Journal of Advances in Developmental Research

E-ISSN: 0976-4844   •   Impact Factor: 9.71

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Machine Learning-Based Sports Player Performance Drop Prediction Using Random Forest and Flask with Visual Analytics

Author(s) Abishek Krishnan T, Sowmeya V
Country India
Abstract The performance of sports players can fluctuate due to various physical, psychological, and environmental factors. Timely identification of performance drops is crucial for coaches, analysts, and management teams to make informed decisions on training, recovery, and game strategies. This project, “Machine Learning-Based Sports Player Performance Drop Prediction Using Random Forest and Flask with Visual Analytics”, aims to develop a predictive system using machine learning algorithms to forecast potential declines in athletes’
performance. The system collects historical performance metrics, physiological data, and contextual match information to train models capable of identifying patterns associated with performance deterioration. A web-based visualization dashboard is integrated to provide real-time insights, performance trends, and alerts, allowing stakeholders to monitor and respond proactively. Additionally, the dashboard supports exporting data and reports in CSV format for further
analysis. The proposed approach not only enhances decision-making in sports management but also contributes to improving player health, training efficiency, and overall team performance.
Keywords Machine Learning, Random Forest, Sports Analytics, Performance Prediction, Web- Based Dashboard, Flask Web Frame Work, Predictive Modeling.
Field Computer
Published In Volume 17, Issue 1, January-June 2026
Published On 2026-05-27
Cite This Machine Learning-Based Sports Player Performance Drop Prediction Using Random Forest and Flask with Visual Analytics - Abishek Krishnan T, Sowmeya V - IJAIDR Volume 17, Issue 1, January-June 2026.
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