Graduate School of Informatics, Nagoya University, Nagoya, Aichi, Japan.
RIKEN Center for Advanced Intelligence Project, Fukuoka, Fukuoka, Japan.
PLoS One. 2023 Apr 13;18(4):e0284318. doi: 10.1371/journal.pone.0284318. eCollection 2023.
While forecasting football match results has long been a popular topic, a practical model for football participants, such as coaches and players, has not been considered in great detail. In this study, we propose a generalized and interpretable machine learning model framework that only requires coaches' decisions and player quality features for forecasting. By further allowing the model to embed historical match statistics, features that consist of significant information, during the training process the model was practical and achieved both high performance and interpretability. Using five years of data (over 1,700 matches) from the English Premier League, our results show that our model was able to achieve high performance with an F1-score of 0.47, compared to the baseline betting odds prediction, which had an F1-score of 0.39. Moreover, our framework allows football teams to adapt for tactical decision-making, strength and weakness identification, formation and player selection, and transfer target validation. The framework in this study would have proven the feasibility of building a practical match result forecast framework and may serve to inspire future studies.
虽然预测足球比赛结果一直是一个热门话题，但针对足球参与者（如教练和球员）的实用模型并没有被详细考虑。在本研究中，我们提出了一个通用且可解释的机器学习模型框架，该框架仅需要教练的决策和球员质量特征即可进行预测。通过进一步允许模型在训练过程中嵌入历史比赛统计数据，该模型在实现高性能和可解释性的同时，使用了来自英超联赛的五年数据（超过 1700 场比赛），我们的结果表明，我们的模型能够实现高绩效，F1 得分为 0.47，而基准投注赔率预测得分为 0.39。此外，我们的框架允许足球团队进行战术决策、强弱识别、阵容和球员选择以及转会目标验证。本研究中的框架证明了构建实用比赛结果预测框架的可行性，并可能为未来的研究提供启示。
