Concurrent Validity and Reliability of Sprinting Force-Velocity Profile Assessed With GPS Devices in Elite Athletes

Running sprint force-velocity-power profile obtained with a low-cost and low frame rate acquisition video technique: reliability and concurrent validity

The Force-velocity (F-v) and Power-velocity (P-v) relationships quantify athlete's horizontal force production capacities during sprinting. Efforts are underway to enhance ecological validity for practitioners and sports coaches. This study provides detailed data comparison of a low frames per second setup (30 Hz; FPSlow) with splits from a high FPS camera to derive F-v and P-v relationships. Sixty-six sprints performed by 11 university track and field athletes (6 male, 5 female) were evaluated. Data were recorded using FPSlow, photocells, and a high-speed camera (240 Hz; MySprint). In the FPSlow setup, bias was 0.17s, and Limits of agreement was 0.09s compared to photocells. ICC was 1.00, and the coefficient of variation (CV) was 1.0% [0.8-1.1%]. Time acquisition comparison between MySprint and FPSlow setups revealed high consistency (ICC = 0.99) and low CV (2.9% [2.8-3.1%]). F-v profile variables exhibited biases from trivial to small, with ICC ranging from moderate to nearly perfect. CV ranged from 2.7% to 11.8%, and improved using the average of three sprints (CV between 1.8% and 8.6%). The 'simple method' applied to data from the low FPS video setup yielded kinetic and kinematic parameters comparable to those obtained by the validated previous method and photocells.

Characterization of Running Intensity in Canadian Football Based on Tactical Position

This study aimed to use a data-driven approach to identify individualized speed thresholds to characterize running demands and athlete workload during games and practices in skill and linemen football players. Data were recorded from wearable sensors over 28 sessions from 30 male Canadian varsity football athletes, resulting in a total of 287 performances analyzed, including 137 games and 150 practices, using a global positioning system. Speed zones were identified for each performance by fitting a 5-dimensional Gaussian mixture model (GMM) corresponding to 5 running intensity zones from minimal (zone 1) to maximal (zone 5). Skill players had significantly higher (p < 0.001) speed thresholds, percentage of time spent, and distance covered in maximal intensity zones compared to linemen. The distance covered in game settings was significantly higher (p < 0.001) compared to practices. This study highlighted the use of individualized speed thresholds to determine running intensity and athlete workloads for American and Canadian football athletes, as well as compare running performances between practice and game scenarios. This approach can be used to monitor physical workload in athletes with respect to their tactical positions during practices and games, and to ensure that athletes are adequately trained to meet in-game physical demands.

Validity and Reliability of the Acceleration-Speed Profile for Assessing Running Kinematics' Variables Derived From the Force-Velocity Profile in Professional Soccer Players

ABSTRACT

Alonso-Callejo, A, García-Unanue, J, Guitart-Trench, M, Majano, C, Gallardo, L, and Felipe, J. Validity and reliability of the acceleration-speed profile for assessing running kinematics' variables derived from the force-velocity profile in professional soccer players. J Strength Cond Res 38(3): 563-570, 2024-The aim of this research is to assess the validity and reliability of the acceleration-speed profile (ASP) for measuring the mechanical variables of running kinematics when compared with the force-velocity profile (FVP) obtained by reference systems. The ASP and FVP of 14 male players of an elite football club were assessed during a competitive microcycle. Three ASPs were tested according to the number and type of sessions included in its plotting (ASP1: 5 training sessions and competitive match; ASP2: 5 training sessions; ASP3: competitive match). Force-velocity profile was tested 4 days before match (MD-4) with a 30-m linear sprint using 3 previously validated devices (encoder, mobile App, and global positioning system). Level of significance was p < 0.05. Acceptable reliability (intraclass correlation coefficient > 0.5) was found between the ASP1 and the encoder for all variables (F 0 -A 0 , V 0 -S 0 , and V max ). The more reliable ASP method was the ASP1 showing a lower bias than the ASP2 and ASP3 methods for almost all variables and reference systems. For ASP1, lower mean absolute error (MAE: 0.3-0.5) and higher correlation (P-M corr: 0.57-0.92) were found on variables related to the velocity in comparison with variables related to the early acceleration phase (F 0 -A 0 ; MAE: 0.49-0.63; P-M corr: 0.13-0.41). Acceleration-speed profile, when computed with data from a complete competitive week, is a reliable method for analyzing variables derived from velocity and acceleration kinematics. From these results, practitioners could implement ASP and the applications of the FVP previously studied, such as resistance training prescription, performance assessment, and return-to-play management.

