Developing Athlete Monitoring Systems in Team Sports: Data Analysis and Visualization

in International Journal of Sports Physiology and Performance
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In professional team sports, the collection and analysis of athlete-monitoring data are common practice, with the aim of assessing fatigue and subsequent adaptation responses, examining performance potential, and minimizing the risk of injury and/or illness. Athlete-monitoring systems should be underpinned by appropriate data analysis and interpretation, to enable the rapid reporting of simple and scientifically valid feedback. Using the correct scientific and statistical approaches can improve the confidence of decisions made from athlete-monitoring data. However, little research has discussed and proposed an outline of the process involved in the planning, development, analysis, and interpretation of athlete-monitoring systems. This review discusses a range of methods often employed to analyze athlete-monitoring data to facilitate and inform decision-making processes. There is a wide range of analytical methods and tools that practitioners may employ in athlete-monitoring systems, as well as several factors that should be considered when collecting these data, methods of determining meaningful changes, and various data-visualization approaches. Underpinning a successful athlete-monitoring system is the ability of practitioners to communicate and present important information to coaches, ultimately resulting in enhanced athletic performance.

Thornton is with La Trobe Sport and Exercise Medicine Research Centre, La Trobe University, Melbourne, VIC, Australia, and Gold Coast Suns Football Club, Carrara, QLD, Australia. Delaney is with the Inst of Sport, Exercise and Active Living, Victoria University, Melbourne, VIC, Australia. Duthie is with the School of Exercise Science, Australian Catholic University, Strathfield, NSW, Australia. Dascombe is with the Applied Sports Science and Exercise Testing Laboratory, University of Newcastle, Ourimbah, NSW, Australia.

Thornton (heidi.thornton@goldcoastfc.com.au) is corresponding author.
  • 1.

    Thorpe R, Atkinson G, Drust B, Gregson W. Monitoring fatigue status in elite team-sport athletes: implications for practice. Int J Sports Physiol Perform. 2017;12(Suppl 2):S-227S-234. PubMed ID: 28095065 doi:10.1123/ijspp.2016-0434

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

    Halson S. Monitoring training load to understand fatigue in athletes. Sports Med. 2014;44(2):139147. doi:10.1007/s40279-014-0253-z

  • 3.

    Buchhiet M. Want to see my report, coach? Sport science reporting in the real world. Aspetar Sports Med J. 2017;6:3642.

  • 4.

    Atkinson G, Nevill A. Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine. Sports Med. 1998;26(4):217238. PubMed ID: 9820922 doi:10.2165/00007256-199826040-00002

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

    Coutts A. Working fast and working slow: the benefits of embedding research in high-performance sport. Int J Sports Physiol Perform. 2016;11(1):12. PubMed ID: 26752203 doi:10.1123/IJSPP.2015-0781

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

    Thorpe R, Atkinson G, Drust B, Gregson W. Monitoring fatigue status in elite team-sport athletes: implications for practice. Int J Sports Physiol Perform. 2017;12(Suppl 2):S-227S-234. PubMed ID: 28095065 doi:10.1123/ijspp.2016-0434

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

    Hopkins WG, Marshall SW, Quarrie KL, Hume PA. Risk factors and risk statistics for sports injuries. Clin J Sport Med. 2007;17(3):208210. PubMed ID: 17513914 doi:10.1097/JSM.0b013e3180592a68

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

    Halson SL. Monitoring training load to understand fatigue in athletes. Sports Med. 2014;44(suppl 2):139147. doi:10.1007/s40279-014-0253-z

  • 9.

    Borresen J, Lambert MI. The quantification of training load, the training response and the effect on performance. Sports Med. 2009;39(9):779795. PubMed ID: 19691366 doi:10.2165/11317780-000000000-00000

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

    Hopkins W. Individual responses made easy. J Appl Physiol. 2015;118(12):14441446. PubMed ID: 25678695 doi:10.1152/japplphysiol.00098.2015

  • 11.

