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The Effects of a Field-Based Priming Session on Perceptual, Physiological, and Performance Markers in Female Rugby Sevens Players

Billy R.J. Mason, Andrew J. McKune, Kate L. Pumpa, Jocelyn K. Mara, Alexander C. Engel, Liam P. Kilduff, and Nick B. Ball

Purpose: This study aimed to determine the effects of a field-based priming session on perceptual, physiological, and performance responses in female rugby sevens athletes. Methods: Thirteen highly trained female rugby sevens players (age: 20.7 [2.0] y; height: 169.3 [4.8] cm; weight: 68.8 [7.9] kg) completed either a 20-minute field-based priming session or a control condition. Perceptual, physiological, and performance variables were collected at baseline (PRE) and 5 (POST5), 30 (POST30), and 120 minutes (POST120) postintervention. Data were analyzed using Bayesian mixed effects models. Results: The priming protocol had a larger increase in mental readiness (maximum a posteriori [MAP] = 20, 95% high-density intervals [HDI] = −4 to 42, probability of direction [PD]% = 95, % in region of practical equivalence [ROPE] = 9.7), physical readiness (MAP = 20.1, 95% HDI = −4.6 to 42.1, PD% = 93, % in ROPE = 10.6), and testosterone (MAP = 14.9, 95% HDI = 0.5 to 27.7, PD% = 98, % in ROPE = 5.6) than the control POST30. Cognitive performance decreased POST120 in the priming condition for congruent (MAP = 0.02, 95% HDI = −0.06 to 0.00, PD% = 95, % in ROPE = 6.4) and incongruent tasks (MAP = 0.00, 95% HDI = −0.07 to 0.00, PD% = 98, % in ROPE = 3.2) when compared with the control. Conclusions: Perceptual and physiological markers improved POST30 in the priming condition. Findings indicate that perceptual and physiological responses to priming were not coupled with performance improvements. Priming was not accompanied by perceptual, physiological, or performance improvements at POST120.

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Next-Generation Models for Predicting Winning Times in Elite Swimming Events: Updated Predictions for the Paris 2024 Olympic Games

Iñigo Mujika, David B. Pyne, Paul Pao-Yen Wu, Kwok Ng, Emmet Crowley, and Cormac Powell

Purpose: To evaluate statistical models developed for predicting medal-winning performances for international swimming events and generate updated performance predictions for the Paris 2024 Olympic Games. Methods: The performance of 2 statistical models developed for predicting international swimming performances was evaluated. The first model employed linear regression and forecasting to examine performance trends among medal winners, finalists, and semifinalists over an 8-year period. A machine-learning algorithm was used to generate time predictions for each individual event for the Paris 2024 Olympic Games. The second model was a Bayesian framework and comprised an autoregressive term (the previous winning time), moving average (past 3 events), and covariates for stroke, gender, distance, and type of event (World Championships vs Olympic Games). To examine the accuracy of the predictions from both models, the mean absolute error was determined between the predicted times for the Budapest 2022 World Championships and the actual results from said championships. Results: The mean absolute error for prediction of swimming performances was 0.80% for the linear-regression machine-learning model and 0.85% for the Bayesian model. The predicted times and actual times from the Budapest 2022 World Championships were highly correlated (r = .99 for both approaches). Conclusions: These models, and associated predictions for swimming events at the Paris 2024 Olympic Games, provide an evidence-based performance framework for coaches, sport-science support staff, and athletes to develop and evaluate training plans, strategies, and tactics, as well as informing resource allocation to athletes, based on their potential for the Paris 2024 Olympic Games.

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Team Behavior and Performance: An Exploration in the Context of Professional Rugby Union

Benjamin G. Serpell, Carmen M. Colomer, Mark R. Pickering, and Christian J. Cook

Purpose: To explore complex system behavior and subsequent team performance in professional rugby union. Methods: Here, we present 2 studies. In the first, we used global positioning system technology to measure player clustering during stoppages in play in nearly 100 games of professional rugby union to explore team (complex system) behavior and performance. In the second, we measured stress hormones (cortisol and testosterone) prior to team meetings and analyzed these relative to amount of time and the frequency with which players looked at peer presenters, as well as subsequent training performance, to explain how stress may lead to behaviors observed in the first study and subsequent match performance. Results: No link between player clustering during stoppages of play and performance was observed. When players (complex system agents) demonstrated greater levels of stress (as indicated by greater cortisol-awakening response and a greater decline in testosterone-to-cortisol ratio across the morning), they tended to look at peer presenters more; however, training quality declined (P = .02). Correlational analysis also showed that training quality was related to testosterone-to-cortisol ratio (P = .04). Conclusions: Team behavior is complex and can be unpredictable. It is possible that under stress, complex system agents (ie, rugby union players) look at (and cluster toward) their teammates more; however, meaningful interaction may not necessarily occur. Furthermore, while complex system (team) analysis may be valuable strategically in rugby union in the context of describing behavior, without understanding “how” or “why” intrateam/interagent behaviors emerge it may have little meaning.

