{"title":"NBA的犯规积累","authors":"Dani Chu","doi":"10.1515/jqas-2019-0119","DOIUrl":null,"url":null,"abstract":"Abstract This paper investigates the fouling time distribution of players in the National Basketball Association. A Bayesian analysis is presented based on the assumption that fouling time distributions follow a gamma distribution. Various insights are obtained including the observation that players accumulate fouls at a rate that increases with the current number of fouls. We demonstrate possible ways to incorporate the fouling time distributions to provide decision support to coaches in the management of playing time.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2020-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Foul accumulation in the NBA\",\"authors\":\"Dani Chu\",\"doi\":\"10.1515/jqas-2019-0119\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract This paper investigates the fouling time distribution of players in the National Basketball Association. A Bayesian analysis is presented based on the assumption that fouling time distributions follow a gamma distribution. Various insights are obtained including the observation that players accumulate fouls at a rate that increases with the current number of fouls. We demonstrate possible ways to incorporate the fouling time distributions to provide decision support to coaches in the management of playing time.\",\"PeriodicalId\":16925,\"journal\":{\"name\":\"Journal of Quantitative Analysis in Sports\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2020-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Quantitative Analysis in Sports\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1515/jqas-2019-0119\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"SOCIAL SCIENCES, MATHEMATICAL METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Quantitative Analysis in Sports","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/jqas-2019-0119","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"SOCIAL SCIENCES, MATHEMATICAL METHODS","Score":null,"Total":0}
Abstract This paper investigates the fouling time distribution of players in the National Basketball Association. A Bayesian analysis is presented based on the assumption that fouling time distributions follow a gamma distribution. Various insights are obtained including the observation that players accumulate fouls at a rate that increases with the current number of fouls. We demonstrate possible ways to incorporate the fouling time distributions to provide decision support to coaches in the management of playing time.
期刊介绍：
The Journal of Quantitative Analysis in Sports (JQAS), an official journal of the American Statistical Association, publishes timely, high-quality peer-reviewed research on the quantitative aspects of professional and amateur sports, including collegiate and Olympic competition. The scope of application reflects the increasing demand for novel methods to analyze and understand data in the growing field of sports analytics. Articles come from a wide variety of sports and diverse perspectives, and address topics such as game outcome models, measurement and evaluation of player performance, tournament structure, analysis of rules and adjudication, within-game strategy, analysis of sporting technologies, and player and team ranking methods. JQAS seeks to publish manuscripts that demonstrate original ways of approaching problems, develop cutting edge methods, and apply innovative thinking to solve difficult challenges in sports contexts. JQAS brings together researchers from various disciplines, including statistics, operations research, machine learning, scientific computing, econometrics, and sports management.
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