Pacing during an elite Olympic distance triathlon: comparison between male and female competitors

Warm-Up in Triathlon: Do Triathletes Follow the Scientific Guidelines?

PURPOSE

Warming up before competition is universally recognized as an effective way to enhance performance. However, only a few articles have directly investigated different warm-up strategies adopted by triathletes and suggested by coaches. The Olympic-distance triathlon is an endurance competition characterized, at least for the elite, by a fast start with a strong correlation to the final position in the race. Thus, executing a proper warm-up protocol would be beneficial in optimizing performance. The present study aimed to provide an overview of the warm-up protocol adopted/suggested by national-caliber triathletes/coaches before an Olympic-distance triathlon race.

METHODS

Online surveys were created and shared between national- and international-caliber Italian, French, and Spanish triathletes and coaches. Information about the rationale, structure, and specific exercises adopted/suggested during personal warm-up protocols was collected. Thereafter, triathletes were grouped according to the discipline sequence reported.

RESULTS

Seventy-nine triathletes and nineteen coaches completed the survey. The cycle-run-swim was the most reported discipline sequence adopted, with a total time of 90.0 (25.0) minutes, against the 62.5 (25.0) minutes suggested by coaches. Conditioning exercises were performed by only 31.6% of triathletes 20 to 10 minutes before the race start.

CONCLUSIONS

Triathletes who took part in this survey adopted very long protocols with the specific intention of including all disciplines. These results highlight the need to raise awareness in triathletes and coaches on the correct warm-up procedures and to stimulate researchers to design studies that directly investigate the effects of different warm-up protocols before competitions.

Cycling Intensity Effect on Running Plus Cycling Performance among Triathletes

Running performance is crucial for triathlon performance. However, the prior bout of cycling may affect the running split time. This study compared the triathletes' cycling plus running (C+R) time, when cycling was performed at three different intensities and running was maximal. A total of 38 athletes (21 males and 17 females) were included. Body composition, maximal oxygen uptake, and functional threshold power (FTP) was evaluated. The participants visited the laboratory three times to cycle 20 km at 80%, 85%, or 90% FTP (in randomized order) and run 5 km as fast as possible. Males ran faster after cycling at 80% FTP than after cycling at 90% FTP (mean difference=35.1 s; CI% 2.2, 68.1 s; p=0.035). The C+R time was faster when cycling at 90% FTP than at 80% FTP (mean difference=57.7 s; CI% 26.1, 89.3 s; p<0.001). For females, no significant difference was observed in the running time after cycling at 80%, 85%, or 90% FTP. The C+R time was faster when cycling at 90% FTP than at 80% FTP (mean difference=80.9 s; CI% 29.7, 132.1 s; p=0.002). In conclusion, to optimize triathlon performance, male and female athletes should cycle at a minimum of 90% FTP.

Swimming Performance in Elite Triathletes: Comparison Between Open Water and Pool Conditions

This study aimed to compare performance, kinematic, and physiological variables between open water and pool swimming conditions in elite triathletes and to examine the associations between conditions on these variables. Fourteen elite triathletes (10 males and 4 females [23.4 ± 3.8 years]) performed two 1500-m swimming tests in open water and in a 25-m pool. Swimming speed, stroke rate (SR), length (SL) and index (SI), heart rate (HR), blood lactate concentrations [La-], and end-exercise oxygen uptake (EEV̇O2) were assessed in both conditions. Lower SL and SI and higher SR were obtained in open water compared with pool swimming (p < 0.05). Moreover, kinematic variables changed as a function of distance in both conditions (p < 0.05). No differences were found in the main physiological variables (HR, [La-], and EEV̇O2) between conditions. Respiratory exchange ratio presented lower values in open water than in pool conditions (p < 0.05), while time constant was higher in open water (p = 0.032). The fastest triathletes in open water obtained the best performance in the pool (r = 0.958; p < 0.001). All kinematic variables, HR and peak [La-] presented positive associations between conditions (r > 0.6; p < 0.05). Despite physiological invariance, triathletes and coaches should monitor specific open water training to adapt their swimming technique to the competitive environment.

Open Water Swimming in Elite Triathletes: Physiological and Biomechanical Determinants

This study aimed (i) to analyze the 1500 m open water swimming performance, (ii) to examine the associations between physiological and biomechanical variables with swimming performance, and (iii) to determine which variables can predict swimming performance in triathletes. Fourteen elite triathletes (23.4±3.8 y) performed a 1500 m test in open water swimming conditions. Swimming performance was assessed using World Aquatics Points Scoring, and data were obtained from the 1500 m open water swimming test. Heart rate, end-exercise oxygen uptake (EE˙VO2) and blood lactate concentrations were measured. The initial 250 m of the 1500 m swimming test presented the highest values of biomechanical variables in males (i. e. swimming speed, stroke rate (SR), length (SL), index (SI)). A decrease in SL was observed in the last 250 m in both sexes. Positive association were found between EE˙VO2 (r=0.513; p=0.030), swimming speed (r=0.873; p<0.001) and SI (r=0.704; p=0.002) with swimming performance. In contrast, time constant of the oxygen uptake (r=-0.500; p=0.034) and buoy-turn times (r=-0.525; p=0.027) were negatively associated with performance. SI was the main predictor (R 2=0.495) of open water swimming performance in triathletes. In conclusion, triathletes and coaches must conduct open water training sessions to maximize SI (i. e. swimming efficiency).

Race Dynamics in Triathlon Mixed-Team-Relay Meaningfully Changes with The New Regulation Towards Paris 2024

Mixed-Team-Relay (MTR) triathlon is a novel Olympic discipline whose performance determinants and tactical behaviors have barely been studied. Additionally, a regulatory change has been made to the male and female relay order for the Paris 2024 Olympics. Therefore, this study aimed to determine the performance determinants and race dynamics as a function of competitive level on the new regulated MTR triathlon. Results from 129 national teams, (516 elite triathletes) across five MTR World Triathlon Series and two MTR European Championships in 2022 and 2023, were analyzed. Split times, average speeds, time behind the race leader (gap), partial and finishing positions, pack position as well as the rank positions of every segment, relay leg, and overall race were computed. Decision tree analyses were conducted as a predictive method for the overall results, and correspondence analyses were conducted to examine the relationship between the different relay legs and segments and the finishing positions. The performance of the fourth leg was the most relevant for overall result (30%), as well as the fourth running leg (16%) and the female legs performance (7%). Medallist relay teams were characterized by displaying a differential speed lower than 0.5 and 0.83 km/h, respectively, from the best-ranking athletes in the Legs 1 and 4. Furthermore, staying in the front pack after the second swimming leg showed a great relationship with achieving a medal position. New MTR triathlon rules shift race dynamics, emphasizing individual efforts in cycling and swimming, while maintaining the crucial importance of running.

Determinants of 1500-m Front-Crawl Swimming Performance in Triathletes: Influence of Physiological and Biomechanical Variables

PURPOSE

To analyze the associations between physiological and biomechanical variables with the FINA (International Swimming Federation) points (ie, swimming performance) obtained in 1500-m front-crawl swimming to determine whether these variables can be used to explain triathletes' FINA points.

METHODS

Fourteen world-class, international and national triathletes (10 male: 23.24 [3.70] y and 4 female: 23.36 [3.76] y) performed a 1500-m front-crawl swimming test in a short-course pool. Heart rate (HR), oxygen uptake (V˙O2), and blood lactate concentrations were obtained before and after the test. HR was also measured during the effort. Highest V˙O2 value (V˙O2peak) was estimated by extrapolation. Clean swimming speed, turn performance, stroke rate, stroke length, and stroke index (SI) were obtained by video analysis.