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Profiling Professional Rugby Union Activity After Peak Match Periods

Samuel T. Howe, Robert J. Aughey, William G. Hopkins, and Andrew M. Stewart

The aim of this investigation was to quantify professional rugby union player activity profiles after the most intense (peak) passages of matches. Movement data were collected from 30 elite and 30 subelite professional rugby union athletes across respective competitive seasons. Accelerometer-derived PlayerLoad and global navigation satellite system–derived measures of mean speed and metabolic power were analyzed using a rolling-average method to identify the most intense 5- to 600-second passages (ie, worst-case scenarios) within matches. Player activity profiles immediately post their peak 5- to 600-second match intensity were identified using 5 epoch duration-matched intervals. Mean speed, metabolic power, and PlayerLoad declined sharply (∼29%–86%) after the most intense 5 to 600 seconds of matches. Following the most intense periods of rugby matches, exercise intensity declined below the average match-half intensity 81% of the time and seldom returned to or exceeded it, likely due to a host of individual physical and physiological characteristics, transient and/or accumulative fatigue, contextual factors, and pacing strategies. Typically, player exercise intensities after the most intense passages of matches were similar between match halves, positional groups, and levels of rugby competition. Accurate identification of the peak exercise intensities of matches and movement thereafter using novel methodologies has improved the limited understanding of professional rugby union player activity profiles following the worst-case scenarios of matches. Findings of the present study may inform match-representative training prescription, monitoring, and tactical match decisions (eg, substitutions and positional changes).

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Mental Fatigue Impairs Tackling Technique in Amateur Rugby Union Players

Demi Davidow, Mitchell Smith, Tayla Ross, Gwyneth Laura James, Lara Paul, Michael Lambert, Ben Jones, and Sharief Hendricks

Purpose: To test the effects of mental fatigue (MF) on tackling technique on the dominant and nondominant shoulders in rugby union. Methods: Twenty male amateur rugby union players and a total of 953 tackles were analyzed. A randomized crossover counterbalanced design was used across a non-MF (control) and an MF condition. During each condition, each player performed 24 tackles, divided into 4 sets of 6 tackles (3 tackles on each shoulder). In the MF condition, players performed the Stroop Task between each set of tackles. A video recording of each tackle was used to evaluate each player’s technical proficiency. A score of 1 point was awarded if a specific technique was performed correctly, and 0 point was given if not. The total score, measured in arbitrary units (AU) out of 11, represents the player’s overall tackling proficiency. Results: Overall, players displayed a significantly lower technical proficiency score in the MF condition compared to control (set 2: control 7.30 [7.04–7.57] AU vs MF 6.91 [6.70–7.12] AU, P = .009, effect size (ES) = 0.30 small and set 3: control 7.34 [7.11–7.57] AU vs MF 6.88 [6.66–7.11] AU, P = .002, ES = 0.37 small). For the nondominant shoulder, players had a significantly lower technical proficiency score during the MF condition at set 2 (control 7.05 [6.68–7.41] AU vs MF 6.69 [6.42–6.96] AU, P = .047, ES = 0.29 small) and set 3 (control 7.14 [6.83–7.45] AU vs MF 6.61 [6.35–6.87] AU, P = .007, ES = 0.49 small). Conclusions: MF can diminish a player’s overall tackling proficiency, especially when tackling on the nondominant shoulder. The physiological mechanism for this finding may be impaired executive function and suboptimal functioning of neural signals and pathways, which result in less skillful coordination of movement. To further understand and explain MF-induced physiological changes in tackling, the feasibility of monitoring brain activity (such as electroencephalogram) and neuromuscular function (such as electromyogram) needs to be investigated. The findings from this study may also contribute to the development of more effective tackle training programs for injury prevention and performance.

