Using Deep Convolutional Neural Networks to Predict Goal-scoring Opportunities in Soccer

Martijn Wagenaar, Emmanuel Okafor, Wouter Frencken, Marco A. Wiering

Abstract

Deep learning approaches have successfully been applied to several image recognition tasks, such as face, object, animal and plant classification. However, almost no research has examined on how to use the field of machine learning to predict goal-scoring opportunities in soccer from position data. In this paper, we propose the use of deep convolutional neural networks (DCNNs) for the above stated problem. This aim is actualized using the following steps: 1) development of novel algorithms for finding goal-scoring opportunities and ball possession which are used to obtain positive and negative examples. The dataset consists of position data from 29 matches played by a German Bundlesliga team. 2) These examples are used to create original and enhanced images (which contain object trails of soccer positions) with a resolution size of 256x256 pixels. 3) Both the original and enhanced images are fed independently as input to two DCNN methods: instances of both GoogLeNet and a 3-layered CNN architecture. A K-nearest neighbor classifier was trained and evaluated on ball positions as a baseline experiment. The results show that the GoogLeNet architecture outperforms all other methods with an accuracy of 67.1%.

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Paper Citation


in Harvard Style

Wagenaar M., Okafor E., Frencken W. and A. Wiering M. (2017). Using Deep Convolutional Neural Networks to Predict Goal-scoring Opportunities in Soccer . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 448-455. DOI: 10.5220/0006194804480455


in Bibtex Style

@conference{icpram17,
author={Martijn Wagenaar and Emmanuel Okafor and Wouter Frencken and Marco A. Wiering},
title={Using Deep Convolutional Neural Networks to Predict Goal-scoring Opportunities in Soccer},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={448-455},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006194804480455},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Using Deep Convolutional Neural Networks to Predict Goal-scoring Opportunities in Soccer
SN - 978-989-758-222-6
AU - Wagenaar M.
AU - Okafor E.
AU - Frencken W.
AU - A. Wiering M.
PY - 2017
SP - 448
EP - 455
DO - 10.5220/0006194804480455