A Meta-Analytic Approach to Swimming Performance Prediction: Reviewing Methods, Datasets, and Research Trends
DOI:
https://doi.org/10.26740/ijok.v5n2.p53-71Keywords:
XAI; PRISMA; PICOC; statisticsAbstract
Background: Pico seems likely to be successful in competitive sports, particularly swimming, including the next Olympic swimming competition. The current manuscript offers a detailed insight into research on the prediction of swimming performance, between 2014 and 2024.
Methods: This Swimming Performance Prediction research used the Systematic Literature Review (SLR) approach. Furthermore, to narrow down the articles relevant to research topics reviewed, this study adhered to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) when performing the systematic review. We find 21 journal publications from the representative studies for seeking identification and analysis for describing research topics or trends, datasets, techniques, methods, evaluations and problems in this research field.
Results: The analyses presented provide detailed information on the topics and trends under investigation in the field of predictions for the prediction of swimming performance, reference to public datasets and the techniques and method often used in comparisons between researchers respectively.
Conclusions: Swimming performance prediction plays an important role in improving training programs, guiding athlete selection, and evaluating progress.
References
Abbott, S., Moulds, K., Salter, J., Romann, M., Edwards, L., & Cobley, S. (2020). Testing the application of corrective adjustment procedures for removal of relative age effects in female youth swimming. Journal of Sports Sciences, 38(10), 1077–1084. https://doi.org/10.1080/02640414.2020.1741956
Amara, S., Chortane, O. G., Negra, Y., Hammami, R., Khalifa, R., Chortane, S. G., & van den Tillaar, R. (2021). Relationship between swimming performance, biomechanical variables and the calculated predicted 1-rm push-up in competitive swimmers. International Journal of Environmental Research and Public Health, 18(21). https://doi.org/10.3390/ijerph182111395
Apriyano, B., Zainuddin, Z. A., Hashim, A. H. M. H., Sayyd, S. M., Mazlan, A. N., Wenando, F. A., Argantos, Ockta, Y., & Anisa, M. F. (2025). Endurance of leg muscle strength and endurance of arm muscle strength to the ability of swimming speed 200 meters breaststroke. Retos, 62, 327–334. https://doi.org/10.47197/retos.v62.109079
Armen, M., Neldi, H., Suud, A., & Alben, C. (2024). The differences effects of lunges vs. squats exercise programs on the swimming speed 50-meters butterfly style: a quasi-experimental study. 2041(2014), 1344–1350.
Banister, E. W., & Calvert, T. W. (1980). Planning for future performance: implications for long term training. Canadian Journal of Applied Sport Sciences. Journal Canadien Des Sciences Appliquees Au Sport, 5(3), 170–176. http://europepmc.org/abstract/MED/6778623
Born, D. P., Stöggl, T., Lorentzen, J., Romann, M., & Björklund, G. (2024). Predicting future stars: Probability and performance corridors for elite swimmers. Journal of Science and Medicine in Sport, 27(2), 113–118. https://doi.org/10.1016/j.jsams.2023.10.017
Busso, T., Denis, C., Bonnefoy, R., Geyssant, A., & Lacour, J. R. (1997). Modeling of adaptations to physical training by using a recursive least squares algorithm. Journal of Applied Physiology, 82(5), 1685–1693. https://doi.org/10.1152/jappl.1997.82.5.1685
Busso, T., Häkkinen, K., Pakarinen, A., Carasso, C., Lacour, J. R., Komi, P. V, & Kauhanen, H. (1990). A systems model of training responses and its relationship to hormonal responses in elite weight-lifters. European Journal of Applied Physiology and Occupational Physiology, 61(1), 48–54. https://doi.org/10.1007/BF00236693
Carvalho, D. D., Goethel, M. F., Silva, A. J., Vilas-Boas, J. P., Pyne, D. B., & Fernandes, R. J. (2024). Swimming Performance Interpreted through Explainable Artificial Intelligence (XAI)—Practical Tests and Training Variables Modelling. Applied Sciences (Switzerland), 14(12), 1–15. https://doi.org/10.3390/app14125218
Chatard, J. C., & Stewart, A. M. (2011). Training load and performance in swimming. World Book of Swimming: From Science to Performance, January 2011, 359–373.
