Have a personal or library account? Click to login

Analysis of Relationship between Training Load and Recovery Status in Adult Soccer Players: a Machine Learning Approach

Open Access
|Jan 2023

References

  1. Ahmad, M. W., Reynolds, J., & Rezgui, Y. (2018). Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees. Journal of cleaner production, 203, 810–821.10.1016/j.jclepro.2018.08.207
  2. Bakdash, J. Z., & Marusich, L. R. (2017). Repeated measures correlation. Frontiers in psychology, 8, 456.10.3389/fpsyg.2017.00456538390828439244
  3. Brink, M. S., Visscher, C., Arends, S., Zwerver, J., Post, W. J., & Lemmink, K. A. (2010). Monitoring stress and recovery: New insights for the prevention of injuries and illnesses in elite youth soccer players. British journal of sports medicine, 44(11), 809–815.10.1136/bjsm.2009.06947620511621
  4. Brownstein, C. G., Dent, J. P., Parker, P., Hicks, K. M., Howatson, G., Goodall, S., & Thomas, K. (2017). Etiology and recovery of neuromuscular fatigue following competitive soccer match-play. Frontiers in physiology, 8, 831.10.3389/fphys.2017.00831566100129118716
  5. Bunker, R. P., & Thabtah, F. (2019). A machine learning framework for sport result prediction. Applied computing and informatics, 15(1), 27–33.10.1016/j.aci.2017.09.005
  6. Cai, J., Luo, J., Wang, S., & Yang, S. (2018). Feature selection in machine learning: A new perspective. Neurocomputing, 300, 70–79.10.1016/j.neucom.2017.11.077
  7. Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C., & Collins, J. J. (2018). Next-generation machine learning for biological networks. Cell, 173(7), 1581–1592.10.1016/j.cell.2018.05.01529887378
  8. Carling, C., Lacome, M., McCall, A., Dupont, G., Le Gall, F., Simpson, B., & Buchheit, M. (2018). Monitoring of post-match fatigue in professional soccer: Welcome to the real world. Sports Medicine, 48(12), 2695–2702.10.1007/s40279-018-0935-z624461629740792
  9. Cawley, G. C., & Talbot, N. L. (2010). On over-fitting in model selection and subsequent selection bias in performance evaluation. The Journal of Machine Learning Research, 11, 2079–2107.
  10. Clemente, F. M., Figueiredo, A. J., Martins, F. M. L., Mendes, R. S., & Wong, D. P. (2016). Physical and technical performances are not associated with tactical prominence in U14 soccer matches. Research in Sports Medicine, 24(4), 352–362.10.1080/15438627.2016.122227727533018
  11. De Beéck, T. O., Jaspers, A., Brink, M. S., Frencken, W. G., Staes, F., Davis, J. J., & Helsen, W. F. (2019). Predicting future perceived wellness in professional soccer: The role of preceding load and wellness. International Journal of Sports Physiology and Performance, 14(8), 1074–1080.10.1123/ijspp.2017-086430702339
  12. Fell, J., & Williams, A. D. (2008). The effect of aging on skeletal-muscle recovery from exercise: Possible implications for aging athletes. Journal of Aging and Physical Activity, 16(1), 97–115.10.1123/japa.16.1.9718268815
  13. Ferreira, P., Le, D. C., & Zincir-Heywood, N. (2019). Exploring feature normalization and temporal information for machine learning based insider threat detection. 2019 15th International Conference on Network and Service Management (CNSM), 1–7.10.23919/CNSM46954.2019.9012708
  14. Fessi, M. S., Nouira, S., Dellal, A., Owen, A., Elloumi, M., & Moalla, W. (2016). Changes of the psychophysical state and feeling of wellness of professional soccer players during pre-season and in-season periods. Research in Sports Medicine, 24(4), 375–386.10.1080/15438627.2016.122227827574867
  15. Foster, C., Hector, L. L., Welsh, R., Schrager, M., Green, M. A., & Snyder, A. C. (1995). Effects of specific versus cross-training on running performance. European journal of applied physiology and occupational physiology, 70(4), 367–372.10.1007/BF008650357649149
  16. Frank, E., & Hall, M. (2001). A simple approach to ordinal classification. European conference on machine learning, 145–156.10.1007/3-540-44795-4_13
  17. Gabbett, T. J. (2016). The training—Injury prevention paradox: Should athletes be training smarter and harder? British journal of sports medicine, 50(5), 273–280.10.1136/bjsports-2015-095788478970426758673
  18. Gjaka, M., Tschan, H., Francioni, F. M., Tishkuaj, F., & Tessitore, A. (2016). MONITORING OF LOADS AND RECOVERY PERCEIVED DURING WEEKS WITH DIFFERENT SCHEDULE IN YOUNG SOCCER PLAYERS. Kinesiologia Slovenica, 22(1).
