Revealing Elite Players in a Sports Network: A Case Study of WTA Players from 2012 to 2021

The WTA network from 2012 to 2021 is used to identify influential players, assess community structure and correlate network centrality with official rankings. Prior studies on tennis competitions have encountered limitations regarding data access, processing speed, variable selection, temporal fluctuations, and correlation methodologies. Nevertheless, these challenges offer prospects for additional exploration. This study uses R Studio and Gephi to do an extensive network analysis of matches played from the loser to the winner in the WTA’s network time series. The analysis focused on degree, betweenness, and eigenvector centralities. In addition, the Louvain and Fast Greedy algorithms were employed to detect player communities, examine interactions, and ascertain influence patterns among elite WTA players. A critical component of this study involves the creation of performance benchmarks. The study assessed player performance by analyzing the correlation between the official WTA rankings and centrality-based rankings and assessing the statistical significance of this association. Later, the application of benchmark standards successfully highlighted players who attained top rankings, showcasing the effectiveness of network centrality as a tool for establishing performance standards in women’s tennis. This approach underlines the potential of datadriven methods in sports analytics, emphasizing the role of centrality measures in identifying high-performing athletes.