ep83b_WorldCup_QuarterFinal.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Episode 83b. 2022 World Cup Prediction with Gradient Boosting (2): Quarter Final (Round of 8)"
      ],
      "metadata": {
        "id": "vLQSi83Dw8cn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[1] Original Article\n",
        "Predicting FIFA 2022 World Cup with ML (SERGIO PESSOA)\n",
        "- https://www.kaggle.com/code/sslp23/predicting-fifa-2022-world-cup-with-ml\n",
        "\n",
        "Soccer World Cup 2018 Winner (DR. JAMES BOND)\n",
        "- https://www.kaggle.com/code/agostontorok/soccer-world-cup-2018-winner\n",
        "\n",
        "\n",
        "\n",
        "[2] Data\n",
        "International football results from 1872 to 2022 (MART JÜRISOO)\n",
        "- https://www.kaggle.com/datasets/martj42/international-football-results-from-1872-to-2017\n",
        "\n",
        "FIFA World Ranking 1992-2022 (ALEX)\n",
        "- https://www.kaggle.com/datasets/cashncarry/fifaworldranking"
      ],
      "metadata": {
        "id": "E8TpA5_sxdnV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# The Project\n",
        "\n",
        "The idea here is to simulate the FIFA 2022 World Cup games with machine learning, in order to predict the competition's winner. The project uses two datasets: [International football results from 1872 to 2022](https://www.kaggle.com/datasets/martj42/international-football-results-from-1872-to-2017) and [FIFA World Ranking 1992-2022](https://www.kaggle.com/datasets/cashncarry/fifaworldranking)\n",
        "\n",
        "I've used the same logic that was used in the [Soccer World Cup 2018 Winner\n",
        "](https://www.kaggle.com/code/agostontorok/soccer-world-cup-2018-winner) notebook, that models the problem as binary classification one. I used this to make more easy to analyze model's results, so the model predicts between win from home team and draw/win from away team. Then, to remove the advantage of away team, I predicted the results changing teams from away and home (because there is not home advantage in World Cup), and used as probabilities the mean of the two predictions. "
      ],
      "metadata": {
        "id": "af9McLQwxZhZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Tensorflow 2.0\n",
        "- The following code ensures that Google CoLab is running the correct version of TensorFlow."
      ],
      "metadata": {
        "id": "LX0jqPlKxJNp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Start CoLab\n",
        "try:\n",
        "    %tensorflow_version 2.x\n",
        "    COLAB = True\n",
        "    print(\"Note: using Google CoLab\")\n",
        "except:\n",
        "    print(\"Note: not using Google CoLab\")\n",
        "    COLAB = False"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_YGmzDmgw881",
        "outputId": "ddcdc095-47b8-463c-efc7-e6a992afbfc5"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n",
            "Note: using Google CoLab\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import Libraries"
      ],
      "metadata": {
        "id": "pl2uAgSNxO--"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import re"
      ],
      "metadata": {
        "id": "RzziJZTRxKWp"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mount Google Colab"
      ],
      "metadata": {
        "id": "CV0uAd-JzUH6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# upload csv file from local drive\n",
        "from google.colab import drive\n",
        "drive.mount('./mount')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29kxXskvzWz1",
        "outputId": "30fcb755-1331-4664-b16f-a9986a1393f6"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at ./mount\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preparation\n",
        "\n",
        "Here, I'll prepare the data to apply feature engineering methods that will create the database in order to apply Machine Learning algorithms."
      ],
      "metadata": {
        "id": "Xw2gU24YyzZR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df =  pd.read_csv(\"/content/mount/MyDrive/Colab Notebooks/ep83a_results.csv\")"
      ],
      "metadata": {
        "execution": {
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        "id": "qePMfv-0wmQy"
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      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"date\"] = pd.to_datetime(df[\"date\"])"
      ],
      "metadata": {
        "execution": {
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      "execution_count": 5,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
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        "execution": {
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        "outputId": "16b3bcb4-fb27-499e-bd63-bcc7d55abb99"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date          0\n",
              "home_team     0\n",
              "away_team     0\n",
              "home_score    4\n",
              "away_score    4\n",
              "tournament    0\n",
              "city          0\n",
              "country       0\n",
              "neutral       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(inplace=True)"
      ],
      "metadata": {
        "execution": {
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          "iopub.execute_input": "2022-11-19T16:03:34.580556Z",
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      "execution_count": 7,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
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      "metadata": {
        "execution": {
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date          datetime64[ns]\n",
              "home_team             object\n",
              "away_team             object\n",
              "home_score           float64\n",
              "away_score           float64\n",
              "tournament            object\n",
              "city                  object\n",
              "country               object\n",
              "neutral                 bool\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset used will be the FIFA Games between 2018, from after the 2018 World Cup to the last games before the 2022 World Cup. The idea is to analyze just the games played at the preparation and classification to WC."
      ],
      "metadata": {
        "id": "btie3cCbwmQz"
      }
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    {
      "cell_type": "code",
      "source": [
        "df.sort_values(\"date\").tail()"
      ],
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        "execution": {
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          "height": 206
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        "outputId": "da91f0a3-3612-4af8-fb1e-536b0d573a9d"
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      "execution_count": 9,
      "outputs": [
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            "text/plain": [
              "            date     home_team away_team  home_score  away_score  \\\n",
              "44195 2022-11-30  Saudi Arabia    Mexico         1.0         2.0   \n",
              "44199 2022-12-01    Costa Rica   Germany         2.0         4.0   \n",
              "44200 2022-12-01       Croatia   Belgium         0.0         0.0   \n",
              "44198 2022-12-01         Japan     Spain         2.0         1.0   \n",
              "44201 2022-12-01        Canada   Morocco         1.0         2.0   \n",
              "\n",
              "           tournament       city country  neutral  \n",
              "44195  FIFA World Cup     Lusail   Qatar     True  \n",
              "44199  FIFA World Cup    Al Khor   Qatar     True  \n",
              "44200  FIFA World Cup  Al Rayyan   Qatar     True  \n",
              "44198  FIFA World Cup  Al Rayyan   Qatar     True  \n",
              "44201  FIFA World Cup       Doha   Qatar     True  "
            ],
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df = df[(df[\"date\"] >= \"2018-8-1\")].reset_index(drop=True)"
      ],
      "metadata": {
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      "execution_count": 10,
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    {
      "cell_type": "code",
      "source": [
        "df.sort_values(\"date\").tail()"
      ],
      "metadata": {
        "execution": {
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              "           date   home_team away_team  home_score  away_score      tournament  \\\n",
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      "source": [
        "df.home_team.value_counts()"
      ],
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        "execution": {
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        "outputId": "3649af09-c02e-47b3-93ba-34632a74658b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "United States    46\n",
              "Mexico           43\n",
              "Qatar            43\n",
              "Japan            40\n",
              "Bahrain          39\n",
              "                 ..\n",
              "Chameria          1\n",
              "Székely Land      1\n",
              "Catalonia         1\n",
              "Cascadia          1\n",
              "Orkney            1\n",
              "Name: home_team, Length: 247, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
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      "cell_type": "code",
      "source": [
        "rank = pd.read_csv(\"/content/mount/MyDrive/Colab Notebooks/ep83a_fifa_ranking-2022-10-06.csv\")"
      ],
      "metadata": {
        "execution": {
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      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rank[\"rank_date\"] = pd.to_datetime(rank[\"rank_date\"])\n",
        "rank = rank[(rank[\"rank_date\"] >= \"2018-8-1\")].reset_index(drop=True)\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:34.839389Z",
          "iopub.execute_input": "2022-11-19T16:03:34.840135Z",
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        "id": "x5IQZwJYwmQ1"
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      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some teams in the World Cup have different names in the ranking's dataset. So, it's needed to adjust. "
      ],
      "metadata": {
        "id": "wO4GX7USwmQ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rank[\"country_full\"] = rank[\"country_full\"].str.replace(\"IR Iran\", \"Iran\").str.replace(\"Korea Republic\", \"South Korea\").str.replace(\"USA\", \"United States\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:34.864296Z",
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      "execution_count": 15,
      "outputs": []
    },
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      "cell_type": "markdown",
      "source": [
        "The merge is made in order to get a dataset FIFA games and its rankings."
      ],
      "metadata": {
        "id": "XQcUCfnOwmQ2"
      }
    },
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      "cell_type": "code",
      "source": [
        "rank = rank.set_index(['rank_date']).groupby(['country_full'], group_keys=False).resample('D').first().fillna(method='ffill').reset_index()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:34.890284Z",
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        "id": "lT2Vx_GOwmQ2"
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      "execution_count": 16,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "df_wc_ranked = df.merge(rank[[\"country_full\", \"total_points\", \"previous_points\", \"rank\", \"rank_change\", \"rank_date\"]], left_on=[\"date\", \"home_team\"], right_on=[\"rank_date\", \"country_full\"]).drop([\"rank_date\", \"country_full\"], axis=1)\n",
        "\n",
        "df_wc_ranked = df_wc_ranked.merge(rank[[\"country_full\", \"total_points\", \"previous_points\", \"rank\", \"rank_change\", \"rank_date\"]], left_on=[\"date\", \"away_team\"], right_on=[\"rank_date\", \"country_full\"], suffixes=(\"_home\", \"_away\")).drop([\"rank_date\", \"country_full\"], axis=1)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:35.712578Z",
          "iopub.execute_input": "2022-11-19T16:03:35.71312Z",
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        "id": "jec7Xm6BwmQ2"
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      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_wc_ranked[(df_wc_ranked.home_team == \"Brazil\") | (df_wc_ranked.away_team == \"Brazil\")].tail(10)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:35.848785Z",
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              "           date    home_team away_team  home_score  away_score  \\\n",
              "2528 2021-11-11       Brazil  Colombia         1.0         0.0   \n",
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              "\n",
              "                        tournament            city      country  neutral  \\\n",
              "2528  FIFA World Cup qualification       São Paulo       Brazil    False   \n",
              "2629  FIFA World Cup qualification        San Juan    Argentina    False   \n",
              "2737  FIFA World Cup qualification           Quito      Ecuador    False   \n",
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              "2894  FIFA World Cup qualification          La Paz      Bolivia    False   \n",
              "2986                      Friendly           Seoul  South Korea    False   \n",
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              "3231                      Friendly        Le Havre       France     True   \n",
              "3300                      Friendly           Paris       France     True   \n",
              "\n",
              "      total_points_home  previous_points_home  rank_home  rank_change_home  \\\n",
              "2528            1820.36               1811.73        2.0               0.0   \n",
              "2629            1738.79               1725.31        6.0               0.0   \n",
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              "2894            1308.12               1324.21       76.0              -1.0   \n",
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              "3300            1837.56               1837.56        1.0               0.0   \n",
              "\n",
              "      total_points_away  previous_points_away  rank_away  rank_change_away  \n",
              "2528            1618.76               1618.40       16.0               0.0  \n",
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              "3231            1393.47               1389.68       60.0               0.0  \n",
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              "    <tr>\n",
              "      <th>2629</th>\n",
              "      <td>2021-11-16</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>FIFA World Cup qualification</td>\n",
              "      <td>San Juan</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>False</td>\n",
              "      <td>1738.79</td>\n",
              "      <td>1725.31</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1820.36</td>\n",
              "      <td>1811.73</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2737</th>\n",
              "      <td>2022-01-27</td>\n",
              "      <td>Ecuador</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>FIFA World Cup qualification</td>\n",
              "      <td>Quito</td>\n",
              "      <td>Ecuador</td>\n",
              "      <td>False</td>\n",
              "      <td>1448.27</td>\n",
              "      <td>1448.74</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1826.35</td>\n",
              "      <td>1826.35</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2770</th>\n",
              "      <td>2022-02-01</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Paraguay</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>FIFA World Cup qualification</td>\n",
              "      <td>Belo Horizonte</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>False</td>\n",
              "      <td>1826.35</td>\n",
              "      <td>1826.35</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1454.52</td>\n",
              "      <td>1454.52</td>\n",
              "      <td>43.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2811</th>\n",
              "      <td>2022-03-24</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Chile</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>FIFA World Cup qualification</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>False</td>\n",
              "      <td>1823.42</td>\n",
              "      <td>1826.35</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1543.16</td>\n",
              "      <td>1543.42</td>\n",
              "      <td>26.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2894</th>\n",
              "      <td>2022-03-29</td>\n",
              "      <td>Bolivia</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>FIFA World Cup qualification</td>\n",
              "      <td>La Paz</td>\n",
              "      <td>Bolivia</td>\n",
              "      <td>False</td>\n",
              "      <td>1308.12</td>\n",
              "      <td>1324.21</td>\n",
              "      <td>76.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1823.42</td>\n",
              "      <td>1826.35</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2986</th>\n",
              "      <td>2022-06-02</td>\n",
              "      <td>South Korea</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Seoul</td>\n",
              "      <td>South Korea</td>\n",
              "      <td>False</td>\n",
              "      <td>1519.54</td>\n",
              "      <td>1522.85</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1832.69</td>\n",
              "      <td>1823.42</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3054</th>\n",
              "      <td>2022-06-06</td>\n",
              "      <td>Japan</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Tokyo</td>\n",
              "      <td>Japan</td>\n",
              "      <td>False</td>\n",
              "      <td>1553.44</td>\n",
              "      <td>1549.82</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1832.69</td>\n",
              "      <td>1823.42</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3231</th>\n",
              "      <td>2022-09-23</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Ghana</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Le Havre</td>\n",
              "      <td>France</td>\n",
              "      <td>True</td>\n",
              "      <td>1837.56</td>\n",
              "      <td>1837.56</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1393.47</td>\n",
              "      <td>1389.68</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3300</th>\n",
              "      <td>2022-09-27</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>Tunisia</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Friendly</td>\n",
              "      <td>Paris</td>\n",
              "      <td>France</td>\n",
              "      <td>True</td>\n",
              "      <td>1837.56</td>\n",
              "      <td>1837.56</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1507.86</td>\n",
              "      <td>1507.86</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we have the data ready to create features based on the games' results and FIFA rankings.\n",
        "\n",
        "# Features Engineering\n",
        "\n",
        "The idea here is to create possible features that have impact on predicting football games. By intuition, we say that features that impact could be:\n",
        "\n",
        "- Past game points made\n",
        "- Past goals scored and suffered\n",
        "- The importance of game (friendly or not)\n",
        "- Rank of the teams \n",
        "- Rank increasement of the teams\n",
        "- Goals made and suffered by ranking faced\n",
        "\n",
        "So, the first thing to do is create the feature that says which team won and how much points they made at game. "
      ],
      "metadata": {
        "id": "U_K-I2quwmQ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df_wc_ranked"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:35.888019Z",
          "iopub.execute_input": "2022-11-19T16:03:35.888746Z",
          "iopub.status.idle": "2022-11-19T16:03:35.894255Z",
          "shell.execute_reply.started": "2022-11-19T16:03:35.888712Z",
          "shell.execute_reply": "2022-11-19T16:03:35.892886Z"
        },
        "trusted": true,
        "id": "jEaRwhofwmQ2"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def result_finder(home, away):\n",
        "    if home > away:\n",
        "        return pd.Series([0, 3, 0])\n",
        "    if home < away:\n",
        "        return pd.Series([1, 0, 3])\n",
        "    else:\n",
        "        return pd.Series([2, 1, 1])\n",
        "\n",
        "results = df.apply(lambda x: result_finder(x[\"home_score\"], x[\"away_score\"]), axis=1)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:35.895765Z",
          "iopub.execute_input": "2022-11-19T16:03:35.896113Z",
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          "shell.execute_reply": "2022-11-19T16:03:36.523931Z"
        },
        "trusted": true,
        "id": "IPeiSYYKwmQ3"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[[\"result\", \"home_team_points\", \"away_team_points\"]] = results"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:36.527404Z",
          "iopub.execute_input": "2022-11-19T16:03:36.527901Z",
          "iopub.status.idle": "2022-11-19T16:03:36.535888Z",
          "shell.execute_reply.started": "2022-11-19T16:03:36.527847Z",
          "shell.execute_reply": "2022-11-19T16:03:36.53508Z"
        },
        "trusted": true,
        "id": "OViovLkswmQ3"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The game points are 3 for win, 1 for draw and 0 for lose and are different than the FIFA rank points that are already in the database. Also, it's supposed that FIFA Rank points and FIFA Ranking of the same team are negative correlated, and we should use only one of them to create new features. This supposition is checked below:"
      ],
      "metadata": {
        "id": "kNin8WqqwmQ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(15, 10))\n",
        "sns.heatmap(df[[\"total_points_home\", \"rank_home\", \"total_points_away\", \"rank_away\"]].corr())\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:36.537495Z",
          "iopub.execute_input": "2022-11-19T16:03:36.537785Z",
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          "shell.execute_reply.started": "2022-11-19T16:03:36.537758Z",
          "shell.execute_reply": "2022-11-19T16:03:36.842239Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "RJcghmh9wmQ3",
        "outputId": "de994c3a-65a6-4c6b-9c24-3cd0cd28704d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we create columns that will help in the creation of the features: ranking difference, points won at the game vs. team faced rank, and goals difference in the game. All features that are not differences should be created for the two teams (away and home)."
      ],
      "metadata": {
        "id": "J9AwN3wswmQ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"rank_dif\"] = df[\"rank_home\"] - df[\"rank_away\"]\n",
        "df[\"sg\"] = df[\"home_score\"] - df[\"away_score\"]\n",
        "df[\"points_home_by_rank\"] = df[\"home_team_points\"]/df[\"rank_away\"]\n",
        "df[\"points_away_by_rank\"] = df[\"away_team_points\"]/df[\"rank_home\"]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:36.84545Z",
          "iopub.execute_input": "2022-11-19T16:03:36.846279Z",
          "iopub.status.idle": "2022-11-19T16:03:36.85671Z",
          "shell.execute_reply.started": "2022-11-19T16:03:36.846216Z",
          "shell.execute_reply": "2022-11-19T16:03:36.855655Z"
        },
        "trusted": true,
        "id": "FgCNf6WFwmQ3"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to create the features, I'll separate the dataset in home team's and away team's dataset, unify them and calculate the past game values. After that, I'll separate again and merge them, retrieving the original dataset. This proccess optimizes the creation of the features."
      ],
      "metadata": {
        "id": "BST-wWQbwmQ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "home_team = df[[\"date\", \"home_team\", \"home_score\", \"away_score\", \"rank_home\", \"rank_away\",\"rank_change_home\", \"total_points_home\", \"result\", \"rank_dif\", \"points_home_by_rank\", \"home_team_points\"]]\n",
        "\n",
        "away_team = df[[\"date\", \"away_team\", \"away_score\", \"home_score\", \"rank_away\", \"rank_home\",\"rank_change_away\", \"total_points_away\", \"result\", \"rank_dif\", \"points_away_by_rank\", \"away_team_points\"]]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:36.858224Z",
          "iopub.execute_input": "2022-11-19T16:03:36.858807Z",
          "iopub.status.idle": "2022-11-19T16:03:36.868856Z",
          "shell.execute_reply.started": "2022-11-19T16:03:36.858774Z",
          "shell.execute_reply": "2022-11-19T16:03:36.86808Z"
        },
        "trusted": true,
        "id": "IZpZl1iDwmQ3"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "home_team.columns = [h.replace(\"home_\", \"\").replace(\"_home\", \"\").replace(\"away_\", \"suf_\").replace(\"_away\", \"_suf\") for h in home_team.columns]\n",
        "\n",
        "away_team.columns = [a.replace(\"away_\", \"\").replace(\"_away\", \"\").replace(\"home_\", \"suf_\").replace(\"_home\", \"_suf\") for a in away_team.columns]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:36.870019Z",
          "iopub.execute_input": "2022-11-19T16:03:36.870553Z",
          "iopub.status.idle": "2022-11-19T16:03:36.87801Z",
          "shell.execute_reply.started": "2022-11-19T16:03:36.87052Z",
          "shell.execute_reply": "2022-11-19T16:03:36.876911Z"
        },
        "trusted": true,
        "id": "czEaJnKMwmQ4"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "team_stats = home_team.append(away_team)#.sort_values(\"date\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:36.882625Z",
          "iopub.execute_input": "2022-11-19T16:03:36.882958Z",
          "iopub.status.idle": "2022-11-19T16:03:36.89592Z",
          "shell.execute_reply.started": "2022-11-19T16:03:36.88293Z",
          "shell.execute_reply": "2022-11-19T16:03:36.894985Z"
        },
        "trusted": true,
        "id": "5RugKTnfwmQ4"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#this column will be used to calculate features for simulation\n",
        "team_stats_raw = team_stats.copy()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:36.897282Z",
          "iopub.execute_input": "2022-11-19T16:03:36.897602Z",
          "iopub.status.idle": "2022-11-19T16:03:36.908703Z",
          "shell.execute_reply.started": "2022-11-19T16:03:36.897575Z",
          "shell.execute_reply": "2022-11-19T16:03:36.90745Z"
        },
        "trusted": true,
        "id": "W_fXwdIUwmQ4"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we have a database ready to create predictive features. They will be:\n",
        "\n",
        "- Mean goals of the team in World Cup Cycle.\n",
        "- Mean goals of the team in last 5 games.\n",
        "- Mean goals suffered of the team in World Cup Cycle.\n",
        "- Mean goals suffered of the team in last 5 games.\n",
        "- Mean FIFA Rank that team faced in World Cup Cycle.\n",
        "- Mean FIFA Rank that team faced in last 5 games.\n",
        "- FIFA Points won at the cycle.\n",
        "- FIFA Points won in last 5 games.\n",
        "- Mean game points at the Cycle.\n",
        "- Mean game points at last 5 games.\n",
        "- Mean game points by rank faced at the Cycle.\n",
        "- Mean game points by rank faced at last 5 games."
      ],
      "metadata": {
        "id": "2z0L72g_wmQ4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stats_val = []\n",
        "\n",
        "for index, row in team_stats.iterrows():\n",
        "    team = row[\"team\"]\n",
        "    date = row[\"date\"]\n",
        "    past_games = team_stats.loc[(team_stats[\"team\"] == team) & (team_stats[\"date\"] < date)].sort_values(by=['date'], ascending=False)\n",
        "    last5 = past_games.head(5)\n",
        "    \n",
        "    goals = past_games[\"score\"].mean()\n",
        "    goals_l5 = last5[\"score\"].mean()\n",
        "    \n",
        "    goals_suf = past_games[\"suf_score\"].mean()\n",
        "    goals_suf_l5 = last5[\"suf_score\"].mean()\n",
        "    \n",
        "    rank = past_games[\"rank_suf\"].mean()\n",
        "    rank_l5 = last5[\"rank_suf\"].mean()\n",
        "    \n",
        "    if len(last5) > 0:\n",
        "        points = past_games[\"total_points\"].values[0] - past_games[\"total_points\"].values[-1]#qtd de pontos ganhos\n",
        "        points_l5 = last5[\"total_points\"].values[0] - last5[\"total_points\"].values[-1] \n",
        "    else:\n",
        "        points = 0\n",
        "        points_l5 = 0\n",
        "        \n",
        "    gp = past_games[\"team_points\"].mean()\n",
        "    gp_l5 = last5[\"team_points\"].mean()\n",
        "    \n",
        "    gp_rank = past_games[\"points_by_rank\"].mean()\n",
        "    gp_rank_l5 = last5[\"points_by_rank\"].mean()\n",
        "    \n",
        "    stats_val.append([goals, goals_l5, goals_suf, goals_suf_l5, rank, rank_l5, points, points_l5, gp, gp_l5, gp_rank, gp_rank_l5])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:36.910217Z",
          "iopub.execute_input": "2022-11-19T16:03:36.910603Z",
          "iopub.status.idle": "2022-11-19T16:03:59.457419Z",
          "shell.execute_reply.started": "2022-11-19T16:03:36.910571Z",
          "shell.execute_reply": "2022-11-19T16:03:59.456223Z"
        },
        "trusted": true,
        "id": "saySQJIcwmQ4"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stats_cols = [\"goals_mean\", \"goals_mean_l5\", \"goals_suf_mean\", \"goals_suf_mean_l5\", \"rank_mean\", \"rank_mean_l5\", \"points_mean\", \"points_mean_l5\", \"game_points_mean\", \"game_points_mean_l5\", \"game_points_rank_mean\", \"game_points_rank_mean_l5\"]\n",
        "\n",
        "stats_df = pd.DataFrame(stats_val, columns=stats_cols)\n",
        "\n",
        "full_df = pd.concat([team_stats.reset_index(drop=True), stats_df], axis=1, ignore_index=False)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.458813Z",
          "iopub.execute_input": "2022-11-19T16:03:59.459159Z",
          "iopub.status.idle": "2022-11-19T16:03:59.485538Z",
          "shell.execute_reply.started": "2022-11-19T16:03:59.459127Z",
          "shell.execute_reply": "2022-11-19T16:03:59.484619Z"
        },
        "trusted": true,
        "id": "WEYPeVT_wmQ4"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "home_team_stats = full_df.iloc[:int(full_df.shape[0]/2),:]\n",
        "away_team_stats = full_df.iloc[int(full_df.shape[0]/2):,:]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.487039Z",
          "iopub.execute_input": "2022-11-19T16:03:59.48751Z",
          "iopub.status.idle": "2022-11-19T16:03:59.494965Z",
          "shell.execute_reply.started": "2022-11-19T16:03:59.487464Z",
          "shell.execute_reply": "2022-11-19T16:03:59.493676Z"
        },
        "trusted": true,
        "id": "VPM1N7TzwmQ4"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The new columns:"
      ],
      "metadata": {
        "id": "fwlPJmfPwmQ5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "home_team_stats.columns[-12:]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.496778Z",
          "iopub.execute_input": "2022-11-19T16:03:59.497154Z",
          "iopub.status.idle": "2022-11-19T16:03:59.514509Z",
          "shell.execute_reply.started": "2022-11-19T16:03:59.49712Z",
          "shell.execute_reply": "2022-11-19T16:03:59.513289Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iXB2iDq-wmQ5",
        "outputId": "b0bc0f39-e45d-4c58-ddf1-b28b01bcdabc"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['goals_mean', 'goals_mean_l5', 'goals_suf_mean', 'goals_suf_mean_l5',\n",
              "       'rank_mean', 'rank_mean_l5', 'points_mean', 'points_mean_l5',\n",
              "       'game_points_mean', 'game_points_mean_l5', 'game_points_rank_mean',\n",
              "       'game_points_rank_mean_l5'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "home_team_stats = home_team_stats[home_team_stats.columns[-12:]]\n",
        "away_team_stats = away_team_stats[away_team_stats.columns[-12:]]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.516147Z",
          "iopub.execute_input": "2022-11-19T16:03:59.51654Z",
          "iopub.status.idle": "2022-11-19T16:03:59.529746Z",
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        },
        "trusted": true,
        "id": "JAT0ik6fwmQ5"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "home_team_stats.columns = ['home_'+str(col) for col in home_team_stats.columns]\n",
        "away_team_stats.columns = ['away_'+str(col) for col in away_team_stats.columns]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.531446Z",
          "iopub.execute_input": "2022-11-19T16:03:59.531844Z",
          "iopub.status.idle": "2022-11-19T16:03:59.541274Z",
          "shell.execute_reply.started": "2022-11-19T16:03:59.531792Z",
          "shell.execute_reply": "2022-11-19T16:03:59.539842Z"
        },
        "trusted": true,
        "id": "XSYLa8wBwmQ5"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to unify the database, is needed to add home_ and away_ suffix for each column. After that, the data is ready to be merged."
