Search

Abstract Ultramarathon running is of increasing popularity, where the time-limited 24-hour run is one of the most popular events. Although we have a high scientific knowledge about different topics for this specific race format, we do not know where the best 24-hour runners originate from and where the fastest races are held. The purpose of the present study was to investigate the origin of these runners and the fastest race locations. A machine learning model based on the XG Boost algorithm was built to predict running speed based on the athlete´s age, gender, country of origin and the country where the race takes place. Model explainability tools were used to investigate how each independent variable would influence the predicted running speed. A sample of 171,358 race records from 63,514 unique runners from 73 countries participating in 24-hour races held in 57 countries between 1807 and 2022 was analyzed. Most of the athletes originated from the USA, France, Germany, Great Britain, Italy, Japan, Russia, Australia, Austria, and Canada. Tunisian athletes achieved the fastest average running speed, followed by runners from Russia, Latvia, Lithuania, Island, Croatia, Slovenia, and Israel. Regarding the country of the event, the ranking looks quite similar to the participation by the athlete, suggesting a high correlation between the country of origin and the country of the event. The fastest 24-hour races are recorded in Israel, Romania, Korea, the Netherlands, Russia, and Taiwan. On average, men were 0.4 km/h faster than women, and the fastest runners belonged to age groups 35–39, 40–44, and 45–49 years. In summary, the 24-hour race format is spread over the world, and the fastest athletes mainly originate from Eastern Europe, while the fastest races were organized in European and Asian countries.

Abstract The majority of participants in Ironman triathlon races are age group athletes. We have extensive knowledge about recreational athletes’ training and competition participation. Nonetheless, Ironman age group triathletes must achieve fast race times to qualify for the Ironman World Championship in Hawaii. They can, therefore, benefit from knowing where the fastest Ironman racecourses in the world are. The aim of the present study was to investigate where the fastest Ironman racecourses for age group triathletes are located in the world. Data from 677,702 Ironman age group finishers’ records (544,963 from men and 132,739 from women) originating from 228 countries and participating in 444 events across 66 different Ironman race locations between 2002 and 2022 were analyzed. Data was analyzed through traditional descriptive statistics and with machine learning regression models. Four algorithms were tested (Random Forest Regressor, XG Boost Regressor, Cat Boot Regressor, and Decision Tree Regressor). The models used gender, age group, country of origin, environmental factors (average air and water temperatures), and the event location as independent variables to predict the final overall race time. Despite the majority of successful Ironman age group triathletes originating from the USA (274,553), followed by athletes from the United Kingdom (55,410) and Canada (38,264), these countries exhibited average overall race times that were significantly slower compared to the fastest countries. Most of the triathletes competed in Ironman Wisconsin (38,545), followed by Ironman Florida (38,157) and Ironman Lake Placid (34,341). The fastest overall race times were achieved in Ironman Copenhagen (11.68 ± 1.38 h), followed by Ironman Hawaii (11.72 ± 1.86 h), Ironman Barcelona (11.78 ± 1.43 h), Ironman Florianópolis (11.80 ± 1.52 h), Ironman Frankfurt (12.03 ± 1.38 h) and Ironman Kalmar (12.08 ± 1.47 h). The fastest athletes originated from Belgium (11.48 ± 1.47 h), followed by athletes from Denmark (11.59 ± 1.40 h), Switzerland (11.62 ± 1.49 h), Austria (11.68 ± 1.50), Finland (11.68 ± 1.40 h) and Germany (11.74 ± 15.1 h). Flat running and cycling courses were associated with faster overall race times. Three of the predictive models identified the ‘country’ and ‘age group’ variables as the most important predictors. Environmental characteristics showed the lowest influence regarding the other variables. The origin of the athlete was the most predictive variable whereas environmental characteristics showed the lowest influence. Flat cycling and flat running courses were associated with faster overall race times. The fastest overall race times were achieved mainly in European races such as Ironman Copenhagen, Ironman Hawaii, Ironman Barcelona, Ironman Florianópolis, Ironman Frankfurt and Ironman Kalmar. The fastest triathletes originated from European countries such as Belgium, Denmark, Switzerland, Austria, Finland, and Germany.

Purpose The IRONMAN® triathlon is an endurance multisport discipline of high popularity. Professional IRONMAN® triathletes need to qualify for the IRONMAN® World Championships and, therefore, would benefit from identifying the fastest race courses. Our purpose was to identify the fastest races held for professional IRONMAN® triathletes competing from 2002 to 2022. Methods This was an observational study, sampling 7,078 race records (380 different events in 55 different event locations) of professional IRONMAN® triathletes of both sexes (4,235 males and 2,843 females). We downloaded information about sex, nationality, both split (swimming, cycling, and running) and overall race times, the event location, and the year. Information about race course characteristics, water temperatures, and air temperatures was obtained. Descriptive statistics were calculated for each event location, and factorial ANOVA tests were used to explore the statistical significance of the results. A confidence interval of 95% was adopted. Results The fastest overall IRONMAN® average race times were achieved during the IRONMAN® Tallinn, IRONMAN® Switzerland held in Thun, and IRONMAN® Des Moines. A lake for the swim split characterized the first five courses, while the bike split featured rolling or hilly terrain. For the run split, most of the courses were characterized as flat. For environmental characteristics, lower water and air temperatures also added time to the average finish time. Conclusions Understanding the fastest race courses and their characteristics would assist professional IRONMAN® triathletes in selecting races that offer the optimal conditions for their competition. Fast IRONMAN® race courses typically feature a lakeside swim course, a rolling or hilly bike course, and a flat run course.

