Leveraging Machine Learning Algorithms for Performance Prediction and Optimization in Sports Data Analytics
DOI:
https://doi.org/10.32628/CSEIT25113375Keywords:
Sports analytics, Sports outcome prediction, performance optimisation, Machine learning (ML), FIFA World Cup dataAbstract
The use of data analytics has revolutionised sports strategy planning and performance optimisation in the last few years. This research delves into the use of data analytics to boost sports performance. It specifically looks at sophisticated methods that can improve team strategy, game results, and individual performance. This study applies machine learning techniques to analyze official badminton match data to predict match outcomes and assess player profiles. By employing algorithms such as XGBoost and Support Vector Machine, the research achieved notable predictive accuracy—XGBoost reached 75%, while SVM attained 74%. The comparative analysis demonstrates the advantage of ensemble and advanced algorithms in capturing complex patterns in sports data. These insights can aid coaches and athletes in tailoring training strategies and identifying areas for improvement. The findings show that using data analytics can assist coaches and athletes in spotting their strengths as well as weaknesses.
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