职业生涯早期的胜利和赛事声望是网球运动员发展轨迹的特征

IF 3 2区 计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Chiara Zappalà, Sandro Sousa, Tiago Cunha, Alessandro Pluchino, Andrea Rapisarda, Roberta Sinatra
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引用次数: 0

摘要

体育运动中的成功是一种复杂的现象,但其研究成果却非常有限。尤其是,我们对网球等运动项目中的成功以及促成成功的因素缺乏深入的科学认识。在此,我们研究了网球运动员职业生涯的发展,以了解职业生涯早期阶段的作用以及特定赛事对运动员轨迹的影响。我们采用了一种将网络科学与网球职业运动员协会(ATP)赛事数据分析相结合的综合方法,并引入了一种基于赛事共同出席网络特征向量中心度的新方法来量化赛事声望。我们重点研究了参加中心赛事与球员表现之间的相互作用,发现球员取得首胜的赛事级别与成为顶级球员有关。这项研究揭示了初始阶段在球员职业生涯发展中的关键作用,为了解网球运动的成功动力提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Early career wins and tournament prestige characterize tennis players’ trajectories

Early career wins and tournament prestige characterize tennis players’ trajectories

Success in sports is a complex phenomenon that has only garnered limited research attention. In particular, we lack a deep scientific understanding of success in sports like tennis and the factors that contribute to it. Here, we study the unfolding of tennis players’ careers to understand the role of early career stages and the impact of specific tournaments on players’ trajectories. We employ a comprehensive approach combining network science and analysis of the Association of Tennis Professionals (ATP) tournament data and introduce a novel method to quantify tournament prestige based on the eigenvector centrality of the co-attendance network of tournaments. Focusing on the interplay between participation in central tournaments and players’ performance, we find that the level of the tournament where players achieve their first win is associated with becoming a top player. This work sheds light on the critical role of the initial stages in the progression of players’ careers, offering valuable insights into the dynamics of success in tennis.

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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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