This study developed a new explainable artificial intelligence algorithm called PassAI, which classifies successful or failed passes in a soccer game and explains its rationale using both tracking and passer's seasonal stats information. This study aimed to address two primary challenges faced by artificial intelligence and machine learning algorithms in the sports domain: how to use different modality data for the analysis and how to explain the rationale of the outcome from multimodal perspectives. To address these challenges, PassAI has two processing streams for multimodal information: tracking image data and passer's stats and classifying pass success and failure. After completing the classification, it provides a rationale by either calculating the relative contribution between the different modality data or providing more detailed contribution factors within the modality. The results of the experiment with 6,349 passes of data obtained from professional soccer games revealed that PassAI showed higher classification performance than state-of-the-art algorithms by >5% and could visualize the rationale of the pass success/failure for both tracking and stats data. These results highlight the importance of using multimodality data in the sports domain to increase the performance of the artificial intelligence algorithm and explainability of the outcomes.

翻译：本研究开发了一种名为PassAI的新型可解释人工智能算法，该算法通过结合球员追踪数据与传球者赛季统计信息，对足球比赛中的成功与失败传球进行分类，并阐释其决策依据。本研究旨在解决人工智能与机器学习算法在体育领域面临的两大核心挑战：如何利用不同模态数据进行比赛分析，以及如何从多模态视角解释算法输出结果的逻辑。为应对这些挑战，PassAI构建了针对多模态信息的双通道处理流程：分别处理追踪图像数据与传球者统计数据，并在此基础上完成传球成败分类。在完成分类后，算法通过计算不同模态数据间的相对贡献度，或提供模态内部更精细的贡献因子来生成决策依据。基于从职业足球比赛中获取的6,349次传球数据进行的实验表明，PassAI的分类性能较现有最优算法提升超过5%，并能对追踪数据与统计数据在传球成败判定中的贡献进行可视化阐释。这些结果凸显了在体育领域运用多模态数据对提升人工智能算法性能及其结果可解释性的重要意义。