Player-Team Heterogeneous Interaction Graph Transformer for Soccer Outcome Prediction

• URL: http://arxiv.org/abs/2507.10626v1
• Date: Mon, 14 Jul 2025 06:43:36 GMT
• Title: Player-Team Heterogeneous Interaction Graph Transformer for Soccer Outcome Prediction
• Authors: Lintao Wang, Shiwen Xu, Michael Horton, Joachim Gudmundsson, Zhiyong Wang,
• Abstract summary: HIGFormer is a novel graph-augmented transformer-based deep learning model for soccer outcome prediction.<n>It captures both fine-grained player dynamics and high-level team interactions.<n>Experiments on the WyScout Open Access dataset, a large-scale real-world soccer dataset, demonstrate that HIGFormer significantly outperforms existing methods in prediction accuracy.
• Score: 8.197004730382396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• From Players to Champions: A Generalizable Machine Learning Approach for Match Outcome Prediction with Insights from the FIFA World Cup [0.0]
  This paper presents a machine learning framework specifically designed to forecast match winners in FIFA World Cup.<n>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.<n> Experimental results on data from the FIFA 2022 World Cup demonstrate our approach's superior accuracy compared to baseline method.
  arXiv  Detail & Related papers  (2025-05-03T19:17:20Z)
• Towards a Unified Transformer-based Framework for Scene Graph Generation and Human-object Interaction Detection [116.21529970404653]
  We introduce SG2HOI+, a unified one-step model based on the Transformer architecture. Our approach employs two interactive hierarchical Transformers to seamlessly unify the tasks of SGG and HOI detection. Our approach achieves competitive performance when compared to state-of-the-art HOI methods.
  arXiv  Detail & Related papers  (2023-11-03T07:25:57Z)
• About latent roles in forecasting players in team sports [47.066729480128856]
  Team sports contain a significant social component that influences interactions between teammates and opponents. We create RolFor, a novel end-to-end model for Role-based Forecasting.
  arXiv  Detail & Related papers  (2023-04-17T13:33:23Z)
• Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution [29.478765505215538]
  This study presents a novel deep learning method, called GATv2-GCN, for predicting player performance in sports. We use a graph attention network to capture the attention that each player pays to each other, allowing for more accurate modeling. We evaluate the performance of our model using real-world sports data, demonstrating its effectiveness in predicting player performance.
  arXiv  Detail & Related papers  (2023-03-29T14:48:51Z)
• A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification [75.93186954061943]
  Action spotting involves understanding the dynamics of the game, the complexity of events, and the variation of video sequences. In this work, we focus on the former by (a) identifying and representing the players, referees, and goalkeepers as nodes in a graph, and by (b) modeling their temporal interactions as sequences of graphs. For the player identification task, our method obtains an overall performance of 57.83% average-mAP by combining it with other modalities.
  arXiv  Detail & Related papers  (2022-11-22T15:23:53Z)
• Where Will Players Move Next? Dynamic Graphs and Hierarchical Fusion for Movement Forecasting in Badminton [6.2405734957622245]
  We focus on predicting what types of returning strokes will be made, and where players will move to based on previous strokes. Existing sequence-based models neglect the effects of interactions between players, and graph-based models still suffer from multifaceted perspectives. We propose a novel Dynamic Graphs and Hierarchical Fusion for Movement Forecasting model (DyMF) with interaction style extractors.
  arXiv  Detail & Related papers  (2022-11-22T12:21:24Z)
• Graph Neural Networks to Predict Sports Outcomes [0.0]
  We introduce a sport-agnostic graph-based representation of game states. We then use our proposed graph representation as input to graph neural networks to predict sports outcomes.
  arXiv  Detail & Related papers  (2022-07-28T14:45:02Z)
• Collusion Detection in Team-Based Multiplayer Games [57.153233321515984]
  We propose a system that detects colluding behaviors in team-based multiplayer games. The proposed method analyzes the players' social relationships paired with their in-game behavioral patterns. We then automate the detection using Isolation Forest, an unsupervised learning technique specialized in highlighting outliers.
  arXiv  Detail & Related papers  (2022-03-10T02:37:39Z)
• Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network [65.11999700562869]
  We propose a novel collaborative prediction unit (CoPU), which aggregates predictions from multiple collaborative predictors according to a collaborative graph. Our methods outperform state-of-the-art works on the three tasks by 28.6%, 17.4% and 21.0% on average.
  arXiv  Detail & Related papers  (2021-07-02T08:20:06Z)
• TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning [87.38675639186405]
  We propose a novel graph neural network approach, called TCL, which deals with the dynamically-evolving graph in a continuous-time fashion. To the best of our knowledge, this is the first attempt to apply contrastive learning to representation learning on dynamic graphs.
  arXiv  Detail & Related papers  (2021-05-17T15:33:25Z)
• GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions [70.9481395807354]
  We propose a Graph of Graphs Neural Network (GoGNN), which extracts the features in both structured entity graphs and the entity interaction graph in a hierarchical way. GoGNN outperforms the state-of-the-art methods on two representative structured entity interaction prediction tasks.
  arXiv  Detail & Related papers  (2020-05-12T03:46:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.