Optimization of training for professional rugby union players: investigating the impact of different small-sided games models on GPS-derived performance metrics

Introduction: Professional rugby union players can improve their performance by engaging in small-sided games (SSGs), which simulate the movement patterns of the game. This study collected metrics related to running performance and mechanical workload and their relative values from both forward and back positions, aiming to explore the impact of different SSGs factors on athlete workload, as well as the workload difference between official games (OGs) and SSGs. Methods: The monitored GPS data were collected from SSGs with different player numbers and pitch sizes (five sessions), SSG rules (5 weeks, four sessions per week), and OGs conducted throughout the year. Additionally, the study compared changes in players' sprinting performance before and after two SSG sessions. Results: Backs had greater workload than forwards. Less space and number of players SSG (4 vs. 4, 660 m2) was conducive to facilitating training for players in acceleration and deceleration. Conversely, larger spaces were associated with improved running performance. However, the introduction of a floater had no significant impact on performance improvement. Additionally, the 7 vs. 4 model (seven players engaged with four opponents) resulted in the greatest workload during medium-hard accelerations (F = 52.76-88.23, p < 0.001, ηp 2 = 0.19-0.28). Japan touch model allowed for more high-speed running training (F = 47.93-243.55, p < 0.001, ηp 2 = 1.52). The workload performed by SSGs can almost cover that of OGs (F = 23.36-454.21, p < 0.05, ηp 2 = 0.03-0.57). In the context of ηp 2, values around 0.01, 0.06 and 0.14 indicate small, medium and large effects respectively. Discussion: However, given the significantly higher workload of SSGs and the slight decrease in sprinting performance, further research is required to examine the training patterns of SSGs. This study provided insight into the impact of player numbers, pitch size, and rules on rugby-specific SSGs. Coaches should optimize SSG setups for enhanced training outcomes, ensuring the long-term development of physical capacity, technical and tactical skills.

A Systematic Review and Meta-Analysis of Wearable Satellite System Technology for Linear Sprint Profiling: Technological Innovations and Practical Applications

ABSTRACT

Cormier, P, Meylan, C, Agar-Newman, D, Geneau, D, Epp-Stobbe, A, Lenetsky, S, and Klimstra, M. A systematic review and meta-analysis of wearable satellite system technology for linear sprint profiling: technological innovations and practical applications. J Strength Cond Res 38(2): 405-418, 2024-An emerging and promising practice is the use of global navigation satellite system (GNSS) technology to profile team-sports athletes in training and competition. Therefore, the purpose of this narrative systematic review with meta-analysis was to evaluate the literature regarding satellite system sensor usage for sprint modeling and to consolidate the findings to evaluate its validity and reliability. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, an electronic search of the databases, PubMed and SPORTDiscus (EBSCO), was conducted. Concurrent validity and reliability studies were considered, and 16 studies were retained for the review from the initial 1,485 studies identified. The effects on outcomes were expressed as standardized mean differences (SMDs, Cohen's d ) for each outcome (i.e., maximal sprint speed [MSS], the acceleration constant [τ], maximal theoretical velocity [ V0 ], relative force [ F0 ], and relative power [P max ]). Effect magnitudes represented the SMD between GNSS-derived and criterion-derived (i.e., radar and laser) and resulted in the following estimates: small for MSS ( d = 0.22, 95% CI 0.01 to 0.42), τ ( d = -0.18, 95% CI -0.60 to 0.23), V0 ( d = 0.14, 95% CI -0.08 to 0.36), relative F0 ( d = 0.15, 95% CI -0.25 to 0.55), and relative P max ( d = 0.21, 95% CI -0.16 to 0.58). No publication bias was identified in meta-analyzed studies and moderator analysis revealed that several factors (sampling rate and sensor manufacturer) influenced the results. Heterogeneity between studies was considered moderate to high. This highlighted the differences between studies in sensor technology differences (i.e., sampling rate, sensor fusion, and satellite network acquisition), processing techniques, criterion technology used, sprint protocols, outcome reporting, and athlete characteristics. These findings may be useful in guiding improvements in sprint modeling using GNSS technology and enable more direct comparisons in future research. Implementation of all-out linear sprint efforts with GNSS technology can be integrated into sport-specific sessions for sprint modeling when robust and consistent data processing protocols are performed, which has important implications for fatigue monitoring, program design, systematic testing, and rehabilitation in individual and team sports.

