This project is a clone of the FaceMash App as shown in the movie "The Social Network". It allows the players to rate two photos competitively. It uses the Elo Algorithm to calculate the rankings, expected probability of winning and losing. This app has been built with the PHP Laravel 5 Framework and uses Bootstrap for the Front-end.
- Features/Technologies:
- Laravel 5 Framework
- Elo Algorithm
- Bootstrap Framework
1.0.0
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Performance isn't measured absolutely; it is inferred from wins, losses, and draws against other players. Players' ratings depend on the ratings of their opponents, and the results scored against them. The difference in rating between two players determines an estimate for the expected score between them. Both the average and the spread of ratings can be arbitrarily chosen. Elo suggested scaling ratings so that a difference of 200 rating points in chess would mean that the stronger player has an expected score (which basically is an expected average score) of approximately 0.75, and the USCF initially aimed for an average club player to have a rating of 1500.
A player's expected score is his probability of winning plus half his probability of drawing. Thus an expected score of 0.75 could represent a 75% chance of winning, 25% chance of losing, and 0% chance of drawing. On the other extreme it could represent a 50% chance of winning, 0% chance of losing, and 50% chance of drawing. The probability of drawing, as opposed to having a decisive result, is not specified in the Elo system. Instead a draw is considered half a win and half a loss.
If Player A has a rating of 
Similarly the expected score for Player B is
When a player's actual tournament scores exceed his expected scores, the Elo system takes this as evidence that player's rating is too low, and needs to be adjusted upward. Similarly when a player's actual tournament scores fall short of his expected scores, that player's rating is adjusted downward. Elo's original suggestion, which is still widely used, was a simple linear adjustment proportional to the amount by which a player overperformed or underperformed his expected score. The maximum possible adjustment per game, called the K-factor, was set at K = 16 for masters and K = 32 for weaker players.
Supposing Player A was expected to score 
This update can be performed after each game or each tournament, or after any suitable rating period. An example may help clarify. Suppose Player A has a rating of 1613, and plays in a five-round tournament. He or she loses to a player rated 1609, draws with a player rated 1477, defeats a player rated 1388, defeats a player rated 1586, and loses to a player rated 1720. The player's actual score is (0 + 0.5 + 1 + 1 + 0) = 2.5. The expected score, calculated according to the formula above, was (0.51 + 0.76 + 0.88 + 0.56 + 0.28) = 2.99. Therefore, the player's new rating is (1613 + 32×(2.5 − 2.99)) = 1597, assuming that a K-factor of 32 is used.
Note that while two wins, two losses, and one draw may seem like a par score, it is worse than expected for Player A because his or her opponents were lower rated on average. Therefore, Player A is slightly penalized. If Player A had scored two wins, one loss, and two draws, for a total score of three points, that would have been slightly better than expected, and the player's new rating would have been (1613 + 32×(3 − 2.99)) = 1613.
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