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Purpose: The relationship between external load and creatine-kinase (CK) response at the team/position or individual level using Global Positioning Systems (GPS) has been studied. This study aimed to compare GPS-derived and Football Movement Profile (FMP) –derived CK-prediction models for national-team soccer players. The second aim was to compare the performance of general and individualized CK prediction models. Methods: Four hundred forty-four national-team soccer players (under 15 [U15] to senior) were monitored during training sessions and matches using GPS. CK was measured every morning from whole blood. The players had 19.3 (18.1) individual GPS-CK pairs, resulting in a total of 8570 data points. Machine learning models were built using (1) GPS-derived or (2) FMP-based parameters or (3) the combination of the 2 to predict the following days’ CK value. The performance of general and individual-specific prediction models was compared. The performance of the models was described by R ² and the root-mean-square error (RMSE, in units per liter for CK values). Results: The FMP model (R ² = .60, RMSE = 144.6 U/L) performed similarly to the GPS-based model (R ² = .62, RMSE = 141.2 U/L) and the combination of the 2 (R ² = .62, RMSE = 140.3 U/L). The prediction power of the general model was better on average (R ² = .57 vs R ² = .37) and for 73% of the players than the individualized model. Conclusions: The results suggest that FMP-based CK-prediction models perform similarly to those based on GPS-derived metrics. General machine learning models’ prediction power was higher than those of the individual-specific models. These findings can be used to monitor postmatch recovery strategies and to optimize weekly training periodization.

Purpose: Optimal ankle dorsiflexion range of motion plays a vital role in attaining the essential crouched posture necessary for excelling in speed skating. The purpose of this study was to determine how the ankle dorsiflexion angle evolves throughout a day of training and to identify the factors that influence this angle. Methods: Thirty short-track speed skaters, from 2 teams, participated in this study. The maximum ankle dorsiflexion angle was obtained in a lunge position facing a wall, using a digital inclinometer. All measures were obtained 3 times per side, 6 times per day, on 2 training days separated by at least a week. We conducted multiple tests to study the impact of repetition, day, side, team level, sex, and moment on the ankle dorsiflexion angle. Results: The 3 times repeated measures and the 2 days of training did not have a significant influence on the results. There was a statistically significant difference between the first time point of the day and the 5 other time points for both ankles. Moreover, the influence of sex and team level was not statistically significant. Conclusions: The results indicate that there are significant changes in ankle dorsiflexion range of motion but only after the first warm-up of the day. Such findings could enable team staff to enhance athletes’ precompetition preparation and tailor ankle mobility training regimens more effectively.

Purpose: This study aimed to enhance the understanding of soccer match peak demands by describing worst-case scenario (WCS) and time spent above 80% and 90% of the WCS for total distance (TD) and high-speed running (HSR). The investigation considered playing level (first team vs under-19 [U19] team) and playing position (center backs, fullbacks, midfielders, and forwards) to assess how WCS and the time spent above specific thresholds vary across different populations. Methods: Data from 31 players in a professional Italian soccer club were collected during the 2022–23 season. Microtechnology devices tracked physical activity during matches. Players were categorized by position, and WCS was determined using rolling averages over a 1-minute period. Time spent above 80% and 90% of WCS for TD and HSR was calculated. Results: The U19 team exhibited higher HSR WCS compared with the first team (∼63 m·min⁻¹ vs ∼56 m·min⁻¹). Midfielders recorded the highest TD WCS (∼208 m·min⁻¹), and forwards exhibited the highest HSR WCS (∼70 m·min⁻¹). The first team spent significantly more time above 80% (∼6 min) and 90% (∼1 min) of TD WCS. Midfielders spent significantly more time above the 80% (∼7 min) of TD WCS, while forwards above the 80% (∼2 min) of HSR WCS. Conclusions: The study emphasizes that WCS used alone may not sufficiently capture real match intensity. Considering the time spent above specific thresholds provides additional insights (ie, between-levels differences and position). Practitioners should consider both WCS and time spent above thresholds for individualized training prescriptions, reflecting differences in playing roles.

Purpose: Global navigation satellite system device–derived metrics are commonly represented by discrete zones with intensity often measured by standardizing volume to per-minute of activity duration. This approach is sensitive to imprecision in duration measurement and can lead to highly variable outcomes—transforming data from zones to a gradient may overcome this problem. The purpose of this study was to critically evaluate this approach for measuring team-sport activity demands. Methods: Data were collected from 129 first-team and 73 academy matches from a Scottish Premiership football club. Gradients were calculated for velocity, acceleration, and deceleration zones, along with per-minute values for several commonly used metrics. Means and 95% CIs were calculated for playing level, as well as first-team positional groups. Within-subject coefficients of variation were also calculated for match level, position, and individual groups. Results: The gradient approach showed consistency with per-minute metrics when measuring playing level and position groups. With coefficients of variation of 10.8% to 26.9%, the gradients demonstrated lower variability than most per-minute variables, which ranged from 10.7% to 84.5%. Conclusions: Gradients are a potentially useful way of describing intensity in team sports and compare favorably to existing intensity variables in their ability to distinguish between match types and position groups, providing evidence that gradient variables can be used to monitor match and training intensity in team sports.

