Relationships Between Model Estimates and Actual Match-Performance Indices in Professional Australian Footballers During an In-Season Macrocycle

in International Journal of Sports Physiology and Performance
Restricted access

Purchase article

USD  $24.95

Student 1 year subscription

USD  $107.00

1 year subscription

USD  $142.00

Student 2 year subscription

USD  $203.00

2 year subscription

USD  $265.00

Purpose: To assess and compare the validity of internal and external Australian football (AF) training-load measures for predicting match exercise intensity (MEI/min) and player-rank score (PRScore) using a variable dose-response model. Methods: A cohort of 25 professional AF players (23 ± 3 y, 188.3 ± 7.2 cm, 87.7 ± 8.4 kg) completed a 24-wk in-season macrocycle. In-season internal training and match load were quantified using session rating of perceived exertion (sRPE) and external load from satellite and accelerometer data. Using a training-impulse (TRIMP) calculation, external load (au) was represented as distance (TRIMPDist), distance ≥4.16 m/s (TRIMPHSDist), and PlayerLoad (TRIMPPL). In-season training load, MEI/min, and PRScore were applied to a variable dose-response model, which provided estimates of MEI/min and PRScore. Predicted MEI/min and PRScore were correlated with actual measures from each match. The magnitude of the difference between MEI/min and PRScore estimates for each model input and the difference between the precision of internal and external load measures to predict MEI/min and PRScore were calculated using the effect size ± 90% confidence interval (CI). Results: Estimates of MEI/min demonstrated very large associations with actual MEI/min (r, 90% CI) (eg, TRIMPDist .76 ± .13, and sRPESkills .73 ± .14). Estimates of PRScore demonstrated associations of large magnitude with actual PRScore using the same inputs. Precision of actual MEI/min was lowest using sRPE compared with (ES ± 90% CI) TRIMPDist, −.67 ± .34, and TRIMPPL, −.91 ± .39. There were trivial and unclear differences in the precision of PRScore estimates between TRIMP and sRPE inputs. Conclusions: Dose-response models from multiple training-load inputs can predict within-individual variation of MEI/min and PRScore. Internal and external training-input methods exhibited comparable predictive power.

Graham, Parfitt, and Eston are with the Alliance for Research in Exercise, Nutrition and Activity, University of South Australia, Adelaide, SA, Australia. Cormack is with the School of Exercise Science, Australian Catholic University, Fitzroy, VIC, Australia.

Graham (Sgraham@pafc.com.au) is corresponding author.
  • 1.

    Busso T. Variable dose-response relationship between exercise training and performance. Med Sci Exerc Sport. 2003;35:1188–1195. doi:10.1249/01.MSS.0000074465.13621.37

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

    Bannister EW, Calvert TW, Savage MV, Bach T. A systems model of training for athletic performance. J Sports Med Exerc Sci. 1975;7:57–61.

    • Search Google Scholar
    • Export Citation
  • 3.

    Wood R, Hayter S, Rowbottom D, Stewart I. Applying a mathematical model to training adaptation in a distance runner. Eur J Appl Physiol. 2005;94:310–316. PubMed doi:10.1007/s00421-005-1319-2

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

    Wallace LK, Slattery KM, Coutts AJ. A comparison of methods for quantifying training load: relationships between modelled and actual training responses. Eur J Sport Sci. 2014;114:11–20. PubMed doi:10.1007/s00421-013-2745-1

    • Search Google Scholar
    • Export Citation
  • 5.

    Chalencon S, Pichot V, Roche F, et al. Modeling of performance and ANS activity for predicting future response to training. Eur J Appl Physiol. 2015;115:589–596. PubMed doi:10.1007/s00421-014-3035-2

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

    Mujika I, Busso T, Lacoste L, Barale F, Geyssant A, Chatard JC. Modeled responses to training and taper in competitive swimmers. Med Sci Sprt Exerc. 1996;28:251–258. doi:10.1097/00005768-199602000-00015

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

    Sanjez AMJ, Galbes O, Fabre-Guery F, et al. Modeling training responses in elite female gymnasts and optimal strategies of overload training and taper. J Sports Sci. 2013;31:1510–1519. doi:10.1080/02640414.2013.786183

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

    Busso T, Hakkinen K, Parakinen A, Kavhanen H, Komi PV, Lacour JR. Hormonal adaptations and modelled responses in elite weightlifters during 6 weeks of training. Eur J Appl Physiol. 1992;64:381–386. doi:10.1007/BF00636228

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

    Borreson J, Lambert MI. The quantification of training load, the training response and the effect on performance. Sports Med. 2009;39:779–795. doi:10.2165/11317780-000000000-00000

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

    Coutts AJ, Duffield R. Validity and reliability of GPS devices for measuring movement demands of team sports. J Sci Med Sports. 2010;13:133–135. doi:10.1016/j.jsams.2008.09.015

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

    Aughey RJ. Applications of GPS technologies to field sports. Int J Sports Physiol Perform. 2011;6:295–310. PubMed doi:10.1123/ijspp.6.3.295

  • 12.

