利用可解释的机器学习技术识别确保法甲冠军位置的关键因素

Q1 Mathematics
Spyridon Plakias, Christos Kokkotis, Michalis Mitrotasios, Vasileios Armatas, Themistoklis Tsatalas, Giannis Giakas
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引用次数: 0

摘要

引言成绩分析对教练来说至关重要,也是一个广泛研究的课题。技术和人工智能(AI)技术的进步彻底改变了体育分析。目的:本文的主要目的是介绍一种强大的、可解释的机器学习(ML)模型,该模型可识别出有助于确保法国足球甲级联赛积分榜前三名位置的关键因素,从而确保参加下一赛季的欧洲冠军联赛。材料和方法:这项回顾性观察研究分析了 2022-23 赛季法甲联赛全部 380 场比赛的数据。数据来自公开访问的网站 "whoscored",包括 34 项表现指标。本研究采用了序列前向特征选择(SFFS)和多种 ML 算法,包括 XGBoost、支持向量机(SVM)和逻辑回归(LR),以创建一个稳健、可解释的模型。为了提高模型的可解释性,还使用了 SHAP(SHapley Additive Explanations)模型。结果K-means 聚类分析将球队分为若干组(TOP TEAMS,3 支球队/REST TEAMS,17 支球队),ML 模型为影响联赛排名的因素提供了重要见解。LR 分类器是表现最好的分类器,准确率为 75.13%,召回率为 76.32%,F1 分数为 48.03%,精确率为 35.17%。发现 "短传 "和 "穿越球 "对模型的预测有积极影响,而 "攻门 "和 "长球 "则有消极影响。结论我们的模型具有令人满意的预测准确性和清晰的结果可解释性,为相关人员提供了有用的信息。具体而言,我们的模型建议在控球阶段采取依靠短传(避免长传)的策略,并以通过传球进入进攻三区和对方禁区为目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying Key Factors for Securing a Champions League Position in French Ligue 1 Using Explainable Machine Learning Techniques
Introduction: Performance analysis is essential for coaches and a topic of extensive research. The advancement of technology and Artificial Intelligence (AI) techniques has revolutionized sports analytics. Aim: The primary aim of this article is to present a robust, explainable machine learning (ML) model that identifies the key factors that contribute to securing one of the top three positions in the standings of the French Ligue 1, ensuring participation in the UEFA Champions League for the following season. Materials and Methods: This retrospective observational study analyzed data from all 380 matches of the 2022–23 French Ligue 1 season. The data were obtained from the publicly-accessed website “whoscored” and included 34 performance indicators. This study employed Sequential Forward Feature Selection (SFFS) and various ML algorithms, including XGBoost, Support Vector Machine (SVM), and Logistic Regression (LR), to create a robust, explainable model. The SHAP (SHapley Additive Explanations) model was used to enhance model interpretability. Results: The K-means Cluster Analysis categorized teams into groups (TOP TEAMS, 3 teams/REST TEAMS, 17 teams), and the ML models provided significant insights into the factors influencing league standings. The LR classifier was the best-performing classifier, achieving an accuracy of 75.13%, a recall of 76.32%, an F1-score of 48.03%, and a precision of 35.17%. “SHORT PASSES” and “THROUGH BALLS” were features found to positively influence the model’s predictions, while “TACKLES ATTEMPTED” and “LONG BALLS” had a negative impact. Conclusions: Our model provided satisfactory predictive accuracy and clear interpretability of results, which gave useful information to stakeholders. Specifically, our model suggests adopting a strategy during the ball possession phase that relies on short passes (avoiding long ones) and aiming to enter the attacking third and the opponent’s penalty area with through balls.
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来源期刊
Applied Sciences
Applied Sciences Mathematics-Applied Mathematics
CiteScore
6.40
自引率
0.00%
发文量
0
审稿时长
11 weeks
期刊介绍: APPS is an international journal. APPS covers a wide spectrum of pure and applied mathematics in science and technology, promoting especially papers presented at Carpato-Balkan meetings. The Editorial Board of APPS takes a very active role in selecting and refereeing papers, ensuring the best quality of contemporary mathematics and its applications. APPS is abstracted in Zentralblatt für Mathematik. The APPS journal uses Double blind peer review.
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