Abstract
Artificial intelligence methods are successfully applied in many areas where a prediction or classification is needed. An example may also be the forecasting of an athlete’s performance, investigated in this article. Powerlifting is a sport of widespread popularity - more and more training people are serious about the competition and prepare professionally for it. This paper aims to present and analyze the athlete’s deadlift score prediction system based on the previous results and the historical data of other lifters. At first, we use the parametrics to choose an athlete with the results similar to the investigated lifter. We propose the artificial neural network application for smoothing empirical hazard and cumulative distribution functions designated for the failed deadlift attempts. We decided to involve quasi-RBF neural networks – involving the sigmoid function and nonlinear least squares learning algorithm. As a result we get the prediction whether the athlete’s deadlift attempt will be valid or not.
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Notes
- 1.
This should be the minimal requirement in any success/failure prediction.
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Rafajłowicz, W., Marszałek, J. (2021). Intelligent Performance Prediction for Powerlifting. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12854. Springer, Cham. https://doi.org/10.1007/978-3-030-87986-0_42
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DOI: https://doi.org/10.1007/978-3-030-87986-0_42
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