Dana Agar-Newman

The purpose of this study was to investigate the relationship between the vertical force–velocity profile (FVP) and swimming start performance. Twenty-four trained/developmental swimmers were initially recruited but only twenty (10 males and 10 females) were considered eligible to be included for all statistical analyses. Subjects had a mean age of 19.84 ± 1.50 years and body mass of 72.34 ± 7.39 kg and completed a FVP consisting of incrementally loaded squat jumps (SJ) and two maximal-effort swimming starts during the same training week. Swimming start performance was quantified using relative dive distance (DD) and time to 10 m (T10). Pearson correlation coefficients were examined between the FVP metrics and the dependent variables DD and T10. Linear mixed models were then calculated for DD and T10 using theoretical maximal force (F₀), theoretical maximal velocity (V₀), and anthropometrics as the predictor variables, accounting for individual differences. The linear mixed model identified a significant interaction of both F₀ and body mass (BM) with sex. Among males, F₀ had a positive relationship with DD and BM had a negative relationship with DD. Among females, F₀ had no overall significant relationship with DD and BM had a positive relationship with DD. None of the FVP variables significantly predicted T10. The results indicate that F₀ derived from the FVP was positively related to relative dive distance in male swimmers. However, the FVP test remains a poor predictor of overall swimming start performance in male and female trained/developmental swimmers.

Horizontal force–velocity profiling (FV) has become a prominent approach to evaluate sprinting performance and force/power characteristics in athletes. As optical timing gates (TGs) are the most prevalent and accessible athlete sprint testing measurement devices, initial FV applications involved measuring sprint performance using TGs placed at the 0.75, 10, 15, 20, 30, and 40 m marks during all-out sprint testing sessions. However, there was uncertainty as to the standardization in the number, placement, and total distance of TG measurements to determine FV outputs. While original investigations have attempted to determine optimal TG measurement configurations, they have not been compared to continuous velocity measurements, nor have they been subject to a statistical optimization approach. Therefore, the objective of this study is to use simulated TG locations, derived from continuous GNSS velocity measurements, to identify the shortest distance with the minimum number of optimal timing-gate locations to obtain accurate estimates of FV metrics. A total of 49 elite women’s rugby sevens athletes performed 40 m sprint tests between 2015 and 2020. Data were collected using GNSS units (STATSports, Ireland), with athlete split times extracted every 2.5 m interval for the first 10 m, and every 5 m for the remainder of the sprint to simulate timing gates. FV metrics and times generated for all the combinations were compared to the standard model and GNSS models, respectively, using a one-way repeated measures analysis of variance (ANOVA) model. The shortest distance with an average percentage difference 0.27% (RMSE 1.43) for the standard model and 0.43% (RMSE 0.08) for the GNSS models were [0.75, 10-15-25] and [0.75, 5-10-30] combinations, respectively, across all FV measures. For individual FV metrics, different combinations can provide better estimations, indicating that certain regions of the sprint are required for the determination of specific metrics. Within this cohort, the best TG combinations for linear sprint testing are 10 to 15 m shorter than the current suggested combination, using three fewer gates. Further, alternative combinations may offer greater accuracy for specific FV metrics. This work provides an approach for the determination of the most accurate TG locations for teams with constraints on time, money, and equipment.

Background: Accurate linear sprint modelling is essential for evaluating athletes’ performance, particularly in terms of force, power, and velocity capabilities. Radar sensors have emerged as a critical tool in capturing precise velocity data, which is fundamental for generating reliable force-velocity (FV) profiles. This study focuses on the fitting of radar sensor data to various sprint modelling techniques to enhance the accuracy of these profiles. Forty-seven university-level athletes (M = 23, F = 24; 1.75 ± 0.1 m; 79.55 ± 12.64 kg) participated in two 40 m sprint trials, with radar sensors collecting detailed velocity measurements. This study evaluated five different modelling approaches, including three established methods, a third-degree polynomial, and a sigmoid function, assessing their goodness-of-fit through the root mean square error (RMSE) and coefficient of determination (r²). Additionally, FV metrics (P_max, F₀, V₀, FV_slope, and DRF) were calculated and compared using ANOVA. Results: Significant differences (p < 0.001) were identified across the models in terms of goodness-of-fit and most FV metrics, with the sigmoid and polynomial functions demonstrating superior fit to the radar-collected velocity data. Conclusions: The results suggest that radar sensors, combined with appropriate modelling techniques, can significantly improve the accuracy of sprint performance analysis, offering valuable insights for both researchers and coaches. Care should be taken when comparing results across studies employing different modelling approaches, as variations in model fitting can impact the derived metrics.