      ],
      "metadata": {
        "id": "jsYADMyRwmQ5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "match_stats = pd.concat([home_team_stats, away_team_stats.reset_index(drop=True)], axis=1, ignore_index=False)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.542725Z",
          "iopub.execute_input": "2022-11-19T16:03:59.543464Z",
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          "shell.execute_reply.started": "2022-11-19T16:03:59.543429Z",
          "shell.execute_reply": "2022-11-19T16:03:59.554636Z"
        },
        "trusted": true,
        "id": "Mpzc8b5SwmQ5"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full_df = pd.concat([df, match_stats.reset_index(drop=True)], axis=1, ignore_index=False)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.557208Z",
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        "trusted": true,
        "id": "Jecpu3xSwmQ5"
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      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full_df.columns"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.572883Z",
          "iopub.execute_input": "2022-11-19T16:03:59.573265Z",
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        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ZClfg-wwmQ6",
        "outputId": "9f5e8bda-58c8-4b44-cadc-790719cb7a23"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['date', 'home_team', 'away_team', 'home_score', 'away_score',\n",
              "       'tournament', 'city', 'country', 'neutral', 'total_points_home',\n",
              "       'previous_points_home', 'rank_home', 'rank_change_home',\n",
              "       'total_points_away', 'previous_points_away', 'rank_away',\n",
              "       'rank_change_away', 'result', 'home_team_points', 'away_team_points',\n",
              "       'rank_dif', 'sg', 'points_home_by_rank', 'points_away_by_rank',\n",
              "       'home_goals_mean', 'home_goals_mean_l5', 'home_goals_suf_mean',\n",
              "       'home_goals_suf_mean_l5', 'home_rank_mean', 'home_rank_mean_l5',\n",
              "       'home_points_mean', 'home_points_mean_l5', 'home_game_points_mean',\n",
              "       'home_game_points_mean_l5', 'home_game_points_rank_mean',\n",
              "       'home_game_points_rank_mean_l5', 'away_goals_mean',\n",
              "       'away_goals_mean_l5', 'away_goals_suf_mean', 'away_goals_suf_mean_l5',\n",
              "       'away_rank_mean', 'away_rank_mean_l5', 'away_points_mean',\n",
              "       'away_points_mean_l5', 'away_game_points_mean',\n",
              "       'away_game_points_mean_l5', 'away_game_points_rank_mean',\n",
              "       'away_game_points_rank_mean_l5'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, to quantify game importance, is created a column that finds competition of the game."
      ],
      "metadata": {
        "id": "1rEVf9H_wmQ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def find_friendly(x):\n",
        "    if x == \"Friendly\":\n",
        "        return 1\n",
        "    else: return 0\n",
        "\n",
        "full_df[\"is_friendly\"] = full_df[\"tournament\"].apply(lambda x: find_friendly(x)) "
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.585423Z",
          "iopub.execute_input": "2022-11-19T16:03:59.585904Z",
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        },
        "trusted": true,
        "id": "XWDzkICxwmQ6"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full_df = pd.get_dummies(full_df, columns=[\"is_friendly\"])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.599091Z",
          "iopub.execute_input": "2022-11-19T16:03:59.599521Z",
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        "trusted": true,
        "id": "YGArixSewmQ6"
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      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full_df.columns"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.622532Z",
          "iopub.execute_input": "2022-11-19T16:03:59.622889Z",
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        },
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        },
        "id": "WP3XjZBCwmQ6",
        "outputId": "810908b2-5b0b-4e81-c853-94f45bced90b"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['date', 'home_team', 'away_team', 'home_score', 'away_score',\n",
              "       'tournament', 'city', 'country', 'neutral', 'total_points_home',\n",
              "       'previous_points_home', 'rank_home', 'rank_change_home',\n",
              "       'total_points_away', 'previous_points_away', 'rank_away',\n",
              "       'rank_change_away', 'result', 'home_team_points', 'away_team_points',\n",
              "       'rank_dif', 'sg', 'points_home_by_rank', 'points_away_by_rank',\n",
              "       'home_goals_mean', 'home_goals_mean_l5', 'home_goals_suf_mean',\n",
              "       'home_goals_suf_mean_l5', 'home_rank_mean', 'home_rank_mean_l5',\n",
              "       'home_points_mean', 'home_points_mean_l5', 'home_game_points_mean',\n",
              "       'home_game_points_mean_l5', 'home_game_points_rank_mean',\n",
              "       'home_game_points_rank_mean_l5', 'away_goals_mean',\n",
              "       'away_goals_mean_l5', 'away_goals_suf_mean', 'away_goals_suf_mean_l5',\n",
              "       'away_rank_mean', 'away_rank_mean_l5', 'away_points_mean',\n",
              "       'away_points_mean_l5', 'away_game_points_mean',\n",
              "       'away_game_points_mean_l5', 'away_game_points_rank_mean',\n",
              "       'away_game_points_rank_mean_l5', 'is_friendly_0', 'is_friendly_1'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And, after that, we select only columns that will help in Features' analysis."
      ],
      "metadata": {
        "id": "5IHA--bjwmQ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_df = full_df[[\"date\", \"home_team\", \"away_team\", \"rank_home\", \"rank_away\",\"home_score\", \"away_score\",\"result\", \"rank_dif\", \"rank_change_home\", \"rank_change_away\", 'home_goals_mean',\n",
        "       'home_goals_mean_l5', 'home_goals_suf_mean', 'home_goals_suf_mean_l5',\n",
        "       'home_rank_mean', 'home_rank_mean_l5', 'home_points_mean',\n",
        "       'home_points_mean_l5', 'away_goals_mean', 'away_goals_mean_l5',\n",
        "       'away_goals_suf_mean', 'away_goals_suf_mean_l5', 'away_rank_mean',\n",
        "       'away_rank_mean_l5', 'away_points_mean', 'away_points_mean_l5','home_game_points_mean', 'home_game_points_mean_l5',\n",
        "       'home_game_points_rank_mean', 'home_game_points_rank_mean_l5','away_game_points_mean',\n",
        "       'away_game_points_mean_l5', 'away_game_points_rank_mean',\n",
        "       'away_game_points_rank_mean_l5',\n",
        "       'is_friendly_0', 'is_friendly_1']]\n",
        "\n",
        "base_df.tail()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.633061Z",
          "iopub.execute_input": "2022-11-19T16:03:59.63344Z",
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          "height": 386
        },
        "id": "Y_eAufk_wmQ6",
        "outputId": "dfa42f69-a393-48b0-b180-686fa81c0721"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           date home_team         away_team  rank_home  rank_away  home_score  \\\n",
              "3331 2022-09-27    Norway            Serbia       36.0       25.0         0.0   \n",
              "3332 2022-09-27    Sweden          Slovenia       20.0       65.0         1.0   \n",
              "3333 2022-09-27    Kosovo            Cyprus      106.0      108.0         5.0   \n",
              "3334 2022-09-27    Greece  Northern Ireland       49.0       58.0         3.0   \n",
              "3335 2022-09-30      Fiji   Solomon Islands      163.0      137.0         1.0   \n",
              "\n",
              "      away_score  result  rank_dif  rank_change_home  ...  \\\n",
              "3331         2.0       1      11.0               0.0  ...   \n",
              "3332         1.0       2     -45.0               0.0  ...   \n",
              "3333         1.0       0      -2.0               0.0  ...   \n",
              "3334         1.0       0      -9.0               1.0  ...   \n",
              "3335         0.0       0      26.0               0.0  ...   \n",
              "\n",
              "      home_game_points_mean  home_game_points_mean_l5  \\\n",
              "3331               1.833333                       2.0   \n",
              "3332               1.607843                       0.6   \n",
              "3333               1.326087                       1.2   \n",
              "3334               1.590909                       2.4   \n",
              "3335               1.500000                       1.4   \n",
              "\n",
              "      home_game_points_rank_mean  home_game_points_rank_mean_l5  \\\n",
              "3331                    0.041984                       0.090235   \n",
              "3332                    0.048520                       0.009231   \n",
              "3333                    0.018478                       0.016825   \n",
              "3334                    0.050090                       0.028040   \n",
              "3335                    0.009432                       0.008868   \n",
              "\n",
              "      away_game_points_mean  away_game_points_mean_l5  \\\n",
              "3331               1.866667                       2.0   \n",
              "3332               1.404762                       1.0   \n",
              "3333               0.767442                       1.0   \n",
              "3334               1.139535                       1.0   \n",
              "3335               1.714286                       2.0   \n",
              "\n",
              "      away_game_points_rank_mean  away_game_points_rank_mean_l5  \\\n",
              "3331                    0.053327                       0.073887   \n",
              "3332                    0.026035                       0.029545   \n",
              "3333                    0.009687                       0.019652   \n",
              "3334                    0.022315                       0.009470   \n",
              "3335                    0.010013                       0.012387   \n",
              "\n",
              "      is_friendly_0  is_friendly_1  \n",
              "3331              1              0  \n",
              "3332              1              0  \n",
              "3333              1              0  \n",
              "3334              1              0  \n",
              "3335              1              0  \n",
              "\n",
              "[5 rows x 37 columns]"
            ],
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>home_team</th>\n",
              "      <th>away_team</th>\n",
              "      <th>rank_home</th>\n",
              "      <th>rank_away</th>\n",
              "      <th>home_score</th>\n",
              "      <th>away_score</th>\n",
              "      <th>result</th>\n",
              "      <th>rank_dif</th>\n",
              "      <th>rank_change_home</th>\n",
              "      <th>...</th>\n",
              "      <th>home_game_points_mean</th>\n",
              "      <th>home_game_points_mean_l5</th>\n",
              "      <th>home_game_points_rank_mean</th>\n",
              "      <th>home_game_points_rank_mean_l5</th>\n",
              "      <th>away_game_points_mean</th>\n",
              "      <th>away_game_points_mean_l5</th>\n",
              "      <th>away_game_points_rank_mean</th>\n",
              "      <th>away_game_points_rank_mean_l5</th>\n",
              "      <th>is_friendly_0</th>\n",
              "      <th>is_friendly_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3331</th>\n",
              "      <td>2022-09-27</td>\n",
              "      <td>Norway</td>\n",
              "      <td>Serbia</td>\n",
              "      <td>36.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.041984</td>\n",
              "      <td>0.090235</td>\n",
              "      <td>1.866667</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.053327</td>\n",
              "      <td>0.073887</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3332</th>\n",
              "      <td>2022-09-27</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>Slovenia</td>\n",
              "      <td>20.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>-45.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.607843</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.048520</td>\n",
              "      <td>0.009231</td>\n",
              "      <td>1.404762</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.026035</td>\n",
              "      <td>0.029545</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3333</th>\n",
              "      <td>2022-09-27</td>\n",
              "      <td>Kosovo</td>\n",
              "      <td>Cyprus</td>\n",
              "      <td>106.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.326087</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.018478</td>\n",
              "      <td>0.016825</td>\n",
              "      <td>0.767442</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.009687</td>\n",
              "      <td>0.019652</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3334</th>\n",
              "      <td>2022-09-27</td>\n",
              "      <td>Greece</td>\n",
              "      <td>Northern Ireland</td>\n",
              "      <td>49.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.590909</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.050090</td>\n",
              "      <td>0.028040</td>\n",
              "      <td>1.139535</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.022315</td>\n",
              "      <td>0.009470</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3335</th>\n",
              "      <td>2022-09-30</td>\n",
              "      <td>Fiji</td>\n",
              "      <td>Solomon Islands</td>\n",
              "      <td>163.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.009432</td>\n",
              "      <td>0.008868</td>\n",
              "      <td>1.714286</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.010013</td>\n",
              "      <td>0.012387</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 37 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_df.isna().sum()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.677195Z",
          "iopub.execute_input": "2022-11-19T16:03:59.677685Z",
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          "shell.execute_reply": "2022-11-19T16:03:59.689958Z"
        },
        "trusted": true,
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          "base_uri": "https://localhost:8080/"
        },
        "id": "xA8LValBwmQ7",
        "outputId": "222bbbdf-4960-4f85-8db8-a1f4a5a6fb18"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date                               0\n",
              "home_team                          0\n",
              "away_team                          0\n",
              "rank_home                          0\n",
              "rank_away                          0\n",
              "home_score                         0\n",
              "away_score                         0\n",
              "result                             0\n",
              "rank_dif                           0\n",
              "rank_change_home                   0\n",
              "rank_change_away                   0\n",
              "home_goals_mean                  101\n",
              "home_goals_mean_l5               101\n",
              "home_goals_suf_mean              101\n",
              "home_goals_suf_mean_l5           101\n",
              "home_rank_mean                   101\n",
              "home_rank_mean_l5                101\n",
              "home_points_mean                   0\n",
              "home_points_mean_l5                0\n",
              "away_goals_mean                  100\n",
              "away_goals_mean_l5               100\n",
              "away_goals_suf_mean              100\n",
              "away_goals_suf_mean_l5           100\n",
              "away_rank_mean                   100\n",
              "away_rank_mean_l5                100\n",
              "away_points_mean                   0\n",
              "away_points_mean_l5                0\n",
              "home_game_points_mean            101\n",
              "home_game_points_mean_l5         101\n",
              "home_game_points_rank_mean       101\n",
              "home_game_points_rank_mean_l5    101\n",
              "away_game_points_mean            100\n",
              "away_game_points_mean_l5         100\n",
              "away_game_points_rank_mean       100\n",
              "away_game_points_rank_mean_l5    100\n",
              "is_friendly_0                      0\n",
              "is_friendly_1                      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The games with NA are the ones who mean could not be calculated (games from the beginning of the dataset). Those will be dropped."
      ],
      "metadata": {
        "id": "lnq7X0OiwmQ7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_df_no_fg = base_df.dropna()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.693642Z",
          "iopub.execute_input": "2022-11-19T16:03:59.694066Z",
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        "id": "-2OKRaaywmQ7"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Analysis\n",
        "\n",
        "Now, we need to analyze all features that were created and check if they have predictive power. Also, if they don't have, we need to create some that have, like differences of home and away teams. To analyze the predictive power, I'll assign draw games as a lose of the home team and will create a binary problem. "
      ],
      "metadata": {
        "id": "53cwqco1wmQ7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = base_df_no_fg"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.708554Z",
          "iopub.execute_input": "2022-11-19T16:03:59.709045Z",
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        "id": "0NBWCUKywmQ7"
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      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def no_draw(x):\n",
        "    if x == 2:\n",
        "        return 1\n",
        "    else:\n",
        "        return x\n",
        "    \n",
        "df[\"target\"] = df[\"result\"].apply(lambda x: no_draw(x))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.71833Z",
          "iopub.execute_input": "2022-11-19T16:03:59.718699Z",
          "iopub.status.idle": "2022-11-19T16:03:59.73603Z",
          "shell.execute_reply.started": "2022-11-19T16:03:59.718665Z",
          "shell.execute_reply": "2022-11-19T16:03:59.734667Z"
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        "trusted": true,
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        },
        "id": "APKzDkpPwmQ7",
        "outputId": "5e5144b0-3d8f-4278-e7a9-45f2d4a003fb"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-44-fd45a11c0adc>:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[\"target\"] = df[\"result\"].apply(lambda x: no_draw(x))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analysis that will be made:\n",
        "\n",
        "- Violin and boxplot to analyze if the features have different distributions according to the target\n",
        "- Scatter plots to analyze correlations"
      ],
      "metadata": {
        "id": "0Gstlmr8wmQ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data1 = df[list(df.columns[8:20].values) + [\"target\"]]\n",
        "data2 = df[df.columns[20:]]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.738026Z",
          "iopub.execute_input": "2022-11-19T16:03:59.738414Z",
          "iopub.status.idle": "2022-11-19T16:03:59.753585Z",
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          "shell.execute_reply": "2022-11-19T16:03:59.752349Z"
        },
        "trusted": true,
        "id": "_NGWUv_5wmQ8"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled = (data1[:-1] - data1[:-1].mean()) / data1[:-1].std()\n",
        "scaled[\"target\"] = data1[\"target\"]\n",
        "violin1 = pd.melt(scaled,id_vars=\"target\", var_name=\"features\", value_name=\"value\")\n",
        "\n",
        "scaled = (data2[:-1] - data2[:-1].mean()) / data2[:-1].std()\n",
        "scaled[\"target\"] = data2[\"target\"]\n",
        "violin2 = pd.melt(scaled,id_vars=\"target\", var_name=\"features\", value_name=\"value\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.755522Z",
          "iopub.execute_input": "2022-11-19T16:03:59.755889Z",
          "iopub.status.idle": "2022-11-19T16:03:59.787051Z",
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          "shell.execute_reply": "2022-11-19T16:03:59.786162Z"
        },
        "trusted": true,
        "id": "fndxY3_gwmQ8"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "sns.violinplot(x=\"features\", y=\"value\", hue=\"target\", data=violin1,split=True, inner=\"quart\")\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:03:59.788844Z",
          "iopub.execute_input": "2022-11-19T16:03:59.789299Z",
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          "height": 723
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        "outputId": "86217f40-8e8e-4148-8471-d1034f4df653"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "sns.violinplot(x=\"features\", y=\"value\", hue=\"target\", data=violin2,split=True, inner=\"quart\")\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:00.439989Z",
          "iopub.execute_input": "2022-11-19T16:04:00.440502Z",
          "iopub.status.idle": "2022-11-19T16:04:01.28762Z",
          "shell.execute_reply.started": "2022-11-19T16:04:00.440458Z",
          "shell.execute_reply": "2022-11-19T16:04:01.286474Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "id": "h5URy99fwmQ8",
        "outputId": "ebb89e28-5038-44a0-aa5e-84bc3dd776d3"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With the plots, we find that rank difference is the only good separator of the data. But, we can create features that get the differences between home and away team and analyze if they are good separating the data."
      ],
      "metadata": {
        "id": "YO2nEjtFwmQ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dif = df.copy()\n",
        "dif.loc[:, \"goals_dif\"] = dif[\"home_goals_mean\"] - dif[\"away_goals_mean\"]\n",
        "dif.loc[:, \"goals_dif_l5\"] = dif[\"home_goals_mean_l5\"] - dif[\"away_goals_mean_l5\"]\n",
        "dif.loc[:, \"goals_suf_dif\"] = dif[\"home_goals_suf_mean\"] - dif[\"away_goals_suf_mean\"]\n",
        "dif.loc[:, \"goals_suf_dif_l5\"] = dif[\"home_goals_suf_mean_l5\"] - dif[\"away_goals_suf_mean_l5\"]\n",
        "dif.loc[:, \"goals_made_suf_dif\"] = dif[\"home_goals_mean\"] - dif[\"away_goals_suf_mean\"]\n",
        "dif.loc[:, \"goals_made_suf_dif_l5\"] = dif[\"home_goals_mean_l5\"] - dif[\"away_goals_suf_mean_l5\"]\n",
        "dif.loc[:, \"goals_suf_made_dif\"] = dif[\"home_goals_suf_mean\"] - dif[\"away_goals_mean\"]\n",
        "dif.loc[:, \"goals_suf_made_dif_l5\"] = dif[\"home_goals_suf_mean_l5\"] - dif[\"away_goals_mean_l5\"]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:01.289414Z",
          "iopub.execute_input": "2022-11-19T16:04:01.289828Z",
          "iopub.status.idle": "2022-11-19T16:04:01.308648Z",
          "shell.execute_reply.started": "2022-11-19T16:04:01.289791Z",
          "shell.execute_reply": "2022-11-19T16:04:01.307286Z"
        },
        "trusted": true,
        "id": "NLUQAkZEwmQ8"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_difs = dif.iloc[:, -8:]\n",
        "scaled = (data_difs - data_difs.mean()) / data_difs.std()\n",
        "scaled[\"target\"] = data2[\"target\"]\n",
        "violin = pd.melt(scaled,id_vars=\"target\", var_name=\"features\", value_name=\"value\")\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.violinplot(x=\"features\", y=\"value\", hue=\"target\", data=violin,split=True, inner=\"quart\")\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:01.310044Z",
          "iopub.execute_input": "2022-11-19T16:04:01.310403Z",
          "iopub.status.idle": "2022-11-19T16:04:02.138901Z",
          "shell.execute_reply.started": "2022-11-19T16:04:01.31037Z",
          "shell.execute_reply": "2022-11-19T16:04:02.137443Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "9adx5-hpwmQ8",
        "outputId": "efd4da29-3b5e-4e65-d4e4-62346af233c5"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With that plot, we see that goal differences are good separator, and goals suffered difference too. Differences between goals made and goals suffered of the teams are not very good separators.\n",
        "\n",
        "Now, we have 5 features:\n",
        "\n",
        "- rank_dif\n",
        "- goals_dif\n",
        "- goals_dif_l5\n",
        "- goals_suf_dif\n",
        "- goals_suf_dif_l5\n",
        "\n",
        "We can create other features, like differences of points made, differences of points made by rank faced and differences of rank faced."
      ],
      "metadata": {
        "id": "5hOwRjfpwmQ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dif.loc[:, \"dif_points\"] = dif[\"home_game_points_mean\"] - dif[\"away_game_points_mean\"]\n",
        "dif.loc[:, \"dif_points_l5\"] = dif[\"home_game_points_mean_l5\"] - dif[\"away_game_points_mean_l5\"]\n",
        "dif.loc[:, \"dif_points_rank\"] = dif[\"home_game_points_rank_mean\"] - dif[\"away_game_points_rank_mean\"]\n",
        "dif.loc[:, \"dif_points_rank_l5\"] = dif[\"home_game_points_rank_mean_l5\"] - dif[\"away_game_points_rank_mean_l5\"]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:02.140465Z",
          "iopub.execute_input": "2022-11-19T16:04:02.140815Z",
          "iopub.status.idle": "2022-11-19T16:04:02.152358Z",
          "shell.execute_reply.started": "2022-11-19T16:04:02.140783Z",
          "shell.execute_reply": "2022-11-19T16:04:02.151089Z"
        },
        "trusted": true,
        "id": "WZvoXKU4wmQ9"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dif.loc[:, \"dif_rank_agst\"] = dif[\"home_rank_mean\"] - dif[\"away_rank_mean\"]\n",
        "dif.loc[:, \"dif_rank_agst_l5\"] = dif[\"home_rank_mean_l5\"] - dif[\"away_rank_mean_l5\"]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:02.154034Z",
          "iopub.execute_input": "2022-11-19T16:04:02.155511Z",
          "iopub.status.idle": "2022-11-19T16:04:02.171935Z",
          "shell.execute_reply.started": "2022-11-19T16:04:02.155475Z",
          "shell.execute_reply": "2022-11-19T16:04:02.170898Z"
        },
        "trusted": true,
        "id": "8J5q9fv2wmQ9"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Also, we can calculate goals made and suffered by rank, and check this difference."
