Relationship Between Training Workloads, Match Workloads, and Match Performance in Elite Netball

in International Journal of Sports Physiology and Performance

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Marni J. SimpsonSchool of Human Movement and Nutrition Sciences, University of Queensland, St Lucia, QLD, Australia
Queensland Firebirds, Netball Queensland, Nathan, QLD, Australia

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David G. JenkinsSchool of Human Movement and Nutrition Sciences, University of Queensland, St Lucia, QLD, Australia
School of Health and Sport Sciences, University of the Sunshine Coast, Sippy Downs, QLD, Australia

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Mark ConnickSchool of Human Movement and Nutrition Sciences, University of Queensland, St Lucia, QLD, Australia

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Vincent G. KellySchool of Human Movement and Nutrition Sciences, University of Queensland, St Lucia, QLD, Australia
School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, QLD, Australia

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Purpose: This study examined the relationships between training workloads, game workloads, and match performance in an elite netball team. Methods: Ten elite female netball athletes were monitored over a complete season. Training and game external workloads were determined through inertial movement units and expressed as absolute PlayerLoad (PL) and change of direction (COD). Monthly workload and training efficiency index were also calculated, which used internal workloads (session rating of perceived exertion and summated heart-rate zones). Game performance was assessed through a performance analysis statistic algorithm called NetPoints. To account for the influence of team game workloads on each other, the average workload for midcourt positions (avgMC) was calculated for each game. Data for each athlete were transformed into z scores, and linear mixed modeling was used to build models to examine the relationships between workloads and game performance. Results: Monthly PL, training efficiency index PL, and avgMC PL were statistically significant (P < .05) and positively related to game PL (z = 0.20–0.35, P < .001–.02). For game COD, statistically significant positive relationships were found between monthly COD (z = 0.29 [0.11], P = .01) and avgMC COD (z = 0.21 [0.09], P = .03). The models for NetPoints found significant negative relationships with monthly PL (z = 0.46 [0.12], P < .001) and COD (z = −0.36 [0.11], P = .01). Conclusions: Higher monthly workloads are related to higher game workload; however, they are also related to decreases in match performance. Therefore, netball practitioners should consider that increases to training workload in a 4-week period prior to a game can influence game workloads and performance.

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  • 1.

    Bourdon PC, Cardinale M, Murray A, et al. Monitoring athlete training loads: consensus statement. Int J Sports Physiol Perform. 2017;12(suppl):S2-161S2-170. doi:10.1123/IJSPP.2017-0208

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

    Anderson L, Triplett-McBride T, Foster C, Doberstein S, Brice G. Impact of training patterns on incidence of illness and injury during a women’s collegiate basketball season. J Strength Cond Res. 2003;17(4):734738. PubMed ID: 14636112

    • Search Google Scholar
    • Export Citation
  • 3.

    Coutts A, Reaburn P, Piva T, Rowsell G. Monitoring for overreaching in rugby league players. Eur J Appl Physiol. 2007;99(3):313324. PubMed ID: 17219174 doi:10.1007/s00421-006-0345-z

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

    Slattery K, Wallace L, Bentley D, Coutts A. Effect of training load on simulated team sport match performance. Appl Physiol Nutr Metab. 2012;37(2):315322. PubMed ID: 22452610 doi:10.1139/h2012-001

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

    Borresen J, Lambert MI. Changes in heart rate recovery in response to acute changes in training load. Eur J Appl Physiol. 2007;101(4):503511. PubMed ID: 17687564 doi:10.1007/s00421-007-0516-6

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

    Campos-Vazquez MA, Toscano-Bendala FJ, Mora-Ferrera JC, Suarez-Arrones LJ. Relationship between internal load indicators and changes on intermittent performance after the preseason in professional soccer players. J Strength Cond Res. 2017;31(6):14771485. PubMed ID: 28538295 doi:10.1519/JSC.0000000000001613

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

    Ferioli RD, Bosio RA, La Torre RA, Carlomagno RD, Connolly RD, Rampinini RE. Different training loads partially influence physiological responses to the preparation period in basketball. J Strength Cond Res. 2018;32(3):790797. PubMed ID: 28146032 doi:10.1519/JSC.0000000000001823

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

    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
  • 9.

    Gastin BP, Fahrner LB, Meyer LD, Robinson LD, Cook LJ. Influence of physical fitness, age, experience, and weekly training load on match performance in elite Australian football. J Strength Cond Res. 2013;27(5):12721279. PubMed ID: 22820206 doi:10.1519/JSC.0b013e318267925f

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

    Johnston RD, Murray NB, Austin DJ. The influence of pre-season training loads on in-season match activities in professional Australian football players. Sci Med Footb. 2018;3(2):143149. doi:10.1080/24733938.2018.1501160

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

    Crang ZL, Hewitt A, Scott TJ, Kelly VG, Johnston RD. Relationship between preseason training load, match performance, and match activities in professional rugby league. J Strengh Cond Res. 2022;36(9):25812588. doi:10.1519/jsc.0000000000003891

