Pacing in age-group freestyle swimmers at The XV FINA World Masters Championships in Montreal 2014

Identifying Differences in Swimming Speed Fluctuation in Age-Group Swimmers by Statistical Parametric Mapping: A Biomechanical Assessment for Performance Development

The aim of this study was to compare the assessment of swimming speed processed as a discrete variable and as a continuous variable in young swimmers. One-hundred and twenty young swimmers (60 boys: age = 12.91 ± 0.86 years; 60 girls: age = 12.46 ± 0.94 years) were analysed. The dataset for each sex was divided into three tiers: (i) tier #1 - best-performing swimmers; (ii) tier #2: intermediate-performing swimmers, and; (iii) tier #3 - poorest-performing swimmers. As a discrete variable, swimming speed showed significant sex and tier effects, and a significant sex*tier interaction (p < 0.001). Speed fluctuation showed a non-significant sex effect (p > 0.05), a significant tier effect (p < 0.001), and a non-significant sex*tier interaction (p > 0.05). As a continuous variable, the swimming speed time-curve presented significant sex and tier effects (p < 0.001) throughout the stroke cycle, and a significant sex*tier interaction (p < 0.05) in some moments of the stroke cycle. Swimming speed fluctuation analysed as a discrete variable and as a continuous variable can be used in a complementary way. Nonetheless, SPM can provide deeper insight into differences within the stroke cycle. Thus, coaches and practitioners should be aware that different knowledge about the swimmers' stroke cycle can be learned by assessing swimming speed using both methods.

Pacing Strategy Models in 1500 m Male Freestyle Long-Course Swimming on the Basis of the All-Time Ranking

In long-distance swimming competitions, a pacing strategy is of considerable importance for the final result and for sporting success. The paper presents the pacing strategy models of the all-time best competitors in 1500 m male freestyle long-course swimming. The top 60 scores were retrieved from official websites. The results were divided into six groups of ten swim times each, with splits of 15 × 100, 5 × 300, 3 × 500, and 2 × 750 m, and then analysis of variance was used. The main effects of the competitor group order revealed with the analysis of variance were statistically significant (p < 0.001). The group effect size turned out very high (η_p² = 0.95). Consecutive groups of competitors achieved significantly slower results. The magnitude of the interaction effects of the competitor group order and distance splits was moderate (η_p² of 0.05-0.09) and statistically not significant. The main effects of the 3 × 500, 5 × 300, and 15 × 100 m splits were high and very high (η_p² of 0.33-0.75) and statistically significant (p < 0.001). The difference between the 2 × 750 m split was statistically not significant. The achieved values of the main effects led to the following trend. In the distance split, the first and last sections did not significantly differ from each other, nor did the middle sections. However, when the middle sections and the first and last sections were compared against each other, a significant discrepancy was observed. The pacing strategies of the best athletes in the history of the competition follow a very similar parabolic trend.

Pacing in World-Class Age Group Swimmers in 200 and 400 m Individual Medley

The present research investigated pacing for world-class age group swimmers competing in individual medley in 200 m and 400 m. Data on 3,242 unique finishers (1,475 women and 1,767 men) competing in four Master World Championships [XV FINA WMC held in Montreal (Canada) in 2014, the XVI FINA WMC held in Kazan (RUS) in 2015, the FINA WMC held in Budapest (HUN) in 2017, and the XVIII FINA WMC held in Gwangju (KOR] in 2019) were analyzed. Men were faster than women among all age groups in both 200 and 400 m. Additionally, differences were found between almost all adjacent age groups, with the exception (p > 0.05) of age groups 25-29 to 30-34, 35-39 to 40-44 years in 200 m races and 25-29 to 30-34, 30-34 to 35-39, 35-39 to 40-44, and 45-49 to 50-54 years in 400 m races. Men showed a higher pacing variation in 200 m among all male age groups and all female age groups up to 69 years. Pace-variation pairwise comparisons between men and women showed no consistencies throughout age groups, with the exception of a higher variation in men in age groups ≥55-year-old. Men were faster for all splits and strokes in both 200 and 400 m, and significant changes were identified for each split and stroke for both men and women in both 200 and 400 m. Front crawl (freestyle, 4th split) was the fastest butterfly (1st split), backstroke (2nd split), and breaststroke (3rd split). In summary, men were faster than women for all age groups in both 200 and 400 m. Men showed a higher pacing variation in 200 m in all age groups, where women had a higher variation in age groups up to 69 years. The fastest stroke for the final spurt was front crawl, followed by butterfly, backstroke, and breaststroke. Based on these findings, coaches should advise their master athletes to focus on the final spurt in both 200 and 400 m individual medley for a fast final race time.