RESULTS

Average 1500-m performance times were 1088 (45) seconds and 1144 (31) seconds for males and females, respectively. HR after the effort, V˙O2peak, aerobic contributions, total energy expenditure, energy cost, and turn performance presented moderate negative associations with swimming performance (r ≈ .5). In contrast, respiratory exchange ratio, anaerobic alactic contribution, clean swimming speed, stroke length, and SI were positively related, with clean swimming speed and SI having a strong large association (r ≈ .7). A multiple stepwise regression model determined that 71% of the variance in FINA points was explained by SI and total energy expenditure, being predictors in 1500-m front-crawl swimming.

CONCLUSIONS

Swimming performance in triathletes was determined by the athletes' energy demands and biomechanical variables. Thus, coaches should develop specific technique skills to improve triathletes' swimming efficiency.

What is the Most Important Leg and Discipline in Triathlon Mixed Team Relays?

This study aimed to determine the importance of the different relay legs and sport disciplines in the overall result of the triathlon Mixed Team Relay (MTR) events. The study analysed the results of 80 Mixed Team Relay triathlon teams (n = 320 professional triathletes) corresponding to the top ten finishers at the World Championships in Hamburg from 2013 to 2020. Split times, average speeds, time behind the race leader (gap), partial and finishing positions, as well as the rank positions of every segment, relay leg, and overall race were computed. Decision tree analyses were conducted as a predictive method for the overall results, and correspondence analyses were conducted to examine the relationship between the different relay legs and segments and the finishing positions. Running was the variable with the greatest importance (32%) in the overall result, followed by female team members (17%) and the third relay leg (17%). The swimming segments (1%) and the fourth relay leg (1%) had the lowest relevance. Medallist relay teams were characterised by cycling and running faster than 10.99 m/s and 5.59 m/s, respectively, with time gaps of less than 43 seconds by the end of the third relay leg. A reliable and accurate prediction model for the medallists' and finalists' team positions in the Mixed Team Relay triathlon was obtained. The running disciplines and performance of female team members, especially in the third leg, were ascertained to be the most significant determinants for the overall Mixed Team Relay result.

Changes in pacing variation with increasing race duration in ultra-triathlon races

Despite the increasing scientific interest in the relationship between pacing and performance in endurance sports, little information is available about pacing and pacing variation in ultra-endurance events such as ultra-triathlons. Therefore, we aimed to investigate the trends of pacing, pacing variation, the influence of age, sex, and performance level in ultra-triathlons of different distances. We analysed 969 finishers (849 men, 120 women) in 46 ultra-triathlons longer than the original Ironman® distance (e.g., Double-, Triple-, Quintuple- and Deca Iron ultra-triathlons) held from 2004 to 2015. Pacing speed was calculated for every cycling and running lap. Pacing variation was calculated as the coefficient of variation (%) between the average speed of each lap. Performance level (i.e., fast, moderate, slow) was defined according to the 33.3 and 66.6 percentile of the overall race time. A multivariate analysis (two-way ANOVA) was applied for the overall race time as the dependent variable with 'sex' and 'age group' as independent factors. Another multivariate model with 'age' and 'sex' as covariates (two-way ANCOVA) was applied with pacing variation (cycling and running) as the dependent variable with 'race' and 'performance level' as independent factors. Different pacing patterns were observed by event and performance level. The general pacing strategy applied was a positive pacing. In Double and Triple Iron ultra-triathlon, faster athletes paced more evenly with less variation than moderate or slower athletes. The variation in pacing speed increased with the length of the race. There was no significant difference in pacing variation between faster, moderate, and slower athletes in Quintuple and Deca Iron ultra-triathlon. Women had a slower overall performance than men. The best overall times were achieved at the age of 30-39 years. Successful ultra-triathlon athletes adapted a positive pacing strategy in all race distances. The variation in pacing speed increased with the length of the race. In shorter ultra-triathlon distances (i.e., Double and Triple Iron ultra-triathlon), faster athletes paced more evenly with less variation than moderate or slower athletes. In longer ultra-triathlon distances (i.e., Quintuple and Deca Iron ultra-triathlon), there was no significant difference in pacing variation between faster, moderate, and slower athletes.

Race development and performance-determining factors in a mass-start cross-country skiing competition

Introduction

Although five of six Olympic events in cross-country skiing involve mass-starts, those events are sparsely examined scientifically. Therefore, in this study, we investigated speed profiles, pacing strategies, group dynamics and their performance-determining impact in a cross-country skiing mass-start competition.

Methods

Continuous speed and position of 57 male skiers was measured in a six-lap, 21.8 km national mass-start competition in skating style and later followed up with an online questionnaire. Skiers ranked from 1 to 40 were split into four performance-groups: R1-10 for ranks 1 to 10, R11-20 for ranks 11 to 20, R21-30 for ranks 21 to 30, and R31-40 for ranks 31 to 40.

Results

All skiers moved together in one large pack for 2.3 km, after which lower-performing skiers gradually lost the leader pack and formed small, dynamic packs. A considerable accordion effect occurred during the first half of the competition that lead to additional decelerations and accelerations and a higher risk of incidents that disadvantaged skiers at the back of the pack. Overall, 31% of the skiers reported incidents, but none were in R1-10. The overall trend was that lap speed decreased after Lap 1 for all skiers and thereafter remained nearly unchanged for R1-10, while it gradually decreased for the lower-performing groups. Skiers in R31-40, R21-30, and R11-20 lost the leader pack during Lap 3, Lap 4, and Lap 5, respectively, and more than 60% of the time-loss relative to the leader pack occurred in the uphill terrain sections. Ultimately, skiers in R1-10 sprinted for the win during the last 1.2 km, in which 2.4 s separated the top five skiers, and a photo finish differentiated first from second place. Overall, a high correlation emerged between starting position and final rank.

Conclusions

Our results suggest that (a) an adequate starting position, (b) the ability to avoid incidents and disadvantages from the accordion effect, (c) tolerate fluctuations in intensity, and (d) maintain speed throughout the competition, particularly in uphill terrain, as well as (e) having well-developed final sprint abilities, are key factors determining performance during skating-style mass-start cross-country skiing competitions.

Cutoff value for predicting success in triathlon mixed team relay

Introduction

The Mixed-Team-Relay (MTR) triathlon is an original race format present on the international scene since 2009, which became an Olympic event at the Tokyo 2020 Games. The aim of this study was to define the probabilities of reaching a victory, a podium, or a finalist rank in a relay triathlon, according to the position of any of the four relayers (Women/Men/Women/Men) during each of the four segments (leg) of the race.

Methods

All MTR results from the World Series, Continental Championships, World Championships from 2009 to 2021 and Tokyo 2020 Olympics have been collected. We calculated the set of probability frequencies of reaching a given final state, according to any transient state during the race. All results are compared with a V' Cramer method.

Results

The frequency of winning is similar at the end of Leg 1 for TOP1 (first position) and TOP2-3 (second and third positions). Then, a difference in the winning-associated frequencies is first observed after the Bike stage of Leg 2, where 47% of TOP1 athletes will win, vs 13% of the TOP2-3.

Discussion

This difference continually increases until the end of the race. Legs 2 and 3 are preponderant on the outcome of the race, the position obtained by each triathlete, especially in swimming and cycling, greatly influences the final performance of the team. Leg 1 allows to maintain contact with the head of the race, while Leg 4 sets in stone the position obtained by the rest of the team.

