row of spectator seats approximately 2.5 m above and 15 m away from the side of the pool. Race analysis software compiled in Matlab 2012 (The MathWorks, Inc., Natick, MA, USA) was used for calibration and image processing to obtain SR, SL, and swim speed (S) for every stroke cycle. Four poolside marks
David Simbaña Escobar, Philippe Hellard, David B. Pyne, and Ludovic Seifert
Elaine Tor, David L. Pease, Kevin A. Ball, and Will G. Hopkins
Time trials are commonly used in the lead-up to competition. A method that evaluates the relationship between time trial and competition performance in swimming would be useful for developing performance-enhancement strategies.
To use linear mixed modeling to identify key parameters that can be used to relate time-trial and competition performance.
Ten swimmers participated in the study. Each swimmer was analyzed during 3 time trials and 1 competition. Race video footage was analyzed to determine several key parameters. Pooling of strokes and distances was achieved by modeling changes in parameters between time trials and competition within each subject as linear predictors of percent change in performance using mixed modeling of log-transformed race times.
When parameters were evaluated as the effect of 2 SD on performance time, there were very large effects of start time (2.6%, 90% confidence interval 1.8–3.3%) and average velocity (–2.3%, –2.8% to –1.8%). There was also a small effect for stroke rate (–0.6%, –1.3% to 0.2%). Further analysis revealed an improvement in performance time of 2.4% between time trials and competition, of which 1.8% (large; 1.4–2.1%) was due to a change in average velocity and 0.9% (moderate; 0.6–1.1%) was due to a change in start time; changes in remaining parameters had trivial effects on performance.
This study illustrates effective analytical strategies for identifying key parameters that can be the focus of training to improve performance in small squads of elite swimmers and other athletes.
Yann Le Meur, Thierry Bernard, Sylvain Dorel, Chris R. Abbiss, Gérard Honnorat, Jeanick Brisswalter, and Christophe Hausswirth
The purpose of the present study was to examine relationships between athlete’s pacing strategies and running performance during an international triathlon competition.
Running split times for each of the 107 finishers of the 2009 European Triathlon Championships (42 females and 65 males) were determined with the use of a digital synchronized video analysis system. Five cameras were placed at various positions of the running circuit (4 laps of 2.42 km). Running speed and an index of running speed variability (IRSVrace) were subsequently calculated over each section or running split.
Mean running speed over the frst 1272 m of lap 1 was 0.76 km-h–1 (+4.4%) and 1.00 km-h–1 (+5.6%) faster than the mean running speed over the same section during the three last laps, for females and males, respectively (P < .001). A significant inverse correlation was observed between RSrace and IRSVrace for all triathletes (females r = -0.41, P = .009; males r = -0.65, P = .002; and whole population -0.76, P = .001). Females demonstrated higher IRSVrace compared with men (6.1 ± 0.5 km-h–1 and 4.0 ± 1.4 km-h–1, for females and males, respectively, P = .001) due to greater decrease in running speed over uphill sections.
Pacing during the run appears to play a key role in high-level triathlon performance. Elite triathletes should reduce their initial running speed during international competitions, even if high levels of motivation and direct opponents lead them to adopt an aggressive strategy.
Thomas Muehlbauer, Christian Schindler, and Stefan Panzer
This study assessed the effect of time spent in several race sectors (S) on finishing time and determined the variance in distribution of skating time and in total race time for official 1000-m sprint races conducted during a competitive season.
Total race and sector times for the first 200 m (S1) and the following two 400-m laps (S2 and S3) of 34 female and 31 male elite speed skaters performed during a series of World Cup Meetings were analyzed.
Overall, skaters started fast, reached their peak in S2, and slowed down in S3, irrespective of race category considered (eg, rank of athlete, number of race, altitude of rink, starting lane). Regression analyses revealed that spending a shorter fraction of time in the last (women in S3: B = 239.1; P < .0001; men in S3: B = 201.5; P < .0001) but not in the first (women in S1: B = -313.1; P < .0001; men in S1: B = -345.6; P < .0001) race sector is associated with a short total race time. Upper- compared with lower-ranked skaters varied less in competition-to-competition sector and total race times (women: 0.02 to 0.33 vs 0.02 to 0.51; men: 0.01 to 0.15 vs 0.02 to 0.57).
This study confirmed that skaters adopted a fast start pacing strategy during official 1000-m sprint races. However, analyses indicate that shortening time in the closing but not in the starting sector is beneficial for finishing fast. In addition, findings suggest that lower-ranked skaters should concentrate training on lowering their competition-to-competition variability in sector times.
Marco J. Konings, Olaf S. Noorbergen, David Parry, and Florentina J. Hettinga
To gain more insight in pacing behavior and tactical positioning in 1500-m short-track speed skating, a sport in which several athletes directly compete in the same race.
