The Science Behind Competition and Winning in Athletics: Using World-Level Competition Data to Explore Pacing and Tactics

Pacing Demands in Competitive Nordic Skiing

BACKGROUND AND PURPOSE

Cross-country skiing, biathlon, and Nordic combined are Winter Olympics sports that involve cross-country skiing in undulating terrain, characterized by various subtechniques and repeated intensity fluctuations. The stochastic interval profile of these sports necessitates the continuous regulation of work and energy expenditure throughout training sessions and competitions, a concept known as pacing. With the advent of technological advancements that allow for the measurement of these features during training and competitions, scientific studies have broadened our understanding of the associated racing and pacing demands. We provide the current scientific overview of pacing demands in competitive cross-country skiing, biathlon, and Nordic combined and propose guidelines for how performance can be enhanced by adjusting pacing behavior.

CONCLUSIONS AND PRACTICAL APPLICATIONS

The study of pacing in skiing has evolved from basic lap-to-lap, or segment, analyses to detailed insights into micropacing strategies. This includes analysis of speed, internal and external power, subtechnique distribution, and associated temporal patterns, combined with subjective ratings of effort. While several objective tools such as heart rate, blood lactate concentration, and speed measurements are widely used in practice, current understanding suggests that these measures should supplement, rather than replace, the use of perceived effort (eg, rating of perceived exertion) to regulate intensity during training and competition in undulating terrain. Therefore, the ability to self-regulate effort appears to be an important performance characteristic and should be developed in adolescents and systematically used to optimize and evaluate the training process and race performance throughout athletes' careers.

The Characteristics of Endurance Events with a Variable Pacing Profile-Time to Embrace the Concept of "Intermittent Endurance Events"?

A variable pacing profile is common in different endurance events. In these races, several factors, such as changes in elevation or race dynamics, lead participants to perform numerous surges in intensity. These surges are so frequent that certain events, such as cross-country (XC) skiing, mountain biking (MTB), triathlon, and road cycling, have been termed "intermittent endurance events". The characteristics of these surges vary depending on the sport: MTB and triathlon require athletes to perform numerous short (<10 s) bouts; XC skiing require periods of short- and moderate-(30 s to 2 min) duration efforts, while road cycling is comprised of a mix of short-, moderate-, and long-duration (>2 min) bouts. These bouts occur at intensities above the maximal metabolic steady state (MMSS), with many efforts performed at intensities above the athletes' maximal aerobic power or speed (MAP/MAS) (i.e., supramaximal intensities). Given the factors that influence the requirement to perform surges in these events, athletes must be prepared to always engage in a race with a highly stochastic pace. The aim of this review is to characterize the variable pacing profile seen in endurance events and to discuss how the performance of multiple maximal and supramaximal surges in intensity can affect how athletes fatigue during a race and influence training strategies that can lead to success in these races.

Footstrike patterns and race performance in the 2017 IAAF World Championship men's 10,000 m final

Midfoot- (MFS) and forefoot-striking (FFS) runners usually switch to rearfoot-striking (RFS) during marathons. However, world-class runners might resist modifications during shorter races. The purpose of this study was to analyse footstrike patterns, ground contact times and running speeds in a World Championship men's 10,000 m final. Footstrike patterns and contact times of the top 12 finishing men (24 ± 5 years) were recorded (150 Hz) during laps 1, 5, 11, 15, 20 and 25. Split times for each 100-m segment were obtained. No RFS patterns were observed; there was no difference between the number of FFS and MFS athletes at any distance (p ≥ 0.581) and no change in the proportions of FFS and MFS occurred (p = 0.383). No link between race performance and footstrike pattern appeared given the similar number who used FFS or MFS and their similar finishing times. Despite slower running speeds and longer contact times in the middle of the race (p ≤ 0.024), no effect on footstrike patterns occurred. The prevalence of anterior footstrike patterns in this world-class race reflects the capability of maintaining fast paces (>22 km/h). Changes in footstrike pattern might accompany the physiological and neuromuscular effects of fatigue over longer distances.