Abstract The 50-km ultra-marathon is a popular race distance, slightly longer than the classic marathon distance. However, little is known about the country of affiliation and age of the fastest 50-km ultra-marathon runners and where the fastest races are typically held. Therefore, this study aimed to investigate a large dataset of race records for the 50-km distance race to identify the country of affiliation and the age of the fastest runners as well as the locations of the fastest races. A total of 1,398,845 50-km race records (men, n = 1,026,546; women, n = 372,299) were analyzed using both descriptive statistics and advanced regression techniques. This study revealed significant trends in the performance of 50-km ultra-marathoners. The fastest 50-km runners came from African countries, while the fastest races were found to occur in Europe and the Middle East. Runners from Ethiopia, Lesotho, Malawi, and Kenya were the fastest in this race distance. The fastest 50-km racecourses, providing ideal conditions for faster race times, are in Europe (Luxembourg, Belarus, and Lithuania) and the Middle East (Qatar and Jordan). Surprisingly, the fastest ultra-marathoners in the 50-km distance were found to fall into the age group of 20–24 years, challenging the conventional belief that peak ultra-marathon performance comes in older age groups. These findings contribute to a better understanding of the performance models in 50-km ultra-marathons and can serve as valuable insights for runners, coaches, and race organizers in optimizing training strategies and racecourse selection.

This study aimed to investigate the trends of finishers in the São Silvestre race in Brazil, taking into account sex, age, and performance levels. A total of 31 ​775 runners (women, n ​= ​13 ​847; men, n ​= ​17 ​928), aged (45.2 ​± ​16.8) years, finishers in the São Silvestre race between 2007 and 2021, were considered in the present analysis. Data (event year, date of birth, sex, and race times) were downloaded from the official race website. The man-Whitney U test, Spearman correlation, and robust regression model were computed. Participation increased over time for both sexes. Regarding age groups, “31–40 years” (women) and “> 60 years” (men) were those with the highest number of finishers. We found a decrease in performance across the years (β ​= ​2.45; p ​

Abstract Pacing during cycling and running in an IRONMAN triathlon has been investigated in only one study with elite IRONMAN triathletes. We have, however, no knowledge of how professional triathletes pace during an IRONMAN World Championship. To investigate the split-by-split speed, pacing strategies and pacing variability in professional female and male IRONMAN World Championship participants in the fastest IRONMAN World Championship ever in IRONMAN Hawaii 2022. For both cycling and running, 25 specific split times were recorded in each discipline. The best 30 men and 30 women overall were chosen from the official IRONMAN website database for further analysis. They were divided into three performance groups: Top 10, 11–20th place, and 21st–30th place. Mean speed, individual linear regressions with the corresponding correlation coefficients, and coefficient of variation were calculated to assess split-by-split speed, pacing strategies, and pacing variability, respectively. In both men’s and women’s cycling and running segments, the top ten participants exhibited faster split times compared to the slower performance groups. Notably, no discernible differences existed between the 11–20th and 21st–30th place in men’s cycling and women’s running times. Conversely, in men’s running and women’s cycling segments, those in the 11–20th place displayed quicker times than those in the 21st–30th place. In the cycling segment across all groups, men demonstrated a more negative pacing pattern (indicating an increase in speed), whereas women exhibited more consistent pacing. In the running segment, the top 10 men and all women’s groups showcased relatively similar slightly positive pacing profiles. However, men ranking 11–20th and 21st–30th displayed more pronounced positive pacing strategies, implying a more significant decline in speed over time. In terms of cycling, the variability in pacing remained relatively consistent across the three performance groups. Conversely, during the running segment, the top ten male triathletes and those in the 11–20th place displayed lower pacing variability than their counterparts in the 21st–30th position place and all women’s groups. In summary, performance and pacing were examined in professional male and female IRONMAN World Championship participants during IRONMAN Hawaii 2022. Top performers showed faster cycling and running split times, with differences in pacing strategies between sexes. The pacing was more consistent in cycling, while running pacing varied more, particularly among male triathletes in different performance groups.