Costa, M. J., Marinho, D. A., Reis, V. M., Silva, A. J., Marques, M. C., Bragada, J. A., & Barbosa, T. M. (2010). Tracking the performance of world-ranked swimmers. Journal of Sports Science and Medicine, 9(3), 411–417.
Crowley, E., Ng, K., Mujika, I., & Powell, C. (2022). Speeding up or Slowing Down? Analysis of Race Results in Elite-level Swimming from 2011-2019 to Predict Future Olympic Games Performances. Measurement in Physical Education and Exercise Science, 26(2), 130–140. https://doi.org/10.1080/1091367X.2021.1952592
de Anda Martín, I. O., Pérez, I. M., Mena, E. B., & Del Arco Paniagua, A. (2024). Impact of psychological and physiological factors on endurance performance prediction. Retos, 61(2010), 774–784. https://doi.org/10.47197/retos.v61.106874
de Jesus, K., de Jesus, K., Ayala, H. V. H., dos Santos Coelho, L., Vilas-Boas, J. P., & Fernandes, R. J. P. (2019). Predicting centre of mass horizontal speed in low to severe swimming intensities with linear and non-linear models. Journal of Sports Sciences, 37(13), 1512–1520. https://doi.org/10.1080/02640414.2019.1574949
Demarie, S., Chirico, E., & Galvani, C. (2022). Prediction and Analysis of Tokyo Olympic Games Swimming Results: Impact of the COVID-19 Pandemic on Swimmers’ Performance. International Journal of Environmental Research and Public Health, 19(4). https://doi.org/10.3390/ijerph19042110
Demirkan, E., Özkadi, T., Alagöz, I., Çağlar, E. Ç., & Çamiçi, F. (2023). Age-related physical and performance changes in young swimmers: The comparison of predictive models in 50-meter swimming performance. Baltic Journal of Health and Physical Activity, 15(2). https://doi.org/10.29359/BJHPA.15.2.04
Donato, A. J., Tench, K., Glueck, D. H., Seals, D. R., Eskurza, I., & Tanaka, H. (2003). Declines in physiological functional capacity with age: A longitudinal study in peak swimming performance. Journal of Applied Physiology, 94(2), 764–769. https://doi.org/10.1152/japplphysiol.00438.2002
Dormehl, S. J., Robertson, S. J., Barker, A. R., & Williams, C. A. (2017). Confirming the Value of Swimming-Performance Models for Adolescents. International Journal of Sports Physiology and Performance, 12(9), 1177–1185. https://doi.org/10.1123/ijspp.2016-0506
Edelmann-Nusser, J., Hohmann, A., & Henneberg, B. (2002). Modeling and prediction of competitive performance in swimming upon neural networks. European Journal of Sport Science, 2(2), 1–10. https://doi.org/10.1080/17461390200072201
Espada, M. C., Santos, F. J., Conceição, A., Louro, H., Ferreira, C. C., Reis, J. F., Pessôa-Filho, D. M., & Pereira, A. (2022). The Effects of 12 Weeks In-Water Training in Stroke Kinematics, Dry-Land Power, and Swimming Sprints Performance in Master Swimmers. Journal of Men’s Health, 18(9). https://doi.org/10.31083/j.jomh1809186
Fitz-Clarke JR, Morton, R. H., & Banister, E. W. (1991). Optimizing athletic performance by influence curves. Journal of Thermal Biology, 71:11, 51–58. https://doi.org/https://doi.org/10.1152/jappl.1991.71.3.1151
Fone, L., & van den Tillaar, R. (2022). Effect of Different Types of Strength Training on Swimming Performance in Competitive Swimmers: A Systematic Review. Sports Medicine - Open, 8(1). https://doi.org/10.1186/s40798-022-00410-5
Guo, W., Soh, K. G., Zakaria, N. S., Hidayat Baharuldin, M. T., & Gao, Y. (2022). Effect of Resistance Training Methods and Intensity on the Adolescent Swimmer’s Performance: A Systematic Review. Frontiers in Public Health, 10(April), 1–10. https://doi.org/10.3389/fpubh.2022.840490
Gurevitch, J., Koricheva, J., Nakagawa, S., & Stewart, G. (2018). Meta-analysis and the science of research synthesis. Nature, 555(7695), 175–182. https://doi.org/10.1038/nature25753