      ],
      "metadata": {
        "id": "RxIPWxGkwmQ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dif.loc[:, \"goals_per_ranking_dif\"] = (dif[\"home_goals_mean\"] / dif[\"home_rank_mean\"]) - (dif[\"away_goals_mean\"] / dif[\"away_rank_mean\"])\n",
        "dif.loc[:, \"goals_per_ranking_suf_dif\"] = (dif[\"home_goals_suf_mean\"] / dif[\"home_rank_mean\"]) - (dif[\"away_goals_suf_mean\"] / dif[\"away_rank_mean\"])\n",
        "dif.loc[:, \"goals_per_ranking_dif_l5\"] = (dif[\"home_goals_mean_l5\"] / dif[\"home_rank_mean\"]) - (dif[\"away_goals_mean_l5\"] / dif[\"away_rank_mean\"])\n",
        "dif.loc[:, \"goals_per_ranking_suf_dif_l5\"] = (dif[\"home_goals_suf_mean_l5\"] / dif[\"home_rank_mean\"]) - (dif[\"away_goals_suf_mean_l5\"] / dif[\"away_rank_mean\"])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:02.181142Z",
          "iopub.execute_input": "2022-11-19T16:04:02.181575Z",
          "iopub.status.idle": "2022-11-19T16:04:02.194506Z",
          "shell.execute_reply.started": "2022-11-19T16:04:02.181537Z",
          "shell.execute_reply": "2022-11-19T16:04:02.193454Z"
        },
        "trusted": true,
        "id": "t4BIk0jSwmQ9"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_difs = dif.iloc[:, -10:]\n",
        "scaled = (data_difs - data_difs.mean()) / data_difs.std()\n",
        "scaled[\"target\"] = data2[\"target\"]\n",
        "violin = pd.melt(scaled,id_vars=\"target\", var_name=\"features\", value_name=\"value\")\n",
        "\n",
        "plt.figure(figsize=(15,10))\n",
        "sns.violinplot(x=\"features\", y=\"value\", hue=\"target\", data=violin,split=True, inner=\"quart\")\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:02.195702Z",
          "iopub.execute_input": "2022-11-19T16:04:02.196486Z",
          "iopub.status.idle": "2022-11-19T16:04:02.802203Z",
          "shell.execute_reply.started": "2022-11-19T16:04:02.19645Z",
          "shell.execute_reply": "2022-11-19T16:04:02.801038Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "id": "XK2gNyVywmQ9",
        "outputId": "4fa8b871-cc45-47d4-d0d8-08bd9927e454"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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AAHCqlE/kAgAAAABIHkIfAAAAADgYoQ8AAAAAHIzQBwAAAAAORugDAAAAAAcj9AEAAACAgxH6AAAAkFLBYFCSFA6HU1wJ4EyEPgAAAKTUrl27JEllZWUprgRwJkIfAAAAUire0xcIBFJcCeBMhD4AAAD0CtFoNNUlAI5E6AOgqqoq1dbWproMAMBpzhiT6hIARyL0AdC3v/1t3XnnnakuAwBwmiP0AcnhSXUBAFLv8OHDOnz4cKrLAACcpqy1qS4BcDR6+gAAANAr0NMHJAehDwAAAL0CoQ9IDkIfAAAAADgYoQ8AAAAAHIzQBwAAAAAORugDAAAAAAcj9AEAAACAgxH6AAAAAMDBCH0AAAAA4GCEPgAAAABwMEIfAAAAADgYoQ8AAAAAHIzQBwAAAAAORugDAAAAAAcj9AEAAACAgxH6AAAAAMDBCH0AAAAA4GCEPgAAAABwMEIfAAAAADgYoQ8AAAC9grU21SUAjkToAwAAQEoZYyQR+oBkIfQBAACgV4hGo6kuAXAkQh8AAABSKt7TByA5CH0AAADoFRjeCSQHoQ8AAOAUhEKhVJcAAF1C6AMAADhJ1lpdddVVmjt3bqpLcYR4Dx/DPIHkIPQBAACcpEgkIr/fr8cffzzVpTiKy8WhKZAM/LIAAABOUiQSSXUJjkJPH5BchD4AAIBTREgBkA4IfQAAAOgVCNFAchD6AAAAThFLDABIB4Q+AACAk+R2uyVxbh+A9EDoAwAAOEnxYYj09AFIB4Q+AACAk0ToA5BOCH0AAAAniQlHAKQTQh8AAMBJIvShN9u6dateffXVVJeBXoTQBwAJZK3VwoULVVdXl+pSAACnqR/96Ed64IEHUl0GehFCHwAk0O7du/XLX/5Sjz/+eKpLAQCcpurr61NdAnoZQh8AJFBzc7Mkafv27SmuBAAAIIbQBwAJFF+zK76GFwAAQKoR+gAAAADAwQh9AJBArN0FAAB6G0IfACSQyxXbrBL6AABAb0HoA4AEivf0RaPRFFcCAAAQQ+gDgARieCcAAOhtCH0AkECEPgAA0NsQ+gAAAADAwQh9AAAAAOBghD4AAAAAcDBCHwAAAAA4GKEPAAAAAByM0AcAAAAADkboAwAAAAAHS2noM8b82RhTaYz5oN1tRcaY14wxZa1/90lljQAAAACQzlLd0/cXSbM/dNudkt6w1o6W9EbrdQAAAADAKUhp6LPWLpJ0+EM3f0bSU62Xn5J0TY8WBQAAAAAOkuqevs4MtNYeaL18UNLAVBYDAAAAAOmsN4a+NtZaK8l2dp8x5mZjzEpjzMqqqqoergwAAAAA0kNvDH2HjDGDJan178rOHmStfcxaO9VaO7V///49WiAAAAAApIveGPpeknRD6+UbJL2YwloAAAAAIK2lesmGv0taKmmsMabcGPNNSb+Q9CljTJmkT7ZeBwAAAACcAk8q39xa++Vj3DWrRwsBgASpr6+XJNXW1qa4EgAAgJjeOLwTANJWPOzV1dWluBIAAIAYQh8AJJAxRpIUm3wYAAAg9Qh9AJBA8dAHAADQWxD6ACCBotGoJMIfAADoPQh9AJBAkUgk1SUAAAB0QOgDgAQKBoOSJJeLzSvgZJy3CyCdcFQCAAkUDoclMbwTcLr4UG4ASAeEPgBIIA4EgdMDv3UA6YTQBwAJ5Ha7JTH0C3A6zt8FkE5OGPqMMQONMU8YYxa2Xp9gjPlm8ksDgPTj8XgkEfoAp6OnD0A66UpP318kvSJpSOv1bZK+n6yCACCdcS4fcHqgYQdAOulK6OtnrX1WUlSSrLVhSYxpAIDj4IAQAAD0Fl0JfU3GmL6SrCQZY6ZLqktqVQCQpgh7AACgt/F04TE/kPSSpJHGmHcl9Zf0+aRWBQBpKj6RCwAAQG9xwtBnrV1tjPm4pLGSjKSt1tpQ0isDgDTE7J3A6YHfOIB0csLQZ4y5/kM3TTHGyFr71yTVBABpy+v1SuKAEHA6fuMA0klXhndOa3fZJ2mWpNWSCH0A8CEM7wROD4Q+AOmkK8M7v9v+ujGmUNIzSasIQApYxUZvo7tcrq7MjwUg3bFOH4B0cipHJ02SShNdCIBUIvABwMmggSex6DkFkqsr5/T9S63LNSgWEidIejaZRQFAuoq3/nMAAzibx9OVM2QAoHfoyhbrwXaXw5L2WGvLk1QPAKQ1Y+g1BU4HhL7koMEMSI6unNP3Tk8UAgBOED9gIfwBzsbwTgDp5JihzxjToKPDOjvcJclaa/OTVhUApKn48E5CH+BshL7EYpsJJNcxQ5+1Nq8nCwEAJ4hEIqkuAUAPIKQASCddHpBujBmg2Dp9kiRr7d6kVAQAaYzhnQBw6jinD0iOE45NMMZcbYwpk7RL0juSdktamOS6AAAAei0adpKDzxVIjq4MSL9X0nRJ26y1pZJmSXo/qVUBQJrigAU4PdAjlViMkgCSqyuhL2StrZHkMsa4rLVvSZqa5LoAIC3FD1g4IAScjd84gHTSlXP6ao0xuZIWS3raGFMpqSm5ZQFAeqKVGjg9xGfqBYB00JWevrckFUi6XdLLknZIuiqZRQFAuiL0AacHZuoFkE66Evo8kl6V9LakPElzW4d7AgA+hOGdwOmBnj4A6eSEoc9ae4+19kxJt0kaLOkdY8zrSa8MANIQkxEApwcadgCkk6709MVVSjooqUbSgOSUAwDpjQNBAADQ23Rlnb5bjTFvS3pDUl9JN1lrJye7MABIR4Q+AADQ23Rl9s6hkr5vrV2b7GIAAGgvGAxq9+7dGjNmTKpLAQAgbXXlnL67CHwA0DXxGf04py8x5syZo5tvvln79+9PdSlABy7XyZwhAwCpxRYLANBrrVy5UpJUXV2d4kqAjmjYSaz40HjCNJAc/LJ6kN/vT3UJAJKMadwTK34A2NLSkuJKnCEajSocDqe6DEcgnCQHYRpIDrZYPeStt97S7NmzNWfOnFSXAiCJmMglsSoqKiRJu3fvTm0hDvHAAw/o9u99L9VlOAKLsycW204guQh9PeSNN96QJC1YsCDFlQAdceCC3iw+QoKevsR4/fXXtXHTplSX4Qj0mCYHPX1AchD6ekhtba0khnii9wkEAqkuwVEY3pkcfE/R29BgBiCdEPp6SE1NjSSpoaEhxZUAHQWDwVSX4CihUCjVJTgSn2ti0QDZfYQ+AOmE0NdD4jsHegHQ23DgkljxEM0QpcRwu92SpKamphRX4izl5eWpLiHtsT8HkE4IfT0kOzu7w99Ab0FPX2IRohPL4/FIkqqqqlJcibPs3Lkz1SWkPSYeAZBOCH09JDc3V5KUn5+f4kqcIRqNqrGxMdVlOEJ9fX2qSwCOKd5jyuLsibVjx45Ul5D26OlLDsI0kByEvh6WlZWV6hIc4fbbb9eVV15JL1UC1NXVpboER2HtrsSKHwAeOngwxZU4y549e1JdQtpj/5NYDIkHkoujkx4Sn248fn4KumfDhg2SGPKVCM3NzakuwZForU6M+HDZCIuKJ9SB/RWpLiHtMblQcrDtBJKD0NdD4rN3spPovvhizRKTESQCoS+x4q3/HLgkRvveFHqlE+fQoUN8R7uJRgj0Vnw30RlCXw+IRCJt6/SxwHD3vfrqq22XDxw4kMJKnIGGiMSKh2gOqBOD0Jc47Rt4/IGgjhw5ksJq0h+TNiVWfJvJEPnu41x9dIZfVg9Yvnx522V6Vbrv3Xffa7vMNO7dR4tgYsWDCQeEidH+c9y1a1cKK0l/8cbHOBrNuoffeGLFJ8bh3L7ua//dDAQCKawEvQmhL8mstfrvv/2t7TqLs3fPoUOHtH17meJ9KBkZGSmtxwnofU6seOhjZr/Ee//991NdQlqL9+xlumNb0L1796aynLRHg1lyEPq6r31vKY3jiCP0JdmGDRu0aeNGhQqHSmIn0R3hcFgPPfSQjMutqK9AEjuHRGCHkFgMmUuOyUVBLVm8iFbrbqiurpYk9fdFlOkx2rZtW4orSm8f7jlF9zAkPnHa9/Sxj+8+p3w3CX1J9vzzz8t4MhUuHNZ2Gz0rJ+/IkSP60Y9+rOXLl6tl6PmyHp8k6SDTuHcbM6AmFuedJU58AixJOn9gUC3+QIfh8jg58eGcLiONzg9q5Qo+y+6Ih2gkFo253df+XGhCX/cR+nBCK1as0DvvvCN//7GSObpUw8aNG1NYVfpZvHixvv6Nb2jtunVqKZ2p0IBxbfe99+4ShtF104EDR4Mzk7p0H406idN+AfHxhSFle6UlS5aksKL0tnnz5rbLU/sHtK+8Qlu2bElhRelt586dqS7BUeIH1oS+7ms/dJvQhzhCXxJYazVv3jzdddfdstlFCg4+q8P9K1euTFFl6WX//v26++679dOf/lS1Ibcax1+lcL/RHR9z4CAHgd3Q0tKiLVuPHvQ1NjamsBpniNAIkTDtFxB3u6ym9fPr7bff6tADiK5pamrS++8va7s+fWBQWR5pzpw5KawqvW3cuCnVJQCdat+5wOiT7qOnD52qqKjQj378Yz344IMKZPdX05jZktvb4TFvv/UmvVPHUVNTo4cffljXXX+9li5boUDJVDWOu0rR7KKPPHZIjtUfHv49E+SconXr1inS7jxTWgS7r/2wGnTPh3tSrjijRdFQUP/+s/9DA8VJeuGFFxQI+GVNbLef7bGaPbRZixYtoiHyFBw+fFh79uxOdRmOxPFR9wSDQc2bP7/tOueedh+hDx34/X49+eSTuuGGr2vVmnXyD5uu5jGXynp9HR73sYEBHTxUqcWLF6eo0t6rrKxMDz74oL74xS/pf55/Xv7CUjVM/KyCgydLx1i355vj6lVdVaWf/u//zXIYp2DJkiUy7RolCM/d52d4Z0JEo1EtX76iw22DsqO6eXyDNm3cqG/ffHOH4Yo4tpqaGv3tb08rXDBU1n10xuMrhrVoSI7V/ffdq/3796ewwvSzYkXH76ZTDgpTKf4Z8ll2z0svvaS62lpZl0dSrDMC3eOUCcQIfd3U1NSk559/Xl/+ylf11FNPqSW/RA1nflahgROkTsalTx0QVHFulN6pVvv27dPTTz+tb9x4o2666SbNX/CymvuUqnHiZ+UvnSmbkXPc548uCOum8Q1av36dvn3zTZwveRLC4bAWLV6iYEFJ222VlZUprCj90fCQOMuXL1dNTbUimfkdbj9/YFA/ObtO/iP7ddutt+rRRx91zA45GcLhsO67/375g0H5h53X4b4Mt/TdiXUKNdfpu7fdyvazi6y1euHFF6XM3Lbb6HnuvnjYY/3DU7d3717913/9l8IFJYp6Y8dPm/hdd5vf7091CQlB6DsFgUBA7733nn7+85/r2s9+Vr///e9VHfKqedzl8o+8WDYj+5jPdRurb41tUE1Nte66887Tbkfh9/u1bNky/f73v9eXv/JVXXfddfrTn/6kHZWN8g+brvqzvqjA8AtlW5dk6IqPDYodBDZVV+i2227Tf/zHf3Q4FwidW7lyperrahXuUyrjik00tHbt2hRXld5Y9ywxwuGw/vT441JmrqI5fT9y/7g+Yd0/7bAuGtyiuXPn6lvfvJEJSToRCAR07733as3q1WoedkGn29XinIjuPrtWLv8Rffe739UjjzxCg+QJLF++XJs3bZJ/0OS22+hNSRwmwzo1fr9f99zzHwrLJf/wC6XWfoeysjIaJLvJKaHPk+oC0oG1Vnv37tWKFSu0fPlyrVm7VqFgUMaTqUDhMIVGjFU0p3+nPXudGVkQ1i0TGvToxo267dZbdO9992vYsGEnfmIain92y5Yt07Jly7Ru3XqFwyEZl0ehvIEKD5uucOEw2XYtpqdifJ+wfn5ejebtydKr77ypN998Ux+74AJ99nOf05QpUzosVIqY+fPny3h9CreuISnFzje97bbb5PGwaTgVH+4psdYyE91Jstbqscce047t29Uy8mJ5jnTegJPtsbpxXJPOGxDU41uk2267Vddff4O++tWv8v1VbBKc++67T2VlZfIPPe8jk2C1V5Ib0b3Tjmju9mw99+yzWjB/nj73+S/o2muvVWFhYQ9W3fvV1NToVw8+KPnyFeo3Wr4970mKfd7jxo07wbNxPPHzoTm3/ORZa/Xggw9qx47tah79qQ6dD+FIRK+88oquvfbaFFaY3tqfFxr5IMwAACAASURBVJnO+3X2jMfQ1NSkVatW6f3339f7y5bpcHy2uKwCBQtHKVxYokjeYMnlPv4LHcN5A4LK9dTp4U3St2++SXf84If69Kc/ncB/QepEo1Ft2rRJixYt0juLFulQ61p6NqtQob5jFC4oViRvkORK7NcvyyN9YWSLLh3q12vlPr21eqneW7pUxUMG66qrP6PZs2dzANNq1apVWrx4sQJDzu7wHT5SW6eNGzfqrLPOOs6z0Rlrrd54803J5ZWisaUvqqqqNGDAgBRXlj5CoZAeeeQR/fOf/1RwwHiFi0qPGfriJhaFdP+0w/rrthw9+eSTeuvNN3Trbd/RtGnT0nbH3B319fX6+9//rmefe05R41HLqFkK9znjhM/L9lh9Y1yTZhX79fyuoJ566inNmfO0Zs36pK655hqNHTv2tPw82/P7/brr7rtVc/iImsZe3mHbuWnTJl166aUprC79xWeZpKfv5IRCIT300EN6/fXXFSieoki7htw8b1QDsq2ee3aurr76arndp3bMerpbv3592+XKykoNHDgwhdWcOkJfO4FAQIsXL9Ybb7yp5SuWKxIOy3gyFcwbrMjwcQrnD5HNzEvY+00oCuu+qUf0yKY8PfDAA1q8aJFu+853NGjQoIS9R086ePCg5s+fr5dfeUVVlZWSy61w3mCFz/iYwgUl3e7N66r8DKvPjWjRVWe0aEVVht7aH9Yf//hHPfH4n/TxT1ysz3/+86d1i2x9fX2spTorPzZJTqsxBSGV1Xm1bt06Qt8pePXVV7Vp40YFBkxQZmVsKve1a9c6pjEn2bZv365f/t//q7Jt2xQceKYCQ8878ZNa5XitbjmzUecPCGjOjj368Y9/rPHjxup/ffFLmjlz5mnR83fo0CH985//1Asvvih/S4tCfUcpMHSarDfrpF5nWF5Ed0xuUEWTW6/u8+mt117Wyy+/rJEjSnXFlVfpU5/6lPLyErcfTAfWWr399tt67LE/6cCB/WoZNUvRnH4dHrNgwXx95StfSduDwd6Ec3S77siRI/rZz/5d69evU2DI2R9ZIsxIumxosx7+4KAWLlyoK6+8MjWFprnVa9a0Xd64cWPa/s6dvyfsgltuuUVVVVU6fPhwbKrgzFwF+45VxFcob812mbBfnpodHYbBearL5K0ukyRZb5b8Iy9uuy9z7/tyNR+WJEUKij/yI2yvyBfVXWfXaeE+n/65dImWLl2qT196qa655hqNGTMmSf/ixNq3b5+++93vtnV/R7L7Klh6kcKFw+QKNihz7zJ5DsemXveP/ISsNzbs4FQ/w6wtC+Ruve94MtzSjEFBzRgUVEWTW29WZGrJ26/r9ddf19lnnaUbvv51nXPOOYn5ENJETU2NfvDDH+rQoUo1j7m0Q29rtsdqYI7l3KhTsGnTJj300K8VzRugcL9RbaHvzTffIPSdQE1Njf77v/9bL730kqw7Qy0jL1a4qPSUXmtK/5Am9T2sxQcytWDvFt1zzz3qU1igT186W7Nnz1Zp6am9bm8VDAb1/vvva8GCBVq2bJmspFCf4QqOOKvTJW5ORnFORN8Y16QvjmrWewcztOjgDv3ud7/To48+opkzL9Lll19+WgydX7NmjR794x+1betW2ewitYz5tCLtJr+SpJmD/Xr/kE9PPvmk7rzzzhRVmt7an3N24MD+tB5C1xMqKys1d+5c/etf8xQKh9Uy4uMK9x3Z6WOn9g9qXGFYj/zhDzrvvPMYfXKSAoGA1q9bLyMrK6OFCxfqggsuUFbWyTWo9Qandejbvn275syZ0zbtd9TjU3DIRIUGTZKMkau5Rjq8I+l1uF3SlWf4dcHAoP61J0uvv7JACxYs0PAzhumSWZ/UhRdeqNLS0l63AYxGo5ozZ46efPJJRSJRRb05shnZChSfc3Sn2EuWLCvOiei6Mc36/IgWvb0/Uy9vXac77rhDZ02erOuuv17nnntur/t8EykcDmvZsmV6+OE/6GBlpZpGfyo2xPZDziry67X331dFRYWKi4tTUGn6ee+993T/Az9X0JWp5pGXyARjQ5PyvFEtX7Zc27dv16hRo1JcZe+ze/du/eMf/9DChQsVCkcU7D9GgeJzJU9mp4///YY8eVzSlWe0aHLf2PDZPQ1uPV12dIbfW89sUGGm1SXFAbmN9Mo+n2obj+jZuXM1d+5cjRxRqk99+lJdfPHFadtS29zcrJUrV2rJkiVavHiJWlqaZTKy5R84SaEB4xI+oiLbY/XJkoA+WRLQ7ga3Fh3I1NLFsfOmB/Tvp9mXXa5Pf/rTKikpOfGLpQFrrcrKyvTuu+9q8ZIl2rljh5SZq5bSmbGDavPRkNs3M6pPFrdo4csvKxgM6qabbtLgwYNTUH36qa+v1z/+8Q899z/Pt93W3Nyi+fPn0yv1IZFIRGVlZXrhhRf02muvKRK1ChWNUHDwZEWzjn3qistI3xzXoP+9wqP7779Pt9/+fY0YMaIHK09PO3fu1MKFC/XyK68oEPDLJclKWrlihW75/76te/7jXp1xxomHzvcmxgnroUydOtWezOKy69ev19/+9jctX75cxu1VoP9YBQeeecLlAbrDU71dWbsW6Y7J9TqnX+i4j20KGS09lKFllT5trY3l8kED+mv6x2bovPPO05QpU+Tz+Y77GskWCAR03/33a/GiRQoVlSowbPpJDyPqjqzN8+VpPKS/XlJzSs8PRqS39/s0b1+Oav1S6fAzdOVVV2vWrFmOOu/v4MGDWrBggf41b76OHK6RMnPUVPoJRfM6HvDmr3pKZ/Vp0dfHNemuZX3Ub1CxfvPb36lv34/OnIjYgeH+/fv1l7/8Ra+99ppsdpGaRl4i68uXq6lGOZte1BdGNOnl8hwNKBmhB37+87QNGYlUXl6upUuX6tXXXlPZtm2Sy61g0UgFB0865oy9vh1vy3t4p0blh7oc+iRp8YFMLT4QC5DZnqgm9AnrvUM+7ayPndNy5pkTdMklszRz5sxe3fIdDodVVlam1atXa+WqVVq/bp0ikUjs1IPCYQoVlSqSP6TTMNKZnDVzlBVtUWl+WNKpfZ553qimDQhq0QGfNh7xylopOztLN910sz7xiU+oT58+ifwIki4YDGrt2rV69913tWTJu6qpqZaMUTR3gIKFwxUaMPaY56DnrfizrhnerCvPaNGLu7P0Snm2onLrmmuv1de+9jVH7U8SqaamRs8995z++cILCvj9sQndjEveI7uV7bGKuDN1//0PaPLkycrIyDjxCzqQtVb79u3TqlWrtHr1aq1avVrNTU0yLo8C/UYrOGjScRt5sjc8rz6RI3p45hFJ0jv7M/WXrbmKWGnM6FG67PIrNGvWLOXn5x/zNU43DQ0NevPNNzVv/vzWfZRLoYJhCvUfo9yyV3XRYL/OGxDUo5vyFTKZuv3739fMmTOVm9szpy91hTFmlbV2aqf3nU6hb/v27frDHx7RmjWrZTKy5O8/XsEB44/ZspxIJxP62qsNGK2tydCa6gxtOpKhQETyej0666yzNX36dE2fPr3HW1gDgYDuuvturV61Sv6h5yk08Mwuz1yaKN0NfXGhqPTewUy9uT9Lu+rdcrlcmjLlHF1yySxdeOGFabcxrK2t1Y4dO7Rz506tWLFCK1askLVW4YIShfqPVbhgaKcL3cdD3w/OatDWWo8eXF8oX3auvnbd9frMZz5z2u5026uurtbq1au1Zs0arVy1KnbeqnEpMHhybPhx66QO8dD3jbGN6pMZ1SOb8uX15ehbN92s6dOnn1bhr6mpSevWrdOqVau0bPkKle+LLWlhc/oqUDRK4b4jTthYFA99v51xWH0yu7+/OtTs0vuHMrW82qd9DbHfwpjRo/SxGRdq+vTpGjNmTEqHLAYCAW3dulXr1q3T+vXrtX7DBgVapwu32UUK5Q1RuHCoIrkDO/0tn0giQl9BRlS3TYwtN3Q44NIfPsjV3kaPAhHTtg2dNeuTve5gSIodSFdWVmrbtm3aunWrtm3b1radlKRwzgCFBoxVpKBEJtSszL3L2p7b2ekJnoaDumZ4sz47ItbDfzjg0j93ZmnRQZ+yfD599WvX6eKLL9agQYMcPxS2Kw4ePKhnnnlG8+bNVzgSVqhPaaynKrtIGeWrlHlgne6YXK8ntuarPiB53G6NHDVSEyacqfHjx2v8+PEqKSlx5MiccDisvXv36mc/+5kaGxvV0NCgcDj2O5UvTyFfH7n8dZI7U9blOuHpMh8OfZJUHzR671CmFh/M0r4Gl7wetz4240Jdfvnlmjp16mk50Us0GtXatWu1YMECvfPOOwqFQrLZRQr0G61w0UhZb6yTpWDln3XRYL9uHNcU2+5tzFNZa8fMwAH9NHLUGI0YMUKjRo3SiBEjVFxcnJLPk9AnaeHChfrVgw/KurxqGTRZof7jJHfPjW491dDXXjAiba31av1hr9Yf9ulAU2yjVzxksGZcOFMf+9jHNHHixKRPWvC73/1O//jHP2LDXY4zDXgyJSr0tbev0a2lhzK1rMqnqmYjj9utadOm6dLZszVjxgx5vd6Evdepuv322yXFDlwuvvhiZWdna8eOHdqwYYO2bStTJBI++uDMXAX6jpJ1eeWp2xd73jHOnfQ2VbWFPkna2+DW33fkaONhr/r1LdL50y/QpEmTNHHiRBUXFztyhyvFNv7V1dXav3+/Dhw4oIqKCh04cEBbt25TeXnsMzTeTAVzBymSNzg2QZGvY8NA+9B3cXFAB5tdenRTvna19jANLSnW1GnnaerUqZo8ebKjJsVoaWnRhg0btHbtWq1evVrbtm1TNBqVcXkUzh2gUOHQ1iVauv5vTnToa+9Ak0urqjO0utqnHXVuWUmFBfk67/zpOv/883Xeeecl/f/ne9/7npqamtTY2ChfVpbKy8sVaT3Qsy6PrNsr686Qf+TFbefpdet86IZDOiM3pPvOq0v4v6VtG1rpU1WLkdfr0fTpF+iTn/ykpk+frszM5DewttdZwNu8Zasa6lv/7cbIZvWRDbXIenyy7gwFh5zVdnqCq7nmpENfXHmjW8/tzNaa6liDmS8zU8NLh2vEiJEqLS1t+1NUVOTY7WlcQ0ODtmzZotdff12vvfa6orIKFo36SA9/PPTdM7VW/bKi2nLEq531ntifRq8Crbu3vJxsjZtwpiZMmKDx48dr3Lhxadej2tzcrJ07d2r79u0qKyvTtrIy7dq1S+FQ6/GhMYq6MxQuGhEbiZaZJ1fL4RN+H6Xjh772dje4tfhAppZWZqkxKOXmZKu0dISGt343hw8frtLS0rTrue+KSCSi3bt3a8mSJZo/f4EqKw/FlmErKlWo3xhFs/t+pDOjfeiTpHBU2njEq70Nbu1r9Ghfc4YONBlFW3dTGV6vSkuHa+So0RoxYoRGjhypESNGqKCg6+tQn4q0DH3GmNmSfivJLelxa+0vjvXYE4W+I0eO6Nprr5V1ZyiSVdhhGEw0u0iBYdM7fV77H9GHBYadH/tSdCJry4KP/ntCLXL761SSE1au13a7dVWS/rQpR9vqvGoMGbVEXIpaqSA/TzMunKmLL75Y55xzTlIC4OVXXKHGQKTTMeQ99Xm6m2pkoiGNK4x9hon4PP+2LVt7G2OfV0lOWF639H5llo74paI+hW3rVmVnH13/pic0NjZq27Zt2rJli5599lm1tLR0nN3M5VY0I08KByS3R9blkX/Ex0/qAPHDoS/ug8NevbrPp231GWpu3Rf1KSzQxEmTNW7cOA0ZMkQDBw7UoEGD1KdPn7Q4eAmFQvrOd76jYDCoQCCg4cOHKxKJqLyiQgcPHFAkEunweJOVr1BGvqzLK1egPnYgnpF9zAPtaFYfZVRubgt9kmStVNHk1geHvfrgsFdb6jIUbH2bvNwcDRkyREOKS2J/t/vTr1+/Xt3yaq3V9u3b9dOf/lQNDQ1H19cyLkVz+insK5S7qVrW7ZWMOaWJnEw4KHfL4bbhne0Nyw3ra2M6X3S4/W/9w746ukln5HX8f64PGm2o8erv23PUGHa17bhzc3P1wx/+MKGzgPr9fi1atEiLFi3Wu+8uaetlimYWxHrx8gYq6s5Q5v6jM8YlbBKsJIa+OGul7fUeLTuUoWVVWaoLSNlZPn3i4ks0e/ZsTZo0KWnbivLyci1ZskSrV6/W5s1b1NBQH7ujNeCFs/sqkt1XkZx+sW1kN5cOig/v/HDoi9vb4NaOeo8qmtwqb/KovDlD9YGjx115uTkqHTHiI2EwXRuDwuGwdu/erc2bN2vTpk3a8MHGth5+4/Yo0G+MggMndjossX3oK83v+PuMtm5Dd9R7tKPOo50NGSpvcil+CDt40ECNn3Cmhg8frpKSEg0dOlTFxcU9vr8+lp07d2rp0qXavn27tm7bpgP797f97o3Xp3BWkSJZRYpkFyma01dRX2G3R1CdKPTFhaLS2uoMfXDYq4pmjyqavGoKHf2OFuTnanjpCJWWjmgLg8OHD096eEmkQCCgLVu2aMOGDVq/foM2fLBBLa2TB0XyhyjYb3RsaZvjbA8+HPo6E4xI+5tbQ2CjW3sbPdrX5FVDu/kt+vXt0xoER2r06NEaNWqUSkpKEjYS4Hihr1dO5GKMcUv6g6RPSSqXtMIY85K1dtOpvN7hw7EdoPX4unzeQzrI8lj1yYyqT6Y0vrBFxblRrawK6K1XYxPBFBbk69OXztZll12W0FnrivoUqWn/gdiePQ0O8k9Fn8yorhru1xdHNmtDjVevlIf0pz/9Sc/NfUbf+Oa3dMUVVyQlUAcCAW3fvl1btmzRli1btGnzZlWUl7fdb3x5CmUNUKSoSNHsPopmFSnqyz/u9zrcb/Qxe2TjAd276qlO759YFNLEopCiVtrf5Na2Oo/K6gLasmqxFi9e3OGxGV6vBg7sr0GDizVo0KCP/OnpFu1AIKDy8nLt3r1bu3fv1p49e7Rj5y4d2F8Rm6W31f6DlbJZBQpn5EmFpXI3Vsq63JLLrZaRl7QdnByv0eJEjIktgF2SG9HsYX6FolJZnUe76j2q8vtVWX1EG8vLtKhFirRrh/N63Bo4cKCGFJdo8ODBGjRoUNvfgwYNUkFBQUqC9r59+/Svf/1Lb739dmyYq1p7pbw5Cg4YGxvy7fbK1VwjV7uW6d4sP8NqxuCg3jngk1VE/rBRQ8iooblB99xzj4YNLdGDD/262+f/zZ8/X//12J9UX1crZeYo2H+swvklsYmVPB2HUbfkdz4ZSFd+051pGXe5ctbM0aHmqB5Y/dGh64kK0cZIowvCGl0Q1p4Gj/I9RnXBZi1cENs3+Xw+/epXv9KkSZOOWWtXxEc+NDc3q66uTnV1dW0NYja7SKHsgYoWnalIdt+2gNfWQ3dk90de72QbH7tiWF5EwzppYChvcqu80aPyJr/K99TplU0b1NJuoEbfoj4qHRHrGSgtLdUFF1zQK3uzampq2gLexo2btGXLFgUCseHIJiNLoax+ihRPUSSnvyK5/SX3iU8VeHJLrnyejo3j+xo7Nub+6Kxa+dxWuxs8er3cp001B7To7UN6M9rxtXyZmXJ7PMrMzNTo0aN19dVXq6Qk1sD2b//2b22P+8pXvqLzzz8/AZ/IUbfffrvq6+tVVVWlxsbWBmbjViQjW+Eh58QCXlZRbD6J1u24p7pMmXuWdvp6J/v9dAUa1STT9ls/UeP4tAGxZLJof6be3p+pQMTIGKk0p1oVu2v1ysb1Hb6jWb5MjRs/oe072tMzg8Z//1LH/7+ysjL99re/VVNTk5qamlRYWKgdO3e2jaCQy6Oo26toTn/5R13SNp/HiRrMolZaU52hB1a7T/hZDh8c2w7FtptS2Cd5XNLZ/YLa1xjQ3k01Wrl8edv+3peZoZEjR2n0mDEaNWqURo0apdLS0oSPjuiVPX3GmAsk/bu19tLW63dJkrX25509/kQ9fdFoVN+66Sbt2luhxlGfUjS3fzLKPq5EDO/sqmBEWleToaWHMrSmJlORqDRlyjm67rrrE7JEwRtvvKF7771Xob4j5T9jRo8Ok41LxvDOE9lR59HcHTnaUuvRyBGl+smddyVkWY3169frnnvuUVNTk/yt5+5IkoxLEW+Owv1HK5LTT5GcfpLn6AQ+iew59TQcVI4nqqG5kS73mr5RnqlFB3wKRWMz0E4oDKna71J1wKPyJrdCHY9z2kJhbV2DMjIylJGRoUsuuUQzZ87UoEGDVFVVpdGjuzdceMuWLfrxj38sv9+vYPBDU8e6MxTKG6RoVh9FswoVzcyXzcyT9WTKU7M9cb36kZDczTUalBVRYWb0pHqhF+3PVNhKXpfVBQODqmxxq8rv0pZarxpDR3uf4nyZGZKMMjIzlZGRoenTp+vjH/+4pk6dmpQwWFlZqf/8zW+09L33JJdL4fxihQrPUKRwaNImckrm8M6uiFrp8c05WnLQpxtuuEHf+MY3Tvm1du3apRtvvFFRGUV9hbLujNhCWvH36oGREu6GQ/K5om3n9LWXyJ7TuPbhMmql+qBLh1pccrnd+vvfnznlg8Ta2lp961vfUm1tbdt5T9adIevJVDhvkAKlMzt9XiK3m1Js29k3M6L+WSf3W//wiBNrpZqAS3/blq198fMjjdQUdikUjZ3b9rEZM3TFFVf0+LlXV1xxRdvleO9Oc3OzWlr8bQFPkqJZRQrnDVQkd4A8R/bIU1chGckad4eRQdabLRNq7WVpd3Ad7+nLdFm5jFVfX1S53thnVpgRUW3w6L+5/ef5xOYcLa+MhUmPS+rniyoYkYJRI8mqMeRWMGo6NKi5jJGV5HK5ZIxRQUGBCgoKlJmZqT/84Q/d7nWZP3++fvPb3yoUDMZGPnizZb3Zsi5Xz42IaqySR7HGF6n7I6KslZ7YkqOd9R4FI0bZ3qhcxqWKZrcC4VhuPffcc3XZZZfrwgsvTPpw7vahb/bs2XK73dqwYYOWr1ihQwcPtt0Xze6rUP4QRfIGygQa5W1t8DnZURKehoNyG6sM16l/N4tzjm4nvziySR6XtKfBo72Nbu1p9Gpvo1ct4aNfVF9mprKys5WVlaWsrCz95je/OeEogLTr6ZNULGlfu+vlkjo0wRhjbpZ0syQNGzbsuC/mcrl037336nu33y67Zb6C/cfFxpIncbbOVMpwS9MGBDVtQFD1wSYtOpCp1zat0R13rNEll1yin/zkJ936Mc6aNUsVFRX685//LG9TpfyDJitUNDIl4a8njSwI665z6rSyKkN/275Lt95yi/7Pz36miy66qFuv+/zzz6um5mh4tYr1SluPT1Ffvtx1++Wu3y+p4xAvd8MhuZtjz/vwTjXjwIZOd6qS2p4jSdGMXNnWiYxawkZ7Gtx6Znu25u05ujE7lt0NHh1sju0YPS5pV0Ps/z/TFdXUfmHV+F0KRY2Kc8IakR9WVYtb1f5GlTdkyLYe6T799NN6+umnJcV+pxPGj9ODD/36lGen3b9/v+obGhQf82NlYgcYkaCsyyMTCSqSN7DDOTu+HW/KhJrlCrXEDsRbJ2QxoRa5Qs3K3LVE1pff6c7B3Vwj685UtN1QJZc/NmyuJuBSXdB0+fPcVhvb8Mc/z6WHjqaBM/uEdCQQC31n5IV1Zp+QqvxuVbW06PVynwIBv6yM5s2bp3nz5mn27Nn64Q9/mNDzUKurq/WNG7+p5ha/AkPOiS0P0IMz9p7Mkg1S14dyTyoK6qrh7RpbJNUFjcrqvNpQ49X6Iz7VtEiDBg7QZZdd1q1/Q25urnJyc9XY0CATCUgut6zp+eG7A7MjuntK/Uk9Z+bggGYOPvlFs+PvEx/ivLwyQwv2Zcsa061FuDds2KDq6uqjN7i9sd92dl9Zl1fZm16SNR7JdNxuttf5QV+sZ/p4a+wGB09u24bkrfiz6oIuNYdP7re+pyH+W3d3CMZ9MqPq64t1V00qCuqKM/za2+jWuwcz9friRVq0aJE8Ho+KiorUt29fPfroo135uE7ZmjVrjg7bljoM4Y64M+Qy7tZeKqOWETPbQom3cosUjYUNYzp+Fq6marnCrcNhjVs6zlrGcSfTiPX/2LvvOLnqev/j78/MbC/pZbPphCSkBxISQg1duhTlCoiIiICK5ao3cPl59V4E0atcK0VQQAFFUAIYkE4ENBASEhII6b33ZPvM9/fHmdnMJpvNlpk9e05ez8cjj92Z2Ww+HM7MOZ9v+Xzyok55UUlyyRty7//JsM61GtetVhsqIlpfEdWzKwsUjyckmbZu3Vp/HT777LP0pS/doIsvvrjZ/2a6eDyuX99zj5fwSVJ+qeqKeipR3EPxop5KFHiJc3qiljqn6rofqURh1xbt3ZO8Wfz9kxarrZSqd9T/nmdXFujZld5ndv/iumZ9BuysiTQ4Pxsb4Ek4r0DWXXNLNfe92Xr33dmKRCLq0qWLunbtqvvuuy/jg5DV1dXasmVL/X7o999/P/mKKZFbpNryY7zPg6LuDZZsxtISaqutbPD/oKmkuimt/W97YXV+fbI4umuNbj16pxJO2lwZ0a2zOivupKrqKlVVV2v7dm+J7vnnn69hw4bqnnvubdW/G9i7dOfcfZLuk7yZvkP9fJ8+ffTbBx/UPffcoxkzZih384dykRwlcgrkYnmq6TOuRZu3pZaNEuSu907IJ5YWasaqhuFme3RVknrk1SqqiF555RX16NFDN9xwQ6N/r7k++9nPavTo0frVr3+txR+/qYI17ypukeRIVo5q+ozJ6vFMNWfP5hKldPv/O93zalVZ63TnHXdowoQJbdo3cNttt2nDhg2qqKhQRUWF8vLztXHDBsWrqhSt2iFZpL6gQ3T7KiWKeiiRX6J4SS9FqpM3VI0cu1TSt7942oda6kJT8s6DKog59Stu+N9fmut046jGLwxDO9dpY2XjN6zn9K885LGMO6k2YaqqM+2tM1XWOX2wYKE2btzY6t43p556qiZPnqzZs2fr7bff1ptvvaWdO7yLiBQwDgAAIABJREFUniXqFInXyNbMVnz7CrmcIq8BcF21FMlVvKBAVUOmNnpuHky8sNsB52bhgqcbJNbpWntxSK3IiJh0Vf25XSvn5I26JkzVcVNlckni888/r5ycHH3zm99s1b/XmFdffVV79+xWvKCrorvXK7p7ff1r2Ry5jlTuaPT1TIk7adGOmJbv9pbbLt2dp03JQ1yQn6djJkzUSSedpFNPPbXNS7p79OihX/7iF3rggQc0c+ZMuZq9SpT0VE2XQarrMkgu9+CfI00t6WxK5fBzGjwumvOopOyuNknZXm36cHuOPtyeow92eMmzmTRx4kRdc83n1a9fv1b/7hNPPFEjR45UZWWlKisr1atXL23Zuk1r17yvfSuYTC4aU+7q2UqU9FS8oIsUP3CGs6065SbUo6DhusJMfnYOLIlrYEmFVuyKaU+taUdNQps2bdKmTZv09a99TVdceaUmTGh0YL/NfvSjHzV47Cyium5HqK5TuZyiyt34Qf3SxPRBoNoeQxs8v//1yaWu7aUH9ostK4wfsLxz/wGedOnHM33mdFet6fcfF2lXjak24e3ZXbYrpi3VOdpSZdpvJag6dypV77IylZX10RFHNN7ovDmi0aj++PjjacteF+iDBQu1d8vHkiSL5aqusLuserdcJOYlJYmDDxJ0dBGTyoq890D3goQqak1bqiL1ifS1n79Gd9z5w4xWrp49e7bWrl3b4DkXzVUip0CJvFJFd65VdJf3eiYHy4tznMoKm14Rla6xczPl4UWF2lTpzUgv2RXTHxYXalNlVJuqYqpNeDPRKZFIRD17dFd5334aNWpUq+8lOmrSt1ZS+tWgb/K5NikpKdG3vvUtXXnllXruuef0+OOPqy45Km9rZqu2aqdX0r4DLnltq4hJXfMSqopHtHr16kP/hWYYP3687rv3Xs2bN0/Tp0/XK6+84lVBi8QU277KKyke9b/iZTZETeqaF9e6ikqtXbu2TcsSY7GY7r333gbP1dTUaNmyZVq0aJEWLVqkhR9+pFUrVyiy8q36n7HcAtXlliiRV6pEfqliW5fWf3+ofT1N2f/DLD3hTZ9JSdfYTMofFu+bSTmpT7U2VES1qTKqoZ1qtaEyqk1VOdpYFVVl2obxwsKCNhcwKCws1IknnqgTTzxRiUSivvz9pk2btHnzZm3YuFEbN27Srs0HJhMlC/6qRG6R4rFCJXKLVNepX3LvZJcGe1ibOr5VA09Q0cKn1S0vccjlnemaujj89qMira8w1SakZ1bka2NlVOsqYlpfmaO9NfuOX25OjgYP7qv+Awbq+OOPb/nBa8LRRx8tSYpW7VQ8uSy2Pff0fmX07gOWdw4oOfiMVWMzUwnnVVXsUxRXZdy0fHeu/rKiSE8t917v3q2LjjpmlC4ZNUojR47UsGHDMl61d8CAAfr+97+vDRs26OWXX9aLL76kFSv+Ja36l+KlZarpeoTqug7K6mfnxopos/f5tGTmNO68pXUFMacPd+TVV5guLirU+AnHaNKkSZo0aZJ69MjMFotf/vKXBzxXXV2t5cuXa8mSJcnKiEu0dOlSVW1NG+HPK/KKM8XylLNxoRL5nZQo6KzqfpMOek4f6nNTat1n56FmW5yTttdEtGZPVOO613jFYPZGVLVXiiekOXPnKuFc1pK+e++9V4sWLdKSJd5x/HjxEq1evax+IMeiMcULuqiuoKti21cqXtBVicKurd53KknXDN9zQCGXxt7rdQlp1Z6o8qJOY7vVaHNVVFuqIvqPWV21pdLqi2XV/7s5nZTTvUxDy/ropP32R/fq1UsFBZlbuVBUVKQJEybU/39xzmnt2rX7ksAPFmj58g31+8sLl7wsFZSqLreTEgWdVNttSP15md5WrCXHNbZliYpiLquz+s5JW6oiWrE7piM71Wr5rhxtr81RZXItbSwaVW5uboN99JkwZcoUDR06NLnMuFJ5eXnauHGj4lU7Fa3a6RVuiuTIRbzB8nincrnc4jYNlufu2aDx3WsOKOTS1HXohN7VOqK0Vmv3xrSpMqLfflSkTZURbarO1ZbKfenGmr0x5eXmqKysTAOH9dPx5eUNirr17t07I3UkOuqevpikjyWdJi/Ze0fSZ5xzCxr7+ZY2Z09JJBJauHCh3njjDb351lv7Cmbkl6qmpEzx0nLVlZZlpI9fe+7pa8yyXVHd/1Gp1lfEdNddd2XlArFnzx69+uqrmv7MM1r88ceyWJ6qenhFHTK9DMyPPX37e3Vtnn67qFiPPfaYysoaL7iQSVVVVVqzZo3Wrl1b/2fNmrVavWaNtm3d0uBnLSdf8dwSxfNKlMgvVSKvVHWd+zbYE5iu5J0HW7ynb/+bwM8cuVcbK6LaWBnVK2vztaUqktxHYapL+7yPRCLq3auH+vYboPLycvXt21fl5eUqL/cKwLRXa4yamhpt2bKlfsR88+bN9d9v2LhRmzZt0t49+25sLSdPdfldFC/wksB4QRclCrscUJhg/5YNzbWtOqINeyPaWBnV5qqoNlVGtLkqpk2VsQaV1CSpS+dSDRg4SP37D1D//v3Vv39/9evXT7169cpqL7B58+bpjjvu1Pr166T8ElV3Gay6boO9pDhL2rKnL+G8ZccLt8X04Y5cLd2VW79folNpsYYfNULDhx+lYcOGadiwYerWreVLezJh5cqVeuWVV/TC3/+uDevXy2K5qu4yWLU9j/LOsQzKdJ++DRUR3f9hsdbsiaky7iVMBfl5GjN2nMaPH6+jjz5aRxxxhK9VaBOJhNavX68VK1Zo1apVWrlypVauXKkVK1fWV/CTJIvmKJ7fSfH8Tkrkd66/6U7kdzpoMljyzoNt2tOXfrNYWefd/K3ZE9XqvVGt2ZOjNRU52pM2sNO9WxcNPsIrAZ8qoDFw4MB2bSlUU1OjlStX1ifWS5Ys1eIlS1Sxd9/npQpKVZvfRYnCbvWJYHrBksY0Vb1T2q8K8vYcLdrh9S5OKSkqVK+yMvXp03gxsY5SyTMlVWwsdT6uWrVKy5ev0Jq1a/a1bpBkuYWqy/POy3hRd8U79W1yVUBK4fwnlVu9I6N7+rZWR7R8V0wrdke1fHdMK/bk1Z+f0WhEgwYN0rBhwzVs2DANHer1rWuvPr81NTVavny5Pv74Y689y4cfafnyZfUVuS0nX7UFXRUv7K5EUXfFi7rJ5RY3e/CyOdU7E84biFi0I0eLdsS0aGdeg8qdJcVF6lNervJy776nT58+9V+7deuWkWWwQW3ZcI6ku+W1bHjQOXf7wX62tUnf/tauXat33nlHs2bN0uz33qtviJso6q7akjLFkxtBW1Pi2a+kb+3eqKavKNA/N+apS5fOmnbLrZo4cWLW/92FCxfq8cf/qDdmviGLRFXd7UjV9B7Vov5cTfE76atLSHfO7aSt1l1PPPmU7013q6urtW7duoYJ4dq1Wr16jbZs3uQtd4pEVdt5gGp7DPMqBaZ9uKQ3Zz8U56RNlREt3hnT4p05Wro7VxsqIg1GVaPRiHr36qW+/frXJ3V9+vRR3759MzZi1R527dqlZcuWafny5Vq2bJmWLlumZUuXqapqX3n2RHFPVfcerbrO/SWzZiV9qRHqxTtztHint6Rwa1rF91g0ql69eqq8bz/16dNHZWVl9SN+ZWVlvt68xONxvfXWW/rLX/+qOe+9J+ecXGEX1XTqr7quA5Uo6JrRGcCWJn1VddLcrbmauyVX87bnaU/ygjto4ACNGTtOo5KzeGVlZR2uxYhzTh988IGeeeYZvfLKq6qrq1W8tEyqqayfWU3fT9aapfNFcx7VwPw9bWrZsLkyUt/TNNXg/ojBg3TclON17LHHasSIEYF4jzvntG3bNq1atar+z4oVXjK4dcvmfT+YX6rq7kNV2/3IAwYwD9Wy4WASyaXZ87bmaOUer5XD5rRJhsKCfA0aNEiDj/Cq+KWSvNLSA7c0dASpnohLly6tnxVc9PFibVi/rv5n9g2cdVW8qJvqugxqUAugsaRvR7VpwbYcfbA9Vwt25GlHcvtt3/I+mjDxWI0bN67+ulJcfGAbiCCKx+PasGFDfSK4cuVKrwp12iCFK+yq2pI+qutUftD70rYmfc5JOVGncd1qvcrdu/K0PXn8o9GIBg0cqKHJBG/YsGFZqTbZVqlVU6lenR99tEjLVyxXokEi2E3xom6Kl5YfcG+UrrGkry7h1TdYtCOmj3bk6ONduapM3uL37tlDY8cfrdGjR+vII49Unz592qUVSxALucg59zdJrauR3Eqp2YaLLrpIdXV1+uijj/Tuu+/q3dmz9eHChYpvmC+LxFRb3Et1nfqqrnO/AxozdwR1Ca+s7Gvr8jV/W47y8nJ1+b9doiuvvFJFRe1TvGbEiBH6/ve/p5UrV+qxxx7Tiy++qNzNH6m2U3/V9hrR5Buro1u5O6qHPi7Wkp0xfec71/ue8ElSXl5efX+n/aVGv1544QU9/8ILqli0TCropKpuQ1Xbc1izSmjvrTW9vTFXC7bnaMmuPO1M5jJFhQUaMXKUJg0cWP/+KS8vV8+ePQNx03copaWlGjdunMaNG1f/nHNOGzdu9JLApUv17HPPaeOSl+UKu6iqbLwSBxnYcE56f2uOnl9dqMW7YvXVTXv26K6xx43RyJEjNXjwYJWVlalHjx4dtj9fNBqtXz67bds2vf7663rttdc0b948ufXvSwWdVN1lkGq7DVH+in/U/73WJisNylseRMJJH26P6bV1+ZqzNU818WTP0qlTdOyxx+roo48ORINhM9Po0aM1evRo3XjjjXruuef01F/+qq27dkgWVSK3MCt70pqjqk7616Y8/WNDvhbt8N7bo0aO1IVTp+rEE0/M6H6d9mJm6tatm7p163ZAZeuKigqtXr1aS5Ys0fPPv6D5899V3rr3VNtpgGp7DlO8pKzF17DquNf7dM6WXM3dlq9d1V4VyX79+mrU6CH1DZwHDRqk3r17d7hBiaaYmXr16qVevXppypQp9c9XVFQ0WG67ZMkSLV22VDWbFsptXKC9g6fKFTTs97arJqJHF+fpg+15WrPHu76WlhTrmOMmasKECTrmmGPUu/eBewHDIhqN1l9L04+lc05Lly7VO++8o3/NmqX58+crvvEDWTSm2uLequ0+VHVdBzb4XQdb3tnYksSqOmnJrhxtqYooatLSPbmqqnP6YFuuenTrqvFTvEGzo446SoMHD+5wCV5jcnNzNXz4cA0fPrz+udQS8NT2mY8WLdKKFQuUWD9PiZLequoz3htsa8Ira/M0a1Oelu7KVXVyKWv/fn11+pTxGjNmjMaMGdMhPxM77ExfS2Rqpq8pFRUVmjdvXv2bbU1yX5wr7KqaLgNV221Io81GU1IzfZlszp5egW5klxoNLo1r1qZczd6arz01Xp+fCy/6pC644ALf+/ts3rxZf/3rX/X09Onas3u3XGFXVfc8qtVVP1Mzfdlqzr5/RT/npA93xPTC6gLN2ZKrTqUl+urNX9Npp53W4tj9VF1drddff11PT5+uBR98oHhJb1UMO1ul7z3S6Ezfqt1RvbgmX29vyldNXCrr1VOjxozVqFGjNHr0aA0cOLBDJL1+qqur02uvvaaHH3lEq1auVGW/Y1WwelaDmb55W3P05PIiLd8VVa+ePXTiSSdr1KhRGjFiRLv2NcqmHTt2aObMmXr5lVf0/ty53gxgNNcrVR7LU02fsa1O+g420+ecNGtTrv66skhr90RUUlykU087XVOnTtXo0aM7bOLcEnV1dXrzzTf1xJ//rA/mz5fFclXVfZhqe49q1bL5ls70rdkT1Utr8/XWxgJV1Tn161uusz9xjk477bRQ33jvb+XKlXrmmWc0Y8bz2rt3jxIlvbR36Fkqmf1wkzN926ojmrvFS/QWbs9VbcIbLJs0+ThNmTJFkyZNCmwj9taKx+OaNWuWfvCDO7SnolIVA6aortsR9TN9nXMT2hPP0ZixYzRhgpfoDRky5LC/1uyvsrJSc+fO1TvvvKM333pbGzesV233I1XVf7IUzWlWc/bVe6J6fV2ePt6Vp1W7vQrRZqbBgwZq9JixGj16tEaNGtUhE5hMqqqq0owZM/TwI7/X9m1bFS8tU3Wf8d4ERVJqpm9gSZ1+t6hYAwf01zETJmrMmDEaPXq0unbt6uN/wT6BXN7ZEu2R9O1v7dq1evvtt/Xqq69pwQKvelVt5wGq7nuMXH6nA34+G0nfQ4sK9eH2HO2ujagyHlFdwttLMeX4E3TGGWdowoQJHW62pbq6Wi+//LKe+POftXzZMllOvqq7DVFNj+EtmjVtr6Rve7XprQ15mrmhUOv2mkpLivXJiy/RpZdeGvgL9fPPP68777xT1WVjlb/xgwZJn3PS0ysK9NTyQuXl5uj0M87UhRdemJG+hGFVXV2tL1z3Ra1etVKS6pO+l9bk6eGPi9WnrLeu+uzVOuOMMzrc+zLTNm/erBdeeEHTpz+jTZs2ejPLPUeqtvuRUqRlidjBlnfurjXdu7BE87bmaED/frriyqt08sknB2L0ubUWLVqkP/7xj3r11VelSFRVPY5STdmYFu07b27St3RnTH9dUaD3t+YqNydHp552ms477zyNHDkyUDNQmVZdXa0ZM2bo7rvvVk3Po5S76cNGk75/rM/Vi2sLtXyXd76X9eqpKSecqOOPP15jxowJ/WdAc2zatEn/9b3vaeGCBd49QDRHeRvmS5Kuu+46XXHFFT5HGBx1dXV6+OGH9fAjj0j5pdoz5AwVLH7xoEnf7lrTU8sK9cq6fOXm5GjEiJEaPWZM/WBkWJbKtlR1dbWeeeYZ/f73f9COHdsVL+2jqj7jlSjppU7vPqheBXXaVBnThIkT9YM77uyQA4skfVm2fv16TZ8+XU/95S+qrq5RVf/Jqu05vMHPZGpPX8JJ87fm6B8b8vTe1jzVppYxnXCiTjjhBB1zzDGBuOlxzmnevHl66qmnNHPmTCUSCcU7lau6x1GKd+4rWdMjetnc07e92vTe5lzN2pynj7bnyEkaOWKEzr/gAk2dOjUQx7e57rrrLv3tb3+TmTS2a0190vfgR0V6bV2+zjjjDH31q18NfILbXmbOnKnbbrtNkpf0xSLS/R8Wa8qUKfrud78bqnOnOeLxuGbOnKlHH3tMHy9aJOUWqqrXSNX2GN7s6pSNJX07qk13zu2izTU5uuGGG3XhhRd2yItvtqxZs0YPPfSQXnzpJVlOvirKJ6qu2xHNWm54qKRvc2VEjy8p1Dub81RaUqxLL/uULrzwwvqm3PD86le/0p/+9CdJapD01SWkPywu0str8zXkiCM09dRTNWXKFA0cOPCwTpYPpq6uTg888IAee+yxBs8///zzre7XejibM2eObrnlVu3O7y6r3HlA0hdPSK+uy9OTy4tVGY/owgsv1DXXXNNh94r6paqqStOnT9fv//Codu3coXincsV2rpGTqW95H/36nns77H1RIPf0BUlZWZmuv/56XXrppfrhXXdp1r/ektVVq6bPoRuONldNXHpjfZ6eX1OkTRWmTqUlOvf80zR16lSNGjUqcDc8ZqaxY8dq7Nix2rJli5577jk9PX26ti15ScovVVXv0artdqTUDss5nJNW741qzuZczdmap2XJkdl+fcv12QtO12mnnab+/ftnPQ4/fO5zn9Pf/va3Bm1K5m3N0Wvr8vXpT39aX/rSl7hRaYGRI0fWf7+pMqqX1hVo/Lhx+v73v39YjuxHo1GdcsopOvnkkzV79mz9/g9/0Nw576hgwweq6jVSNb1GtLgwVk1c+un8Ttpal6cf//hHGjs2c5+zQdG3b1/deuutuuyyy/STn/5UH334hup2rFTlwBNaXW064bxmwU8uL5LFcnXNNVfosssu63AVDzuK6667TjNmPK/du/fti9pdY/rFglJ9uD2myy+/XNddd13grs3tLRaL6frrr9fo0aN1yy23SJKGDh1KwtdK48eP16c//Sn97ne/UyKW12BLdFWddOfczlq2K6rx48fpK1/5qgYPHuxfsB1Yfn6+PvWpT+n888/X008/rd899JCqkgfzxpu+3GETvkM5/O5Csqhbt276we23684779RLL70kxWtU03dCmwqW7K41vbY2Ty+sLdKuamnEiKN0w2Wf0oknnhiam8ju3bvr6quv1hVXXKF//OMfeuyxx7Vo0ZvK37hAFf2PO+SG2tZINbX+V3IP5OYK7//R8OHD9IVPeUtwDoeR2Z49e2rkyFFauMBbUpNw0mNLi1Xep4+uvfba0P/3Z1p6sZAZq/MVjcU07ZZbQvNebS0zq+9ZtWDBAj300EOaNWuW8rYsUkX/KYp3Km/273pyubdk7vbbv3tYJnzphg4dql/+4hd64okndP/99yv24XTtHTxViaLuLfo926oi+vXCEi3aEdOU447T177+9dDsNc2WnJwcjR49Sm+95fVO3VYd0e3vddGOuphuueXbOvPMM32OMFjSC5b4XYMg6C655BL94dFHVVtTLSUXVDgnPbioWCt2x3Trrbfo9NNP5/reDAUFBbr88su1ZMkS775ealNfZr8d3nciWRCLxTRt2jQVFhZq+vTpilbvUuXgk1v0O5yTluyK6bW1efrn5nzVxqVjJ07UZ664QmPHjg3tGzUWi9XPCrz11lv62c9/ro2LZqim5whV95vQqlYZ+6uoM72xLk+vri/U+r2mWDSqCRMn6OoTTtRxxx3nW58uPw0bNrQ+6XtzQ57W7onov/79i+3WWydMzEzde/TQls2blXCm06eeys3zfkaOHKm77rpLc+bM0U9+8lOt/vgFVfcZp5o+4w85QLapMqK/ry7QOeeck/Hm80EVjUZ1+eWXa9y4cbr1P2+TffQ3VQw8QXXdmjeCv3xXVP87v7NqLU/Tpn1DZ555ZmivMZk2dOjQ+qTvz0sLtKMupv/7v59pxIgRPkcWTPn5+aqqqmKpYRuVlJRoynHH6fXXX69/7uW1efrnxjx94QvX6owzzvAxumAaOnRofdLXvXvLBtU6EpK+LIhGo/r617+ufv366Ze//KXyl89UXad+h/x7u2tN/1ifp9fXe0VD8vPydPY5Z+qTn/zkYTUFb2Y6/vjjdcwxx+j+++/Xk08+qZw961Ux6JRWNyreXWN6fnW+XlpXqMpaaeTIEbrynHN10kknBXaaPlNSjeUTTvrriiINPXKITj65ZQMV2GfggAHastnr8TVp0iSfo+m4xo8fr9/85n799Kc/1fPPPy+rqVD1wClN7uf9y/ICRWM5+vznP9+OkQbD8OHD9Zv779N/3nabFnzwmqqrd3tFXppI4DZURHTX+51V3KWHfv6jH2vAgAHtGHHwlZd7M9Sr98T03pZcffryS0n42qCgoEBVVVWH3d7nbDgumfQ5efc/jy0p1uTJk/SZz3zG79ACKX12L8iDYiR9WWJmuuyyy7Rnzx499NBDihccPFnZUhnRMysL9I+N3qzeiBFH6Ypzz9PUqVMP6/0U+fn5+spXvqLJkyfrf26/XZGPntHewaco3rn5++u2VUX0wup8vbK+UDVxp5NOOllXXHEFVSjTpMoMf7QjRzUJ001XXBnoDzW/lZeXK1VY6nAarGmNvLw8fec731GPHj30yCOPKFJXpcojTml0Vn/1npje2pCvT19+SaBHWrOpS5cu+ulPfqIf/vCHevnll6VErbfFoBHOSfd/WKpIXpHu/r+f1Q/+oPlSZexnb8lVSXER1SbbKJXsxZONs9F66deef2zIU21C+uIXO0Zf4SAKy4AYSV+WHXPMMXrooYcUqa044LWEk55dWaCnVxRKkajOPuecw25WrzkmTpyoBx94QP8xbZoWL35FFUPPOuTfWbk7qhmrCvSvTXlyFtHUqVN11VVXaeDAgdkPOGBSVflqEqZoNKLjjjvO54iCLX2JcNh7G2WCmenaa69Vly5d9LOf/1yFi19UxZAzDujf+ZflhSosLODG+hByc3N16623Kj8/X88995xcbpFqex51wM/N2ZKjxTuj+ta3biTha6X0JfAnnzL1sF81kikMOrZd+raC19cXasSIo7i3bIOcnOZVm+7oSPray36dMWoT0i8+KNGcLbk65ZRTdOONN7L3pwndunXTT/73f/WlG27Q2hVvKB5rfAZ01e6onlhWqPe35io/L08XXXyeLr30Um5qmpDeULRXzx4srWmj9P5GBQUtb559uLr44otVXFysH9xxhwqWvabKIac1WJq4dFdMV10V/P6Y7SESiegb3/iGNm/ZolnvzFK8uJcShQ0bBz+3qkhlvXrqrLMOPYiGxqVX5uT6nTnMRrVdakBid21Eu2ulf/vEOT5HFGxhKcYWjv+KDqx///4yM0X2bq5/zjnpgQ+LNWdLrm6++WZ98pOf9DHC4CgpKdEt06bppptuUqR6b4PX6hLSE0sL9fyaAhUXFenaay/XRRddxA1iM/To0aP++6Kiw7Mhayal3wgyYt0yZ555pnbt2qVf/OIXytm4ULW9RzZ4/bzzzvMpsuCJRqO6Zdo0XfXZq5VY+Zb2Dj+3Poletce79H/ls58Ozc2M39IHz9A6fF5mzv4tL1jB0zZh+ZxkOCXLOnfurFNPPVWxin1NxN/emKu3Nubp85//PAlfC40cOVLjxo9Pbz2j6rj04/c7acbqAp133vl69LHHddVVV5HwNVPDmanDdw9ppjjnDv1DOKhLLrlEx06apIJ178lq9i2LLykuZrlsC3Xu3FlfvulGRfZsUmzL4gavRaMR2gpkUPrgGVqHz87MSZ8tHXLEYPZBt1FY+m2S9LWDq666qv776rjp8WUlGj58GHtTWumUtMqSzkn3LSzRhzu8Vhnf/OY3SfZaKH10tZhjB5+ZmW7+6lcVUUK56+bWPz+I/SitcuaZZ2r4UUepYN17Ury2/vkhRxzBZ2UbpY/+c1PddqlEheQvs4YfRUXZtgrLkuNw/Fd0cOnFQ15bl6fAXIyzAAAgAElEQVQdVdKNN94UmpGD9pZeEvvtjbl6Z3Ourr/+S+xNyQBmUtouLBcHP5WXl+ucc85R3tbF9YkK52brmJluuvFGqaZCOZs/qn++azeSlLZKL+5wOPZ4zRaSvszq27ev3yGgg+DupJ2k1lcv3J6rkSNGaMyYMT5HFFyp3kiS9OyqIg05YrA+9alP+RhR8KUusenHFq1DU/vMuOCCC+QScUWTS+OZlWq90aNHa9z48crftLD+uf33/KDl0gd4UlWQ0XYkfZnVp08fv0NAB0HS1046d+5c//0nzqGKUlukV5dcsyeiM886m9mVNvOWeFLltO2Ywc+MIUOGqLRTp/p2NxzXtrns0kul6r2K1FVJIknJhPTkhCIkbZc6hiR9mcV+U6Rwp9xO0it7TZ482cdIgm//xq00Ws8cltC1XSKR8DuEUDAzjRk9uv4xDZvbZtKkSSoq3jdbynu97RhszCwS5+xg6TFS+MRqJ6WlpfXfs+G7bdL3UeTEoiR9GdSlSxe/Qwi8uro6v0MIjQkTJtR/zw1228RiMY0fN7b+MUlf24WljHtHkUr6GDjLLLYcIIWraDth/0TmpN/8TZo8WYWFtBnIFJqJt11qRopR67abNGlS/ffpgz1onfSiYjQTbzvOycwi6cuOmpoav0NAB0HS104YEcyO008/w+8QQoURwbZLzfSR9LVd+h5TZvraLn12r3fv3j5GEg4kfZlF0pcdJH1I4SraTlJJH8UIMiNV/XTcuHE+RxIuDE60HXvPsoPiDm2Xvnw7fZ85WofPy+xggCezuCYhhU+sdpIa/WcEKzPuvvtu7dixo0FVVADhxYBZ26W3veDGuu0YiMis1PFklURmcd+JFD7128muXbskcZHIlEgkwkg1OiRG/7ODvbttxzHMLK7nCAIGeJDCmdBOtm7d6ncIANoBRZuyI70CMlqHlRGZxYxUdnBcM4vBHqSQ9LWT6urq+u8ZHQTCi2I42VFUVOR3CIFHS5bMYgYFQUDShxQ+sdpJ+puOUSwgvFJJH+/zzCovL/c7hMBj6XFmkfQhCGjFhBQ+sdpJqicSS7+AcKMYQXYwS4WOhvc4goDBCaRwJrSTAQMGSJKGDx/ucyQAsinVE4kLLRBuvMcBBAlrPdrJ1VdfrT179ujqq6/2OxQAWURzdgAA0NGQ9LWT3Nxcff3rX/c7DABZluqJRNIHhBv9zwAECWsTACCDSPYyi8pz6KioxA0gSEj6AAAdVl5eniRusNHxMNMHIEhI+gAgg1IzfSQpmZFqM8ANNjoa3uMAgoSkDwAyiOWdmZWqkEjSh46GcxJAkJD0AQA6rGg0KkmKx+M+RwI0RMsGAEHCJxYAoMNiuSw6qtSABAAEAUkfAGQQyzszK5XscVwzo1evXsrPz/c7jFDgnAQQJPTpA4AMYmYqs0j6Muvuu+9WTU2N32GEAss7AQQJSR8AZBDJSWaRRGdWWVmZ3yGEBu91AEHCMBUAZFCq4Ag3hJmRk5MjSaqtrfU5EgAAgoukDwAyiDLumZWamWKmDwCA1mN5JwBkEHvQMuvLX/6y+vfvr4EDB/odCgAAgUXSBwDosPr27asbb7zR7zAAAAg0lncCQAZReAQAAHQ0JH0AkEGUcQcODwzsAAgS7k4AIINSSR83hAAAoKMg6QOADCLpAw4PvMcBBAlJHwBkUCzm1cfihhAIN9qzAAgSkj4AyCBm+oDDQzwe9zsEAGg2kj4AyAKSPiDcSPoABAlJHwAAQAvV1tb6HQIANBtJHwBkUKpPX+orgHDiPQ4gSEj6ACCDUss6uSEEwo2enACChE8sAMignj17SpI6derkcyQAsikajfodAgA0G0kfAGRQbm6uJKm4uNjnSABkU6o9CwAEAUkfAGQQyzuBwwPLOwEECZ9YAAAALcTADoAgIekDAABoIZI+AEFC0gcAGURTduDwQNIHIEhI+gAggxKJhCQq+wEAgI6DpA8AMig101dUVORzJAAAAB7qDQNABg0aNEiTJ0/WNddc43coAAAAkkj6ACCjcnJydOedd/odBgAAQD2WdwIAAABAiJH0AQAAAECIkfQBAAAAQIj5kvSZ2WVmtsDMEmY2Yb/XppnZEjNbZGZn+REfAAAAAISFX4VcPpB0saR70580sxGSLpc0UlIfSS+Z2VDnXLz9QwQAAACA4PNlps8596FzblEjL10o6XHnXLVzbrmkJZKObd/oAAAAACA8OtqevnJJq9Mer0k+dwAz+6KZvWtm727evLldggMAAACAoMna8k4ze0lS70ZeutU593Rbf79z7j5J90nShAkTXFt/HwAAQHM5x60HgODIWtLnnDu9FX9traR+aY/7Jp8DAADoMBKJhN8hAECzdbTlndMlXW5meWY2SNKRkmb5HBMAAEADJH0AgsSX6p1m9klJP5fUQ9JzZjbXOXeWc26Bmf1J0kJJdZJuonInkH3FxcXq0qWL32EAQGCQ9AEIEl+SPufcXyT95SCv3S7p9vaNCDi83XPPPcrNzfU7DAAIDJI+AEHiV58+AB1I3759/Q4BAALFzPwOAQCaraPt6QMAAOjwIhFuoQAEB59YAAAALUTSByBI+MQCAABoIZI+AEHCJxYAAEALsacPQJCQ9AEAALQQSR+AICHpAwAAAIAQI+kDAAAAgBAj6QMAAACAECPpAwAAAIAQI+kDAAAAgBAj6QMAAACAECPpAwAAAIAQI+kDAACAr5xzfocAhBpJHwAAAACEGEkfAAAAOgQz8zsEIJRI+gAAAOCr1PJOkj4gO0j6AAAAACDESPoAAADQIVDQBcgOkj4AAIAWIjnJLJZ1AtlF0gcAANBCJH3ZwXEFsoOkDwAAoIVITjJr4MCBkqRBgwb5GwgQUiR9AAAALZRIJPwOIVQ6deokSSopKfE5EiCcSPoAAABaiKQPQJCQ9AEAAABAiJH0AQAAAECIkfQBAAAAQIiR9AEAALRQNBr1OwQAaDaSPgAAgBYi6QMQJCR9AAAALWRmfocAAM1G0gcAAAAAIUbSBwAAAAAhRtIHAAAAACFG0gcAAAAAIUbSBwAAAAAhRtIHAAAAACFG0gcAAAAAIUbSBwAAAAAhRtIHAAAAACFG0gcAAAAAIUbSBwAAAAAhRtIHAAAAACFG0gcAAAAAIUbSBwAAgA7BOed3CEAokfQBAADAV2YmiaQPyBaSPgAAAPgqlfQByA6SPgAAAPgqEvFuSROJhM+RAOFE0gcAAABfpWb6SPqA7Ij5HQAAAAAOb6m9fCzzREc0fPhwTZo0ye8w2oSkDwAAAL5KzfBFo1GfIwEO9Otf/zrwAxIs7wQAAICvJk+eLEkaMWKEz5EABwp6wicx0wcAAACfTZkyRc8995yKior8DgUIJWb6AAAA4DsSPiB7SPoAAAAAIMRI+gAAAAAgxEj6AAAAACDESPoAAAAAIMRI+gAAAAAgxEj6AAAAACDESPoAAAAAIMRI+gAAAAAgxEj6AAAAACDESPoAAAAAIMRI+gAAAAAgxEj6AAAAACDESPoAAAAAIMR8SfrM7Edm9pGZzTOzv5hZ57TXppnZEjNbZGZn+REfAAAAAISFXzN9L0oa5ZwbI+ljSdMkycxGSLpc0khJZ0v6lZlFfYoRAAAAAALPl6TPOfd351xd8uE/JfVNfn+hpMedc9XOueWSlkg61o8YAQAAACAMOsKevs9LmpH8vlzS6rTX1iSfAwAAAAC0Qixbv9jMXpLUu5GXbnXOPZ38mVsl1Un6Qyt+/xclfVGS+vfv34ZIAQAAACC8spb0OedOb+p1M/ucpPMkneacc8mn10rql/ZjfZPPNfb775N0nyRNmDDBNfYzAAAAAHC486t659mSvi3pAudcRdpL0yVdbmZ5ZjZI0pGSZvkRIwAAAACEQdZm+g7hF5LyJL1oZpL0T+fcl5xzC8zsT5IWylv2eZNzLu5TjAAAAAAQeL4kfc65IU28druk29sxHAAAAAAIrY5QvRMAAAAAkCUkfQAAAAAQYiR9AAAAABBiJH0AAAAAEGIkfQAAAAAQYiR9AAAAABBiJH0AAAAAEGIkfQAAAAAQYiR9AAAAABBiJH0AAAAAEGIkfQAAAAAQYiR9AAAAABBiJH0AAAAAEGIkfQAAAAAQYiR9AAAAABBiJH0AAAAAEGIkfQAAAAAQYiR9AAAAABBiJH0AAAAAEGIkfQAAAAAQYiR9AAAAABBiJH0AAABAiJx00kmKRLjNxz4xvwMAAAAAkDnTpk3T7t27/Q4DHQhJHwAAABAiBQUFKigo8DsMdCDM+wIAAABAiJH0AQAAAECIkfQBAAAAQIiR9AEAAABAiJH0AQAAAECIkfQBAAAAQIiR9AEAAABAiJH0AQAAAECIkfQBAAAAQIiR9AEAAABAiJH0AQAAAECIkfQBAAAAQIiR9AEAAABAiJH0AQAAAECIkfQBAAAAQIiR9AEAAABAiJH0AQAAtFJpaanfIQDAIcX8DgAAACCITj75ZJ177rl+hwEAh0TSBwAA0Arf+973/A4BAJqF5Z0AAAAAEGIkfQAAAAAQYiR9AAAAABBiJH0AAAAAEGIkfQAAAAAQYiR9AAAAABBiJH0AAAAAEGIkfQAAAAAQYiR9AAAAABBiJH0AAAAAEGIkfQAAAAAQYiR9AAAAABBiJH0AAAAAEGIkfQAAAAAQYiR9AAAAABBiJH0AAAAAEGIkfQAAAAAQYiR9AAAAABBiJH0AAAAAEGIkfQAAAAAQYiR9AAAAABBiJH0AAAAAEGLmnPM7hjYzs82SVvodRzN0l7TF7yBChOOZORzLzOJ4ZhbHM7M4npnDscwsjmdmcTwzJyjHcoBzrkdjL4Qi6QsKM3vXOTfB7zjCguOZORzLzOJ4ZhbHM7M4npnDscwsjmdmcTwzJwzHkuWdAAAAABBiJH0AAAAAEGIkfe3rPr8DCBmOZ+ZwLDOL45lZHM/M4nhmDscysziemcXxzJzAH0v29AEAAABAiDHTBwAAAAAhRtIHAAAAACFG0gcAAAAAIUbSBwAAcAhmNsjvGMLEzC5LfuW4Au2ApC/LzOwuMys1sxwze9nMNpvZlX7HBZjZJxp57kt+xBImZlZsZkebWWe/YwkyM/thc55Dy3B+tsmfJcnMXvY7kJCYlvz6pK9RhJSZDTKzi81suN+xBI2Zdd/v8ZVm9jMz+6KZmV9xtRVJX/ad6ZzbJek8SSskDZH0LV8jCiAzOzvt+05m9oCZzTOzR82sl5+xBdhtZnZq6oGZfVvShT7GE0hm9qu070+QtFDS/0qab2bn+BZY8J3RyHMHDFSgaZyfGRUxs1skDTWzb+z/x+/gAmirmf1d0iAzm77/H7+DCxoz+2va9xdKekXS+ZKeNrPP+RVXQP099Y2Z/aekqyTNlndd+olfQbVVzO8ADgM5ya/nSnrCObczwIMEfvqBpOeT3/+vpPXyPswulnSvpIt8iivILpD0rJl9S9LZkoaLpK81Jqd9/9+SLnLOvWdmgyX9SdLf/AkrmMzsBkk3ShpsZvPSXiqR9KY/UQUa52fmXC7vWhOTdz6ibc6VdLSkR+Rd19E2A9K+/46kU51zy5OzVi9L+p0vUQVT+o36xZJOdM7tNbNHJb3nU0xtRtKXfc+Y2UeSKiXdYGY9JFX5HFPQTXDOjUt+/1Mzu9rXaALKObfFzC6Q9JK8EaxLHY0726rUOfeeJDnnlpkZqyla7lFJMyTdIek/0p7f7Zzb5k9IocH52QbOuUWSfmhm85xzM/yOJ+icczWS/mlmU5xzm/2OJwTSr98x59xyqf5an/AppqAqMLPx8lZERp1zeyXJOVdrZnF/Q2s9kr7s+66kuyTtdM7FzaxC3gwLWqZncvmMSSo1M0tLULhxaQEz262GF4dcSYMlXZo8rKX+RBZYw5MzUiZpoJl1cc5tT95Q5/ocW+A453ZK2plcUrPBOVdtZqdIGmNmDzvndvgbYeBwfmaImV3pnPu9pBFmdtT+rzvnArvsyw9mdrdz7muSHjSzAwYcnXPcK7XMWDPbJe+9nmdmZc659WaWKynqc2xBs0H7lnFuSzuW3STV+RhXm5D0Zd/bzrmjUw+S08Mz5S1pQPPdr33LaR6S1F3SZjPrLWmub1EFkHOOZUmZtf/N397k166S/l87xxImT0qaYGZDJN0n6Wl5s4DsQ2sZzs/MKUp+LfY1ivB4JPn1x75GERLOuYMldoWSrm/PWILOOXfKQV7aIemkdgwlo4zVXNmRTEbKJf1e0me0b31wqaR7nHNUU4LvzKxc3j6A+gEg59wb/kUEeMzsPefc0ckCQ5XOuZ+b2Rzn3Hi/YwMAhJeZNTkxk1omHzTM9GXPWZI+J6mvGlb62S3pFj8CCrJDVUZjWU3LJcvff1peNb/UGnUniaSvBcxsvhoul61/SZJzzo1p55DCotbM/k3SZ+UVbZL2FcZCM3F+Zo6Z/ayp151zX22vWMKgiXNTksS52TJpWzdSkwypY5t6r7N1o/maKizkJJ3axOsdFklfljjnHpL0kJld4pyjB03bsSQx8y6SNMw5V+13IAF3nt8BhNQ1kr4k6fZkBbpB2rccDM3H+Zk5s5Nfj5c0QtIfk48vkzd4hpZJnZs3Jb+m3t9XqolkEI1r7taN1L7ebMcTZM65qc35OTM7wzn3YrbjyRSWd2aZmeVJukTSQDVcQvd9v2IKMzOb5py7w+84gsDMZki6zDm3x+9YDgdm9rZz7ji/4wAaw/nZfGb2T0knOOfqko9zJM10zk1u+m+iMY0t204t7/YrpjDj2GZO0I4lM33Z97SknfJGCJlRyb7L5JV6x6FVSJprZi8r7dxkiVLW5PsdQJAcZOnXTknvSvof59zW9o8q1Dg/m6+LvP35qRYixcnn0DpmZsc7595MPpgiqnJnE82iMydQx5KkL/v6OufO9juIw0ig3oA+m578g/bBsoqWmSFvr+mjyceXy6tCt0Fek+HzG/9raCXOz+a7U9IcM3tV3jXnJEn/5WtEwXatvLYNnZKPd0j6vI/xhB3v9cwJ1LEk6cu+t8xstHNuvt+BHCYC9Qb0U3LfKdBRnb7fspn5aRU9r/QtKhz2nHO/TS6Pn5R86jvOuQ2p181spHNugT/RBY9zbra8HnOdko93pr9uZldzvQLajunz7DtB0mwzW2Rm88xsfrJRLrKDmb5mMrMjzezPZrbQzJal/vgdV4hxbrZM1MyOTT0ws4na12A4sM1xOzDOzxZwzm1wzj2d/LNhv5cpONQKzrmd+yd8STe3ezABlCx21awfzWogIWBmlyW/HuqYrsh+NJlD0pd9n5B0pKQz5S1HOk8sS8qmJ/wOIEB+K+nX8m6gp0p6WF5fSbSCmX2ikee+lPbwqnYMJwy+IOkBM1tuZiskPSDpOjMrEvt2WyzZoqWp5zg/M4eb6szieDbPnyUpuU+/Kae1QyxBNy35tcnq+865i9shloyhemeWmFmpc26XmXVt7HXn3LbGnkfTzOwuSf8jqVLS85LGSPq6c45kpYXMbLZz7hgzm++cG53+nN+xBZGZvSXpP51zryQff1vSVOfcAckgmu9gS77QMo1VmTOzefRCy7ygVfTr6DiezWNmc+QNfN8g6af7v04/4+YzsxflbReaKGnm/q875y5o96AygD192fOovFm92WrYLFPJx4P9CCoEznTOfdvMPilvWv1iec3ESfpartrMIpIWm9mXJa2VV4UOrXOBpGfN7FuSzpY0XNKF/oYUbGZ2rqSRkvLNvI9Q2t20jJndIOlGSYP321pQIulNf6ICWoSZvua5XF7/3ZjobdxW50o6Wt5S7aYatQcKSV+WOOfOS35t7hprNE9O8uu5kp5wzu1M3QyixW6WVw3xq5L+W94Sz6t9jSjAnHNbzOwCSS/JG+y51LGUotXM7B555+dUSb+RdKmkWb4GFUyPyquEeoek/0h7fjcrTrKmxu8AgsDMbnbO/V96u4aDYHCiGZxziyT9MDmDP8PveILMOVcj6Z9mNsU5t9nveDKF5Z3tIHkjeFLy4WvOuWf9jCfIzOxOeSNZlZKOldRZ0rPOuUlN/kU0YGZRST90zv2737EEnZntVsOqsbny9kk6Sc45V+pLYAGXWnqY9rVY0gzn3Il+xxZEZnaEpDXOuWozO0Xe0viHnXM7/I0seMyssaWGOyWtTDVsx6GZ2Vzn3DiWb2aGmV3pnPu9mX1TjVQyZ3ln85nZ3c65r5nZM2r8WLK8EwdKJikTJf0h+dTNyZGDW3wMK8i+K+kuSTudc3Ezq5C3rA4tkDx2J/gdRxg451hGkx2Vya8VZtZH0lZJZT7GE3RPSppgZkMk3SfpaXmzgOf4GlUw/Ure0q958pYejpK0QFInM7vBOfd3P4MLkA/NbLGkPvstPTZ5A2bsN22ZouRXtmm0XaoC7499jSLDmOnLsuQH2TjnXCL5OCppDh9mrXOQYgSMEraCmf1aUrm8jd97U887557yLaiAM7NySQOUNqDmnHvDv4iCy8xuk/RzeZXmfilvtPV+59z/8zWwgErrcfhtSZXOuZ+b2Rzn3Hi/YwsaM3tK0m2pXnxmNkLS9yV9W9JTzrlxfsYXJGbWW9ILamTw1jm3sv0jAsKLmb720VlSau9EJz8DCarkhaFcUoGZjde+jd2l8vb9oOXy5c2enJr2nJNE0tcKyfL3n5a0UFI8+bSTV2gILeSc++/kt0+a2bOS8tMreJrZGc65F/2JLpBqzezfJH1W+9oG5TTx8zi4oenN151zC81suHNuGXvMWybZ43Cs33GEgZn9rKnXnXNfba9Ygs7M5quRZZ0pQZ24IenLvjskzTGzV+UlKiep4WZ6NM9Zkj4nqa+k9HXpuyWxVLYVnHPXNPW6mU1zztEPrfkukjTMOVftdyBhkzym+x/XH0oi6Wu+ayR9SdLtzrnlyabDNBFvnQXJlRKPJx9/WtJCM8uTVOtfWMFkZsvV+L4pqpy3zOzk1+MljZD0x+Tjy+QNRqL5zkt+vSn5NfVZeaWaSAY7OpZ3tgMzK5O3r89Jeic5soVWMLNLnHNNNstEZrBstmXMbIaky5xze/yO5XDA0kT4xcwK5LXBSO2LflPePr8qSYV8BrSMmXVLe5gvL0npylLu1jGzf0o6IVVUyMxyJM10zk32N7Lgaew6E+R7I2b62sdx8i4OTt4x/4u/4QTas2b2GUkD1XDfFL27Mo91Si1TIWmumb2stFkpltRkDSOWLXCQ5Uo7Jb0r6X+cc1vbP6pgcs5Vyuvd1Vj/LhK+Fmrk3LvbzGZLIulrnS7ytr6kthUVJ59Dy1l6SxEzmyIp4nNMrUbSl2Vm9itJQyQ9lnzqejM73Tl3UxN/DQf3tLwbldk6cLkXMoub6paZnvwDdEQz5O01fTT5+HJ5+6E3SPqd9u3zwyGY2fGS/ksHFm1iOWIr7NcCIyJpgrg/bYs7deC2ov/yNaLgulbSg2aWqsexQ9LnfYynTVjemWVm9pGko1JNms0sImmBc+4ofyMLJjP7wDk3yu84Dgcsn4OfzMz2b25vZnmpPZNm9pRz7mJ/oguepiofm9l859xov2ILmuR1/evyBh9TRZsam7FCMySTk5Q6SSsk/TjZbBytkCx+l+pf/K/0bUVmNjK9EBEOLZX0pRcTSz5/tXPuIX+iajlGUrJviaT+klKlh/sln0PrvGVmo51z8/0O5DDwhN8BBImZHSmvcNMIeftSJDH63wYPKG1ENdmc/Wl5LRxEwtdiUTM71jk3S5LMbKKkaPI1Goq3zE7n3Ay/gwgL59xUv2MIm2SS9/RBXn5EXp9JNNP+yV6amyUFJukL7LrUACmR14D0teRo1kJJpWY23cxYCtZyJ0iabWaLzGyemc3fr6krmsnM7jKzUjPLMbOXzWyzmV2Zet059wM/4wug30r6tbwb6KmSHpb0e18jCrY1yeXxMrMukv4ujmdbfEHSA2a23MxWyEuqrzOzInmDFWi+V83sR2Z2nJkdnfrjd1BBZWY3J69FZma/MbP3zOxMv+MKMfbrZ06gjiXLO7PMzE5u6nXn3OvtFUsYmNmAxp6niWvLmdlc59w4M/ukvPLE35D0hnOOnkmtYGaznXPHpC+VSz3nd2xBZWZ3yStIcIykO6nc23YHW6aE5ttvOWKKc86d2sjzOAQze985N9bMzpLXVuQ/JT0S1AqJHV2Qq092NEE7lizvzLJDJXVm9rZz7rj2iieozKzUObdLXl8+ZEaqMfO5kp5wzu2ksXCbVCf37C42sy9LWiuvahpawMzSl23+S9JtkmZJcmZ2sXPuKX8iCz4zO1fSSEn5qfc6lY9bjuWIGZe68Jwj6WHn3ALjYoRgCNR5StLnv/xD/wjkVZw7T97GeaeGbzQniX1TLfdMsiBBpaQbzKyHvD5TaJ2b5VVD/Kqk/5a3xPNqXyMKpv2rSM6RN0Bxvrz3OklfK5jZPfLOz6mSfiPpUnnJNJrJzK50zv3ezL7R2OvOuZ+0d0whMdvM/i5pkKRpZlYiKeFzTGFW43cAHZ2Z3eyc+7/0dg0H0dRrHQ7LO30WtKlhhIeZ5UkqkleUIJ7c21PsnNvoc2iBY2ZRST90zv2737EAjTGzec65MWlfiyXNcM6d6HdsQWFm1zvn7jWz7zb2unPue+0dUxgkV0iMk7TMObcj2ay93Dk3L/k61SZb4CD7S3dKWplq2I6mpW1/CdU9OjN9CBwzu0Be3xlJes0596yf8QTY2+kfZs65vWY2U1T1arFk0nyC33GESXLm+TpJA9WwF1pgeyT5rDL5tcLM+kjaKqnMx3gCxzl3b/LbnzvntqW/ZmaDfAgpFJxzCUnvpT3eKu/8TKHaZMv8St7xmidvVdQoSQskdTKzG5xzf/czuID40MwWS+qzX7FAk7d/d4xPcbUJSV+WpPeTOtSPZj2YEDGzOyVNlPSH5FM3m9kU59wtPoYVKMn+PeWSCsxsvPadg6Xyln+hdeYkK/I+Ib1bN+cAAB3VSURBVGlv6kn2oLXa05JmSnpJab3Q0GrPmllnST+Sd4PtJN3vb0iB9YyZfSK5z1xmdpS89z09ZLOD+6SWWSfp2tTsqJmNkPR9Sd+WtzyepO8QnHP/lrxXekHSBX7Hkyks78yStKa3jzj3/9u79yg7qzrN498HTHc0IQEdRRREbZWr3ERAAvSArSh4AbFxteiIoAtGR20dL9iKgE4r06CODdoiYrjb6lInXMTB9kKQe0higHhZINDdy7FnrYhJ0AQCPPPHu485KYpKnVOnatd76vmsVevU+55zUk+yKlXvfvfev5/fOsbrdrd951Rma7Nyx2Wvcmews6xuWVvvutQg6W3A8cC+wJKup9YCF2aQ0h9JC0c57cxM9aezvKZ2jmFUlnbP7q7gKekVtn9QMVZrlII4H6YpgrUTTXuW42wvrxpsSA3bErvJJulO27uPdi4/V2e2zPRNnj+T9GbgwBHV6ICNd/8z4OvL1kBnac38mkHayPZFwEWSjkkJ/MGx/faxnpf0UdvphzZ+V0k6wvb3agcZNmUVysiVKP8TyKBvHGxfLWkWzYzJVsDRtn9VOVZEx12S/gn453L8JmBludmzoV6s9pF0L82qiE3YbmXxwMz0TZKyv+c44FhgZBP23P3vk6S/Ac4Efkyz5OMQ4BTb36garIXKL4BjePyeqZRwnwS5W90bSWtpCg09RHOh0tlLMa9qsCElaZntvWvnmM4kncOmF4AvB+4B7gOw/d4KsYaepJttH1A7R1tIejLwLqCzz/wGmn1+64Gn2H6wVra2KUWFOmYDfw081fYnKkWakAz6JpmkE21fUDvHMJG0Hc2+PgO32f5t5UitJOn7NBW9bqdrz5Ttz1YLNcRyUR3TWW5KbF5ZGv+EyiqK6FGqTUabSLrd9ktq5+hHlndOEkmH2f4R8MBYyzujLy+juYNlmu/h79aN01rb235V7RAzSO6w9UjSNsAL6epnantxvUQxk2VQN2lSbXKAJC0ATgd2ZNNVPK1ckljTiBsSW9DUQmjt2Km1wVvgEOBHbGworBGPGfT1QdKXgBcAXy+nTpL0V7bfXTFWW90o6cW276gdZIZIBboeSHoHTcP77YHlwAHATcBhNXO1lSR5xNKeEVWm75v6VO00ykV1Z+lxLqr7k2qTg3UB8H5GrOKJvnSvfHqE5ufksXWiTFwGfZNnraQPAHeycbAHuds/UYcBu3QuXiRdRHNHMHp3EHB82aj8EC3vP9MC36odoGXeR7OM+2bbh0raGfh05UxtdgHwp73kpTn7Ipp9adh+3IqUeEK5qB6sF3U3X7e9UtLOtn8t5V5ZH1bbvqZ2iGFg+9DaGQYpg77JM7c87kRz4bKI5qL6tcCttUINgbuB5wD3l+Mdyrno3atrBxgmkv4B+B80TbC/D+wBvN/2pQC2M2DpzXrb6yV1ZqR+IWmn2qFa7N8lfcn2u8qy2atJn75+5aJ6sFJtcrB+LOksmlnSP1Xptb20XqR2kvQ+YCFNS6vzaZYhn9LWJccp5DLJJC0GjrS9thxvBVxt+5C6ydpJ0nU0g+hbaWZN96PpNbcawPbQNNGcLJLm2V4j6amjPW/7d6Odj7F1+h9JOhp4DfABYLHtPStHayVJ3wXeDvwtzQz/A8As20dUDdZi5cbEPOAlwJlp2dIfSWcCW5KL6oFItcnBkvTjUU7bdpbG90jSz2zvKelw4GTg48AlbS16lZm+ybct8HDX8cPlXPSnlWVyp5nLaQYlt7Pp0mPKcfal9GdWeTwS+Jbt1Vma1D/bR5dPTy8XMfNpZlCjByMKid0CnEq5aSbpDSkq1pf9y+O+XedM9pv2xfY6mr1To1WOzoCvR8O2JLGyzi/xI4CLbd+lFv9iz0zfJJP0MZpNn50Kk0cB30iT5skh6SbbL6udI2aecvf/KJrlnfsBWwNX2d5/zDfG40jaErjL9s61s7SdpIVjPJ2esVFdqk0OhqS32L601JN4HNufm+pMbVd+fj4beB6wJ80M/0/a2rIhg74pUEq+HlwOF9teVjPPMEsvtN5Ieh1NpVlofpBdVTNPm5X9J3No9vs8KmkOMNf2f1SO1kqSFgHvsf2vtbNEjCTpSGA3Nm0n8sl6idpL0i8YpTCO7VXVQrWQpJNsnyfptNGet33GVGdqO0lbAHsBv7b9+9Ks/dm2V5Tnd+suQjTdZdAXQyUNhsevzEy9FLisnPobmmb3f1cvVXuN9r2X78f+lf3Qe9MsRfxD53z27fZH0tOBdwLPZdPZlMz09UjSl4GnAIcCXwXeCNxq+8SqwVpK0i1ZETE4kp46cm++pOfZvrdWpmHVtt/x2dMXMXMdAexl+zH4U/uLZUAGfT2Q9Eya5R9PlrQ3G/cAzKO5MIz+nFo7wJBZBFwP/AtpMzBRB9reQ9IK22dI+iyQap79S7XJwbpS0qttrwGQtAtNy6Dd68YaSq3a35dBX7TCiCbCY7500sMMl62Bzh3B+TWDtNjhwPE0TcS790ysJQPovtm+bqzns3+3Z0+x/ZHaIYbE+vL4R0nPAlYB21XM03YpjDNYn6YZ+B1J0zbsYuC4upGGVquWS2bQF21xE7CPpEtsv3WM1431XGzqM8CyUhlRNHv7TqkbqX1sXwRcJOmYlMCfUrM3/5LocpWkI2x/r3aQIXClpK2Bs4ClNBd+6XnYp1SbHCzbV0uaBVwLbAUcbftXlWPFNJA9fdEKku6kuXv1KeBDI59P2fH+SNqOZl+fafbz/bZypNYqhVyO4fF7plLcYRK0bS9FbZLW0hQaeoim4bVoqnfOqxqsZUphhwNs31iO/xyYbXt13WTtk2qTgyXpHDadeXo5cA9wH4Dt91aINdQk3Wz7gNo5xiszfdEWJ9MsT9gaeO2I50yzFyB69zKahrim+Xnw3bFfHmNYBKymqUA3nqXIEVPG9la1MwwD249J+iJNkSHKtoP8f+/PnPKY783BWDLi+PYqKYZIqb4/0mrgftuPtGnAB5npi5aRdKLtC2rnGAaSvgS8APh6OfUm4B7b766Xqr0k3Wk7G+UnaLz7d9OepXeStgFeyKZtBhbXS9ROks6m2XLwHeciasJSbTKmK0k3A/sAK2hWR+wO3EVTA+G/2r62YryeZdAXrSDpMNs/kvSG0Z7P8s7eld5Iu3QuWsqypbts71I3WTtJ+gpwju07amdps86yzc3t35W0u+07pzJbm0l6B/A+moJDy4EDgJtsp1hGj7qWyj5CU9QlS2UnQNINwOOqTeYmWn9GaXbf+f5Ms/seSfoOcGqnF5+kXYFPAh+muemzV818vcryzmiLQ4Af0SztNOWHWNdjBn29uxt4DnB/Od6hnIv+HAQcL+lemuVenV+0e9SN1Tp/JunNwIGj3eTp3ODJgK9n76PZv3uz7UMl7UyzTzp6tLmlsm1r2DwNpNrkYF3AKM3uoy8v6v6/bHulpJ1t/1pqX7H4DPqiLdaWzd53snGwBy0rlzvNbAX8XNKtNP+O+wFLJF0BaYLdh1fXDjAksn93cqy3vV5SZwntLyTtVDvUkLqEZklYjEOqTQ7catvpGzkYd0n6J+Cfy/GbgJWlgNOGerH6k0FftMXc8rgTzd3qRTQDv9cCt9YK1XKfqB1gGEiaV5Ylra2dZRjY/inwU0lLsn93oP69tBn438APJD3Axln+GKz2TQFUMEq1yfk01Sb/m6RUm+xfmt0PzvHAu4C/Lcc3AB+kGfC1rtVI9vRFq0haDBxpe2053gq42vYhdZMNnzS/Hh9JV9l+TVnW2T0LDdlH0bPs3518kv6S5gL7+7Yfrp1n2KSdyPhIettYz5ceqNGj0nt3JGf/bmSmL9pmW6D7IuXhci4GL82vx8H2a8rj82pnGRLZvztgkrakKdK0M4Dt6ypHisigbpKk2f3gjFIUB4C23szNoC/a5mLgVkmdfnJHARfWizPUsgygR5JeRzNoAfiJ7atq5mmp7N8dMNuPSvqlpOfY/tfaedpMTfWG7W3/2xgvy+xpD1JtcvBKUZzd2LQ9yyfrJWqtoSqKk0FftIrtv5d0DXBwOfV228tqZooAkHQmzX7Ty8qp90k60PbfVYzVRtm/Ozm2oSlKcCvwh87JFGzqjW1L+h7w4jFe06qGzdPAUF1Y1ybpy8BTaPacfRV4I/nZ2a+hKoqTPX0RM0yaX08OSSuAvWw/Vo63BJalZUN/sn93sMo+vsfJUs/eSboIONf2bbWzDANJt9jev3aOYSFphe09uh7nAtfYPnizb45NlJu5WzIkRXEy0xcx89wEbLb5NTDWczG6rYHflc/n1wwyBLJ/d4A2N7hL4aae7A8cJ+l+mlnT9OScmFSbHKz15fGPkp4FrAK2q5inzTo3I/btOmeglUVxMuiLmHnS/HpyfAZYViqniWZv3yl1I7Va9u9OrRRuGr/DawcYMkN1YT0NXFnas5wFLKX5tzy/bqR2GraiOFneGTHDSDqIpvn1scAVI5627ROmPtVwkLQdzT40A7fZ/m3lSK0maR827t9dnP27kydtBnpTfo6+0PZCSU8H5tq+t3aumNkkbQEcYPvGcvznwGzbq+smaxdJb7F9aSkq9ji2PzfVmQYhM30RM0yaX0+qlwEH0Qz6ngR8d+yXx1jK8q4s8YppRdJpNLNSOwELgVnApcCCmrnaLNUmB8P2Y5K+COxdjh+ia8lsjNuc8rhV1RQDlkFfxAzTaX4NPDDW8s7ojaQvAS8Avl5OnSTpr2y/u2KsmOHGW7iJja0xYvOOprmoXgpg+zel0FD0IdUmB+6Hko4BvuMs5+uL7fPKp+fY/l33c5Ja25M3g76ImSfNryfHYcAunV+ypcLfXXUjRaRw0yR4uLRu6Pxfn7O5N8SYDuyqNnmGpM8CQ1Mmv4KTgA8Aj0haz8ZCQ/PqxmqlKyW92vYaAEm7AN8Cdq8bqz8Z9EXMPGl+PTnuBp4D3F+OdyjnImpK4abB+6ak84CtJb0TOIEUypiIVJscINtjzjpL2s12bkiOz6dpBn5H0iznvpimJkIrZdAXMfOk+fXk2Ar4eWl+bWA/YImkKyBNsKOak2kuUram+T/eLTP7fbB9tqRXAGuAFwGfsP2DyrHaLNUmp9YlQIo2jYPtqyXNAq6l+R1/tO1fVY7Vt1TvjJih0vx6sJ6o+XVHmmBHTZJOTOGmwZH0TJobO6nUOwGpNjn1JC2zvXftHNOZpHPYdPXTy4F7gPsAbL+3QqwJy0xfxMyV5tcDlObXMR2lcNPgSXoH8AmavdECzpH0Sdtfq5usfVJtsorM9mzekhHHt1dJMWAZ9EXMXGl+PbXS/DpqSOGmwfsQsLftVQCSngbcCGTQ159Um4xpxfZFtTNMhgz6ImYo238v6Ro2Nr9+e5pfT6pczEQNKdw0eKuAtV3Ha8u56E+qTQ6IJAHb2/63MV728BjPRRdJC4DTgR1pxkyd783n18zVrwz6ImawNL+OGHop3DR4dwO3SFpEM3h+PbCiDK6x/bma4dom1SYHp7QS+R7w4jFec8AURmq7C4D30yzvfLRylgnLoC8iYgLS/DqmM9tnwJ8KN+3TVbjpdODqitHa7J7y0bGoPKZB++RItcneLJX0Utu31Q4yBFbbHpqekaneGRExAZKW2t5s82tJu6cXWtQi6ZfAHp0bFKVK4grbO9VNNnwknWP7PbVzDItUm+yNpF8AL6DpGfsHNi5J3KNqsBaSdCawJc3e5z/d3C2rpFonM30REROT5tfRBincNHUW1A4wZDI70ZvDawcYIvuXx327zhk4rEKWCcugLyJiYtL8Oqa9FG6KmBls3y/pIOCFthdKejob9/ZGD2wfWjvDIGXQFxExAbZ/CvxU0pI0v47pLIWbYrpJtcnBk3QazczUTsBCYBZwKZmB7oukI4Hd6Gq7ZPuT9RL1L4O+iIgJSPPriBghRZvGKdUmJ8XRNM3ulwLY/o2kFBnqg6QvA08BDgW+CryRFlc9zqAvImJi0vw6YoaStAUw1/aartNfqJWnpVJtcrAeLoNpA0iaUztQix1oew9JK2yfIemzQGureWbQFxExMWl+HTGDSLqcZi/vo8BtwDxJX7B9FoDtCyvGa6P9geMkpdrkYHxT0nnA1pLeCZwAnF85U1utL49/lPQsYBWwXcU8E5JBX0TExKT5dcTMsqvtNZKOo7nrfwpN8+az6sZqrVSbHCDbZ0t6BbAGeBHwCds/qByrra6UtDXN/+2lNDdzWzuAzqAvImIC0vw6YsaZJWkWTduLc21v6Cyli96l2uSkuAN4Ms0g5Y7KWVqpLN3+oe3fA9+WdBUw2/bqytH6tkXtABERQ2JbNq0y93A5FxHD5TzgPmAOsFjSjjSzKtGHUm3yI8BHy6lOtcnog6R30KwyeQNN4ZGbJZ1QN1X72H4M+GLX8UNtHvAByM7NqYiIiZL0MeBYoLv59Tdsf6ZeqoiYCpKeZPuR2jnaSNJySrVJ23uXcyuyp68/kn5JU4BkVTl+GnCj7Z3qJmsfSWcDNwHf8RAMmLK8MyJiANL8OmK4lYJNY/nclAQZPqk2OVirgLVdx2vLuejdScAHgEckrWdjkaF5dWP1J4O+iIgBSfPriKGWXmeTI9UmB+tu4BZJi2j29L0eWNG5aWE7NyfGyfaY/+cl7Wb7rqnKM1FZ3hkRERER1ZRqk68sh9em2mT/yh7JJ9QpPhYTJ2mp7X1q5xivzPRFREREjJOk2cCJwG7A7M552ymW0b9UmxyQzQ3qJJ1j+z1TlWfIafMvmT5SvTMiIiJi/C4BnknTX+46YHs23UMVPUi1ySm3oHaAIdKq5ZJZ3hkRERExTpKW2d67U2Gy9Oy73vYBtbO1UapNTq22LUmcztr2b5mZvoiIiIjx21Aefy9pd2A+8IyKedou1SZj2lFjh8287OHNPD+tZE9fRERExPh9RdI2wKnAFcDc8nn0J9Ump1ar9qHVUtqIfA948RivadXsfgZ9EREREeNk+6vl0+uA59fMMiTuKR8di8pjWmRMkKQtgLm213Sd/kKtPC20VNJLbd9WO8ggZE9fRERExDhJmg+cDhxcTv0E+JTt1bUyDbNUm+yNpMuBk4FHgduAecAXbJ9VNVgLSfoF8ALgfuAPbGzOvkfVYH3KoC8iIiJinCR9G7gTuKiceiuwp+031Es1vNpWLKM2Sctt7yXpOGAf4BTg9rYOVGqStONo523fP9VZBiGFXCIiIiLG7y9sn2b71+XjDLLMM6aPWaWi7FHAFbY30LLWAtNFGdztABxWPv8jLR47tTZ4RERERAXrJB3UOZC0AFhXMU9Et/OA+4A5wOIyW7VmzHfEqCSdBnwE+Gg5NQu4tF6iicnyzoiIiIhxkrQncDFNqwaAB4C32V5RL9Xw6vRFrJ2jzSQ9yfYjtXO0jaTlwN7A0s73YKc/Z91k/Un1zoiIiIjxeznNfr655fhB4KWStrC9vF6s9ku1yf51WlyMIa0vevdwad1gAElzageaiCzvjIiIiBi/fWmqI86jme07CXgVcL6kD9cM1kaSLpc0r1xQ3wmslPShzvO2L6wWrl222sxH9O6bks4Dtpb0TuBfgPMrZ+pblndGREREjJOkxcARth8sx3OBq2kGfrfb3rVmvrZJtcmYziS9AnhlObzW9g9q5pmILO+MiIiIGL9nAA91HW8AtrW9TtJDT/CeeGLd1SbPtb2hs5wueidpNnAisBswu3Pe9gnVQrXbHcCTaSqg3lE5y4RkeWdERETE+F0G3CLptFLd7wbg8rI8cWXdaK2UapODdQnwTOBw4Dpge2Bt1UQtJekdwK3AG4A3AjdLau3gOcs7IyIiInogaV9gQTm8wfaSmnmGTapN9q9T7bRTZbLMol5v+4Da2dpG0i+BA22vKsdPA260vVPdZP3J8s6IiIiIHpRBXgZ6E5Bqk5NmQ3n8vaTdgd/SLEmO3q1i01nSteVcK2XQFxERERFTLRUlJ8dXJG0DnApcQdNa5NS6kVrrbpql3Ito9vS9HljRuWFhu1U3JrK8MyIiIiIiokvZs/uEbJ8xVVkGIYO+iIiIiKgi1SYHS9J84HTg4HLqJ8CnbK+ulWlYSTrH9ntq5xivVO+MiIiIiFpSbXKwvkZT/fTY8rEWWFg10fBasPmXTB+Z6YuIiIiIKlJtcrA6ze43dy4mTtJS2/vUzjFememLiIiIiFpGVpucT6pNTsQ6SQd1DiQtANZVzBPTRKp3RkREREQtqTY5WCcDF5e9fQAPAG+rmGeYqXaAXmR5Z0RERETEEOjqfzi3PD4IrAZut728Tqr2k7QFMNf2mq5zx9u+sF6q3mR5Z0RERERUIWm+pM9LWlI+zu6apYre7Usz2zePZqnsScCrgPMlfbhmsLaRdLmkeZLmAHcCKyV9qPN8mwZ8kEFfRERERNSTapODtT2wj+0P2v7vwEto9kgeAhxfM1gL7Vpm9o4CrgGeB7y1bqT+ZU9fRERERNTyF7aP6To+Q1KWIfbvGcBDXccbgG1tr5P00BO8J0Y3q1STPQo41/YGSa3dF5dBX0RERETUsk7SQbZ/Cqk2OQCXAbdIWlSOXwtcXpYorqwXq5XOA+4DfgYslrQjzax0K6WQS0RERERUIWlP4GKa/WdQqk3aXlEvVbtJ2peNjcNvsL2kZp5hIulJth+pnaMfGfRFRERERBWpNhnTTdf35Khsf26qsgxSlndGRERERC37lo8raPqeHQesAE6W9C3b/1AzXMxIW9UOMBky0xcRERERVUhaDBxh+8FyPBe4mqbNwO22d62ZL2JYZKYvIiIiImpJtcmYliTNBk4EdgNmd87bPqFaqAlIn76IiIiIqKVTbfI0SacBN5BqkzE9XAI8EzgcuI6mB+LaqokmIMs7IyIiIqKaVJuM6UjSMtt7S1phe4/Ss+962wfUztaPLO+MiIiIiGrKIC8DvZhuNpTH30vaHfgtzXLkVsqgLyIiIiIiYlNfkbQNcCpNddm55fNWyvLOiIiIiIiIIZZCLhEREREREV0kzZf0eUlLysfZkubXztWvDPoiIiIiIiI29TVgDXBs+VgLLKyaaAKyvDMiIiIiIqKLpOW299rcubbITF9ERERERMSm1kk6qHMgaQGwrmKeCclMX0RERERERBdJewIXA519fA8Ab7O9ol6q/mXQFxERERER0UXSB8qnc8vjg8Bq4Hbby+uk6l+Wd0ZERERERGxqX+BkYB7NbN9JwKuA8yV9uGawfmSmLyIiIiIiooukxcARth8sx3OBq2kGfrfb3rVmvl5lpi8iIiIiImJTzwAe6jreAGxre92I863wpNoBIiIiIiIippnLgFskLSrHrwUulzQHWFkvVn+yvDMiIiIiImIESfsCC8rhDbaX1MwzERn0RUREREREDLHs6YuIiIiIiBhiGfRFREREREQMsQz6IiJiRpL0Xkk/l3RZj+97rqQ3T1auiIiIQcugLyIiZqp3Aa+wfVyP73su0POgT9KWvb4nIiJiEDLoi4iIGUfSl4HnA9dI+pikr0m6VdIySa8vr3mupOslLS0fB5a3nwkcLGm5pPdLOl7SuV1/9lWS/nP5/EFJn5X0M+Blkt5Svs5ySedJ2rJ8XCjpTkl3SHr/1P5rRETEsMugLyIiZhzbJwO/AQ4F5gA/sr1fOT6r9GH6fzQzgfsAbwL+sbz9FOB623vZ/vxmvtQc4BbbewKryp+zwPZewKPAccBewLNt7277xcDCQf5dIyIi0pw9IiJmulcCr5P0wXI8G3gOzaDwXEmdAdqL+vizHwW+XT5/OfAS4DZJAE+mGVheCTxf0jnA1cC1ff49IiIiRpVBX0REzHQCjrH9y01OSqcD/wHsSbMyZv0TvP8RNl05M7vr8/W2H+36OhfZ/ujjAkh7AocDJwPHAif0/teIiIgYXZZ3RkTETPd/gPeoTL9J2rucnw/8X9uPAW8FOoVY1gJbdb3/PmAvSVtI2gHY7wm+zg+BN0p6Rvk6T5W0o6T/BGxh+9vAx4F9BvdXi4iIyExfRETEp4D/BayQtAVwL/Aa4EvAtyX9F+D7wB/K61cAj5biLBeW994LrAR+Diwd7YvYXinp48C15etsAN4NrAMWlnMAj5sJjIiImAjZrp0hIiIiIiIiJkmWd0ZERERERAyxDPoiIiIiIiKGWAZ9ERERERERQyyDvoiIiIiIiCGWQV9ERERERMQQy6AvIiIiIiJiiGXQFxERERERMcQy6IuIiIiIiBhi/x80LFALitiVrgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Due to the low values, the violin plot was not a good choice to analyze if features are really separating the data in this case. We will see then the boxplot:"
      ],
      "metadata": {
        "id": "A26A5Ag0wmQ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "sns.boxplot(x=\"features\", y=\"value\", hue=\"target\", data=violin)\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:02.803708Z",
          "iopub.execute_input": "2022-11-19T16:04:02.804614Z",
          "iopub.status.idle": "2022-11-19T16:04:03.419825Z",
          "shell.execute_reply.started": "2022-11-19T16:04:02.804576Z",
          "shell.execute_reply": "2022-11-19T16:04:03.418709Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "id": "9-OzOpQOwmQ9",
        "outputId": "26612a63-a6a1-4b65-912b-8a3359b426bb"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Difference of points (full and last 5 games), difference of points by ranking faced (full and last 5 games) and difference of rank faced (full and last 5 games) are good features. Also, some of the generated features have very similar distributions which will be analyzed using scatterplots."
