UWEC CERCA 2026

The utilization of analytics in women’s professional tennis, the Women’s Tennis Association (WTA), has occurred much more recently when compared to other professional sports. Despite unique difficulties in predicting match outcomes, there have been a spate of recent articles overviewing probabilistic and data-mining methods to do so. Our research work: (1) performed a literature review of data-mining methods for men’s professional tennis (ATP) and women’s professional tennis (WTA); (2) integrated knowledge about predictive statistics learned from prior years’ data-exploration; and (3) adds generalizations, efficiencies, documentation, and new features to functions used to summarize and organize data for predictive models.Data for player statistics and results of WTA tournaments was obtained from a GitHub repository under a Creative Commons license. We computed and analyzed a set of related summary statistics for use in comparison of data-mining methods. We discuss the data-analytic methods and compare predictive abilities. We edited original functions in R with the focus of wrangling the data across an appropriate time window, court surface, and player rank and of summarizing statistics for individual players and opponents. The process and final forms of the functions, along with the dissemination process via an R package, are described.