Donnie S. Strack’s research while affiliated with University of Technology Sydney and other places

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Publications (6)


External Load Fluctuations Across an Amateur Athletic Union Basketball Season
  • Article

December 2023

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80 Reads

The Journal of Strength and Conditioning Research

Constantine W Kutson

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Donnie Strack

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Kutson, CW, Russell, JL, Strack, D, Coutts, AJ, and McLean, BD. External load fluctuations across an Amateur Athletic Union basketball season. J Strength Cond Res XX(X): 000–000, 2023—Amateur Athletic Union (AAU) competitions are an important component of the developmental pathway for youth basketball athletes. Despite its relative importance, there is currently a paucity of research investigating the physical demands in AAU basketball. Therefore, the purpose of this study was to examine the physical demands encountered over the course of an AAU basketball season. External training load was quantified using inertial sensors (Catapult T6) from one male AAU basketball team (age: 17.5 ± 0.5 years, height: 197.3 ± 10.0 cm, and mass: 89.4 ± 11.6 kg) over the course of the 2021 AAU season and categorized post hoc into high-, medium-, and low-minute groups based on mean playing minutes. After player categorization, 2 linear mixed models were constructed, one for PlayerLoad (PL) and one for duration, to examine the differences across player category, month of the season, and activity types (practices or games). The results show that the highest training loads were encountered by high-minute players, who had total PLs of 9,766 ± 1,516 AU, 13,207 ± 2,561 AU, and 7,071 ± 2,122 AU during April, May, and June, respectively. Highly variable training loads were also evident over the course of a season, with peak PL values as high as 4,921 AU per week. Practitioners should be aware that AAU basketball players experience variable loads throughout the season, which peak around congested competition/tournament periods. In addition, players with high game minutes accumulate the most load over the course of a season. This information may be used to better inform planning and periodizing strategies during developmental phases.


'Avoidance Preening', Displacement Behavior and Co-Dependency in Professional Team Sport: When Wants Become More Important Than Needs
  • Article
  • Full-text available

August 2022

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73 Reads

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1 Citation

International Journal of Sports Physical Therapy

An athlete's body plays an important role in their performance and well-being. However, game-relevant skills are better determinants of success, compared with physical fitness, in technically-driven team sports. In the professional era, over utilization of resources, in pursuit of physical optimization, can detract from time spent on priorities. Athletes' non-strategic, time-demanding focus on physical preparation/treatments resembles avian 'avoidance preening', whereby stressful situations trigger birds to excessively preen in place of more productive activities. The purpose of this commentary is to explore the behaviors of resource-rich professional teams and the roles of staff dedicated to optimizing physical performance, including circumstances that foster avoidance behavior and create the potential for practitioners to encourage co-dependent relationships with athletes. To cultivate healthy/productive environments, the following is recommended: I) recognition of non-productive avoidance behaviors; II) eschewing unjustified, fear promoting, pathoanatomical language; III) fostering collaborative approaches; IV) encouraging utilization of psychology services; V) recognizing that optimal physical function and feeling good is rarely the primary goal in professional team sports. Level of evidence: 5.

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Conceptual framework for physical training in professional basketball, adapted from the work by Jeffries et al. [12] Prescription represents the short and long-term planning and execution of training, competition and travel over the course of the season (i.e., nature and organization of training sessions and travel)
External load represents the physical demands associated with training, competition, and travel during the season, and training load is the specific stimulus induced by both training sessions and competition. Internal load represents the psychophysiological responses occurring during the execution of training. Contextual factors are defined as factors that are not part of the main training process, such as environmental, social, and cultural factors, but can influence the training process or outcome. Individual factors are characteristics of the individual athlete, such as genetics, psychological traits and states, and training background, which can influence the training process or outcome. Training effects can be acute or chronic, and positive or negative, effects caused and occurring after the training session, and can be assessed using functional, subjective, physiological, biomechanical and cognitive measures. The bidirectional arrow represents a reciprocal nature of interactions between training effects and individual/contextual factors. For example, a negative training effect (e.g., increased fatigue or poor sleep) can act as an individual factor influencing the internal training load in the subsequent session. Sports performance outcomes are defined as the result of the balance between positive and negative training effects.
Covariates included in model specification for athlete-reported outcome measures
Covariates included in model specification for countermovement jump measures
Frequencies of recovery days during previous 3 days across all players
Means, SD’s and Pearson correlations among athlete-reported outcome measures and independent variables

