Relationships Between Different Internal and External Training Load Variables and Elite International Women’s Basketball Performance

in International Journal of Sports Physiology and Performance
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Purpose: To investigate the relationships between internal and external training load (TL) metrics with elite international women’s basketball performance. Methods: Sessional ratings of perceived exertion, PlayerLoad/minute, and training duration were collected from 13 elite international-level female basketball athletes (age 29.0 [3.7] y, stature 186.0 [9.8] cm, body mass 77.9 [11.6] kg) during the 18 weeks prior to the International Basketball Federation Olympic qualifying event for the 2016 Rio Olympic Games. Training stress balance, differential load, and the training efficiency index were calculated with 3 different smoothing methods. These TL metrics and their change in the last 21 days prior to competition were examined for their relationship to competition performance as coach ratings of performance. Results: For a number of TL variables, there were consistent significant small to moderate correlations with performance and significant small to large differences between successful and unsuccessful performances. However, these differences were only evident for external TL when using exponentially weighted moving averages to calculate TL. The variable that seemed most sensitive to performance was the change in training efficiency index in the last 21 days prior to competition (performance r = .47–.56, P < .001 and difference between successful and unsuccessful performance P < .001, f2 = 0.305–0.431). Conclusions: Internal and external TL variables were correlated with performance and distinguished between successful and unsuccessful performances among the same players during international women’s basketball games. Manipulating TL in the last 3 weeks prior to competition may be worthwhile for basketball players’ performance, especially in internal TL.

Coyne, Newton, and Haff are with the Centre for Exercise and Sports Science Research, Edith Cowan University, Joondalup, WA, Australia. Coyne is also with the UFC Performance Inst, Las Vegas, NV, USA. Coutts is with the Human Performance Research Centre, University of Technology Sydney (UTS), Moore Park, NSW, Australia; and the School of Sport, Exercise and Rehabilitation, University of Technology Sydney (UTS), Moore Park, Moore Park, NSW, Australia. Haff is also with Directorate of Psychology and Sport, University of Salford, Salford, Greater Manchester, United Kingdom.

Coyne (coach@josephcoyne.com) is corresponding author.
  • 1.

    Stojanovic E, Stojiljkovic N, Scanlan AT, Dalbo VJ, Berkelmans DM, Milanovic Z. The activity demands and physiological responses encountered during basketball match-play: a systematic review. Sports Med. 2018;48(1):111135. PubMed ID: 29039018 doi:10.1007/s40279-017-0794-z

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

    McLean BD, Strack D, Russell J, Coutts AJ. Quantifying physical demands in the national basketball association-challenges around developing best-practice models for athlete care and performance. Int J Sports Physiol Perform. 2019;14(4):414420. PubMed ID: 30039990 doi:10.1123/ijspp.2018-0384

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

    Fox JL, Scanlan AT, Stanton R. A review of player monitoring approaches in basketball: current trends and future directions. J Strength Cond Res. 2017;31(7):20212029. PubMed ID: 28445227 doi:10.1519/JSC.0000000000001964

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

    Fox JL, Stanton R, Sargent C, Wintour SA, Scanlan AT. The association between training load and performance in team sports: a systematic review. Sports Med. 2018;48(12):27432774. PubMed ID: 30225537 doi:10.1007/s40279-018-0982-5

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

    Reina Román M, García-Rubio J, Feu S, Ibáñez SJ. Training and competition load monitoring and analysis of women’s amateur basketball by playing position: approach study. Front Psychol. 2019;9:2689. PubMed ID: 30687163 doi:10.3389/fpsyg.2018.02689

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

    Ransdell LB, Murray T, Gao Y, Jones P, Bycura D. A 4-year profile of game demands in elite Women’s Division I college basketball. J Strength Cond Res. 2020;34(3):632638. PubMed ID: 31842134 doi:10.1519/JSC.0000000000003425

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

    Paulauskas H, Kreivyte R, Scanlan AT, Moreira A, Siupsinskas L, Conte D. Monitoring workload in elite female basketball players during the in-season phase: weekly fluctuations and effect of playing time. Int J Sports Physiol Perform. 2019;14(7):941948. PubMed ID: 30676809 doi:10.1123/ijspp.2018-0741

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

    Impellizzeri FM, Marcora SM, Coutts AJ. 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
  • 9.

    Coyne JOC, Gregory Haff G, Coutts AJ, Newton RU, Nimphius S. The current state of subjective training load monitoring—a practical perspective and call to action. Sports Med Open. 2018;4(1):58. PubMed ID: 30570718 doi:10.1186/s40798-018-0172-x

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

    Delaney JA, Duthie GM, Thornton HR, Pyne DB. Quantifying the relationship between internal and external work in team sports: development of a novel training efficiency index. Sci Med Football. 2018;2(2):149158. doi:10.1080/24733938.2018.1432885

    • Crossref
    • 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:10.1007/s40279-017-0830-z

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

    Graham SR, Cormack S, Parfitt G, Eston R. Relationships between model predicted and actual match performance in professional Australian Footballers during an in-season training macrocycle. Int J Sports Physiol Perform. 2017:13(3):339346. PubMed ID: 28714739 doi:10.1123/ijspp.2017-0026

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

    Lazarus BH, Stewart AM, White KM, et al. Proposal of a global training load measure predicting match performance in an elite team sport. Front Physiol. 2017;8:930. PubMed ID: 29209229 doi:10.3389/fphys.2017.00930

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

    Staunton C, Wundersitz D, Gordon B, Kingsley M. Construct validity of accelerometry-derived force to quantify basketball movement patterns. Int J Sports Med. 2017;38(14):10901096. PubMed ID: 28965347 doi:10.1055/s-0043-119224

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

    Bourdon PC, Cardinale M, Murray A, et al. Monitoring athlete training loads: consensus statement. Int J Sports Physiol Perform. 2017;12(suppl 2):161170. PubMed ID: 28463642 doi:10.1123/IJSPP.2017-0208

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

    Banister EW, Calvert TW, Savage MV, Bach TM. A systems model of training for athletic performance. Aust J Sci Med. 1975;7(3):5761.