Individual In-Situ GPS-Derived Acceleration-Speed Profiling: Toward Automatization and Refinement in Male Professional Rugby Union Players

BACKGROUND

Recently a proof-of-concept was proposed to derive the soccer players' individual in-situ acceleration-speed (AS) profile from global positioning system (GPS) data collected over several sessions and games. The present study aimed to propose an automatized method of individual GPS-derived in-situ AS profiling in a professional rugby union setting.

METHOD

AS profiles of forty-nine male professional rugby union players representing 61.5 million positions, from which acceleration was derived from speed during 51 training sessions and 11 official games, were analyzed. A density-based clustering algorithm was applied to identify outlier points. Multiple AS linear relationships were modeled for each player and session, generating numerous theoretical maximal acceleration (A0), theoretical maximal running speed (S0) and AS slope (ASslope, i.e., overall orientation of the AS profile). Each average provides information on the most relevant value while the standard deviation denotes the method accuracy. In order to assess the reliability of the AS profile within the data collection period, data were compared over two 2-week phases by the inter-class correlation coefficient. A0 and S0 between positions and type of sessions (trainings and games) were compared using ANOVA and post hoc tests when the significant threshold had been reached.

RESULTS

All AS individual profiles show linear trends with high coefficient of determination (r2 > 0.81). Good reliability (Inter-class Correlation Coefficient ranging from 0.92 to 0.72) was observed between AS profiles, when determined 2 weeks apart for each player. AS profiles depend on players' positions, types of training and games. Training and games data highlight that highest A0 are obtained during games, while greatest S0 are attained during speed sessions.

CONCLUSIONS

This study provides individual in-situ GPS-derived AS profiles with automatization capability. The method calculates an error of measurement for A0 and S0, of paramount importance in order to improve their daily use. The AS profile differences between training, games and playing positions open several perspectives for performance testing, training monitoring, injury prevention and return-to-sport sequences in professional rugby union, with possible transferability to other sprint-based sports.

KEY POINTS

AS profiles computed from rugby union GPS data provide positional benchmarks during training and competition. This study provides automatic detection of atypical data and the computation of error measurement of theoretical maximal acceleration and speed components. This refinement constitutes a step forward for a daily use of ecological data by considering data collection and method reliabilities. This easy-to-implement approach may facilitate its use to the performance management process (talent identification, training monitoring and individualization, return-to-sport).

Effects of Tactical Periodization on Workload, Physical Fitness, and Well-Being in Professional Rugby Union Players During a Preseason Period

