A Novel Method to Characterize the Pacing Profile of Elite Male 1500-m Freestyle Swimmers.

PURPOSE: Pacing, or the distribution of energy expenditure, is particularly important in swimming; however, there is limited research examining pacing profiles in long-distance freestyle events. This study aimed to characterize the pacing profiles of elite male 1500-m freestyle swimmers using a novel method to provide a detailed analysis of different race segments. METHODS: The race data for 327 male 1500-m freestyle long-course races between 2010 and 2019 were analyzed retrospectively. The raw 50-m split times for each lap were converted to a percentage of overall race time. The races were classified as a fast-, average-, or slow-start strategy (laps 1-2); as an even, negative, or positive pacing strategy (laps 3-28); and as a fast-, average-, or slow-finish strategy (laps 29-30) to give an overall pacing profile. RESULTS: Slow- and average-start strategies were associated with faster overall 1500-m times than a fast-start strategy (mean = -21.2 s; 90% confidence interval, -11.4 to -32.3 s, P = .00). An even pacing strategy in laps 3 to 28 yielded faster overall 1500-m times than a positive pacing strategy (-8.4 s, -3.9 to -13.0 s, P = .00). The overall 1500-m times did not differ substantially across the finish strategies (P = .99). The start strategy differed across age groups and nationalities, where younger swimmers and swimmers from Australia and Great Britain typically spent a lower percentage of race time in laps 1 to 2 (faster start strategy; -0.10%, -0.01% to -0.23%, P ≤ .02). CONCLUSION: Adopting a relatively slower start strategy helps conserve energy for the latter stages of a 1500-m freestyle race.

The Influence of Muscle Fiber Typology on the Pacing Strategy of 200-m Freestyle Swimmers.

PURPOSE: To determine the influence of muscle fiber typology (MFT) on the pacing strategy of elite swimmers competing in the 200-m freestyle event. METHOD: The top 3 career-best performances from 25 elite 200-m freestyle swimmers were analyzed-12 women (1:58.0 [0:01.3] min:s) and 13 men (1:48.4 [0:02.5]). Muscle carnosine concentration was quantified by proton magnetic resonance spectroscopy in the gastrocnemius and soleus muscles and expressed as a carnosine aggregate z score (CAZ score) relative to an age- and gender-matched nonathlete control group to estimate MFT. Linear regression models were employed to examine the influence of MFT on the percentage of overall race time spent in each 50-m lap. RESULTS: Swimmers with a higher CAZ score spent a greater percentage of race time in lap 3 compared with swimmers with a lower CAZ score (0.1%, 0.0% to 0.2%; mean, 90% confidence interval, P = .02). For every 1% increase in the percentage of race time spent in lap 1, the percentage of race time spent in lap 3 decreased by 0.4% for swimmers with a higher CAZ score (0.2% to -0.5%, P = .00, r = -.51), but not for swimmers with a lower CAZ score (-0.1%, -0.3% to 0.1%, P = .28, r = -.18). The percentage of race time spent in lap 4 decreased by 0.8% for higher-CAZ-score swimmers (-0.5% to -1.0%, P = .00, r = -.66) and by 0.9% for lower-CAZ-score swimmers (-0.6% to -1.3%, P = .00, r = -.65) when lap 1 percentage increased by 1%. CONCLUSION: MFT may influence the pacing strategy of swimmers in the 200-m freestyle event, which provides an avenue for maximizing individualized pacing strategies of elite swimmers.

Predicting performance in 4 x 200-m freestyle swimming relay events.

