The Rice Competitive Edge Lab offers a collaborative environment for students to gain outside-the-classroom research experience working on the hardest problems in sport analytics. This research focus is specifically designed to prepare students for the most competitive R&D jobs in professional sports by tackling complex, open-ended questions.
Students in the RCE Lab have presented their research papers at the New England Symposium on Statistics in Sports, the Cascadia Symposium on Statistics in Sports, Saberseminar, the Women in Sports Data Symposium, and the Carnegie Mellon Sports Analytics Conference. These presentations serve as excellent resume builders that help Rice students stand out to potential employers in a crowded job market.
The lab's big-data research into pitching injuries spans multiple independent, high-visibility projects. Our researchers currently manage data science initiatives funded directly by Major League Baseball, alongside a separate project backed by the Center for Human Performance for advanced UCL imaging. Concurrently, the lab utilizes its computational expertise to analyze high-fidelity kinematic tracking data from the Rice Baseball Pitching Lab.
If you are a Rice student interested in getting involved with the Rice Competitive Edge Lab, contact Scott Powers.
Student Conference Presentations
| Student | Title | Conference(s) |
|---|---|---|
| Lou Zhou | GPS: A metric for evaluating goalkeeper positioning | 2026 ASI Summit |
| Elisabeth Millington | A comps-based approach for interpreting tree-based predictions with an application to the NFL draft | 2025 CMSAC 2025 NESSIS |
| Ruoqian (Judy) Zhu | Ball path curvature and in-game free throw shooting proficiency in the National Basketball Association | 2025 NESSIS |
| Jacob Hahn | The Two-Foot Rule: A game theoretic analysis of the pickoff limit in Major League Baseball | 2024 CASSIS 2024 Saberseminar |
| Elizabeth Sepúlveda | A statistical approach to sport climbing difficulty and progression | 2024 CASSIS |
| Naomi Consiglio | Modifying k-means clustering to optimize positioning in volleyball | 2024 WiSD Symposium |
| Jeff Brover | Do we learn more about AAA batters when they face better pitches? | 2024 Saberseminar |
| Andrew Kang | Not All Features Are Created Equal: Player clustering and evaluation | 2024 Opta Forum |
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