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

    Heasman J, Dawson B, Berry J, Stewart G. Development and validation of a player impact ranking system in Australian football. Int J Perform Anal Sport. 2008;8(3):156171. doi:10.1080/24748668.2008.11868457

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

    Hiscock D, Dawson B, Heasman J, Peeling P. Game movements and player performance in the Australian Football League. Int J Perform Anal Sport. 2012;12(3):531545. doi:10.1080/24748668.2012.11868617

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

    Saunders N, McLean SG, Fox AS, Otago L. Neuromuscular dysfunction that may predict ACL injury risk: A case report. Knee. 2014;21(3):789792. PubMed ID: 24529986 doi:10.1016/j.knee.2014.01.005

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

    Saunders N, Otago L. Elite netball injury surveillance: implications for injury prevention. J Sci Med Sport. 2009;12:S63. doi:10.1016/j.jsams.2008.12.148

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

    Young CM, Gastin PB, Sanders N, Mackey L, Dwyer DB. Player load in elite netball: match, training and positional comparisons. Int J Sports Physiol Perform. 2016;11(8):10741079. PubMed ID: 27001768 doi:10.1123/ijspp.2015-0156

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

    Luteberget LS, Holme BR, Spencer M. Reliability of wearable Inertial measurement units to measure physical activity in team handball. Int J Sports Physiol Perform. 2018;13(4):467. PubMed ID: 28872371 doi:10.1123/ijspp.2017-0036

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

    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
    • Search Google Scholar
    • Export Citation
  • 19.

    Foster C, Florhaug JA, Franklin J, et al. A new approach to monitoring exercise training. J Strength Cond Res. 2001;15(1):109. PubMed ID: 11708692

    • Search Google Scholar
    • Export Citation
  • 20.

    Bannister EW. Modeling elite athletic performance. In: MacDougall JD, Wenger HA, Green HJ, eds. Physiological Testing of the High Performance Athlete. Human Kinetics; 1991:403424.

    • Search Google Scholar
    • Export Citation
  • 21.

    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 Footb. 2018;2(2):149156. doi:10.1080/24733938.2018.1432885

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

    Simpson MJ, Jenkins DG, Scanlan AT, Kelly VG. Relationships between external- and internal-workload variables in an elite female netball team and between playing positions. Int J Sports Physiol Perform. 2020;15(6):841846. PubMed ID: 32163926 doi:10.1123/ijspp.2019-0619

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

    Castillo D, Raya-González J, Scanlan AT, Sánchez-Díaz S, Lozano D, Yanci J. The influence of physical fitness attributes on external demands during simulated basketball matches in youth players according to age category. Physiol Behav. 2021;233:113354113354. PubMed ID: 33561474 doi:10.1016/j.physbeh.2021.113354

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

    Harper DJ, Kiely J. Damaging nature of decelerations: do we adequately prepare players? BMJ Open Sport Exerc Med. 2018;4(1):e000379. PubMed ID: 30112183 doi:10.1136/bmjsem-2018-000379

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

    Byrne C, Twist C, Eston R. Neuromuscular function after exercise-induced muscle damage: theoretical and applied implications. Sports Med. 2004;34(1):4969. PubMed ID: 14715039 doi:10.2165/00007256-200434010-00005

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

    Sullivan C, Bilsborough JC, Cianciosi M, Hocking J, Cordy JT, Coutts AJ. Factors affecting match performance in professional Australian football. Int J Sports Physiol Perform. 2014;9(3):561566. PubMed ID: 23981383 doi:10.1123/ijspp.2013-0183

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

    Sullivan C, Bilsborough JC, Cianciosi M, Hocking J, Cordy J, Coutts AJ. Match score affects activity profile and skill performance in professional Australian Football players. J Sci Med Sport. 2014;17(3):326331. PubMed ID: 23770325 doi:10.1016/j.jsams.2013.05.001

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

    Kempton T, Coutts AJ. Factors affecting exercise intensity in professional rugby league match-play. J Sci Med Sport. 2015;19(6):504508. PubMed ID: 26117160 doi:10.1016/j.jsams.2015.06.008

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

    McCaskie CJ, Young WB, Fahrner BB, Sim M. Association between preseason training and performance in elite Australian football. Int J Sports Physiol Perform. 2019;14(1):6875. doi:10.1123/ijspp.2018-0076

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

    Caparrós T, Alentorn-Geli E, Myer GD, et al. The relationship of practice exposure and injury rate on game performance and season success in professional male basketball. J Sports Sci Med. 2016;15(3):397402. PubMed ID: 27803617

    • Search Google Scholar
    • Export Citation
  • 31.

    Riboli A, Semeria M, Coratella G, Esposito F. Effect of formation, ball in play and ball possession on peak demands in elite soccer. Biol Sport. 2021;38(2):195205. PubMed ID: 34079164 doi:10.5114/biolsport.2020.98450

    • Crossref
    • Search Google Scholar
    • Export Citation
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