ABSTRACT

Hu, X, Boisbluche, S, Philippe, K, Maurelli, O, Li, S, Xu, B, and Prioux, J. Effects of tactical periodization on workload, physical fitness, and well-being in professional rugby union players during a preseason period. J Strength Cond Res 38(1): 105-115, 2024-Tactical periodization (TP) emerged approximately 30 years ago and has recently gained considerable attention in rugby union (RU). It aims to develop specific physical fitness components with 3 acquisition days (strength, endurance, and speed). However, no study has investigated the effects of TP on workload, physical fitness, and well-being across an RU preseason. This study aimed to determine how RU players' workload response to TP focusing on positional differences, observe the influence of a TP preseason training program on aerobic fitness and neuromuscular performance between positions, and analyze the variation of well-being reported by forwards and backs from the 3 acquisition days. Thirty-two male players completed a 6-week TP protocol. External and internal workload variables were recorded through global positioning systems and session rating of perceived exertion (s-RPE) separately. Fitness assessments included Bronco and countermovement jump (CMJ) tests. The sum of well-being indices was measured using the Hooper index. Kruskal-Wallis H tests revealed that the highest values of PlayerLoad slow, PlayerLoad slow percentage, and s-RPE were found on endurance day and the lowest on speed day. Mann-Whitney U tests showed that 15 external workload parameters were higher in backs than forwards for each acquisition day. Small improvements were observed on the Bronco test. No differences were observed in CMJ performance during the preseason period and well-being values between acquisition days. This study provides unique insights into external and internal workload variables during each acquisition day. Furthermore, it highlights TP as an efficient theoretical concept to use in an RU context.

Minimal Number of Events Required for Acceleration-Speed Profiling in Elite Women's Soccer

PURPOSE

To determine the minimum number of events (training or matches) for producing valid acceleration-speed (AS) profiles from global navigation satellite system (GNSS) data.

METHODS

Nine elite female soccer players participated in a 4-week training camp consisting of 19 events. AS profile metrics calculated from different combinations of athlete events were compared to force-velocity (FV) profile metrics from 2 × 40-m stand-alone sprint effort trials, using the same GNSS 10-Hz technology. Force-velocity profiles were calculated, from which AS profiles were obtained. AS profiles from training and matches were generated by plotting acceleration and speed points and performing a regression through the maximal points to obtain the AS metrics (theoretical maximal speed, x-intercept [in meters per second], theoretical maximal acceleration, y-intercept [in meters per second squared], and the slope per second). A linear mixed model was performed with the AS metrics as the outcome variables, the number of events as a fixed effect, and the participant identifier as a mixed effect. Dunnett post hoc multiple comparisons were used to compare the means of each number of event grouping (1-19 events) to those estimated from the dedicated sprint test.

RESULTS

Theoretical maximal speed and theoretical maximal acceleration means were no longer significantly different from the isolated sprint reference with 9 to 19 (small to trivial differences = -0.31 to -0.04 m·s-1, P = .12-.99) and 6 to 19 (small differences = -0.4 to -0.28 m·s-2, P = .06-.79) events, and the slopes were no longer different with 1 to 19 events (trivial differences = 0.06-0.03 s-1, P = .35-.99).

CONCLUSIONS

AS profiles can be estimated from a minimum of 9 days of tracking data. Future research should investigate methodology resulting in AS profiles estimated from fewer events.

Are non-starters accumulating enough load compared with starters? Examining load, wellness, and training/match ratios of a European professional soccer team

BACKGROUND

The aims of the study were to: (i) compare accumulated load and wellness between starters and non-starters of a European professional soccer team; (ii) analyze the relationships between wellness and load measures and; (iii) compare training/match ratio (TMr) of external and internal load between starters and non-starters.

METHODS

Ten players were considered starters while seven were classified as non-starters over a 16-week period in which six training sessions and match day (MD) were considered in each weekly micro-cycle. The following measures were used: wellness (fatigue, quality of sleep, muscle soreness, stress, and mood); load (rated of perceived exertion (RPE), session-RPE (s-RPE), high-speed running (HSR), sprinting, accelerations (ACC) and decelerations (DEC)). Accumulated wellness/load were calculated by summing all training and match sessions, while TMr was calculated by dividing accumulated training load by match data for all load measures and each player. Mann-Whitney U test was used for wellness variables, while independent T-test was used for the remaining variables to compare groups. Moreover, relationships among variables were explored using the Spearman's Rho correlation coefficient.