Lap times and intermediate rankings of elite 1500-m short-track-skating competitors were collected over the season 2012–13 (N = 510, 85 races). Two statistical approaches were used to assess pacing behavior and tactical positioning. First, lap times were analyzed using a MANOVA, and for each lap differences between sex, race type, final rankings, and stage of competition were determined. Second, Kendall tau b correlations were used to assess relationships between intermediate and final rankings. In addition, intermediate rankings of the winner of each race were examined.
In 1500 m (13.5 laps of 111.12 m), correlations between intermediate and final ranking gradually increased throughout the race (eg, lap 1, r = .05; lap 7, r = .26; lap 13, r = .85). Moreover, the percentage of race winners skating in the leading position was over 50% during the last 3 laps. Top finishers were faster than bottom-place finishers only during the last 5 laps, with on average 0.1- to 1.5-s faster lap times of the race winners compared with the others during the last 5 laps.
Although a fast start led to faster finishing times, top finishers were faster than bottom-placed finishers only during the last 5 laps. Moreover, tactical positioning at 1 of the foremost positions during the latter phase of the race appeared to be a strong determinant of finishing position.
Olaf S. Noorbergen, Marco J. Konings, Dominic Micklewright, Marije T. Elferink-Gemser, and Florentina J. Hettinga
To explore pacing behavior and tactical positioning during the shorter 500- and 1000-m short-track competitions.
Lap times and intermediate rankings of elite 500- and 1000-m short-track-skating competitors were collected over the 2012–13 season. First, lap times were analyzed using a MANOVA, and for each lap, differences between sex, race type, final ranking, and stage of competition were determined. Second, Kendall tau-b correlations were used to assess relationships between intermediate and final rankings. In addition, intermediate rankings of the winner of each race were examined.
Top-placed athletes appeared faster than bottom-placed athletes in every lap in the 500-m, while in the 1000-m no differences were found until the final 4 laps (P < .05). Correlations between intermediate and final rankings were already high at the beginning stages of the 50-m (lap 1: r = .59) but not for the 1000-m (lap 1: r = .21).
Although 500- and 1000-m short-track races are both relatively short, fundamental differences in pacing behavior and tactical positioning were found. A fast-start strategy seems to be optimal for 500-m races, while the crucial segment in 1000-m races seems to be from the 6th lap to the finish line (ie, after ± 650 m). These findings provide evidence to suggest that athletes balance between choosing an energetically optimal profile and the tactical and positional benefits that play a role when riding against an opponent, as well as contributing to developing novel insights in exploring athletic behavior when racing against opponents.
Daniel J. Daly, Stefka K. Djobova, Laurie A. Malone, Yves Vanlandewijck, and Robert D. Steadward
A video race analysis was conducted on 100-m freestyle performances of 72 male and 62 female finalists at the Sydney 2000 Paralympic Games. Races were won or lost in the second half of each 50-m race lap and differences in speed between swimmers were more related to stroke length than stroke rate. Within-race speed changes were more related to changes in stroke rate. Stroke rate changes were also responsible for speed changes between qualifying heats and finals in the first part of races, while stroke length was responsible for better speed maintenance at the end of races. Results indicate that Paralympic finalists use race speed patterns similar to able-bodied elite swimmers.
Daniel J. Daly, Laurie A. Malone, David J. Smith, Yves Vanlandewijck, and Robert D. Steadward
A video race analysis was conducted at the Atlanta Paralympic Games swimming competition. The purpose was to describe the contribution of clean swimming speed, as well as start, turn, and finish speed, to the total race performance in the four strokes for the men’s 100 m events. Start, turn, and finish times, as well as clean swimming speed during four race sections, were measured on videotapes during the preliminary heats (329 swims). Information on 1996 Olympic Games finalists (N = 16) was also available. In Paralympic swimmers, next to clean swimming speed, both turning and finishing were highly correlated with the end race result. Paralympic swimmers do start, turn, and finish slower than Olympic swimmers but in direct relation to their slower clean swimming speed. The race pattern of these components is not different between Paralympic and Olympic swimmers.
-0295 Monitoring the Effect of Race-Analysis Parameters on Performance in Elite Swimmers Elaine Tor * David L. Pease * Kevin A. Ball * Will G. Hopkins * 7 2014 9 4 633 636 10.1123/ijspp.2013-0205 Performance of Maximum Number of Repetitions With Cluster-Set Configuration Eliseo Iglesias-Soler * Eduardo
Yuji Matsuda, Yoshihisa Sakurai, Keita Akashi, and Yasuyuki Kubo
velocity during swimming is much more difficult and complex than it is for land movements, and the experimental environment for monitoring the CoM velocity during swimming is quite limited. Alternatively, the sacral marker or fixed-point method has been used for race analysis during competitions and