Abstract Background The sex difference in the three split disciplines (swimming, cycling, and running) and overall race times in triathlon races has mainly been investigated for the Olympic distance and IRONMAN® triathlon formats, but not for the half IRONMAN® distance, i.e., the IRONMAN® 70.3. The aim of the present study was to investigate the sex differences in IRONMAN® 70.3 by age group in 5-year intervals for the split disciplines of this race. Data from 823,459 records (625,393 males and 198,066 females) of all age group finishers (in 5-year intervals) competing in all official IRONMAN® 70.3 races held worldwide between 2004 and 2020 were analyzed, and sex differences by age group and split disciplines were evaluated. Results Males were faster than females in all split disciplines and all age groups. The sex difference was lower in swimming than in cycling and running and less pronounced for triathletes between 20 and 50 years of age. After the age of 60 years, females were able to reduce the sex difference to males in swimming and cycling, but not in running, where the reduction in the sex difference started after the age of 70 years. The lowest sex difference was in the age group 75 + years for swimming and cycling and in the age group 30–34 years for running. Across age groups, the sex difference was U-shaped in swimming and running, with an increase after 18–24 years in swimming and after 40–44 years in running. In contrast, the sex difference decreased continuously with the increasing age for cycling. Conclusions In conclusion, the study found that the sex difference in performance decreases with age in the IRONMAN® 70.3 race distance. However, females did not outperform males at older ages. Notably, sex differences were observed across different disciplines, with swimming displaying lower differences compared to cycling and running. These findings underscore the complex interplay between age, sex, and performance in endurance sports, emphasizing the need for additional research to understand the factors influencing these differences.

Abstract Knowing which discipline contributes most to a triathlon performance is important to plan race pacing properly. To date, we know that the running split is the most decisive discipline in the Olympic distance triathlon, and the cycling split is the most important discipline in the full-distance Ironman® triathlon. However, we have no knowledge of the Ironman® 70.3. This study intended to determine the most crucial discipline in age group athletes competing from 2004 to 2020 in a total of 787 Ironman® 70.3 races. A total of 823,459 athletes (198,066 women and 625,393 men) from 240 different countries were analyzed and recorded in 5-year age groups, from 18 to 75 + years. Correlation analysis, multiple linear regression, and two-way ANOVA were applied, considering p

Background: For Ironman® triathlon, it has been reported that most of the fnishers and the fastest women and men in Ironman® Hawaii originated from the United States of America (USA). We have, however, no knowledge of where the fastest race courses in the Ironman® 70.3 triathlon took place. We aim to analyse where the Ironman® 70.3 races were held and where the fastest split and overall race times were achieved. Methods: The athletes’ sex, age group, country of origin, and split times for swimming, running, cycling, and transitioning were obtained from the official Ironman® website. To investigate the locations of the fastest Ironman® 70.3 competitions between 2004 and 2020, a full sample of 852,721 qualifying records throughout 197 different event locations was processed. These race records were aggregated by location, and each location’s split and full finish times were calculated. Data analysis was performed first for the full sample (all race records), and then for an elite sub-sample consisting of the top 100 males and top 100 females records in each location. Results: For the full sample, the fastest overall race times were achieved in Ironman® 70.3 Zell am See (Austria). For the top 100 athletes sub-sample, the Ironman® 70.3 European Championship Elsinore and Ironman® 70.3 World Championship were the fastest courses. Conclusion: These results are useful for athletes’ strategic planning and inform event organisers about the strengths of different courses, aiding in the optimisation and promotion of future Ironman® 70.3 races worldwide. Keywords: endurance, performance, half-distance Ironman®, multi-sport, running.

IntroductionOur study examined 16,611 records of professional triathletes from 163 Ironman® 70.3 races across 97 countries (2004-2020). The aim was to identify the most predictive discipline—swim, bike, or run—for overall race time.MethodsWe used correlation matrices to compare the dependent variable “finish time” with independent variables “swim time,” “bike time,” and “run time.” This analysis was conducted separately for male and female athletes. Additionally, univariate and multiple linear regression models assessed the strength of these associations.ResultsThe results indicated that “bike time” had the strongest correlation with finish time (0.85), followed by “run time” (0.75 for females, 0.82 for males) and “swim time” (0.46 for females, 0.63 for males). Regression models confirmed “bike time” as the strongest predictor of overall race time (R² = 0.8), with “run time” and “swim time” being less predictive.DiscussionThe study concludes that in Ironman 70.3 races, “bike time” is the most significant predictor of overall race performance for both sexes, suggesting a focus on cycling in training and competition strategies. It also highlights a smaller performance gap between genders in swimming than in cycling or running.

The present study intended to determine the nationality of the fastest 100-mile ultra-marathoners and the country/events where the fastest 100-mile races are held. A machine learning model based on the XG Boost algorithm was built to predict the running speed from the athlete's age (Age group), gender (Gender), country of origin (Athlete country) and where the race occurred (Event country). Model explainability tools were then used to investigate how each independent variable influenced the predicted running speed. A total of 172,110 race records from 65,392 unique runners from 68 different countries participating in races held in 44 different countries were used for analyses. The model rates Event country (0.53) as the most important predictor (based on data entropy reduction), followed by Athlete country (0.21), Age group (0.14), and Gender (0.13). In terms of participation, the United States leads by far, followed by Great Britain, Canada, South Africa, and Japan, in both athlete and event counts. The fastest 100-mile races are held in Romania, Israel, Switzerland, Finland, Russia, the Netherlands, France, Denmark, Czechia, and Taiwan. The fastest athletes come mostly from Eastern European countries (Lithuania, Latvia, Ukraine, Finland, Russia, Hungary, Slovakia) and also Israel. In contrast, the slowest athletes come from Asian countries like China, Thailand, Vietnam, Indonesia, Malaysia, and Brunei. The difference among male and female predictions is relatively small at about 0.25 km/h. The fastest age group is 25-29 years, but the average speeds of groups 20-24 and 30-34 years are close. Participation, however, peaks for the age group 40-44 years. The model predicts the event location (country of event) as the most important predictor for a fast 100-mile race time. The fastest race courses were occurred in Romania, Israel, Switzerland, Finland, Russia, the Netherlands, France, Denmark, Czechia, and Taiwan. Athletes and coaches can use these findings for their race preparation to find the most appropriate racecourse for a fast 100-mile race time.