Running Variability in Marathon-Evaluation of the Pacing Variables

Background and Objectives: Pacing analyses for increasingly popular long-distance running disciplines have been in researchers' spotlight for several years. In particular, assessing pacing variability in long-distance running was hardly achievable since runners must repeat long-running trials for several days. Potential solutions for these problems could be multi-stage long-distance running disciplines. Therefore, this study aimed to assess the long-distance running variability as well as the reliability, validity, and sensitivity of the variables often used for pacing analyses. Materials and Methods: This study collected the split times and finish times for 20 participants (17 men and three women; mean age 55.5 years ± 9.5 years) who completed the multiday marathon running race (five marathons in 5 days), held as part of the Bretzel Ultra Tri in Colmar, France, in 2021. Seven commonly used pacing variables were subsequently calculated: Coefficient of variation (CV), Change in mean speed (CS), Change in first lap speed (CSF), Absolute change in mean speed (ACS), Pace range (PR), Mid-race split (MRS), and First 32 km-10 km split (32-10). Results: Multi-stage marathon running showed low variability between days (Intraclass correlation coefficient (ICC) > 0.920), while only the CV, ACS, and PR variables proved to have moderate to good reliability (0.732 < ICC < 0.785). The same variables were also valid (r > 0.908), and sensitive enough to discern between runners of different performance levels (p < 0.05). Conclusions: Researchers and practitioners who aim to explore pacing in long-distance running should routinely utilize ACS, CV, and PR variables in their analyses. Other examined variables, CS, CSF, MRS, and 32-10, should be used cautiously. Future studies might try to confirm these results using different multi-stage event's data as well as by expanding sensitivity analysis to age and gender differences.

Racewalking on a treadmill alters gait characteristics without increasing risk of disqualification

PURPOSE

Treadmill training is useful for racewalking training; however, it may affect gait characteristics and lead to non-legal techniques. The aim of this study was to determine the kinematic differences between treadmill and overground conditions during racewalking at different speeds.

METHODS

Twenty-two elite racewalkers participated in this study. They racewalked under treadmill and overground conditions at high and medium speeds. A 12-camera motion analysis system was used to record the racewalking trials.

RESULTS

Significant condition by speed interactions were detected in step frequency and pelvis rotation angle; step frequency decreased while pelvis rotation angle increased from overground to treadmill conditions at high speed. Compared to overground conditions, racewalkers decreased the ankle dorsiflexion angle at heel strike and increased hip flexion, shoulder hyperextension, and elbow flexion angles at heel strike and hip and shoulder extension angles at toe-off under treadmill conditions. Compared to medium speed, racewalkers decreased the contact time, hip flexion, and shoulder hyperextension at heel strike, and ankle plantarflexion and shoulder extension angles at toe-off, and increased flight time, step length, and elbow flexion angle at heel strike at high speed.

CONCLUSION

Several kinematic differences during racewalking were detected between treadmill and overground conditions, with more differences detected at high speed, indicating that treadmill racewalking, especially at high speed, has different gait characteristics. However, no differences were detected in flight time and knee angle under treadmill conditions compared to overground conditions, indicating that racewalking on a treadmill does not increase the risk of disqualification.

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.

Impact of marathon performance on muscles stiffness in runners over 50 years old

Introduction

The research examines the relationship between marathon performance and muscle stiffness changes from pre to marathon in recreational runners aged 50+ years.

Methods

Thirty-one male long-distance runners aged 50-73 years participated in the experiment. The muscle stiffness of quadriceps and calves was measured in two independent sessions: the day before the marathon and 30 min after the completed marathon run using a Myoton device.

Results and Discussion

The 42.195-km run was completed in 4.30,05 h ± 35.12 min, which indicates an intensity of 79.3% ± 7.1% of HRmax. The long-term, low-intensity running exercise (marathon) in older recreational runners and the low level of HRmax and VO2max showed no statistically significant changes in muscle stiffness (quadriceps and calves). There was reduced muscle stiffness (p = 0.016), but only in the triceps of the calf in the dominant (left) leg. Moreover, to optimally evaluate the marathon and adequately prepare for the performance training program, we need to consider the direct and indirect analyses of the running economy, running technique, and HRmax and VO2max variables. These variables significantly affect marathon exercise.

Pacing Behaviour Development and Acquisition: A Systematic Review

BACKGROUND

The goal-directed decision-making process of effort distribution (i.e. pacing) allows individuals to efficiently use energy resources as well as to manage the impact of fatigue on performance during exercise. Given the shared characteristics between pacing behaviour and other skilled behaviour, it was hypothesized that pacing behaviour would adhere to the same processes associated with skill acquisition and development.

METHODS

PubMed, Web of Science and PsycINFO databases between January 1995 and January 2022 were searched for articles relating to the pacing behaviour of individuals (1) younger than 18 years of age, or (2) repeatedly performing the same exercise task, or (3) with different levels of experience.

RESULTS

The search resulted in 64 articles reporting on the effect of age (n = 33), repeated task exposure (n = 29) or differing levels of experience (n = 13) on pacing behaviour. Empirical evidence identifies the development of pacing behaviour starts during childhood (~ 10 years old) and continues throughout adolescence. This development is characterized by an increasingly better fit to the task demands, encompassing the task characteristics (e.g. duration) and environment factors (e.g. opponents). Gaining task experience leads to an increased capability to attain a predetermined pace and results in pacing behaviour that better fits task demands.