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Understanding ‘monitoring’ data–the association between measured stressors and athlete responses within a holistic basketball performance framework

June 2022

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906 Reads

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9 Citations

This study examined associations between cumulative training load, travel demands and recovery days with athlete-reported outcome measures (AROMs) and countermovement jump (CMJ) performance in professional basketball. Retrospective analysis was performed on data collected from 23 players (mean±SD: age = 24.7±2.5 years, height = 198.3±7.6 cm, body mass = 98.1±9.0 kg, wingspan = 206.8±8.4 cm) from 2018–2020 in the National Basketball Association G-League. Linear mixed models were used to describe variation in AROMs and CMJ data in relation to cumulative training load (previous 3- and 10-days), hours travelled (previous 3- and 10-day), days away from the team’s home city, recovery days (i.e., no travel/minimal on-court activity) and individual factors (e.g., age, fatigue, soreness). Cumulative 3-day training load had negative associations with fatigue, soreness, and sleep, while increased recovery days were associated with improved soreness scores. Increases in hours travelled and days spent away from home over 10 days were associated with increased sleep quality and duration. Cumulative training load over 3 and 10 days, hours travelled and days away from home city were all associated with changes in CMJ performance during the eccentric phase. The interaction of on-court and travel related stressors combined with individual factors is complex, meaning that multiple athletes response measures are needed to understand fatigue and recovery cycles. Our findings support the utility of the response measures presented (i.e., CMJ and AROMs), but this is not an exhaustive battery and practitioners should consider what measures may best inform training periodization within the context of their environment/sport.


Percentage duration spent on-court work in pre-season (PRE) and regular season (REGULAR) based on activity category (A), drill type (B), and tactical emphasis (C). Game = any league competitive event; Team = basketball-specific court work done as a team, Individual = basketball-specific court work not done with the team, Simulated = predominantly non-scripted drills, focused on game-like physical contact, pace, and situations, Skill = predominantly scripted drills with limited physical contact, focused on skill development, Offense = basketball activity with predominantly offensive emphasis, Defense = basketball activity with predominantly defensive emphasis, Both = basketball activity with equal offensive and defensive emphasis.
Average weekly integrated load (A) and duration (B) during the regular season, by rotation status.
Descriptive statistics of total player weeks of integrated load and duration across participant categories.
Quantifying Training and Game Demands of a National Basketball Association Season

December 2021

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478 Reads

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15 Citations

Purpose: There are currently no data describing combined practice and game load demands throughout a National Basketball Association (NBA) season. The primary objective of this study was to integrate external load data garnered from all on-court activity throughout an NBA season, according to different activity and player characteristics. Methods: Data from 14 professional male basketball players (mean ± SD; age, 27.3 ± 4.8 years; height, 201.0 ± 7.2 cm; body mass, 104.9 ± 10.6 kg) playing for the same club during the 2017–2018 NBA season were retrospectively analyzed. Game and training data were integrated to create a consolidated external load measure, which was termed integrated load. Players were categorized by years of NBA experience (1-2y, 3-5y, 6-9y, and 10 + y), position (frontcourt and backcourt), and playing rotation status (starter, rotation, and bench). Results: Total weekly duration was significantly different (p < 0.001) between years of NBA playing experience, with duration highest in 3–5 year players, compared with 6–9 (d = 0.46) and 10+ (d = 0.78) year players. Starters experienced the highest integrated load, compared with bench (d = 0.77) players. There were no significant differences in integrated load or duration between positions. Conclusion: This is the first study to describe the seasonal training loads of NBA players for an entire season and shows that a most training load is accumulated in non-game activities. This study highlights the need for integrated and unobtrusive training load monitoring, with engagement of all stakeholders to develop well-informed individualized training prescription to optimize preparation of NBA players.