  19. Hader, K., Rumpf, M. C., Hertzog, M., Kilduff, L. P., Girard, O., & Silva, J. R. (2019). Monitoring the athlete match response: Can external load variables predict post-match acute and residual fatigue in soccer? A systematic review with meta-analysis. Sports medicine-open, 5(1), 1–19.10.1186/s40798-019-0219-7690163431820260
  20. Halson, S. L. (2014). Monitoring training load to understand fatigue in athletes. Sports medicine, 44(2), 139–147.10.1007/s40279-014-0253-z421337325200666
  21. Impellizzeri, F. M., Rampinini, E., Coutts, A. J., Sassi, A., & Marcora, S. M. (2004). Use of RPE-based training load in soccer. Medicine & Science in sports & exercise, 36(6), 1042–1047.10.1249/01.MSS.0000128199.23901.2F
  22. Impellizzeri, F. M., Rampinini, E., & Marcora, S. M. (2005). Physiological assessment of aerobic training in soccer. Journal of sports sciences, 23(6), 583–592.10.1080/0264041040002127816195007
  23. Jaspers, A., De Beéck, T. O., Brink, M. S., Frencken, W. G., Staes, F., Davis, J. J., & Helsen, W. F. (2018). Relationships between the external and internal training load in professional soccer: What can we learn from machine learning? International journal of sports physiology and performance, 13(5), 625–630.10.1123/ijspp.2017-029929283691
  24. Johnson, D. R., & Creech, J. C. (1983). Ordinal measures in multiple indicator models: A simulation study of categorization error. American Sociological Review, 398–407.10.2307/2095231
  25. Jones, C. M., Griffiths, P. C., & Mellalieu, S. D. (2017). Training load and fatigue marker associations with injury and illness: A systematic review of longitudinal studies. Sports medicine, 47(5), 943–974.10.1007/s40279-016-0619-5539413827677917
  26. Kalkhoven, J. T., Watsford, M. L., Coutts, A. J., Edwards, W. B., & Impellizzeri, F. M. (2021). Training load and injury: Causal pathways and future directions. Sports Medicine, 51(6), 1137–1150.10.1007/s40279-020-01413-633400216
  27. Kang, H. (2013). The prevention and handling of the missing data. Korean journal of anesthesiology, 64(5), 402.10.4097/kjae.2013.64.5.402366810023741561
  28. Kensert, A., Alvarsson, J., Norinder, U., & Spjuth, O. (2018). Evaluating parameters for ligand-based modeling with random forest on sparse data sets. Journal of cheminformatics, 10(1), 1–10.10.1186/s13321-018-0304-9675560030306349
  29. Kenttä, G., & Hassmén, P. (1998). Overtraining and recovery. Sports medicine, 26(1), 1–16.10.2165/00007256-199826010-000019739537
  30. Lacome, M., Simpson, B., Broad, N., & Buchheit, M. (2018). Monitoring players’ readiness using predicted heart-rate responses to soccer drills. International Journal of Sports Physiology and Performance, 13(10), 1273–1280.10.1123/ijspp.2018-002629688115
  31. Malone, S., Owen, A., Newton, M., Mendes, B., Collins, K. D., & Gabbett, T. J. (2017). The acute: Chonic workload ratio in relation to injury risk in professional soccer. Journal of science and medicine in sport, 20(6), 561–565.10.1016/j.jsams.2016.10.01427856198
  32. Malone, S., Owen, A., Newton, M., Mendes, B., Tiernan, L., Hughes, B., & Collins, K. (2018). Wellbeing perception and the impact on external training output among elite soccer players. Journal of science and medicine in sport, 21(1), 29–34.10.1016/j.jsams.2017.03.01928442275
  33. Mandorino, M., Figueiredo, A. J., Cima, G., & Tessitore, A. (2021). A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players. International Journal of Computer Science in Sport, 20(2), 147–163.10.2478/ijcss-2021-0009
