Seasonal Linear Predictivity in National Football Championships

Abstract

Predicting the results of sport matches and competitions is an arising research field, benefiting from the growing amount of available data and the novel data analytics techniques. Excellent forecasts can be achieved by advanced machine learning methods applied to detailed historical data, especially in very popular sports such as football (soccer). Here we show that, despite the large number of confounding factors, the results of a football team in longer competitions (e.g., a national league) follow a basically linear trend useful for predictive purposes, too. In support of this claim, we present a set of experiments of linear regression on a database collecting the yearly results of 707 teams playing in 22 divisions from 11 countries, in 20 football seasons.