An Intelligent Mobile Application for Sports Recommendations and Performance Prediction using Open AI and Machine Learning

This research focuses on the development of an intelligent mobile application to address challenges in selecting suitable sports and predicting athletic performance. The problem of early dropout in youth sports and performance stagnation due to mismatched expectations underscores the need for solutions. The proposed application combines OpenAI and machine learning algorithms to evaluate user inputs, including physical attributes, psychological traits, and environmental factors, to recommend appropriate sports and predict future performance. Key technologies include generative AI for personalized sports recommendations and machine learning algorithms for performance prediction in golf, trained on professional player data. Challenges such as data accuracy, generalization of algorithms, and prompt optimization were tackled through user feedback and rigorous performance testing. The use of accuracy measurement validated the system's reliability and adaptability, demonstrating its usefulness. By offering accurate and scalable solutions, the application has the potential to create sustained athletic engagement and enhance decision-making for users across.

Authors

Publication Details

Published
2025-02-22
DOI
https://doi.org/10.5121/csit.2025.150420
Primary Topic
Sports Analytics and Performance
Type
article
Field-Weighted Citation Impact
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article

An Intelligent Mobile Application for Sports Recommendations and Performance Prediction using Open AI and Machine Learning

Hongya Qu, Ivan Revilla
Sports Analytics and Performance
article

An Intelligent Mobile Application for Sports Recommendations and Performance Prediction using Open AI and Machine Learning

Hongya Qu, Ivan Revilla
article en

Abstract

This research focuses on the development of an intelligent mobile application to address challenges in selecting suitable sports and predicting athletic performance. The problem of early dropout in youth sports and performance stagnation due to mismatched expectations underscores the need for solutions. The proposed application combines OpenAI and machine learning algorithms to evaluate user inputs, including physical attributes, psychological traits, and environmental factors, to recommend appropriate sports and predict future performance. Key technologies include generative AI for personalized sports recommendations and machine learning algorithms for performance prediction in golf, trained on professional player data. Challenges such as data accuracy, generalization of algorithms, and prompt optimization were tackled through user feedback and rigorous performance testing. The use of accuracy measurement validated the system's reliability and adaptability, demonstrating its usefulness. By offering accurate and scalable solutions, the application has the potential to create sustained athletic engagement and enhance decision-making for users across.

Openalex Percentile: Top 94%
Sports Analytics and Performance
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An Intelligent Mobile Application for Sports Recommendations and Performance Prediction using Open AI and Machine Learning — Hongya Qu, Ivan Revilla · (2025) | TGRS Research Map | TGRS