Predicting eggbeater kick performances from hip joint function testing in artistic swimming
DOI:
https://doi.org/10.51224/SRXIV.227Keywords:
artistic swimming, eggbeater kick, hip joint function, machine learning, performanceAbstract
The eggbeater kick is an important skill in artistic swimming necessary to lift the body above water level. Previous attempts to model its performance included complex biomechanical parameters that cannot be easily used to guide strength and conditioning training. The objective of this study was to model the relationship between hip strength and eggbeater performance through a machine learning algorithm. We assessed hip function of 92 elite artistic swimmers with six easily performed isometric tests. These data were fed to a gradient boosting model to predict three technical variables: body boost height [BB-H], eggbeater height [EB-H] and eggbeater force [EB-F]. Group mean differences () between predicted and measured variables were reported. Then, the model was used to propose training tips for two hypothetical case studies. Our model predicted performances with errors within the resolution of the scale used during competitions: absolute error of and in EB-H and BB-H, respectively. The predicted performance was similar to the measured one for all technical tests (EB-F: ; EB-H: ; BB-H: ). We illustrated some of the important predictors (hip internal rotation, abduction, and left-right imbalances) of the eggbeater kick performance and highlighted personalized strategies to improve performance.
Metrics
References
Alentejano T, Marshall D, Bell G. A Time–Motion Analysis of Elite Solo Synchronized Swimming. International Journal of Sports Physiology and Performance. 2008;3(1):31-40. doi:10.1123/ijspp.3.1.31
Sanders RH. Analysis of the Eggbeater Kick Used to Maintain Height in Water Polo. Journal of Applied Biomechanics. 1999;15(3):284-291. doi:10.1123/jab.15.3.284
Homma M, Homma M. Coaching Points for the Technique of the Eggbeater Kick in Synchronized Swimming Based on Three-Dimensional Motion Analysis. Sports Biomechanics. 2005;4(1):73-87. doi:10.1080/14763140508522853
van Duijn T, Button C, Masters RSW. A Multidisciplinary Comparison of Different Techniques Among Skilled Water Treaders. Frontiers in Physiology. 2021;12. Accessed May 3, 2022. https://www.frontiersin.org/article/10.3389/fphys.2021.719788
FINA. FINA Artistic Swimming Manual for Judges, Coaches and Referees. Fédération Internationale de Natation; 2018.
Sanders RH. A Model of Kinematic Variables Determining Height Achieved in Water Polo Boosts. Journal of Applied Biomechanics. 1999;15(3):270-283. doi:10.1123/jab.15.3.270
Sanders R. Strength, flexibility and timing in the eggbeater kick. In: ; 2002.
Zinner C, Sperlich B, Krueger M, Focke T, Reed J, Mester J. Strength, Endurance, Throwing Velocity and in-Water Jump Performance of Elite German Water Polo Players. Journal of Human Kinetics. 2015;45(1):149-156. doi:10.1515/hukin-2015-0015
Oliveira N, Sanders RH. Kinematic and kinetic evidence for functional lateralization in a symmetrical motor task: the water polo eggbeater kick. Exp Brain Res. 2015;233(3):947-957. doi:10.1007/s00221-014-4166-8
Oliveira N, Saunders DH, Sanders RH. The Effect of Fatigue-Induced Changes in Eggbeater-Kick Kinematics on Performance and Risk of Injury. International Journal of Sports Physiology and Performance. 2016;11(1):141-145. doi:10.1123/ijspp.2015-0057
Maier T, Meister D, Trösch S, Wehrlin JP. Predicting biathlon shooting performance using machine learning. Journal of Sports Sciences. 2018;36(20):2333-2339. doi:10.1080/02640414.2018.1455261
