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Abstract: Curtis, M, Kupperman, N, Westbrook, J, Weltman, AL, Hart, J, and Hertel, J. Neuromuscular performance and the intensity of external training load during the preseason in National Collegiate Athletic Association Division I men's collegiate basketball players. J Strength Cond Res 39(1): 54-61, 2025-The aim of the study was to determine whether acute changes in neuromuscular performance can be detected through countermovement jumps (CMJs) conducted pre- and postpractice sessions in conditions of high or low intensity measured by microsensors technology. Using an observational repeated measures design, data were collected from 10 male collegiate basketball players. Countermovement jump data were collected before and after practice exposures over 4 weeks of preseason. Select CMJ kinetics were compared in conditions of high and low training load intensity to detect neuromuscular performance changes in displacement of the center of mass and kinetics. Kinetic measures were categorized as output, underpinning, and strategy-related variables. We investigated "output" defined as displacement (jump height [JH]), "underpinning" defined as force-related (mean eccentric force, mean concentric force, force at zero velocity), and "strategy" defined as time-related (countermovement depth [CMD], eccentric duration (EccDur), concentric duration [ConcDur]) variables. There were significant condition × time interactions in CMJ variables namely eccentric mean force (EccForce), force at zero velocity (Force@0), CMDepth, EccDur, and ConcDur. In conditions of high intensity, players had significant, but small decreases in EccForce and Force@0, with small increases in CMD, EccDur, and ConDur, respectively. However, there were no significant decreases in JH. High-intensity practice exposures did not impact neuromuscular performance specific to "output," suggesting that collegiate basketball athletes can maintain JH despite alterations in "underpinning" and "strategy-related" variables. This could have relevance in understanding how fatigue associated with higher-intensity training exposures may potentially alter jump strategy and force production capacities due to external load intensity in collegiate basketball athletes., (Copyright © 2024 National Strength and Conditioning Association.)

Supervised machine learning (ML) offers an exciting suite of algorithms that could benefit research in sport science. In principle, supervised ML approaches were designed for pure prediction, as opposed to explanation, leading to a rise in powerful, but opaque, algorithms. Recently, two subdomains of ML-explainable ML, which allows us to "peek into the black box," and interpretable ML, which encourages using algorithms that are inherently interpretable-have grown in popularity. The increased transparency of these powerful ML algorithms may provide considerable support for the hypothetico-deductive framework, in which hypotheses are generated from prior beliefs and theory, and are assessed against data collected specifically to test that hypothesis. However, this paper shows why ML algorithms are fundamentally different from statistical methods, even when using explainable or interpretable approaches. Translating potential insights from supervised ML algorithms, while in many cases seemingly straightforward, can have unanticipated challenges. While supervised ML cannot be used to replace statistical methods, we propose ways in which the sport sciences community can take advantage of supervised ML in the hypothetico-deductive framework. In this manuscript we argue that supervised machine learning can and should augment our exploratory investigations in sport science, but that leveraging potential insights from supervised ML algorithms should be undertaken with caution. We justify our position through a careful examination of supervised machine learning, and provide a useful analogy to help elucidate our findings. Three case studies are provided to demonstrate how supervised machine learning can be integrated into exploratory analysis. Supervised machine learning should be integrated into the scientific workflow with requisite caution. The approaches described in this paper provide ways to safely leverage the strengths of machine learning-like the flexibility ML algorithms can provide for fitting complex patterns-while avoiding potential pitfalls-at best, like wasted effort and money, and at worst, like misguided clinical recommendations-that may arise when trying to integrate findings from ML algorithms into domain knowledge. KEY POINTS: Some supervised machine learning algorithms and statistical models are used to solve the same problem, y = f(x) + ε, but differ fundamentally in motivation and approach. The hypothetico-deductive framework-in which hypotheses are generated from prior beliefs and theory, and are assessed against data collected specifically to test that hypothesis-is one of the core frameworks comprising the scientific method. In the hypothetico-deductive framework, supervised machine learning can be used in an exploratory capacity. However, it cannot replace the use of statistical methods, even as explainable and interpretable machine learning methods become increasingly popular. Improper use of supervised machine learning in the hypothetico-deductive framework is tantamount to p-value hacking in statistical methods., Competing Interests: Declarations Ethics approval and consent to participate Not Applicable. Consent for publication Not Applicable. Competing interests Jordan Rodu, Alexandra F. Dejong Lempke, Natalie Kupperman, and Jay Hertel all declare that they have no conflicts of interest pertinent to this manuscript., (© 2024. The Author(s).)

