From Players to Champions: A Generalizable Machine Learning Approach for Match Outcome Prediction with Insights from the FIFA World Cup

TL;DR

This paper introduces a machine learning framework that combines team and player data to accurately predict FIFA World Cup match outcomes, demonstrating improved performance and providing new insights into player synergy and team dynamics.

Contribution

It presents a novel, player-centric predictive model for FIFA matches that integrates multi-year data, advanced classification, and optimization techniques, surpassing traditional aggregate approaches.

Findings

Our model outperforms baseline methods in accuracy.

Incorporating individual player metrics improves prediction quality.

Insights into player synergy and team strategies are gained.

Abstract

Accurate prediction of FIFA World Cup match outcomes holds significant value for analysts, coaches, bettors, and fans. This paper presents a machine learning framework specifically designed to forecast match winners in FIFA World Cup. By integrating both team-level historical data and player-specific performance metrics such as goals, assists, passing accuracy, and tackles, we capture nuanced interactions often overlooked by traditional aggregate models. Our methodology processes multi-year data to create year-specific team profiles that account for evolving rosters and player development. We employ classification techniques complemented by dimensionality reduction and hyperparameter optimization, to yield robust predictive models. Experimental results on data from the FIFA 2022 World Cup demonstrate our approach's superior accuracy compared to baseline method. Our findings highlight the importance of incorporating individual player attributes and team-level composition to enhance predictive performance, offering new insights into player synergy, strategic match-ups, and tournament progression scenarios. This work underscores the transformative potential of rich, player-centric data in sports analytics, setting a foundation for future exploration of advanced learning architectures such as graph neural networks to model complex team interactions.