Hamidi Rad, M., Aminian, K., Gremeaux, V., Massé, F., & Dadashi, F. (2021). Swimming Phase-Based Performance Evaluation Using a Single IMU in Main Swimming Techniques. Frontiers in Bioengineering and Biotechnology, 9(December), 1–10. https://doi.org/10.3389/fbioe.2021.793302
Hohmann, A. (1992). Analysis of delayed training effects in the preparation of the West german water polo team for the 1988 Olympic Games. Biomechanics and Medicine in Swimming. Swimming Science VI, 213–218. https://open-archive.sport-iat.de/bms/6_213-217_Hohmann.pdf
Hołub, M., Stanula, A., Baron, J., Głyk, W., Rosemann, T., & Knechtle, B. (2021). Predicting breaststroke and butterfly stroke results in swimming based on Olympics history. Int. J. Environ. Res. Public Health, 18(12), 6621. https://doi.org/10.3390/ijerph18126621
Kitchenham, B., & Charters, S. M. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report, Ver. 2.3 EBSE Technical Report. EBSE, January 2007.
Lima, L. P., Pontes-Silva, A., Oliveira, I. P. de, Quaresma, F. R. P., & Maciel, E. da S. (2025). Mathematical model for predicting handgrip strength in Quilombola children and adolescents: a cross-sectional study. Retos, 62, 26–30. https://doi.org/10.47197/retos.v62.108681
Liu, C., Xu, B., Wan, K., Sun, Q., Wang, R., Feng, Y., Shao, H., Liu, T., & Wang, R. (2024). Improved prediction of swimming talent through random forest analysis of anthropometric and physiological phenotypes. Phenomics, 4(5), 465–472. https://doi.org/10.1007/s43657-024-00176-8
Lobato, C. H., de Lima Rocha, M., de Almeida-Neto, P. F., & de Araújo Tinôco Cabral, B. G. (2023). Influence of advancing biological maturation in months on muscle power and sport performance in young swimming athletes. Sport Sciences for Health, 19(2), 487–494. https://doi.org/10.1007/s11332-022-01026-8
Mabweazara, S. Z., Leach, L., & Andrews, B. S. (2017). Predicting swimming performance using state anxiety. South African Journal of Psychology, 47(1), 110–120. https://doi.org/10.1177/0081246316645060
Marinho, D. A., Ferreira, M. I., Barbosa, T. M., Vilaça-Alve, J., Costa, M. J., Ferraz, R., & Neiva, H. P. (2020). Energetic and biomechanical contributions for longitudinal performance in master swimmers. Journal of Functional Morphology and Kinesiology, 5(2). https://doi.org/10.3390/jfmk5020037
Mitchell, L. J. G., Rattray, B., Fowlie, J., Saunders, P. U., & Pyne, D. B. (2020). The impact of different training load quantification and modelling methodologies on performance predictions in elite swimmers. European Journal of Sport Science, 20(10), 1329–1338. https://doi.org/10.1080/17461391.2020.1719211
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Antes, G., Atkins, D., Barbour, V., Barrowman, N., Berlin, J. A., Clark, J., Clarke, M., Cook, D., D’Amico, R., Deeks, J. J., Devereaux, P. J., Dickersin, K., Egger, M., Ernst, E., Gøtzsche, P. C., … Tugwell, P. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7). https://doi.org/10.1371/journal.pmed.1000097
Morais, J. E., Barbosa, T. M., Forte, P., Bragada, J. A., Castro, F. A. d. S., & Marinho, D. A. (2023). Stability analysis and prediction of pacing in elite 1500 m freestyle male swimmers. Sports Biomechanics, 22(11), 1496–1513. https://doi.org/10.1080/14763141.2020.1810749
Morais, J. E., Barbosa, T. M., Forte, P., Silva, A. J., & Marinho, D. A. (2021). Young Swimmers’ Anthropometrics, Biomechanics, Energetics, and Efficiency as Underlying Performance Factors: A Systematic Narrative Review. Frontiers in Physiology, 12(September). https://doi.org/10.3389/fphys.2021.691919