Discovering the sluggishness of triathlon running - using the attractor method to quantify the impact of the bike-run transition

Running in a triathlon, a so-called brick run, is uniquely influenced by accumulated load from its preceding disciplines. Crucially, however, and irrespective of race type, the demands of a triathlon always exceed the sum of its parts. Triathletes of all levels commonly report subjectively perceived incoordination within the initial stages of the cycle run transition (T2). Although minimizing it, and its influence on running kinematics, can positively impact running and overall triathlon performance, the mechanisms behind the T2 effect remain unclear. In the present study, we assessed the influence of the pre-load exercise mode focusing on the biomechanical perspective. To analyze inertial sensor-based raw data from both legs, the so-called Attractor Method was applied. The latter represents a sensitive approach, allowing to quantify subtle changes of cyclic motions to uncover the transient effect, a potentially detrimental transient phase at the beginning of a run. The purpose was to analyze the impact of a pre-load on the biomechanics of a brick run during a simulated Olympic Distance triathlon (without the swimming section). Therefore, we assessed the influence of pre-load exercise mode on running pattern (δM) and precision (δD), and on the length of the transient effect (t_T) within a 10 km field-based run in 22 well-trained triathletes. We found that δD, but not δM, differed significantly between an isolated run (I_Run) and when it was preceded by a 40 km cycle (T_Run) or an energetically matched run (R_Run). The average distance ran until overcoming the transient phase (t_T) was 679 m for T_Run, 450 m for R_Run, and 29 4 m for I_Run. The results demonstrated that especially the first kilometer of a triathlon run is prone to an uncoordinated running sensation, which is also commonly reported by athletes. That is, i) the T2 effect appeared more linked to variability in running style than to running style per se ii) run t_T distance was influenced by preceding exercise load mode, being greater for a T_Run than for the R_Run condition, and iii) the Attractor Method seemed to be a potentially promising method of sensitively monitoring T2 adaptation under ecologically valid conditions.

Biomechanical and physiological implications to running after cycling and strategies to improve cycling to running transition: A systematic review

OBJECTIVES

This systematic review summarises biomechanical, physiological and performance factors affecting running after cycling and explores potential effective strategies to improve performance during running after cycling.

DESIGN

Systematic review.

METHODS

The literature search included all documents available until 14th December 2021 from Medline, CINAHL, SportDiscus, and Scopus. Studies were screened against the Appraisal tool for Cross-sectional Studies to assess methodological quality and risk of bias. After screening the initial 7495 articles identified, fulltext screening was performed on 65 studies, with 39 of these included in the systematic review.

RESULTS

The majority of studies observed detrimental effects, in terms of performance, when running after cycling compared to a control run. Unclear implications were identified from a biomechanical and physiological perspective with studies presenting conflicting evidence due to varied experimental designs. Changes in cycling intensity and cadence have been tested but conflicting evidence was observed in terms of biomechanical, physiological and performance outcomes.

CONCLUSIONS

Because methods to simulate cycle to run transition varied between studies, findings were conflicting as to whether running after cycling differed compared to a form of control run. Although most studies presented were rated high to very high quality, it is not possible to state that prior cycling does affect subsequent running, from a physiological point of view, with unclear responses in terms of biomechanical outcomes. In terms of strategies to improve running after cycling, it is unclear if manipulating pedalling cadence or intensity affects subsequent running performance.

A Study of Race Pacing in the Running Leg of the Japan University Triathlon Championship

Choosing an appropriate pacing strategy is important for good triathlon performance. In the Japan Student Triathlon Championship held in 2020, the men's category was divided into two groups, which was a different racing style from the previous races that all athletes start at the same time. It is highly likely that the performance level will vary as grouping was performed according to the competence of each player. The aim of this study was to understand the relationship of the total time and time of each leg between the superior performance group and the inferior performance group, as well as the difference in pacing during running in participants of the 2020 Japan University Triathlon Championship Watarase Competition, which was held under unconventional conditions. We analyzed 153 male athletes (Group A: 77; Group B: 76) who completed the race. The total race time, leg time, and average speed in each leg and its variation coefficient were evaluated based on the official results of the competition and footage recorded during the race. The results showed that the total time and leg time for each leg were significantly shorter in Group A compared to those in Group B (p < 0.05). In both groups, the Lap 4 run was significantly slower than those of Laps 1–3 (p < 0.05), while there was no significant difference in the running speed to average speed ratio across all laps between the groups (p < 0.05). Thus, there was a difference in running speed between the groups, but no significant difference in pacing. The results of this study serve as basic data for examining superior pacing strategies, although further studies on a wide range of competition levels are necessary.

Elite Triathlete Profiles in Draft-Legal Triathlons as a Basis for Talent Identification

Draft-legal triathlons are the main short-distance races worldwide and are those on which talent-identification programs are usually focused. Performance in these races depends on multiple factors; however, many investigations do not focus on elite triathletes. Therefore, the aim of this narrative review was to carry out a systematic literature search to define the elite female and male triathlete profiles and their competition demands in draft-legal triathlons. This will allow us to summarize the main determinant factors of high-level triathletes as a basis for talent detection. A comprehensive review of Web of Science and Scopus was performed using the search strategy: Triathl* and (performance or competition or profile) and (elite or professional or “high performance” or “high level” or talent). A total of 1325 research documents were obtained, and after screening following the criteria, only 83 articles were selected. After data synthesis, elite triathlete aspects such as age, physiological, anthropometric, and psychosocial profile or competition demands were studied in the scientific literature. Thus, it is essential that when implementing talent identification programs, these factors must be considered. However, constant updating is needed due the continuous regulatory changes and the need of triathletes to adapt to these new competition demands.

The beginning of success: Performance trends and cut-off values for junior and the U23 triathlon categories

Background

This study sought to determine cut-off values for each triathlon discipline to achieve podium in Junior (short distance; 750 m swim, 20 km cycle and 5 km run) and U23 (standard/Olympic distance; 1.5 km swim, 40 km cycle and 10 km run) triathlon events. Additionally, we aimed to investigate which discipline has the largest relationship with overall Junior and U23 triathlon performance, and the effect of sex and time in performance trends.

Methods

We included all data from Junior and U23 official races (International Triathlon Federation; ITU) of Junior (n = 3,314 finishes) and U23 (n = 5,092 finishes) categories held from 1999 to 2018.

Results

Men were significantly faster than women in both Junior (11.13%) and U23 (12.28%) categories. Swimming and cycling times were faster in 2009-2018 than in the 1999-2008 decade for men (3.36%; 6.49%), women junior (6.50%; 7.09%), men (0.15%; 3.46%) and women U23 (1.61%; 3.31%) respectively. Cycling was the discipline with the greatest influence on overall triathlon performance in Junior and U23 categories, regardless of sex or rank position. The cut-off values for the Junior category were (men/women): swimming, 9.2/9.4 min; cycling, 31.9/38.2 min; running, 16.8/18.9 min. U23's cut-off values were (men/women): swim, 18.0/19.4 min, cycling: 63.4/70.1 min; run, 33.9/38.7 min.

Conclusion

Cycling was the discipline with the greatest influence on overall performance for both men and women in Junior and U23 categories. Moreover, swimming and cycling performances increased over the years for both sexes.

