Institute of Physical Education, Inner Mongolia Normal University, Hohhot 010020, China.
Comput Intell Neurosci. 2022 Aug 2;2022:6341495. doi: 10.1155/2022/6341495. eCollection 2022.
Track and field is an important part of sports. Track and field athletes are an important reserve force for the development of national sports. An accurate assessment of track and field athletes' performance can help them develop more appropriate training programs and improve their performance. In order to assess the performance of track and field athletes better, this paper proposes an improved logistic regression method. Firstly, this method uses factor analysis to reduce the data dimensions of the factors that affect the performance of track and field athletes, and uses the principal component analysis to select common factors and their corresponding values. Then, according to the common factors, a binary logistic regression model is established to evaluate the performance of track and field athletes. Experiments show that the method can effectively evaluate the performance of track and field athletes and is suitable for athletes of different track and field sports. It has high accuracy, fast evaluation efficiency, and good universality of performance evaluation. For different numbers of athletes, the proposed method has a lower error evaluation index, higher evaluation accuracy, and better evaluation quality. Compared with the other two methods, the proposed method has the shortest evaluation time and is more effective for the performance evaluation of track and field athletes.
田径是体育的重要组成部分。田径运动员是国家体育发展的重要后备力量。准确评估田径运动员的表现可以帮助他们制定更合适的训练计划，提高他们的表现。为了更好地评估田径运动员的表现，本文提出了一种改进的逻辑回归方法。首先，该方法使用因子分析来减少影响田径运动员表现的因素的数据维度，并使用主成分分析来选择共同因素及其对应的值。然后，根据共同因素，建立二项逻辑回归模型来评估田径运动员的表现。实验表明，该方法可以有效地评估田径运动员的表现，适用于不同田径项目的运动员。它具有较高的准确性、快速的评估效率和良好的性能评估通用性。对于不同数量的运动员，所提出的方法具有较低的误差评估指标、较高的评估准确性和更好的评估质量。与其他两种方法相比，所提出的方法具有最短的评估时间，对于田径运动员的表现评估更为有效。
