SMU Data Science Review
Abstract
Women’s beach volleyball is one of the fastest growing collegiate sports today. The increase in popularity has come with an increase in valuable scholarship opportunities across the country. With thousands of athletes to sort through, college scouts depend on websites that aggregate tournament results and rank players nationally. This project partnered with the company Volleyball Life, who is the current market leader in the ranking space of junior beach volleyball players. Utilizing the tournament information provided by Volleyball Life, this study explored replacements to the current ranking systems, which are designed to aggregate player points from recent tournament placements. Three probabilistic/modern ranking techniques were tested, specifically an Elo variant, TrueSkill, and a random walker graph network. This study found that Elo could predict match outcomes with a 13% higher accuracy than the preexisting systems and TrueSkill with an 11% higher accuracy.
Recommended Citation
Stewart, Cameron; Mazel, Michael; and Sadler, Bivin
(2022)
"Application of Probabilistic Ranking Systems on Women’s Junior Division Beach Volleyball,"
SMU Data Science Review: Vol. 6:
No.
2, Article 2.
Available at:
https://scholar.smu.edu/datasciencereview/vol6/iss2/2
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Included in
Applied Statistics Commons, Data Science Commons, Numerical Analysis and Scientific Computing Commons, Probability Commons, Sports Studies Commons, Statistical Models Commons, Theory and Algorithms Commons
