Analysis of Sports Performance Prediction Model Based on GA-BP Neural Network Algorithm

Area of Science:

• Sports Science
• Artificial Intelligence
• Machine Learning

Background:

• Traditional sports performance prediction methods are time-consuming and lack accuracy.
• This limits the development of targeted training plans for athletes.
• Accurate performance prediction is crucial for optimizing athletic development.

Purpose of the Study:

• To develop an advanced sports performance prediction model using the GA-BP neural network algorithm.
• To compare the efficacy of the GA-BP model against the traditional BP neural network.
• To enhance the scientific basis for formulating athlete training strategies.

Main Methods:

• Implementation of a Genetic Algorithm-Backpropagation (GA-BP) neural network model.
• Experimental analysis comparing GA-BP with the standard BP neural network.
• Utilizing quality training indicators and sports training results to build athlete models.

Main Results:

• The GA-BP neural network demonstrated a significantly faster convergence speed compared to the BP neural network.
• GA-BP achieved the desired error accuracy in a shorter timeframe.
• The GA-BP model provided higher accuracy, improved stability, and better prediction effects.

Conclusions:

• The GA-BP neural network algorithm effectively overcomes the limitations of traditional BP networks for sports performance prediction.
• This model offers higher application value for coaches and athletes.
• It enables more intuitive identification of athlete strengths and weaknesses for personalized training.