Pantazopoulos A, Maragoudakis M. Sports & Nutrition Data Science using Gradient Boosting Machines. In: Proceedings of the 10th Hellenic Conference on Artificial Intelligence. SETN ’18. Association for Computing Machinery; 2018:1-7. doi:10.1145/3200947.3201060
Ofoghi B, Zeleznikow J, Dwyer D, Macmahon C. Modelling and analysing track cycling Omnium performances using statistical and machine learning techniques. Journal of Sports Sciences. 2013;31(9):954-962. doi:10.1080/02640414.2012.757344
Abut F, Akay MF. Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances. MDER. 2015;8:369-379. doi:10.2147/MDER.S57281
Natekin A, Knoll A. Gradient boosting machines, a tutorial. Frontiers in Neurorobotics. 2013;7. Accessed May 5, 2022. https://www.frontiersin.org/article/10.3389/fnbot.2013.00021
Lundberg SM, Lee SI. A Unified Approach to Interpreting Model Predictions. In: Guyon I, Luxburg UV, Bengio S, et al., eds. Advances in Neural Information Processing Systems. Vol 30. Curran Associates, Inc.; 2017:4765-4774. https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf
Desmyttere G, Gaudet S, Begon M. Test-retest reliability of a hip strength assessment system in varsity soccer players. Physical Therapy in Sport. 2019;37:138-143. doi:10.1016/j.ptsp.2019.03.013
Dorogush AV, Ershov V, Gulin A. CatBoost: gradient boosting with categorical features support. arXiv:181011363 [csLG]. Published online October 24, 2018. Accessed May 3, 2022. http://arxiv.org/abs/1810.11363
Kruschke JK. Bayesian estimation supersedes the t test. J Exp Psychol Gen. 2013;142(2):573-603. doi:10.1037/a0029146
Nebro AJ, Durillo JJ, Garcia-Nieto J, Coello Coello CA, Luna F, Alba E. SMPSO: A new PSO-based metaheuristic for multi-objective optimization. In: 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making(MCDM). ; 2009:66-73. doi:10.1109/MCDM.2009.4938830
Cichanowski HR, Schmitt JS, Johnson RJ, Niemuth PE. Hip Strength in Collegiate Female Athletes with Patellofemoral Pain. Medicine & Science in Sports & Exercise. 2007;39(8):1227-1232. doi:10.1249/mss.0b013e3180601109
Scott DA, Bond EQ, Sisto SA, Nadler SF. The intra- and interrater reliability of hip muscle strength assessments using a handheld versus a portable dynamometer anchoring station. Archives of Physical Medicine and Rehabilitation. 2004;85(4):598-603. doi:10.1016/j.apmr.2003.07.013
Stirn I, Strmecki J, Strojnik V. The Examination of Different Tests for the Evaluation of the Efficiency of the Eggbeater Kicks. Journal of Human Kinetics. 2014;41(1):215-226. doi:10.2478/hukin-2014-0049
Yanagi H, Amano K, Sakai T, Hara A. Vertical Force Exerted during Eggbeater Kick in Water Polo. In: ; 1995:1016-1017.
Bernards JR, Sato K, Haff GG, Bazyler CD. Current Research and Statistical Practices in Sport Science and a Need for Change. Sports. 2017;5(4):87. doi:10.3390/sports5040087
Carter JEL, Ackland TR. Kinanthropometry in Aquatic Sports: A Study of World Class Athletes. Human Kinetics; 1994.
Grimston SK, Hay JG. Relationships among anthropometric and stroking characteristics of college swimmers. Medicine & Science in Sports & Exercise. 1986;18(1):60-68.
Yamamura C, Zushi S, Takata K, Ishiko T, Matsui N, Kitagawa K. Physiological Characteristics of Well-Trained Synchronized Swimmers in Relation to Performance Scores. Int J Sports Med. 1999;20(04):246-251. doi:10.1055/s-2007-971125
Oliveira N, Fernandes R, Sarmento M, et al. Muscle Activity During the Typical Water Polo Eggbeater Kick. International Journal of Aquatic Research and Education. 2010;4(2). doi:https://doi.org/10.25035/ijare.04.02.07
Downloads
Posted
License
Copyright (c) 2022 Romain Martinez, Najoua Assila, Élodie Monga-Dubreuil , Gauthier Desmyttere, Mickaël Begon
This work is licensed under a Creative Commons Attribution 4.0 International License.
