Project Details
Description
Team sports currently enjoy great popularity, specifically soccer is the sport with the largest number of practitioners in the world. Currently, the distribution of training load and the competition demands to which athletes are subjected are being analyzed for better performance with Big Data techniques.
When it comes to data modeling and visualization, there are several challenges such as finding the techniques that best adapt to the nature of the data and finding the most complete way to display the information for clubs in such a way that understand the reason for determining the optimal training load. Within AI, Machine Learning techniques deserve special attention, which are based on the application of algorithms with the aim of recognizing patterns and trends in data in order to make predictions, which is already a reality in sports sciences. It is a field in continuous expansion in which the first research is beginning to appear. The latest studies have grouped performance factors using the Principal Components technique without being able to optimize the response to a training load model.
This study aims to formulate a prediction of workload results for players through a supervised learning analysis of logistic regression, seeking a paradigm shift in the way of analyzing and understanding training load.
When it comes to data modeling and visualization, there are several challenges such as finding the techniques that best adapt to the nature of the data and finding the most complete way to display the information for clubs in such a way that understand the reason for determining the optimal training load. Within AI, Machine Learning techniques deserve special attention, which are based on the application of algorithms with the aim of recognizing patterns and trends in data in order to make predictions, which is already a reality in sports sciences. It is a field in continuous expansion in which the first research is beginning to appear. The latest studies have grouped performance factors using the Principal Components technique without being able to optimize the response to a training load model.
This study aims to formulate a prediction of workload results for players through a supervised learning analysis of logistic regression, seeking a paradigm shift in the way of analyzing and understanding training load.
General Objective
Desarrollar un modelo predictivo de carga física externa para futbolistas.
Research Lines
Ciencias Aplicadas al Análisis del Movimiento Humano
Actividad Física y Rendimiento Deportivo
Actividad Física y Rendimiento Deportivo
| Status | Finished |
|---|---|
| Effective start/end date | 1/01/25 → 31/12/25 |
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