CONCLUSIONS

Similar to skilled behaviour, physical maturation and cognitive development likely drive the development of pacing behaviour. Pacing behaviour follows established processes of skill acquisition, as repeated task execution improves the match between stimuli (e.g. task demands and afferent signals) and actions (i.e. continuing, increasing or decreasing the exerted effort) with the resulting exercise task performance. Furthermore, with increased task experience attentional capacity is freed for secondary tasks (e.g. incorporating opponents) and the goal selection is changed from achieving task completion to optimizing task performance. As the development and acquisition of pacing resemble that of other skills, established concepts in the literature (e.g. intervention-induced variability and augmented feedback) could enrich pacing research and be the basis for practical applications in physical education, healthcare, and sports.

Physiological Responses and Performance During a 3-Minute Cycle Time Trial: Standard Paced Versus All-Out Paced

PURPOSE

To compare performance and physiological responses between a standard-paced 3-minute time trial (TTSP, ie, pacing based on normal intention) and a consistently all-out-paced 3-minute time trial (TTAOP).

METHODS

Sixteen well-trained male cyclists completed the TTSP and TTAOP, on separate days of testing, on a cycling ergometer with power output and respiratory variables measured. Time trials were preceded by 7 × 4-minute submaximal stages of increasing intensity with the linear relationship between power output and metabolic rate used to estimate the contribution from aerobic and anaerobic energy resources. The time course of anaerobic and aerobic contributions to power output was analyzed using statistical parametric mapping.

RESULTS

Mean power output was not different between the 2 pacing strategies (TTSP = 417 [43] W, TTAOP = 423 [41] W; P = 0.158). TTAOP resulted in higher peak power output (P < .001), mean ventilation rate (P < .001), mean heart rate (P = .044), peak accumulated anaerobically attributable work (P = .026), post-time-trial blood lactate concentration (P = .035), and rating of perceived exertion (P = .036). Statistical parametric mapping revealed a higher anaerobic contribution to power output during the first ∼30 seconds and a lower contribution between ∼90 and 170 seconds for TTAOP than TTSP. The aerobic contribution to power output was higher between ∼55 and 75 seconds for TTAOP.

CONCLUSIONS

Although there was no significant difference in performance (ie, mean power output) between the 2 pacing strategies, differences were found in the distribution of anaerobically and aerobically attributable power output. This implies that athletes can pace a 3-minute maximal effort very differently but achieve the same result.

Pacing and packing behavior in elite and world record performances at Berlin marathon

The aim of this study was to compare pacing and packing behaviors between sex and performance level at elite Berlin marathon races. Official electronic split and finishing times from 279 (149 male and 130 female) marathon performances, including 5 male world records, were obtained from eleven Berlin marathon races held from 2008 to 2018, and from two previous world records and the second world all-time fastest performance also achieved at that same Berlin course. Male performances displaying an even pacing behavior were significantly faster than those adopting a positive behavior (p < 0.001; d = 0.75). Male world records were characterized by even profiles with fast endspurts, being especially remarkable at world all-time two fastest performances which were assisted by the use of a new shoe technology. Female marathon runners decreased their speed less than men during the second half marathon and especially from the 35^th km onwards (p < 0.001; 0.51 ≤ d≤0.55). The latest race stages were usually run individually in both sexes. Significant pace differences between performance groups at every race segment were found in women (p < 0.01; 1.0 ≤ d≤2.0), who also covered an important part of the race alone. Prior to participation in meet marathon races such as Berlin marathon, elite runners should select the group that they will join during the race according to their current performance level as a preassigned pace set by a pacemaker will be adopted. Therefore, they could follow an even rather than positive pacing behavior which will allow them to achieve a more optimal performance.

Consistency of pacing profile according to performance level in three different editions of the Chicago, London, and Tokyo marathons

Running pacing has become a focus of interest over recent years due to its relationship with performance, however, it is still unknown the consistency of each race in different editions. The aim of this study is to analyze the consistency of pacing profile in three consecutive editions of three marathon races. A database of 282,808 runners, compiled from three different races (Chicago, London, and Tokyo Marathon) and three editions (2017, 2018, and 2019) was analyzed. Participants were categorized according to their time performance in the marathon, every 30 min from 2:30 h to sub-6 h. The relative speed of each section for each runner was calculated as a percentage of the average speed for the entire race. The intraclass correlation coefficients (ICC) of relative speed at the different pacing section, taking into account the runner time categories, was excellent over the three marathon editions (ICC > 0.93). The artificial intelligence model showed an accuracy of 86.8% to classify the runners' data in three marathons, suggesting a consistency between editions with identifiable differences between races. In conclusion, although some differences have been observed between editions in certain sections and marathon runner categories, excellent consistency of the pacing profile was observed. The study of pacing profile in a specific marathon can, therefore, be helpful for runners, coaches and marathon organizers for planning the race and improving its organization.