Open access

Synthetic Data as a Strategy to Resolve Data Privacy and Confidentiality Concerns in the Sport Sciences: Practical Examples and an R Shiny Application

Mitchell Naughton, Dan Weaving, Tannath Scott, and Heidi Compton

Purpose: There has been a proliferation in technologies in the sport performance environment that collect increasingly larger quantities of athlete data. These data have the potential to be personal, sensitive, and revealing and raise privacy and confidentiality concerns. A solution may be the use of synthetic data, which mimic the properties of the original data. The aim of this study was to provide examples of synthetic data generation to demonstrate its practical use and to deploy a freely available web-based R Shiny application to generate synthetic data. Methods: Openly available data from 2 previously published studies were obtained, representing typical data sets of (1) field- and gym-based team-sport external and internal load during a preseason period (n = 28) and (2) performance and subjective changes from before to after the posttraining intervention (n = 22). Synthetic data were generated using the synthpop package in R Studio software, and comparisons between the original and synthetic data sets were made through Welch t tests and the distributional similarity standardized propensity mean squared error statistic. Results: There were no significant differences between the original and more synthetic data sets across all variables examined in both data sets (P > .05). Further, there was distributional similarity (ie, low standardized propensity mean squared error) between the original observed and synthetic data sets. Conclusions: These findings highlight the potential use of synthetic data as a practical solution to privacy and confidentiality issues. Synthetic data can unlock previously inaccessible data sets for exploratory analysis and facilitate multiteam or multicenter collaborations. Interested sport scientists, practitioners, and researchers should consider utilizing the shiny web application (SYNTHETIC DATA—available at https://assetlab.shinyapps.io/SyntheticData/).

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Monitoring Changes in Lower-Limb Strength and Power in Elite Athletes With the Countermovement-Jump and Keiser Leg-Press Tests

Sondre Nysether, Will G. Hopkins, Fredrik Mentzoni, Gøran Paulsen, Thomas A. Haugen, and Paul A. Solberg

Purpose: To determine the utility of countermovement-jump and Keiser leg-press tests for tracking changes in elite athletes of different sports. Methods: Elite athletes of the Norwegian Olympic Federation (126 individuals from 18 sports) performed countermovement-jump and Keiser tests on 2 to 11 occasions between 2014 and 2021. Separate analyses were performed for male and female alpine skiing, male and female handball, male ice hockey, and males and females of other sports. Means and standard deviations of consecutive change scores were combined with short-term error of measurement (3.7%–7.0%) and smallest important changes (2.0%–3.6%, defined by standardization) to determine the proportions of athletes who experienced decisive changes in 2 senses: first, the athlete did not get substantially worse or better (>90% chance of either), and second, the athlete did get substantially worse or better (>90% chance of either). Results: Averaged over sports, Keiser peak power and relative peak power had the highest proportions of decisive changes in the first (60% and 55%) and second senses (25% and 28%). The velocity intercept of the force–velocity relationship had the lowest proportions in the first and second senses (29% and 11%), while jump height, Keiser mean power, relative mean power, the force intercept, and the slope of the force–velocity relationship had similar proportions (40%–53% and 15%–21%). Conclusions: With the possible exception of the Keiser test velocity intercept, the proportions of observed decisive changes in elite athletes using Keiser measures and countermovement-jump height between tests appear adequate for the measures to be useful for routine monitoring.

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Adapting Training to the Menstrual Cycle

Xanne A.K. Janse de Jonge and Belinda M. Thompson

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Volume 18 (2023): Issue 7 (Jul 2023)

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Between-Seasons Variability of Cyclists’ Peak Performance: A Longitudinal Analysis of “Real-World” Power Output Data in Male Professional Cyclists

Pedro L. Valenzuela, Manuel Mateo-March, Xabier Muriel, Mikel Zabala, Alejandro Lucia, David Barranco-Gil, and Jesús G. Pallares

Purpose: The record power profile (RPP) has gained popularity as a method of monitoring endurance cycling performance. However, the expected variation of cyclists’ performance between seasons remains unknown. We aimed to assess the between-seasons variability of peak performance (assessed through the RPP) in male professional cyclists. Methods: The study followed a longitudinal observational design. Sixty-one male professional cyclists (age 26 [5] y) with power output data from both training sessions and competitions were analyzed for a median of 4 consecutive seasons (range 2–12). The highest mean maximum power values attained for different durations (from 10 s to 30 min), as well as the resulting critical power, were determined for each season. Within-cyclist variability between seasons was assessed, and the upper threshold of expected changes (ie, twice the normal coefficient of variation) was determined. Results: All mean maximum power values showed an overall high agreement and low variability between seasons (intraclass correlation coefficient [ICC] = .76–.88 and coefficient of variation [CV] = 3.2%–5.9%), with the lowest variability observed for long efforts (>1 min). Critical power showed an ICC and CV of .79 (95% CI, .70–.85) and 3.3% (95% CI, 3.0%–3.7%), respectively. Upper thresholds of expected variation were <12% for short efforts (≤1 min) and <8% for long efforts. Conclusions: “Real-world” peak performance assessed through the RPP shows a low variability between seasons in male professional cyclists—especially for long efforts—with expected variation being around 6% and 3% for short (≤1 min) and long efforts, respectively, and with changes >12% and >8%, respectively, being infrequent for these effort durations.