Hypothesis Testing with Men's and Women's Soccer Matches.ipynb

{"cells":[{"source":"![A soccer pitch for an international match.](soccer-pitch.jpg)\n\nYou're working as a sports journalist at a major online sports media company, specializing in soccer analysis and reporting. You've been watching both men's and women's international soccer matches for a number of years, and your gut instinct tells you that more goals are scored in women's international football matches than men's. This would make an interesting investigative article that your subscribers are bound to love, but you'll need to perform a valid statistical hypothesis test to be sure!\n\nWhile scoping this project, you acknowledge that the sport has changed a lot over the years, and performances likely vary a lot depending on the tournament, so you decide to limit the data used in the analysis to only official `FIFA World Cup` matches (not including qualifiers) since `2002-01-01`.\n\nYou create two datasets containing the results of every official men's and women's international football match since the 19th century, which you scraped from a reliable online source. This data is stored in two CSV files: `women_results.csv` and `men_results.csv`.\n\nThe question you are trying to determine the answer to is:\n\n> Are more goals scored in women's international soccer matches than men's?\n\nYou assume a **10% significance level**, and use the following null and alternative hypotheses:\n\n$H_0$ : The mean number of goals scored in women's international soccer matches is the same as men's.\n\n$H_A$ : The mean number of goals scored in women's international soccer matches is greater than men's.","metadata":{},"id":"3ed0edb2-696b-49ab-9db0-681defcce695","cell_type":"markdown"},{"source":"# Are more goals scored in women's international soccer matches than men's?","metadata":{},"cell_type":"markdown","id":"61995a2e-b6bf-4f46-b439-9ba9c197228c"},{"source":"# Imports\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport pingouin\nfrom scipy.stats import mannwhitneyu\n\n# Load men's and women's datasets\nmen = pd.read_csv(\"men_results.csv\")\nwomen = pd.read_csv(\"women_results.csv\")","metadata":{"executionCancelledAt":null,"executionTime":3634,"lastExecutedAt":1705936261308,"lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"# Imports\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport pingouin\nfrom scipy.stats import mannwhitneyu\n\n# Load men's and women's datasets\nmen = pd.read_csv(\"men_results.csv\")\nwomen = pd.read_csv(\"women_results.csv\")"},"id":"84a3c4c8-534b-4d1a-9919-17e05208b2ca","cell_type":"code","execution_count":1,"outputs":[]},{"source":"# Filter the data for the time range and tournament\nmen[\"date\"] = pd.to_datetime(men[\"date\"])\nmen_subset = men[(men[\"date\"] > \"2002-01-01\") & (men[\"tournament\"].isin([\"FIFA World Cup\"]))]\nwomen[\"date\"] = pd.to_datetime(women[\"date\"])\nwomen_subset = women[(women[\"date\"] > \"2002-01-01\") & (women[\"tournament\"].isin([\"FIFA World Cup\"]))]\nmen_subset\n","metadata":{"executionCancelledAt":null,"executionTime":181,"lastExecutedAt":1705936384255,"lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"# Filter the data for the time range and tournament\nmen[\"date\"] = pd.to_datetime(men[\"date\"])\nmen_subset = men[(men[\"date\"] > \"2002-01-01\") & (men[\"tournament\"].isin([\"FIFA World Cup\"]))]\nwomen[\"date\"] = pd.to_datetime(women[\"date\"])\nwomen_subset = women[(women[\"date\"] > \"2002-01-01\") & (women[\"tournament\"].isin([\"FIFA World Cup\"]))]\nmen_subset\n"},"cell_type":"code","id":"c04d8d2e-5cd3-4add-ae22-896d6551a114","outputs":[{"output_type":"execute_result","data":{"application/com.datacamp.data-table.v2+json":{"table":{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"Unnamed: 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2019-07-06  ...          2  FIFA World Cup\n4469        4469 2019-07-07  ...          0  FIFA World Cup\n\n[200 rows x 7 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>date</th>\n      <th>home_team</th>\n      <th>away_team</th>\n      <th>home_score</th>\n      <th>away_score</th>\n      <th>tournament</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1600</th>\n      <td>1600</td>\n      <td>2003-09-20</td>\n      <td>Nigeria</td>\n      <td>North Korea</td>\n      <td>0</td>\n      <td>3</td>\n      <td>FIFA World Cup</td>\n    </tr>\n    <tr>\n      <th>1601</th>\n      <td>1601</td>\n      <td>2003-09-20</td>\n      <td>Norway</td>\n      <td>France</td>\n      <td>2</td>\n      <td>0</td>\n      <td>FIFA World Cup</td>\n    </tr>\n    <tr>\n      <th>1602</th>\n      <td>1602</td>\n      <td>2003-09-20</td>\n      <td>Germany</td>\n      <td>Canada</td>\n      <td>4</td>\n      <td>1</td>\n      <td>FIFA World Cup</td>\n    </tr>\n    <tr>\n      <th>1603</th>\n      <td>1603</td>\n      <td>2003-09-20</td>\n      <td>Japan</td>\n      <td>Argentina</td>\n      <td>6</td>\n      <td>0</td>\n      <td>FIFA World Cup</td>\n    </tr>\n    <tr>\n      <th>1604</th>\n      <td>1604</td>\n      <td>2003-09-21</td>\n      <td>United States</td>\n      <td>Sweden</td>\n      <td>3</td>\n      <td>1</td>\n      <td>FIFA World Cup</td>\n    </tr>\n    <tr>\n      <th>...</th>\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>4465</th>\n      <td>4465</td>\n      <td>2019-06-29</td>\n      <td>Germany</td>\n      <td>Sweden</td>\n      <td>1</td>\n      <td>2</td>\n      <td>FIFA World Cup</td>\n    </tr>\n    <tr>\n      <th>4466</th>\n      <td>4466</td>\n      <td>2019-07-02</td>\n      <td>England</td>\n      <td>United States</td>\n      <td>1</td>\n      <td>2</td>\n      <td>FIFA World Cup</td>\n    </tr>\n    <tr>\n      <th>4467</th>\n      <td>4467</td>\n      <td>2019-07-03</td>\n      <td>Netherlands</td>\n      <td>Sweden</td>\n      <td>1</td>\n      <td>0</td>\n      <td>FIFA World Cup</td>\n    </tr>\n    <tr>\n      <th>4468</th>\n      <td>4468</td>\n      <td>2019-07-06</td>\n      <td>England</td>\n      <td>Sweden</td>\n      <td>1</td>\n      <td>2</td>\n      <td>FIFA World Cup</td>\n    </tr>\n    <tr>\n      <th>4469</th>\n      <td>4469</td>\n      <td>2019-07-07</td>\n      <td>United States</td>\n      <td>Netherlands</td>\n      <td>2</td>\n      <td>0</td>\n      <td>FIFA World Cup</td>\n    </tr>\n  </tbody>\n</table>\n<p>200 rows × 7 columns</p>\n</div>"},"metadata":{},"execution_count":4}],"execution_count":4},{"source":"### Create group and goals_scored columns","metadata":{},"cell_type":"markdown","id":"1957b145-c4fa-4b2a-8161-8e470056458c"},{"source":"\nmen_subset[\"group\"] = \"men\"\nwomen_subset[\"group\"] = \"women\"\nmen_subset[\"goals_scored\"] = men_subset[\"home_score\"] + men_subset[\"away_score\"]\nwomen_subset[\"goals_scored\"] = women_subset[\"home_score\"] + women_subset[\"away_score\"]","metadata":{"executionCancelledAt":null,"executionTime":18,"lastExecutedAt":1705936499663,"lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"\nmen_subset[\"group\"] = \"men\"\nwomen_subset[\"group\"] = \"women\"\nmen_subset[\"goals_scored\"] = men_subset[\"home_score\"] + men_subset[\"away_score\"]\nwomen_subset[\"goals_scored\"] = women_subset[\"home_score\"] + women_subset[\"away_score\"]"},"cell_type":"code","id":"4eb45b23-aee0-46ac-ba7d-074f778d8604","outputs":[],"execution_count":6},{"source":"### Determine normality using histograms","metadata":{},"cell_type":"markdown","id":"46ab2a47-4936-4387-a00f-300bb6c555c1"},{"source":"men_subset[\"goals_scored\"].hist()\nplt.show()\nplt.clf()","metadata":{"executionCancelledAt":null,"executionTime":245,"lastExecutedAt":1705936502969,"lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"# Determine normality using histograms\nmen_subset[\"goals_scored\"].hist()\nplt.show()\nplt.clf()"},"cell_type":"code","id":"3ac631d3-089f-45b3-a8d6-b80d791f3900","outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}}],"execution_count":7},{"source":"women_subset[\"goals_scored\"].hist()\nplt.show()\nplt.clf()","metadata":{"executionCancelledAt":null,"executionTime":157,"lastExecutedAt":1705936563678,"lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"women_subset[\"goals_scored\"].hist()\nplt.show()\nplt.clf()"},"cell_type":"code","id":"91631d04-567d-4523-85c5-e64b4b3fba1f","outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}}],"execution_count":8},{"source":"### _**Goals