In-game soccer outcome prediction with offline reinforcement learning

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Pegah Rahimian, Balazs Mark Mihalyi, Laszlo Toka
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引用次数: 0

Abstract

Predicting outcomes in soccer is crucial for various stakeholders, including teams, leagues, bettors, the betting industry, media, and fans. With advancements in computer vision, player tracking data has become abundant, leading to the development of sophisticated soccer analytics models. However, existing models often rely solely on spatiotemporal features derived from player tracking data, which may not fully capture the complexities of in-game dynamics. In this paper, we present an end-to-end system that leverages raw event and tracking data to predict both offensive and defensive actions, along with the optimal decision for each game scenario, based solely on historical game data. Our model incorporates the effectiveness of these actions to accurately predict win probabilities at every minute of the game. Experimental results demonstrate the effectiveness of our approach, achieving an accuracy of 87% in predicting offensive and defensive actions. Furthermore, our in-game outcome prediction model exhibits an error rate of 0.1, outperforming counterpart models and bookmakers’ odds.

Abstract Image

利用离线强化学习预测场内足球比赛结果
预测足球比赛的结果对包括球队、联赛、投注者、博彩业、媒体和球迷在内的各利益相关方都至关重要。随着计算机视觉技术的进步,球员跟踪数据变得越来越丰富,从而推动了复杂足球分析模型的发展。然而,现有模型通常仅依赖于从球员跟踪数据中提取的时空特征,这可能无法完全捕捉到复杂的比赛动态。在本文中,我们介绍了一种端到端系统,该系统利用原始事件和跟踪数据来预测进攻和防守行动,并根据历史比赛数据预测每个比赛场景的最佳决策。我们的模型结合了这些行动的有效性,可以准确预测比赛每一分钟的获胜概率。实验结果证明了我们方法的有效性,在预测进攻和防守行动方面达到了 87% 的准确率。此外,我们的比赛结果预测模型误差率为 0.1,优于同类模型和博彩公司赔率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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