This is an Elo Rating calculator specifically tweaked for soccer matches.
It can calculate a given team rating based on a history of matches spread out over different seasons/leagues, give you the likelihood of Team A beating Team B, import and export data do JSON files, personalize the algorithm, and many more features.
From Wikipedia:
The Elo rating system is a method for calculating the relative skill levels of players in competitor-versus-competitor games such as chess. It is named after its creator Arpad Elo, a Hungarian-born American physics professor.
This implementation has a few tweaks to adjust to soccer matches, more notably, the addition of a variable that changes the points spread in a given match according to the goal difference.
For detailed information about the formulas and a bit more of theory, please visit the Wikipedia Page.
The initial rating value is 1500 points, while the default K is 20 (but it's easily changeable).
Also, after each season, you can regress the points of all teams by 1/3 to the mean by setting true the RegressToMean variable. This is particularly useful in competitive leagues where there a lot of transfers and new players at the begginning of each new season (default is true).
Higher ranking teams will still get an advantage, but it will be more competitive.
EloCalculator calculator = new EloCalculator();
try {
calculator = new EloCalculator.Builder()
.setK(20)
.setLeagues("C:\\Soccer\\Data.json")
.setRegressTowardMean(true)
.build();
} catch (IOException ex) {
Logger.getLogger(MainClass.class.getName()).log(Level.SEVERE, null, ex);
}calculator.calculateRatings();You can save it (and load it later, so you don't need to recalculate).
try {
calculator.saveTeamsJSONFile("C:\\Soccer\\Ratings_Jan_2017.json");
} catch (IOException ex) {
Logger.getLogger(MainClass.class.getName()).log(Level.SEVERE, null, ex);
}Or you could get your data directly. In this example, we only print the rating in descending order.
System.out.println("Ranking: ");
List<Team> teams = calculator.getTeams(true);
for(Team t : teams)
System.out.println(((int) t.getLastRating().getRating()) + " " + t.getName()); [
{
"champion": "Palmeiras",
"name": "Campeonato Brasileiro 2016",
"matches": [
{
"home": "Vitória",
"away": "Palmeiras",
"homeGoals": 1,
"awayGoals": 2,
"date": "Dec 11, 2016 12:00:00 AM"
},
{
"home": "Palmeiras",
"away": "Atlético-PR",
"homeGoals": 4,
"awayGoals": 0,
"date": "May 14, 2016 12:00:00 AM"
}
],
"year": 2016
}
]A few things I have in mind for the future...
- Simulations: Be able to simulate a League X number of times, randomly, based on the winning expectancy of each team and return the most likely wins by match, and possible champions, for both eliminatory and point-based championships (such as UEFA and La Liga, respectively);
- Increase K for matches between great rivals and classic matches. So when Real Madrid and Barcelona play a match against each other, K could be increased by X, because this match would be more important than Real Madrid against Villarreal;
- Multiple names for teams: Some teams change names during its history, and connecting multiple names for the same team could help have a more accurate historical rating. E.g.: São Paulo FC, which until 1935 was known as São Paulo da Floresta;
-
Decrease points for teams that didn't play for X months. So that when a team is demoted to a lesser competitive league, his rating will be affected for how long he stays there.
- World Football Elo Ratings' Website
- World Football Elo Ratings at Wikipedia
- Club Elo website
- FiveThirtyEight's Soccer Power Index
- A guide to ESPN's SPI ratings
The MIT License (MIT)
Copyright (c) 2016 Victor Santiago
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