Not All About the Effort? A Comparison of Playing Intensities During Winning and Losing Game Quarters in Basketball

in International Journal of Sports Physiology and Performance
View More View Less
Restricted access

Purchase article

USD  $24.95

Student 1 year online subscription

USD  $114.00

1 year online subscription

USD  $152.00

Student 2 year online subscription

USD  $217.00

2 year online subscription

USD  $289.00

Purpose: To compare peak and average intensities encountered during winning and losing game quarters in basketball players. Methods: Eight semiprofessional male basketball players (age = 23.1 [3.8] y) were monitored during all games (N = 18) over 1 competitive season. The average intensities attained in each quarter were determined using microsensors and heart-rate monitors to derive relative values (per minute) for the following variables: PlayerLoad, frequency of high-intensity and total accelerations, decelerations, changes of direction, jumps, and total inertial movement analysis events combined, as well as modified summated-heart-rate-zones workload. The peak intensities reached in each quarter were determined using microsensors and reported as PlayerLoad per minute over 15-second, 30-second, 1-minute, 2-minute, 3-minute, 4-minute, and 5-minute sample durations. Linear mixed models and effect sizes were used to compare intensity variables between winning and losing game quarters. Results: Nonsignificant (P > .05), unclear–small differences were evident between winning and losing game quarters in all variables. Conclusions: During winning and losing game quarters, peak and average intensities were similar. Consequently, factors other than the intensity of effort applied during games may underpin team success in individual game quarters and therefore warrant further investigation.

Fox and Scanlan are with the School of Health, Medical, and Applied Sciences and the Human Exercise and Training Laboratory, Central Queensland University, Rockhampton, QLD, Australia. Green is with the Sacramento Kings, Sacramento, CA, USA.

Fox (j.fox2@cqu.edu.au) is corresponding author.
  • 1.

    Scanlan A, Dascombe B, Reaburn P. A comparison of the activity demands of elite and sub-elite Australian men’s basketball competition. J Sport Sci. 2011;29(11):11531160. doi:10.1080/02640414.2011.582509

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

    Sansone P, Tschan H, Foster C, Tessitore A. Monitoring training load and perceived recovery in female basketball: implications for training design. J Strength Cond Res. 2020; 34(10):29292936. doi:10.1519/JSC.0000000000002971

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

    Vetter RE, Yu H, Foose AK. Effects of moderators on physical training programs: a Bayesian approach. J Strength Cond Res. 2017;31(7):18681878. doi:10.1519/JSC.0000000000001585

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

    Fox JL, Stanton R, Scanlan AT. A comparison of training and competition demands in semiprofessional male basketball players. Res Q Exerc Sport. 2018;89(1):103111. doi:10.1080/02701367.2017.1410693

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

    Fox JL, Conte D, Stanton R, McLean B, Scanlan AT. The application of accelerometer-derived moving averages to quantify peak demands in basketball: a comparison of sample duration, playing role, and session type  [published online ahead of print February 14, 2020]. J Strength Cond Res. doi:10.1519/JSC.0000000000003486

    • Search Google Scholar
    • Export Citation
  • 6.

    Alonso E, Miranda N, Zhang S, Sosa C, Trapero J, Lorenzo J, Lorenzo A. Peak match demands in young basketball players: approach and applications. Int J Env Res Pub Health. 2020;17(7):2256. doi:10.3390/ijerph17072256

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

    Tierney P, Tobin DP, Blake C, Delahunt E. Attacking 22 entries in rugby union: running demands and differences between successful and unsuccessful entries. Scan J Med Sci Sports. 2017;27(12):19341941. doi:10.1111/sms.12816

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

    Fox JL, Stanton R, Sargent C, O’Grady CJ, Scanlan AT. The impact of contextual factors on game demands in starting, semi-professional, male basketball players. Int J Sports Physiol Perform. 2019;15(4):450456. doi:10.1123/ijspp.2019-0203

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

    Zhang S, Lorenzo A, Gomez M, Liu H, Goncalves B, Sampaio J. Players’ technical and physical performance profiles and game-to-game variation in NBA. Int J Perform Anal Sport. 2017;17(4):466483. doi:10.1080/24748668.2017.1352432

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

    Fernández-Leo A, Gomez-Carmona CD, Garcia-Rubio J, Ibanez SJ. Influence of contextual variables on physical and technical performance in male amateur basketball: a case study. Int J Env Res Pub Health. 2020;17(4):1193. doi:10.3390/ijerph17041193

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

    Barrett S, Midgley A, Lovell R. PlayerLoad: reliability, convergent validity, and influence of unit position during treadmill running. Int J Sports Physiol Perform. 2014;9(6):945952. doi:10.1123/ijspp.2013-0418

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

    Lutebegert LS, Holme BR, Spencer M. Reliability of wearable inertial measurement units to measure physical activity in team handball. Int J Sports Physiol Perform. 2017;13:467476.

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

    Scanlan AT, Fox JL, Poole JL, et al. A comparison of traditional and modified Summated-Heart-Rate-Zones models to measure internal training load in basketball players. Meas Phys Educ Exerc Sci. 2018;22(4):303309. doi:10.1080/1091367X.2018.1445089

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

    Berkelmans DM, Dalbo VJ, Fox JL, et al. Influence of different methods to determine maximum heart rate on training load outcomes in basketball players. J Strength Cond Res. 2018;32(11):31773185. doi:10.1519/JSC.0000000000002291

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

    Salazar H, Castellano J. Most demanding passages in Basketball: a preliminary study. Sport Perform Sci. 2019. https://sportperfsci.com/wp-content/uploads/2019/08/SPSR73_Salazar_190807_final.pdf.

    • Search Google Scholar
    • Export Citation
  • 16.

    Hopkins WA. A scale of magnitudes for Effect Statistics. SportSci. 2006. http://www.sportsci.org/resource/stats/index.html.

All Time Past Year Past 30 Days
Abstract Views 277 277 56
Full Text Views 15 15 0
PDF Downloads 25 25 0