Gender Effect on the Relationship between Talent Identification Tests and Later World Triathlon Series Performance

Background: We examined the explanatory power of the Spanish triathlon talent identification (TID) tests for later World Triathlon Series (WTS)-level racing performance as a function of gender. Methods: Youth TID (100 m and 1000 m swimming and 400 m and 1000 m running) test performance times for when they were 14–19 years old, and WTS performance data up to the end of 2017, were obtained for 29 female and 24 male “successful” Spanish triathletes. The relationships between the athletes’ test performances and their later best WTS ranking positions and performance times were modeled using multiple linear regression. Results: The swimming and running TID test data had greater explanatory power for best WTS ranking in the females and for best WTS position in the males (R²a = 0.34 and 0.37, respectively, p ≤ 0.009). The swimming TID times were better related to later race performance than were the running TID times. The predictive power of the TID tests for WTS performance was, however, low, irrespective of exercise mode and athlete gender. Conclusions: These results confirm that triathlon TID tests should not be based solely on swimming and running performance. Moreover, the predictive value of the individual tests within the Spanish TID battery is gender specific.

Fast or slow start? The role of running strategies in triathlon

OBJECTIVES

To investigate the impact of fast-start, steady or slow-start strategies of the running fraction in sprint triathlon on oxygen consumption, perception of fatigue and blood lactate.

DESIGN

Thirteen male triathletes (age; 36.4 ± 10.8 yy, height 174.8 ± 7.9 cm, body mass 70.6 ± 11.1 kg; V'O_2max 62.4 ± 8.9 ml min^-1 kg^-1; mean ± SD) attended the laboratory five times in order to complete two incremental tests and three subsequent cycle-run sessions.

METHODS

Three experimental randomized sessions with different effort distribution were compared. The intensities of the 1st running kilometer were set at 95%, 100% and 105% of the second ventilatory threshold for slow, continuous and fast start protocol respectively. Measurement of ventilatory variables, blood lactate and ratings of perceived exertion were collected throughout all sessions.

RESULTS

A meaningful difference was found between the slow versus fast start protocol in V'O₂ (SE = 0.58, P = 0.0005), BLa^- (SE = 0.21, P = 0.0097), HR (SE = 1.23, P = 0.0011) and RPE (SE = 2.83, P = 0.0047) values. No differences in-between protocols were found at the end of the running bout whatever the condition.

CONCLUSIONS

Differences in physiological parameters were found between protocols during the first kilometer, not at the end of exercise. The fast start appears to be more correct and useful for performance in racing setting and may be used as a strategy without impacting the remaining running bout in ecological setting.

Running Your Best Triathlon Race

Negative or evenly paced racing strategies often lead to more favorable performance outcomes for endurance athletes. However, casual inspection of race split times and observational studies both indicate that elite triathletes competing in Olympic-distance triathlon typically implement a positive pacing strategy during the last of the 3 disciplines, the 10-km run. To address this apparent contradiction, the authors examined data from 14 International Triathlon Union elite races over 3 consecutive years involving a total of 725 male athletes. Analyses of race results confirm that triathletes typically implement a positive running pace strategy, running the first lap of the standard 4-lap circuit substantially faster than laps 2 (∼7%), 3 (∼9%), and 4 (∼12%). Interestingly, mean running pace in lap 1 had a substantially lower correlation with 10-km run time (r = .82) than both laps 2 and 3. Overall triathlon race performance (ranking) was best associated with run performance (r = .82) compared with the swim and cycle sections. Lower variability in race pace during the 10-km run was also reflective of more successful run times. Given that overall race outcome is mainly explained by the 10-km run performance, with top run performances associated with a more evenly paced strategy, triathletes (and their coaches) should reevaluate their pacing strategy during the run section.

Cut-Off Values in the Prediction of Success in Olympic Distance Triathlon

Cut-off points and performance-related tools are needed for the development of the Olympic distance triathlon. The purposes of the present study were (i) to determine cut-off values to reach the top three positions in an Olympic distance triathlon; (ii) to identify which discipline present the highest influence on overall race performance and if it has changed over the decades. Data from 1989 to 2019 (n = 52,027) from all who have competed in an official Olympic distance triathlon events (World Triathlon Series and Olympics) were included. The cut-off value to achieve a top three position was calculated. Linear regressions were applied for performance trends overall and for the top three positions of each race. Men had cut-off values of: swimming = 19.5 min; cycling = 60.7 min; running = 34.1 min. Women's cut-off values were: swimming = 20.7 min; cycling = 71.6 min; running = 38.1 min. The running split seemed to be the most influential in overall race time regardless of rank position or sex. In conclusion, cut-offs were established, which can increase the chances of achieving a successful rank position in an Olympic triathlon. Cycling is the discipline with the least influence on overall performance for both men and women in the Olympic distance triathlon. This influence pattern has not changed in the last three decades.

The Explanatory Capacity of Talent Identification Tests for Performance in Triathlon Competitions: A Longitudinal Analysis

The aim of this study was to determine the explanatory capacity of the Spanish Triathlon Federation’s talent identification tests in relation to performance in competition in subsequent years. We used an exploratory longitudinal study design to establish the relationship between talent identification tests completed by 247 triathletes (97 women and 150 men) aged from 14 to 19 years and the results they obtained over the years in competition. The battery of tests included freestyle swimming (100 and 1000 m) and running (400 and 1000 m). The results indicate that the explanatory capacity of these tests for split places in competition in the corresponding discipline was highest in the 1000-m swimming test, with a value of 0.34 for the adjusted coefficient of determination (R₂a) (p ≤ 0.001), followed by the 1000-m running and 100-m swimming tests, where the highest R²a values were 0.26 and 0.19, respectively. No significant model was found for the 400-m running test. It was concluded that the explanatory capacity of the tests analysed for predicting performance in the discipline in competition was low. However, it was higher for the swimming and running tests of longer distance.

SEX AND AGE-RELATED CHANGES IN PERFORMANCE IN THE DUATHLON WORLD CHAMPIONSHIPS

ABSTRACT Objective Our study analyses differences in performance between sexes, and changes in performance between age groups at Olympic distance during the ITU Duathlon World Championships, held between 2005 and 2016. During this period, a total of 9,772 duathletes were analysed (6,739 men and 3,033 women). Methods Two-way analyses of variance (ANOVA) were used to examine sex- and age-related differences in performance (time, percentage of time and performance ratio) in the first running and cycling legs, the second running leg, and total race for the top 10 male and female athletes in each age group at the Duathlon World Championships. Results The age group with the highest participation, in both male and female categories, was 40-44 years, and it was found that the mean age of female finisher participants across all age groups was 23.5±12. With regards to performance, the best results for total race time and the cycling segment were achieved in the 30-34-year age group, for both male and female athletes. With regards to performance in the first and third segments (running legs), the best times were achieved in the 25-29 and 30-34 age groups, for men and women respectively. Conclusion According to the results of our study, the best results in the professional career of a duathlete are achieved at between 30 and 35 years, therefore the athlete should incorporate this factor into their training plan. Level of evidence III; Retrospective comparative study.

