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Identifying coordination strategies used by sprinters and features that differentiate these strategies will aid in understanding different technical approaches to initial sprint acceleration. Moreover, multiple effective coordination strategies may be available to athletes of similar ability, which typical group-based analyses may mask. This study aimed to identify sub-groups of sprinters based on thigh-thigh and shank-foot coordination during initial acceleration, and assess sprint performance across different combinations of coordination strategies. Angular kinematics were obtained from 21 sprinters, and coordination determined using vector coding methods, with step 1 and steps 2-4 separated for analysis. Performance was assessed using metrics derived from velocity-time profiles. Using hierarchical cluster analysis, three distinct coordination strategies were identified from thigh-thigh and shank-foot coordination in step 1 and two strategies in steps 2-4. Coordination strategies primarily differed around early flight thigh-thigh coordination and early stance shank-foot coordination in step 1, while timing of reversals in thigh rotation characterised differences in later steps. Higher performers tended to have greater lead thigh and foot dominance in step 1 and early swing thigh retraction in steps 2-4. The novel application of cluster analysis to coordination provides new insights into initial acceleration technique in sprinters, with potential considerations for training and performance.

Objectives: The aims of this study were to: i) identify performance indicators associated with match outcomes in the United Rugby Championship; ii) compare the efficacy of isolated and relative datasets to predict match outcome; and iii) investigate whether reduced statistical models can reproduce predictive accuracy., Design: Retrospective analysis of key performance indicators in the United Rugby Championship., Methods: Twenty-seven performance indicators were selected from 96 matches (2020-21 United Rugby Championship). Random forest classification was completed on isolated and relative datasets, using a binary match outcome (win/lose). Maximum relevance and minimum redundancy performance indicator selection was utilised to reduce models. In addition, models were tested on 53 matches from the 2021-22 season to ascertain prediction accuracy., Results: Within the 2020-21 datasets, the full models correctly classified 83% of match performances for the relative dataset and 64% for isolated data, the equivalent reduced models classified 85% and 66% respectively. The reduced relative model successfully predicted 90% of match performances in the 21-22 season, highlighting that five performance indicators were significant: kicks from hand, metres made, clean breaks, turnovers conceded and scrum penalties., Conclusions: Relative performance indicators were more effective in predicting match outcomes than isolated data. Reducing features used in random forest classification did not degrade prediction accuracy, whilst also simplifying interpretation for practitioners. Increased kicks from hand, metres made, and clean breaks compared to the opposition, as well as fewer scrum penalties and turnovers conceded were all indicators of winning match outcomes within the United Rugby Championship., Competing Interests: Declaration of interest statement All authors confirm they have no conflicts or competing interests in publishing this work., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The orientation of the ground reaction force (GRF) vector is a key determinant of human sprint acceleration performance and has been described using ratio of forces (RF) which quantifies the ratio of the antero-posterior component to the resultant GRF. Different methods have previously been used to calculate step-averaged RF, and this study therefore aimed to compare the effects of three calculation methods on two key "technical" ability measures: decline in ratio of forces (D RF ) and theoretical maximal RF at null velocity (RF 0 ). Twenty-four male sprinters completed maximal effort 60 m sprints from block and standing starts on a fully instrumented track (force platforms in series). RF-horizontal velocity profiles were determined from the measured GRFs over the entire acceleration phase using three different calculation methods for obtaining an RF value for each step: A) the mean of instantaneous RF during stance, B) the step-averaged antero-posterior component divided by the step-averaged resultant GRF, C) the step-averaged antero-posterior component divided by the resultant of the step-averaged antero-posterior and vertical components. Method A led to significantly greater RF 0 and shallower D RF slopes than Methods B and C. These differences were very large (Effect size Cohen's d = 2.06 - 4.04) and varied between individuals due to differences in the GRF profiles, particularly during late stance as the acceleration phase progressed. Method B provides RF values which most closely approximate the mechanical reality of step averaged accelerations progressively approaching zero and it is recommended for future analyses although it should be considered a ratio of impulses., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Lower-limb wearable resistance (WR) provides a specific and targeted overload to the musculature involved in sprint running, however, it is unknown if greater impact forces occur with the additional limb mass. This study compared the contact times and ground reaction force waveforms between sprint running with no load and 2% body mass (BM) shank-positioned WR over 30 m. Fifteen male university-level sprint specialists completed two maximum effort sprints with each condition in a randomized order. Sprint running with shank WR resulted in trivial changes to contact times at 5 m, 10 m, and 20 m (effect size [ES] = <0.20, p > 0.05) and a small, significant increase to contact time at 30 m by 1.94% (ES = 0.25, p = 0.03). Significant differences in ground reaction force between unloaded and shank loaded sprint running were limited to the anterior-posterior direction and occurred between 20% and 30% of ground contact at 10 m, 20 m, and 30 m. Shank WR did not result in greater magnitudes of horizontal or vertical forces during the initial impact portion of ground contact. Practitioners can prescribe shank WR training with loads ≤2% BM without concern for increased risk of injurious impact forces.