RESULTS

The main results showed that non-starters presented higher significant values for fatigue (p < 0.019; g = 0.24) and lower significant values for duration (p < 0.006; ES = 1.81) and s-RPE (p < 0.001; ES = 2.69) when compared to starters. Moreover, positive and very large correlation was found between quality of sleep and RPE, while negative and very large correlation were found between stress and deceleration, and mood and deceleration (all, p < 0.05). Finally, non-starters presented higher values in all TMr than starters, namely, RPE (p = 0.001; g = 1.96), s-RPE (p = 0.002; g = 1.77), HSR (p = 0.001; g = 2.02), sprinting (p = 0.002; g = 4.23), accelerations (p = 0.001; g = 2.72), decelerations (p < 0.001; g = 3.44), and duration (p = 0.003; g = 2.27).

CONCLUSIONS

In conclusion, this study showed that non-starters produced higher TMr in all examined variables despite the lower match and training durations when compared with starters, suggesting that physical load was adjusted appropriately. Additionally, higher RPE was associated with improved sleep while higher number of decelerations were associated with decreased wellness, namely, stress and mood for non-starters.

Concurrent Validity and Reliability of the Sprint Force-Velocity Profile Assessed with K-AI Wearable Tech

Establishing a sprint acceleration force-velocity profile is a way to assess an athlete's sprint-specific strength and speed production capacities. It can be determined in field condition using GNSS-based (global navigation satellite system) devices. The aims of this study were to (1) assess the inter-unit and the inter-trial reliability of the force-velocity profile variables obtained with K-AI Wearable Tech devices (50 Hz), (2) assess the concurrent validity of the input variables (maximal sprint speed and acceleration time constant), and (3) assess the validity of the output variables (maximal force output, running velocity and power). Twelve subjects, including one girl, performed forty-one 30 m sprints in total, during which the running speed was measured using two GPS (global positioning system) devices placed on the upper back and a radar (Stalker® Pro II Sports Radar Gun). Concurrent validity, inter-device and inter-trial reliability analyses were carried out for the input and output variables. Very strong to poor correlation (0.99 to 0.38) was observed for the different variables between the GPS and radar devices, with typical errors ranging from small to large (all < 7.6%). Inter-unit reliability was excellent to moderate depending on the variable (ICC values between 0.65 and 0.99). Finally, for the inter-trial reliability, the coefficients of variation were low to very low (all < 5.6%) for the radar and the GPS. The K-AI Wearable Tech used in this study is a concurrently valid and reliable alternative to radar for assessing a sprint acceleration force-velocity profile.

Do non-contact injuries occur during high-speed running in elite football? Preliminary results from a novel GPS and video-based method

OBJECTIVES

Understanding how injuries occur (inciting circumstances) is useful for developing etiological hypotheses and prevention strategies. The aims of this study were 1) to evaluate the feasibility of a method combining video and Global Positioning System data to estimate the speed and acceleration of activities leading to injuries and 2) to use this method to analyse the inciting circumstances leading to non-contact injuries.

DESIGN

Retrospective descriptive study.

METHODS

Injury inciting circumstances from 46 elite players over three seasons were analysed from video recordings and from external load measures collected through Catapult Vector S7 Global Positioning System.

RESULTS

In total 34 non-contact injuries were analysed. Sixteen out of the seventeen hamstring injuries occurred when players were running for (median and interquartile range) 16.75 m (8.42-26.65 m) and achieved a peak speed of 29.28 km·h-1 (26.61-31.13 km·h-1) which corresponded to 87.55 % of players' maximal speed (78.5 %-89.75 %). Of the three adductor injuries, one occurred whilst the player was decelerating without the ball, one occurred whilst the player was accelerating and controlling the ball at knee level, and one occurred whilst the player was performing an instep kick. Two quadriceps injuries occurred whilst the players were kicking either whilst walking or running.

CONCLUSIONS

From the preliminary results reported in this study most hamstring injuries occurred when players ran >25 km·h-1 and above 80 % of their maximal speed. This study suggests that this novel approach can allow a detailed and standardised analysis of injury inciting circumstances.

