Data Reduction Approaches to Athlete Monitoring in Professional Australian Football

in International Journal of Sports Physiology and Performance
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Purpose: To apply data reduction methods to athlete-monitoring measures to address the issue of data overload for practitioners of professional Australian football teams. Methods: Data were collected from 45 professional Australian footballers from 1 club during the 2018 Australian Football League season. External load was measured in training and matches by 10-Hz OptimEye S5 and ClearSky T6 GPS units. Internal load was measured via the session rate of perceived exertion method. Perceptual wellness was measured via questionnaires completed before training sessions with players providing a rating (1–5 Likert scale) of muscle soreness, sleep quality, fatigue, stress, and motivation. Percentage of maximum speed was calculated relative to individual maximum velocity recorded during preseason testing. Derivative external training load measures (total daily, weekly, and monthly) were calculated. Principal-component analyses (PCAs) were conducted for Daily and Chronic measures, and components were identified via scree plot inspection (eigenvalue > 1). Components underwent orthogonal rotation with a factor loading redundancy threshold of 0.70. Results: The Daily PCA identified components representing external load, perceived wellness, and internal load. The Chronic PCA identified components representing 28-d speed exposure, 28-d external load, 7-d external load, and 28-d internal load. Perceived soreness did not meet the redundancy threshold. Conclusions: Monitoring player exposure to maximum speed is more appropriate over chronic than short time frames to capture variations in between-matches training-cycle duration. Perceived soreness represents a distinct element of a player’s perception of wellness. Summed-variable and single-variable approaches are novel methods of data reduction following PCA of athlete monitoring data.

Ryan and Coutts are with the Human Performance Research Centre, University of Technology Sydney (UTS), Sydney, NSW, Australia. Ryan, Kempton, and Coutts are with the Carlton Football Club, Melbourne, VIC, Australia.

Ryan (sam.ryan@carltonfc.com.au) is corresponding author.
  • 1.

    Ryan S, Kempton T, Impellizzeri F, Coutts A. Training monitoring in professional Australian football: theoretical basis and recommendations for coaches and scientists. Sci Med Football. 2020;4(1):5258. doi:10.1080/24733938.2019.1641212

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

    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:10.1123/ijspp.2013-0444

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

    Williams S, Trewartha G, Cross MJ, Kemp SPT, Stokes KA. Monitoring what matters: a systematic process for selecting training-load measures. Int J Sports Physiol Perform. 2017;12(suppl 2):S2101S2106. PubMed ID: 27834553 doi:10.1123/ijspp.2016-0337

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

    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:10.1249/01.MSS.0000128199.23901.2F

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

    Colby MJ, Dawson B, Peeling P, Heasman J, Rogalski B, Drew M, Stares J. Improvement of prediction of noncontact injury in elite Australian footballers with repeated exposure to established high-risk workload scenarios. Int J Sports Physiol Perform. 2018;13(9):11301135. PubMed ID: 29543079 doi:10.1123/ijspp.2017-0696

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

    Ryan S, Coutts AJ, Hocking J, Dillon PA, Whitty A, Kempton T. Physical preparation factors that influence technical and physical match performance in professional Australian football. Int J Sports Physiol Perform. 2018;13(8):10211027. PubMed ID: 29466065 doi:10.1123/ijspp.2017-0640

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

    Weaving D, Jones B, Ireton M, Whitehead S, Till K, Beggs CB. Overcoming the problem of multicollinearity in sports performance data: a novel application of partial least squares correlation analysis. PLoS One. 2020;14(2):e0211776. PubMed ID: 30763328 doi:10.1371/journal.pone.0211776

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

    Weaving D, Dalton NE, Black C, Darrall-Jones J, Phibbs PJ, Gray M, Jones B, Roe GAB. 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:10.1123/ijspp.2017-0565

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

    Currell K, Jeukendrup AE. Validity, reliability and sensitivity of measures of sporting performance. Sports Med. 2008;38(4):297316. PubMed ID: 18348590 doi:10.2165/00007256-200838040-00003

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

    Robertson S, Kremer P, Aisbett B, Tran J, Cerin E. Consensus on measurement properties and feasibility of performance tests for the exercise and sport sciences: a Delphi study. Sports Med. 2017;3(1):2.

    • Search Google Scholar
    • Export Citation
  • 11.