  34. Mandorino, M., Figueiredo, A. J., Cima, G., & Tessitore, A. (2022a). Predictive Analytic Techniques to Identify Hidden Relationships between Training Load, Fatigue and Muscle Strains in Young Soccer Players. Sports, 10(1), 3.10.3390/sports10010003882288835050968
  35. Mandorino, M., Figueiredo, A. J., Cima, G., & Tessitore, A. (2022b). The Impact of External and Internal Load on Recovery Status of Adult Soccer Players: A Machine Learning Approach. International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, 122–125.10.1007/978-3-030-99333-7_20
  36. Mandorino, M., Figueiredo, A. J., Condello, G., & Tessitore, A. (2022). The influence of maturity on recovery and perceived exertion, and its relationship with illnesses and non-contact injuries in young soccer players. Biology of Sport, 39(4), 839–848.10.5114/biolsport.2022.109953953636936247948
  37. Murray, N. B., Gabbett, T. J., Townshend, A. D., & Blanch, P. (2017). Calculating acute: Chronic workload ratios using exponentially weighted moving averages provides a more sensitive indicator of injury likelihood than rolling averages. British Journal of Sports Medicine, 51(9), 749–754.10.1136/bjsports-2016-09715228003238
  38. Murugesan, G., Saghafi, B., Davenport, E., Wagner, B., Urban, J., Kelley, M., Jones, D., Powers, A., Whitlow, C., & Stitzel, J. (2018). Single season changes in resting state network power and the connectivity between regions distinguish head impact exposure level in high school and youth football players. Medical Imaging 2018: Computer-Aided Diagnosis, 10575, 105750F.
  39. Nédélec, M., McCall, A., Carling, C., Legall, F., Berthoin, S., & Dupont, G. (2012). Recovery in soccer. Sports medicine, 42(12), 997–1015.10.2165/11635270-000000000-00000
  40. Nikolaidis, P. T., Clemente, F. M., van der Linden, C. M., Rosemann, T., & Knechtle, B. (2018). Validity and reliability of 10-Hz global positioning system to assess in-line movement and change of direction. Frontiers in physiology, 9, 228.10.3389/fphys.2018.00228586286529599725
  41. Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in health sciences education, 15(5), 625–632.10.1007/s10459-010-9222-y20146096
  42. Ray, S. (2019). A quick review of machine learning algorithms. 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon), 35–39.10.1109/COMITCon.2019.8862451
  43. Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological methods, 17(3), 354.10.1037/a002931522799625
  44. Robitzsch, A. (2020). Why ordinal variables can (almost) always be treated as continuous variables: Clarifying assumptions of robust continuous and ordinal factor analysis estimation methods. Frontiers in Education, 5, 177.10.3389/feduc.2020.589965
  45. Rossi, A., Pappalardo, L., Cintia, P., Iaia, F. M., Fernández, J., & Medina, D. (2018). Effective injury forecasting in soccer with GPS training data and machine learning. PloS one, 13(7), e0201264.10.1371/journal.pone.0201264605946030044858
  46. Rossi, A., Perri, E., Pappalardo, L., Cintia, P., & Iaia, F. M. (2019). Relationship between External and Internal Workloads in Elite Soccer Players: Comparison between Rate of Perceived Exertion and Training Load. Applied Sciences, 9(23), 5174.10.3390/app9235174
