Modeling the Prediction of the Session Rating of Perceived Exertion in Soccer: Unraveling the Puzzle of Predictive Indicators

in International Journal of Sports Physiology and Performance
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Purpose: To predict the session rating of perceived exertion (sRPE) in soccer and determine its main predictive indicators. Methods: A total of 70 external-load indicators (ELIs), internal-load indicators, individual characteristics, and supplementary variables were used to build a predictive model. Results: The analysis using gradient-boosting machines showed a mean absolute error of 0.67 (0.09) arbitrary units (AU) and a root-mean-square error of 0.93 (0.16) AU. ELIs were found to be the strongest predictors of the sRPE, accounting for 61.5% of the total normalized importance (NI), with total distance as the strongest predictor. The included internal-load indicators and individual characteristics accounted only for 1.0% and 4.5%, respectively, of the total NI. Predictive accuracy improved when including supplementary variables such as group-based sRPE predictions (10.5% of NI), individual deviation variables (5.8% of NI), and individual player markers (17.0% of NI). Conclusions: The results showed that the sRPE can be predicted quite accurately using only a relatively limited number of training observations. ELIs are the strongest predictors of the sRPE. However, it is useful to include a broad range of variables other than ELIs, because the accumulated importance of these variables accounts for a reasonable component of the total NI. Applications resulting from predictive modeling of the sRPE can help coaching staff plan, monitor, and evaluate both the external and internal training load.

Geurkink, Lievens, Boone, and Bourgois are with the Dept of Movement and Sports Sciences, and Vandewiele, de Turck, and Ongenae, the Dept of Information Technology, Ghent University, Ghent, Belgium. Geurkink, Matthys, and Bourgois are with the Performance and Sports Sciences Dept, KAA Ghent (Football Club), Ghent, Belgium.

Bourgois (jan.bourgois@ugent.be) is corresponding author.
International Journal of Sports Physiology and Performance
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References
  • 1.

    Viru AViru M. Nature of training effects. Exerc Sport Sci. 2000;6(7):6795.

  • 2.

    Impellizzeri FMRampinini EMarcora SM. Physiological assessment of aerobic training in soccer. J Sports Sci. 2005;23(6):583592. PubMed ID: 16195007 doi:10.1080/02640410400021278

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Cummins COrr RO’Connor HWest C. Global positioning systems (GPS) and microtechnology sensors in team sports: a systematic review. Sport Med. 2013;43(10):10251042. PubMed ID: 23812857 doi:10.1007/s40279-013-0069-2

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4.

    Algrøy EAHetlelid KJSeiler SStray Pedersen JI. Quantifying training intensity distribution in a group of Norwegian professional soccer players. Int J Sports Physiol Perform. 2011;6(1):7081. PubMed ID: 21487151 doi:10.1123/ijspp.6.1.70

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 5.

    Little TWilliams AG. Measures of exercise intensity during soccer training drills with professional soccer players. J Strength Cond Res. 2007;21(2):367371. PubMed ID: 17530957 doi:10.1519/R-19445.1

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Alexiou HCoutts AJ. A comparison of methods used for quantifying internal training load in women soccer players. Int J Sports Physiol Perform. 2008;3(3):320330. PubMed ID: 19211944 doi:10.1080/02640424.2015.1088166

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    Impellizzeri FMRampinini ECoutts AJSassi AMarcora SM. Use of RPE-based training load in soccer. Med Sci Sports Exerc. 2004;36(6):10421047. PubMed ID: 15179175 doi:10.1249/01.MSS.0000128199.23901.2F

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Borresen JLambert MI. Quantifying training load: a comparison of subjective and objective methods. Int J Sports Physiol Perform. 2008;3:1630. PubMed ID: 19193951 doi:10.1123/ijspp.3.1.16

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9.

    Gaudino PIaia FMStrudwick AJet al. Factors influencing perception of effort (session rating of perceived exertion) during elite soccer training. Int J Sports Physiol Perform. 2015;10(7):860864. PubMed ID: 25671338 doi:10.1123/ijspp.2014-0518

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Haddad MChaouachi AWong DPet al. Influence of fatigue, stress, muscle soreness and sleep on perceived exertion during submaximal effort. Physiol Behav. 2013;119:185189. PubMed ID: 23816982 doi:10.1016/j.physbeh.2013.06.016

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Timmons JA. Variability in training-induced skeletal muscle adaptation. J Appl Physiol. 2011;110(3):846853. PubMed ID: 21030666 doi:10.1152/japplphysiol.00934.2010

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Hoff J. Soccer specific aerobic endurance training. Br J Sports Med. 2002;36(3):218221. PubMed ID: 12055120 doi:10.1136/bjsm.36.3.218

  • 13.