Effects of The Performance Level and Race Distance on Pacing in Ultra-Triathlons

The aim of this study was to examine the effects of the performance level and race distance on pacing in ultra-triathlons (Double, Triple, Quintuple and Deca), wherein pacing is defined as the relative time (%) spent in each discipline (swimming, cycling and running). All finishers (n = 3,622) of Double, Triple, Quintuple and Deca Iron ultra-triathlons between 1985 and 2016 were analysed and classified into quartile groups (Q1, Q2, Q3 and Q4) with Q1 being the fastest and Q4 the slowest. Performance of all non-finishers (n = 1,000) during the same period was also examined. Triple and Quintuple triathlons (24.4%) produced the highest rate of non-finishers, and Deca Iron ultra-triathlons produced the lowest rate (18.0%) (χ² = 12.1, p = 0.007, φC = 0.05). For the relative swimming and cycling times (%), Deca triathletes (6.7 ± 1.5% and 48.8 ± 4.9%, respectively) proved the fastest and Double (9.2 ± 1.6% and 49.6 ± 3.6%) Iron ultra-triathletes were the slowest (p < 0.008) with Q4 being the fastest group (8.3 ± 1.6% and 48.8 ± 4.3%) and Q1 the slowest one (9.5 ± 1.5% and 50.9 ± 3.0%) (p < 0.001). In running, Double triathletes were relatively the fastest (41.2 ± 4.0%) and Deca (44.5 ± 5.4%) Iron ultra-triathletes the slowest (p < 0.001) with Q1 being the fastest (39.6 ± 3.3%) and Q4 the slowest group (42.9 ± 4.7%) (p < 0.001). Based on these findings, it was concluded that the fastest ultra-triathletes spent relatively more time swimming and cycling and less time running, highlighting the importance of the role of the latter discipline for the overall ultra-triathlon performance. Furthermore, coaches and ultra-triathletes should be aware of differences in pacing between Double, Triple, Quintuple and Deca Iron triathlons.

Sex Difference in Triathlon Performance

This brief review investigates how sex influences triathlon performance. Performance time for both Olympic distance and Ironman distance triathlons, and physiological considerations are discussed for both elite and non-elite male and female triathletes. The relative participation of female athletes in triathlon has increased over the last three decades, and currently represents 25-40% of the total field. Overall, the sex difference in both Olympic and Ironman distance triathlon performance has narrowed across the years. Sex difference differed with exercise mode and exercise duration. For non-elite Ironman triathletes, the sex difference in swimming time (≈12%) is lower than that which was evidenced for cycling (≈15%) and running (≈18%). For elite triathletes, sex difference in running performance is greater for Olympic triathlon (≈14%) than it is for Ironman distance triathlon (≈7%). Elite Ironman female triathletes have reduced the gap to their male counterparts to less than 10% for the marathon. The sex difference in triathlon performance is likely to be due to physiological (e.g., VO2max) and morphological (e.g., % body fat) factors but hormonal, psychological and societal (e.g., lower participation rate) differences should also be considered. Future studies should address the limited evidence relating sex difference in physiological characteristics such as lactate threshold, exercise economy or peak fat oxidation.

The Rise of Elite Short-Course Triathlon Re-Emphasises the Necessity to Transition Efficiently from Cycling to Running

Transitioning efficiently between cycling and running is considered an indication of overall performance, and as a result the cycle–run (C–R) transition is one of the most researched areas of triathlon. Previous studies have thoroughly investigated the impact of prior cycling on running performance. However, with the increasing number of short-course events and the inclusion of the mixed relay at the 2020 Tokyo Olympics, efficiently transitioning from cycle–run has been re-emphasised and with it, any potential limitations to running performance among elite triathletes. This short communication provides coaches and sports scientists a review of the literature detailing the negative effects of prior variable-cycling on running performance experienced among elite, short-course and Olympic distance triathletes; as well as discussing practical methods to minimise any negative impact of cycling on running performance. The current literature suggests that variable-cycling negatively effects running ability in at least some elite triathletes and that improving swimming performance, drafting during cycling and C–R training at race intensity could improve an athlete’s triathlon running performance. It is recommended that future research clearly define the performance level, competitive format of the experimental population and use protocols that are specific to the experimental population in order to improve the training and practical application of the research findings.

Men vs. women world marathon records' pacing strategies from 1998 to 2018

The aim of this study was to analyse the pacing strategies adopted by elite male and female marathon runners when setting every world record since 1998. For data analysis, the total distance of the marathon was divided into eight sections of 5 km and a final section of 2.195 km, and the relative average speed of each section was calculated individually. Female athletes maintained similar speeds in the first and second half of the marathon (ES = 0.22, small effect, p = 0.705), whereas male athletes increased their speed as the marathon progressed (ES = 1.18, moderate effect, p = 0.011). However, no differences were observed between men and women in either the first (ES = 0.56, small effect, p = 0.290), or in the second half of the marathon (ES = 0.60, moderate effect, p = 0.266). When comparing the women's world records (1998-2003) vs. men's records (1998-2018) by sections, we observed differences at the beginning of the race (second section, ES = 0.89, moderate effect) and at the end (last section, ES = 0.87, moderate effect). The pace variations during the race were similar between male athletes and that of women with male pacemakers (1.53% ± 0.60 vs. 1.68% ± 0.84, respectively). However, a trend towards higher pace variations during the race in the female records with female pacemakers was observed (2.28% ± 0.95). This study shows how male and female marathon records in the last 20 years have been set using different pacing strategies. While men used a negative strategy (faster finishing), women used a less uniform pacing strategy.

Is the Bike Segment of Modern Olympic Triathlon More a Transition towards Running in Males than It Is in Females?

In 2009, the International Triathlon Union created a new triathlon race format: The World Triathlon Series (WTS), for which only athletes with a top 100 world ranking are eligible. Therefore, the purpose of this study was to analyze the influence of the three disciplines on performance within all the WTS Olympic distance races within two Olympic cycles, and to determine whether their relative contribution changed over the years. Methods: For each of a total of 44 races, final race time and position as well as split times (and positions), and summed time (and position) at each point of the race were collected and included in the analysis. Athletes were divided into 4 groups according to their final race placing (G1: 1st–3rd place; G2: 4–8th place; G3: 8–16th place and G4: ≥17th place). Two-way multivariate ANOVAs were conducted to compare the main effects of years and rank groups. For females, there were significant differences in the swim and bike segment only between G4 and the other groups (p range from 0.001–0.029), whilst for the run segment each group differed significantly from each other (p < 0.001). For males, there were significant differences in swim only between G4 and the other groups (p range from 0.001–0.039), whilst for the running segment each group differed significantly from the others (p < 0.001). Although we found running to be the segment where there were significant differences between performance groups, it is apparently important for overall success that a good runner be positioned with the first cycling pack. However, bike splits were not different between either of the four male groups or between the first 3 groups of the females. At this very high level of performance, at least in the males, the bike leg seems to be a smooth transition towards running.

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.

Sprinting After Having Sprinted: Prior High-Intensity Stochastic Cycling Impairs the Winning Strike for Gold

Bunch riding in closed circuit cycling courses and some track cycling events are often typified by highly variable power output and a maximal sprint to the finish. How criterium style race demands affect final sprint performance however, is unclear. We studied the effects of 1 h variable power cycling on a subsequent maximal 30 s sprint in the laboratory. Nine well-trained male cyclists/triathletes (O_2peak 4.9 ± 0.4 L⋅min^-1; mean ± SD) performed two 1 h cycling trials in a randomized order with either a constant (CON) or variable (VAR) power output matched for mean power output. The VAR protocol comprised intervals of varying intensities (40–135% of maximal aerobic power) and durations (10 to 90 s). A 30 s maximal sprint was performed before and immediately after each 1 h cycling trial. When compared with CON, there was a greater reduction in peak (-5.1 ± 6.1%; mean ± 90% confidence limits) and mean (-5.9 ± 5.2%) power output during the 30 s sprint after the 1 h VAR cycle. Variable power cycling, commonly encountered during criterium and triathlon races can impair an optimal final sprint, potentially compromising race performance. Athletes, coaches, and staff should evaluate training (to improve repeat sprint-ability) and race-day strategies (minimize power variability) to optimize the final sprint.

