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A Tactile Automated Passive-Finger Stimulator TAPS

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A Bayesian approach to predict performance in football: a case study.

Gabriel G Ribeiro¹, Lilia C C da Costa¹, Paulo H Ferreira¹

¹Department of Statistics, Federal University of Bahia, Salvador, Brazil.

Frontiers in Sports and Active Living

|March 31, 2025

Summary

Predicting football match outcomes is challenging due to unpredictability. This study used advanced Poisson regression models to analyze attack and defense dynamics, improving prediction accuracy for the Brazilian Championship Series A (Brasileirão).

Keywords:

Bayesian inference dynamic models dynamic zero-inflated Poisson football prediction quantitative football performance

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Area of Science:

Sports Analytics
Statistical Modeling
Football Research

Background:

Football's inherent unpredictability, exemplified by the Brazilian Championship Series A (Brasileirão), poses challenges for result prediction.
Understanding team attack and defense dynamics is crucial for improving football match outcome forecasting.
Previous models often struggle to capture the dynamic nature of team performance throughout a season.

Purpose of the Study:

To develop and evaluate data-driven models for predicting Brazilian Championship Series A (Brasileirão) match results for the 2022 season.
To identify key attack and defense patterns influencing match outcomes.
To quantify team-specific dynamic performances in attack and defense.

Main Methods:

Utilized 10 variations of the Poisson countable regression model, incorporating hierarchy, overdispersion, time-varying parameters, and informative priors.
Adopted data from the 2021 Brazilian Championship Series A as informative priors for team attack and defense advantage estimations.
Employed the de Finetti measure and leave-one-out cross-validation for forecast quality assessment and model comparison.

Main Results:

The dynamic Poisson model with zero inflation demonstrated superior performance in predicting match outcomes compared to other models evaluated.
The methodology successfully quantified team attack and defense dynamic performances.
Models presented satisfactory goodness-of-fit and forecast accuracy, validated by established statistical metrics.

Conclusions:

The dynamic Poisson model with zero inflation offers a novel and effective approach for football match prediction, representing a first in this context.
The developed data-driven models provide valuable insights into team performance dynamics, aiding both prediction and performance analysis.
An interactive online framework (Shiny app) was created to disseminate the study's findings and facilitate access to predictive results.