IntroductionMany studies indicate a considerable impact of optimal pacing on long-distance running performance. Given that the amount of carbohydrates in metabolic processes increases supralinearly with the running intensity, we may observe differences between the pacing strategies of two long-distance races and different performance levels of runners. Accordingly, the present study aimed to examine the differences in pacing strategies between marathon and half-marathon races regarding the performance levels of runners.MethodsThe official results and split times from a total of 208,760 (marathon, N = 75,492; half-marathon, N = 133,268) finishers in the “Vienna City Marathon” between 2006 and 2018 were analyzed. The percentage of the average change of speed for each of the five segments (CS 1–5), as well as the absolute change of speed (ACS) were calculated. The CS 1–5 for the marathon are as follows: up to the 10th km, 10th – 20th km, 20th – 30th km, 30th – 40th km, and from the 40th km to the 42.195 km. For the half-marathon, the CS 1–5 are half of the marathon values. Four performance groups were created as quartiles of placement separately for sexes and races: high-level (HL), moderate to high-level (MHL), moderate to low-level (MLL), and low-level (LL).ResultsPositive pacing strategies (i.e., decrease of speed) were observed in all performance groups of both sex and race. Across CS 1–5, significant main effects (p

Abstract The purposes of this study were (i) to describe differences in participation in 100-mile ultra-marathons by continent; (ii) to investigate differences in performance between continents; and (iii) to identify the fastest runners by continent and country. Data from 148,169 athletes (119,408 men), aged 18–81 years, and finishers in a 100-miles ultra-marathon during 1870–2020 were investigated. Information about age, gender, origin, performance level (top three, top 10, top 100) was obtained. Kruskal–Wallis tests and linear regressions were performed. Athletes were mostly from America and Europe. A macro-analysis showed that the fastest men runners were from Africa, while the fastest women runners were from Europe and Africa. Women from Sweden, Hungary and Russia presented the best performances in the top three, top 10 and top 100. Men from Brazil, Russia and Lithuania were the fastest. The lowest performance and participation were observed for runners from Asia. In summary, in 100-miles ultra-marathon running, the majority of athletes were from America, but for both sexes and performance levels, the fastest runners were from Africa. On a country level, the fastest women were from Sweden, Hungary and Russia, while the fastest men were from Brazil, Russia and Lithuania.

Abstract It is well known that weather and pacing have an influence on elite marathon performance. However, there is limited knowledge about the effect of weather on running speed in elite marathoners. The aim of the present cross-sectional study was to investigate potential associations between running speed and weather variables in elite runners competing in the ‘New York City Marathon’ between 1999 and 2019. Data from all official female and male finishers with name, sex, age, calendar year, split times at 5 km, 10 km, 15 km, 20 km, 25 km, 30 km, 35 km, 40 km and finish and hourly values for temperature (°Celsius), barometric pressure (hPa), humidity (%) and sunshine duration (min) between 09:00 a.m. and 04:00 p.m. were obtained from official websites. A total of 560,731 marathon runners' records were available for analysis (342,799 men and 217,932 women). Pearson and Spearman correlation analyses were performed between the average running speed and the weather variables (temperature, pressure, humidity and sunshine). Ordinary Least Squares (OLS) regressions were also performed. The runner´s records were classified into four performance groups (all runners, top 100, top 10 and top 3) for comparison. Differences in running speed between the four performance groups were statistically significant (p

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.

Background: The two aspects of the influence of environmental conditions on marathon running performance and pacing during a marathon have been separately and widely investigated. The influence of environmental conditions on the pacing of age group marathoners has, however, not been considered yet.Objective: The aim of the present study was to investigate the association between environmental conditions (i.e., temperature, barometric pressure, humidity, precipitation, sunshine, and cloud cover), gender and pacing of age group marathoners in the “New York City Marathon”.Methodology: Between 1999 and 2019, a total of 830,255 finishes (526,500 males and 303,755 females) were recorded. Time-adjusted averages of weather conditions for temperature, barometric pressure, humidity, and sunshine duration during the race were correlated with running speed in 5 km-intervals for age group runners in 10 years-intervals.Results: The running speed decreased with increasing temperatures in athletes of age groups 20–59 with a pronounced negative effect for men aged 30–64 years and women aged 40–64 years. Higher levels of humidity were associated with faster running speeds for both sexes. Sunshine duration and barometric pressure showed no association with running speed.Conclusion: In summary, temperature and humidity affect pacing in age group marathoners differently. Specifically, increasing temperature slowed down runners of both sexes aged between 20 and 59 years, whereas increasing humidity slowed down runners of 80 years old.

