Using Machine Learning Algorithms for Jumping Distance Prediction of Male Long Jumpers

Öz

The long jump is defined as an athletic event, and it has also been a standard event in modern Olympic Games. The purpose of the athletes is to make the distance as far as possible from a jumping point. The main purpose of this study was to determine the most successful machine learning algorithm in the prediction of the long jump distance of male athletes. In this paper, we used age and velocity variables for predicting the long jump performance of athletes. During the research, 328 valid jumps belonging to 73 Turkish male athletes were used as data. In determining the most successful algorithm, mean absolute error (MAE), root mean square error (RMSE), Mean Squared Error (MSE), R2 score, Explained Variance Score (EVS), and Mean Squared Logarithmic Error (MSLE) values were taken into consideration. The outcomes of the analysis showed that long jump performance can be determined by chosen independent variables. The 5-fold cross-validation technique was used for the performance evaluation of the models. As a result of the experimental tests, the Gradient Boosting Regression Trees (GBRT) algorithm reached the best result with an MSE value of 0.0865. In this study, it was concluded that the machine learning approach suggested can be used by trainers to determine the long jump performance of male athletes.

Anahtar Kelimeler

• Long jump performance
• machine learning
• run-up velocity

Kaynakça

1. Açıkada, C., Arıtan, S., & Yazıcıoğlu, M. V. (1993). Balkan Gençler Şampiyonası Uzun Atlama Yaklaşma Koşusunun Analizi. [Analysis of the 1992 Balkan Junior Championship Long Jump Approach Run.]. Atlet Bilim ve Teknoloji Dergisi, 9, pp. 34-40.
2. Bayraktar, I., & Çilli, M. (2018). Estimation of jumping distance using run-up velocity for male long jumpers. Pedagogics, psychology, medical-biological problems of physical training, 22(3), pp. 124-129. https://doi.org/10.15561/18189172.2018.0302
3. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
4. Bridgett, L. A., Galloway, M., & Linthorne, N. P. (2002). The effect of run-up speed on long jump performance. ISBS-Conference Proceedings Archive.
5. Bridgett, L. A., & Linthorne, N. P. J. J. o. s. s. (2006). Changes in long jump take-off technique with increasingrun-up speed. 24(8), pp. 889-897. https://doi.org/10.1080/02640410500298040
6. Bunker, R., & Susnjak, T. (2022). The Application of Machine Learning Techniques for Predicting Match Results in Team Sport: A Review. Journal of Artificial Intelligence Research, 73, 1285-1322. https://doi.org/10.1613/jair.1.13509
7. Cox, L. A. (2002). Data mining and causal modeling of customer behaviors. Telecommunication Systems, 21(2-4), pp. 349-381. https://doi.org/10.1023/A:1020911018130
8. Derse, E., Hansen, J., Tim, O., & Stolley, S. (2012). Track and Field Coaching Manual: LA84 Foundation.

