Designing a prediction model for athlete's sports performance using neural network

Accurately predicting the performance in various sports is crucial in comprehending training characteristics and enhancing physical education and training processes. The increasing significance of sports in China has enhanced the interest for developing prediction models to assess athletes' sports performance, accurately. These models will greatly contribute to improving sports performance and implementing scientific training methods. This paper proposes a prediction model for athletes' sports performance using Neural Networks as the underlying framework. This research aims to enhance sports performance and scientific training by developing a reliable and effective prediction model. First, the proposed model utilizes neural network algorithms, including error backpropagation and genetic algorithms, to train and optimize the prediction model. Second, it analyzes temporal patterns, extracts statistical features, and adapts to new data by ensuring reliability and effectiveness of the proposed model. Third, this paper conducts an error analysis by comparing the prediction errors between our proposed method and traditional models to evaluate the accuracy of the prediction model. In addition, this paper conducts an error analysis by comparing the prediction errors between our proposed method and traditional models. Finally, the results demonstrate that our proposed method achieves a maximum error of 36.12%, with the highest error rate in the BP network prediction being 6.76%. Furthermore, we compared our predicted results with multiple linear regression and other existing prediction methods, and our proposed method demonstrated superior accuracy with an overall prediction accuracy of 97.6%. Our suggested solution surpasses previous approaches with much higher accuracy, reduced latency, improved recall, and increased scalability.

Automated summary

This research proposes a prediction model for athletes' sports performance using Neural Networks as the underlying framework, aiming to enhance sports performance and scientific training. The model utilizes neural network algorithms for training and optimization, analyzes temporal patterns, extracts statistical features, and evaluates its accuracy by comparing prediction errors with traditional models. The results show that the proposed method achieves an overall prediction accuracy of 97.6%, surpassing previous approaches with higher accuracy, reduced latency, improved recall, and increased scalability.