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Purpose: This study aimed to determine the accuracy of a 4 split time modelling method to generate velocity-time and velocity-distance variables in elite male 100-m sprinters and subsequently to assess the roles of key sprint parameters with respect to 100-m sprint performance. Additionally, this study aimed to assess the differences between faster and slower sprinters in key sprint variables that have not been assessed in previous work., Methods: Velocity-time and velocity-distance curves were generated using a mono-exponential function from 4 split times for 82 male sprinters during major athletics competitions. Key race variables-maximum velocity, the acceleration time constant (τ), and percentage of velocity lost (v Loss )-were derived for each athlete. Athletes were divided into tertiles, based on 100-m time, with the first and third tertiles considered to be the faster and slower groups, respectively, to facilitate further analysis., Results: Modelled split times and velocities displayed excellent accuracy and close agreement with raw measures (range of mean bias was -0.2% to 0.2%, and range of intraclass correlation coefficients (ICCs) was 0.935 to 0.999) except for 10-m time (mean bias was 1.6% ± 1.3%, and the ICC was 0.600). The 100-m sprint performance time and all 20-m split times had a significant near-perfect negative correlation with maximum velocity (r ≥ -0.90) except for the 0 to 20-m split time, where a significantly large negative correlation was found (r = -0.57). The faster group had a significantly higher maximum velocity and τ (p < 0.001), and no significant difference was found for v Loss (p = 0.085)., Conclusion: Coaches and researchers are encouraged to utilize the 4 split time method proposed in the current study to assess several key race variables that describe a sprinter's performance capacities, which can be subsequently used to further inform training., Competing Interests: Competing interests The authors declare that they have no competing interests., (Copyright © 2019. Production and hosting by Elsevier B.V.)

Abstract: Healy, R, Kenny, IC, and Harrison, AJ. Resistance training practices of sprint coaches. J Strength Cond Res 35(7): 1939-1948, 2021-This study describes the results of a survey of resistance training practices of sprint coaches. This study investigated why sprint coaches prescribe resistance training to their athletes, what exercises they select, and what factors are involved with their selection. Forty-one of 73 (56%) sprint coaches with mean ± SD coaching experience of 8.4 ± 6.4 years were included in this study. Coaches completed an online questionnaire consisting of 5 sections: (a) informed consent, (b) coach background information, (c) coach education and qualifications, (d) coaches' views on resistance training, and (e) exercise selection and preference. The results showed that coaches prescribe resistance training to their sprint athletes to develop strength and power, which they believe will transfer to sprint performance. Coaches prescribed a wide variety of traditional, ballistic, and plyometric exercises, with the hurdle jump found to be the most widely prescribed exercise (93% of coaches surveyed). Coaches selected exercises for a variety of reasons; however, the 3 most prominent reasons were: (a) performance adaptations; (b) practicality; and (c) the targeting of muscles/muscle groups. Coaches prioritized exercises that specifically developed strength, power, and reactive strength for their sprint athletes. This research can be used to develop educational resources for sprint coaches who wish to use resistance training with their athletes. In addition, sprint coaches can use the data presented to expand their current exercise repertoire and resistance training practices., (Copyright © 2019 National Strength and Conditioning Association.)

Healy, R, Smyth, C, Kenny, IC, and Harrison, AJ. Influence of reactive and maximum strength indicators on sprint performance. J Strength Cond Res 33(11): 3039-3048, 2019-The primary aim of this study was to assess the relationship between reactive and maximal strength measures with 40 m sprint performance and mechanical properties. Fourteen male and 14 female sprinters participated in this study. On the first day, subjects performed 40 m sprints with 10-m split times recorded in addition to maximal theoretical velocity, maximal theoretical force and peak horizontal power, which were calculated from force-velocity relationships. On the second day, subjects performed isometric midthigh pulls (IMTPs) with peak force (PF) and relative PF calculated, drop jumps (DJs) and vertical hopping where the reactive strength index (RSI) was calculated as jump height (JH) divided by contact time (CT). Pearson correlations were used to assess the relationships between measures and independent samples t-tests were used to assess the differences between men and women. No significant correlations were found between DJ and hopping RSI and sprint measures. A significant strong positive correlation was found between IMTP PF and peak horizontal power in men only (r = 0.61). The male sprinters performed significantly better in all recorded measures apart from hopping (CT, JH and RSI) and DJ CT where no significant differences were found. The lack of association between reactive and maximal strength measures with sprint performance is potentially because of the test's prolonged CTs relative to sprinting and the inability to assess the technical application of force. Several methods of assessing reactive strength are needed that can better represent the demands of the distinct phases of sprinting e.g., acceleration, maximum velocity.

Purpose: To assess the relationships between reactive strength measures and associated kinematic and kinetic performance variables achieved during drop jumps. A secondary aim was to highlight issues with the use of reactive strength measures as performance indicators., Methods: Twenty-eight national- and international-level sprinters, 14 men and 14 women, participated in this cross-sectional analysis. Athletes performed drop jumps from a 0.3-m box onto a force platform with dependent variables contact time (CT), landing time, push-off time, flight time, jump height (JH), reactive strength index (RSI, calculated as JH/CT), reactive strength ratio (RSR, calculated as flight time/CT), and vertical leg-spring stiffness recorded., Results: A Pearson correlation test found very high to near-perfect relationships between RSI and RSR (r = .91-.97), with mixed relationships between RSI, RSR, and the key performance variables (men: r = -.86 to -.71 between RSI/RSR and CT, r = .80-.92 between RSI/RSR and JH; women: r = -.85 to -.56 between RSR and CT, r = .71 between RSI and JH)., Conclusions: The method of assessing reactive strength (RSI vs RSR) may be influenced by the performance strategies adopted, that is, whether athletes achieve their best reactive strength scores via low CTs, high JHs, or a combination. Coaches are advised to limit the variability in performance strategies by implementing upper and/or lower CT thresholds to accurately compare performances between individuals.

Purpose: Pacing strategies are key to overall performance outcome in distance-running events. Presently, no literature has examined pacing strategies used by masters athletes of all running levels during a competitive marathon. Therefore, this study aimed to examine masters athletes' pacing strategies, categorized by gender, age, and performance level., Methods: Data were retrieved from the 2015 TSC New York City Marathon for 31,762 masters athletes (20,019 men and 11,743 women). Seven performance-classification (PC) groupings were identified via comparison of overall completion time compared with current world records, appropriate to age and gender. Data were categorized via, age, gender, and performance level. Mean 5-km speed for the initial 40 km was calculated, and the fastest and slowest 5-km-split speeds were identified and expressed as a percentage faster or slower than mean speed. Pace range, calculated as the absolute sum of the fastest and slowest split percentages, was then analyzed., Results: Significant main effects were identified for age, gender, and performance level (P < .001), with performance level the most determining factor. Athletes in PC1 displayed the lowest pace range (14.19% ± 6.66%), and as the performance levels of athletes decreased, pace range increased linearly (PC2-PC7, 17.52% ± 9.14% to 36.42% ± 18.32%). A significant interaction effect was found for gender × performance (P < .001), with women showing a smaller pace range (-3.81%)., Conclusions: High-performing masters athletes use more-controlled pacing strategies than their lower-ranked counterparts during a competitive marathon, independent of age and gender.