Pacing in World-Class Age Group Swimmers in 100 and 200 m Freestyle, Backstroke, Breaststroke, and Butterfly

Pacing in swimming has been investigated in pool swimming for elite-standard and age group freestyle swimmers, but little is known about pacing in age group swimmers competing at world class level in backstroke, breaststroke, and butterfly. The aim of this study was to investigate pacing for age group swimmers competing at world class level in 100 and 200 m in the four single disciplines (freestyle, backstroke, breaststroke and butterfly). Data on 18,187 unique finishers competing in four FINA Master World Championships between 2014 and 2019 were analyzed. The sample included 3334 women and 14,853 men. Swimming speed decreased with increasing age (p < 0.05). Freestyle was the fastest and breaststroke the slowest (p < 0.05) stroke. Women and men were faster in 100 m (p < 0.05) than in 200 m. Backstroke was the stroke with the lowest and butterfly with the highest coefficient of variation in swimming speed. One hundred meters had a higher coefficient of variation in swimming speed than breaststroke (p < 0.05). For 100 m, swimming speed decreased for all strokes and all age groups during the second lap. For 200 m, swimming speed was the fastest for all strokes and all age groups during the first lap. In summary, the FINA World Masters Championships presented the unique characteristic that, when all competitors were considered, (i) swimming speed decreased with increasing age, (ii) women and men were faster in 100 m than in 200 m, (iii) freestyle was the fastest stroke and (iv) the largest increase in swimming time for 100 m all strokes and all age groups occurred during the second (out of two) lap and for 200 m, swimming speed was the fastest for all strokes and age groups during the first lap. These findings should help coaches to develop age- and event-tailored pacing strategies.

Sex Differences in Swimming Disciplines-Can Women Outperform Men in Swimming?

In recent years, the interest of female dominance in long-distance swimming has grown where several newspaper articles have been published speculating about female performance and dominance—especially in open-water ultra-distance swimming. The aim of this narrative review is to review the scientific literature regarding the difference between the sexes for all swimming strokes (i.e., butterfly, backstroke, breaststroke, freestyle and individual medley), different distances (i.e., from sprint to ultra-distances), extreme conditions (i.e., cold water), different ages and swimming integrated in multi-sports disciplines, such as triathlon, in various age groups and over calendar years. The influence of various physiological, psychological, anthropometrical and biomechanical aspects to potentially explain the female dominance was also discussed. The data bases Scopus and PUBMED were searched by April 2020 for the terms ’sex–difference–swimming’. Long-distance open-water swimmers and pool swimmers of different ages and performance levels were mainly investigated. In open-water long-distance swimming events of the ’Triple Crown of Open Water Swimming’ with the ’Catalina Channel Swim’, the ’English Channel Swim’ and the ’Manhattan Island Marathon Swim’, women were about 0.06 km/h faster than men. In master swimmers (i.e., age groups 25–29 to 90–94 years) competing in the FINA (Fédération Internationale de Natation) World Championships in pool swimming in freestyle, backstroke, butterfly, breaststroke, individual medley and in 3000-m open-water swimming, women master swimmers appeared able to achieve similar performances as men in the oldest age groups (i.e., older than 75–80 years). In boys and girls aged 5–18 years—and listed in the all-time top 100 U.S. freestyle swimming performances from 50 m to 1500 m—the five fastest girls were faster than the five fastest boys until the age of ~10 years. After the age of 10 years, and until the age of 17 years, however, boys were increasingly faster than girls. Therefore, women tended to decrease the existing sex differences in specific age groups (i.e., younger than 10 years and older than 75–80 years) and swimming strokes in pool-swimming or even to overperform men in long-distance open-water swimming (distance of ~30 km), especially under extreme weather conditions (water colder than ~20 °C). Two main variables may explain why women can swim faster than men in open-water swimming events: (i) the long distance of around 30 km, (ii) and water colder than ~20 °C. Future studies may investigate more detailed (e.g., anthropometry) the very young (<10 years) and very old (>75–80 years) age groups in swimming

Pacing During 200-m Competitive Masters Swimming

Breen, D, Powell, C, and Anderson, R. Pacing during 200-m competitive masters swimming. J Strength Cond Res 34(7): 1903-1910, 2020-Pacing strategies are key to overall performance outcome, particularly in swimming given the large resistive properties of water. However, no studies examining how swimming stroke, gender, age, or performance level affect pacing strategies during 200-m races. This study aimed to examine masters athletes pacing strategies categorized by stroke, gender, age, and performance level. Data were retrieved from World and European masters swimming championships and contained data for 4,272 performances. Performances were coded for stroke, gender, age, and performance classification (PC). Performance classification was based on comparison to the appropriate masters world record. Performances were then normalized, with split times being expressed as a percentage faster or slower than average 50-m split time to determine relative pace. Coefficient of variation (CV) of 50-m time was examined across splits. The main effect for stroke was examined at each split, whereas gender, age, and PC were examined for split-1 pace and CV. An alpha level of 0.05 was set to denote statistical significance. A main effect for stroke was identified at each split (all p < 0.001; (Equation is included in full-text article.)-split-1 = 0.292; (Equation is included in full-text article.)-split-2 = 0.040; (Equation is included in full-text article.)-split-3 = 0.058; (Equation is included in full-text article.)-split-4 = 0.162). A main effect for PC was identified for split-1 pace and CV within all strokes (all p < 0.001), except for breaststroke (both p > 0.775). Masters athletes exhibit different pacing patterns across strokes, whereas lower ranked athletes also display less even pacing and a faster relative start compared with higher-ranked athletes. Individual analyses of pacing strategies may be necessary.

Pacing in lane-based head-to-head competitions: A systematic review on swimming

Athletes' energy distribution over a race (e.g. pacing behaviour) varies across different sports. Swimming is a head-to-head sport with unique characteristics, such as propulsion through water, a multitude of swimming stroke types and lane-based racing. The aim of this paper was to review the existing literature on pacing behaviour in swimming. According to PRISMA guidelines, 279 articles were extracted using the PubMed and Web of Science databases. After the exclusion process was conducted, 16 studies remained. The findings of these studies indicate that pacing behaviour is influenced by the race distance and stroke type. Pacing behaviours in swimming and time-trial sports share numerous common characteristics. This commonality can most likely be attributed to the lane-based racing set-up. The low efficiency of swimming resulting from propulsion through the water induces a rapid accumulation of blood lactate, prompting a change in swimmers' biomechanical characteristics, with the goal of minimising changes in velocity throughout the race. Although the literature on youth swimmers is scarce, youth swimmers demonstrate more variable pacing profiles and have more difficulty in selecting the most beneficial energy distribution.

The Differences in Pacing Among Age Groups of Amateur Cross-Country Skiers Depend on Performance

Pacing strategies have mainly been investigated for runners, but little is known for cross-country skiers. The aim of the present study was to examine the effects of performance and age on pacing strategies in cross-country skiing. All finishers (women, n = 19,375; men, n = 86,190) in the 'Engadin Ski Marathon' (42 km) between 1998 and 2016 were analysed for the percentage change of speed at 10 km (Change A), 20 km (Change B) and 35 km (Change C). They were classified in performance groups according to quartiles of average race speed (Q1, Q2, Q3 and Q4) and in 5-year age groups (<20, 20-24, 25-29… 85-89 years). Men were faster than women by +14.3% (15.2 ± 4.0 vs. 13.3 ± 3.3 km/h; p < 0.001, η² = 0.215). A small impact of age group × performance group interaction on Change A was shown in women (p < 0.001, η² = 0.026) and men (p < 0.001, η² = 0.025), where Q1 augmented and Q4 attenuated the decrease in speed with aging. However, the impact of age group × performance group interaction on Change B and C was trivial (p = 0.002, η² ≤ 0.010). Based on these findings, it was concluded that the differences in pacing among age groups depended on the performance level. Thus, the coaches and fitness trainers working with cross-country skiers should advise their athletes to consider both age and performance.

The Combined Effect of Aging and Performance Level on Pacing in Duathlon - the "ITU Powerman Long Distance Duathlon World Championships"

The role of age and performance level has been investigated in runners such as marathoners, but not in multi-sports athletes such as duathletes (running, cycling, and running). Thus, the aim of the present study was to examine the combined effects of aging and performance level on pacing of duathletes competing in two different race distances. Pacing (defined as the relative contribution of cycling time, %, to the overall race time) was analyzed for 6,671 duathletes competing from 2003 to 2017 in the short distance race (10 km first run, 50 km cycling and 5 km second run) or long distance race (10 km first run, 150 km cycling and 30 km second run) of “Powerman Zofingen,” the “ITU Powerman Long Distance Duathlon World Championships.” Men were faster, older, and spent less time (%) in cycling than women in both distances races (p < 0.001). Younger age groups spent more time (%) in cycling than their older counterparts in women (both short and long distance, p = 0.036, η_p² = 0.031, p = 0.025, η_p² = 0.044, respectively) and men (long distance race, p < 0.001, η_p² = 0.016). Fast performance groups spent more time (%) in cycling than their slower counterparts in short (women, p < 0.001, η_p² = 0.057; men, p < 0.001, η_p² = 0.035) and long distance (women, p < 0.001, η_p² = 0.070; men, p < 0.001, η_p² = 0.052). A small age group × performance group interaction on cycling time (%) was observed in the men’s short distance (p = 0.001, η_p² = 0.020) – but not in the long distance or in women – with smaller differences between performance groups in the older than in the younger age groups. Women, young and fast duathletes were relatively slower in cycling than men, old and slow duathletes; that was, old duathletes were relatively faster in cycling than in running. Moreover, there was indication that the difference in pacing among performance groups might be attenuated with aging. Since fast duathletes were relatively faster in running than in cycling, slow duathletes should be encouraged to cycle slower and run faster.

