{"title":"哪种运动的可预测性更高?团队运动可预测性的跨学科分析","authors":"Michele Coscia","doi":"10.1140/epjds/s13688-024-00448-3","DOIUrl":null,"url":null,"abstract":"<p>Professional sports are a cultural activity beloved by many, and a global hundred-billion-dollar industry. In this paper, we investigate the trends of match outcome predictability, assuming that the public is more interested in an event if there is some uncertainty about who will win. We reproduce previous methodology focused on soccer and we expand it by analyzing more than 300,000 matches in the 1996-2023 period from nine disciplines, to identify which disciplines are getting more/less predictable over time. We investigate the home advantage effect, since it can affect outcome predictability and it has been impacted by the COVID-19 pandemic. Going beyond previous work, we estimate which sport management model – between the egalitarian one popular in North America and the rich-get-richer used in Europe – leads to more uncertain outcomes. Our results show that there is no generalized trend in predictability across sport disciplines, that home advantage has been decreasing independently from the pandemic, and that sports managed with the egalitarian North American approach tend to be less predictable. We base our result on a predictive model that ranks team by analyzing the directed network of who-beats-whom, where the most central teams in the network are expected to be the best performing ones. Our results are robust to the measure we use for the prediction.</p>","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"43 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Which sport is becoming more predictable? A cross-discipline analysis of predictability in team sports\",\"authors\":\"Michele Coscia\",\"doi\":\"10.1140/epjds/s13688-024-00448-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Professional sports are a cultural activity beloved by many, and a global hundred-billion-dollar industry. In this paper, we investigate the trends of match outcome predictability, assuming that the public is more interested in an event if there is some uncertainty about who will win. We reproduce previous methodology focused on soccer and we expand it by analyzing more than 300,000 matches in the 1996-2023 period from nine disciplines, to identify which disciplines are getting more/less predictable over time. We investigate the home advantage effect, since it can affect outcome predictability and it has been impacted by the COVID-19 pandemic. Going beyond previous work, we estimate which sport management model – between the egalitarian one popular in North America and the rich-get-richer used in Europe – leads to more uncertain outcomes. Our results show that there is no generalized trend in predictability across sport disciplines, that home advantage has been decreasing independently from the pandemic, and that sports managed with the egalitarian North American approach tend to be less predictable. We base our result on a predictive model that ranks team by analyzing the directed network of who-beats-whom, where the most central teams in the network are expected to be the best performing ones. Our results are robust to the measure we use for the prediction.</p>\",\"PeriodicalId\":11887,\"journal\":{\"name\":\"EPJ Data Science\",\"volume\":\"43 1\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-01-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EPJ Data Science\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1140/epjds/s13688-024-00448-3\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EPJ Data Science","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1140/epjds/s13688-024-00448-3","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Which sport is becoming more predictable? A cross-discipline analysis of predictability in team sports
Professional sports are a cultural activity beloved by many, and a global hundred-billion-dollar industry. In this paper, we investigate the trends of match outcome predictability, assuming that the public is more interested in an event if there is some uncertainty about who will win. We reproduce previous methodology focused on soccer and we expand it by analyzing more than 300,000 matches in the 1996-2023 period from nine disciplines, to identify which disciplines are getting more/less predictable over time. We investigate the home advantage effect, since it can affect outcome predictability and it has been impacted by the COVID-19 pandemic. Going beyond previous work, we estimate which sport management model – between the egalitarian one popular in North America and the rich-get-richer used in Europe – leads to more uncertain outcomes. Our results show that there is no generalized trend in predictability across sport disciplines, that home advantage has been decreasing independently from the pandemic, and that sports managed with the egalitarian North American approach tend to be less predictable. We base our result on a predictive model that ranks team by analyzing the directed network of who-beats-whom, where the most central teams in the network are expected to be the best performing ones. Our results are robust to the measure we use for the prediction.
期刊介绍:
EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.