Player-Team Heterogeneous Interaction Graph Transformer for Soccer Outcome Prediction

TL;DR

HIGFormer is a novel graph transformer model that captures multi-level heterogeneous interactions among players and teams to improve soccer match outcome prediction, outperforming existing methods on real-world data.

Contribution

The paper introduces HIGFormer, a multi-level graph-augmented transformer that models both player and team interactions for more accurate soccer outcome prediction.

Findings

HIGFormer achieves superior prediction accuracy over existing models.

The model provides insights into player performance evaluation.

Extensive experiments validate the effectiveness of the approach.

Abstract

Predicting soccer match outcomes is a challenging task due to the inherently unpredictable nature of the game and the numerous dynamic factors influencing results. While it conventionally relies on meticulous feature engineering, deep learning techniques have recently shown a great promise in learning effective player and team representations directly for soccer outcome prediction. However, existing methods often overlook the heterogeneous nature of interactions among players and teams, which is crucial for accurately modeling match dynamics. To address this gap, we propose HIGFormer (Heterogeneous Interaction Graph Transformer), a novel graph-augmented transformer-based deep learning model for soccer outcome prediction. HIGFormer introduces a multi-level interaction framework that captures both fine-grained player dynamics and high-level team interactions. Specifically, it comprises (1) a Player Interaction Network, which encodes player performance through heterogeneous interaction graphs, combining local graph convolutions with a global graph-augmented transformer; (2) a Team Interaction Network, which constructs interaction graphs from a team-to-team perspective to model historical match relationships; and (3) a Match Comparison Transformer, which jointly analyzes both team and player-level information to predict match outcomes. Extensive experiments on the WyScout Open Access Dataset, a large-scale real-world soccer dataset, demonstrate that HIGFormer significantly outperforms existing methods in prediction accuracy. Furthermore, we provide valuable insights into leveraging our model for player performance evaluation, offering a new perspective on talent scouting and team strategy analysis.