Application of Artificial Intelligence in Sports Performance Analysis

Abstract: AI is increasingly playing a significant role in sports today, and it's transforming the way athletes are evaluated, trained, and monitored. It enables coaches, sports scientists and athletes to explore vast quantities of performance data and find patterns that they might not otherwise be able to see through traditional methods. Technologies like machine learning, computer vision, wearable technology, predictive analytics, and automated video analysis are also being applied to a growing number of technical skills, physical performance, tactical movements, training workload, recovery and injury risk studies. Such technologies can deliver real-time and personalized data to coaches to create training plans based on the unique requirements of athletes and make informed decisions. AI can also aid in talent identification and performance prediction by integrating data collected from physical, physiological, biomechanical, and psychological factors. While these are advantages, there are also drawbacks to using AI in sports. Data quality, privacy, cost, technical, algorithmic bias and ethical considerations should be taken seriously. The article will explore the key applications of AI in sports performance analysis, as well as its potential benefits in enhancing athlete development and performance, and its current challenges and future prospects.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-31
DOI
https://doi.org/10.5281/zenodo.23754712
Primary Topic
Sports Performance and Training
Type
article
Field-Weighted Citation Impact
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article

Application of Artificial Intelligence in Sports Performance Analysis

Ramdas R. Jadhav
2 links
Zenodo (CERN European Organization for Nuclear Research)
Sports Performance and Training
article

Application of Artificial Intelligence in Sports Performance Analysis

Ramdas R. Jadhav
article en

Abstract

Abstract: AI is increasingly playing a significant role in sports today, and it's transforming the way athletes are evaluated, trained, and monitored. It enables coaches, sports scientists and athletes to explore vast quantities of performance data and find patterns that they might not otherwise be able to see through traditional methods. Technologies like machine learning, computer vision, wearable technology, predictive analytics, and automated video analysis are also being applied to a growing number of technical skills, physical performance, tactical movements, training workload, recovery and injury risk studies. Such technologies can deliver real-time and personalized data to coaches to create training plans based on the unique requirements of athletes and make informed decisions. AI can also aid in talent identification and performance prediction by integrating data collected from physical, physiological, biomechanical, and psychological factors. While these are advantages, there are also drawbacks to using AI in sports. Data quality, privacy, cost, technical, algorithmic bias and ethical considerations should be taken seriously. The article will explore the key applications of AI in sports performance analysis, as well as its potential benefits in enhancing athlete development and performance, and its current challenges and future prospects.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 10%
Sports Performance and Training
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Application of Artificial Intelligence in Sports Performance Analysis — Ramdas R. Jadhav · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS