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Patrycja Lipinska, Sian V. Allen and Will G. Hopkins

Purpose:

Pacing has a substantial effect on endurance performance. The authors characterize pacing and identify its parameters for optimal performance in 1500-m freestyle swimming.

Methods:

Web sites provided 50-m lap and 1500-m race times for 330 swims of 24 elite male swimmers. Pacing for each swim was characterized with 7 parameters derived from a general linear model: linear and quadratic coefficients for the effect of lap number; reductions from predicted time for first, second, penultimate, and last laps; and lap-time variability. Scatter plots of race time vs each parameter for each swimmer were used to identify optimum values of parameters.

Results:

Most scatterplots showed only weak relationships between the parameter and performance, but one-third to one-half of swimmers had an optimum value of the parameter that was substantially different from their mean value. A large improvement in performance time (1.4% ± 0.9%, mean ± SD) could be achieved generally by reversing the sign of the linear parameter to make the slowest lap occur earlier in the race. Small to moderate improvements might also accrue by changing the quadratic parameter, by making the first and second laps slower and the penultimate and last laps faster, and reducing lap-time variability.

Conclusions:

This approach to analysis of pacing may help improve performance in swimmers and other endurance athletes in sports with multiple laps, but data from many competitions are required.

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Sian V. Allen, Tom J. Vandenbogaerde and Will G. Hopkins

Many national sporting organizations recruit talented athletes to well-resourced centralized training squads to improve their performance.

Purpose:

To develop a method to monitor performance progression of swimming squads and to use this method to assess the progression of New Zealand’s centralized elite swimming squad.

Methods:

Best annual long-course competition times of all New Zealand swimmers with at least 3 y of performances in an event between 2002 and 2013 were downloaded from takeyourmarks.com (~281,000 times from ~8500 swimmers). A mixed linear model accounting for event, age, club, year, and elite-squad membership produced estimates of mean annual performance for 175 swim clubs and mean estimates of the deviation of swimmers’ performances from their individual quadratic trajectories after they joined the elite squad. Effects were evaluated using magnitude-based inferences, with a smallest important improvement in swim time of –0.24%.

Results:

Before 2009, effects of elite-squad membership were mostly unclear and trivial to small in magnitude. Thereafter, both sexes showed clear additional performance enhancements, increasing from large in 2009 (males –1.4% ± 0.8%, females –1.5% ± 0.8%; mean ± 90% confidence limits) to extremely large in 2013 (males –6.8% ± 1.7%, females –9.8% ± 2.9%). Some clubs also showed clear performance trends during the 11-y period.

Conclusions:

Our method of quantifying deviations from individual trends in competition performance with a mixed model showed that Swimming New Zealand’s centralization strategy took several years to produce substantial performance effects. The method may also be useful for evaluating performance-enhancement strategies introduced at national or club level in other sports.

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Kaitlyn J. Weiss, Sian V. Allen, Mike R. McGuigan and Chris S. Whatman

Purpose:

To establish the relationship between the acute:chronic workload ratio and lower-extremity overuse injuries in professional basketball players over the course of a competitive season.

Methods:

The acute:chronic workload ratio was determined by calculating the sum of the current week’s session rating of perceived exertion of training load (acute load) and dividing it by the average weekly training load over the previous 4 wk (chronic load). All injuries were recorded weekly using a self-report injury questionnaire (Oslo Sports Trauma Research Center Injury Questionnaire20). Workload ratios were modeled against injury data using a logistic-regression model with unique intercepts for each player.

Results:

Substantially fewer team members were injured after workload ratios of 1 to 1.49 (36%) than with very low (≤0.5; 54%), low (0.5–0.99; 51%), or high (≥1.5; 59%) workload ratios. The regression model provided unique workload–injury trends for each player, but all mean differences in likelihood of being injured between workload ratios were unclear.

Conclusions:

Maintaining workload ratios of 1 to 1.5 may be optimal for athlete preparation in professional basketball. An individualized approach to modeling and monitoring the training load–injury relationship, along with a symptom-based injury-surveillance method, should help coaches and performance staff with individualized training-load planning and prescription and with developing athlete-specific recovery and rehabilitation strategies.

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Sian V. Allen, Tom J. Vandenbogaerde, David B. Pyne and Will G. Hopkins

Talent identification and development typically involve allocation of resources toward athletes selected on the basis of early-career performance.

Purpose:

To compare 4 methods for early-career selection of Australia’s 2012 Olympic-qualifying swimmers.

