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Arturo Casado and Andrew Renfree
Purpose: To assess tactical and performance factors associated with progression from qualification rounds in the 800-m and 1500-m running events at the 2017 International Association of Athletics Federations World Championships. Methods: Official results were used to access final and intermediate positions and times, as well as performance characteristics of competitors. Shared variance between intermediate positions and rank order lap times with finishing positions were calculated, along with probability of automatic qualification, for athletes in each available race position at the end of every 400-m lap. Differences in race positions and lap times relative to season’s best performances were assessed between automatic qualifiers, fastest losers, and nonqualifiers. Results: Race positions at the end of each 400-m lap remained more stable through 800-m races than 1500-m races. Probability of automatic qualification decreased with both race position and rank order lap times on each lap, although rank order lap times accounted for a higher degree of shared variance than did intermediate position. In the 1500-m event, fastest losers ran at a higher percentage of season’s best speed and adopted positions closer to the race lead in the early stages. This was not the case in the 800-m. Conclusions: Intermediate positioning and the ability to produce a fast final race segment are strongly related to advancement from qualification rounds in middle-distance running events. The adoption of a more “risky” strategy characterized by higher speeds relative to season’s best may be associated with an increased likelihood of qualification as fastest losers in the 1500-m event.
Andrew Renfree and Alan St Clair Gibson
Purpose:
To analyze pacing strategies displayed by athletes achieving differing levels of performance during an elite-level marathon race.
Methods:
Competitors in the 2009 IAAF Women’s Marathon Championship were split into groups 1, 2, 3, and 4 comprising the first, second, third, and fourth 25% of finishers, respectively. Final, intermediate, and personal-best (PB) times of finishers were converted to mean speeds, and relative speed (% of PB speed) was calculated for intermediate segments.
Results:
Mean PB speed decreased from groups 1 to 4, and speeds maintained in the race were 98.5% ± 1.8%, 97.4% ± 3.2%, 95.0% ± 3.1%, and 92.4% ± 4.4% of PB speed for groups 1–4 respectively. Group 1 was fastest in all segments, and differences in speed between groups increased throughout the race. Group 1 ran at lower relative speeds than other groups for the first two 5-km segments but higher relative speeds after 35 km. Significant differences (P < .01) in the percentage of PB speed maintained were observed between groups 1 and 4 and groups 2 and 4 in all segments after 20 km and groups 3 and 4 from 20 to 25 km and 30 to 35 km.
Conclusions:
Group 1 athletes achieved better finishing times relative to their PB than athletes in other groups, who selected unsustainable initial speeds resulting in subsequent significant losses of speed. It is suggested that psychological factors specific to a major competitive event influenced decision making by athletes, and poor decisions resulted in final performances inferior to those expected based on PB times.
Andrew Renfree, Arturo Casado, Gonzalo Pellejero, and Brian Hanley
Purpose: To determine different relationships between, and predictive ability of, performance variables at intermediate distances with finishing time in elite male 10,000-m runners. Methods: Official electronic finishing and 100-m split times of the men’s 10,000-m finals at the 2008 and 2016 Olympic Games and IAAF World Championships in 2013 and 2017 were obtained (125 athlete performances in total). Correlations were calculated between finishing times and positions and performance variables related to speed, position, time to the leader, and time to the runner in front at 2000, 4000, 6000, 8000, and 9900 m. Stepwise linear-regression analysis was conducted between finishing times and positions and these variables across the race. One-way analysis of variance was performed to identify differences between intermediate distances. Results: The SD and kurtosis of mean time, skewness of mean time, and position and time difference to the leader were either correlated with or significantly contributed to predictions of finishing time and position at at least one of the analyzed distances (.81 ≥ r ≥ .30 and .001 ≤ P ≤ .03, respectively). These variables also displayed variation across the race (.001 ≤ P ≤ .05). Conclusions: The ability to undertake a high degree of pace variability, mostly characterized by acceleration in the final stages, is strongly associated with achievement of high finishing positions in championship 10,000-m racing. Furthermore, the adoption and maintenance of positions close to the front of the race from the early stages are important to achieve a high finishing position.
Hugh Trenchard, Andrew Renfree, and Derek M. Peters
Purpose:
Drafting in cycling influences collective behavior of pelotons. Although evidence for collective behavior in competitive running events exists, it is not clear if this results from energetic savings conferred by drafting. This study modeled the effects of drafting on behavior in elite 10,000-m runners.
Methods:
Using performance data from a men’s elite 10,000-m track running event, computer simulations were constructed using Netlogo 5.1 to test the effects of 3 different drafting quantities on collective behavior: no drafting, drafting to 3 m behind with up to ~8% energy savings (a realistic running draft), and drafting up to 3 m behind with up to 38% energy savings (a realistic cycling draft). Three measures of collective behavior were analyzed in each condition: mean speed, mean group stretch (distance between first- and last-placed runner), and runner-convergence ratio (RCR), which represents the degree of drafting benefit obtained by the follower in a pair of coupled runners.
Results:
Mean speeds were 6.32 ± 0.28, 5.57 ± 0.18, and 5.51 ± 0.13 m/s in the cycling-draft, runner-draft, and no-draft conditions, respectively (all P < .001). RCR was lower in the cycling-draft condition but did not differ between the other 2. Mean stretch did not differ between conditions.
Conclusions:
Collective behaviors observed in running events cannot be fully explained through energetic savings conferred by realistic drafting benefits. They may therefore result from other, possibly psychological, processes. The benefits or otherwise of engaging in such behavior are as yet unclear.
Andrew Renfree, Louise Martin, Ashley Richards, and Alan St Clair Gibson
Purpose:
This study examined individual contributions to overall pacing strategy during 2- and 5-km rowing trials in a coxless-4 boat.
Methods:
A crew of 4 male rowers performed maximal-effort on-water trials over 2 and 5 km, and power output during every individual stroke was measured for each crew member. Mean overall boat and individual rower stroke power were calculated for each 25% epoch (25% of total strokes taken), and power for each individual epoch was calculated as a percentage of mean power maintained over the entire distance. The coefficient of variation was used to determine stroke-to-stroke and epoch-to-epoch variability for individual rowers and the overall boat.
Results:
In both trials, the overall pacing strategy consisted of a high power output in the initial 25% that decreased in the middle 50% and increased again in the final 25%. However, individual rower data indicate wide variation in individual power profiles that did not always mimic the overall boat profile.
Conclusions:
This study demonstrates that overall boat power profiles during 2- and 5-km rowing trials are similar to velocity profiles previously reported for individual ergometry and on-water racing events. However, this overall profile is achieved despite considerable variation in individual rower profiles. Further research is warranted to determine the mechanisms through which individual contributions to overall pacing strategy are regulated and the effectiveness or otherwise of seemingly disparate individual strategies on overall performance.
Everton C. do Carmo, Renato Barroso, Andrew Renfree, Saulo Gil, and Valmor Tricoli
The effects of an enforced fast start on long-distance performance are controversial and seem to depend on the athlete’s capacity to delay and tolerate metabolic disruption. The aim of this study was to investigate the effects of an enforced start on 10-km-running performance and the influence of the some physiological and performance variables on the ability to tolerate an enforced fast start during the running. Fifteen moderately trained runners performed two 10-km time trials (TTs): free pacing (FP-TT) and fast start (FS-TT). During FS-TT, speed during the first kilometer was 6% higher than in FP-TT. Maximal oxygen uptake (VO2max), peak velocity (PV), velocity associated with VO2max (vVO2max), ventilatory threshold, and running economy at 10 and 12 km/h and FP-TT average velocity (AV-10 km) were individually determined. There were no differences between FP-TT and FS-TT performance (45:01 ± 4:08 vs 45:11 ± 4:46 min:s, respectively, P = .4). Eight participants improved (+2.2%) their performance and were classified as positive responders (PR) and 7 decreased (–3.3%) performance and were classified as negative responders (NR). Running speed was significantly higher for PR between 6 and 9.2 km (P < .05) during FS-TT. In addition, PR presented higher PV (P = .02) and vVO2max (P = .01) than NR, suggesting that PV and vVO2max might influence the ability to tolerate a fast-start strategy. In conclusion, there was an individual response to the enforced fast-start strategy during 10-km running, and those who improved performance also presented higher vVO2max and PV, suggesting a possible association between these variables and response to the strategy adopted.
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.
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.