The Effect of Sex and Performance Level on Pacing in Duathlon

The purpose of the present research was to study the effect of sex and performance on pacing in short (Run1-10 km, Bike-50 km and Run2-5 km) and long distance (Run1-10 km, Bike-150 km and Run2-30 km) in the Powerman World Championship ‘Powerman Zofingen’. All finishers (n = 6671; women, n = 1037; men, n = 5634) competing either in the short or long distance versions of ‘Powerman Zofingen’ from 2003 to 2017 were analyzed for the time spent in each discipline (Run1, Bike and Run2), and in transition (Tran) from Run1 to Bike (Tran1) and from Bike to Run2 (Tran2). Athletes were ranked in quartile (Q) groups (Q1, Q2, Q3 and Q4), with Q1 the fastest and Q4 the slowest. In short distance, in both sexes, a medium discipline/transition × quartile interaction on relative time was observed (p < 0.001, η²_p = 0.103 and η²_p = 0.119, respectively), where Q1 was relatively the fastest in Tran1, Tran2 and Run2, and the slowest in Bike (p < 0.001). In long distance, in both sexes, a large discipline/transition × quartile interaction on relative time was observed (p < 0.001, η²_p = 0.208 and η²_p = 0.180, respectively), where Q1 was relatively the fastest in Tran1, Tran2 and Run2, and the slowest in Bike (p < 0.001). In summary, a similar trend of variation of pacing by performance level was observed in both sexes and distances with the fastest duathletes being the fastest in Run2 and both transitions, and the slowest in Bike.

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 profile in the main international open-water swimming competitions

PURPOSE

Different aspects of pacing in endurance events have been investigated, however, there are very limited information on pacing strategies during open-water swimming. The aim was to describe and compare the pacing profile used by male and female open-water swimmers (OW-swimmers) during the 5-, 10- and 25 km races in the main international competitions.

METHODS

A total of 438 performances were analysed for 5 km, 579 for 10 km and 189 for 25 km, from 2012 to 2017. Swimmers were divided into four groups based on finishing time. G1 whose finishing times were within 0.5% of the winner's time, G2 between 0.51% and 1% slower than winner's time; G3 between 1.1% and 2% slower than winner's time; G4 over 2% of winner's time. Kolmogorov-Smirnov test was used to verify the normal distribution of data and repeated measures ANOVA was performed.

RESULTS

G1 adopted a negative pacing and significantly increased the speed in the last split compared with the other groups during the 5-, 10- and 25-km races in both males and females (p < .001). During the 5- and 10-km race, the last split speed of G1 was significantly faster compared to the other groups in both males and females (p < .05).

CONCLUSIONS

OW-swimmers that used a conservative approach remaining in G1 until the finish of the race, increase the possibility to win a medal in the main international competitions.

Pacing Strategy of a Full Ironman Overall Female Winner on a Course With Major Elevation Changes

Pryor, JL, Adams, WM, Huggins, RA, Belval, LN, Pryor, RR, and Casa, DJ. Pacing strategy of a full Ironman overall female winner on a course with major elevation changes. J Strength Cond Res 32(11): 3080-3087, 2018-The purpose of this study was to use a mixed-methods design to describe the pacing strategy of the overall female winner of a 226.3-km Ironman triathlon. During the race, the triathlete wore a global positioning system and heart rate (HR)-enabled watch and rode a bike outfitted with a power and cadence meter. High-frequency (every km) analyses of mean values, mean absolute percent error (MAPE), and normalized graded running pace and power (accounting for changes in elevation) were calculated. During the bike, velocity, power, cadence, and HR averaged 35.6 km·h, 199 W, 84 rpm, and 155 b·min, respectively, with minimal variation except for velocity (measurement unit variation [MAPE]: 7.4 km·h [20.3%], 11.8 W [7.0%], 3.6 rpm [4.6%], 3 b·min [2.3%], respectively). During the run, velocity and HR averaged 13.8 km·h and 154 b·min, respectively, with velocity varying four-fold more than HR (MAPE: 4.8% vs. 1.2%). Accounting for elevation changes, power and running pace were less variable (raw [MAPE] vs. normalized [MAPE]: 199 [7.0%] vs. 204 W [2.7%]; 4:29 [4.8%] vs. 4:24 min·km [3.6%], respectively). Consistent with her planned pre-race pacing strategy, the triathlete minimized fluctuations in HR and watts during the bike and run, whereas velocity varied with changes in elevation. This case report provides observational evidence supporting the utility of a pacing strategy that allows for an oscillating velocity that sustains a consistent physiological effort in full Ironman races.

Changes in Contributions of Swimming, Cycling, and Running Performances on Overall Triathlon Performance Over a 26-Year Period

Figueiredo, P, Marques, EA, and Lepers, R. Changes in contributions of swimming, cycling, and running performances on overall triathlon performance over a 26-year period. J Strength Cond Res 30(9): 2406-2415, 2016-This study examined the changes in the individual contribution of each discipline to the overall performance of Olympic and Ironman distance triathlons among men and women. Between 1989 and 2014, overall performances and their component disciplines (swimming, cycling and running) were analyzed from the top 50 overall male and female finishers. Regression analyses determined that for the Olympic distance, the split times in swimming and running decreased over the years (r = 0.25-0.43, p ≤ 0.05), whereas the cycling split and total time remained unchanged (p > 0.05), for both sexes. For the Ironman distance, the cycling and running splits and the total time decreased (r = 0.19-0.88, p ≤ 0.05), whereas swimming time remained stable, for both men and women. The average contribution of the swimming stage (∼18%) was smaller than the cycling and running stages (p ≤ 0.05), for both distances and both sexes. Running (∼47%) and then cycling (∼36%) had the greatest contribution to overall performance for the Olympic distance (∼47%), whereas for the Ironman distance, cycling and running presented similar contributions (∼40%, p > 0.05). Across the years, in the Olympic distance, swimming contribution significantly decreased for women and men (r = 0.51 and 0.68, p < 0.001, respectively), whereas running increased for men (r = 0.33, p = 0.014). In the Ironman distance, swimming and cycling contributions changed in an undulating fashion, being inverse between the two segments, for both sexes (p < 0.01), whereas running contribution decreased for men only (r = 0.61, p = 0.001). These findings highlight that strategies to improve running performance should be the main focus on the preparation to compete in the Olympic distance; whereas, in the Ironman, both cycling and running are decisive and should be well developed.

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.

Men are more likely than women to slow in the marathon

Studies on nonelite distance runners suggest that men are more likely than women to slow their pace in a marathon.

PURPOSE

This study determined the reliability of the sex difference in pacing across many marathons and after adjusting women's performances by 12% to address men's greater maximal oxygen uptake and also incorporating information on racing experience.

METHODS

Data were acquired from 14 US marathons in 2011 and encompassed 91,929 performances. For 2929 runners, we obtained experience data from a race-aggregating Web site. We operationalized pace maintenance as the percentage change in pace observed in the second half of the marathon relative to the first half. Pace maintenance was analyzed as a continuous variable and as two categorical variables, as follows: "maintain the pace," defined as slowing <10%, and "marked slowing," defined as slowing ≥30%.

RESULTS

The mean change in pace was 15.6% and 11.7% for men and women, respectively (P < 0.0001). This sex difference was significant for all 14 marathons. The odds for women were 1.46 (95% confidence interval, 1.41-1.50; P < 0.0001) times higher than men to maintain the pace and 0.36 (95% confidence interval, 0.34-0.38; P < 0.0001) times that of men to exhibit marked slowing. Slower finishing times were associated with greater slowing, especially in men (interaction, P < 0.0001). However, the sex difference in pacing occurred across age and finishing time groups. Making the 12% adjustment to women's performances lessened the magnitude of the sex difference in pacing but not its occurrence. Although greater experience was associated with less slowing, controlling for the experience variables did not eliminate the sex difference in pacing.