Morais, J. E., Barbosa, T. M., Gonjo, T., & Marinho, D. A. (2023). Using Statistical Parametric Mapping as a statistical method for more detailed insights in swimming: a systematic review. Frontiers in Physiology, 14(June), 1–12. https://doi.org/10.3389/fphys.2023.1213151
Morais, J. E., Barbosa, T. M., Neiva, H. P., Marques, M. C., & Marinho, D. A. (2022). Young Swimmers’ Classification Based on Performance and Biomechanical Determinants: Determining Similarities Through Cluster Analysis. Motor Control, 26(3), 396–411. https://doi.org/10.1123/mc.2021-0126
Mujika, I., Pyne, D. B., Wu, P. P.-Y., Ng, K., Crowley, E., & Powell, C. (2023). Next-generation models for predicting winning times in elite swimming events: Updated predictions for the Paris 2024 Olympic Games. Int. J. Sports Physiol. Perform., 18(11), 1269–1274. https://doi.org/10.1123/ijspp.2023-0174
Nicol, E., Pearson, S., Saxby, D., Minahan, C., & Tor, E. (2022). Stroke Kinematics, Temporal Patterns, Neuromuscular Activity, Pacing and Kinetics in Elite Breaststroke Swimming: A Systematic Review. Sports Medicine - Open, 8(1). https://doi.org/10.1186/s40798-022-00467-2
Nugent, F., Comyns, T., Kearney, P., & Warrington, G. (2019). Ultra-Short Race-Pace Training (USRPT) In Swimming: Current Perspectives. Open Access Journal of Sports Medicine, 10(October), 133–144. https://doi.org/10.2147/OAJSM.S180598
Nurmukhanbetova, D., Gussakov, I., & Yermakhanova, A. (2023). The influence of the low-volume high-intensity method training on the indicators of speed and strength qualities of young high skill level swimmers. Retos, 50, 446–455. https://doi.org/10.47197/retos.v50.98492
Okoli, C., & Schabram, K. (2012). A Guide to Conducting a Systematic Literature Review of Information Systems Research. SSRN Electronic Journal, 10(2010). https://doi.org/10.2139/ssrn.1954824
Podrihalo, O., Podrigalo, L., Jagiełło, W., Iermakov, S., & Yermakova, T. (2021). Substantiation of methods for predicting success in artistic swimming. International Journal of Environmental Research and Public Health, 18(16). https://doi.org/10.3390/ijerph18168739
Post, A. K., Koning, R. H., Visscher, C., & Elferink-Gemser, M. T. (2022). The importance of reflection and evaluation processes in daily training sessions for progression toward elite level swimming performance. Psychology of Sport and Exercise, 61, 102219. https://doi.org/https://doi.org/10.1016/j.psychsport.2022.102219
Pyne, D. B., Trewin, C. B., & Hopkins, W. G. (2004). Progression and variability of competitive performance of Olympic swimmers. Journal of Sports Sciences, 22(7), 613–620. https://doi.org/10.1080/02640410310001655822
Rico-González, M., Pino-Ortega, J., Clemente, F. M., & Arcos, A. L. (2022). Guidelines for performing systematic reviews in sports science. Biology of Sport, 39(2), 463–471. https://doi.org/10.5114/BIOLSPORT.2022.106386
Sadewa, Y. R., Sumaryanto, S., Sumarjo, S., & Ika, K. Y. (2024). Relationship of muscle strength, power, and leg flexibility with the swim start of the butterfly style. Retos, 55, 163–169. https://doi.org/10.47197/retos.v55.103106
Santos, C. C., Fernandes, R. J., Marinho, D. A., & Costa, M. J. (2023). From Entry to Finals: Progression and Variability of Swimming Performance at the 2022 FINA World Championships. Journal of Sports Science and Medicine, July, 417–424. https://doi.org/10.52082/jssm.2023.417
Sridana, R., Tomoliyus, Sukamti, E. R., Prabowo, T. A., & Abrori, R. B. (2024). The Effect of Coaching Style on Performance of Athletes Through Anxiety as Mediating Variable in Adolescent Swimmers. Retos, 55, 241–248. https://doi.org/10.47197/RETOS.V55.103150
