A data-driven approach to predicting the most valuable player in a game

IF 0.9 Q3 MATHEMATICS, APPLIED Computational and Mathematical Methods Pub Date : 2021-02-26 DOI:10.1002/cmm4.1155
Francisco P. Romero, Catalina Lozano-Murcia, Julio A. Lopez-Gomez, Eusebio Angulo Sanchez-Herrera, Eduardo Sanchez-Lopez
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引用次数: 5

Abstract

The identification of outstanding behaviors is a matter of essential importance in sports analytics. However, analyzing how human experts select each match's most valuable player (MVP) according to objective and subjective factors is a great challenge. This article proposes a data-driven approach for sports team performance based on the weighted aggregation of statistical indicators. The proposal is divided into two approaches: The first conducts a principal component analysis to examine the relationship between each game's statistical indicators. The other addresses a meta-heuristic analysis to weight the attributes and choose the MVPs optimally. Finally, we apply the proposed approach to the 2018 European Men's Handball Championship and take the “Player of the Match” of each game as an example to illustrate its usefulness and efficacy. We perform multiple analyses, including a comparison with a fuzzy multi-criteria decision-making method that show that the data-driven approach can predict the “Player of the Match” in most matches. It also allows us to estimate and quantify the expert evaluations, which are often difficult to obtain in a disaggregated form.

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预测游戏中最有价值的玩家的数据驱动方法
在体育分析中,优秀行为的识别是一个至关重要的问题。然而,分析人类专家如何根据客观和主观因素选择每场比赛的最有价值球员(MVP)是一个巨大的挑战。本文提出了一种基于统计指标加权聚合的体育团队绩效数据驱动方法。该建议分为两种方法:第一种方法是进行主成分分析,以检查每款游戏的统计指标之间的关系。另一个解决了一个元启发式分析,以加权属性并选择最佳mvp。最后,我们将该方法应用于2018年欧洲男子手球锦标赛,并以每场比赛的“全场最佳球员”为例来说明其实用性和有效性。我们进行了多项分析,包括与模糊多标准决策方法的比较,结果表明数据驱动方法可以预测大多数比赛中的“比赛最佳球员”。它还使我们能够估计和量化专家的评估,而这些评估通常很难以分类的形式获得。
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