An Evaluation of Training Load Measures for Drills in Women’s Collegiate Lacrosse

in International Journal of Sports Physiology and Performance
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Purpose: To statistically evaluate the internal and external load metrics in different types of lacrosse drills. Methods: A total of 25 Division I collegiate female lacrosse players wore a heart rate monitor and a global positioning system during preseason training sessions. Seven measures determined training load, 2 internal measures and 5 external measures, across 5 different types of drills: stickwork, small-sided games, individual skills, conditioning, and team drills. Principal component analysis was used to determine which internal and external load variables were most associated with each drill type. Results: Stickwork extracted 2 principal components, explaining 45% and 17% of the variance. Small-sided games extracted 1 principal component, explaining 51% of the variance. Individual skills extracted 2 components, explaining 39% and 22% of the variance. Conditioning extracted 2 components, explaining 44% and 24% of the variance. Team drills extracted 2 components, explaining 52% and 18% of the variance. Conclusions: In 4 out of 5 training modes, the inclusion of both internal and external training-load measures was necessary to accurately decipher training load. For most drills, the first component is related to measures of external load, and the second component described the balance between internal and external load measures. Small-sided games extracted only external measures including the following: accelerations, total distance, and average speed. These results show that a combination of internal and external load measures is required to determine training load during certain training modes. This information can help coaches make decisions about desired training load for practice sessions.

The authors are with the Dept of Physical Therapy, Campbell University, Buies Creek, NC, USA.

Bunn (bunnj10@gmail.com) is corresponding author.
  • 1.

    Malone S, Owen A, Newton M, Mendes B, Collins KD, Gabbett TJ. The acute:chronic workload ratio in relation to injury risk in professional soccer. J Sci Med Sport. 2017;20(6):561565. PubMed ID: 27856198 doi:

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

    Malone S, Roe M, Doran DA, Gabbett TJ, Collins K. High chronic training loads and exposure to bouts of maximal velocity running reduce injury risk in elite Gaelic football. J Sci Med Sport. 2017;20(3):250254. PubMed ID: 27554923 doi:

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

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

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

    Gabbett TJ, Jenkins DG. Relationship between training load and injury in professional rugby league players. J Sci Med Sport. 2011;14(3):204209. PubMed ID: 21256078 doi:

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

    Watson A, Brickson S, Brooks A, Dunn W. Subjective well-being and training load predict in-season injury and illness risk in female youth soccer players. Br J Sports Med. 2017;51(3):194199. PubMed ID: 27919919 doi:

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

    Gabbett TJ. Debunking the myths about training load, injury and performance: empirical evidence, hot topics and recommendations for practitioners. Br J Sports Med. 2018;54(1):5866. PubMed ID: 30366966 doi:

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

    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:

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

    Wing C. Monitoring athlete load: data collection methods and practical recommendations. Strength Cond J. 2018;40(4):2639. doi:

  • 9.

    Manzi V, Iellamo F, Impellizzeri F, D’Ottavio S, Castagna C. Relation between individualized training impulses and performance in distance runners. Med Sci Sports Exerc. 2009;41(11):20902096. PubMed ID: 19812506 doi:

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

    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
  • 11.

    Stagno KM, Thatcher R, van Someren KA. A modified TRIMP to quantify the in-season training load of team sport players. J Sports Sci. 2007;25(6):629634. PubMed ID: 17454529 doi:

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

    Akenhead R, Nassis GP. Training load and player monitoring in high-level football: current practice and perceptions. Int J Sports Physiol Perform. 2016;11(5):587593. PubMed ID: 26456711 doi:

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

    Weaving D, Jones B, Marshall P, Till K, Abt G. Multiple measures are needed to quantify training loads in professional rugby league. Int J Sports Med. 2017;38(10):735740. PubMed ID: 28783849 doi:

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

    Weaving D, Marshall P, Earle K, Nevill A, Abt G. Combining internal- and external-training-load measures in professional rugby league. Int J Sports Physiol Perform. 2014;9(6):905912. PubMed ID: 24589469 doi:

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

    Weaving D, Beggs C, Dalton-Barron N, Jones B, Abt G. Visualizing the complexity of the athlete-monitoring cycle through principal-component analysis. Int J Sports Physiol Perform. 2019;14(9):13041310. PubMed ID: 31569072 doi:

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

    NCAA. Student-Athlete Participation: 1981–1982 to 2018–2019. Available at https://ncaaorg.s3.amazonaws.com/research/sportpart/2018-19RES_SportsSponsorshipParticipationRatesReport.pdf

    • Search Google Scholar
    • Export Citation
  • 17.