Biomechanics of World-Class 800 m Women at the 2017 IAAF World Championships

The 800 m race challenges the aerobic and anaerobic energy systems, and athletes adopt a technique that allows for running efficiency as well as sprinting speeds. The aim of this novel study was to compare important kinematic variables between the two laps of the 2017 IAAF World Championships women's final. Video data (150 Hz) were collected of all eight finalists on both laps at a distance approximately 50 m from the finish line along the home straight. Running speed, step length, cadence, temporal variables, sagittal plane joint angles, and shank angle at initial contact were measured. Running speed was faster on lap 2 (p = 0.008) because of large increases in cadence (p = 0.012). These higher cadences resulted in large decreases in contact times (p = 0.031) and in flight times (p = 0.016) on lap 2. Greater knee flexion and ankle plantarflexion (p ≤ 0.039) at initial contact on lap 2 seemed partly responsible for shorter swing times (p = 0.016), and which accompanied a decrease in shank angle at initial contact from lap 1 (7°) to a more vertical position on lap 2 (4°) (p = 0.008). Coaches should note that the need for higher cadence, horizontal impulse production during shorter contact times, and the adoption of forefoot striking require strength and neural system training to allow for athletes to increase cadence during the sprint finish. Increasing cadence (and not step length) was the driving factor for faster finishing speeds in the women's 800 m.

Expanding the Gap: An Updated Look Into Sex Differences in Running Performance

Males consistently outperform females in athletic endeavors, including running events of standard Olympic distances (100 m to Marathon). The magnitude of this percentage sex difference, i.e., the sex gap, has evolved over time. Two clear trends in sex gap evolution are evident; a narrowing of the gap during the 20th century, followed by a period of stability thereafter. However, an updated perspective on the average sex gap from top 20 athlete performances over the past two decades reveals nuanced trends over time, indicating the sex gap is not fixed. Additionally, the sex gap varies with performance level; the difference in absolute running performance between males and females is lowest for world record/world lead performances and increases in lower-ranked elite athletes. This observation of an increased sex gap with world rank is evident in events 400 m and longer and indicates a lower depth in female competitive standards. Explanations for the sex difference in absolute performance and competition depth include physical (physiological, anatomical, neuromuscular, biomechanical), sociocultural, psychological, and sport-specific factors. It is apparent that females are the disadvantaged sex in sport; therefore, measures should be taken to reduce this discrepancy and enable both sexes to reach their biological performance potential. There is scope to narrow the sex performance gap by addressing inequalities between the sexes in opportunities, provisions, incentives, attitudes/perceptions, research, and media representation.

World and European Rowing Medallists Pace With Smaller Variation Than Their Competitors

Purpose: To establish the relation between pacing pattern and performance, within sex, and number of crew members, at the very highest performance level in World class rowing.

Methods: Pacing profiles based on official 500 m split times in 106 A-finals with six contesting boat crews (n = 636 crews), in recent World (2017–2019) and European (2017–2021) championships, were analyzed. The coefficient of variation (CV) and sum of relative differences (SRD) of the split times, and normalized velocities in the four segments of the race, were compared between performance levels, that is, placement (1st–6th), and subgroups based on sex (female or male) and number of crew members (one, two, or four). Statistical tests and resulting p-values and effect sizes (Cohen's d) were used to assess differences between groups.

Results: The pacing profiles of the medallists had smaller variation than those of the non-podium finishers (CV = 1.72% vs. CV = 2.00%; p = 4 × 10−7, d = 0.41). Compared to the non-podium finishers, the medallists had lower normalized velocities in the first and second segments of the race, slightly higher in the third segment and higher in the fourth segment. Female crews paced somewhat more evenly than male crews. No significant differences were found in the evenness of pacing profiles between singles, doubles/pairs and quads/fours. Analyses of SRD were overall consistent with analyses of CV.

Conclusion: Medal winners in major rowing championships use a more even pacing strategy than their final competitors, which could imply that such a strategy is advantageous in rowing.

