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

IF 3 2区 计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS EPJ Data Science Pub Date : 2024-01-29 DOI:10.1140/epjds/s13688-024-00448-3
Michele Coscia
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引用次数: 0

摘要

职业体育是一项深受大众喜爱的文化活动,也是一项价值千亿美元的全球性产业。在本文中,我们研究了比赛结果可预测性的趋势,假定如果谁会获胜存在一定的不确定性,那么公众就会对某项赛事更感兴趣。我们重现了之前以足球为重点的研究方法,并对其进行了扩展,分析了 1996-2023 年间九个领域的 30 多万场比赛,以确定哪些领域的比赛随着时间的推移变得更可预测/更不可预测。我们研究了主场优势效应,因为它可以影响结果的可预测性,而且受到 COVID-19 大流行病的影响。在以往工作的基础上,我们估算了在北美流行的平均主义模式和欧洲使用的富者越富模式之间,哪种体育管理模式会导致更不确定的结果。我们的研究结果表明,各体育项目的可预测性并没有普遍的趋势,主场优势的减少与大流行病无关,而采用北美平均主义方式管理的体育项目往往更难预测。我们的结果基于一个预测模型,该模型通过分析 "谁击败谁 "的有向网络对球队进行排名,网络中最核心的球队有望成为表现最好的球队。我们的结果与我们用于预测的衡量标准密切相关。
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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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.

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来源期刊
EPJ Data Science
EPJ Data Science MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -
CiteScore
6.10
自引率
5.60%
发文量
53
审稿时长
13 weeks
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.
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