Comparison of acceleration-speed profiles from training and competition to individual maximal sprint efforts

This study aimed to (1) compare "in-situ" monitored acceleration-speed (ASin-situ) profile metrics from training/competition data in elite female soccer players to similar metrics from profiles developed from isolated maximal sprint efforts (ASsprint) and; (2) compare the confidence interval (CI) and a Tukey boxplot (BP) outlier removal technique on the training/competition data to derive ASin-situ profiles. Fifteen national team soccer players participated in a 4-week camp while wearing 10 Hz GNSS units. Towards the middle of the camp, 2 × 40 m isolated maximal sprints were performed. ASin-situ profiles (theoretical maximum acceleration A0 in m∙s-2 and speed S0 in m∙s-1) were computed using the CI and BP techniques with training/competition data. The sprint data were modelled separately to construct horizontal force-velocity (FV) profiles, from which ASsprint profiles were derived. Bland-Altman analysis was used to assess agreement between the CI- and BP-derived ASin-situ profiles to the ASsprint profiles, as well as regression analysis for systematic and proportional bias. Additionally, 1-way ANOVAs with Tukey posthoc compared the metrics between each method of analysis. Using the BP method, good agreement of the ASin-situ with ASsprint profile metrics A0/S0 was displayed, whereas good to moderate agreement was shown for the CI. The CI technique showed a proportional bias for A0/S0. Good to excellent intertrial reliability was demonstrated for isolated sprint metrics. Both BP and CI techniques provided comparable ASin-situ profiles to ASsprint profiles. This current research demonstrated that ASin-situ profiling is applicable in elite women's soccer and will have further application in many team sports.

Assessing the agreement between a global navigation satellite system and an optical-tracking system for measuring total, high-speed running, and sprint distances in official soccer matches

This study aimed to compare the agreement of total distance (TD), high-speed running (HSR) distance, and sprint distance during 16 official soccer matches between a global navigation satellite system (GNSS) and an optical-tracking system. A total of 24 male soccer players, who are actively participating in the Polish Ekstraklasa professional league, were included in the analysis conducted during official competitions. The players were systematically monitored using Catapult GNSS (10-Hz, S7) and Tracab optical-tracking system (25-Hz, ChyronHego). TD, HSR distance, sprint distance, HSR count (HSRC), and sprint count (SC) were collected. The data were extracted in 5-min epochs. A statistical approach was employed to visually examine the relationship between the systems based on the same measure. Additionally, R2 was utilized as a metric to quantify the proportion of variance accounted for by a variable. To assess agreement, Bland-Altman plots were visually inspected. The data from both systems were compared using the estimates derived from the intraclass correlation (ICC) test and Pearson product-moment correlation. Finally, a paired t-test was employed to compare the measurements obtained from both systems. The interaction between Catapult and Tracab systems revealed an R2 of 0.717 for TD, 0.512 for HSR distance, 0.647 for sprint distance, 0.349 for HSRC, and 0.261 for SC. The ICC values for absolute agreement between the systems were excellent for TD (ICC = 0.974) and good for HSR distance (ICC = 0.766), sprint distance (ICC = 0.822). The ICC values were not good for HSRCs (ICC = 0.659) and SCs (ICC = 0.640). t-test revealed significant differences between Catapult and Tracab for TD (p < 0.001; d = -0.084), HSR distance (p < 0.001; d = -0.481), sprint distance (p < 0.001; d = -0.513), HSRC (p < 0.001; d = -0.558), and SC (p < 0.001; d = -0.334). Although both systems present acceptable agreement in TD, they may not be perfectly interchangeable, which sports scientists and coaches must consider when using them.

