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Towards Sustainable Athletic Excellence: A Predictive Framework for Player Performance and Injury Mitigation

Author(s) Abhinav Balasubramanian
Country United States
Abstract In modern sports, balancing peak player performance with injury prevention remains a key priority for teams and sports professionals. This paper introduces a unified machine learning framework that integrates performance prediction and injury risk assessment to support data-driven decision-making in sports management. The proposed framework outlines the use of regression models for predicting player performance and classification models for estimating injury probability. A detailed methodology for feature engineering is presented, incorporating critical factors such as workload patterns, recovery timelines, historical injury data, and recent performance trends. This dual-purpose framework has the potential to enhance load management strategies, optimize team lineups, and enable personalized training regimens tailored to individual athletes. Furthermore, key challenges, practical applications, and avenues for future research are discussed, offering a foundational approach for advancing predictive sports analytics aimed at fostering long-term player development and team success.
Keywords Artificial Intelligence (AI), Machine Learning in Sports, Sports Analytics, Performance Prediction, Injury Risk Assessment, Player Load Management
Field Engineering
Published In Volume 4, Issue 10, October 2023
Published On 2023-10-04
Cite This Towards Sustainable Athletic Excellence: A Predictive Framework for Player Performance and Injury Mitigation - Abhinav Balasubramanian - IJLRP Volume 4, Issue 10, October 2023. DOI 10.5281/zenodo.14673160
DOI https://doi.org/10.5281/zenodo.14673160
Short DOI https://doi.org/g8z64k
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