AIM: The aim was to predict and understand variations in swimmer performance between individual and relay events, and develop a predictive model for the 4x200-m swimming freestyle relay event to help inform team selection and strategy. DATA AND METHODS: Race data for 716 relay finals (4 x 200-m freestyle) from 14 international competitions between 2010-2018 were analysed. Individual 200-m freestyle season best time for the same year was located for each swimmer. Linear regression and machine learning was applied to 4 x 200-m swimming freestyle relay events. RESULTS: Compared to the individual event, the lowest ranked swimmer in the team (-0.62 s, CI = [-0.94, -0.30]) and American swimmers (-0.48 s [-0.89, -0.08]) typically swam faster 200-m times in relay events. Random forest models predicted gold, silver, bronze and non-medal with 100%, up to 41%, up to 63%, and 93% sensitivity, respectively. DISCUSSION: Team finishing position was strongly associated with the differential time to the fastest team (mean decrease in Gini (MDG) when this variable was omitted = 31.3), world rankings of team members (average ranking MDG of 18.9), and the order of swimmers (MDG = 6.9). Differential times are based on the sum of individual swimmer's season's best times, and along with world rankings, reflect team strength. In contrast, the order of swimmers reflects strategy. This type of analysis could assist coaches and support staff in selecting swimmers and team orders for relay events to enhance the likelihood of success.

Pacing and Performance in Swimming: Differences Between Individual and Relay Events.

PURPOSE: Although pacing is considered crucial for success in individual swimming events, there is a lack of research examining pacing in relays. The authors investigated the impact of start lap and pacing strategy on swimming performance and whether these strategies differ between relays and the corresponding individual event. METHODS: Race data for 716 relay (4 × 200-m freestyle) finals from 14 international competitions between 2010 and 2018 were analyzed retrospectively. Each swimmer's individual 200-m freestyle season's best time for the same year was used for comparison. Races were classified as a fast, average, or slow start lap strategy (lap 1) and as an even, negative, or positive pacing strategy (laps 2-4) to give an overall race strategy, for example, average start lap even pacing. RESULTS: A fast start lap strategy was associated with slower 200-m times (range 0.5-0.9 s, P ≤ .04) irrespective of gender, and positive pacing led to slower 200-m (0.4-0.5 s, P ≤ .03) times in females. A fast start lap strategy led to positive pacing in 71% of swimmers. Half of the swimmers changed pacing strategy, with 13% and 7% more female and male swimmers, respectively, displaying positive pacing in relays compared with individual events. In relays, a fast start lap and positive pacing was utilized more frequently by swimmers positioned on second to fourth relay legs (+13%) compared with lead-off leg swimmers (+3%). CONCLUSION: To maximize performance, swimmers should be more conservative in the first lap and avoid unnecessary alterations in race strategy in relay events.

Pacing in Swimming: A Systematic Review.

BACKGROUND: Pacing strategy, or how energy is distributed during exercise, can substantially impact athletic performance and is considered crucial for optimal performance in many sports. This is particularly true in swimming given the highly resistive properties of water and low mechanical efficiency of the swimming action. OBJECTIVES: The aim of this systematic review was to determine the pacing strategies utilised by competitive swimmers in competition and their reproducibility, and to examine the impact of different pacing strategies on kinematic, metabolic and performance variables. This will provide valuable and practical information to coaches and sports science practitioners. DATA SOURCES: The databases Web of Science, Scopus, SPORTDiscus and PubMed were searched for published articles up to 1 August 2017. STUDY SELECTION: A total of 23 studies examining pool-based swimming competitions or experimental trials in English-language and peer-reviewed journals were included in this review. RESULTS: In short- and middle-distance swimming events maintenance of swimming velocity is critical, whereas in long-distance events a low lap-to-lap variability and the ability to produce an end spurt in the final lap(s) are key. The most effective strategy in the individual medley (IM) is to conserve energy during the butterfly leg to optimise performance in subsequent legs. The pacing profiles of senior swimmers remain relatively stable irrespective of opponents, competition stage or type, and performance time. CONCLUSION: Implementing event-specific pacing strategies should benefit the performance of competitive swimmers. Given differences between swimmers, there is a need for greater individualisation when considering pacing strategy selection across distances and strokes.