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Purpose: This study aimed to determine whether the initial levels of aerobic fitness and hematological variables in young female road cyclists are related to their athletic performance development during their careers. Methods: Results of graded exercise tests on a cycle ergometer and total hemoglobin mass (tHb-mass) measurements were analyzed in 34 female road cyclists (age 18.6 [1.9] y). Among them, 2 groups were distinguished based on their competitive performance (Union Cycliste Internationale world ranking) over the following 8 years. Areas under the curve in receiver-operating-characteristic curves were calculated as indicators of elite-performance prediction. Results: Initial graded exercise test variables (peak power, peak oxygen uptake, and power at 4 mmol/L blood lactate) were not significantly different in elite (n = 13) versus nonelite (n = 21) riders. In contrast, elite riders had higher tHb-mass expressed either in absolute measures (664 [75] vs 596 [59] g, P = .006) or normalized to body mass (11.2 [0.8] vs 10.3 [0.7] g/kg, P = .001) and fat-free mass (14.4 [0.9] vs 13.1 [0.9] g/kg, P < .001). Absolute and relative erythrocyte volumes were significantly higher in elite subjects (P ranged from < .001 to .006). Of all the variables analyzed, the relative tHb-mass had the highest predictive ability to reach the elite level (area under the curve ranged from .82 to .85). Conclusion: Measurement of tHb-mass can be a helpful tool in talent detection to identify young female road cyclists with the potential to reach the elite level in the future.

Purpose: This study investigated the test–retest reliability and usefulness of the foot–ankle rebound-jump test (FARJT) for measuring foot–ankle reactive strength metrics in athletes. Methods: Thirty-six highly trained, healthy athletes (5 female; 21.5 [3.9] y; 1.80 [0.10] m; 72.7 [10.4] kg) performed 8 repeated bilateral vertical foot–ankle rebound jumps on 2 testing days. Testing days were 1 week apart, and these sessions were preceded by a familiarization session. Reactive strength metrics were calculated by dividing jump height (in meters) by contact time (in seconds) for the reactive strength index (RSI) and flight time (in seconds) by contact time (in seconds) for the reactive strength ratio (RSR). The mean of 4 jumps (excluding the first and last 2 jumps) on each testing session were considered for RSI and RSR reliability and usefulness analysis. Results: We found a high reliability of the FARJT for RSI (intraclass correlation coefficient [ICC] > .90 and coefficient of variation [CV] = 12%) and RSR (ICC ≥ .90 and CV = 8%). Regarding their usefulness, both RSI and RSR were rated as “marginal” in detecting the smallest worthwhile change (typical error > smallest worthwhile change) and “good” in detecting a moderate change in performance. Conclusions: The results showed that a FARJT is a highly reliable test for measuring foot–ankle reactive strength in athletes and useful for quantifying changes, for example, following a training block. However, its usefulness as an accurate daily or weekly monitoring tool in practice is questionable.

Purpose: To examine associations between exercise heart rate (HR_ex) during a continuous-fixed submaximal fitness test (CF-SMFT) and an intermittent-variable protocol (semistandardized kicking drill [SSD]) in Australian Football athletes, controlling for external intensities, within-session scheduling, and environmental conditions. Methods: Forty-four professional male Australian Football athletes (22.8 [8.0] y) were monitored over 10 sessions involving a 3-minute CF-SMFT (12 km·h⁻¹) as the first activity and a SSD administered 35.7 (8.0) minutes after the CF-SMFT. Initial heart rate and HR_ex were collected, with external intensities measured as average velocity (in meters per minute) and average acceleration–deceleration (in meters per second squared). Environmental conditions were sampled. A penalized hierarchical linear mixed model was tuned for a Bayesian information criterion minima using a 10-fold cross-validation, with out-of-sample prediction accuracy assessed via root-mean-squared error. Results: SSD average acceleration–deceleration, initial heart rate, temperature, and ground hardness were significant moderators in the tuned model. When model covariates were held constant, a 1%-point change in SSD HR_ex associated with a 0.4%-point change in CF-SMFT HR_ex (95% CI, 0.3–0.5). The tuned model predicted CF-SMFT HR_ex with an average root-mean-squared error of 2.64 (0.57) over the 10-fold cross-validation, with 74% and 86% of out-of-sample predictions falling within 2.7%-points and 3.7%-points, respectively, from observed values, representing the lower and upper limits for detecting meaningful changes in HR_ex according to the documented typical error. Conclusions: Our findings support the use of an SSD to monitor physiological state in Australian Football athletes, despite varied scheduling within session. Model predictions of CF-SMFT HR_ex from SSD HR_ex closely aligned with observed values, considering measurement imprecision.

Introduction: In distance running, pacing is characterized by changes in speed, leading to runners dropping off the leader’s pace until a few remain to contest victory with a final sprint. Pacing behavior has been well studied over the last 30 years, but much remains unknown. It might be related to finishing position, finishing time, and dependent on critical speed (CS), a surrogate of physiologic capacity. We hypothesized a relationship between CS and the distance at which runners “fell behind” and “let go” from the leader or were “outsprinted” as contributors to performance. Methods: 100-m split times were obtained for athletes in the men’s 10,000-m at the 2008 Olympics (N = 35). Split times were individually compared with the winner at the point of “falling behind” (successive split times progressively slower than the winner), “letting go” (large increase in time for distance compared with winner), or “outsprinted” (falling behind despite active acceleration) despite being with the leader with 400 m remaining. Results: Race times ranged between 26:55 and 29:23 (world record = 26:17). There were 3 groups who fell behind at ∼1000 (n = 11), ∼6000 (n = 16), and ∼9000 m (n = 2); let go at ∼4000 (n = 10), ∼7000 (n = 14), and ∼9500 m (n = 5); or were outkicked (n = 6). There was a moderate correlation between CS and finishing position (r = .82), individual mean pace (r = .79), “fell behind” distance (r = .77), and “let go” distance (r = .79). D′ balance was correlated with performance in the last 400 m (r = .87). Conclusions: Athletes displayed distinct patterns of falling behind and letting go. CS serves as a moderate predictor of performance and final placing. Final placing during the sprint is related to preservation of D′ balance.

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.