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Elo algorithm implementation in Python

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# Elo Python Ranking

The elo formula is a method of ranking chess players by calculating relative skill. It has found successful applications in team sports. A python package has been developed to calulate expected probability of victory based on prior skill rankings and update the rankings following a result.

```from elosports.elo import Elo
eloLeague = Elo(k = 20)
eloLeague.expectResult(eloLeague.ratingDict['Daniel'],eloLeague.ratingDict['Harry'])```

The difference in ratings (relative score) determines the probability of victory in a potential match-up. After a result concludes, the difference determines how many points the victor gains and defeated loses. A few points transfer from the loser to the winner when the higher rated player wins. Many points transfer when the lower-rated player wins.

The long-term average for teams is 1500 and values generally range from 1200 to 1800.

## k-value

`eloLeague = Elo(k = 20)`

The k-factor determines how the rating reacts to new results. If the value is set too high the ratings will jump around too much and set too low it will take a long time to recognize greatness.

## g-value

`eloLeague = Elo(k= 20, g = 1)`

The g-value or margin of value multiplier introduces a way of preventing autocorrelation.

`eloLeague = Elo(k = 20, homefield = 100)`

Home-field advantage is pre-determined. In the NBA and NFL, FiveThirtyEight gives home-court advantages of around 100 Elo points. In the case of two evenly-matched teams, Elo favors the home team.

## Expected Score

The formula for determining the expected probabilistic score can found: https://en.wikipedia.org/wiki/Elo_rating_system

`eloLeague.expectResult(eloLeague.ratingDict['Daniel'],eloLeague.ratingDict['Harry'])`

## Update Rankings

`eloLeague.gameOver(winner = "Daniel, loser = "Harry")`

## Tutorial

A tutorial with NFL (American football) simulated Elo rankings can be found in the tutorial section.

Elo algorithm implementation in Python

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