Prediction of The Optimal Performance of Running Athletes
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Abstract
This research is a scientific study between disciplines, especially mathematics and sports. The problem in this research is how to model the optimal time prediction for the performance of athletic athletes, especially running, based on data from national and international championship results. The aim of this research is to find a prediction model for optimal performance times for running athletes based on national and international time record data. The solving method in this research uses mathematical methods, namely the least squares method and forecasting with the help of Excel and SPSS programs. Through this research, it is hoped that it will become input for scientific information, especially for athletics (running) so that it can predict athlete performance based on optimal time prediction models so that training targets are well directed. Athletes can provide positive motivation from these predictions. Practically, for coaches, teachers and sports coaches, athletics can be a basis for creating training programs to create better performance. The optimal time prediction model produced in this research can provide an overview of running achievements both nationally and internationally. Apart from that, it can also be used to compare national and international achievements so that it can be used as evaluation material. The optimal time prediction model for running athletes' performance for short distances of 100m and 200m was obtained with a model that was significant at the 0.05 level of significance.
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References
Clemen, R. T. (1989). Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5(4), 559–583.
Deb, C., Zhang, F., Yang, J., Lee, S. E., & Shah, K. W. (2017). A review on time series forecasting techniques for building energy consumption. Renewable and Sustainable Energy Reviews, 74, 902–924.
Gaudet, S. (2014). A physical model of sprinting. Journal of Biomechanics, 47(12), 2933–2940.
Heazlewood, T. (2006). Prediction versus reality: The use of mathematical models to predict elite performance in swimming and athletics at the Olympic Games. Journal of Sports Science & Medicine, 5(4), 480.
Hidayah, N. (2010). MODEL PREDIKSI WAKTU OPTIMAL PADA CABANG OLAHRAGA RENANG BERDASARKAN HASIL KEJUARAAN DUNIA. Jurnal Kepelatihan Olahraga, 2(1), 69–75.
Keller, J. B. (1973). A theory of competitive running. Phys. Today, 26(9), 43–47.
Mureika, J. R. (1997). A simple model for predicting sprint-race times accounting for energy loss on the curve. Canadian Journal of Physics, 75(11), 837–851.
Mureika, J. R. (1998). How Good Can We Get? Using mathematical models to predict the future of athletics. ArXiv Preprint Physics/9803034.
Péronnet, F., & Thibault, G. (1989). Mathematical analysis of running performance and world running records. Journal of Applied Physiology, 67(1), 453–465.
Vandewalle, H. (2017). Mathematical modeling of the running performances in endurance exercises: comparison of the models of Kennelly and Péronnet-Thibaut for World records and elite endurance runners. Distances (M), 3000(5000), 10000.
Woodside, W. (1991). The optimal strategy for running a race (a mathematical model for world records from 50 m to 275 km). Mathematical and Computer Modelling, 15(10), 1–12.