  47. Sansone, P., Tschan, H., Foster, C., & Tessitore, A. (2020). Monitoring training load and perceived recovery in female basketball: Implications for training design. The Journal of Strength & Conditioning Research.10.1519/JSC.000000000000297130589724
  48. Saw, A. E., Main, L. C., & Gastin, P. B. (2016). Monitoring the athlete training response: Subjective self-reported measures trump commonly used objective measures: A systematic review. British journal of sports medicine, 50(5), 281–291.10.1136/bjsports-2015-094758478970826423706
  49. Sawczuk, T., Jones, B., Scantlebury, S., & Till, K. (2018). Relationships between training load, sleep duration, and daily well-being and recovery measures in youth athletes. Pediatric exercise science, 30(3), 345–352.10.1123/pes.2017-019029478381
  50. Selmi, O., Gonçalves, B., Ouergui, I., Sampaio, J., & Bouassida, A. (2018). Influence of well-being variables and recovery state in physical enjoyment of professional soccer players during small-sided games. Research in Sports Medicine, 26(2), 199–210.10.1080/15438627.2018.143154029376416
  51. Selmi, O., Ouergui, I., Castellano, J., Levitt, D., & Bouassida, A. (2020). Effect of an intensified training period on well-being indices, recovery and psychological aspects in professional soccer players. European Review of Applied Psychology, 70(6), 100603.10.1016/j.erap.2020.100603
  52. Shan, G., Zhang, H., & Jiang, T. (2020). Correlation coefficients for a study with repeated measures. Computational and mathematical methods in medicine, 2020.10.1155/2020/7398324713676132300374
  53. Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524.10.1016/j.asoc.2019.105524
  54. Tessitore, A., Meeusen, R., Cortis, C., & Capranica, L. (2007). Effects of different recovery interventions on anaerobic performances following preseason soccer training. The Journal of Strength & Conditioning Research, 21(3), 745–750.10.1519/00124278-200708000-00015
  55. Thorpe, R. T., Strudwick, A. J., Buchheit, M., Atkinson, G., Drust, B., & Gregson, W. (2015). Monitoring fatigue during the in-season competitive phase in elite soccer players. International journal of sports physiology and performance, 10(8), 958–964.10.1123/ijspp.2015-000425710257
  56. Thorpe, R. T., Strudwick, A. J., Buchheit, M., Atkinson, G., Drust, B., & Gregson, W. (2017). The influence of changes in acute training load on daily sensitivity of morning-measured fatigue variables in elite soccer players. International journal of sports physiology and performance, 12(s2), S2-107-S2-113.10.1123/ijspp.2016-043327918668
  57. Vescovi, J. D., Klas, A., & Mandic, I. (2019). Investigating the relationships between load and recovery in women’s field hockey–Female Athletes in Motion (FAiM) study. International Journal of Performance Analysis in Sport, 19(5), 672–682.10.1080/24748668.2019.1647731
  58. Zhang, C.-X., Wang, G.-W., & Zhang, J.-S. (2012). An empirical bias–variance analysis of DECORATE ensemble method at different training sample sizes. Journal of Applied Statistics, 39(4), 829–850.10.1080/02664763.2011.620949
Language: English
Page range: 1 - 16
Published on: Jan 17, 2023
Published by: International Association of Computer Science in Sport
In partnership with: Paradigm Publishing Services
Publication frequency: 2 times per year

© 2023 M. Mandorino, A.J. Figueiredo, G. Cima, A. Tessitore, published by International Association of Computer Science in Sport
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.