Predicting Athlete Performance Readiness Through Countermovement Jump Analysis
Publication Date
Spring 4-16-2025
Presentation Length
Poster/Gallery presentation
College
College of Sciences & Mathematics
Department
Math and Computer Science, Department of
Student Level
Undergraduate
Faculty Advisor
Christina Davis
Presentation Type
Poster
Summary
The aim of this project is to answer the research question: Can force plate measurements from a countermovement jump accurately predict an athlete's performance readiness? To test this research question, Linear Discriminate Analysis, Random Forest, Receiver Operating Characteristic, and Principal Component Analysis models were built with Python in Visual Studio Code. These models used seven metrics to predict athlete readiness: Average Braking Force, Average Propulsive Power, Time to Takeoff, Left Average Braking Force, Right Average Braking Force, L|R Average Braking Force, and Peak Propulsive Power. Athlete readiness was classified by calculating the quintiles of the Jump Height values of all athletes. Overall, the results from this project demonstrate that the Hawkins Dynamic Force-Plate Countermovement Jump can predict athlete readiness with an average accuracy of 80.38% using the models outlined above.
Recommended Citation
Moore, Emory, "Predicting Athlete Performance Readiness Through Countermovement Jump Analysis" (2025). SPARK Symposium Presentations. 121.
https://repository.belmont.edu/spark_presentations/121