Pacing profiles and tactical behaviors of elite runners

The pacing behaviors used by elite athletes differ among individual sports, necessitating the study of sport-specific pacing profiles. Additionally, pacing behaviors adopted by elite runners differ depending on race distance. An "all-out" strategy, characterized by initial rapid acceleration and reduction in speed in the later stages, is observed during 100 m and 200 m events; 400 m runners also display positive pacing patterns, which is characterized by a reduction in speed throughout the race. Similarly, 800 m runners typically adopt a positive pacing strategy during paced "meet" races. However, during championship races, depending on the tactical approaches used by dominant athletes, pacing can be either positive or negative (characterized by an increase in speed throughout). A U-shaped pacing strategy (characterized by a faster start and end than during the middle part of the race) is evident during world record performances at meet races in 1500 m, 5000 m, and 10,000 m events. Although a parabolic J-shaped pacing profile (in which the start is faster than the middle part of the race but is slower than the endspurt) can be observed during championship 1500 m races, a negative pacing strategy with microvariations of pace is adopted by 5000 m and 10,000 m runners in championship races. Major cross country and marathon championship races are characterized by a positive pacing strategy; whereas a U-shaped pacing strategy, which is the result of a fast endspurt, is adopted by 3000 m steeplechasers and half marathoners. In contrast, recent world record marathon performances have been characterized by even pacing, which emphasizes the differences between championship and meet races at distances longer than 800 m. Studies reviewed suggest further recommendations for athletes. Throughout the whole race, 800 m runners should avoid running wide on the bends. In turn, during major championship events, 1500 m, 5000 m, and 10,000 m runners should try to run close to the inside of the track as much as possible during the decisive stages of the race when the speed is high. Staying within the leading positions during the last lap is recommended to optimize finishing position during 1500 m and 5000 m major championship races. Athletes with more modest aims than winning a medal at major championships are advised to adopt a realistic pace during the initial stages of long-distance races and stay within a pack of runners. Coaches of elite athletes should take into account the observed difference in pacing profiles adopted in meet races vs. those used in championship races: fast times achieved during races with the help of one or more pacemakers are not necessarily replicated in winner-takes-all championship races, where pace varies substantially. Although existing studies examining pacing characteristics in elite runners through an observational approach provide highly ecologically valid performance data, they provide little information regarding the underpinning mechanisms that explain the behaviors shown. Therefore, further research is needed in order to make a meaningful impact on the discipline. Researchers should design and conduct interventions that enable athletes to carefully choose strategies that are not influenced by poor decisions made by other competitors, allowing these athletes to develop more optimal and successful behaviors.

Determinants of Performance in Paced and Maximal 800-m Running Time Trials

PURPOSE

We aimed to identify the underpinning physiological and speed/mechanical determinants of different types of 800-m running time trials (i.e., with a positive or negative pacing strategy) and key components within each 800-m time trial (i.e., first and final 200-m).

METHODS

Twenty trained male 800-m runners (800-m personal best time (min:s): 1:55.10 ± 0:04.44) completed a maximal 800-m time trial (800MAX) and one pacing trial, whereby runners were paced for the first lap and speed was reduced by 7.5% (800PACE) relative to 800MAX, while the last lap was completed in the fastest time possible. Anaerobic speed reserve, running economy, the velocity corresponding with VO2peak (VVO2peak), maximal sprint speed (MAXSS), maximal accumulated oxygen deficit and sprint force-velocity-power profiles were derived from laboratory and field testing. Carnosine content was quantified by proton magnetic resonance spectroscopy in the gastrocnemius and soleus and expressed as a carnosine aggregate Z-score (CAZ-score) to estimate muscle typology. Data were analysed using multiple stepwise regression analysis.

RESULTS

MAXSS and vVO2peak largely explained the variation in 800MAX time (r2 = 0.570; P = 0.020), while MAXSS was the best explanatory variable for the first 200-m time in 800MAX (adjusted r2 = 0.661, P < 0.001). Runners with a higher CAZ-score (i.e., higher estimated percentage of type II fibres) reduced their last lap time to a greater extent in 800PACE relative to 800MAX (adjusted r2 = 0.413, P < 0.001), while better maintenance of mechanical effectiveness during sprinting, a higher CAZ-score and vVO2peak was associated with a faster final 200-m time during 800PACE (adjusted r2 = 0.761, P = 0.001).

CONCLUSION

These findings highlight that diversity in the physiological and speed/mechanical characteristics of male middle-distance runners may be associated with their suitability for different 800-m racing strategies in order to have the best chance of winning.

Pace and motor control optimization for a runner

We present a model which encompasses pace optimization and motor control effort for a runner on a fixed distance. We see that for long races, the long term behaviour is well approximated by a turnpike problem, that allows to define an approximate optimal velocity. We provide numerical simulations quite consistent with this approximation which leads to a simplified problem. The advantage of this simplified formulation for the velocity is that if we have velocity data of a runner on a race, and have access to his [Formula: see text], then we can infer the values of all the physiological parameters. We are also able to estimate the effect of slopes and ramps.

Effects of Isomaltulose Ingestion on Thermoregulatory Responses during Exercise in a Hot Environment

Isomaltulose is a low glycemic and insulinemic carbohydrate available as a constituent of sports drinks. However, it remains unclear whether thermoregulatory responses (sweating and cutaneous vasodilation) after isomaltulose drink ingestion differ from those of sucrose and water during exercise in a hot environment. Ten young healthy males consumed 10% sucrose, 10% isomaltulose, or water drinks. Thirty-five minutes after ingestion, they cycled for fifteen minutes at 75% peak oxygen uptake in a hot environment (30 °C, 40% relative humidity). Sucrose ingestion induced greater blood glucose concentration and insulin secretion at the pre-exercise state, compared with isomaltulose and/or water trials, with no differences during exercise in blood glucose. Change in plasma volume did not differ between the three trials throughout the experiment, but both sucrose and isomaltulose ingestions similarly increased plasma osmolality, as compared with water (main beverage effect, p = 0.040)-a key response that potentially delays the onset of heat loss responses. However, core temperature thresholds and slopes for heat loss responses were not different between the trials during exercise. These results suggest that ingestion of isomaltulose beverages induces low glycemic and insulinemic states before exercise but does not alter thermoregulatory responses during exercise in a hot environment, compared with sucrose or water.

