Authors:
Zixue Zeng
1
and
Bingyu Pan
2
Affiliations:
1
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, U.S.A.
;
2
School of Sports Engineering, Beijing Sports University, Xinxi Road no.48, HaiDian District, Beijing, China
Keyword(s):
Football, Association Football Positions, BP Neural Network, Machine Learning.
Abstract:
The prediction of the player's positions, or determining which position a player is suitable for based on sports performance and physiological indicators, plays a major role in association football. This research is based on the public dataset provided by Wyscout, from which player-related indicators are extracted and processed. Six indicators, including the accuracy of shot, the accuracy of simple pass, the accuracy of glb (Ground loose ball), the accuracy of defending duel,the accuracy of air duel, the accuracy of attacking duel, are selected according to the ANOVA (analysis of variance) test, and being imported into BP neural network for training. Since the neural network has three hyperparameters: training rate, iterations, and the number of neurons in the hidden layer, it is required to use the k-fold cross-validation to evaluate by which hyperparameter pair the model predict best. It is found that when the learning rate is set to 0.0125 and the hidden layer neuron is set to 6,
the average accuracy of the cross-check is the highest, which is 73%. When iterations reach 300, the accuracy curve tends to converge. The final accuracy rate can reach 77%.
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