A physics-based algorithm to perform predictions in football leagues

TL;DR

This paper introduces a physics-inspired stochastic algorithm that predicts football league outcomes by incorporating home advantage and team performance, validated against FiveThirtyEight's SPI predictions.

Contribution

The work extends a previous stochastic model by including home advantage and demonstrates its effectiveness in predicting league results using minimal data.

Findings

The algorithm accurately predicts league outcomes in complex seasons.

It performs well even with limited input data.

Predictions align closely with established forecasts like FiveThirtyEight.

Abstract

In this work, we extended a stochastic model for football leagues based on the team's potential [R. da Silva et al. Comput. Phys. Commun. \textbf{184} 661--670 (2013)] for making predictions instead of only performing a successful characterization of the statistics on the punctuation of the real leagues. Our adaptation considers the advantage of playing at home when considering the potential of the home and away teams. The algorithm predicts the tournament's outcome by using the market value or/and the ongoing team's performance as initial conditions in the context of Monte Carlo simulations. We present and compare our results to the worldwide known SPI predictions performed by the "FiveThirtyEight" project. The results show that the algorithm can deliver good predictions even with a few ingredients and in more complicated seasons like the 2020 editions where the matches were played without fans in the stadiums.