      ],
      "metadata": {
        "id": "UOwNYSWKwmQ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.jointplot(data = data_difs, x = 'goals_per_ranking_dif', y = 'goals_per_ranking_dif_l5', kind=\"reg\")\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:03.421537Z",
          "iopub.execute_input": "2022-11-19T16:04:03.421921Z",
          "iopub.status.idle": "2022-11-19T16:04:05.800371Z",
          "shell.execute_reply.started": "2022-11-19T16:04:03.421889Z",
          "shell.execute_reply": "2022-11-19T16:04:05.799125Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "fQbYDNgkwmQ9",
        "outputId": "fcea27e8-a4a2-4d99-c1ea-852db8feaf01"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Goals difference by ranking faced and its last 5 games version has very similar distributions. So, we will use only the full version (goals_per_ranking_dif)."
      ],
      "metadata": {
        "id": "xgV3yE7lwmQ-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.jointplot(data = data_difs, x = 'dif_rank_agst', y = 'dif_rank_agst_l5', kind=\"reg\")\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:05.801975Z",
          "iopub.execute_input": "2022-11-19T16:04:05.80236Z",
          "iopub.status.idle": "2022-11-19T16:04:06.869617Z",
          "shell.execute_reply.started": "2022-11-19T16:04:05.802326Z",
          "shell.execute_reply": "2022-11-19T16:04:06.86833Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "PbxAt6YEwmQ-",
        "outputId": "4c42dc80-7c24-4189-bb0f-46acb408f0e8"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.jointplot(data = data_difs, x = 'dif_points', y = 'dif_points_l5', kind=\"reg\")\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:06.871322Z",
          "iopub.execute_input": "2022-11-19T16:04:06.871667Z",
          "iopub.status.idle": "2022-11-19T16:04:07.722616Z",
          "shell.execute_reply.started": "2022-11-19T16:04:06.871636Z",
          "shell.execute_reply": "2022-11-19T16:04:07.721179Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "FodcSrDJwmQ-",
        "outputId": "d6de25d1-965e-476b-8868-31a9523319f7"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.jointplot(data = data_difs, x = 'dif_points_rank', y = 'dif_points_rank_l5', kind=\"reg\")\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:07.724887Z",
          "iopub.execute_input": "2022-11-19T16:04:07.725602Z",
          "iopub.status.idle": "2022-11-19T16:04:10.453428Z",
          "shell.execute_reply.started": "2022-11-19T16:04:07.72556Z",
          "shell.execute_reply": "2022-11-19T16:04:10.452318Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "9BeSCV5UwmQ-",
        "outputId": "e5a89139-e786-470a-c171-caa7f270eb7d"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For the differences of rank faced, game points by rank faced and mean game points by rank faced, the two versions (full and 5 games) are not so similar. So, we will use both.\n",
        "\n",
        "Based on that, final features are:\n",
        "\n",
        "\n",
        "- rank_dif\n",
        "- goals_dif\n",
        "- goals_dif_l5\n",
        "- goals_suf_dif\n",
        "- goals_suf_dif_l5\n",
        "- dif_rank_agst\n",
        "- dif_rank_agst_l5\n",
        "- goals_per_ranking_dif\n",
        "- dif_points_rank\n",
        "- dif_points_rank_l5\n",
        "- is_friendly"
      ],
      "metadata": {
        "id": "n_z1aRJBwmQ-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_db(df):\n",
        "    columns = [\"home_team\", \"away_team\", \"target\", \"rank_dif\", \"home_goals_mean\", \"home_rank_mean\", \"away_goals_mean\", \"away_rank_mean\", \"home_rank_mean_l5\", \"away_rank_mean_l5\", \"home_goals_suf_mean\", \"away_goals_suf_mean\", \"home_goals_mean_l5\", \"away_goals_mean_l5\", \"home_goals_suf_mean_l5\", \"away_goals_suf_mean_l5\", \"home_game_points_rank_mean\", \"home_game_points_rank_mean_l5\", \"away_game_points_rank_mean\", \"away_game_points_rank_mean_l5\",\"is_friendly_0\", \"is_friendly_1\"]\n",
        "    \n",
        "    base = df.loc[:, columns]\n",
        "    base.loc[:, \"goals_dif\"] = base[\"home_goals_mean\"] - base[\"away_goals_mean\"]\n",
        "    base.loc[:, \"goals_dif_l5\"] = base[\"home_goals_mean_l5\"] - base[\"away_goals_mean_l5\"]\n",
        "    base.loc[:, \"goals_suf_dif\"] = base[\"home_goals_suf_mean\"] - base[\"away_goals_suf_mean\"]\n",
        "    base.loc[:, \"goals_suf_dif_l5\"] = base[\"home_goals_suf_mean_l5\"] - base[\"away_goals_suf_mean_l5\"]\n",
        "    base.loc[:, \"goals_per_ranking_dif\"] = (base[\"home_goals_mean\"] / base[\"home_rank_mean\"]) - (base[\"away_goals_mean\"] / base[\"away_rank_mean\"])\n",
        "    base.loc[:, \"dif_rank_agst\"] = base[\"home_rank_mean\"] - base[\"away_rank_mean\"]\n",
        "    base.loc[:, \"dif_rank_agst_l5\"] = base[\"home_rank_mean_l5\"] - base[\"away_rank_mean_l5\"]\n",
        "    base.loc[:, \"dif_points_rank\"] = base[\"home_game_points_rank_mean\"] - base[\"away_game_points_rank_mean\"]\n",
        "    base.loc[:, \"dif_points_rank_l5\"] = base[\"home_game_points_rank_mean_l5\"] - base[\"away_game_points_rank_mean_l5\"]\n",
        "    \n",
        "    model_df = base[[\"home_team\", \"away_team\", \"target\", \"rank_dif\", \"goals_dif\", \"goals_dif_l5\", \"goals_suf_dif\", \"goals_suf_dif_l5\", \"goals_per_ranking_dif\", \"dif_rank_agst\", \"dif_rank_agst_l5\", \"dif_points_rank\", \"dif_points_rank_l5\", \"is_friendly_0\", \"is_friendly_1\"]]\n",
        "    return model_df"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:10.455185Z",
          "iopub.execute_input": "2022-11-19T16:04:10.455687Z",
          "iopub.status.idle": "2022-11-19T16:04:10.468041Z",
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          "shell.execute_reply": "2022-11-19T16:04:10.466758Z"
        },
        "trusted": true,
        "id": "V8gpNoMvwmQ-"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_db = create_db(df)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:10.469566Z",
          "iopub.execute_input": "2022-11-19T16:04:10.469955Z",
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          "shell.execute_reply.started": "2022-11-19T16:04:10.469906Z",
          "shell.execute_reply": "2022-11-19T16:04:10.491802Z"
        },
        "trusted": true,
        "id": "BdsRh5S3wmQ-"
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      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_db"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:10.494759Z",
          "iopub.execute_input": "2022-11-19T16:04:10.495103Z",
          "iopub.status.idle": "2022-11-19T16:04:10.525671Z",
          "shell.execute_reply.started": "2022-11-19T16:04:10.495072Z",
          "shell.execute_reply": "2022-11-19T16:04:10.524653Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "W3yeF-BewmQ-",
        "outputId": "4c9e6dc5-0bc6-48a0-fb11-ed829f5f76b3"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       home_team         away_team  target  rank_dif  goals_dif  goals_dif_l5  \\\n",
              "4       Barbados              Cuba       1     -21.0   2.000000           2.0   \n",
              "6       Barbados              Cuba       1     -21.0   1.000000           1.0   \n",
              "31      Mongolia              Guam       1      -4.0   4.000000           4.0   \n",
              "32         Nepal            Bhutan       0     -22.0   1.000000           1.0   \n",
              "33    Bangladesh          Pakistan       0      -7.0  -1.000000          -1.0   \n",
              "...          ...               ...     ...       ...        ...           ...   \n",
              "3331      Norway            Serbia       1      11.0  -0.038095          -0.8   \n",
              "3332      Sweden          Slovenia       1     -45.0   0.030812           0.2   \n",
              "3333      Kosovo            Cyprus       0      -2.0   0.667341           0.6   \n",
              "3334      Greece  Northern Ireland       0      -9.0   0.067653           0.2   \n",
              "3335        Fiji   Solomon Islands       0      26.0  -1.119048          -0.6   \n",
              "\n",
              "      goals_suf_dif  goals_suf_dif_l5  goals_per_ranking_dif  dif_rank_agst  \\\n",
              "4          1.000000               1.0               0.037037     -92.000000   \n",
              "6          0.500000               0.5               0.008511     -35.500000   \n",
              "31        -1.000000              -1.0               0.021622       0.000000   \n",
              "32         0.000000               0.0               0.004975       7.000000   \n",
              "33        -0.500000              -0.5              -0.007200      30.500000   \n",
              "...             ...               ...                    ...            ...   \n",
              "3331      -0.230159               0.0              -0.005282       9.014286   \n",
              "3332       0.137255               0.2               0.009459     -25.102241   \n",
              "3333      -0.463094               0.0               0.007625       7.037917   \n",
              "3334      -0.369979              -1.2              -0.002915      16.024841   \n",
              "3335      -0.634921              -0.8              -0.006351      -7.476190   \n",
              "\n",
              "      dif_rank_agst_l5  dif_points_rank  dif_points_rank_l5  is_friendly_0  \\\n",
              "4                -92.0         0.018519            0.018519              0   \n",
              "6                -35.5         0.008897            0.008897              0   \n",
              "31                 0.0         0.016216            0.016216              1   \n",
              "32                 7.0         0.000000            0.000000              1   \n",
              "33                30.5        -0.010437           -0.010437              1   \n",
              "...                ...              ...                 ...            ...   \n",
              "3331              -3.4        -0.011343            0.016348              1   \n",
              "3332              10.2         0.022485           -0.020314              1   \n",
              "3333               1.6         0.008791           -0.002827              1   \n",
              "3334               0.6         0.027775            0.018570              1   \n",
              "3335               5.8        -0.000581           -0.003519              1   \n",
              "\n",
              "      is_friendly_1  \n",
              "4                 1  \n",
              "6                 1  \n",
              "31                0  \n",
              "32                0  \n",
              "33                0  \n",
              "...             ...  \n",
              "3331              0  \n",
              "3332              0  \n",
              "3333              0  \n",
              "3334              0  \n",
              "3335              0  \n",
              "\n",
              "[3201 rows x 15 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>home_team</th>\n",
              "      <th>away_team</th>\n",
              "      <th>target</th>\n",
              "      <th>rank_dif</th>\n",
              "      <th>goals_dif</th>\n",
              "      <th>goals_dif_l5</th>\n",
              "      <th>goals_suf_dif</th>\n",
              "      <th>goals_suf_dif_l5</th>\n",
              "      <th>goals_per_ranking_dif</th>\n",
              "      <th>dif_rank_agst</th>\n",
              "      <th>dif_rank_agst_l5</th>\n",
              "      <th>dif_points_rank</th>\n",
              "      <th>dif_points_rank_l5</th>\n",
              "      <th>is_friendly_0</th>\n",
              "      <th>is_friendly_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Barbados</td>\n",
              "      <td>Cuba</td>\n",
              "      <td>1</td>\n",
              "      <td>-21.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.037037</td>\n",
              "      <td>-92.000000</td>\n",
              "      <td>-92.0</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Barbados</td>\n",
              "      <td>Cuba</td>\n",
              "      <td>1</td>\n",
              "      <td>-21.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.008511</td>\n",
              "      <td>-35.500000</td>\n",
              "      <td>-35.5</td>\n",
              "      <td>0.008897</td>\n",
              "      <td>0.008897</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Mongolia</td>\n",
              "      <td>Guam</td>\n",
              "      <td>1</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.021622</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.016216</td>\n",
              "      <td>0.016216</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Nepal</td>\n",
              "      <td>Bhutan</td>\n",
              "      <td>0</td>\n",
              "      <td>-22.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.004975</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Bangladesh</td>\n",
              "      <td>Pakistan</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>-0.007200</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>30.5</td>\n",
              "      <td>-0.010437</td>\n",
              "      <td>-0.010437</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3331</th>\n",
              "      <td>Norway</td>\n",
              "      <td>Serbia</td>\n",
              "      <td>1</td>\n",
              "      <td>11.0</td>\n",
              "      <td>-0.038095</td>\n",
              "      <td>-0.8</td>\n",
              "      <td>-0.230159</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.005282</td>\n",
              "      <td>9.014286</td>\n",
              "      <td>-3.4</td>\n",
              "      <td>-0.011343</td>\n",
              "      <td>0.016348</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3332</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>Slovenia</td>\n",
              "      <td>1</td>\n",
              "      <td>-45.0</td>\n",
              "      <td>0.030812</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.137255</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.009459</td>\n",
              "      <td>-25.102241</td>\n",
              "      <td>10.2</td>\n",
              "      <td>0.022485</td>\n",
              "      <td>-0.020314</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3333</th>\n",
              "      <td>Kosovo</td>\n",
              "      <td>Cyprus</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>0.667341</td>\n",
              "      <td>0.6</td>\n",
              "      <td>-0.463094</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.007625</td>\n",
              "      <td>7.037917</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.008791</td>\n",
              "      <td>-0.002827</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3334</th>\n",
              "      <td>Greece</td>\n",
              "      <td>Northern Ireland</td>\n",
              "      <td>0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>0.067653</td>\n",
              "      <td>0.2</td>\n",
              "      <td>-0.369979</td>\n",
              "      <td>-1.2</td>\n",
              "      <td>-0.002915</td>\n",
              "      <td>16.024841</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.027775</td>\n",
              "      <td>0.018570</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3335</th>\n",
              "      <td>Fiji</td>\n",
              "      <td>Solomon Islands</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>-1.119048</td>\n",
              "      <td>-0.6</td>\n",
              "      <td>-0.634921</td>\n",
              "      <td>-0.8</td>\n",
              "      <td>-0.006351</td>\n",
              "      <td>-7.476190</td>\n",
              "      <td>5.8</td>\n",
              "      <td>-0.000581</td>\n",
              "      <td>-0.003519</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3201 rows × 15 columns</p>\n",
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              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# The model\n",
        "\n",
        "Now that we have a database ready and with columns with predictive power, we can start our modelling. \n",
        "\n",
        "Two models will be tested: Random Forest and Gradient Boosting. The selected will be the one with best recall."
      ],
      "metadata": {
        "id": "U-m1R4d4wmQ-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = model_db.iloc[:, 3:]\n",
        "y = model_db[[\"target\"]]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:10.527167Z",
          "iopub.execute_input": "2022-11-19T16:04:10.5275Z",
          "iopub.status.idle": "2022-11-19T16:04:10.540069Z",
          "shell.execute_reply.started": "2022-11-19T16:04:10.527471Z",
          "shell.execute_reply": "2022-11-19T16:04:10.538758Z"
        },
        "trusted": true,
        "id": "IZLphfVVwmQ_"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:10.541485Z",
          "iopub.execute_input": "2022-11-19T16:04:10.541963Z",
          "iopub.status.idle": "2022-11-19T16:04:10.555071Z",
          "shell.execute_reply.started": "2022-11-19T16:04:10.541918Z",
          "shell.execute_reply": "2022-11-19T16:04:10.553935Z"
        },
        "trusted": true,
        "id": "zR3Ll1m2wmQ_"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2, random_state=1)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:10.556653Z",
          "iopub.execute_input": "2022-11-19T16:04:10.557282Z",
          "iopub.status.idle": "2022-11-19T16:04:10.579573Z",
          "shell.execute_reply.started": "2022-11-19T16:04:10.55723Z",
          "shell.execute_reply": "2022-11-19T16:04:10.578308Z"
        },
        "trusted": true,
        "id": "vJbcM8xhwmQ_"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gb = GradientBoostingClassifier(random_state=5)\n",
        "\n",
        "params = {\"learning_rate\": [0.01, 0.1, 0.5],\n",
        "            \"min_samples_split\": [5, 10],\n",
        "            \"min_samples_leaf\": [3, 5],\n",
        "            \"max_depth\":[3,5,10],\n",
        "            \"max_features\":[\"sqrt\"],\n",
        "            \"n_estimators\":[100, 200]\n",
        "         } \n",
        "\n",
        "gb_cv = GridSearchCV(gb, params, cv = 3, n_jobs = -1, verbose = False)\n",
        "\n",
        "gb_cv.fit(X_train.values, np.ravel(y_train))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:04:10.583801Z",
          "iopub.execute_input": "2022-11-19T16:04:10.58428Z",
          "iopub.status.idle": "2022-11-19T16:05:06.413026Z",
          "shell.execute_reply.started": "2022-11-19T16:04:10.584205Z",
          "shell.execute_reply": "2022-11-19T16:05:06.411945Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8oirnR2zwmQ_",
        "outputId": "ae186b70-d51e-439c-c9c7-7ab1154cbcc4"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, estimator=GradientBoostingClassifier(random_state=5),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'learning_rate': [0.01, 0.1, 0.5],\n",
              "                         'max_depth': [3, 5, 10], 'max_features': ['sqrt'],\n",
              "                         'min_samples_leaf': [3, 5],\n",
              "                         'min_samples_split': [5, 10],\n",
              "                         'n_estimators': [100, 200]},\n",
              "             verbose=False)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gb = gb_cv.best_estimator_"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:05:06.414295Z",
          "iopub.execute_input": "2022-11-19T16:05:06.414622Z",
          "iopub.status.idle": "2022-11-19T16:05:06.419211Z",
          "shell.execute_reply.started": "2022-11-19T16:05:06.41459Z",
          "shell.execute_reply": "2022-11-19T16:05:06.41831Z"
        },
        "trusted": true,
        "id": "OhrNQ4-rwmQ_"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gb"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:05:06.420657Z",
          "iopub.execute_input": "2022-11-19T16:05:06.421212Z",
          "iopub.status.idle": "2022-11-19T16:05:06.435299Z",
          "shell.execute_reply.started": "2022-11-19T16:05:06.421179Z",
          "shell.execute_reply": "2022-11-19T16:05:06.434184Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w7AI2NJpwmQ_",
        "outputId": "688eadd6-250c-416a-9b09-000ddb0c48cd"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(max_features='sqrt', min_samples_leaf=5,\n",
              "                           min_samples_split=5, random_state=5)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "params_rf = {\"max_depth\": [20],\n",
        "                \"min_samples_split\": [10],\n",
        "                \"max_leaf_nodes\": [175],\n",
        "                \"min_samples_leaf\": [5],\n",
        "                \"n_estimators\": [250],\n",
        "                 \"max_features\": [\"sqrt\"],\n",
        "                }\n",
        "\n",
        "rf = RandomForestClassifier(random_state=1)\n",
        "\n",
        "rf_cv = GridSearchCV(rf, params_rf, cv = 3, n_jobs = -1, verbose = False)\n",
        "\n",
        "rf_cv.fit(X_train.values, np.ravel(y_train))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:05:06.436954Z",
          "iopub.execute_input": "2022-11-19T16:05:06.437373Z",
          "iopub.status.idle": "2022-11-19T16:05:09.237012Z",
          "shell.execute_reply.started": "2022-11-19T16:05:06.437335Z",
          "shell.execute_reply": "2022-11-19T16:05:09.235743Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Xkf_GZDwmQ_",
        "outputId": "564932fc-fe08-4f51-febf-08c5d2e543b0"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, estimator=RandomForestClassifier(random_state=1), n_jobs=-1,\n",
              "             param_grid={'max_depth': [20], 'max_features': ['sqrt'],\n",
              "                         'max_leaf_nodes': [175], 'min_samples_leaf': [5],\n",
              "                         'min_samples_split': [10], 'n_estimators': [250]},\n",
              "             verbose=False)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf = rf_cv.best_estimator_"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:05:09.238658Z",
          "iopub.execute_input": "2022-11-19T16:05:09.239014Z",
          "iopub.status.idle": "2022-11-19T16:05:09.243741Z",
          "shell.execute_reply.started": "2022-11-19T16:05:09.238982Z",
          "shell.execute_reply": "2022-11-19T16:05:09.24254Z"
        },
        "trusted": true,
        "id": "F9OtRPPrwmQ_"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:05:09.244957Z",
          "iopub.execute_input": "2022-11-19T16:05:09.245707Z",
          "iopub.status.idle": "2022-11-19T16:05:09.25858Z",
          "shell.execute_reply.started": "2022-11-19T16:05:09.245658Z",
          "shell.execute_reply": "2022-11-19T16:05:09.257274Z"
        },
        "trusted": true,
        "id": "gAEPyhatwmQ_"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def analyze(model):\n",
        "    fpr, tpr, _ = roc_curve(y_test, model.predict_proba(X_test.values)[:,1]) #test AUC\n",
        "    plt.figure(figsize=(15,10))\n",
        "    plt.plot([0, 1], [0, 1], 'k--')\n",
        "    plt.plot(fpr, tpr, label=\"test\")\n",
        "\n",
        "    fpr_train, tpr_train, _ = roc_curve(y_train, model.predict_proba(X_train.values)[:,1]) #train AUC\n",
        "    plt.plot(fpr_train, tpr_train, label=\"train\")\n",
        "    auc_test = roc_auc_score(y_test, model.predict_proba(X_test.values)[:,1])\n",
        "    auc_train = roc_auc_score(y_train, model.predict_proba(X_train.values)[:,1])\n",
        "    plt.legend()\n",
        "    plt.title('AUC score is %.2f on test and %.2f on training'%(auc_test, auc_train))\n",
        "    plt.show()\n",
        "    \n",
        "    plt.figure(figsize=(15, 10))\n",
        "    cm = confusion_matrix(y_test, model.predict(X_test.values))\n",
        "    sns.heatmap(cm, annot=True, fmt=\"d\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:05:09.259978Z",
          "iopub.execute_input": "2022-11-19T16:05:09.260323Z",
          "iopub.status.idle": "2022-11-19T16:05:09.272577Z",
          "shell.execute_reply.started": "2022-11-19T16:05:09.260293Z",
          "shell.execute_reply": "2022-11-19T16:05:09.271361Z"
        },
        "trusted": true,
        "id": "0Oha0WPYwmQ_"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "analyze(gb)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:05:09.276381Z",
          "iopub.execute_input": "2022-11-19T16:05:09.27677Z",
          "iopub.status.idle": "2022-11-19T16:05:09.852558Z",
          "shell.execute_reply.started": "2022-11-19T16:05:09.276736Z",
          "shell.execute_reply": "2022-11-19T16:05:09.85139Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "d_MZ_6S8wmRA",
        "outputId": "71e74a36-707b-4b4c-83b7-9ced72e01a32"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "analyze(rf)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:05:09.853848Z",
          "iopub.execute_input": "2022-11-19T16:05:09.854178Z",
          "iopub.status.idle": "2022-11-19T16:05:10.836476Z",
          "shell.execute_reply.started": "2022-11-19T16:05:09.854147Z",
          "shell.execute_reply": "2022-11-19T16:05:10.835294Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "h2ZOrfYrwmRA",
        "outputId": "2404d6f9-b27e-4428-96e2-299b3d30f82c"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Random Forest model is a little bit better but seems to underfit. So, we will use the Gradient Boosting Model."
      ],
      "metadata": {
        "id": "Qq1fhvfiwmRA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from operator import itemgetter"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:05:10.838026Z",
          "iopub.execute_input": "2022-11-19T16:05:10.838427Z",
          "iopub.status.idle": "2022-11-19T16:05:10.844444Z",
          "shell.execute_reply.started": "2022-11-19T16:05:10.838392Z",
          "shell.execute_reply": "2022-11-19T16:05:10.843164Z"
        },
        "trusted": true,
        "id": "7RzoTXWYwmRA"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# WC Simulation\n",
        "\n",
        "The first thing is to create FIFA World Cup games. To do this, I'll get in the wikipedia the teams and group phase matches."