CONCLUSIONS

The sex difference in pacing is robust. It may reflect sex differences in physiology, decision making, or both.

Performance analysis and prediction in triathlon

Performance in triathlon is dependent upon factors that include somatotype, physiological capacity, technical proficiency and race strategy. Given the multidisciplinary nature of triathlon and the interaction between each of the three race components, the identification of target split times that can be used to inform the design of training plans and race pacing strategies is a complex task. The present study uses machine learning techniques to analyse a large database of performances in Olympic distance triathlons (2008-2012). The analysis reveals patterns of performance in five components of triathlon (three race "legs" and two transitions) and the complex relationships between performance in each component and overall performance in a race. The results provide three perspectives on the relationship between performance in each component of triathlon and the final placing in a race. These perspectives allow the identification of target split times that are required to achieve a certain final place in a race and the opportunity to make evidence-based decisions about race tactics in order to optimise performance.

Pacing strategies during the swim, cycle and run disciplines of sprint, Olympic and half-Ironman triathlons

PURPOSE

This study investigated the influence of distance on self-selected pacing during the swim, cycle and run disciplines of sprint, Olympic and half-Ironman (HIM) distance triathlon races.

METHOD

Eight trained male triathletes performed the three individual races in <2 months. Participants' bikes were fitted with Schoberer Rad Meßtechnik to monitor speed, power output and heart rate during the cycle discipline. Global positioning system was worn to determine speed and heart rate during the swim and run disciplines.

RESULT

An even swim pacing strategy was adopted across all distances. A more stochastic pacing was observed during the HIM cycle [standard deviation of exposure variation analysis (EVASD) = 3.21 ± 0.61] when compared with the sprint cycle discipline (EVASD = 3.84 ± 0.44, p = 0.018). Only 20.9 ± 4.1 % of the cycling time was spent more than 10 % above the mean power output in the HIM, compared with 43.8 ± 2.9 % (p = 0.002) and 37.7 ± 11.1 % (p = 0.039) during the sprint and Olympic distance triathlons, respectively. Conversely, 13.6 ± 5.1 % of the cycling time was spent 5-10 % below the mean power output during the HIM, compared with 5.9 ± 1.2 % (p = 0.034) and 8.0 ± 5.1 % (p = 0.045) during the sprint and Olympic distance triathlons, respectively. A negative pacing strategy was adopted during the sprint distance run, compared with positive pacing strategy during the Olympic and HIM.

CONCLUSION

Results of this study suggest that pacing strategies during triathlon are highly influenced by distance and discipline, and highlight the importance of developing pacing strategies based on distance, strengths and individual fitness.

Tracking career performance of successful triathletes

PURPOSE

Tracking athletes' performances over time is important but problematic for sports with large environmental effects. Here we have developed career performance trajectories for elite triathletes, investigating changes in swim, cycle, run stages, and total performance times while accounting for environmental and other external factors.

METHODS

Performance times of 337 female and 427 male triathletes competing in 419 international races between 2000 and 2012 were obtained from triathlon.org. Athletes were categorized according to any top 16 placing at World Championships or Olympics between 2008 and 2012. A mixed linear model accounting for race distance (sprint and Olympic), level of competition, calendar-year trend, athlete's category, and clustering of times within athletes and races was used to derive athletes' individual quadratic performance trajectories. These trajectories provided estimates of age of peak performance and predictions for the 2012 London Olympic Games.

RESULTS

By markedly reducing the scatter of individual race times, the model produced well-fitting trajectories suitable for comparison of triathletes. Trajectories for top 16 triathletes showed different patterns for race stages and differed more among women than among men, but ages of peak total performance were similar for men and women (28 ± 3 yr, mean ± SD). Correlations between observed and predicted placings at Olympics were slightly higher than those provided by placings in races before the Olympics.

CONCLUSIONS

Athletes' trajectories will help identify talented athletes and their weakest and strongest stages. The wider range of trajectories among women should be taken into account when setting talent identification criteria. Trajectories offer a small advantage over usual race placings for predicting men's performance. Further refinements, such as accounting for individual responses to race conditions, may improve utility of performance trajectories.

Pacing profiles and pack running at the IAAF World Half Marathon Championships

The aim of this study was to describe the pacing profiles and packing behaviour of athletes competing in the IAAF World Half Marathon Championships. Finishing and split times were collated for 491 men and 347 women across six championships. The mean speeds for each intermediate 5 km and end 1.1 km segments were calculated, and athletes grouped according to finishing time. The best men and women largely maintained their split speeds between 5 km and 15 km, whereas slower athletes had decreased speeds from 5 km onwards. Athletes were also classified by the type of packing behaviour in which they engaged. Those who ran in packs throughout the race had smaller decreases in pace than those who did not, or who managed to do so only to 5 km. While some athletes' reduced speeds from 15 to 20 km might have been caused by fatigue, it was also possibly a tactic to aid a fast finish that was particularly beneficial to medallists. Those athletes who ran with the same competitors throughout sped up most during the finish. Athletes are advised to identify rivals likely to have similar abilities and ambitions and run with them as part of their pre-race strategy.

Factors influencing pacing in triathlon

Triathlon is a multisport event consisting of sequential swim, cycle, and run disciplines performed over a variety of distances. This complex and unique sport requires athletes to appropriately distribute their speed or energy expenditure (ie, pacing) within each discipline as well as over the entire event. As with most physical activity, the regulation of pacing in triathlon may be influenced by a multitude of intrinsic and extrinsic factors. The majority of current research focuses mainly on the Olympic distance, whilst much less literature is available on other triathlon distances such as the sprint, half-Ironman, and Ironman distances. Furthermore, little is understood regarding the specific physiological, environmental, and interdisciplinary effects on pacing. Therefore, this article discusses the pacing strategies observed in triathlon across different distances, and elucidates the possible factors influencing pacing within the three specific disciplines of a triathlon.

Effects of deceptive running speed on physiology, perceptual responses, and performance during sprint-distance triathlon

OBJECTIVE

This study examined the effects of speed deception on performance, physiological and perceptual responses, and pacing during sprint-distance triathlon running.

METHODS

Eight competitive triathletes completed three simulated sprint-distance triathlons (0.75 km swim, 20 km bike, 5 km run) in a randomised order, with swimming and cycling sections replicating baseline triathlon performance. During the first 1.66 km of the run participants maintained an imposed speed, completing the remaining 3.33 km as quickly as possible. Although the participants were informed that initially prescribed running speed would reflect baseline performance, this was true during only one trial (Tri-Run100%). As such, other trials were either 3% faster (Tri-Run103%), or 3% slower (Tri-Run97%) than baseline during this initial period.

RESULTS

Performance during Tri-Run103% (1346±108 s) was likely faster than Tri-Run97% (1371±108 s), and possibly faster than Tri-Run100% (1360±125 s), with these differences likely to be competitively meaningful. The first 1.66 km of Tri-Run103% induced greater physiological strain compared to other conditions, whilst perceptual responses were not significantly different between trials.

CONCLUSIONS

It appears that even during 'all-out' triathlon running, athletes maintain some form of 'reserve' capacity which can be accessed by deception. This suggests that expectations and beliefs have a practically meaningful effect on pacing and performance during triathlon, although it is apparent that an individual's conscious intentions are secondary to the brains sensitivity to potentially harmful levels of physiological and perceptual strain.