Purpose: The aim of this study was to analyze the reliability and validity of the predicted distance–time relationship in the severe-intensity domain from a 3-minute all-out running test (3MT). Methods: Twelve runners performed two 3MTs (test #1 and test #2) on an outdoor 400-m track after familiarization. Eighteen-hertz Global Positioning System data were used to estimate critical speed (CS) and distance covered above CS (D′). Time to cover 1200 and 3600 m (T1200 and T3600, respectively) was predicted using CS and D′ estimates from each 3MT. Eight runners performed 2 time trials in a single visit to assess real T1200 and T3600. Intraclass correlation coefficients (ICCs) and standard errors of measurement were calculated for reliability analysis. Results: Good to excellent reliability was found for CS, T1200, and T3600 estimates from 3MT (ICC  > .95, standard error of measurement between 1.3% and 2.2%), and poor reliability was found for D′ (ICC = .55, standard error of measurement = 27%). Predictions from 3MT were significantly correlated to actual T1200 (r = .87 and .85 for test #1 and test #2, respectively) and T3600 (r = .91 and .82 for test #1 and test #2, respectively). The calculation of error prediction showed a systematic error between predicted and real T3600 (6.4% and 7.8% for test #1 and test #2, respectively, P < .01) contrary to T1200 (P > .1). Random error was between 4.4% and 6.1% for both distances. Conclusions: Despite low reliability of D′, 3MT yielded a reliable predicted distance–time relationship allowing repeated measures to evidence change with training adaptation. However, caution should be taken with prediction of performance potential of a single individual because of substantial random error and significant underestimation of T3600.

Purpose: To compare the kinematic profile of 2 underwater pullout breaststroke techniques. Methods: Sixteen swimmers (9 men, 20.67 [2.71] y old; 7 women, 18.86 [0.83] y old) performed 3 × 25-m breaststroke using 2 pullout breaststroke techniques: Fly-Kick first and Combined. A speedometer was used to assess the peak and the mean velocity during the glide, propulsion, and recovery phases of both techniques, as well as for the total underwater sequence. The underwater distance was retrieved from video footage and was considered for each pullout technique. The range of motion of the knee during the fly-kick was also retrieved, and the time to complete the 25 m was considered the performance outcome, accompanied by the mean velocity, stroke rate, stroke length, and stroke index. Results: Velocity–time series showed different profiles between pullout techniques (P ≤ .05) mostly in the glide and propulsion phases for males and females, respectively. The mean velocity of 25 m was shown to be greater in females when using the Fly-Kick first technique (P = .05, d = 0.36). Greater values in total underwater distance and knee range of motion were also observed for this technique in both cohorts.  Conclusions: Female swimmers presented a higher performance when using the Fly-Kick first technique. Different kinematic profiles arise when swimmers use different underwater pullout techniques where the Fly-Kick first may allow them to reach higher kinematical standard.

Background: The force–velocity relationship suggests that maximal power (P _max) can only be produced in optimal torque (T _opt) and cadence (C _opt). However, the cadence at which mean maximal power (MMP) is produced has never been studied. This study aimed to determine the individual MMP–cadence relationship from in situ data. Method: We analyzed 1 year of data from 14 under-19 cyclists and calculated the MMP for each cadence between 50 and 120 rpm for 2-, 5-, and 20-minute durations. The MMP–cadence relationship was fit with a second-order polynomial function. The goodness of fit (r ²) and odd-day–even-day absolute and relative reliability were evaluated, respectively, for P _max, T _opt, and C _opt. Results: The goodness of fit was very high for every duration studied. T _opt and P _max, but not C _opt, were significantly higher for shorter durations. P _max was significantly correlated only with T _opt for the 3 durations (r ² = .63, .71, and .64 for 2, 5, and 20 min, respectively). Discussion: Evaluation of the MMP–cadence relationship from in situ data is feasible and reliable for 2-, 5-, and 20-minute durations. This profiling approach would enable better detection of the strengths and weaknesses of cyclists and make it possible to design more effective training interventions. Practical Applications: The analysis makes it possible to identify the torque versus cadence component that individually limits power production. Knowing the C _opt for a given duration of maximal effort could help athletes choose the right gear ratio and regulate cadence during a race in order to maximize performance.