Finding the Signal in the Noise—Interday Reliability and Seasonal Sensitivity of 84 Countermovement Jump Variables in Professional Basketball Players

December 2021

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226 Reads

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26 Citations

The Journal of Strength and Conditioning Research

Mercer, RAJ, Russell, JL, McGuigan, LC, Coutts, AJ, Strack, DS, and McLean, BD. Finding the signal in the noise-interday reliability and seasonal sensitivity of 84 countermovement jump variables in professional basketball players. J Strength Cond Res 37(2): 394-402, 2023-This study examined the measurement characteristics of countermovement jump (CMJ) variables in basketball athletes using different variable selection criteria. Test-retest reliability (noise) and seasonal variability (signal) CMJ data were collected from 13 professional basketball athletes playing for the same club throughout 1 competitive season. Interday reliability (coefficient of variation [CV] and intraclass correlation coefficients) were calculated over 3 preseason tests conducted on 3 consecutive days. To evaluate sensitivity, signal-to-noise ratio (SNR) was calculated by dividing seasonal variability (CV) from 8 in-season CMJ tests (collected from November to February) by preseason reliability (CV). Players performed 3 CMJs each testing day, and 3 data analysis techniques were applied: a single variable from the trial with either the best jump height (BestJH; calculated by flight time) or the best flight time to contraction time (BestFT:CT) and mean output across 3 jumps (Mean3). Mean3 was the most reliable data analysis technique, with 79 and 82 of 84 variables displaying lower interday CVs compared with BestJH and BestFT:CT, respectively. Overall, many CMJ measures display seasonal changes that are greater than the inherent noise, with 77 variables producing SNR of >1.00 for Mean3 compared with 65 and 58 variables for BestJH and BestFT:CT, respectively. To improve reliability and sensitivity, it is recommended that practitioners use the average of multiple CMJ trials and regularly reassess measurement characteristics specific to their cohort and environment.


Fig. 1 Flowchart illustrating the search and inclusion/exclusion strategy
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Summary of youth (under 18 years) basketball load monitoring studies
Measuring Physical Demands in Basketball: An Explorative Systematic Review of Practices Key Points

January 2021

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3,367 Reads

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102 Citations

Sports Medicine

Background Measuring the physical work and resultant acute psychobiological responses of basketball can help to better understand and inform physical preparation models and improve overall athlete health and performance. Recent advancements in training load monitoring solutions have coincided with increases in the literature describing the physical demands of basketball, but there are currently no reviews that summarize all the available basketball research. Additionally, a thorough appraisal of the load monitoring methodologies and measures used in basketball is lacking in the current literature. This type of critical analysis would allow for consistent comparison between studies to better understand physical demands across the sport. Objectives The objective of this systematic review was to assess and critically evaluate the methods and technologies used for monitoring physical demands in competitive basketball athletes. We used the term ‘training load’ to encompass the physical demands of both training and game activities, with the latter assumed to provide a training stimulus as well. This review aimed to critique methodological inconsistencies, establish operational definitions specific to the sport, and make recommendations for basketball training load monitoring practice and reporting within the literature. Methods A systematic review of the literature was performed using EBSCO, PubMed, SCOPUS, and Web of Science to identify studies through March 2020. Electronic databases were searched using terms related to basketball and training load. Records were included if they used a competitive basketball population and incorporated a measure of training load. This systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO Registration # CRD42019123603), and approved under the National Basketball Association (NBA) Health Related Research Policy. Results Electronic and manual searches identified 122 papers that met the inclusion criteria. These studies reported the physical demands of basketball during training (n = 56), competition (n = 36), and both training and competition (n = 30). Physical demands were quantified with a measure of internal training load (n = 52), external training load (n = 29), or both internal and external measures (n = 41). These studies examined males (n = 76), females (n = 34), both male and female (n = 9), and a combination of youth (i.e. under 18 years, n = 37), adults (i.e. 18 years or older, n = 77), and both adults and youth (n = 4). Inconsistencies related to the reporting of competition level, methodology for recording duration, participant inclusion criteria, and validity of measurement systems were identified as key factors relating to the reporting of physical demands in basketball and summarized for each study. Conclusions This review comprehensively evaluated the current body of literature related to training load monitoring in basketball. Within this literature, there is a clear lack of alignment in applied practices and methodological framework, and with only small data sets and short study periods available at this time, it is not possible to draw definitive conclusions about the true physical demands of basketball. A detailed understanding of modern technologies in basketball is also lacking, and we provide specific guidelines for defining and applying duration measurement methodologies, vetting the validity and reliability of measurement tools, and classifying competition level in basketball to address some of the identified knowledge gaps. Creating alignment in best-practice basketball research methodology, terminology and reporting may lead to a more robust understanding of the physical demands associated with the sport, thereby allowing for exploration of other research areas (e.g. injury, performance), and improved understanding and decision making in applying these methods directly with basketball athletes.

