Early career wins and tournament prestige characterize tennis players’ trajectories
- Chiara Zappalà(Creator),
- Sandro Sousa(Creator),
- Tiago Cunha(Creator),
- Alessandro Pluchino(Creator),
- Andrea Rapisarda(Creator),
- Roberta Sinatra(Creator)
- Complexity Science Hub,
- Corvinus University of Budapest,
- Institute for Scientific Interchange Foundation,
- Istituto Nazionale di Fisica Nucleare Sezione di Catania,
- ,
Dataset:
Dataset Types
Dataset
Dataset Information
Date made available
01/01/2024Publisher
figshareDescription
Abstract 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.
Cite this dataset
Brief description
Chiara Zappalà, Sandro Sousa, Tiago Cunha, Alessandro Pluchino, Andrea Rapisarda, Roberta Sinatra. (2024). Early career wins and tournament prestige characterize tennis players’ trajectories. 10.6084/m9.figshare.c.7192249.v1
Funding Details
Open access funding provided by Corvinus University of Budapest. CZ acknowledges the support of 101086712-LearnData-HORIZON-WIDERA-2022-TALENTS-01 financed by EUROPEAN RESEARCH EXECUTIVE AGENCY (REA) (https://cordis.europa.eu/project/id/101086712), the Erasmus Mobility Network, and the Danish Data Science Academy (DDSA) for funding her visits to the research group of RS. AP and AR acknowledge partial financial support of PRIN 2017WZFTZP Stochastic forecasting in complex systems. RS and SS acknowledge support from Villum Fonden through the Villum Young Investigator program (project number: 00037394).