Methods:

Performance times from 5738 Australian swimmers in individual Olympic events at 101 competitions from 2000 to 2012 were analyzed as percentages of world-record times using 4 methods that retrospectively simulated early selection of swimmers into a talent-development squad. For all methods, squad-selection thresholds were set to include 90% of Olympic qualifiers. One method used each swimmer’s given-year performance for selection, while the others predicted each swimmer’s 2012 performance. The predictive methods were regression and neural-network modeling using given-year performance and age and quadratic trajectories derived using mixed modeling of each swimmer’s annual best career performances up to the given year. All methods were applied to swimmers in 2007 and repeated for each subsequent year through 2011.

Results:

The regression model produced squad sizes of 562, 552, 188, 140, and 93 for the years 2007 through 2011. Corresponding proportions of the squads consisting of Olympic qualifiers were 11%, 11%, 32%, 43%, and 66%. Neural-network modeling produced similar outcomes, but the other methods were less effective. Swimming Australia’s actual squads ranged from 91 to 67 swimmers but included only 50−74% of Olympic qualifiers.

Conclusions:

Large talent-development squads are required to include most eventual Olympic qualifiers. Criteria additional to age and performance are needed to improve early selection of swimmers to talent-development squads.

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Gareth N. Sandford, Sian V. Allen, Andrew E. Kilding, Angus Ross and Paul B. Laursen

Purpose: In recent years (2011–2016), men’s 800-m championship running performances have required greater speed than previous eras (2000–2009). The “anaerobic speed reserve” (ASR) may be a key differentiator of this performance, but profiles of elite 800-m runners and their relationship to performance time have yet to be determined. Methods: The ASR—determined as the difference between maximal sprint speed (MSS) and predicted maximal aerobic speed (MAS)—of 19 elite 800- and 1500-m runners was assessed using 50-m sprint and 1500-m race performance times. Profiles of 3 athlete subgroups were examined using cluster analysis and the speed reserve ratio (SRR), defined as MSS/MAS. Results: For the same MAS, MSS and ASR showed very large negative (both r = −.74 ± .30, ±90% confidence limits; very likely) relationships with 800-m performance time. In contrast, for the same MSS, ASR and MAS had small negative relationships (both r = −.16 ± .54; possibly) with 800-m performance. ASR, 800-m personal best, and SRR best defined the 3 subgroups along a continuum of 800-m runners, with SRR values as follows: 400–800 m ≥ 1.58, 800 m ≤ 1.57 to ≥ 1.48, and 800–1500 m ≤ 1.47 to ≥ 1.36. Conclusion: MSS had the strongest relationship with 800-m performance, whereby for the same MSS, MAS and ASR showed only small relationships to differences in 800-m time. Furthermore, the findings support the coaching observation of three 800-m subgroups, with the SRR potentially representing a useful and practical tool for identifying an athlete’s 800-m profile. Future investigations should consider the SRR framework and its application for individualized training approaches in this event.

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Gareth N. Sandford, Simon Pearson, Sian V. Allen, Rita M. Malcata, Andrew E. Kilding, Angus Ross and Paul B. Laursen

Purpose: To assess the longitudinal evolution of tactical behaviors used to medal in men’s 800-m Olympic Games (OG) or world-championship (WC) events in the recent competition era (2000–2016). Methods: Thirteen OG and WC events were characterized for 1st- and 2nd-lap splits using available footage from YouTube. Positive pacing strategies were defined as a faster 1st lap. Season’s best 800-m time and world ranking, reflective of an athlete’s “peak condition,” were obtained to determine relationships between adopted tactics and physical condition prior to the championships. Seven championship events provided coverage of all medalists to enable determination of average 100-m speed and sector pacing of medalists. Results: From 2011 onward, 800-m OG and WC medalists showed a faster 1st lap by 2.2 ± 1.1 s (mean, ±90% confidence limits; large difference, very likely), contrasting a possibly faster 2nd lap from 2000 to 2009 (0.5, ±0.4 s; moderate difference). A positive pacing strategy was related to a higher world ranking prior to the championships (r = .94, .84–.98; extremely large, most likely). After 2011, the fastest 100-m sector from 800-m OG and WC medalists was faster than before 2009 by 0.5, ±0.2 m/s (large difference, most likely). Conclusions: A secular change in tactical racing behavior appears evident in 800-m championships; since 2011, medalists have largely run faster 1st laps and have faster 100-m sector-speed requirements. This finding may be pertinent for training, tactical preparation, and talent identification of athletes preparing for 800-m running at OGs and WCs.