Anaerobic performance in youth has received little attention partly due to the lack of a "gold-standard" measurement. However, force-velocity-power (F-v-P) profiling recently showed high reliability and validity in trained adults. Therefore, the aim was to determine the reliability of F-v-P profiling in children and adolescents. Seventy-five children (60 boys, 15 girls; age: 14.1 ± 2.6 years) completed three 30 m sprints. Velocity was measured at 46.875 Hz using a radar device. The F-v-P profile was fitted to a velocity-time curve allowing instantaneous power variables to be calculated. Reliability was assessed using the intra-class correlation coefficient (ICC), coefficient of variation (CV), standard error of measurement (SEM) and smallest worthwhile change (SWC). High reliability was evident for absolute peak (P peak ) and mean power (P mean ), P peak and P mean expressed relative to body mass, peak and mean velocity, 30 m sprint time, peak horizontal force (F 0 ), relative F 0 , mechanical efficiency index and fatigue rate (ICC: 0.75-0.88; CV: 1.9-9.4%) with time to peak power demonstrating moderate reliability (ICC: 0.50; CV: 9.5%). The F-v-P model demonstrated at least moderate reliability for all variables. This therefore provides a potential alternative for paediatric researchers assessing sprint performance and the underlying kinetics.

Objectives: The primary aim of this study was to examine whether accuracy of rugby union match prediction outcomes differed dependent on the method of data analysis (i.e., isolated vs. descriptively converted or relative data). A secondary aim was to then use the most appropriate method to investigate the performance indicators (PI's) most relevant to match outcome., Methods: Data was 16 PI's from 127 matches across the 2016-17 English Premiership rugby season. Given the binary outcome (win/lose), a random forest classification model was built using these data sets. Predictive ability of the models was further assessed by predicting outcomes from data sets of 72 matches across the 2017-18 season., Results: The relative data model attained a balanced prediction rate of 80% (95% CI - 75-85%) for 2016-17 data, whereas the isolated data model only achieved 64% (95% CI - 58-70%). In addition, the relative data model correctly predicted 76% (95% CI - 68-84%) of the 2017-18 data, compared with 70% (95% CI - 63-77%) for the isolated data model. From the relative data model, 10 PI's had significant relationships with game outcome; kicks from hand, clean breaks, average carry distance, penalties conceded when the opposition have the ball, turnovers conceded, total metres carried, defenders beaten, ratio of tackles missed to tackles made, total missed tackles, and turnovers won., Conclusions: Outcomes of Premiership rugby matches are better predicted when relative data sets are utilised. Basic open-field abilities based around an effective kicking game, ball carrying abilities, and not conceding penalties when the opposition are in possession are the most relevant predictors of success., (Copyright © 2018 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)