The influence of environmental conditions has been investigated for different marathon races, but not for the Berlin Marathon, the fastest marathon race course in the world. The aim of this study was to investigate the potential influence of environmental conditions such as temperature, precipitation, sunshine, and atmospheric pressure on marathon race times in the Berlin Marathon since its first event in 1974–2019. A total of n = 882,540 valid finisher records were available for analysis, of which 724,135 correspond to male and 158,405 to female runners. We performed analyses regarding performance levels considering all finishers, the top 3, the top 10, and the top 100 women and men. Within the 46 years of Berlin marathons under study, there was some level of precipitation for 18 years, and 28 years without any rain. Sunshine was predominant in 25 of the events, whilst in the other 21, cloud cover was predominant. There was no significant trend with time in any of the weather variables (e.g., no increase in temperature across the years). Overall runners became slower with increasing temperature and sunshine duration, however, elite runners (i.e., top 3 and top 10) seemed to run faster and improved their race times when the temperature increased (with women improving more than men). Top 10 women seemed to benefit more from increasing temperatures than top 10 males, and male top 100 runners seemed to benefit more from increasing temperatures than female top 100 runners. In the top three sub-group, no differences were observed between male and female correlations. In summary, in marathoners competing in the Berlin Marathon between 1974 and 2019, increasing temperatures and sunshine duration showed a different effect on different performance levels where overall runners (i.e., the general mass of runners) became slower with increasing temperature and sunshine duration, but elite runners (i.e., top 3, top 10) became faster with increasing temperatures where sex differences exist.

The aspect of participation and performance trends in marathon running has been investigated mainly in marathons held in the United States of America (e.g., “New York City Marathon,” “Boston Marathon”), but not for the fastest course in the world, the “Berlin Marathon” held in Berlin, Germany. This study aimed to examine trends in participation and performance in the “Berlin Marathon” on all its previous 46 editions from 1974 to 2019, the largest dataset ever studied in this event with 696,225 finishers (after data cleaning). Athletes in all age groups increased their participation, except for male athletes aged 20–49 years and athletes of both sexes above 79 years of age. This overall increase in participation was more pronounced in women, but still, there are more men than women participating in “Berlin Marathon” nowadays. All age group athletes decreased their performance across years overall, whereas the top ten recreational athletes improved their performance over the years. Our findings improved the knowledge about the evolution of male and female marathoners across calendar years, especially for the fastest marathon race in the world, the “Berlin Marathon.”

Objective: To analyze the proportion of dedication in each triathlon discipline (swimming, cycling, and running) and the importance of each separate discipline to predict overall performance of elite triathletes across different triathlon distances.Methods: Data from 2015 to 2020 (n = 16,667) from official races and athletes in Sprint, Olympic distance, IM 70.3 (Half-Ironman distance), and IM 140.6 (Full-Ironman distance) competitions were included. The proportion of each discipline was calculated individually and compared using general linear models by event distance, sex, and performance level. Automatic linear regression models were applied for each distance considering overall performance as the dependent variable.Results: A within-distance analysis showed that the best predictor for Sprint is cycling, for Olympic is swimming, for IM 70.3 is cycling, and for IM 140.6 is running. A between-distance analysis revealed that swimming is a better predictor in Olympic distance than in other triathlon distances. Cycling is a poor predictor for overall performance in IM 140.6, and the importance of running to predict overall performance is the highest in IM 140.6 and diminishes with decreasing race distance.Conclusion: Each discipline represents a different relative portion and importance to predict overall performance depending on the triathlon distance. Swimming is the most important predictor discipline in Sprint- and Olympic-distance triathlon, cycling in IM 70.3, and running in IM 140.6.

The effect of different environmental conditions such as temperature, wind, barometric pressure, and precipitation has been well investigated in elite marathoners, but not by age categories (i.e., age group marathoners). The aim of the study was to investigate the potential influence of environmental conditions such as temperature, precipitation, and atmospheric pressure on marathon performance in age group marathoners competing in the ‘Berlin Marathon’ from 1974 to 2019. A total of 869,474 valid finisher records were available for analysis, of which 711,136 correspond to males and 158,338 to females. The influence of temperature, atmospheric pressure, and precipitation on marathon race times was investigated in age group marathoners grouped in 5-year-intervals. Within the 46 years of Berlin marathons under investigation, there was some level of precipitation for 18 years, and 28 years without any rain. Sunshine was predominant in 25 of the events, whilst in the other 21 years, cloud cover was predominant. Marathon race times were significantly and positively correlated with age (i.e., older runners were slower than younger runners) where the correlation was higher for males than for females. Marathon race times were significantly and positively correlated with both the hours of sunshine and the daily maximum temperature. The fastest marathon runners (meaning the minimum times) achieved the fastest race times on race days with higher maximum temperatures (i.e., 15–30°C). Daily maximum temperatures showed an influence on age group marathoners from age group 35–40 years and older. Higher precipitation levels impaired performance across most age groups. In summary, higher daily maximum temperatures (i.e., >15°C) and higher precipitation levels impaired performance of master marathoners (i.e., 35–40 years and older) competing in the ‘Berlin Marathon’ in the last 45 years. Master marathoners should start in marathon races with temperatures < 15°C and no precipitation in order to achieve a fast marathon race time.

