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There is a common expression in sports that “there is no ‘I’ in team.” However, collectively, there is actually a very important “I” in sport teams—the individual athlete/player. Each player has his or her own unique characteristics including physical, physiological, and psychological traits. Due to these unique characteristics, each player requires individual provision—whether it be an injury risk profile and targeted prevention strategy or treatment/rehabilitation for injury, dietary regimen, recovery, or psychological intervention. The aim of this commentary is to highlight how 4 high-performance teams from various professional football codes are analyzing individual player data.

Ward is with the Seattle Seahawks, Seattle, WA. Coutts is with the Faculty of Health, University of Technology Sydney (UTS), Moore Park, Australia. Pruna is with FC Barcelona, Barcelona, Spain. McCall is with Arsenal Football Club, London, United Kingdom, and Edinburgh Napier University, Edinburgh, United Kingdom.

McCall (amccall@arsenal.co.uk) is corresponding author.
  • 1.

    Buchheit M. Houston, we still have a problem. Int J Sports Physiol Perform. 2017;12(8):11111114. PubMed ID: 28714760 doi:10.1123/ijspp.2017-0422

  • 2.

    Coutts AJ. Challenges in developing evidence-based practice in high-performance sport. Int J Sports Physiol Perform. 2017;12(6):717718. PubMed ID: 28832264 doi:10.1123/IJSPP.2017-0455

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

    Foster C, Snyder A, Welsh R. Monitoring of training, warm up, and performance in athletes. In: Lehmann M, Foster C, Gastmann U, Keizer H, Steinacker JM, eds. Overload, Performance Incompetence and Regeneration in Sport. New York, NY: Kluwer Academic/Plenum Publishers; 1999:4352.

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

    Foster C, Daines E, Hector L, Snyder AC, Welsh R. Athletic performance in relation to training load. Wis Med J. 1996;95(6):370374. PubMed ID: 8693756

  • 5.

    Hooper SL, Mackinnon LT. Monitoring overtraining in athletes: recommendations. Sports Med. 1995;20(5):321327. PubMed ID: 8571005 doi:10.2165/00007256-199520050-00003

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

    Rushall BS. A tool for measuring stress tolerance in elite athletes. J Appl Sport Psychol. 1990;2(1):5166. doi:10.1080/10413209008406420

  • 7.

    Coutts AJ, Crowcroft S, Kempton T. Developing athlete monitoring systems: theoretical basis and practical applications In: Kellmann M, Beckmann B, eds. Sport, Recovery and Performance: Interdisciplinary Insights (pp. 1932). Abingdon, England: Routledge; 2017.

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

    Sands WA, Kavanaugh AA, Murray SR, McNeal JR, Jemni M. Modern techniques and technologies applied to training and performance monitoring. Int J Sports Physiol Perform. 2017;12(suppl 2):263272. PubMed ID: 27918664 doi:10.1123/ijspp.2016-0405

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

    Hopkins WG. A spreadsheet for monitoring an individual’s changes and trends. Sport Sci. 2017;21:59.

  • 10.

    Kwok OM, Underhill AT, Berry JW, Luo W, Elliot TR, Yoon M. Analyzing longitudinal data with multilevel models: an example with individuals living with lower extremity intra-articular fractures. Rehabil Psychol. 2008;53(3):370386. PubMed ID: 19649151 doi:10.1037/a0012765

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

    Nourbakhsh MR, Ottenbacher KJ. The statistical analysis of single-subject data: a comparative examination. Phys Ther. 1994;74(8):768776. PubMed ID: 8047564 doi:10.1093/ptj/74.8.768

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

    Sands WA, McNeal JR, Stone M. Plaudits and pitfalls in studying elite athletes. Percept Mot Skills. 2005;100:2224. doi:10.2466/pms.100.1.22-24

  • 13.

    Buchheit M. The numbers will love you back in return—I promise. In J Sports Physiol Perform. 2016;11(4):551554. PubMed ID: 27164726 doi:10.1123/ijspp.2016-0214

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

    Hopkins WG, Batterham AM. Error rates, decisive outcomes and publication bias with several inferential methods. Sports Med. 2016;46(10):15631573. PubMed ID: 26971328 doi:10.1007/s40279-016-0517-x

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

    Buchheit M. Monitoring training status with HR measure: do all roads lead to Rome? Front Physiol. 2014;5:73. PubMed ID: 24578692 doi:10.3389/fphys.2014.00073

  • 16.

    van Shaik P, Weston M. Magnitude-based inference and its application in user research. Int J Hum Comput Stud. 2016;88:3850. doi:10.1016/j.ijhcs.2016.01.002

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

    Plews DJ, Laursen PB, Kilding AE, Buchheit M. Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison. Eur J Appl Physiol. 2012;112(11):37293741. PubMed ID: 22367011 doi:10.1007/s00421-012-2354-4

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

    Robertson S, Bartlett JD, Gastin PB. Red, Amber, or Green? Athlete monitoring in team sport: the need for decision-support systems. Int J Sports Physiol Perform. 2017;12(suppl 2): S273S279. PubMed ID: 27967289 doi:10.1123/ijspp.2016-0541

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

    Orme JG, Cox ME. Analyzing single-subject design data using statistical process control charts. Social Work Research. 2001;25(2):115127. doi:10.1093/swr/25.2.115

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

    Ritchie D, Hopkins WG, Buchheit M, Courdy J, Bartlett JD. Quantification of training ad 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
  • 21.

    Moreiera A, Bilsborough JC, Sullivan CJ, CIancosi M, Coutts AJ. Training periodization of professional Australian football players during an entire Australian Football League season. Int J Sports Physiol Perform. 2015;10(5): 566571. PubMed ID: 25405365 doi:10.1123/ijspp.2014-0326

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

    Williams S, West S, Cross MJ, Stokes KA. Better way to determine the actute:chronic workload ratio? Br J Sports Med. 2017;51(3): 209210. PubMed ID: 27650255 doi:10.1136/bjsports-2016-096589

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

    Gastin PG, Meyer D, Robertson 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
  • 24.

    Cnaan A, Laird NM, Slasor P. Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. Stat Med. 1997;16(20):23492380. PubMed ID: 9351170 doi:10.1002/(SICI)1097-0258(19971030)16:20<2349::AID-SIM667>3.0.CO;2-E

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

    Gelman A, Hill J. Data Analysis Using Regression and Multilevel/Hierarchical Models. New York, NY: Cambridge University Press; 2007.

  • 26.

    Malone JJ, Di Michele R, Morgans R, Burgess D, Morton JP, Drust B. Seasonal training load quantification in elite English premier league soccer players. Int J Sports Physiol Perform. 2015;10(4):489497. PubMed ID: 25393111 doi:10.1123/ijspp.2014-0352

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

    Kempton T, Coutts AJ. Factors affecting exercise intensity in professional rugby league. J Sci Med Sport. 2016;19(6):504508. PubMed ID: 26117160 doi:10.1016/j.jsams.2015.06.008

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
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