scored is not normally distributed, so use Wilcoxon-Mann-Whitney test of two groups**_","metadata":{},"cell_type":"markdown","id":"c58f103f-0be4-4d0f-aad3-3dd87218544c"},{"source":"# Combine women's and men's data and calculate goals scored in each match\nboth = pd.concat([women_subset, men_subset], axis=0, ignore_index=True)\n\n# Transform the data for the pingouin Mann-Whitney U t-test/Wilcoxon-Mann-Whitney test\nboth_subset = both[[\"goals_scored\", \"group\"]]\nboth_subset_wide = both_subset.pivot(columns=\"group\", values=\"goals_scored\")\n\n# Perform right-tailed Wilcoxon-Mann-Whitney test with pingouin\nresults_pg = pingouin.mwu(x=both_subset_wide[\"women\"],\n                          y=both_subset_wide[\"men\"],\n                          alternative=\"greater\")\n\n# Alternative SciPy solution: Perform right-tailed Wilcoxon-Mann-Whitney test with scipy\nresults_scipy = mannwhitneyu(x=women_subset[\"goals_scored\"],\n                             y=men_subset[\"goals_scored\"],\n                             alternative=\"greater\")\nprint(results_pg)\nprint(results_scipy)","metadata":{"executionCancelledAt":null,"executionTime":27,"lastExecutedAt":1705936814205,"lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"# Combine women's and men's data and calculate goals scored in each match\nboth = pd.concat([women_subset, men_subset], axis=0, ignore_index=True)\n\n# Transform the data for the pingouin Mann-Whitney U t-test/Wilcoxon-Mann-Whitney test\nboth_subset = both[[\"goals_scored\", \"group\"]]\nboth_subset_wide = both_subset.pivot(columns=\"group\", values=\"goals_scored\")\n\n# Perform right-tailed Wilcoxon-Mann-Whitney test with pingouin\nresults_pg = pingouin.mwu(x=both_subset_wide[\"women\"],\n                          y=both_subset_wide[\"men\"],\n                          alternative=\"greater\")\n\n# Alternative SciPy solution: Perform right-tailed Wilcoxon-Mann-Whitney test with scipy\nresults_scipy = mannwhitneyu(x=women_subset[\"goals_scored\"],\n                             y=men_subset[\"goals_scored\"],\n                             alternative=\"greater\")\nprint(results_pg)\nprint(results_scipy)"},"cell_type":"code","id":"1720123c-9deb-4137-bfd8-51ffcf78c410","outputs":[{"output_type":"stream","name":"stdout","text":"       U-val alternative     p-val       RBC      CLES\nMWU  43273.0     greater  0.005107 -0.126901  0.563451\nMannwhitneyuResult(statistic=43273.0, pvalue=0.005106609825443641)\n"}],"execution_count":10},{"source":"# Extract p-value as a float\np_val = results_pg[\"p-val\"].values[0]\n\n# Determine hypothesis test result using sig. level\nif p_val <= 0.01:\n    result = \"reject\"\nelse:\n    result = \"fail to reject\"\n\nresult_dict = {\"p_val\": p_val, \"result\": result}\nprint(result_dict)","metadata":{"executionCancelledAt":null,"executionTime":17,"lastExecutedAt":1705936842471,"lastScheduledRunId":null,"lastSuccessfullyExecutedCode":"# Extract p-value as a float\np_val = results_pg[\"p-val\"].values[0]\n\n# Determine hypothesis test result using sig. level\nif p_val <= 0.01:\n    result = \"reject\"\nelse:\n    result = \"fail to reject\"\n\nresult_dict = {\"p_val\": p_val, \"result\": result}\nprint(result_dict)"},"cell_type":"code","id":"240d6ee4-57d0-43f8-bc25-72ba834ce736","outputs":[{"output_type":"stream","name":"stdout","text":"{'p_val': 0.005106609825443641, 'result': 'reject'}\n"}],"execution_count":11},{"source":"## _**Conclusion:** Since p-value < 0.05, we can reject the null hypothesis. Therefore, the mean number of goals scored in women's international soccer matches is larger than men's_","metadata":{},"cell_type":"markdown","id":"611db32a-3f89-40a8-815d-60e4d158e628"}],"metadata":{"colab":{"name":"Welcome to DataCamp Workspaces.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.10"}},"nbformat":4,"nbformat_minor":5}