Aim: This study investigated the influence of weather conditions on running performance in female and male age group runners in the largest marathon in the world, the “New York City Marathon.”Methods: The analysis included data from 1,280,557 finishers the “New York City Marathon” from the years 1970 to 2019. Linear mixed models for men and women finishers with race time (min) as dependent variable and 5-year age groups, temperature, wind and relative humidity tertiles (low, medium, high) as independent factors and finisher as random intercept was performed. Additional models with an interaction between age groups and one weather variable each were performed.Results: Temperature was positively associated with race time while wind speed and humidity were negatively associated (p < 0.001). Men were significantly greater affected wind speed and humidity than women (p < 0.001 for interaction) but not by temperature (p = 0.17 for interaction). With an average of 8 min longer race time, high temperature had the greatest effect on race time. The effect of high humidity on race time was significantly increased in 40–59 years old men and 25–65 years old women. High temperatures had an increased effect on race time in 30–64 years old men and 40–64 years old women. The inverse association between race time and high wind speed was pronounced in finishers with younger age.Conclusion: Performance was lower on days with high temperature, low humidity and low wind speed. Men seemed to benefit more from higher humidity and wind speed than women. Aged (70 +) finishers were not greater affected by high temperatures.

The “Berlin Marathon” is the fastest marathon racecourse in the world and has witnessed 11 world records (WRs; eight in men and three in women). Weather conditions can have an important impact on race time and we therefore examined the influence of environmental conditions (i.e., temperature, sunshine, precipitation, barometric pressure, and cloud cover) on WRs and elite (i.e., winner, top three and top 10 finishers) marathon performances of men and women at the “Berlin Marathon” between 1974 and 2019. Average world record marathon times in men were 2:03:52 ± 0:01:19 h:min:s and 2:25:05 ± 0:08:25 h:min:s in females (p < 0.05). Male competitions were held 44 times (mean winning time: 2:09:48 ± 0:09:15 h:min:s) and female competitions 41 times (mean winning time: 02:30:35 ± 0:19:09 h:min:s; p < 0.05). World record performances were set at mean temperatures of 18.61 ± 2.59°C for men and 13.07 ± 4.01°C for women (p > 0.05). The ideal environmental conditions for world record performances for men were temperatures of 18.61°C (p > 0.05), sunny, mostly dry days, with higher atmospheric pressure and little cloud cover (all p > 0.05). In women, ideal conditions for world records performances were temperatures of 13.07°C (p > 0.05), with low atmospheric pressure (p > 0.05), but significantly more rain (p < 0.05), and with no sunshine (p < 0.05) and cloud cover (p < 0.05). With elite performances, the ideal temperatures were of 17.36 ± 4.33°C for men and 17.93 ± 4.07°C for women (p > 0.05), with little to no rain, and moderate cloud cover and sunshine (p > 0.05). In summary, novel findings are, that environmental conditions in world records performances differ between men and women, with women obtaining world records in bad weather (with rain, cloud cover, and no sunshine) and men in good weather (sunny, mostly dry days, with little cloud cover). Larger sample sizes are needed to examine sex differences and environmental conditions on world record marathon performances.

Background and Objectives: Increases in the number of participants in time-limited ultra-marathons have been reported. However, no information is available regarding the trends in participation, performance and age in 12 h and 24 h time-limited events. The aim of the study was to describe the trends in runners’ participation, performance and age in 12 h and 24 h ultra-marathons for both sexes and to identify the age of peak performance, taking into account the ranking position and age categories. Materials and Methods: The sample comprised 210,455 runners in time-limited ultra-marathons (female 12 h = 23,706; female 24 h = 28,585; male 12 h = 61,594; male 24 h = 96,570) competing between 1876 and 2020 and aged 18 to 86 years. The age of peak performance was tested according to their ranking position (first–third; fourth–tenth and >tenth position) and taking into account their running speed in different age categories (60 years), using the Kruskal–Wallis test, followed by the Bonferroni adjustment. Results: An increase in the number of participants and a decrease in running speed were observed across the years. For both events, the sex differences in performance decreased over time. The sex differences showed that male runners performed better than female runners, but the lowest differences in recent years were observed in the 24 h ultra-marathons. A positive trend in age across the years was found with an increase in mean age (“before 1989” = 40.33 ± 10.07 years; “1990–1999” = 44.16 ± 10.37 years; “2000–2009” = 45.99 ± 10.33 years; “2010–2020” = 45.62 ± 10.80 years). Male runners in 24 h races were the oldest (46.13 ± 10.83 years), while female runners in 12 h races were the youngest (43.46 ± 10.16 years). Athletes ranked first–third position were the youngest (female 12 h = 41.19 ± 8.87 years; female 24 h = 42.19 ± 8.50 years; male 12 h = 42.03 ± 9.40 years; male 24 h = 43.55 ± 9.03 years). When age categories were considered, the best performance was found for athletes aged between 41 and 50 years (female 12 h 6.48 ± 1.74 km/h; female 24 h 5.64 ± 1.68 km/h; male 12 h 7.19 ± 1.90 km/h; male 24 h 6.03 ± 1.78 km/h). Conclusion: A positive trend in participation in 12 h and 24 h ultra-marathons was shown across the years; however, athletes were becoming slower and older. The fastest athletes were the youngest ones, but when age intervals were considered, the age of peak performance was between 41 and 50 years.

