Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Document Type
Event
Start Date
22-4-2026 4:00 PM
Description
This study examines whether early-season performance metrics can support player evaluation under the NCAA’s shortened transfer window. Using data from Conference USA and the Mid-American Conference, we modeled offensive (UASE) and defensive (DAR) efficiency with multiple predictive methods. Across both full-season and 9-game datasets, DAR was more predictable, with higher R² and lower RMSE values. Linear Regression consistently performed best for DAR, while KNN and Random Forest performed best for UASE depending on the dataset. Results show that meaningful performance patterns can be identified early in the season, even with limited data. These findings suggest analytics can help programs make faster, more informed decisions for recruiting, roster planning, and player retention.
Included in
UC-087-236 Early Prediction of Player Performance
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
This study examines whether early-season performance metrics can support player evaluation under the NCAA’s shortened transfer window. Using data from Conference USA and the Mid-American Conference, we modeled offensive (UASE) and defensive (DAR) efficiency with multiple predictive methods. Across both full-season and 9-game datasets, DAR was more predictable, with higher R² and lower RMSE values. Linear Regression consistently performed best for DAR, while KNN and Random Forest performed best for UASE depending on the dataset. Results show that meaningful performance patterns can be identified early in the season, even with limited data. These findings suggest analytics can help programs make faster, more informed decisions for recruiting, roster planning, and player retention.