Purpose: The purpose of this research was to investigate the relationship between horizontal jump distance, 10 m time, and 30–40 m time in multi-sport athletes separated by sex and sprint speed. Methods: A total of 1352 athletes (742 males and 610 females) performed 40 m sprints, standing broad jumps (SBJs), and standing triple jumps (STJs). Data were separated by sex and then grouped as fast, average, and slow using the K-Means algorithm in three conditions (acceleration, max speed, and combined). Results: Regression models explained 84.01% of the variance (F(7,757) = 574.5, p < 0.001) for the 10 m times with mass, speed group, and sex as significant predictors and 88.51% of the variance (F = (7,757 = 841.6, p < 0.001) for the 30–40 m times with SBJ, STJ, speed group, sex, and the interaction of sex and group as significant predictors. Conclusions: These results suggest that when examining general athlete physical performance, horizontal jump tests and max speed sprint times can be used equivalently to stratify athletes. However, it is important to group athletes by speed and sex before being able to predict sprint ability from horizontal jump tests. Further, athlete mass is a significant factor in the prediction of acceleration ability but not maximum speed, and horizontal jumps were significant factors in the prediction of max speed but not acceleration.

To evaluate if takeoff speed can be predicted using peak-speed from a linear position transducer (LPT), 21 rowing athletes performed hexagonal-bar jumps in line with the National Team testing protocol. Predictive validity was assessed by comparing peak-speed from a LPT to the criterion measure takeoff speed collected from force plates. The relationship between peak-speed and takeoff speed was: Takeoff Speed = 1.21∙PeakSpeed-0.68, and the correlation was statistically significant (r= 0.990, p < .05). The Bland-Altman plot indicated that the 95% limits of agreement ranged from −0.130 m·s⁻¹ to 0.125 m·s⁻¹, with points above and below the zero line, suggesting there is no systematic bias. The mean difference in speed was 0.18%. This research shows that takeoff speed can be accurately predicted from peak-speed using a LPT. This allows practitioners to quickly and accurately estimate jump height when accessibility and portability of testing equipment are limiting factors in the field.

Agar-Newman, DJ, Tsai, MC, and Klimstra, M. The validity of applying a simple three-factor computational model to calculate force, power, and speed using hexagonal bar jumps. J Strength Cond Res 36(8): 2108–2114, 2022—The development of athlete specific force–speed profiles can be accomplished through testing ballistic movements, enabling athlete comparisons and to direct training interventions. However, field-based assessments relying on the squat jump or countermovement jump may lack specificity for some sports or be contraindicated for some athletes. Therefore, the purpose of this study was to assess the validity of a three-factor computational model using system mass, push-off distance, and jump height to calculate force, speed, and power for the hexagonal bar (hex-bar) jump. Twenty-one university varsity rowing athletes (12 females and 9 males, 20.40 ± 2.60 years, 78.56 ± 13.68 kg, 1.77 ± 0.08 m, and strength training history of 3.57 ± 2.69 years) were purposefully sampled. Testing consisted of jumps at loads starting at 28.55 kg and increasing by 10-kg increments to 78.55 kg or until technical failure occurred. Validity was assessed by comparing the three-factor computational model to the criterion force–time measures from a force plate. The results show force (mean bias = 85.38 N, SE = 5.41, 95% confidence limit 1,576.85–1,598.19), speed (mean bias = 0.00 m·s⁻¹, SE = , 95% confidence limit 0.72–0.72), and power (mean bias = 73.36 W, SE = 3.90, 95% confidence limit 1,166.61–1,181.97) can be computed using a three-factor computational model using the hex-bar jump. In conclusion, jump height from a hex-bar jump can be used with a simple three-factor computational model to calculate valid measures of force, speed, and power. This allows practitioners in the field to use a movement that may be more sport-specific or safe, to calculate kinetic and kinematic measures without encountering the issues of cost and portability associated with force plates.

Tanding triple jump [STJ]) and sprint speed (initial sprint speed [ISS] and maximum sprint speed [MSS]) in elite female rugby athletes. Data were collected from provincial, under 20 international fifteens players, in addition to senior sevens international level female rugby athletes (n = 114). Body weight, SLJ, STJ, 10-m sprint speed (ISS), 30- to 40-m sprint speed (MSS), initial sprint momentum, and maximal sprint momentum were analyzed. When categorized by horizontal jumping ability, there was a significant difference in sprint speeds (p < 0.001) between the top 50% and bottom 50% groups. Examining the relationship between horizontal jumping tasks and sprinting speed revealed a stronger correlation in the slowest 50% of athletes compared with the fastest 50%. A linear regression developed from STJ and body weight adequately predicted ISS (r = 0.645, p < 0.001) and MSS (r = 0.761, p < 0.001). In conclusion, horizontal jumping tasks can be used as a valuable performance test to identify differences of sprinting ability in elite female rugby players. However, the relationship between horizontal jumping tasks and sprinting speed seems to decrease in faster athletes. Further, STJ and body weight can be used to predict both ISS and MSS. Based on these data, it is suggested that only STJ be collected when identifying potential sprinting talent in female rugby athletes and caution be used when generalizing results across varying levels of athletes....