      ],
      "metadata": {
        "id": "n9jwZ9WUwmRA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matches = []\n",
        "groups = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]\n",
        "group_count = 0 \n",
        "\n",
        "table = {\n",
        "    'A': [ ['Qatar', 0, []],  ['Ecuador', 0, []],  ['Senegal', 0, []],  ['Netherlands', 0, []] ] , \n",
        "    'B': [ ['England', 0, []],  ['Iran', 0, []],  ['United States', 0, []],  ['Wales', 0, []] ] ,\n",
        "    'C': [ ['Argentina', 0, []],  ['Saudi Arabia', 0, []],  ['Mexico', 0, []],  ['Poland', 0, []] ] ,\n",
        "    'D': [ ['France', 0, []],  ['Australia', 0, []],  ['Denmark', 0, []],  ['Tunisia', 0, []] ] ,\n",
        "    'E': [ ['Spain', 0, []],  ['Costa Rica', 0, []],  ['Germany', 0, []],  ['Japan', 0, []] ] ,\n",
        "    'F': [ ['Belgium', 0, []],  ['Canada', 0, []],  ['Morocco', 0, []],  ['Croatia', 0, []] ] ,\n",
        "    'G': [ ['Brazil', 0, []],  ['Serbia', 0, []],  ['Switzerland', 0, []],  ['Cameroon', 0, []] ] ,\n",
        "    'H': [ ['Portugal', 0, []],  ['Ghana', 0, []],  ['Uruguay', 0, []],  ['South Korea', 0, []] ]\n",
        "}\n",
        "\n",
        "matches = [\n",
        "  ('A', 'Qatar', 'Ecuador'),  ('A', 'Senegal', 'Netherlands'),  ('A', 'Qatar', 'Senegal'),\n",
        "  ('A', 'Netherlands', 'Ecuador'),  ('A', 'Ecuador', 'Senegal'),  ('A', 'Netherlands', 'Qatar'),\n",
        "\n",
        "  ('B', 'England', 'Iran'),  ('B', 'United States', 'Wales'),  ('B', 'Wales', 'Iran'),\n",
        "  ('B', 'England', 'United States'),  ('B', 'Wales', 'England'),  ('B', 'Iran', 'United States'),\n",
        "\n",
        "  ('C', 'Argentina', 'Saudi Arabia'),  ('C', 'Mexico', 'Poland'),  ('C', 'Poland', 'Saudi Arabia'),\n",
        "  ('C', 'Argentina', 'Mexico'),  ('C', 'Poland', 'Argentina'),  ('C', 'Saudi Arabia', 'Mexico'),\n",
        "\n",
        "  ('D', 'Denmark', 'Tunisia'),  ('D', 'France', 'Australia'),  ('D', 'Tunisia', 'Australia'),\n",
        "  ('D', 'France', 'Denmark'),  ('D', 'Australia', 'Denmark'),  ('D', 'Tunisia', 'France'),\n",
        "\n",
        "  ('E', 'Germany', 'Japan'),  ('E', 'Spain', 'Costa Rica'),  ('E', 'Japan', 'Costa Rica'),\n",
        "  ('E', 'Spain', 'Germany'),  ('E', 'Japan', 'Spain'),  ('E', 'Costa Rica', 'Germany'),\n",
        "\n",
        "  ('F', 'Morocco', 'Croatia'),  ('F', 'Belgium', 'Canada'),  ('F', 'Belgium', 'Morocco'),\n",
        "  ('F', 'Croatia', 'Canada'),  ('F', 'Croatia', 'Belgium'),  ('F', 'Canada', 'Morocco'),\n",
        "\n",
        "  ('G', 'Switzerland', 'Cameroon'),  ('G', 'Brazil', 'Serbia'),  ('G', 'Cameroon', 'Serbia'),\n",
        "  ('G', 'Brazil', 'Switzerland'),  ('G', 'Serbia', 'Switzerland'),  ('G', 'Cameroon', 'Brazil'),\n",
        "\n",
        "  ('H', 'Uruguay', 'South Korea'),  ('H', 'Portugal', 'Ghana'),  ('H', 'South Korea', 'Ghana'),\n",
        "  ('H', 'Portugal', 'Uruguay'),  ('H', 'Ghana', 'Uruguay'),  ('H', 'South Korea', 'Portugal')\n",
        " ]\n",
        "    \n"
      ],
      "metadata": {
        "id": "kh366b7y5K8L"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "table"
      ],
      "metadata": {
        "execution": {
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          "shell.execute_reply": "2022-11-19T16:05:11.804937Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qJavvrN4wmRA",
        "outputId": "477680b8-6332-425d-d24e-08314a6426b0"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'A': [['Qatar', 0, []],\n",
              "  ['Ecuador', 0, []],\n",
              "  ['Senegal', 0, []],\n",
              "  ['Netherlands', 0, []]],\n",
              " 'B': [['England', 0, []],\n",
              "  ['Iran', 0, []],\n",
              "  ['United States', 0, []],\n",
              "  ['Wales', 0, []]],\n",
              " 'C': [['Argentina', 0, []],\n",
              "  ['Saudi Arabia', 0, []],\n",
              "  ['Mexico', 0, []],\n",
              "  ['Poland', 0, []]],\n",
              " 'D': [['France', 0, []],\n",
              "  ['Australia', 0, []],\n",
              "  ['Denmark', 0, []],\n",
              "  ['Tunisia', 0, []]],\n",
              " 'E': [['Spain', 0, []],\n",
              "  ['Costa Rica', 0, []],\n",
              "  ['Germany', 0, []],\n",
              "  ['Japan', 0, []]],\n",
              " 'F': [['Belgium', 0, []],\n",
              "  ['Canada', 0, []],\n",
              "  ['Morocco', 0, []],\n",
              "  ['Croatia', 0, []]],\n",
              " 'G': [['Brazil', 0, []],\n",
              "  ['Serbia', 0, []],\n",
              "  ['Switzerland', 0, []],\n",
              "  ['Cameroon', 0, []]],\n",
              " 'H': [['Portugal', 0, []],\n",
              "  ['Ghana', 0, []],\n",
              "  ['Uruguay', 0, []],\n",
              "  ['South Korea', 0, []]]}"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Above, we also store the team's points at the group and its probabilities of win in each game. The mean of team's wins probabilities will be used as tiebreaker when two teams have the same number of points."
      ],
      "metadata": {
        "id": "lrWM8nQWwmRA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matches[:48]"
      ],
      "metadata": {
        "execution": {
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          "shell.execute_reply": "2022-11-19T16:05:11.818299Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZwMMhiYwmRB",
        "outputId": "c6152b27-6a19-4d53-cae4-ddb1ed425ac2"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('A', 'Qatar', 'Ecuador'),\n",
              " ('A', 'Senegal', 'Netherlands'),\n",
              " ('A', 'Qatar', 'Senegal'),\n",
              " ('A', 'Netherlands', 'Ecuador'),\n",
              " ('A', 'Ecuador', 'Senegal'),\n",
              " ('A', 'Netherlands', 'Qatar'),\n",
              " ('B', 'England', 'Iran'),\n",
              " ('B', 'United States', 'Wales'),\n",
              " ('B', 'Wales', 'Iran'),\n",
              " ('B', 'England', 'United States'),\n",
              " ('B', 'Wales', 'England'),\n",
              " ('B', 'Iran', 'United States'),\n",
              " ('C', 'Argentina', 'Saudi Arabia'),\n",
              " ('C', 'Mexico', 'Poland'),\n",
              " ('C', 'Poland', 'Saudi Arabia'),\n",
              " ('C', 'Argentina', 'Mexico'),\n",
              " ('C', 'Poland', 'Argentina'),\n",
              " ('C', 'Saudi Arabia', 'Mexico'),\n",
              " ('D', 'Denmark', 'Tunisia'),\n",
              " ('D', 'France', 'Australia'),\n",
              " ('D', 'Tunisia', 'Australia'),\n",
              " ('D', 'France', 'Denmark'),\n",
              " ('D', 'Australia', 'Denmark'),\n",
              " ('D', 'Tunisia', 'France'),\n",
              " ('E', 'Germany', 'Japan'),\n",
              " ('E', 'Spain', 'Costa Rica'),\n",
              " ('E', 'Japan', 'Costa Rica'),\n",
              " ('E', 'Spain', 'Germany'),\n",
              " ('E', 'Japan', 'Spain'),\n",
              " ('E', 'Costa Rica', 'Germany'),\n",
              " ('F', 'Morocco', 'Croatia'),\n",
              " ('F', 'Belgium', 'Canada'),\n",
              " ('F', 'Belgium', 'Morocco'),\n",
              " ('F', 'Croatia', 'Canada'),\n",
              " ('F', 'Croatia', 'Belgium'),\n",
              " ('F', 'Canada', 'Morocco'),\n",
              " ('G', 'Switzerland', 'Cameroon'),\n",
              " ('G', 'Brazil', 'Serbia'),\n",
              " ('G', 'Cameroon', 'Serbia'),\n",
              " ('G', 'Brazil', 'Switzerland'),\n",
              " ('G', 'Serbia', 'Switzerland'),\n",
              " ('G', 'Cameroon', 'Brazil'),\n",
              " ('H', 'Uruguay', 'South Korea'),\n",
              " ('H', 'Portugal', 'Ghana'),\n",
              " ('H', 'South Korea', 'Ghana'),\n",
              " ('H', 'Portugal', 'Uruguay'),\n",
              " ('H', 'Ghana', 'Uruguay'),\n",
              " ('H', 'South Korea', 'Portugal')]"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I will use last game stats as the stats for each team involved in the game. Like, for Brazil vs Serbia, the stats of Brazil is the ones they had in the last game and for Serbia too."
      ],
      "metadata": {
        "id": "nqt_ZZrkwmRB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def find_stats(team_1):\n",
        "#team_1 = \"Qatar\"\n",
        "    past_games = team_stats_raw[(team_stats_raw[\"team\"] == team_1)].sort_values(\"date\")\n",
        "    last5 = team_stats_raw[(team_stats_raw[\"team\"] == team_1)].sort_values(\"date\").tail(5)\n",
        "\n",
        "    team_1_rank = past_games[\"rank\"].values[-1]\n",
        "    team_1_goals = past_games.score.mean()\n",
        "    team_1_goals_l5 = last5.score.mean()\n",
        "    team_1_goals_suf = past_games.suf_score.mean()\n",
        "    team_1_goals_suf_l5 = last5.suf_score.mean()\n",
        "    team_1_rank_suf = past_games.rank_suf.mean()\n",
        "    team_1_rank_suf_l5 = last5.rank_suf.mean()\n",
        "    team_1_gp_rank = past_games.points_by_rank.mean()\n",
        "    team_1_gp_rank_l5 = last5.points_by_rank.mean()\n",
        "\n",
        "    return [team_1_rank, team_1_goals, team_1_goals_l5, team_1_goals_suf, team_1_goals_suf_l5, team_1_rank_suf, team_1_rank_suf_l5, team_1_gp_rank, team_1_gp_rank_l5]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:05:11.820833Z",
          "iopub.execute_input": "2022-11-19T16:05:11.821233Z",
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          "shell.execute_reply": "2022-11-19T16:05:11.831022Z"
        },
        "trusted": true,
        "id": "BlRLYVwXwmRB"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def find_features(team_1, team_2):\n",
        "    rank_dif = team_1[0] - team_2[0]\n",
        "    goals_dif = team_1[1] - team_2[1]\n",
        "    goals_dif_l5 = team_1[2] - team_2[2]\n",
        "    goals_suf_dif = team_1[3] - team_2[3]\n",
        "    goals_suf_dif_l5 = team_1[4] - team_2[4]\n",
        "    goals_per_ranking_dif = (team_1[1]/team_1[5]) - (team_2[1]/team_2[5])\n",
        "    dif_rank_agst = team_1[5] - team_2[5]\n",
        "    dif_rank_agst_l5 = team_1[6] - team_2[6]\n",
        "    dif_gp_rank = team_1[7] - team_2[7]\n",
        "    dif_gp_rank_l5 = team_1[8] - team_2[8]\n",
        "    \n",
        "    return [rank_dif, goals_dif, goals_dif_l5, goals_suf_dif, goals_suf_dif_l5, goals_per_ranking_dif, dif_rank_agst, dif_rank_agst_l5, dif_gp_rank, dif_gp_rank_l5, 1, 0]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-11-19T16:05:11.834296Z",
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          "shell.execute_reply": "2022-11-19T16:05:11.845638Z"
        },
        "trusted": true,
        "id": "Zgif2N-hwmRB"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we are able to simulate.\n",
        "\n",
        "Since the model simulates if team 1 will win or not win, it's needed to create some criteria to define a draw. Also, since we have not home advantage at World Cup, the idea is to predict game two times, changing team 1 and team 2. The team with the highest mean of probabilities will be assigned as winner. At the group phase, if \"home team\" win as team 1 and loses as team 2, or if \"home team\" win as team 2 and loses at team 1, there will be assigned a draw in that match."
      ],
      "metadata": {
        "id": "fXQjmk7ZwmRB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2022 World Cup Group Round Results:\n",
        "\n",
        "- A: Netherlands 7, Senegal 6, Ecuador 4, Qatar 0\n",
        "- B: England 7, United States 5, Iran 3, Wales 1\n",
        "- C: Argentina 6, Poland 4, Mexico 4, Saudi Arabia 3\n",
        "- D: France 6, Australia 6, Tunisia 4, Denmark 1\n",
        "- E: Japan 6, Spain 4, Germany 4, Costa Rica 3\n",
        "- F: Morocco 7, Croatia 5, Belgium 4, Canada 0\n",
        "- G: Brazil 6, Switzerland 6, Cameroon 4, Serbia 1\n",
        "- H: Portugal 6, South Korea 4, Uruguay 4, Ghana 3"
      ],
      "metadata": {
        "id": "v265FFN_IAyF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The group phase predicted no surprises, or maybe the draw between Brazil and Switzerland or France and Denmark. For the playoffs phase, I'll predict and show it graphically as made [here](https://www.kaggle.com/code/agostontorok/soccer-world-cup-2018-winner). "
      ],
      "metadata": {
        "id": "41cS8yn_wmRB"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VqMwiyL5WpnH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Simulate playoffs by each knockout round"
      ],
      "metadata": {
        "id": "7e_07kRRWwjN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## [1] Round of 16"
      ],
      "metadata": {
        "id": "UOl5yFFU1q7j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "playoffs16 = {\"Round of 16\": []}\n",
        "advanced =[\n",
        "    ['Netherlands', 'Senegal'], ['England', 'United States'], ['Argentina', 'Poland'], ['France', 'Australia'],\n",
        "    ['Japan', 'Spain'], ['Morocco', 'Croatia'], ['Brazil', 'Switzerland'], ['Portugal', 'South Korea']\n",
        "]"
      ],
      "metadata": {
        "id": "wRM9jdZ8qSdi"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for p in playoffs16.keys():\n",
        "    playoffs16[p] = []\n",
        "\n",
        "actual_round = \"\"\n",
        "next_rounds = []\n",
        "\n",
        "for p in playoffs16.keys():\n",
        "    if p == \"Round of 16\":\n",
        "        control = []\n",
        "        for a in range(0, len(advanced*2), 1):\n",
        "            if a < len(advanced):\n",
        "                if a % 2 == 0:\n",
        "                    control.append((advanced*2)[a][0])\n",
        "                else:\n",
        "                    control.append((advanced*2)[a][1])\n",
        "            else:\n",
        "                if a % 2 == 0:\n",
        "                    control.append((advanced*2)[a][1])\n",
        "                else:\n",
        "                    control.append((advanced*2)[a][0])\n",
        "\n",
        "        playoffs16[p] = [[control[c], control[c+1]] for c in range(0, len(control)-1, 1) if c%2 == 0]\n",
        "        \n",
        "        for i in range(0, len(playoffs16[p]), 1):\n",
        "            game = playoffs16[p][i]\n",
        "            \n",
        "            home = game[0]\n",
        "            away = game[1]\n",
        "            team_1 = find_stats(home)\n",
        "            team_2 = find_stats(away)\n",
        "\n",
        "            features_g1 = find_features(team_1, team_2)\n",
        "            features_g2 = find_features(team_2, team_1)\n",
        "            \n",
        "            probs_g1 = gb.predict_proba([features_g1])\n",
        "            probs_g2 = gb.predict_proba([features_g2])\n",
        "            \n",
        "            team_1_prob = (probs_g1[0][0] + probs_g2[0][1])/2\n",
        "            team_2_prob = (probs_g2[0][0] + probs_g1[0][1])/2\n",
        "            \n",
        "            if actual_round != p:\n",
        "                print(\"-\"*10)\n",
        "                print(\"Starting simulation of %s\"%(p))\n",
        "                print(\"-\"*10)\n",
        "                print(\"\\n\")\n",
        "            \n",
        "            if team_1_prob < team_2_prob:\n",
        "                print(\"%s vs. %s: %s advances with prob %.2f\"%(home, away, away, team_2_prob))\n",
        "                next_rounds.append(away)\n",
        "            else:\n",
        "                print(\"%s vs. %s: %s advances with prob %.2f\"%(home, away, home, team_1_prob))\n",
        "                next_rounds.append(home)\n",
        "            \n",
        "            game.append([team_1_prob, team_2_prob])\n",
        "            playoffs16[p][i] = game\n",
        "            actual_round = p"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IOXhnCahWpqy",
        "outputId": "c1694da7-47db-4ab8-ef59-589e9544ead0"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------\n",
            "Starting simulation of Round of 16\n",
            "----------\n",
            "\n",
            "\n",
            "Netherlands vs. United States: United States advances with prob 0.60\n",
            "Argentina vs. Australia: Argentina advances with prob 0.80\n",
            "Japan vs. Croatia: Croatia advances with prob 0.61\n",
            "Brazil vs. South Korea: Brazil advances with prob 0.80\n",
            "Senegal vs. England: England advances with prob 0.66\n",
            "Poland vs. France: France advances with prob 0.66\n",
            "Spain vs. Morocco: Spain advances with prob 0.67\n",
            "Switzerland vs. Portugal: Portugal advances with prob 0.61\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "next_rounds_predicted = next_rounds\n",
        "next_rounds_results = ['Netherlands', 'Argentina', 'Croatia', 'Brazil',\n",
        " 'England', 'France', 'Morocco', 'Portugal']"
      ],
      "metadata": {
        "id": "OiE17rk7WpuI"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(next_rounds_predicted)\n",
        "print(next_rounds_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yMy-jEHDWpxp",
        "outputId": "bab4e4b4-12be-4983-fd2d-ccfc4c690037"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['United States', 'Argentina', 'Croatia', 'Brazil', 'England', 'France', 'Spain', 'Portugal']\n",
            "['Netherlands', 'Argentina', 'Croatia', 'Brazil', 'England', 'France', 'Morocco', 'Portugal']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2022 World Cup Knockout Round Actual Results (Round of 16)\n",
        "\n",
        "- Netherlands 3 vs. United States 1\n",
        "- Argentina 2 vs. Australia 1\n",
        "- England 3 vs. Senegal 0\n",
        "- France 3 vs. Poland 1\n",
        "- Croatia 4 vs. Japan 1\n",
        "- Brazil 4 vs. South Korea 1\n",
        "- Morocco 3 vs. Spain 0 \n",
        "- Portugal 6 vs. Switzerland 1"
      ],
      "metadata": {
        "id": "-avTsmZF3Uvr"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BsNc0EpsWp06"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## [2] Quarter Final (Round of 8)"
      ],
      "metadata": {
        "id": "JyJNhoNtaY8M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "playoffs8 = {\"Round of 8\": []}\n",
        "advanced = next_rounds_results\n",
        "next_rounds = advanced\n",
        "print(advanced)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oSrPgNOPqnQT",
        "outputId": "541072b7-c9e5-45e1-bc9f-0ca0928ee2c5"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Netherlands', 'Argentina', 'Croatia', 'Brazil', 'England', 'France', 'Morocco', 'Portugal']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for p in playoffs8.keys():\n",
        "    playoffs8[p] = []\n",
        "\n",
        "\n",
        "for p in playoffs8.keys():\n",
        "        playoffs8[p] = [[next_rounds[c], next_rounds[c+1]] for c in range(0, len(next_rounds)-1, 1) if c%2 == 0]\n",
        "        next_rounds = []\n",
        "        for i in range(0, len(playoffs8[p])):\n",
        "            game = playoffs8[p][i]\n",
        "            home = game[0]\n",
        "            away = game[1]\n",
        "            team_1 = find_stats(home)\n",
        "            team_2 = find_stats(away)\n",
        "            \n",
        "            features_g1 = find_features(team_1, team_2)\n",
        "            features_g2 = find_features(team_2, team_1)\n",
        "            \n",
        "            probs_g1 = gb.predict_proba([features_g1])\n",
        "            probs_g2 = gb.predict_proba([features_g2])\n",
        "            \n",
        "            team_1_prob = (probs_g1[0][0] + probs_g2[0][1])/2\n",
        "            team_2_prob = (probs_g2[0][0] + probs_g1[0][1])/2\n",
        "            \n",
        "            if actual_round != p:\n",
        "                print(\"-\"*10)\n",
        "                print(\"Starting simulation of %s\"%(p))\n",
        "                print(\"-\"*10)\n",
        "                print(\"\\n\")\n",
        "            \n",
        "            if team_1_prob < team_2_prob:\n",
        "                print(\"%s vs. %s: %s advances with prob %.2f\"%(home, away, away, team_2_prob))\n",
        "                next_rounds.append(away)\n",
        "            else:\n",
        "                print(\"%s vs. %s: %s advances with prob %.2f\"%(home, away, home, team_1_prob))\n",
        "                next_rounds.append(home)\n",
        "            game.append([team_1_prob, team_2_prob])\n",
        "            playoffs8[p][i] = game\n",
        "            actual_round = p"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B5GApPr_aYFt",
        "outputId": "9324c38a-2811-435a-939a-a026941589b7"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------\n",
            "Starting simulation of Round of 8\n",
            "----------\n",
            "\n",
            "\n",
            "Netherlands vs. Argentina: Argentina advances with prob 0.64\n",
            "Croatia vs. Brazil: Brazil advances with prob 0.68\n",
            "England vs. France: France advances with prob 0.51\n",
            "Morocco vs. Portugal: Portugal advances with prob 0.75\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "next_rounds_predicted = next_rounds\n",
        "#next_rounds_results = ['www', 'xxx', 'yyy', 'zzz']\n",
        "next_rounds_results = next_rounds"
      ],
      "metadata": {
        "id": "lN2aMQaIaYNS"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(next_rounds_predicted)\n",
        "print(next_rounds_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qikCnCtxaYTB",
        "outputId": "01cfb7d1-5e08-46ab-abbf-ea9f005188ff"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Argentina', 'Brazil', 'France', 'Portugal']\n",
            "['Argentina', 'Brazil', 'France', 'Portugal']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2022 World Cup Knockout Round Actual Results (Quarter Final)\n",
        "\n",
        "- XXX vs. YYY\n",
        "- WWW vs. ZZZ"
      ],
      "metadata": {
        "id": "1PZlPdwq3v0A"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "clDg8x6zzkhd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## [3] Semi Final (Round of 4)"
      ],
      "metadata": {
        "id": "iBSHHWzLxJZf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "playoffs4 = {\"Round of 4\": []}\n",
        "advanced = next_rounds_results\n",
        "next_rounds = advanced\n",
        "print(advanced)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oIkXl4h8aYVc",
        "outputId": "9a063c6e-6ad8-4a88-fc7c-4a244d50c288"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Argentina', 'Brazil', 'France', 'Portugal']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for p in playoffs4.keys():\n",
        "    playoffs4[p] = []\n",
        "\n",
        "\n",
        "for p in playoffs4.keys():\n",
        "        playoffs4[p] = [[next_rounds[c], next_rounds[c+1]] for c in range(0, len(next_rounds)-1, 1) if c%2 == 0]\n",
        "        next_rounds = []\n",
        "        for i in range(0, len(playoffs4[p])):\n",
        "            game = playoffs4[p][i]\n",
        "            home = game[0]\n",
        "            away = game[1]\n",
        "            team_1 = find_stats(home)\n",
        "            team_2 = find_stats(away)\n",
        "            \n",
        "            features_g1 = find_features(team_1, team_2)\n",
        "            features_g2 = find_features(team_2, team_1)\n",
        "            \n",
        "            probs_g1 = gb.predict_proba([features_g1])\n",
        "            probs_g2 = gb.predict_proba([features_g2])\n",
        "            \n",
        "            team_1_prob = (probs_g1[0][0] + probs_g2[0][1])/2\n",
        "            team_2_prob = (probs_g2[0][0] + probs_g1[0][1])/2\n",
        "            \n",
        "            if actual_round != p:\n",
        "                print(\"-\"*10)\n",
        "                print(\"Starting simulation of %s\"%(p))\n",
        "                print(\"-\"*10)\n",
        "                print(\"\\n\")\n",
        "            \n",
        "            if team_1_prob < team_2_prob:\n",
        "                print(\"%s vs. %s: %s advances with prob %.2f\"%(home, away, away, team_2_prob))\n",
        "                next_rounds.append(away)\n",
        "            else:\n",
        "                print(\"%s vs. %s: %s advances with prob %.2f\"%(home, away, home, team_1_prob))\n",
        "                next_rounds.append(home)\n",
        "            game.append([team_1_prob, team_2_prob])\n",
        "            playoffs4[p][i] = game\n",
        "            actual_round = p"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XXmq5XLnxYbQ",
        "outputId": "4e2e1d81-1a70-4bc2-f71d-6075c45483fb"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------\n",
            "Starting simulation of Round of 4\n",
            "----------\n",
            "\n",
            "\n",
            "Argentina vs. Brazil: Argentina advances with prob 0.52\n",
            "France vs. Portugal: Portugal advances with prob 0.61\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "next_rounds_predicted = next_rounds\n",
        "#next_rounds_results = ['xxx', 'yyy']\n",
        "next_rounds_results = next_rounds"
      ],
      "metadata": {
        "id": "qgZ64DNTxYeZ"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(next_rounds_predicted)\n",
        "print(next_rounds_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KccZHd9OxYia",
        "outputId": "f585ce95-7c45-4361-eeb2-7adf14e5741a"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Argentina', 'Portugal']\n",
            "['Argentina', 'Portugal']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2022 World Cup Knockout Round Actual Results (Semi Final)\n",
        "\n",
        "- XXX vs. YYY\n",
        "\n"
      ],
      "metadata": {
        "id": "EcHEbUnP4Ah1"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xCSAu1JDysrJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## [4] Final (Round of 2)"
      ],
      "metadata": {
        "id": "qr9lLqGlyt8l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "playoffs2 = {\"Round of 2\": []}\n",
        "advanced = next_rounds_results\n",
        "next_rounds = advanced\n",
        "print(advanced)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MvWds9t-ysum",
        "outputId": "974cbb80-8057-4b7f-b538-c84cbfc5d23c"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Argentina', 'Portugal']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for p in playoffs2.keys():\n",
        "    playoffs2[p] = []\n",
        "\n",
        "\n",
        "for p in playoffs2.keys():\n",
        "        playoffs2[p] = [[next_rounds[c], next_rounds[c+1]] for c in range(0, len(next_rounds)-1, 1) if c%2 == 0]\n",
        "        next_rounds = []\n",
        "        for i in range(0, len(playoffs2[p])):\n",
        "            game = playoffs2[p][i]\n",
        "            home = game[0]\n",
        "            away = game[1]\n",
        "            team_1 = find_stats(home)\n",
        "            team_2 = find_stats(away)\n",
        "            \n",
        "            features_g1 = find_features(team_1, team_2)\n",
        "            features_g2 = find_features(team_2, team_1)\n",
        "            \n",
        "            probs_g1 = gb.predict_proba([features_g1])\n",
        "            probs_g2 = gb.predict_proba([features_g2])\n",
        "            \n",
        "            team_1_prob = (probs_g1[0][0] + probs_g2[0][1])/2\n",
        "            team_2_prob = (probs_g2[0][0] + probs_g1[0][1])/2\n",
        "            \n",
        "            if actual_round != p:\n",
        "                print(\"-\"*10)\n",
        "                print(\"Starting simulation of %s\"%(p))\n",
        "                print(\"-\"*10)\n",
        "                print(\"\\n\")\n",
        "            \n",
        "            if team_1_prob < team_2_prob:\n",
        "                print(\"%s vs. %s: %s advances with prob %.2f\"%(home, away, away, team_2_prob))\n",
        "                next_rounds.append(away)\n",
        "            else:\n",
        "                print(\"%s vs. %s: %s advances with prob %.2f\"%(home, away, home, team_1_prob))\n",
        "                next_rounds.append(home)\n",
        "            game.append([team_1_prob, team_2_prob])\n",
        "            playoffs2[p][i] = game\n",
        "            actual_round = p"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HVrmQHQAysxl",
        "outputId": "0630d549-8717-4856-d85a-b3c0af37dcd8"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------\n",
            "Starting simulation of Round of 2\n",
            "----------\n",
            "\n",
            "\n",
            "Argentina vs. Portugal: Argentina advances with prob 0.55\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "next_rounds_predicted = next_rounds\n",
        "#next_rounds_results = ['xxx']\n",
        "next_rounds_results = next_rounds"
      ],
      "metadata": {
        "id": "_dnSBkIuys0e"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(next_rounds_predicted)\n",
        "print(next_rounds_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DfgDPL9Qys3N",
        "outputId": "29fdbd1d-8bac-4417-ef4a-ed8f53c863a4"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Argentina']\n",
            "['Argentina']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2022 World Cup Knockout Round Actual Results (Final)\n",
        "\n",
        "- WWW"
      ],
      "metadata": {
        "id": "7ZQZeRlC4IXP"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jqlMJFnXys5u"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}