9. Eetvelde, H., Mendonça, L. D., Ley, C., Seil, R., & Tischer, T. (2021). Machine Learning Methods In Sport Injury Prediction And Prevention: A Systematic Review. Journal of Experimental Orthopaedics, 8(1), 1-15. https://doi.org/10.1186/s40634-021-00346-x
10. Fukasiro, S., & Wakavama, A. (1992). The men’s long jump. New Studies in Athletics, 7(1), pp. 53-56.
11. Fuller, D., Ferber, R., & Stanley, K. (2022). Why Machine Learning (ML) Has Failed Physical Activity Research and How We Can Improve. BMJ Open Sport & Exercise Medicine, 8(1), e001259. http://dx.doi.org/10.1136/bmjsem-2021-001259
12. Hay, J. G. (1986). The Biomechanics of the Long Jump. Exercise and Sport Sciences Reviews/Series, 14, pp. 401-446. Retrieved from ://WOS:A1986E165200014
13. Hay, J. G. (1993). Citius, Altius, Longius (Faster, Higher, Longer) - the Biomechanics of Jumping for Distance. Journal of Biomechanics, 26, pp. 7-21. https://doi.org/10.1016/0021-9290(93)90076-Q
14. Hay, J. G., & Miller, J. A. (1985). Techniques Used in the Transition from Approach to Takeoff in the Long Jump. International Journal of Sport Biomechanics, 1(2), pp. 174-184. doi:10.1123/ijsb.1.2.174
15. Hay, J. G., Miller, J. A., & Canterna, R. W. (1986). The Techniques of Elite Male Long Jumpers. Journal of Biomechanics, 19(10), pp. 855-866. https://doi.org/10.1016/0021-9290(86)90136-3
16. Haykin, S. S. (2009). Neural networks and learning machines: Pearson education Upper Saddle River, NJ.
17. Hommel, H. (2009). Long Jump (Final Report) - Scientific Research Project Biomechanical Analyses at the IAAF World CH in Athletics Berlin 2009. https://www.iaaf.org/development/research
18. Hornik, K., Stinchcombe, M., & White, H. (1990). Universal Approximation of an Unknown Mapping and Its Derivatives Using Multilayer Feedforward Networks. Neural Networks, 3(5), pp. 551-560. https://doi.org/10.1016/0893-6080(90)90005-6
19. Lees, A., Graham-Smith, P., & Fowler, N. J. J. o. a. B. (1994). A biomechanical analysis of the last stride, touchdown, and takeoff characteristics of the men's long jump. 10(1), pp. 61-78. https://doi.org/10.1123/jab.10.1.61
20. Linthorne, N. P. (2008). Routledge Handbook of Biomechanics and Human Movement Science: Taylor & Francis. https://doi.org/10.4324/9780203889688
21. Maier, T., Meister, D., Trösch, S., & Wehrlin, J. P. (2018). Predicting biathlon shooting performance using machine learning. Journal of sports sciences, 36(20), pp. 2333-2339. https://doi.org/10.1080/02640414.2018.1455261
22. Mikhailov, N. G., Yakunin, N. A., & Aleshinsky, S. Y. (1981). Biomechanical assesment of take-off in long jump. Teoria i Praktika Fizicheskoi Kultury, 5, pp. 13-15.
23. Mishra, M. K., & Rathore, V. S. (2016). Speed and agility as predictors of long jump performance of male athletes. Turkish Journal of Sport and Exercise, 18(2), pp. 27-33.
24. Moura, N. A., Moura, T. F., & Borin, J. P. (2005). Approach speed and performance in the horizontal jumps: What do Brazilian athletes do? IAF NEW STUDIES IN ATHLETICS, 20(3), pp. 43-48.
25. Musa, R. M., Anwar, P. P. A. M., Taha, Z., Chang, S. W., Fakhri, A. N. A., & Abdullah, M. R. (2019). A machine learning approach of predicting high potential archers by means of physical fitness indicators. PLOS ONE, 14(1), pp. 1-12. https://doi.org/10.1371/journal.pone.0209638
26. Ofoghi, B., Zeleznikow, J., MacMahon, C., & Dwyer, D. (2010). A Machine Learning Approach to Predicting Winning Patterns in Track Cycling Omnium. IFIP Advances in Information and Communication Technology presented at the meeting of Third IFIP TC12 International Conference on Artificial Intelligence (AI) / Held as Part of World Computer Congress (WCC), Brisbane, Australia. https://doi.org/10.1007/978-3-642-15286-3_7
27. Rahim, M. A., Lee, E. L. Y., Malek, N. F., Suwankhong, D., & Nadzalan, A. M. (2020). Relationship Between Physical Fitness and Long Jump Performance. International Journal of Scientific & Technology Research, 9(4):1795-1797.
28. Schulek, A. (2002). Long jump with supramaximal and normal speed. IAF NEW STUDIES IN ATHLETICS, 17(2), pp. 37-46.
29. Takahashi, K., & Wakahara, T. (2019). Association Between Trunk And Gluteus Muscle Size And Long Jump Performance. PloS one, 14(11), e0225413. https://doi.org/10.1371/journal.pone.0225413
30. Tiupa, V., Aleshinsky, S., Primakov, I., & Pereverzev, A. (1982). The biomechanics of the movement of the body’s general centre of mass during the long jump. Teoria i Praktika Fizicheskoi Kultury, 5, pp. 21-32.
31. Whiteside, D., Cant, O., Connolly, M., & Reid, M. (2017). Monitoring Hitting Load in Tennis Using Inertial Sensors and Machine Learning. International Journal of Sports Physiology and Performance, 12(9), pp. 1212-1217. https://doi.org/10.1123/ijspp.2016-0683