Staunton, C. A., Romann, M., Björklund, G., & Born, D.-P. (2024a). Diving into a pool of data: Using principal component analysis to optimize performance prediction in women’s short-course swimming. Journal of Sports Sciences, 42(6), 519–526. https://doi.org/10.1080/02640414.2024.2346670
Staunton, C. A., Romann, M., Björklund, G., & Born, D. P. (2024b). Diving into a pool of data: Using principal component analysis to optimize performance prediction in women’s short-course swimming. Journal of Sports Sciences, 42(6), 519–526. https://doi.org/10.1080/02640414.2024.2346670
Staunton, C. A., Romann, M., Björklund, G., & Born, D. P. (2024c). Streamlining performance prediction: data-driven KPIs in all swimming strokes. BMC Research Notes, 17(1), 1–7. https://doi.org/10.1186/s13104-024-06714-x
Sun, X., Davis, J., Schulte, O., & Liu, G. (2020). Cracking the Black Box: Distilling Deep Sports Analytics. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 3154–3162. https://doi.org/10.1145/3394486.3403367
Tomaszewski, M., Lukanova-Jakubowska, A., Majorczyk, E., & Dzierżanowski, Ł. (2024). From data to decision: Machine learning determination of aerobic and anaerobic thresholds in athletes. PLoS ONE, 19(8). https://doi.org/10.1371/journal.pone.0309427
Veiga, S., Cala, A., Mallo, J., & Navarro, E. (2013). A new procedure for race analysis in swimming based on individual distance measurements. Journal of Sports Sciences, 31(2), 159–165. https://doi.org/10.1080/02640414.2012.723130
Wilk, R., Fidos-Czuba, O., Rutkowski, Ł., Kozłowski, K., Wiśniewski, P., Maszczyk, A., Stanula, A., & Roczniok, R. (2015). Predicting competitive swimming performance. Central European Journal of Sport Sciences and Medicine |, 9(1), 105–112.
Wu, P. P.-Y., Garufi, L., Drovandi, C., Mengersen, K., Mitchell, L. J. G., Osborne, M. A., & Pyne, D. B. (2022). Bayesian prediction of winning times for elite swimming events. Journal of Sports Sciences, 40(1), 24–31. https://doi.org/10.1080/02640414.2021.1976485
Yuan, R., & Han, Y. (2022a). Factor Analysis and Regression Prediction Model of Swimmers’ Performance Structure Based on Mixed Genetic Neural Network. Computational Intelligence and Neuroscience, 2022(1), 2052975. https://doi.org/https://doi.org/10.1155/2022/2052975
Yuan, R., & Han, Y. (2022b). Factor Analysis and Regression Prediction Model of Swimmers’ Performance Structure Based on Mixed Genetic Neural Network. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/2052975
Zhao, H., Li, W., Gan, L., & Wang, S. (2023). Designing a prediction model for athlete’s sports performance using neural network. Soft Computing, 27(19), 14379–14395. https://doi.org/10.1007/s00500-023-09091-y
Zuozienė, I. J., & Poderys, J. (2018). Laboratory Assessments and Field Tests in Predicting Competitive Performance of Swimmers. Baltic Journal of Sport and Health Sciences, 3(86), 115–119. https://doi.org/10.33607/bjshs.v3i86.276
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Indonesian Journal of Kinanthropology (IJOK)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Please find rights and licenses inIndonesian Journal of Kinanthropology (IJOK). By submitting articles/ article manuscripts, the author agrees to this policy. No special documents required.
The copyright of the received article once accepted for publication shall be assigned to the journal as the publisher of the journal. The intended copyright includes the right to publish the article in various forms (including reprints). The journal maintains the publishing rights to the published articles.
Abstract views: 0
,
PDF Downloads: 1