The interaction of competition and terrain on pacing during cross-county running races in junior athletes

The aim of this study was to assess pacing during cross-country racing in relation to race length, surface-terrain and racing dynamics. Athletes (n = 148) competed for school teams (U15, U17, U20) over 2.9, 4.2, 6.3 km, respectively. The course lap (2.067 m) was timed using UHF-RFID and divided into six sections based on surface-terrain and length (549, 619, 207, 338, 150, 156m). Overall pace differed between races (4.56 ± 0.30, 4.42 ± 0.38, 4.40 ± 0.31 m∙s^-1, p=0.042) but not for the top-20 of each race (p=0.174). Moreover, within lap pacing followed a repeated reverse-"J"-parabolic strategy for all races. Race length (no. of laps) and category affected pacing with regards to finish position banding (1st-10th, 11th-20th) where effect size differences showed large to extremely large (1.21 - >4.00) difference between top-10 and other bands. Exceptions to this included the finish sprint where it was typically trivial-moderate (<0.2-1.2) and in key areas where social facilitation occurred. Athlete/coaches need to be aware of between and within lap variations in pace/effort, the effects of race dynamics, and spectator influences, while all athletes need to be educated as part of their training to run their own optimal strategy for best performance.

Pacing behaviour of middle-long distance running & race-walking athletes at the IAAF U18 and U20 World Championship finals

ABSTRACTThe current study analysed the pacing behaviour of athletes competing in the middle-long track event finals of the IAAF Under 18 and Under 20 World Championships between 2015 and 2018. Official finishing times, 1000-m split times and positioning data of 116 female and 153 male athletes, competing in the middle-long distance running (3000 m, 5000 m and 10,000 m) and race walking (5000 m and 10,000 m) events, were gathered. Repeated measures analysis of variance, with 1000-m speed as within-subjects factor and final ranking (medallist, Top 8 or Top 12, rest of the field) as between-subjects factor, was performed to compare the pacing behaviour between athletes. Positioning of the athletes was analysed by Kendall tau-b (T_b) correlation between the intermediate position and final position. Overall, medallists increased their speed throughout a race, with the exception of the 5000 m running event, in which a parabolic pacing behaviour was exhibited. The 1000-m segment in which a significant (P > 0.05) difference in speed was exhibited between differently ranked athletes coincided with a strong (T_b> 0.7) correlation between intermediate and final positioning. These combined results point towards a separation between the athletes during the race, as the Top 8 or Top 12 and the rest of the field are unable to match the speed of the medallists. The distance, discipline, sex, age category and behaviour of competitors all influence the pacing behaviour of young track athletes during international level competition, emphasising the importance and complexity of developing adequate pacing behaviour in track athletes. HighlightsThe distribution of effort over an exercise task (i.e. pacing) is an important factor in endurance exercise competition. The pacing behaviour of athletes is determined by many aspects, such as the biomechanical characteristics of the task, the exercise environment and the age of the athleteSport specific competition data from youth athlete sports events can provide insight into the pacing behaviour of developing athletes. These insights could assist in the optimalisation of the development of pacing behaviour of young athletes in the future.The current study analysed the 1000-m split times and positioning data of athletes competing in the running (3000 m, 5000 m and 10,000) and racewalking (5000 m and 10,000m) finals of the U18 and U20 IAAF world-championship.The difference in final ranking impacted the pacing behaviour of the athletes, with the medallists increasing their speed throughout the race and the other athletes separating from the medallists either early (rest of field) or later in the race (Top 8 or Top 12 athletes).

Differentiating Endurance-and Speed-Adapted Types of Elite and World Class Milers According to Biomechanical, Pacing and Perceptual Responses during a Sprint Interval Session

The aim was to compare pacing, biomechanical and perceptual responses between elite speed-and endurance-adapted milers during a sprint interval training session (SIT). Twenty elite and world-class middle-distance runners (male: n = 16, female: n = 4; 24.95 ± 5.18 years; 60.89 ± 7 kg) were classified as either speed- or endurance-adapted milers according to their recent performances at 800 m or longer races than 1500 m (10 subjects per group). Participants performed 10 repetitions of 100 m sprints with 2 min of active recovery between each, and performance, perceptual and biomechanical responses were collected. The difference between accumulated times of the last and the first five repetitions was higher in speed-adapted milers (ES = 1.07) displaying a more positive pacing strategy. A higher coefficient of variation (CV%) was displayed across the session by speed-adapted milers in average repetition time, contact time, and affective valence (ES ≥ 1.15). Speed-adapted milers experienced lower rates of valence after the 4th repetition excepting at the 8th repetition (ES ≥ 0.99). Speed-adapted milers may need to display a more positive pacing profile than endurance-adapted milers and, therefore, would experience lower levels of affective valence and a more rapid increase of ground contact time during a SIT.

