Seasonal Linear Predictivity in National Football Championships

TL;DR

This paper demonstrates that football team results in long-term competitions follow a linear trend, enabling effective predictions despite complex influencing factors, based on extensive historical data analysis.

Contribution

It introduces the idea that linear models can predict football results over long seasons, challenging the assumption that complex models are always necessary.

Findings

Linear regression effectively predicts team results in long football seasons.

Results show a strong linear trend in team performance over time.

The approach works across multiple countries and divisions.

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.