From Intuition to Intelligence: A Data Science Framework for Modern Sports Analytics
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Abstract
The use of Data Science and Competitive Sports represents a major paradigm shift from using intuition to coach to making decisions based on evidence. In this study, a comprehensive Data Science Framework for Modern Sports Analytics is developed. It combines the collection of multi-modal data, the construction of signal-processing pipelines, the generation of advanced features through feature engineering, and the application of Machine Learning (ML) inference engines. A systematic review of the Literature from 2019 to 2025 was conducted to examine how Artificial Intelligence (AI) sub-disciplines such as supervised learning, unsupervised clustering, deep sequential models, and Explainable AI (XAI) were used in the three primary areas of Performance Optimization, Tactical Strategy, and Business Intelligence. Additionally, the challenges of integrating real-time data into the Data Science pipeline, the Ethical Implications of Collecting Biometric Data of Athletes, and the Increasing Use of Explainable AI to Build Trust with Coaches and Stakeholders are discussed. The proposed Data Science Framework addresses the gap that exists between Fragmented Single-Modality Research and Holistic Real-Time Models of Performance. Indicators of experimental validation across recent studies have shown Statistically Significant Improvements in Match Outcome Prediction Accuracy and Injury Risk Forecasting when Multi-Modal Pipelines were employed. As both a Structured Review and a Forward-Looking Blueprint for Researchers and Practitioners looking to Leverage Data Science for Competitive Advantage in Sports, this Study will be a valuable resource.
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