    Devine NF, Hegedus EJ, Nguyen AD, Ford KR, Taylor JB. External match load in women’s collegiate lacrosse [published online ahead of print February 4, 2020]. J Strength Cond Res. PubMed ID: 32028463 doi:

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

    Hauer R, Tessitore A, Hauer K, Tschan H. Activity profile of international female lacrosse players [published online ahead of print July 22, 2019]. J Strength Cond Res. PubMed ID: 31343545 doi:

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

    Malone JJ, Lovell R, Varley MC, Coutts AJ. Unpacking the black box: applications and considerations for using GPS devices in sport. Int J Sports Physiol Perform. 2017;12(suppl 2):S218S226. PubMed ID: 27736244 doi:

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

    Malone S, Duran D, Collins K, Morton JP, McRoberts A. Accuracy and reliability of the VX Sport global positioning system in intermittent activity. Poster presented at: Proceedings of the 19th Annual Congress for the European College of Sports Science; July, 2014. Amsterdam, The Netherlands.

    • Search Google Scholar
    • Export Citation
  • 21.

    Alphin KL, Sisson OM, Hudgins BL, Noonan CD, Bunn JA. Accuracy assessment of a GPS device for maximum sprint speed. Int J Exerc Sci. 2020;13(4):273280. PubMed ID: 32148634

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

    R Core Team. R: A language and environment for statistical computing. 2013. www.R-project.org/

  • 23.

    Beaton D, Fatt CRC, Abdi H. An ExPosition of multivariate analysis with the singular value decomposition in R. Comput Stat Data Anal. 2014; 72:176189. doi:

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

    Abdi H, Williams LJ. Principal component analysis. WIREs Comp Stat. 2010;2(4):433459. doi:

  • 25.

    Berry KJ, Johnston JE, Mielke PW Jr. Permutation methods. WIREs Comp Stat. 2011;3(6):527542. doi:

  • 26.

    Peres-Neto PR, Jackson DA, Somers KM. How many principal components? Stopping rules for determining the number of non-trivial axes revisited. Comput Stat Data Anal. 2005;49(4):974997. doi:

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

    Beaton D, Abdi H, Filbey FM. Unique aspects of impulsive traits in substance use and overeating: specific contributions of common assessments of impulsivity. Am J Drug Alcohol Abuse. 2014;40(6):463475. PubMed ID: 25115831 doi:

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

    Hesterberg T. Bootstrap. WIREs Comp Stat. 2011;3(6):497526. doi:

  • 29.

    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:

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

    Vanrenterghem J, Nedergaard NJ, Robinson MA, Drust B. Training load monitoring in team sports: a novel framework separating physiological and biomechanical load-adaptation pathways. Sports Med. 2017;47(11):21352142. PubMed ID: 28283992 doi:

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

    Weaving D, Jones B, Ireton M, Whitehead S, Till K, Beggs CB. Overcoming the problem of multicollinearity in sports performance data: a novel application of partial least squares correlation analysis. PLoS One. 2019;14(2):e0211776. PubMed ID: 30763328 doi:

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

    Gaudino P, Alberti G, Iaia FM. Estimated metabolic and mechanical demands during different small-sided games in elite soccer players. Hum Mov Sci. 2014; 36:123133. PubMed ID: 24968370 doi:

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

    Hill-Haas S, Dawson B, Impellizzeri FM, Coutts AJ. Physiology of small-sided games training in football. Sports Med. 2011;41(3):199220. PubMed ID: 21395363 doi:

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

    Hodgson C, Akenhead R, Thomas K. Time-motion analysis of acceleration demands of 4v4 small-sided soccer games played on different pitch sizes. Hum Mov Sci. 2014;33(1):2532. doi:

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

    Akubat I, Patel E, Barrett S, Abt G. Methods of monitoring the training and match load and their relationship to changes in fitness in professional youth soccer players. J Sports Sci. 2012;30(14):14731480. PubMed ID: 22857397 doi:

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

    Weaving D, Dalton NE, Black C, et al. The same story or a unique novel? Within-participant principal-component analysis of measures of training load in professional rugby union skills training. Int J Sports Physiol Perform. 2018;13(9):11751181. PubMed ID: 29584514 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
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