Reliability of individual acceleration-speed profile in-situ in elite youth soccer players

The aims of this study were to describe differences in the acceleration-speed (A-S) profile in-situ and to assess the week-to-week reliability of the A-S profile in-situ over a given training cycle of elite youth soccer players, in relation to the number of sessions included and analyse the effect of the inclusion or not of a specific sprint session. In this retrospective study, 18 male elite U19 football players (179.4 ± 7.1 cm; 69.0 ± 9.5 kg) participated. GPS data collected from three consecutive typical training weeks were used to calculate different combinations of A-S profile in-situ variables (theoretical maximal acceleration [A₀], theoretical maximal speed [S₀] and the slope of the acceleration-speed [AS_slope]). The number (and content) of sessions affected mainly S₀ while A₀ remained similar with or without a sprint session. The reliability of the A-S profile in-situ is more related to the spread of points rather than a specific number of sessions (and thus points) and was improved when a high percentage of maximum speed (i.e. ≥ 95%) was reached. The present study showed low week-to-week variability for A₀, S₀ and AS_slope. However, practitioners need to make sure that the values cover a sufficient range of raw data [20-95% of maximum speed] to build a clear and consistent linear regression, and in turn extrapolate meaningful A-S profile values.

Peak Running, Mechanical, and Physiological Demands of Professional Men's Field Hockey Matches

The aim of this study was to investigate the peak running, mechanical, and physiological demands of players of different positions in professional men's field hockey matches. Eighteen professional male field hockey players participated in the study, and data were collected in eleven official matches. Players wore GPS units (Vector S7, Catapult Sports) and heart rate (HR) monitors (Polar H1, Polar Electros) to collect physical and physiological data. Physical and physiological output of forwards, midfielders, and defenders in full matches and during 1-min peak periods was analysed. For all metrics and positions, the values identified for the 1-min peak periods were greater than the average values of match play (p < 0.05). In terms of 1-min peak period Player Load, all three positions were significantly different from each other. Forwards achieved the highest Player Load per minute, while defenders the lowest. The distance per minute, high-speed distance per minute, and the relative average heart rate of defenders were significantly lower than of midfielders and forwards (p < 0.05). The current study revealed the peak running, mechanical, and physiological demands of professional men's field hockey matches. It is recommended when prescribing training programmes, to consider not only match average demands, but also peak demands. Forwards and midfielders displayed similar peak demands, while defenders had the lowest demands in all metrics except the number of accelerations and decelerations per minute. Player Load per minute can be used to identify the differences in peak mechanical demands between forwards and midfielders.

Concurrent Validity and Reliability of Different Technologies for Sprint-Derived Horizontal Force-Velocity-Power Profiling

ABSTRACT

Cormier, P, Tsai, M-C, Meylan, C, Agar-Newman, D, Epp-Stobbe, A, Kalthoff, Z, and Klimstra, M. Concurrent validity and reliability of different technologies for sprint-derived horizontal force-velocity-power profiling. J Strength Cond Res XX(X): 000-000, 2022-This study evaluated the validity and reliability of common systems to assess sprint-derived horizontal force-velocity-power (FVPH) profile metrics. Two double constellation athlete monitoring systems (STATSports Apex, Catapult Vector S7) and one timing gate system were compared with a radar gun for the computation of FVPH metrics. Intersystem validity was assessed using intraclass correlation coefficients (ICC), Pearson's correlation coefficients (R2), and Bland-Altman plots with absolute and percent agreement. Intrasystem reliability was assessed with agreement bias and ICC. STATSports demonstrated moderate agreement for F0, Pmax, τ, and Drf (8.62, 6.46, -9.81, and 9.96%, respectively) and good agreement for V0 and MSS (-2.18 and -1.62%). Catapult displayed good agreement across all metrics (F0, V0, Pmax, MSS, τ, and Drf: -0.96, -0.89, -1.85, -0.84, 0.38, and -0.27%, respectively). Timing gates demonstrated good agreement with V0 and MSS (-2.62 and -1.71%) and poor agreement with F0, Pmax, τ, and Drf (19.17, 16.64, -20.49, and 20.18%, respectively). Intrasystem reliability demonstrated good agreement (<2% bias) with very large to near-perfect ICC (0.84-0.99) for Catapult and STATSports systems. Overall, GPS/GNSS 10 Hz technology is reliable across devices and can provide moderate-to-good accuracy of FVPH metrics in single maximal effort sprints. However, Catapult provided better agreement for more FVPH metrics than STATSports, which may be related to differences in proprietary algorithms. Also, modeling timing gate data using current FVPH profiling techniques results in poor bias that requires greater investigation. GPS/GNSS data can be used for FVPH profiling, which could inform performance and rehabilitation processes.