    Ryan S, Pacecca E, Tebble J, Hocking J, Kempton T, Coutts AJ. Measurement characteristics of athlete monitoring tools in professional Australian football. Int J Sports Physiol Perform. 2020;15(4):457463. doi:10.1123/ijspp.2019-0060

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

    Gallo TF, Cormack SJ, Gabbett TJ, Lorenzen CH. Self-reported wellness profiles of professional Australian football players during the competition phase of the season. J Strength Cond Res. 2017;31(2):495502. PubMed ID: 27243912 doi:10.1519/JSC.0000000000001515

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

    Gastin PB, Meyer D, Robinson D. Perceptions of wellness to monitor adaptive responses to training and competition in elite Australian football. J Strength Cond Res. 2013;27(9):25182526. PubMed ID: 23249820 doi:10.1519/JSC.0b013e31827fd600

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

    Buchheit M, Racinais S, Bilsborough JC, Bourdon PC, Voss SC, Hocking J, Cordy J, Mendez-Villanueva A, Coutts AJ. Monitoring fitness, fatigue and running performance during a pre-season training camp in elite football players. J Sci Med Sport. 2013;16(6):550555. PubMed ID: 23332540 doi:10.1016/j.jsams.2012.12.003

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

    Johnston RJ, Watsford ML, Pine MJ, Spurrs RW, Murphy A, Pruyn EC. Movement demands and match performance in professional Australian football. Int J Sports Med. 2012;33(2):8993. PubMed ID: 22095328 doi:10.1055/s-0031-1287798

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

    Nicolella DP, Torres-Ronda L, Saylor KJ, Schelling X. Validity and reliability of an accelerometer-based player tracking device. PLoS One. 2018;13(2):e0191823. PubMed ID: 29420555 doi:10.1371/journal.pone.0191823

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

    Whitehead S, Till K, Weaving D, Jones B. The use of microtechnology to quantify the peak match demands of the football codes: a systematic review. Sports Med. 2018;48(11):25492575. PubMed ID: 30088218 doi:10.1007/s40279-018-0965-6

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

    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
  • 19.

    Scott TJ, Black CR, Quinn J, Coutts AJ. Validity and reliability of the session-RPE method for quantifying training in Australian football: a comparison of the CR10 and CR100 scales. J Strength Cond Res. 2013;27(1):270276. PubMed ID: 22450253 doi:10.1519/JSC.0b013e3182541d2e

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

    Hair JF. Multivariate Data Analysis: A Global Perspective. Upper Saddle River, NJ/London, UK: Pearson Education; 2010.

  • 21.

    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:10.1519/JSC.0000000000000323

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

    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:10.1016/j.jsams.2008.09.015

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

    Kempton T, Sullivan C, Bilsborough JC, Cordy J, Coutts AJ. Match-to-match variation in physical activity and technical skill measures in professional Australian Football. J Sci Med Sport. 2015;18(1):109113. PubMed ID: 24444753 doi:10.1016/j.jsams.2013.12.006

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.

    Saw AE, Main LC, Gastin PB. Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review. Br J Sports Med. 2016;50(5):281291. PubMed ID: 26423706 doi:10.1136/bjsports-2015-094758

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

    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:10.1055/s-0043-114007

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

    Impellizzeri F, Marcora S, Coutts A. Internal and external training load: 15 years on. Int J Sports Physiol Perform. 2019;14(2):270273. PubMed ID: 30614348 doi:10.1123/ijspp.2018-0935

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

    Ritchie D, Hopkins WG, Buchheit M, Cordy J, Bartlett JD. Quantification of training and competition load across a season in an elite Australian football club. Int J Sports Physiol Perform. 2016;11(4):474479. PubMed ID: 26355304 doi:10.1123/ijspp.2015-0294

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

    Henderson M, Fransen J, McGrath JJ, Harries SK, Poulos N, Coutts A. Situational factors affecting rugby sevens match performance. Sci Med Football. 2019;3(4):275280. doi:10.1080/24733938.2019.1609070

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

    Hoppe MW, Baumgart C, Polglaze T, Freiwald J. Validity and reliability of GPS and LPS for measuring distances covered and sprint mechanical properties in team sports. PLoS One. 2018;13(2):e0192708. PubMed ID: 29420620 doi:10.1371/journal.pone.0192708

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
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