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Performance of machine learning models in application to beach volleyball data.

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Arndt, C., & Brefeld, U. (2016), Predicting the future performance of soccer players. Statistical Analysis and Data Mining: The ASA Data Science Journal. 9. 373-382.10.1002/sam.11321Search in Google Scholar

Bialkowski, A., Lucey, P., Carr, P., Yue, Y., Sridharan, S., & Matthews, I. (2015). Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data. In Proceedings - IEEE International Conference on Data Mining, ICDM.Search in Google Scholar

Bianchi, F., Facchinetti, T., & Zuccolotto, P. (2017). Role revolution: Towards a new meaning of positions in basketball. Electronic Journal of Applied Statistical Analysis, 10(3), 712–734.Search in Google Scholar

Bunker, R. P., & Thabtah, F. (2019). A machine learning framework for sport result prediction. Applied Computing and Informatics, 15(1), 27–33.10.1016/j.aci.2017.09.005Search in Google Scholar

Chen, T., & Guestrin, C. (2016). {XGBoost}: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). New York, NY, USA: ACM.Search in Google Scholar

Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.Search in Google Scholar

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.Search in Google Scholar

Claudino, J. G., Capanema, D. de O., de Souza, T. V., Serrão, J. C., Machado Pereira, A. C., & Nassis, G. P. (2019). Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: A Systematic Review. Sports Medicine - Open, 5(1).10.1186/s40798-019-0202-3660992831270636Search in Google Scholar

Dick, U., & Brefeld, U. (2019). Learning to Rate Player Positioning in Soccer. Big Data, 7(1), 71–82.10.1089/big.2018.005430672712Search in Google Scholar

Esman, G. (2017, April 18). Splunk and Tensorflow for Security: Catching the Fraudster with Behavior Biometrics. Retrieved from https://www.splunk.com/blog/2017/04/18/deep-learning-with-splunk-and-tensorflow-for-security-catching-the-fraudster-in-neural-networks-with-behavioral-biometrics.htmlSearch in Google Scholar

Hassan, A., Schrapf, N., Ramadan, W., & Tilp, M. (2017a). Evaluation of tactical training in team handball by means of artificial neural networks. Journal of Sports Sciences, 35(7).10.1080/02640414.2016.118380427211106Search in Google Scholar

Hassan, A., Schrapf, N., & Tilp, M. (2017b). The prediction of action positions in team handball by non-linear hybrid neural networks. International Journal of Performance Analysis in Sport, 17(3), 293–302.10.1080/24748668.2017.1336688Search in Google Scholar

Howard, J., et. al. (2018). fast.ai. GitHub. Retrieved from https://github.com/fastai/fastaiSearch in Google Scholar

Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167.Search in Google Scholar

Kautz, T., Groh, B. H., Hannink, J., Jensen, U., Strubberg, H., & Eskofier, B. M. (2017). Activity recognition in beach volleyball using a Deep Convolutional Neural Network. Data Mining and Knowledge Discovery, 31(6), 1678–1705.10.1007/s10618-017-0495-0Search in Google Scholar

Kempe, M., Grunz, A., & Memmert, D. (2015). Detecting tactical patterns in basketball: Comparison of merge self-organising maps and dynamic controlled neural networks. European Journal of Sport Science, 15(4), 249–255.10.1080/17461391.2014.93388224993662Search in Google Scholar

Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.Search in Google Scholar

Kocmi, T., & Bojar, O. (2017). An exploration of word embedding initialization in deep-learning tasks. arXiv preprint arXiv:1711.09160.Search in Google Scholar

Lames, M., & McGarry, T. (2007). On the search for reliable performance indicators in game sports. International Journal of Performance Analysis in Sport, 7(1), 62-79.10.1080/24748668.2007.11868388Search in Google Scholar

Lapham A.C., & Bartlett, R.M. (1995) The use of artificial intelligence in the analysis of sports performance: A review of applications in human gait analysis and future directions for sports biomechanics, Journal of Sports Sciences, 13(3), 229-237.10.1080/026404195087322327563290Search in Google Scholar

Leicht, A. S., Gómez, M. A., & Woods, C. T. (2017). Explaining match outcome during the men’s basketball tournament at the olympic games. Journal of Sports Science and Medicine, 16(4), 468–473.Search in Google Scholar

Link, D. (2014). A toolset for beach volleyball game analysis based on object tracking. Int. J. Comp. Sci. Sport 13, 24–35Search in Google Scholar

Link, D., & Hoernig, M. (2017). Individual ball possession in soccer. PLoS ONE. 12(7): e0179953. https://doi.org/10.1371/journal.pone.017995310.1371/journal.pone.0179953550322528692649Search in Google Scholar

Link, D. (2018). Data Analytics in Professional Soccer. Springer Fachmedien Wiesbaden.10.1007/978-3-658-21177-6Search in Google Scholar