The purpose of this paper was to quantify internal and external loads completed by collegiate volleyball athletes during a competitive season. Eleven players were sampled (using accelerometers and subjective wellness surveys) during the practice ( n = 55) and game ( n = 30) sessions over the 2019 season. Longitudinal data were evaluated for trends across the preseason, non-conference play, and conference play periods. Data were also analyzed with respect to positional groups. Longitudinal analysis of accelerometer data showed higher workload demand during practices than games. Positional group differences were most when evaluating jump count and height. Setters accrued over twice as many jumps in a practice than during a game and had similar overall jump counts in practice to attacking positions. Average team wellness values varied with time in the season, especially during times of congested travel. This is the first study to look at both game and practice workload and wellness measures in collegiate women's volleyball. The results suggest athlete monitoring can be used to understand the demands of volleyball and used in the future to enhance practice and recovery day design to optimize athlete well-being., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Kupperman, Curtis, Saliba and Hertel.)

Context: Athlete monitoring via wearable technology is often used in soccer athletes. Although researchers have tracked global outcomes across soccer seasons, little information exists on athlete loads during individual practice drills. Understanding these demands is important for athletic trainers in making decisions about return to play., Objective: To provide descriptive information on total distance, total player load (PL), total distance per minute, and PL per minute for practice drill structures and game play by player position among female soccer athletes across a competitive season., Design: Retrospective observational study., Setting: National Collegiate Athletic Association Division I university., Patients or Other Participants: A total of 32 female collegiate soccer players (age = 20 ± 1 years, height = 168.75 ± 4.28 cm)., Intervention(s): Athletes wore a single global positioning system and triaxial accelerometer unit during all practices and games in a single soccer season. Individual practice drills were labeled by the team's strength and conditioning coach and binned into physical, technical and tactical skills and large- and small-sided competition drill structures., Main Outcome Measure(s): Descriptive analyses were used to assess the median total distance, total PL, total distance per minute, and PL per minute by drill structure and player position (defender, forward or striker, and midfielder) during practices and games., Results: Large- and small-sided competition drills imposed the greatest percentage of workload across all measures for each position (approximately 20% of total practice), followed by physical drills. When comparing technical and tactical skills drills, we found that technical skills drills required athletes to cover a greater distance (approximately 17% for technical skills and 15% for tactical skills), and tactical skills drills required higher play intensity during practices across all positions (approximately 18% for technical skills and 13% for tactical skills). Defenders had the highest median PL outcomes of all positions during practices., Conclusions: Different practice drill types imposed various levels of demands, which simulated game play, on female soccer athletes. Athletic trainers and other clinicians may use this information in formulating objective return-to-play guidelines for injured collegiate women's soccer players., (© by the National Athletic Trainers' Association, Inc.)

Objective: To evaluate the current literature regarding the utility of global positioning system (GPS)-derived workload metrics in determining musculoskeletal injury risk in team-based field-sport athletes., Data Sources: PubMed entries from January 2009 through May 2019 were searched using terms related to GPS, player workload, injury risk, and team-based field sports., Study Selection: Only studies that used GPS metrics and had injury as the main outcome variable were included., Data Extraction: Total distance, high-speed running, and acute : chronic workload ratios were the most common GPS metrics analyzed, with the most frequent sports being soccer, rugby, and Australian rules football., Data Synthesis: Many distinct workload metrics were associated with increased injury risk in individual studies performed in particular sport circumstances; however, the body of evidence was inconclusive as to whether any specific metrics could consistently predict injury risk across multiple team-based field sports., Conclusions: Our results were inconclusive in determining if any GPS-derived workload metrics were associated with an increased injury risk. This conclusion is due to a myriad of factors, including differences in injury definitions, workload metrics, and statistical analyses across individual studies., (© by the National Athletic Trainers' Association, Inc.)