  • 17.

    Hulin BT, Gabbett TJ, Blanch P, Chapman P, Bailey D, Orchard JW. Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers. Br J Sports Med. 2014;48(8):708712. PubMed ID: 23962877 doi:10.1136/bjsports-2013-092524

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

    Coyne JOC, Nimphius S, Newton RU, Haff GG. Does mathematical coupling matter to the acute to chronic workload ratio? A case study from elite sport. Int J Sports Physiol Perform. 2019;14(10):14471454.

    • Search Google Scholar
    • Export Citation
  • 19.

    Lolli L, Batterham AM, Hawkins R, et al. The acute-to-chronic workload ratio: an inaccurate scaling index for an unnecessary normalisation process? Br J Sports Med. 2019;53(24):15101512. PubMed ID: 29899049 doi:10.1136/bjsports-2017-098884

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

    Wang C, Vargas JT, Stokes T, Steele R, Shrier I. Analyzing activity and injury: lessons learned from the acute: chronic workload ratio. Sports Med. 2020;50(7):12431254. PubMed ID: 32125672 doi:10.1007/s40279-020-01280-1

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

    Allen H, Coggan A. Training and Racing With A Powermeter. 2nd ed. Boulder, CO: Velopress; 2010.

  • 22.

    Tysoe A, Moore IS, Ranson C, McCaig S, Williams S. Bowling loads and injury risk in male first class county cricket: is differential load an alternative to the acute-to-chronic workload ratio? J Sci Med Sport. 2020;23(6):569573. PubMed ID: 31982300 doi:10.1016/j.jsams.2020.01.004

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

    Williams S, West S, Cross MJ, Stokes KA. Better way to determine the acute: chronic workload ratio? Br J Sports Med. 2016;51(3):209. PubMed ID: 27650255 doi:10.1136/bjsports-2016-096589

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

    Murray NB, Gabbett TJ, Townshend AD, Blanch P. Calculating acute: chronic workload ratios using exponentially weighted moving averages provides a more sensitive indicator of injury likelihood than rolling averages. Br J Sports Med. 2016;51(9):749. PubMed ID: 28003238 doi:10.1136/bjsports-2016-097152

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

    Raysmith BP, Drew MK. Performance success or failure is influenced by weeks lost to injury and illness in elite Australian track and Field athletes: a 5-year prospective study. J Sci Med Sport. 2016;19(10):778783. PubMed ID: 23982902 doi:10.1123/ijspp.2013-0121

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

    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
  • 27.

    Delaney JA, McKay BA, Thornton HR, Murray A, Duthie GM. Training efficiency and athlete wellness in collegiate female soccer. Sports Perform Sci Rep. 2018;1(19):13.

    • Search Google Scholar
    • Export Citation
  • 28.

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

  • 29.

    Coyne JOC, Haff G.G., Coutts A.C., Netwon R.U. & Nimphius S. The relationship between internal training load variables during a taper and elite weightlifting success. National Strength & Conditioning Association National Conference; 2018; Indianapolis, IN, U.S.

    • Search Google Scholar
    • Export Citation
  • 30.

    Bosquet L, Montpetit J, Arvisais D, Mujika I. Effects of tapering on performance: a meta-analysis. Med Sci Sports Exerc. 2007;39(8):13581365. PubMed ID: 17762369 doi:10.1249/mss.0b013e31806010e0

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31.

    Moraes H, Aoki MS, Freitas CG, Arruda A, Drago G, Moreira A. SIgA response and incidence of upper respiratory tract infections during intensified training in youth basketball players. Biol Sport. 2017;34(1):4955. PubMed ID: 28416898 doi:10.5114/biolsport.2017.63733

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

    Miloski B, Aoki MS, de Freitas CG, et al. Does testosterone modulate mood states and physical performance in young basketball players? J Strength Cond Res. 2015;29(9):24742481. PubMed ID: 25734781 doi:10.1519/JSC.0000000000000883

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

    Mooney M, O’Brien B, Cormack S, Coutts A, Berry J, Young W. The relationship between physical capacity and match performance in elite Australian football: a mediation approach. J Sci Med Sport. 2011;14(5):447452. PubMed ID: 21530392 doi:10.1016/j.jsams.2011.03.010

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

    Efficiency (basketball). Wikipedia. https://en.wikipedia.org/wiki/Efficiency_(basketball). Published 2020. Accessed April 15, 2020.

  • 35.

    Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc. 2009;41(1):312. PubMed ID: 19092709 doi:10.1249/MSS.0b013e31818cb278

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

    Aiken LS, West SG. Multiple Regression: Testing and Interpreting Interactions. Newbury Park: Sage; 1991.

  • 37.

    Cohen J. A power primer. Psychol Bull. 1992;112(1):155159. PubMed ID: 19565683 doi:10.1037//0033-2909.112.1.155

  • 38.

    Thomas L, Mujika I, Busso T. A model study of optimal training reduction during pre-event taper in elite swimmers. J Sports Sci. 2008;26(6):643652. PubMed ID: 18344135 doi:10.1080/02640410701716782

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

    Hidalgo-Muñoz AR, Béquet AJ, Astier-Juvenon M, et al. Respiration and heart rate modulation due to competing cognitive tasks while driving. Front Hum Neurosci. 2019;12:525. PubMed ID: 30687043 doi:10.3389/fnhum.2018.00525

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
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