A Model for World-Class 10,000 m Running Performances: Strategy and Optimization

The distribution of energetic resources in world-class distance running is a key aspect of performance, with athletes relying on aerobic and anaerobic metabolism to greater extents during different parts of the race. The purpose of this study is to model 10,000 m championship performances to enable a deeper understanding of the factors affecting running speed and, given that more than half the race is run on curves, to establish the effect of the bends on performance. Because a limitation of time split data is that they are typically averaged over 100-m or 1,000-m segments, we simulate two 10,000 m runners' performances and thus get access to their instantaneous speed, propulsive force and anaerobic energy. The numerical simulations provide information on the factors that affect performance, and we precisely see the effect of parameters that influence race strategy, fatigue, and the ability to speed up and deal with bends. In particular, a lower anaerobic capacity leads to an inability to accelerate at the end of the race, and which can accrue because of a reliance on anaerobic energy to maintain pace in an athlete of inferior running economy. We also see that a runner with a worse running economy is less able to speed up on the straights and that, in general, the bends are run slower than the straights, most likely because bend running at the same pace would increase energy expenditure. Notwithstanding a recommendation for adopting the accepted practices of improving aerobic and anaerobic metabolism through appropriate training methods, coaches are advised to note that athletes who avoid mid-race surges can improve their endspurt, which are the differentiating element in closely contested championship races.

Effect of marathon characteristics and runners' time category on pacing profile

This study aimed to analyse differences in pacing profiles in four marathon competitions and to explore that pacing per time category. A database of 91,493 runners gathered from 4 different races was analysed (Valencia, Chicago, London and Tokyo Marathon). Participants were categorized in accordance with their completion time. The relative speed of each section for each runner was calculated as a percentage of the average speed for the entire race. In the four marathons studied, the first 5 km differed widely, presenting London the highest relative speeds (5 km: CI95% London vs. Valencia [12.1, 13.6%], p < 0.001 and ES = 2.1; London vs. Chicago [5.5, 7.1%], p < 0.001 and ES = 1.1; London vs. Tokyo [15.2, 16.8%], p < 0.001 and ES = 2.3). Races did not differ at each section for high-performance runners (sub-2:30), but differences between races increased as the time category increases (e.g. 35 km and sub-3:00: CI95% London vs. Tokyo [-3.1, -1.8%], p < 0.001 and ES = 0.7; 35 km and sub-5:00: London vs. Tokyo [-9.8, -9.2%], p < 0.001 and ES = 1.3). The difference in relative speed between the first and second half of the marathon was higher in London than in the other marathons (e.g. CI95% London vs. Valencia [10.3, 10.8%], p < 0.001 and ES = 1.3). In conclusion, although race characteristics affect pacing, this effect was higher as the category time increases. Race pacing characteristics should be taken into consideration for runners and coaches choosing the race and working on pacing strategies, for researches to extrapolate or interpret results, or for race organizations to improve its pacing characteristics.Highlights The first 5 km differed widely on pacing profiles between the four marathons assessed.London had the highest relative speeds in the first 5 km.Race characteristics affect pacing, but this effect was higher as the category time increases.The difference in relative speed between the first and second half of the marathon was higher in London than in the other marathons.

Men's and Women's World Championship Marathon Performances and Changes With Fatigue Are Not Explained by Kinematic Differences Between Footstrike Patterns

World-class marathon runners make initial contact with the rearfoot, midfoot or forefoot. This novel study analyzed kinematic similarities and differences between rearfoot and non-rearfoot strikers within the men's and women's 2017 IAAF World Championship marathons across the last two laps. Twenty-eight men and 28 women, equally divided by footstrike pattern, were recorded at 29.5 and 40 km (laps 3 and 4, respectively) using two high-definition cameras (50 Hz). The videos were digitized to derive spatiotemporal and joint kinematic data, with additional footage (120 Hz) used to identify footstrike patterns. There was no difference in running speed, step length or cadence between rearfoot and non-rearfoot strikers during either lap in both races, and these three key variables decreased in athletes of either footstrike pattern to a similar extent between laps. Men slowed more than women between laps, and overall had greater reductions in step length and cadence. Rearfoot strikers landed with their foot farther in front of the center of mass (by 0.02-0.04 m), with non-rearfoot strikers relying more on flight distance for overall step length. Male rearfoot strikers had more extended knees, dorsiflexed ankles and hyperextended shoulders at initial contact than non-rearfoot strikers, whereas female rearfoot strikers had more flexed hips and extended knees at initial contact. Very few differences were found at midstance and toe-off. Rearfoot and non-rearfoot striking techniques were therefore mostly indistinguishable except at initial contact, and any differences that did occur were very small. The spatiotemporal variables that differed between footstrike patterns were not associated with faster running speeds and, ultimately, neither footstrike pattern prevented reductions in running speeds. The only joint angle measured at a specific gait event to change with fatigue was midswing knee flexion angle in men. Coaches should thus note that encouraging marathon runners to convert from rearfoot to non-rearfoot striking is unlikely to provide any performance benefits, and that training the fatigue resistance of key lower limb muscle-tendon units to avoid decreases in step length and cadence are more useful in preventing reductions in speed during the later stages of the race.