López-Valenciano A., Ayala F., Puerta J.M., De Ste Croix M., Vera-Garcia F.J., Hernández-Sánchez S., Ruiz-Pérez I., & Myer G.D. (2018). A preventive model for muscle injuries: a novel approach based on learning algorithms. Med Sci Sports Exerc. 50(5). 915–92710.1249/MSS.0000000000001535658236329283933Search in Google Scholar

Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities improve neural network acoustic models. In Proc. Icml. 30(1), 3-9.Search in Google Scholar

Melis, D. A., & Jaakkola, T. (2018). Towards robust interpretability with self-explaining neural networks. In Advances in Neural Information Processing Systems (pp. 7775-7784).Search in Google Scholar

Net gains - the evolution of beach volleyball. (2016, July 21). Retrieved from https://www.olympic.org/news/net-gains-the-evolution-of-beach-volleyballSearch in Google Scholar

Parmar, N., James, N., Hughes, M., Jones, H., & Hearne, G. (2017). Team performance indicators that predict match outcome and points difference in professional rugby league. International Journal of Performance Analysis in Sport, 17(6), 1044–1056.10.1080/24748668.2017.1419409Search in Google Scholar

Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., ... & Lerer, A. (2017). Automatic differentiation in PyTorch. In Long Beach, California, USA: Autodiff Workshop.Search in Google Scholar

Perl, J., & Memmert, D. (2011). Net-Based Game Analysis by Means of the Software Tool SOCCER. International Journal of Computer Science in Sport, 10(2), 77–84.Search in Google Scholar

Peterson, K. D. (2018) Recurrent Neural Network to Forecast Sprint Performance, Applied Artificial Intelligence, 32(7-8), 692-70610.1080/08839514.2018.1505214Search in Google Scholar

Rosenblatt, F. (1961). Principles of neurodynamics. perceptrons and the theory of brain mechanisms (No. VG-1196-G-8). Cornell Aeronautical Lab Inc Buffalo NY.Search in Google Scholar

Russell, S., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach - Third Edition. Pearson.Search in Google Scholar

Schrapf, N., Alsaied, S., & Tilp, M. (2017). Tactical interaction of offensive and defensive teams in team handball analysed by artificial neural networks. Mathematical and Computer Modelling of Dynamical Systems, 23(4), 363–371.10.1080/13873954.2017.1336733Search in Google Scholar

Smith, L. N. (2017). Cyclical learning rates for training neural networks. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 464-472). IEEE.10.1109/WACV.2017.58Search in Google Scholar

Smith, L. N. (2018). A disciplined approach to neural network hyper-parameters: Part 1--learning rate, batch size, momentum, and weight decay. arXiv preprint arXiv:1803.09820.Search in Google Scholar

Tümer, A. E., & Koçer, S. (2017). Prediction of team league’s rankings in volleyball by artificial neural network method. International Journal of Performance Analysis in Sport, 17(3), 202–211.10.1080/24748668.2017.1331570Search in Google Scholar

Vales-Alonso, J., Chaves-Dieguez, D., Lopez-Matencio, P., Alcaraz, J. J., Parrado-Garcia, F. J., & Gonzalez-Castano, F. J. (2015). SAETA: A Smart Coaching Assistant for Professional Volleyball Training. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(8), 1138–1150.Search in Google Scholar

Van Haaren, J., Ben Shitrit, H., Davis, J., & Fua, P. (2016). Analyzing volleyball match data from the 2014 world championships using machine learning techniques. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, (pp. 627-634)., New York, NY, USA. ACM.10.1145/2939672.2939725Search in Google Scholar

Wang, K., & Zemel, R. (2016). Classifying NBA Offensive Plays Using Neural Networks. MIT Sloan Sports Analytics Conference, 1–9.Search in Google Scholar

Wang, Y., Zhao, Y., Chan, R. H. M., & Li, W. J. (2018). Volleyball Skill Assessment Using a Single Wearable Micro Inertial Measurement Unit at Wrist. IEEE Access, 6, 13758–13765.10.1109/ACCESS.2018.2792220Search in Google Scholar

Weissbock, J., & Inkpen, D. (2014). Combining textual pre-game reports and statistical data for predicting success in the national hockey league. In Canadian Conference on Artificial Intelligence (pp. 251-262). Springer, Cham.10.1007/978-3-319-06483-3_22Search in Google Scholar

Zetou, E., Moustakidis, A., Tsigilis, N., & Komninakidou, A. (2007). Does Effectiveness of Skill in Complex I Predict Win in Men’s Olympic Volleyball Games? Journal of Quantitative Analysis in Sports, 3(4).10.2202/1559-0410.1076Search in Google Scholar

eISSN:
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Langue:
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Sujets de la revue:
Computer Sciences, Databases and Data Mining, other, Sports and Recreation, Physical Education