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  • Author: Graham J. Mytton x
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Andrew Renfree, Graham J. Mytton, Sabrina Skorski and Alan St Clair Gibson

Purpose:

To identify tactical factors associated with progression from preliminary rounds in middle-distance running events at an international championship.

Methods:

Results from the 2012 Olympic Games were used to access final and intermediate positions, finishing times, and season-best (SB) times for competitors in men’s and women’s 800-m and 1500-m events (fifteen 800-m races and ten 1500-m races). Finishing times were calculated as %SB, and Pearson product–moment correlations were used to assess relationships between intermediate and finishing positions. Probability (P) of qualification to the next round was calculated for athletes in each available intermediate position.

Results:

There were no significant differences in finishing times relative to SB between qualifiers and nonqualifiers. In the 800-m, correlation coefficients between intermediate and final positions were r = .61 and r = .84 at 400 m and 600 m, respectively, whereas in the 1500-m, correlations were r = .35, r = .43, r = .55, and r = .71 at 400 m, 800 m, 1000 m, and 1200 m, respectively. In both events, probability of qualification decreased with position at all intermediate distances. At all points, those already in qualifying positions were more likely to qualify for the next round.

Conclusions:

The data demonstrate that tactical positioning at intermediate points in qualifying rounds of middle-distance races is a strong determinant of qualification. In 800-m races it is important to be in a qualifying position by 400 m. In the 1500-m event, although more changes in position are apparent, position at intermediate distances is still strongly related to successful qualification.

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Graham J. Mytton, David T. Archer, Alan St Clair Gibson and Kevin G. Thompson

Purpose:

To assess the reliability and stability of 400-m swimming and 1500-m running competitions to establish the number of samples needed to obtain a stable pacing profile. Coaches, athletes, and researchers can use these methods to ensure that sufficient data are collected before training and race strategies are constructed or research conclusions are drawn.

Method:

Lap times were collected from 5 world and European championship finals between 2005 and 2011, resulting in the capture of data from 40 swimmers and 55 runners. A cumulative mean for each lap was calculated, starting with the most recent data, and the number of races needed for this to stabilize to within 1% was reported. Typical error for each lap was calculated for athletes who had competed in more than 1 final.

Results:

International swimmers demonstrated more reproducible performances than runners in 3 of the 4 laps of the race (P < .01). Variance in runners’ lap times significantly decreased by 1.7–2.7% after lap 1, whereas variance in swimmers’ lap times tended to increase by 0.1–0.5% after lap 1. To establish a stable profile, at least ten 400-m swimmers and forty-four 1500-m runners must be included.

Conclusions:

A stable race profile was observed from the analysis of 5 events for 1500-m running and 3 events for 400-m swimming. Researchers and athletes can be more certain about the pacing information collected from 400-m swimming than 1500-m running races, as the swimming data are less variable, despite both events being of similar duration.

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Graham J. Mytton, David T. Archer, Kevin G. Thompson, Andrew Renfree and Alan St Clair Gibson

The collection of retrospective lap times from video footage is a potentially useful research tool to analyze the pacing strategies in any number of competitive events. The aim of this study was to validate a novel method of obtaining running split-time data from publically available video footage. Videos of the 1500-m men’s final from the 2004 and 2008 Olympics, 2005 and 2009 World Championships, and 2010 European Championships were obtained from the YouTube Web site, and split times were collected from all competitors using frame-by-frame playback. The typical error of video split times ranged between 0.02 s and 0.11 s for the 4 laps when compared with official split times. Video finishing times were also similar to official finishing times (typical error of 0.04 s). The method was shown to be highly reliable with a typical error of 0.02 s when the same video was analyzed on 2 occasions separated by 8 mo. Video data of track races are widely available; however, camera angles are not always perpendicular to the start/finish line, and some slower athletes may cross the line after the camera has panned away. Nevertheless, the typical errors reported here show that when appropriate camera angles are available this method is both valid and reliable.

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Graham J. Mytton, David T. Archer, Louise Turner, Sabrina Skorski, Andrew Renfree, Kevin G. Thompson and Alan St Clair Gibson

Purpose:

Previous literature has presented pacing data of groups of competition finalists. The aim of this study was to analyze the pacing patterns displayed by medalists and nonmedalists in international competitive 400-m swimming and 1500-m running finals.

Methods:

Split times were collected from 48 swimming finalists (four 100-m laps) and 60 running finalists (4 laps) in international competitions from 2004 to 2012. Using a cross-sectional design, lap speeds were normalized to whole-race speed and compared to identify variations of pace between groups of medalists and nonmedalists. Lap-speed variations relative to the gold medalist were compared for the whole field.

Results:

In 400-m swimming the medalist group demonstrated greater variation in speed than the nonmedalist group, being relatively faster in the final lap (P < .001; moderate effect) and slower in laps 1 (P = .03; moderate effect) and 2 (P > .001; moderate effect). There were also greater variations of pace in the 1500-m running medalist group than in the nonmedalist group, with a relatively faster final lap (P = .03; moderate effect) and slower second lap (P = .01; small effect). Swimming gold medalists were relatively faster than all other finalists in lap 4 (P = .04), and running gold medalists were relatively faster than the 5th- to 12th-placed athletes in the final lap (P = .02).

Conclusions:

Athletes who win medals in 1500-m running and 400-m swimming competitions show different pacing patterns than nonmedalists. End-spurtspeed increases are greater with medalists, who demonstrate a slower relative speed in the early part of races but a faster speed during the final part of races than nonmedalists.