Research on Effectiveness Evaluation and Optimization of Baseball Teaching Method Based on Machine Learning

TL;DR

This study employs machine learning models to predict students' baseball training scores, evaluating teaching effectiveness and providing optimization suggestions for personalized training strategies.

Contribution

It introduces a data-driven evaluation method using machine learning to assess and enhance baseball teaching effectiveness, highlighting key performance factors.

Findings

K-Neighbors and Gradient Boosting regressors outperform others in prediction accuracy.

Cumulative hits and runs are key factors influencing scores.

Data-driven evaluation supports personalized teaching plans.

Abstract

In modern physical education, data-driven evaluation methods have gradually attracted attention, especially the quantitative prediction of students' sports performance through machine learning model. The purpose of this study is to use a variety of machine learning models to regress and predict students' comprehensive scores in baseball training, so as to evaluate the effectiveness of the current baseball teaching methods and put forward targeted training optimization suggestions. We set up a model and evaluate the performance of students by collecting many characteristics, such as hitting times, running times and batting. The experimental results show that K-Neighbors Regressor and Gradient Boosting Regressor are excellent in comprehensive prediction accuracy and stability, and the R score and error index are significantly better than other models. In addition, through the analysis of feature importance, it is found that cumulative hits and cumulative runs are the key factors affecting students' comprehensive scores. Based on the results of this study, this paper puts forward some suggestions on optimizing training strategies to help students get better performance in baseball training. The results show that the data-driven teaching evaluation method can effectively support physical education and promote personalized and refined teaching plan design.