Background and Objectives: The 6-h ultra-marathon is the shortest time-limited ultra-marathon race, but little has been investigated regarding this race format. Previously, only the age of peak performance in the context of longer time-limited ultra-marathons was determined. The purpose of this study was to investigate the trends in 6-h ultra-marathon races from 1982 to 2020 for female and male ultra-runners, the participation and performance by countries, the age of peak performance, and the differences in performance regarding countries. Materials and Methods: The sample included 23,203 female ultra-runners, aged 18–83 years, and 87,264 male ultra-runners, aged 18–85 years, who were finishers in a 6-h ultra-marathon held between 1982 and 2020. The age of peak performance was tested using the Kruskal–Wallis test, followed by the Bonferroni Correction. The difference in performance by countries was verified using a linear regression model with the fastest runners from Russia in women, and Tunisia in men, used as reference. Results: Over the years, the men-to-women ratio decreased. The mean age was 43.20 ± 9.30 years for female and 46.09 ± 10.17 years for male runners. Athletes in younger age groups were faster than athletes in older age groups. Most female and male participants originated from Germany. Women from Russia (10.01 ± 1.28 km/h) and men from Tunisia (12.16 ± 1.46 km/h) were the fastest. Conclusions: In summary, in 6-h ultra-marathons held between 1982 and 2020, the participation for both women and men increased, while the men-to-women ratio decreased. The mean age was higher in men compared to women. Most female and male runners originated from Germany, but the fastest women were from Russia, and the fastest men from Tunisia. Future studies need to investigate whether Russian women and Tunisian men are also the best in other distance-limited ultra-marathon races, such as 12-h and 24-h.

Background: Pacing, defined as percentage changes of speed between successive splits, has been extensively studied in running and cycling endurance sports; however, less information about the trends in change of speed during cross-country (XC) ski racing is available. Therefore, the aim of the present study was to examine the effect of performance (quartiles of race time (Q), with Q1 the fastest and Q4 the slowest) level on pacing in the Vasaloppet ski race, the largest XC skiing race in the world. Methods: For this purpose, we analyzed female (n = 19,465) and male (n = 164,454) finishers in the Vasaloppet ski race from 2004 to 2017 using a one-way (2 sexes) analysis of variance with repeated measures to examine percentage changes of speed between 2 successive splits. Overall, the race consisted of 8 splits. Results: The race speeds of Q1, Q2, Q3, and Q4 were 13.6 ± 1.8, 10.6 ± 0.5, 9.2 ± 0.3, and 8.1 ± 0.4 km/h, respectively, among females and 16.7 ± 1.7, 13.1 ± 0.7, 10.9 ± 0.6, and 8.9 ± 0.7 km/h, respectively, among males. The overall pacing strategy of finishers was variable. A small sex × split interaction on speed was observed (η2 = 0.016, p < 0.001), with speed difference between sexes ranging from 14.9% (Split 7) to 27.0% (Split 1) and larger changes in speed between 2 successive splits being shown for females (p < 0.001, η2 = 0.004). A large performance × split interaction on speed, with Q1 presenting the smallest changes of speed between splits, was shown for females (η2 = 0.149, p < 0.001) and males (η2 = 0.169, p < 0.001). Conclusion: Male and fast XC skiers are more even pacers. Coaches and athletes should develop tailored sex- and performance-level pacing strategies; for instance, they should advise fast XC skiers to start fast and maintain their speed, rather than starting slowly and trying to make up time by going faster at times during the race. Keywords: Endurance exercise, Race speed, Sex, Sport performance, Winter sport

Abstract Background The participation of master cross-country (XC) skiers in training and competition has increased during the last decades; however, little is known yet about whether these athletes differ from their younger counterparts in aspects of performance such as pacing. Therefore, the aim of the present study was to examine the combined effect of age and performance (race time) on pacing in cross-country (XC) skiing. We analyzed all finishers (n = 79,722) in ‘Vasaloppet’ from 2012 to 2017, the largest cross-country skiing race in the world, classified according to their race time into 10 groups: 3-4 h, 4-5 h, ..., 12-13 h. Results A trivial main effect of sex on total pace range was observed (p

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, ηp2 = 0.031, p = 0.025, ηp2 = 0.044, respectively) and men (long distance race, p < 0.001, ηp2 = 0.016). Fast performance groups spent more time (%) in cycling than their slower counterparts in short (women, p < 0.001, ηp2 = 0.057; men, p < 0.001, ηp2 = 0.035) and long distance (women, p < 0.001, ηp2 = 0.070; men, p < 0.001, ηp2 = 0.052). A small age group × performance group interaction on cycling time (%) was observed in the men’s short distance (p = 0.001, ηp2 = 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.

