发表机构
Al Akhawayn University（阿赫韦因大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出与球队无关的Sim2Win框架，利用公开赛事数据构建战术画像与特征，训练分类器预测足球赛前胜平负概率，在未见过的球队上表现优于基线模型，为足球预测提供了可行替代方案。
AI 中文摘要
职业足球的赛前战术决策高度依赖主观专家分析和基于球队身份的侦察系统，这类系统无法推广到未见过的球队。本文提出Sim2Win，一种与球队无关的、基于事件的赛前战术推荐框架，它将比赛结果预测重新定义为战术决策支持问题。利用来自11项赛事、涵盖178支球队和1411场球队比赛记录的StatsBomb公开事件数据，Sim2Win构建了五场滚动战术画像，设计了4种可解释的战术特征比率，通过K-Means将球队行为聚类为8种比赛风格，并训练13个分类器，从战术对阵表征中估计胜、平、负概率。该系统不使用球队名称或身份特征，因此可推广到训练期间从未见过的球队。严格的留一赛事交叉验证（LOCO）评估表明，Sim2Win在完全未见过的球队上实现了平均ROC-AUC为0.704、平均准确率为55.4%，在全部21次ROC-AUC比较中均优于ELO、Pi-Rating和GAP基线，在21次准确率比较中19次优于上述基线。在所有评估模型中，CatBoost实现了最强的分布内性能，准确率达60.90%。这些发现表明，行为战术表征在分布偏移下提供了可迁移的预测信号，为依赖球队身份的足球预测系统提供了可行替代方案。
英文摘要
Pre-match tactical decision-making in professional football relies heavily on subjective expert analysis and identity-based scouting systems that cannot generalize to unseen teams. This paper presents Sim2Win, a team-agnostic, event-based pre-match tactical recommendation framework that reframes match outcome prediction as a tactical decision-support problem. Using StatsBomb open event data from eleven competitions spanning 178 teams and 1,411 team-match records, Sim2Win constructs five-match rolling tactical profiles, engineers four interpretable tactical feature ratios, clusters team behaviors into eight playstyles via K-Means, and trains thirteen classifiers to estimate win, draw, and loss probabilities from tactical matchup representations. The system operates without team names or identity features, enabling generalization to teams never seen during training. A rigorous Leave-One-Competition-Out (LOCO) evaluation demonstrates that Sim2Win achieves a mean ROC-AUC of 0.704 and mean accuracy of 55.4% on completely unseen teams, outperforming ELO, Pi-Rating, and GAP baselines on all 21 ROC-AUC comparisons and 19 of 21 accuracy comparisons. Among all evaluated models, CatBoost achieved the strongest in-distribution performance with 60.90% accuracy. These findings suggest that behavioral tactical representations provide transferable predictive signal under distribution shift and offer a viable alternative to identity-dependent football prediction systems.
Comments15 pages, 5 figures. Code available at https://github.com/Mouad-Ze/SIM2WIN
