Relationship Between Subjective and External Training Load Variables in Youth Soccer Players

in International Journal of Sports Physiology and Performance
Restricted access

Purchase article

USD  $24.95

Student 1 year online subscription

USD  $112.00

1 year online subscription

USD  $149.00

Student 2 year online subscription

USD  $213.00

2 year online subscription

USD  $284.00

Purpose: To quantify and describe relationships between subjective and external measures of training load in professional youth soccer players. Methods: Data from differential ratings of perceived exertion (dRPE) and 7 measures of external training load were collected from 20 professional youth soccer players over a 46-week season. Relationships were described by repeated-measures correlation, principal component analysis, and factor analysis with oblimin rotation. Results: Significant positive (.44 ≤ r ≤ .99; P < .001) within-individual correlations were obtained across dRPE and all external training load measures. Correlation magnitudes were found to decrease when training load variables were expressed per minute. Principal component analysis provided 2 components, which described 83.3% of variance. The first component, which described 72.9% of variance, was heavily loaded by all measures of training load, while the second component, which described 10.4% of the variance, appeared to have a split between objective and subjective measures of volume and intensity. Exploratory factor analysis identified 4 theoretical factors, with correlations between factors ranging from .5 to .8. These factors could be theoretically described as objective volume, subjective volume, objective running, and objective high-intensity measures. Removing dRPE measures from the analysis altered the structure of the model, providing a 3-factor solution. Conclusions: The dRPE measures are significantly correlated with a range of external training load measures and with each other. More in-depth analysis showed that dRPE measures were highly related to each other, suggesting that, in this population, they would provide practitioners with similar information. Further analysis provided characteristic groupings of variables.

Maughan is with the Aberdeen Football Club, Aberdeen, Scotland. Maughan and MacFarlane are with the University of Glasgow, Glasgow, Scotland. Swinton is with the Robert Gordon University, Aberdeen, Scotland.

Maughan (patrick.maughan@afc.co.uk) is corresponding author.
  • 1.

    Malone JJ, Lovell R, Varley MC, Coutts AJ. Unpacking the black box: applications and considerations for using GPS devices in sport. Int J Sports Physiol Perform. 2017;12(suppl):S218S226. doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2.

    Coutts AJ, Duffield R. Validity and reliability of GPS devices for measuring movement demands of team sports. J Sci Med Sport. 2010;13(1):133135. PubMed ID: 19054711 doi:

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

    Akenhead R, Nassis GP. Training load and player monitoring in high-level football: current practice and perceptions. Int J Sports Physiol Perform. 2016;11(5):587593. PubMed ID: 26456711 doi:

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

    Johnston RJ, Watsford ML, Kelly SJ, Pine MJ, Spurrs RW. Validity and interunit reliability of 10 Hz and 15 Hz GPS units for assessing athlete movement demands. J Strength Cond Res. 2014;28(6):16491655. PubMed ID: 24276300 doi:

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

    Impellizzeri FM, Rampinini E, Coutts AJ, Sassi A, Marcora SM. Use of RPE-based training load in soccer. Med Sci Sports Exerc. 2004;36(6):10421047. PubMed ID: 15179175 doi:

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

    Weston M. Difficulties in determining the dose–response nature of competitive soccer matches. J Athl Enhanc. 2013;2(1):12. doi:

  • 7.

    McLaren SJ, Smith A, Bartlett JD, Spears IR, Weston M. Differential training loads and individual fitness responses to pre-season in professional rugby union players. J Sports Sci. 2018;36(21):24382446. PubMed ID: 29629620 doi:

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

    Jaspers A, Brink MS, Probst SG, Frencken WG, Helsen WF. Relationships between training load indicators and training outcomes in professional soccer. Sports Med. 2017;47(3):533544. PubMed ID: 27459866 doi:

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

    McLaren SJ, Graham M, Spears IR, Weston M. The sensitivity of differential ratings of perceived exertion as measures of internal load. Int J Sports Physiol Perform. 2016;11(3):404406. PubMed ID: 26218099 doi:

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

    McLaren SJ, Smith A, Spears IR, Weston M. A detailed quantification of differential ratings of perceived exertion during team-sport training. J Sci Med Sport. 2017;20(3):290295. PubMed ID: 27451269 doi:

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

    McLaren SJ, Macpherson TW, Coutts AJ, Hurst C, Spears IR, Weston M. The relationships between internal and external measures of training load and intensity in team sports: a meta-analysis. Sports Med. 2018;48(3):641658. PubMed ID: 29288436 doi:

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

    Gaudino P, Iaia FM, Strudwick AJ, et 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:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.