    Batterham A, Hopkins W. Making meaningful inferences about magnitudes. Int J Sports Physiol Perform. 2006;1(1):5057. PubMed ID: 19114737 doi:10.1123/ijspp.1.1.50

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

    Scott T, Thornton H, Scott M, Dascombe B, Duthie G. Differences between relative and absolute speed and metabolic thresholds in rugby league. Int J Sports Physiol Perform. 2018;13(3):298304. PubMed ID: 28657854 doi:10.1123/ijspp.2016-0645

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

    Clarke A, Anson J, Pyne D. Physiologically based GPS speed zones for evaluating running demands in women’s rugby sevens. J Sports Sci. 2015;33(11):11011108. PubMed ID: 25510337 doi:10.1080/02640414.2014.988740

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

    Buchheit M, Hammond K, Bourdarios P, et al. Relative match intensities at high altitude in highly-trained young soccer players (ISA3600). J Sports Sci Med. 2015;14(1):98102. PubMed ID: 25729296.

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

    Lacome M, Piscione J, Hager JP, Bourdin M. A new approach to quantifying physical demand in rugby union. J Sports Sci. 2014;32(3):290300. PubMed ID: 24016296 doi:10.1080/02640414.2013.823225

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

    Malone J, Lovell R, Varley M, Coutts A. Unpacking the black box: Applications and considerations for using GPS devices in sport. Int J Sports Physiol Perform. 2017;12(Suppl 2):S-218S-226. PubMed ID: 27736244 doi:10.1123/ijspp.2016-0236

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

    Thornton H, Nelson A, Delaney J, Serpiello F, Duthie G. Interunit reliability and effect of data-processing methods of global positioning systems. Int J Sports Physiol Perform. 2019;14(4):432438. doi:10.1123/ijspp.2018-0273

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

    Carey D, Crossley K, Whiteley R, et al. Modelling training loads and injuries: the dangers of discretization. Med Sci Sports Exerc. 2018;50(11):22672276. PubMed ID: 29933352 doi:10.1249/MSS.0000000000001685

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

    Altman D, Royston P. The cost of dichotomising continuous variables. BMJ. 2006;332(7549):1080. PubMed ID: 16675816 doi:10.1136/bmj.332.7549.1080

  • 20.

    Bennette C, Vickers A. Against quantiles: categorization of continuous variables in epidemiologic research, and its discontents. BMC Med Res Methodol. 2012;12(1):21. doi:10.1186/1471-2288-12-21

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

    R: A Language and Environment for Statistical Computing [computer program]. Version 3.5.2. Vienna, Austria: R Foundation for Statistical Computing; 2015.

    • Search Google Scholar
    • Export Citation
  • 22.

    McLean B, Coutts A, Kelly V, McGuigan M, Cormack S. Neuromuscular, endocrine, and perceptual fatigue responses during different length between-matches microcycles in professional rugby league players. Int J Sports Physiol Perform. 2010;5(3):367383. PubMed ID: 20861526 doi:10.1123/ijspp.5.3.367

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

    Gabbett T. The training-injury prevention paradox: should athletes be training smarter and harder? Br J Sports Med. 2016;50(5):273280. PubMed ID: 26758673 doi:10.1136/bjsports-2015-095788

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

    Hulin B, Gabbett T, Blanch P, Chapman P, Bailey D, Orchard J. Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers. Br J Sports Med. 2013;48:708712. PubMed ID: 23962877 doi:10.1136/bjsports-2013-092524

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

    Hulin B, Gabbett T, Lawson D, Caputi P, Sampson J. 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
  • 26.

    Murray N, Gabbett T, Townshend A, Hulin B, McLellan C. Individual and combined effects of acute and chronic running loads on injury risk in elite Australian footballers. Scand J Med Sci Sports. 2017;27(9):990998. PubMed ID: 27418064 doi:10.1111/sms.12719

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

    Carey D, Blanch P, Ong K, Crossley K, Crow J, Morris M. Training loads and injury risk in Australian football-differing acute: chronic workload ratios influence match injury risk. Br J Sports Med. 2017;51(16):12151220. PubMed ID: 27789430 doi:10.1136/bjsports-2016-096309

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

    Lolli L, Batterham A, Hawkins R, et al. Mathematical coupling causes spurious correlation within the conventional acute-to-chronic workload ratio calculations [published online ahead of print November 3, 2017]. Br J Sports Med. doi:10.1136/bjsports-2017-098110

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

    Buchheit M. Applying the acute:chronic workload ratio in elite football: worth the effort? Br J Sports Med. 2017;51(18):13251327. PubMed ID: 27852586 doi:10.1136/bjsports-2016-097017

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

    Williams S, West S, Cross M, Stokes K. 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
  • 31.