Increases in speed do not change gait symmetry or variability in world-class race walkers

The aim of this study was to analyse changes in gait variability and symmetry with increasing speed in race walkers. Eighteen international athletes race walked on an instrumented treadmill at speeds of 11, 12, 13 and 14 km·h^-1 in a randomised order for 3 min each. Spatiotemporal and ground reaction force data were recorded for 30 s at each speed. Gait variability was measured using median absolute deviation and inter-leg symmetry was measured using the symmetry angle. There was an overall effect of speed on all absolute values except push-off force, but symmetry and variability (except flight time) did not change with increased speed, step length and step frequency. Most athletes were asymmetrical for at least one variable, but none was asymmetrical for more than half of the variables measured. Therefore, being asymmetrical or having higher variability (<5%) in a few variables is normal. Taking all findings together, practitioners should exercise caution when deciding on the need for corrective interventions and should not be concerned that increasing gait speed could increase injury risk through changes to athletes' asymmetry. Race walking coaches should test at competition speeds to ensure that flight times, and any variability or asymmetry, are measured appropriately.

Successful Pacing Profiles of Olympic Men and Women 3,000 m Steeplechasers

The presence of barriers in the steeplechase increases energy cost and makes successful pacing more difficult. This was the first study to analyze pacing profiles of successful (qualifiers for the final/Top 8 finalists) and unsuccessful (non-qualifiers/non-Top 8 finalists) Olympic steeplechasers across heats and finals, and to analyze differences between race sections (e.g., water jump vs. home straight). Finishing and section splits were collected for 77 men and 84 women competing at the 2008 and 2016 Olympic Games. Competitors were divided into groups based on finishing position (in both rounds analyzed). After a quick opening 228 m (no barriers), men who qualified for the final or finished in the Top 8 in the final had even paces for the first half with successive increases in speed in the last three laps; unsuccessful pacing profiles were more even. Successful women had mostly even paces for the whole race, and less successful athletes slowed after Lap 2. Women started the race relatively quicker than men, resulting in slower second half speeds. The best men completed most race sections at the same speed, but less successful men were slower during the water jump section, suggesting less technically proficiency. Similarly, women were slower during this section, possibly because its landing dimensions are the same as for men and have a greater effect on running speed. Coaches should note the different pacing profiles adopted by successful men and women steeplechasers, and the importance of technical hurdling skills at the water jump.

How to build a new athletic track to break records

We introduce a new optimal control model which encompasses pace optimization and motor control effort for a runner on a fixed distance. The system couples mechanics, energetics, neural drive to an economic decision theory of cost and benefit. We find how effort is minimized to produce the best running strategy, in particular, in the bend. This allows us to discriminate between different types of tracks and estimate the discrepancy between lanes. Relating this model to the optimal path problem called the Dubins path, we are able to determine the geometry of the optimal track and estimate record times.

Optimal Development of Youth Athletes Toward Elite Athletic Performance: How to Coach Their Motivation, Plan Exercise Training, and Pace the Race

Elite athletes have invested many years in training and competition to reach the elite level. One very important factor on the road to elite performance is the decision-making process regarding the regulation of effort over time, termed as pacing behavior. The regulation of effort is vital for optimal athletic performance during a single race and over a longer period of time (e.g., a competitive season) as an inadequate regulation could result in a higher risk of injuries, overtraining, and drop-out. Despite this, there is limited knowledge on how young athletes learn and develop the abilities related to pacing. Pacing behavior of athletes develops from childhood throughout adolescence and is thought to be closely connected to physical maturation, the development of pre-frontal cortical related (meta-) cognitive functions, as well as the gathering of experience with exercise tasks. The motivation of an athlete can critically influence how an athlete paces a single race, but also how they distribute their effort over a longer period of time. Coaches are advised to closely monitor the development of pacing behavior during adolescence (e.g., by gathering split times, and related physiological measurement, during training and competition), as well as the underlying factors including physical maturation (meta-) cognitive development and the motivation of young athletes. Furthermore, pacing behavior development could be aided by providing training in which the task, individual, and environment are manipulated. Hereby, presenting athletes with the opportunity to gain experience in situations which closely resemble the perceptual-motor conditions of upcoming competitions.