This study examined the relationship of weather conditions, together with sex and country of origin, with running performance in the Boston Marathon from 1972 to 2018. A total of 580,990 observations from 382,209 different finishers were analyzed using Generalized Additive Mixed Models. Different groups and subgroups were considered such as all runners, near elite 101:200 finishers, near elite 21:100, annual top ten finishers and annual winners. Weather conditions, over the hours of the event, were average air temperature (°C), total precipitations (mm), wet-bulb globe temperature (WBGT) (°C), wind speed (km/h), wind direction (head wind, side wind, tail wind) and barometric pressure (hPa). These effects were examined in a multi-variable model, together with: sex, country of origin, calendar year, an interaction term country:sex and a spline smooth term in function of calendar year and sex. The average temperature, when increasing by 1°C, was related to worsened performance (by 00:01:47 h:min:sec for all finishers and by 00:00:20 h:min:sec for annual winners). Also, the pressure and wet-bulb globe temperature, when increasing, were related to worsened performances. Tail wind improved performances of all groups. Increasing precipitation was significantly (p

Background and objective: The recent availability of data from mass-participation running events has allowed researchers to examine pacing from the perspective of non-elite distance runners. Based on an extensive analysis of the literature, we concluded that no study utilizing mass-participation events data has ever directly compared pacing in the 10-km race, with other long-distance races. Therefore, the main aim of this study was to assess and compare pacing between 10-km runners and marathoners, in regards to their sex and age. Materials and methods: For the purpose of this study, official results from the Oslo marathon (n = 8828) and 10-km race (n = 16,315) held from 2015 to 2018 were included. Results: Both 10-km runners and marathoners showed positive pacing strategies. Moreover, two-way analysis of variance showed that women were less likely to slow in the marathon than men (9.85% in comparison to 12.70%) however, not in the 10-km race (3.99% in comparison to 3.38%). Finally, pace changing is more prominent in youngest and oldest marathoners comparing to the other age groups (12.55% in comparison to 10.96%). Conclusions: Based on these findings, practitioners should adopt different training programmes for marathoners in comparison to shorter long-distance runners.

Background and objectives: The COVID-19 outbreak has become a major health and economic crisis. The World Health Organization declared it a pandemic in March 2020, and many sporting events were canceled. Materials and Methods: We examined the effects of the COVID-19 pandemic on endurance and ultra-endurance running (UER) and analyzed finishes and events during the COVID-19 pandemic (observation period March 2020–October 2020) to the same time period pre-COVID-19 outbreak (March 2019–October 2019). Results: Endurance finishes decreased during the pandemic (459,029 to 42,656 (male: 277,493 to 25,582; female 181,536 to 17,074; all p < 0.001). Similarly, the numbers of endurance events decreased (213 vs. 61 events; p < 0.001). Average marathon finishing times decreased during the pandemic in men (5:18:03 ± 0:16:34 vs. 4:43:08 ± 0:25:08 h:min:s (p = 0.006)) and women (5:39:32 ± 0:19:29 vs. 5:14:29 ± 0:26:36 h:min:s (p = 0.02)). In UER, finishes decreased significantly (580,289 to 110,055; p < 0.001) as did events (5839 to 1791; p < 0.001). Popular event locations in United States, France, UK, and Germany decreased significantly (p < 0.05). All distance and time-limited UER events saw significant decreases (p < 0.05). Conclusions: The COVID-19 pandemic has had a significant effect on endurance and UER, and it is unlikely that running activities return to pre-pandemic levels any time soon. Mitigation strategies and safety protocols should be established.

This study investigated gender differences in performance and participation and the role of nationality during one century in one of the largest cross-country (XC) skiing events in the world, the ‘Vasaloppet‘ in Sweden. The total number of female and male athletes who finished (n = 562,413) this race between 1922 and 2017 was considered. Most of the finishers were Swedish (81.03% of women and 88.39% of men), followed by Norwegians and Finnish. The overall men-to-women ratio was 17.5. A gender × nationality association was observed for participation (χ2 = 1,823.44, p < 0.001, φ = 0.057), with the men-to-women ratio ranging from 6.7 (USA) to 19.1 (Sweden). For both genders, the participation (%) of Swedish decreased, and that of all other nationalities (except Swiss) increased across years. Regarding the mean race time, men were faster than women by 14.5% (7 h 52 min 17 s versus 9 h 00 min 55 s, respectively). A trivial gender×nationality interaction regarding the race time was observed (p < 0.001, η2 < 0.001), with gender differences ranging from 4.4% (USA) to 22.0% (Iceland). The race time increased across calendar years for both women (r = 0.45, p = 0.006, moderate magnitude) and men (r = 0.25, p = 0.015, small magnitude). On the basis of these findings, we concluded that a relatively small number of women pariticipates in XC skiing. Therefore, the development of public health policies targeting the participation of women in XC skiing should be a concern in the countries with a tradition of this sport.

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, η2p = 0.103 and η2p = 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, η2p = 0.208 and η2p = 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.