A collection of probability models for predicting the outcome of football matches and entire leagues.
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.
In probability theory and statistics, the Poisson distribution, named after French mathematician Siméon Denis Poisson, is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant rate and independently of the time since the last event.
The idea behind this model is that the number of goals scored by a team is a Poisson distribution and independent from the other team.
Having that in mind, we calculate the expected number of goals that both teams team would score (considering the team's past matches), and calculate all the score probabilities with a given goal limit.
This model can give you not only the most likely winner, but the most likely scores as well.
The software can also simulate an entire league and generate metrics based on it, such as the teams with the most chances to win and probabilities for a team to end up in the lowest or highest end of the standings.
This software uses Guice as a dependency injection library, so always remember to add the modules when creating the injector.
For a simple example, let's say you want simulation metrics for a a list of leagues.
First, you create your injector and get your League metrics instance:
Injector injector = Guice.createInjector(new Modules());
final Key<Metrics> key = Key.get(Metrics.class, Names.named(Modules.BRAZILIAN_CHAMPIONSHIP_METRICS_ELO)); //Or Modules.BRAZILIAN_CHAMPIONSHIP_METRICS_POISSON
Metrics metrics = injector.getInstance(key);Then load your data from the JSON files:
List<League> allLeagues = SerializerUtil.getLeagues("/Users/Me/DataOne.json", "/Users/Me/DataTwo.json", "/Users/Me/InfiniteData.json");
List<League> leaguesToSimulate = SerializerUtil.getLeagues("/Users/Me/LeaguesToSimulate.json");
List<Match> allMatches = new ArrayList<>();
allLeagues.forEach(x -> allMatches.addAll(x.getMatches()));
final int simulations = 100 * 1000;
final int historyLimit = -1; // Define the limit of matches to use as history to calculate probabilities, or use -1 to use it all.
Map<League, LeagueMetrics> leagueMetrics = metrics.generate(allMatches, historyLimit, leaguesToSimulate, simulations);If you'd like you can just serialize your data to a CSV file.
SerializerUtil.saveMetricsToCsv("/Users/Me/MyData.csv", leagueMetrics.values());- Input leagues and matches from JSON;
- Poisson calculations model;
- Elo calculations model;
- Implement metrics for more types of championships;
The MIT License (MIT)
Copyright (c) 2018 Victor Domingos Santiago
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