发表机构
Paderborn University; SG Flensburg-Handewitt（帕德博恩大学; 弗伦斯堡-汉德维特体育俱乐部）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文将足球的xT与VAEP框架适配至手球，开发H-xT与H-VAEP模型，利用手球德甲数据验证其有效性并发布代码，实现了手球球员的合理评估。
AI 中文摘要
职业手球中传统的球员评估依赖基础的统计数据指标或启发式指数，无法对多球员的组织进攻链进行合理评价。尽管足球（soccer）分析已采用预期威胁（Expected Threat，xT）和通过概率估计评估动作价值（Valuing Actions by Estimating Probabilities，VAEP），但这些基于事件的动作评估框架尚未应用于手球领域。本文首次全面将xT与VAEP适配并应用于手球，利用了手球德甲五个赛季的追踪衍生事件数据。我们采用手球专属的场地分区布局开发了Handball-xT（H-xT），通过模拟表明其比标准矩形网格更具鲁棒性。我们通过调整特征空间并选择合适的上下文长度以限制球队身份泄露，优化了Handball-VAEP（H-VAEP）。评估显示，H-VAEP能产出异常稳定、具有区分度且直观的球员评分，突出了组织进攻的作用。最后，我们发布了完整代码仓库，以助力职业俱乐部部署这些模型。
英文摘要
Traditional player evaluation in professional handball relies on basic box-score metrics or heuristic indices, which fail to credit the multi-player build-up chain. While football (soccer) analytics has adopted Expected Threat (xT) and Valuing Actions by Estimating Probabilities (VAEP), these event-based action valuation frameworks have not yet been adapted to handball. In this paper, we present the first comprehensive adaptation and evaluation of xT and VAEP for handball, utilizing five seasons of tracking-derived event data from the Handball Bundesliga. We develop Handball-xT (H-xT) using a handball-native court zoning layout, demonstrating via simulations that it is systematically more robust than standard rectangular grids. We optimize Handball-VAEP (H-VAEP) by tailoring its feature space and selecting the context length to limit team-identity leakage. Our evaluation shows that H-VAEP yields exceptionally stable, discriminative, and intuitive player ratings that highlight build-up play. Finally, we release our complete code repository to help professional clubs deploy these models.
Comments13 pages, 6 figures, 1 table. Accepted at the 13th Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA 2026), co-located with ECML PKDD 2026
