Multivariate approaches to investigate the home and away behavior of football teams playing football matches

IF 1 4区数学 Q3 STATISTICS & PROBABILITY

Computational Statistics Pub Date : 2024-09-17 DOI:10.1007/s00180-024-01553-7

Antonello D’Ambra, Pietro Amenta, Antonio Lucadamo

{"title":"Multivariate approaches to investigate the home and away behavior of football teams playing football matches","authors":"Antonello D’Ambra, Pietro Amenta, Antonio Lucadamo","doi":"10.1007/s00180-024-01553-7","DOIUrl":null,"url":null,"abstract":"<p>Compared to other European competitions, participation in the Uefa Champions League is a real “bargain” for football clubs due to the hefty bonuses awarded based on performance during the group qualification phase. To perform successfully in football depends on several multidimensional factors, and analyzing the main ones remains challenging. In the performance study, little attention has been paid to teams’ behavior when playing at home and away. Our study combines statistical techniques to develop a procedure to examine teams’ performance. Several considerations make the 2022–2023 Serie A league season particularly interesting to analyze with our approach. Except for Napoli, all the teams showed different home-and-away behaviors concerning the results obtained at the season’s end. Ball possession and corners have positively influenced scored points in both home and away games with a different impact. The precision indicator was not an essential variable. The procedure highlighted the negative roles played by offside, as well as yellow and red cards.</p>","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s00180-024-01553-7","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}

引用次数: 0

Abstract

Compared to other European competitions, participation in the Uefa Champions League is a real “bargain” for football clubs due to the hefty bonuses awarded based on performance during the group qualification phase. To perform successfully in football depends on several multidimensional factors, and analyzing the main ones remains challenging. In the performance study, little attention has been paid to teams’ behavior when playing at home and away. Our study combines statistical techniques to develop a procedure to examine teams’ performance. Several considerations make the 2022–2023 Serie A league season particularly interesting to analyze with our approach. Except for Napoli, all the teams showed different home-and-away behaviors concerning the results obtained at the season’s end. Ball possession and corners have positively influenced scored points in both home and away games with a different impact. The precision indicator was not an essential variable. The procedure highlighted the negative roles played by offside, as well as yellow and red cards.

Abstract Image

查看原文本刊更多论文

研究足球队主客场比赛行为的多元方法

与其他欧洲赛事相比，参加欧洲冠军联赛对足球俱乐部来说是真正的 "实惠"，因为根据小组资格赛阶段的表现可获得高额奖金。要想在足球比赛中取得好成绩，取决于多个多维因素，而分析其中的主要因素仍具有挑战性。在成绩研究中，人们很少关注球队在主客场比赛中的表现。我们的研究结合了统计技术，制定了一套考察球队表现的程序。有几个因素使得 2022-2023 赛季的意甲联赛特别值得用我们的方法进行分析。除那不勒斯外，所有球队在赛季结束时的主客场表现都不尽相同。在主客场比赛中，控球率和角球对得分都有积极影响，但影响程度不同。精确度指标并非重要变量。该程序强调了越位以及黄牌和红牌的负面作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Computational Statistics 数学-统计学与概率论

CiteScore

2.90

自引率

0.00%

发文量

122

审稿时长

>12 weeks

期刊介绍： Computational Statistics (CompStat) is an international journal which promotes the publication of applications and methodological research in the field of Computational Statistics. The focus of papers in CompStat is on the contribution to and influence of computing on statistics and vice versa. The journal provides a forum for computer scientists, mathematicians, and statisticians in a variety of fields of statistics such as biometrics, econometrics, data analysis, graphics, simulation, algorithms, knowledge based systems, and Bayesian computing. CompStat publishes hardware, software plus package reports.