Performance in Olympic triathlon: changes in performance of elite female and male triathletes in the ITU World Triathlon Series from 2009 to 2012

This study investigated the changes in performance and sex difference in performance of the world best triathletes at the ITU (International Triathlon Union) World Triathlon Series (i.e. 1.5 km swimming, 40 km cycling and 10 km running) during the 2009-2012 period including the 2012 London Olympic Games. Changes in overall race times, split times and sex difference in performance of the top ten women and men of each race were analyzed using single and multi-level regression analyses. Swimming and running split times remained unchanged whereas cycling split times (ß = 0.003, P < 0.001) and overall race times (ß = 0.003, P < 0.001) increased significantly for both women and men. The sex difference in performance remained unchanged for swimming and cycling but decreased for running (ß = -0.001, P = 0.001) from 14.9 ± 2.7% to 13.2 ± 2.6% and for overall race time (ß = -0.001, P = 0.006) from 11.9 ± 1.2% to 11.4 ± 1.4%. The sex difference in running (14.3 ± 2.4%) was greater (P < 0.001) compared to swimming (9.1 ± 5.1%) and cycling (9.5 ± 2.7%). These findings suggest that (i) the world’s best female short-distance triathletes reduced the gap with male athletes in running and total performance at short distance triathlon with drafting during the 2009-2012 period and (ii) the sex difference in running was greater compared to swimming and cycling. Further studies should investigate the reasons why the sex difference in performance was greater in running compared to swimming and cycling in elite short-distance triathletes.

Temporal activity in particular segments and transitions in the olympic triathlon

The Olympic Triathlon is a combined endurance sport. It includes back-to-back swimming, cycling, running and the transition between events (T1 & T2). The aim of the current study was to analyse the possible relationship between the Lost Time T1 & T2 and overall performance. The results showed that the percentages of total time corresponding to each part of the race were: 16.2% for swimming, 0.74% for the swimming-cycling transition (T1), 53.07% for cycling, 0.47% for the cycling-running transition (T2) and 29.5% for running. The correlations between each part of the race and the final placing were: r=0.36 for swimming, r=0.25 for T1, r=0.62 for the cycling, r=0.33 for T2, and r=0.83 for the running. Also, values of r=0.34 & r=0.43 were obtained for Lost Time T1 and Lost Time T2, respectively. In conclusion, losing less time during T2 has been demonstrated to be related to obtaining a better final result.

Cycling attributes that enhance running performance after the cycle section in triathlon

PURPOSE

To determine how cycling with a variable (triathlon-specific) power distribution affects subsequent running performance and quantify relationships between an individual cycling power profile and running ability after cycling.

METHODS

Twelve well-trained male triathletes (VO2peak 4.9 ± 0.5 L/min; mass 73.5 ± 7.7 kg; mean ± SD) undertook a cycle VO2peak and maximal aerobic power (MAP) test and a power profile involving 6 maximal efforts (6 s to 10 min). Each subject then performed 2 experimental 1-h cycle trials, both at a mean power of 65% MAP, at either variable power (VAR) ranging from 40% to 140% MAP or constant power (CON) followed by an outdoor 9.3-km time-trial run. Subjects also completed a control 9.3-km run with no preceding exercise.

RESULTS

The 9.3-km run time was 42 ± 37 s slower (mean ± 90% confidence limits [CL]) after VAR (35:32 ± 3:18 min:s, mean ± SD) compared with CON cycling (34:50 ± 2:49 min:s). This decrement after VAR appeared primarily in the first half of the run (35 ± 20 s; mean ± 90% CL). Higher blood lactate and rating of perceived exertion after 1 h VAR cycling were moderately correlated (r = .51-.55; ± ~.40) with a larger decrement in run performance. There were no clear associations between the power-profile test and decrement in run time after VAR compared with CON.

CONCLUSIONS

A highly variable power distribution in cycling is likely to impair 10-km triathlon run performance. Training to lower physiological and perceptual responses during cycling should limit the negative effects on triathlon running.

Longitudinal changes in response to a cycle-run field test of young male national "talent identification" and senior elite triathlon squads

This study investigated the changes in cardiorespiratory response and running performance of 9 male "Talent Identification" (TID) and 6 male Senior Elite (SE) Spanish National Squad triathletes during a specific cycle-run (C-R) test. The TID and SE triathletes (initial age 15.2 ± 0.7 vs. 23.8 ± 5.6 years, p = 0.03; V(O2)max 77.0 ± 5.6 vs. 77.8 ± 3.6 ml · kg(-1) · min(-1), nonsignificant) underwent 3 tests through the competitive period and the preparatory period, respectively, of 2 consecutive seasons: test 1 was an incremental cycle test to determine the ventilatory threshold (Th(vent)); test 2 (C-R) was 30-minute constant load cycling at the Th(vent) power output followed by a 3-km time-trial run; and test 3 (isolated control run [R]) was an isolated 3-km time-trial control run, in randomized counterbalanced order. In both seasons, the time required to complete the C-R 3-km run was greater than for R in TID (11:09 ± 00:24 vs. 10:45 ± 00:16 min:ss, p < 0.01 and 10:24 ± 00:22 vs. 10:04 ± 00:14, p = 0.006, for season 2005-2006 and 2006-2007, respectively) and SE (10:15 ± 00:19 vs. 09:45 ± 00:30, p < 0.001 and 09:51 ± 00:26 vs. 09:46 ± 00:06, p = 0.02 for season 2005-2006 and 2006-2007, respectively). Compared with the first season, the completion of the time-trial run was faster in the second season (6.6%, p < 0.01 and 6.4%, p < 0.01, for C-R and R tests, respectively) only in TID. Changes in post cycling run performance were accompanied by changes in pacing strategy, but there were only slight or nonsignificant changes in the cardiorespiratory response. Thus, the negative effect of cycling on performance may persist, independently of the period, over 2 consecutive seasons in TID and SE triathletes; however, improvements over time suggests that monitoring running pacing strategy after cycling may be a useful tool to control performance and training adaptations in TID.

Kinanthropometric differences between 1997 World championship junior elite and 2011 national junior elite triathletes

OBJECTIVES

In 1997, anthropometry measures were made to determine the body size and shapes of both senior and junior elite triathletes. Since then, the junior event distance has changed and the optimal morphology of participants may have evolved. Thus the objective of this study was to compare the morphology of 1997 World championship junior elite triathlon competitors with junior elite competitors in 2011.

DESIGN

Comparative study of junior elite triathlete kinanthropometry.

METHODS

Twenty-nine males and 20 females junior elite competitors in the 1997 Triathlon World Championships underwent 26 anthropometric measurements. Results were compared with 28 male and 14 female junior elite triathletes who competed in the 2011 Australian National Junior Series, as qualifying for 2011 Triathlon World Championships. Comparisons were made on the raw scores, as well as somatotype, and body proportional scores.

RESULTS

Both male and female junior elite triathletes in the 2011 group were significantly more ectomorphic than their 1997 counterparts. The 2011 triathletes were also proportionally lighter, with significantly smaller flexed arm and thigh girths, and femur breadths. The 2011 males recorded significantly longer segmental lengths and lower endomorphy values than the 1997 junior males.

CONCLUSIONS

Junior elite triathlete morphology has evolved during the past 14 years possibly as a result of changing race distance and race tactics, highlighting the importance of continually monitoring and updating such anthropometric data.