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- W4297030760 abstract "Artificial neural networks (ANN) are able to detect patterns in sports games. The aim of the present study is to predict the setting speed and its target area within the complex I in volleyball using ANN. For the analysis, 289 rallies from one setter of the 2nd Austrian Volleyball League Women were considered. Player positions and ball trajectory data prior to the setter's pass were recorded. Subsequently, ANN software NeuroDimension was trained with 60% of the datasets to predict the target area and setting speed (supervised learning). The rest was used for cross-validation (15%) and predictions (25%). The accordance of the predicted and the real values was assessed by the percentage of correct predictions. To rate the prediction quality, the results of the ANN were compared with predictions from high-level volleyball coaches. The ANN correctly predicted the target area in 68.1% and the setting speed in 79.2%, which was significantly higher than by chance (p<0.01). The accuracy of the ANN-predicted target areas was 2.8% higher than from coaches (not significant). The ANN's prediction rate for the setting speed was significantly higher by 14.6%. Information from ANN predictions could be helpful to support coaches to train the athlete's skills to anticipate opponents’ playing actions and to train the setter of the own team." @default.
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- W4297030760 date "2022-01-01" @default.
- W4297030760 modified "2023-09-25" @default.
- W4297030760 title "An Artificial Neural Network Predicts Setter's Setting Behavior in Volleyball Similar or Better than Experts" @default.
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- W4297030760 doi "https://doi.org/10.1016/j.ifacol.2022.09.163" @default.
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