The Relationship Among Acceleration, Deceleration and Changes of Direction in Repeated Small Sided Games

The change of direction (COD) ability is perhaps the most significant fitness component in team sport games. One of the best ways to develop COD as well as other components of the game in soccer is the regular inclusion of small sided games (SSGs) in the training process. Therefore, the aim of this research was to determine changes in physiological and kinematic variables in repeated SSGs in youth soccer players. Additionally, we investigated the relationship between selected IMA variables such as acceleration, deceleration and changes of direction. Participants included sixteen U17 soccer players from a 2^nd league professional team with a high aerobic capacity. The study design involved six 3-min 4 × 4 SSGs with goalkeepers and with a 3-min rest interval between games, during two training sessions played on a field of 25 x 35 m. The results showed that the intervention protocol consisting of repeated SSGs generated an intensity below the anaerobic threshold. This allowed for the maintenance of all variables (Heart Rate, Total Distance Covered, Velocity, Acceleration, Deceleration, Change of Direction) at a similar level throughout the subsequent six SSGs. The analysis revealed that in the six SSGs, players performed the most Acc and Dec, then COD Right and the least COD Left. From the third game on, a decrease in the number of COD Right was noticed. We hypothesized that progressive neuromuscular fatigue on the dominant side caused a more symmetrical trend in COD.

Sprint Acceleration Mechanical Outputs Derived from Position- or Velocity-Time Data: A Multi-System Comparison Study

To directly compare five commonly used on-field systems (motorized linear encoder, laser, radar, global positioning system, and timing gates) during sprint acceleration to (i) measure velocity−time data, (ii) compute the main associated force−velocity variables, and (iii) assess their respective inter-trial reliability. Eighteen participants performed three 40 m sprints, during which five systems were used to simultaneously and separately record the body center of the mass horizontal position or velocity over time. Horizontal force−velocity mechanical outputs for the two best trials were computed following an inverse dynamic model and based on an exponential fitting of the position- or velocity-time data. Between the five systems, the maximal running velocity was close (7.99 to 8.04 m.s−1), while the time constant showed larger differences (1.18 to 1.29 s). Concurrent validity results overall showed a relative systematic error of 0.86 to 2.28% for maximum and theoretically maximal velocity variables and 4.78 to 12.9% for early acceleration variables. The inter-trial reliability showed low coefficients of variation (all <5.74%), and was very close between all of the systems. All of the systems tested here can be considered relevant to measure the maximal velocity and compute the force−velocity mechanical outputs. Practitioners are advised to interpret the data obtained with either of these systems in light of these results.

Dehydration, Wellness, and Training Demands of Professional Soccer Players during Preseason

Purpose

Our study is aimed at analyzing the relationships between water loss and a professional soccer team's internal and external training load throughout the first three months of a season, covering all the preseason and the first two months of the competitive season.

Methods

This study followed an observational analytic design. Twenty-seven athletes (age: 25.5 ± 4.1 years, height: 180.7 ± 8.2 cm, and body mass: 78.4 ± 8.7 kg) were included in the study, conducted over the first three months of the season. Players were weighed at the beginning and end of all training sessions to estimate fluid losses. They were asked to complete a wellness questionnaire and indicate the color of the first urine of the day upon their arrival at the practice session. Additionally, all sessions were monitored for locomotor demands.

Results

We found a positive correlation between urine color and sprint distance (r = 0.46, p = 0.01) and a positive correlation between dehydration and rating of perceived exertion (r = 0.44, p = 0.015), whereas a negative correlation between dehydration and number of acceleration (r = −0.39, p = 0.034).

Conclusions

Dehydration increased perceived physical exertion. Regularly monitoring training load and changes in body mass, as well as raising awareness about hydration, can contribute to cognitive and physical performance.