    Boyd LJ, Ball K, Aughey RJ. Quantifying external load in Australian football matches and training using accelerometers. Int J Sports Physiol Perform. 2013;8:44–51. PubMed doi:10.1123/ijspp.8.1.44

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

    Coutts A, Reaburn P, Murphy A, Pine ML, Impellizzeri FM. Validity of the session RPE method for determining training load in team sport athletes. J Sci Med Sports. 2003;6:525.

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

    Taha T. Systems modeling of the relationship between training and performance. Sports Med. 2003;33:1061–1073. PubMed doi:10.2165/00007256-200333140-00003

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

    Halson SL. Monitoring training load to understand fatigue in athletes. Sports Med. 2014;44:139–147. doi:10.1007/s40279-014-0253-z

  • 16.

    Hergerud J, Engen LC, Wisloff U. Aerobic endurance training improves soccer performance. Med Sci Sport Exerc. 2001;33:1925–1931. doi:10.1097/00005768-200111000-00019

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

    Impellizzeri F, Marcora S, Catagna C. Physiological and performance effects of generic versus specific aerobic training in soccer players. Int J Sports Med. 2006;27:483–492. PubMed doi:10.1055/s-2005-865839

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

    Mooney MM, Cormack SJ, Coutts AJ, Berry J, Young WB. The relationship between physical capacity and match performance in elite Australian football: a mediation approach. J Sci Med Sports. 2011;14:447–452. doi:10.1016/j.jsams.2011.03.010

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

    Augey RJ. Increased high-intensity activity in elite Australian football finals matches. Int J Sports Physiol Perform. 2011;6:367–379. doi:10.1123/ijspp.6.3.367

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

    Bauer MA, Young W, Fahrner B, Harvey J. GPS variables most related to match performance in an elite Australian football team. Int J Perform Analysis Sport 2015;15:187–202. doi:10.1080/24748668.2015.11868786

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

    Sullivan C, Bilsborough JC, Cianciosi M, Hocking J, Cordy JT, Coutts A. Factors affecting match performance in professional Australian football. Int J Sports Physiol Perform. 2014;9:561–566. PubMed doi:10.1123/ijspp.2013-0183

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

    Foster C. Monitoring training in athletes with reference to overtraining syndrome. Med Sci Sport Exerc. 1998;30:1164–1168. doi:10.1097/00005768-199807000-00023

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

    Boyd LJ, Ball K, Aughey RJ. The reliability of MinimaxX accelerometers for measuring physical activity in Australian football. Int J Sports Physiol Perform. 2011;6:311–321. PubMed doi:10.1123/ijspp.6.3.311

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

    Edwards S. High performance training and racing. In: Edwards S, ed. The Heart Rate Monitor Book. Sacramento, CA: Fleet Feet Press; 1993:113–123.

    • Search Google Scholar
    • Export Citation
  • 25.

    Busso T, Denis C, Bonnefoy R, Geyssant A, Lacour JR. Modeling of adaptations to physical training by using a recursive least squares algorithm. J Appl Physiol. 1997;82:1685–1693. PubMed

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

    Hopkins WG. Confidence limits and magnitude based Inferences from p-values. http://www.sportsci.org/resource/stats/. 2012.

    • Export Citation
  • 27.

    Busso T, Hakkinen K, Pakarinen A, et al. A systems model of training responses and its relationship to hormonal responses in elite weight-lifters. Eur J Appl Physiol. 1990;61:48–54. doi:10.1007/BF00236693

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

    Gallo T, Cormack S, Gabbett TJ, Williams MD, Lorenzen C. Characteristics impacting on session rating of perceived exertion training load in Australian footballers. J Sports Sci. 2015;33:467–475. PubMed doi:10.1080/02640414.2014.947311

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

    Casamichana D, Castellano J, Galleja-Gozalez J, San Roman J, Castagna C. Relationship between indicators of training load in soccer players J Strength Cond Research. 2013;27:369–374. doi:10.1519/JSC.0b013e3182548af1

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 95 95 8
Full Text Views 17 17 0
PDF Downloads 5 5 0