A Skellam regression model for quantifying positional value in soccer

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2020-12-03 DOI:10.1515/JQAS-2019-0122

K. Pelechrinis, Wayne L. Winston

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引用次数: 5

Abstract

Abstract Soccer is undeniably the most popular sport world-wide and everyone from general managers and coaching staff to fans and media are interested in evaluating players’ performance. Metrics applied successfully in other sports, such as the (adjusted) +/− that allows for division of credit among a basketball team’s players, exhibit several challenges when applied to soccer due to severe co-linearities. Recently, a number of player evaluation metrics have been developed utilizing optical tracking data, but they are based on proprietary data. In this work, our objective is to develop an open framework that can estimate the expected contribution of a soccer player to his team’s winning chances using publicly available data. In particular, using data from (i) approximately 20,000 games from 11 European leagues over eight seasons, and, (ii) player ratings from the FIFA video game, we estimate through a Skellam regression model the importance of every line (attackers, midfielders, defenders and goalkeeping) in winning a soccer game. We consequently translate the model to expected league points added above a replacement player (eLPAR). This model can further be used as a guide for allocating a team’s salary budget to players based on their expected contributions on the pitch. We showcase similar applications using annual salary data from the English Premier League and identify evidence that in our dataset the market appears to under-value defensive line players relative to goalkeepers.

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足球场上位置价值量化的Skellam回归模型

无可否认，足球是世界上最受欢迎的运动，从总经理、教练组到球迷和媒体，每个人都对球员的表现感兴趣。在其他运动中成功应用的指标，如(调整后的)+/−，允许在篮球队球员之间划分学分，由于严重的共线性，在应用于足球时表现出一些挑战。最近，许多玩家评价指标都是利用光学跟踪数据开发出来的，但它们都是基于专有数据。在这项工作中，我们的目标是开发一个开放的框架，可以使用公开可用的数据来估计足球运动员对其球队获胜机会的预期贡献。特别是，使用来自(i)来自11个欧洲联赛超过8个赛季的大约20,000场比赛的数据，以及(ii)来自FIFA视频游戏的球员评级，我们通过Skellam回归模型估计每条线(进攻者，中场，后卫和守门员)在赢得足球比赛中的重要性。因此，我们将模型转换为替换球员(eLPAR)之上的预期联赛积分。这个模型可以进一步用作指导，根据球员在球场上的预期贡献来分配球队的工资预算。我们使用英超联赛的年薪数据展示了类似的应用程序，并确定了在我们的数据集中市场似乎低估了后卫球员而不是门将的证据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.