    Cummins C, Delaney J, Thornton H, Duthie G. Training load prior to injury in professional rugby league players. J Sci Med Sport. 2017;20(suppl 3):5354. doi:10.1016/j.jsams.2017.09.300

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

    Thornton H, Delaney J, Duthie G, Dascombe B. Effects of preseason training on the sleep characteristics of professional rugby league players. Int J Sports Physiol Perform. 2018;13(2):176182. PubMed ID: 28530487 doi:10.1123/ijspp.2017-0119

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Murray N, Gabbett T, Townshend A. The use of relative speed zones in Australian football: are we really measuring what we think we are? Int J Sports Physiol Perform. 2018;13(4):442451. PubMed ID: 28872423 doi:10.1123/ijspp.2017-0148

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

    Tu YK, Gilthorpe MS. Revisiting the relation between change and initial value: a review and evaluation. Stat Med. 2007;26(2):443457. PubMed ID: 16526009 doi:10.1002/sim.2538

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35.

    Windt J, Gabbett TJ. Is it all for naught? What does mathematical coupling mean for acute: chronic workload ratios? [published online ahead of print May 28, 2018]. Br J Sports Med. doi:10.1136/bjsports-2017-098925

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36.

    Moore D, McCabe G, Craig B. Introduction to the Practice of Statistics. 6th ed. New York, NY: WH Freeman; 2009.

  • 37.

    Ghasemi A, Zahediasl S. Normality tests for statistical analysis: a guide for non-statisticians. Int J Endocrinol Metab. 2012;10(2):486489. PubMed ID: 23843808 doi:10.5812/ijem.3505

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.

    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):S-273S-279. PubMed ID: 27967289 doi:10.1123/ijspp.2016-0541

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 39.

    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):313. PubMed ID: 19092709 doi:10.1249/MSS.0b013e31818cb278

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 41.

    Hopkins W. Measures of reliability in sports medicine and science. Sports Med. 2000;30(1):115. PubMed ID: 10907753 doi:10.2165/00007256-200030010-00001

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Pyne D. Interpreting the results of fitness testing. Paper presented at: International Science and Football Symposium; 2003, Melbourne, Australia.

    • Export Citation
  • 43.

    Bernards J, Sato K, Haff G, Bazyler C. Current research and statistical practices in sport science and a need for change. Sports. 2017;5(4):87. doi:10.3390/sports5040087

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 44.

    Rhea M. Determining the magnitude of treatment effects in strength training research through the use of the effect size. J Strength Cond Res. 2004;18(4):918920. PubMed ID: 15574101

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45.

    Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Erlbaum; 1988.

  • 46.

    Hopkins W. A spreadsheet for deriving a confidence interval, mechanistic inference and clinical inference from a p value. Sportscience. 2007;11:1620. www.sportsci.org/2007/wghinf.htm.

    • Search Google Scholar
    • Export Citation
  • 47.

    Welsh A, Knight E. “Magnitude-based inference”: a statistical review. Med Sci Sports Exerc. 2015;47(4):874884. PubMed ID: 25051387 doi:10.1249/MSS.0000000000000451

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 48.

    Sainani K. The Problem with “Magnitude-Based Inference”. Med Sci Sports Exerc. 2018;50(10):21662176. PubMed ID: 29683920 doi:10.1249/MSS.0000000000001645

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 49.

    Hopkins W, Batterham A. 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
  • 50.

    Mengersen K, Drovandi C, Robert C, Pyne D, Gore C. Bayesian estimation of small effects in exercise and sports science. PLoS ONE. 2016;11(4):0147311. doi:10.1371/journal.pone.0147311

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 51.

    Hopkins W, Batterham A, Pyne D, Impellizzeri F. Misplaced decimal places. Scand J Med Sci Sports. 2011;21(6):867868. PubMed ID: 22126719 doi:10.1111/j.1600-0838.2011.01393.x

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 52.

    Weissgerber T, Milic N, Winham SJ, Garovic V. Beyond bar and line graphs: time for a new data presentation paradigm. PLoS Biol. 2015;13(4):e1002128. PubMed ID: 25901488 doi:10.1371/journal.pbio.1002128

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
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