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.

Pacing Decision Making in Sport and the Effects of Interpersonal Competition: A Critical Review

An athlete's pacing strategy is widely recognised as an essential determinant for performance during individual events. Previous research focussed on the importance of internal bodily state feedback, revealed optimal pacing strategies in time-trial exercise, and explored concepts such as teleoanticipation and template formation. Recently, human-environment interactions have additionally been emphasized as a crucial determinant for pacing, yet how they affect pacing is not well understood. Therefore, this literature review focussed on exploring one of the most important human-environment interactions in sport competitions: the interaction among competitors. The existing literature regarding the regulation of exercise intensity and the effect of competition on pacing and performance is critically reviewed in this paper. The PubMed, CINAHL and Web of Science electronic databases were searched for studies about pacing in sports and (interpersonal) competition between January 2000 to October 2017, using the following combination of terms: (1) Sports AND (2) Pacing, resulting in 75 included papers. The behaviour of opponents was shown to be an essential determinant in the regulation of exercise intensity, based on both observational (N = 59) and experimental (N = 16) studies. However, adjustment in the pacing response related to other competitors appears to depend on the competitive situation and the current internal state of the athlete. The findings of this review emphasize the importance of what is happening around the athlete for the outcome of the decision-making process involved in pacing, and highlight the necessity to incorporate human-environment interactions into models that attempt to explain the regulation of exercise intensity in sports and exercise.

Pacing in age group marathoners in the "New York City Marathon"

The aim of the study was to investigate how women and men age group runners pace during a large city marathon. We analysed changes in running speed by splits of 5 km in 20,283 women and 28,282 men age group runners competing in the 2015 edition of the "New York City Marathon". A moderate split×sex interaction on running speed (p < 0.001, η² = 0.108) was observed with men showing a larger decrease in speed from the fastest split (5-10 km) to the slowest one (35-40 km) than women (21.1 vs. 16.7%), and a different pattern was observed in the 25-30 km split (increase in women, decrease in men). A trivial split×age group interaction on speed was observed in women (p < 0.001, η² = 0.003) and men (p < 0.001, η² = 0.004). In summary, men and women of all age groups reduced running speed during the marathon with a final spurt in the last segment (i.e. 40-42.2 km).

Sex differences in pacing during 'Ultraman Hawaii'

Background

To date, little is known for pacing in ultra-endurance athletes competing in a non-stop event and in a multi-stage event, and especially, about pacing in a multi-stage event with different disciplines during the stages. Therefore, the aim of the present study was to examine the effect of age, sex and calendar year on triathlon performance and variation of performance by events (i.e., swimming, cycling 1, cycling 2 and running) in ‘Ultraman Hawaii’ held between 1983 and 2015.

Methods

Within each sex, participants were grouped in quartiles (i.e., Q1, Q2, Q3 and Q4) with Q1 being the fastest (i.e., lowest overall time) and Q4 the slowest (i.e., highest overall time). To compare performance among events (i.e., swimming, cycling 1, cycling 2 and running), race time in each event was converted in z score and this value was used for further analysis.

Results

A between-within subjects ANOVA showed a large sex × event (p = 0.015, η² = 0.014) and a medium performance group × event interaction (p = 0.001, η² = 0.012). No main effect of event on performance was observed (p = 0.174, η² = 0.007). With regard to the sex × event interaction, three female performance groups (i.e., Q2, Q3 and Q4) increased race time from swimming to cycling 1, whereas only one male performance group (Q4) revealed a similar trend. From cycling 1 to cycling 2, the two slower female groups (Q3 and Q4) and the slowest male group (Q4) increased raced time. In women, the fastest group decreased (i.e., improved) race time from swimming to cycling 1 and thereafter, maintained performance, whereas in men, the fastest group decreased race time till cycling 2 and increased it in the running.

Conclusion

In summary, women pace differently than men during ‘Ultraman Hawaii’ where the fastest women decreased performance on day 1 and could then maintain on day 2 and 3, whereas the fastest men worsened performance on day 1 and 2 but improved on day 3.