FALL 2025

AUTHOR

Koray S. Abramson, Pine Crest School

ABSTRACT

The United States Tennis Association (USTA) has historically used point-per-round rankings to determine competitive tournament entry and seeding, but this system often rewards participation over quality of play and can be distorted by random draw effects. Alternative systems such as Universal Tennis Rating (UTR) and World Tennis Number (WTN) use algorithmic predictive modeling based on prior head-to-head results to estimate player ability across gender, age, and geography. Although previous studies (Im, 2023; Kiely, 2025; Krall, 2025; Mayew, 2023) have evaluated predictive accuracy between these two systems using smaller, elite-level samples, large-scale analyses spanning all competitive levels of U.S. junior tennis remain limited.

This study addresses that gap through a comprehensive, multi-level analysis of 70,822 USTA junior matches (scraped from January–July 2024), evaluating UTR, WTN, and USTA rankings for both accuracy and bias. Overall, UTR predicted 78.5%, WTN 74.2%, and USTA 70.1% of matches correctly, respectively, with statistically significant differences. Geographic bias was evident across systems, favoring players from less-competitive sections. In matches between similarly rated opponents, players from stronger sections won 61.7% (USTA), 59.0% (WTN), and 53.9% (UTR), indicating systematic underestimation of those cohorts.

By combining a large-scale comparative analysis with the first known bias assessment of these systems, this study extends prior evaluations and contextualizes newer findings. The results demonstrate that UTR is the most accurate and least-biased predictor of match outcomes, supporting the adoption of algorithmic, data-driven rating frameworks such as UTR over traditional point-per-round ranking systems in junior tennis.