    Brink MSFrencken WGPJordet GLemmink KAPM. Coaches’ and players’ perceptions of training dose: not a perfect match. Int J Sports Physiol Perform. 2014;9(3):497502. PubMed ID: 24235774 doi:10.1123/IJSPP.2013-0009

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 14.

    Doeven SHBrink MSFrencken WGPLemmink KAPM. Impaired player-coach perceptions of exertion and recovery during match congestion. Int J Sports Physiol Perform. 2017;12(9):11511156. PubMed ID: 28095076 doi:10.1123/ijspp.2016-0363

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    Murphy APDuffield RKellett AReid M. Comparison of athlete-coach perceptions of internal and external load markers for elite junior tennis training. Int J Sports Physiol Perform. 2014;9(5):751756. PubMed ID: 24231360 doi:10.1123/IJSPP.2013-0364

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Wallace LCoutts ABell JSimpson NSlattery K. Using session-RPE to monitor training load in swimmers. Strength Cond J. 2008;30(6):7276. doi:10.1519/ssc.0b013e31818eed5f

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 17.

    Foster CFlorhaug JAFranklin Jet al. A new approach to monitoring exercise training. J Strength Cond Res. 2001;15(1):109115. PubMed ID: 11708692 doi:10.1519/00124278-200102000-00019

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18.

    Edwards S. The heart rate monitor book. Med Sci Sports Exerc. 1994;26(5):647. doi:10.1249/00005768-199405000-00020

  • 19.

    Boone JVaeyens RSteyaert AVanden Bossche LBourgois J. Physical fitness of elite Belgian soccer players by player position. J Strength Cond Res. 2012;26(8):20512057. PubMed ID: 21986697 doi:10.1519/JSC.0b013e318239f84f

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    Baguet AEveraert IHespel PPetrovic MAchten EDerave W. A new method for non-invasive estimation of human muscle fiber type composition. PLoS ONE. 2011;6(7):21956. PubMed ID: 21760934 doi:10.1371/journal.pone.0021956

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21.

    Simoneau JBouchard C. Genetic determinism of fiber human skeletal muscle type proportion in human skeletal muscle. FASEB J. 1995;9(11):10911095. PubMed ID: 7649409 doi:10.1096/fasebj.9.11.7649409

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    Jaspers ADe Beéck TOBrink MSFrencken WGStaes FDavis JJHelsen WF. Relationships between the external and internal training load in professional soccer: what can we learn from machine learning? Int J Sports Physiol Perform. 2018;13(5):625630. doi:10.1123/ijspp.2017-0299

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Rossi APerri ETrecroci ASavino MAlberti GIaia FM. GPS data reflect players’ internal load in soccer. Paper presented at: IEEE International Conference on Data Mining Workshops (ICDMW); November 18–21 2017. New Orleans, LA. https://ieeexplore.ieee.org/document/8215756. Accessed September 20 2017.

    • Export Citation
  • 24.

    Burgess DJ. The research doesn’t always apply: practical solutions to evidence-based training load monitoring in elite team sports. Int J Sports Physiol Perform. 2017;12(suppl 2):S21362141. PubMed ID: 27967277 doi:10.1123/ijspp.2016-0608

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Carey DLOng KMorris MECrow JCrossley KM. Predicting ratings of perceived exertion in Australian football players: methods for live estimation. Int J Comput Sci Sport. 2016;15(2):6477. doi:10.1515/ijcss-2016-0005

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 26.

    Vandewiele GGeurkink YLievens MOngenae FDe Turck FBoone J. Enabling training personalization by predicting the session Rate of Perceived Exertion (sRPE). Paper presented at: Machine learning and data mining for sports analytics ECML/PKDD; 2017; Skopje, Macedonia.

    • Search Google Scholar
    • Export Citation
  • 27.

    Bartlett JDO’Connor FPitchford NTorres-Ronda LRobertson SJ. Relationships between internal and external training load in team-sport athletes: evidence for an individualized approach. Int J Sports Physiol Perform. 2017;12(2):230234. PubMed ID: 27194668 doi:10.1123/ijspp.2015-0791

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28.

    Hulin BTGabbett TJLawson DWCaputi PSampson JA. The acute:chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players. Br J Sports Med. 2016;50(4):231236. PubMed ID: 26511006 doi:10.1136/bjsports-2015-094817

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29.

    Williams SWest SCross MJStokes KA. Better way to determine the acute:chronic workload ratio? Br J Sports Med. 2016;51(3):209210. PubMed ID: 27650255 doi:10.1136/bjsports-2016-096589

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30.

    Thorpe RTStrudwick AJBuchheit MAtkinson GDrust BGregson W. Tracking morning fatigue status across in-season training weeks in elite soccer players. Int J Sports Physiol Perform. 2016;11(7):947952. PubMed ID: 26816390 doi:10.1123/ijspp.2015-0490

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
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