Citations (5)


... This reference database decreases instances of overdiagnosis and frees clinicians up from unnecessary treatment strategies. 41 liMitations In line with clinical practice patterns, we used a digital inclinometer to measure ROM without any stabilization equipment (ie, belts). Additional stabilization would improve the validity and/or reliability of these measurements. ...

Reference:

Establishing a Reference Database for Select Clinical Measures in National Basketball Association Players
'Avoidance Preening', Displacement Behavior and Co-Dependency in Professional Team Sport: When Wants Become More Important Than Needs

International Journal of Sports Physical Therapy

... 9 Different frameworks and guidelines have been suggested for collecting reliable CMJ data, as well as metric selection processes. [9][10][11][12][13][14][15][16] For instance, Mercer et al. have highlighted that in order to improve the reliability and sensitivity of CMJ-derived variables, it is recommended that practitioners use the average of multiple CMJ trials (e.g. three trials) and regularly reassess measurement characteristics specific to the cohort and environment. 12 Furthermore, Kershner et al. 15 have highlighted the importance of considering that specific instruction can significantly alter the efficiency and performance of a skill such as the vertical jump. ...

Understanding ‘monitoring’ data–the association between measured stressors and athlete responses within a holistic basketball performance framework

... Basketball requires players to adhere to strict practice schedules, maintain physical fitness, and execute strategic plays during games. This discipline translates into better time management, organizational skills, and resilience, which are valuable traits for social and academic life (Russell et al., 2021). Additionally, the ability to think strategically and make quick decisions under pressure helps players adapt to social dynamics and resolve conflicts effectively, further facilitating their integration into the social fabric of the university. ...

Quantifying Training and Game Demands of a National Basketball Association Season

... The CV, representing the within-subject variation by dividing the standard deviation by the mean, was the most reported reliability metric for both aerobic fitness and RSA testing. A CV value of less than 10% is widely acknowledged as acceptable, yet this threshold may be arbitrary [104], as "highly" variable outcome measures may be sensitive to change [105]. Consequently, a good understanding of the context at hand is required. ...

Finding the Signal in the Noise—Interday Reliability and Seasonal Sensitivity of 84 Countermovement Jump Variables in Professional Basketball Players
  • Citing Article
  • December 2021

The Journal of Strength and Conditioning Research

... The internal and external loads of athletes are determined through monitoring [10][11][12][13], which has been frequently used in recent years to follow the training and game demands [13], and depending on the training load, whether an athlete has absorbed the training is determined by monitoring the skin temperature via thermography [14]. Infrared thermography has recently become more popular in terms of providing insights into heat production and distribution in exercise physiology [15][16][17][18], and in recent years, it has become a widely used method to monitor the training responses and injury risks of athletes [19][20][21]. ...

Measuring Physical Demands in Basketball: An Explorative Systematic Review of Practices Key Points

Sports Medicine