Application of Neural and Regression Models in Sports Results Prediction
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- 作者：Adam Maszczyk ; a.maszczyk@awf.katowice.pl" class="auth_mail ; Artur Go艂a艣 ; Przemys艂aw Pietraszewski ; Robert Roczniok ; Adam Zaj膮c ; Arkadiusz Stanula
- 关键词：sport selection ; linear models ; non-linear models ; Artificial Neural Networks ; optimization
- 刊名：Procedia - Social and Behavioral Sciences
- 出版年：19 March, 2014
- 年：2014
- 卷：117
- 期：Complete
- 页码：482-487
- 全文大小：248 K
文摘
The investigation was aimed at comparing regression and neural models with respect to their accuracy of predicting sports results. The present study involved a group of 116 javelin throwers, aged 18卤0.5 years. The statistical analysis was initially done by the Shapiro-Wilk normality test and by the homogeneity test. The correlation matrix and regression analysis revealed four predictors (cross step, specific power of the arms and the trunk, specific power of the abdominal muscles and the grip power).Subsequently, non-linear regression models as well as neural models were built. Thus, to verify our models, the sports results were predicted for the group of 20 javelin throwers from the Polish National Team and tested by comparing the model- generated predictions with their actual data. The non-linear regression models and perceptron networks structured as 4-3-1, demonstrated their capacity for making generalizations and predicting sports results. Moreover, the difference in the value of absolute errors was 12.68 m (between true and estimated performances in the group of 20 Polish javelin throwers), thus favouring the neural models. The analysis of the above data clearly shows that the neural model does better at predicting sports results than the regression model. Therefore, the investigation demonstrated a significantly greater accuracy of prediction for perceptron models.