    Weaving D, Marshall P, Earle K, Nevill A, Abt G. Combining internal-and external-training-load measures in professional rugby league. Int J Sports Physiol Perform. 2014;9(6):905912. PubMed ID: 24589469 doi:

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

    Foster C, Florhaug JA, Franklin J, et al. A new approach to monitoring exercise training. J Strength Cond Res. 2001;15(1):109115. PubMed ID: 11708692

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

    Weaving D, Dalton NE, Black C, et al. The same story or a unique novel? Within-participant principal-component analysis of measures of training load in professional rugby union skills training. Int J Sports Physiol Perform. 2018;13(9):11751181. PubMed ID: 29584514 doi:

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

    Weaving D, Jones B, Marshall P, Till K, Abt G. Multiple measures are needed to quantify training loads in professional rugby league. Int J Sports Med. 2017;38(10):735740. PubMed ID: 28783849 doi:

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

    Jones RN, Greig M, Mawéné Y, Barrow J, Page RM. The influence of short-term fixture congestion on position specific match running performance and external loading patterns in English professional soccer. J Sports Sci. 2019;37(12):13381346. PubMed ID: 30563419 doi:

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

    Barrett S, Midgley A, Lovell R. PlayerLoad: reliability, convergent validity, and influence of unit position during treadmill running. Int J Sports Physiol Perform. 2014;9(6):945952. PubMed ID: 24622625 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Bakdash JZ, Marusich LR. Repeated measures correlation. Front Psychol. 2017;8:456. PubMed ID: 28439244 doi:

  • 20.

    Hopkins W, Marshall S, Batterham A, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc. 2009;41(1):3. PubMed ID: 19092709 doi:

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

    Kaiser HF. The application of electronic computers to factor analysis. Educ Psychol Meas. 1960;20(1):141151. doi:

  • 22.

    Federolf P, Reid R, Gilgien M, Haugen P, Smith G. The application of principal component analysis to quantify technique in sports. Scand J Med Sci Sports. 2014;24(3):491499. PubMed ID: 22436088 doi:

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

    Josse J, Husson F. missMDA: a package for handling missing values in multivariate data analysis. J Stat Softw. 2016;70(1):131. doi:

  • 24.

    Bartlett MS. A note on the multiplying factors for various χ2 approximations. J R Stat Soc B. 1954:296298.

  • 25.

    Rosseel Y, Oberski D, Byrnes J, et al. Lavaan: Latent variable analysis (Version 0.5-23.1097). 2017. Retrieved from https://cran.r-project.org/web/packages/lavaan/

    • Search Google Scholar
    • Export Citation
  • 26.

    Dodd AL, Mansell W, Sadhnani V, Morrison AP, Tai S. Principal components analysis of the hypomanic attitudes and positive predictions inventory and associations with measures of personality, cognitive style and analogue symptoms in a student sample. Behav Cogn Psychother. 2010;38(1):1533. PubMed ID: 19857364 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 27.

    Rampinini E, Alberti G, Fiorenza M, et al. Accuracy of GPS devices for measuring high-intensity running in field-based team sports. Int J Sports Med. 2015;36(1):4953. PubMed ID: 25254901

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

    Buchheit M, Laursen PB. High-intensity interval training, solutions to the programming puzzle. Sports Med. 2013;43(10):927954. PubMed ID: 23832851 doi:

  • 29.

    Abt G, Lovell R. The use of individualized speed and intensity thresholds for determining the distance run at high-intensity in professional soccer. J Sports Sci. 2009;27(9):893898. PubMed ID: 19629838 doi:

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

    Faude O, Kindermann W, Meyer T. Lactate threshold concepts: how valid are they? Sports Med. 2009;39(6):469490. PubMed ID: 19453206 doi:

  • 31.

    Williams S, Trewartha G, Cross MJ, Kemp SP, Stokes KA. Monitoring what matters: a systematic process for selecting training-load measures. Int J Sports Physiol Perform. 2017;12(suppl):S2-101S2-106. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 32.

    Casamichana D, Castellano J. The relationship between intensity indicators in small-sided soccer games. J Hum Kinet. 2015;46(1):119128.

  • 33.

    Los Arcos A, Méndez-Villanueva A, Yanci J, Martínez-Santos R. Respiratory and muscular perceived exertion during official games in professional soccer players. Int J Sports Physiol Perform. 2016;11(3):301304. PubMed ID: 26217923 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 34.

    Vanrenterghem J, Nedergaard NJ, Robinson MA, Drust B. Training load monitoring in team sports: a novel framework separating physiological and biomechanical load-adaptation pathways. Sports Med. 2017;47(11):21352142. PubMed ID: 28283992 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 535 535 74
Full Text Views 20 20 3
PDF Downloads 23 23 5