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
In the era of the NCAA transfer portal, collegiate basketball coaches face the critical challenge of identifying and targeting elite recruits within a condensed 15-day window. This study investigates the predictability of elite player performance by analyzing postseason award winners within the Coastal Athletic Association (CAA). Utilizing game-by-game data on player efficiency, usage percentages, and Player Efficiency Ratings (PER), we implemented an Exponentially Weighted Moving Average (EWMA) control chart—a technique from the Statistical Process Control (SPC) family—to monitor performance signals. Our results indicate that the EWMA model successfully identifies future award-winning players after an average of only 8.58 games. By providing a 22-game lead before the season’s conclusion, this model offers coaches a significant strategic advantage, allowing for the early identification of potential transfer targets before the portal officially opens. These findings suggest that SPC-based modeling is a highly effective tool for predictive sports analytics and carries broad applications for future collegiate recruitment strategies.
Included in
UC-011-171 Beyond Postseason Awards: Predicting Accolades via Real-Time Control Chart Signals in the NCAA Transfer Era
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
In the era of the NCAA transfer portal, collegiate basketball coaches face the critical challenge of identifying and targeting elite recruits within a condensed 15-day window. This study investigates the predictability of elite player performance by analyzing postseason award winners within the Coastal Athletic Association (CAA). Utilizing game-by-game data on player efficiency, usage percentages, and Player Efficiency Ratings (PER), we implemented an Exponentially Weighted Moving Average (EWMA) control chart—a technique from the Statistical Process Control (SPC) family—to monitor performance signals. Our results indicate that the EWMA model successfully identifies future award-winning players after an average of only 8.58 games. By providing a 22-game lead before the season’s conclusion, this model offers coaches a significant strategic advantage, allowing for the early identification of potential transfer targets before the portal officially opens. These findings suggest that SPC-based modeling is a highly effective tool for predictive sports analytics and carries broad applications for future collegiate recruitment strategies.