A 4-Year Profile of Game Demands in Elite Women's Division I College Basketball

Abstract

Ransdell, LB, Murray, T, Gao, Y, Jones, P, and Bycura, D. A 4-year profile of game demands in elite women's Division I college basketball. J Strength Cond Res 34(3): 632-638, 2020-Workload for a Division I women's collegiate basketball team (0.817 win percentage) was examined by: (a) season, (b) player position, and (c) game outcome (wins vs. losses). Female athletes (n 5 6, mean 19.7 6 1.5 years, at beginning of study) wore Catapult S5 units during 91.8% of games over a 4-year period. Average PlayerLoad, PlayerLoad per minute (PL•min 21), high inertial movement analysis (high-IMA), and jumps were quantified using Catapult Openfield software (version 1.14.1+). Data were checked for normality and log-or square-root-transformed when they were non-normal. A series of linear mixed model analyses were conducted to detect differences in PlayerLoad, PL•min 21 , high-IMA, and jumps by season, position, and game outcome. PL•min 21 and jumps data were not normal, so they were transformed, analyses were run; because there were no differences in findings, data are reported in original units to allow for comparisons with other studies. Cohen's d and confidence intervals were provided as additional information about the strength of reported differences. The 3 most consistent findings were that across a 4-year period, jumps increased, PL•min 21 was higher in guards compared with posts, and high-IMA was higher in losses compared with wins. Other workload patterns were inconsistent, and inappropriate for making conclusive statements. Therefore, comparing jumps across multiple seasons, PL•min 21 by player position and high-IMA in losses are important; in addition, all data can be used to profile National Collegiate Athletic Association Division I women's basketball players and set game workload expectations.

Associations Between Two Athlete Monitoring Systems Used to Quantify External Training Loads in Basketball Players

“…In contrast, the present data were higher than that previously reported in collegiate men's basketball players during the preseason [15,29], as well as higher than that detected in men's professional players [30]. However, the present study paralleled the PL experienced in both elite NCAA Division I collegiate women's players during game play [31] and semi-professional men's players during competition [10]. Numerous factors can influence the eTL experienced during basketball practices and games, which would explain the array of eTL observed during basketball activity currently in the literature, including the training phase, the team's style of play, player's skillset, player experience, the number of players in each drill, the size of the playing area, and even the technical or tactical emphasis of a drill.…”

“…Interestingly, the elevated PL/min values coupled with the lower total PL values in the present study suggests that the primary driver attenuating the total PL observed during this off-season training phase likely relates to the shorter duration of practice in the off-season rather than a moderation in practice intensity. The observed PL/min of the present study was lower than that experienced in elite collegiate women's players, as well as in professional players [31,32], but greater than semi-professional players during competition [10]. The higher PL/min during the off-season training block, compared to that previously reported during the preseason in collegiate men [16] likely reflects the off-season training block practices incorporating more individual skill development work, with the structure of drills during practice including more players at a time, ultimately reducing the amount of time players are not incorporated in activity.…”

“…While not examined in the present study, conflicting data exist ascertaining positional differences in eTL among basketball players [16,31,40,41]. To the author's knowledge, no available data have examined differences in distance traveled between positions in collegiate basketball athletes, which may be a key index warranting investigation in the future.…”

Assessing the External Load Associated With High-Intensity Activities Recorded During Official Basketball Games

“…Recent research has increasingly focused on quantifying the external demands imposed on basketball players during games (Conte et al, 2020;Ferioli et al, 2020b,c). The assessment of external load in basketball becomes particularly useful when also accounting for the role of contextual factors such as playing position and ball possession status, given that differences have been shown to exist with respect to these variables (Puente et al, 2017;Stojanović et al, 2018;Vázquez-Guerrero et al, 2018, 2019bPino-Ortega et al, 2019;Ferioli et al, 2020b;Fernández-Leo et al, 2020;García et al, 2020;Ransdell et al, 2020). Hence, practitioners could use these data to structure training sessions with a clearer understanding of how different activity patterns contribute to the total external load and what differences exist between playing positions and activities which are performed either with or without ball.…”

“…In addition, IMUs provide objective data available immediately after a training session or game, thus resulting as an easier and faster tool to collect and process data than TMA (Portes et al, 2020). Due to these advantages, IMUs are one of the main technologies adopted by basketball practitioners and sport scientists (Schelling and Torres, 2016;Pino-Ortega et al, 2019;Vázquez-Guerrero et al, 2019a,b;García et al, 2020;Portes et al, 2020;Ransdell et al, 2020). However, IMUs are much more expensive than TMA, which limits their applicability in several contexts, such as non-elite practice (Fox et al, 2017).…”