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.

Acute Effects of Short-Term Massage Procedures on Neuromechanical Contractile Properties of Rectus Femoris Muscle.

Background and Objectives: In many sports, maintaining muscle work at an optimal level despite fatigue is crucial. Therefore, it is essential to discover the most efficient way of recovery. This study aimed to evaluate and compare the acute effects of four different recovery methods on muscle neuromechanical properties. Materials and Methods: The research was conducted using a randomized, quasi-experimental, repeated-measures design. Fourteen healthy and active male students of the Faculty of Sport and Physical Education (age 25.1 ± 3.9 years) were included in this study. The tensiomyography was used to evaluate muscle responses after four different types of short-term recovery methods (passive rest, percussive mechanical, vibro-mechanical, and manual massage) on the rectus femoris muscle on four occasions: baseline, post fatigue, post recovery and prolonged recovery. Results: The ANOVA revealed that muscle fatigue decreased maximal vertical muscle displacement (Dm) and muscle contraction time (Tc) in post fatigue compared to the baseline. The most important finding shows that only the vibro-mechanical massage resulted in an increase in Tc in the prolonged recovery compared to the post fatigue (p = 0.028), whereas only manual massage showed no differences in Dm from the baseline in post-recovery (p = 0.148). Moreover, both manual and vibro-mechanical massages increased Dm and Tc in prolonged recovery, indicating no differences from the baseline (all p > 0.05), thus showing signs of muscle recovery. Percussion mechanical massage and passive rest did not show indices of muscle recovery. Conclusions: Manual massage could induce immediate positive changes in Dm by reducing muscle stiffness. In addition, vibro-mechanical and manual massage improved muscle tissue by rapidly returning Dm and Tc values to baseline at prolonged recovery measurement (5 min after the fatigue protocol). These findings can benefit sports practitioners, and physical therapists in developing the best recovery method after muscle fatigue.

Palm Temperature Differences after Static and Dynamic Load on High Bar.

Thermal imaging is used in various fields of industry and research to measure temperature and its possible differences. Since there is a lack of research and literature on palm temperatures and prevention of blisters on hands, our question was how palm temperature differs in human hands after different loads (Hang and Swing in Hang) for 30 s on a high bar. Thirty-eight students from the Faculty of Sport at the University of Ljubljana were measured with a high-quality thermal imaging camera. Palm temperatures were measured before the load was applied, immediately after and every 30 s for a period of 5 min after the load. Each hand was divided into nine different regions of interest (ROIs). Mean (XA), standard deviation (SD), maximum and minimum, and number of pixels were calculated. We found that there was no difference between the left and right hand. The temperature right after the load was applied decreased significantly for both loads and then increased above the level before the load was applied. After the static load, the temperature reached a constant higher level after 3 min. After the dynamic load, the temperatures continued to increase throughout the measurement period. Further investigation is needed to determine the time period in which the hand temperature reaches the temperature before the load is applied.

Pacing in Long-Distance Running: Sex and Age Differences in 10-km Race and Marathon.

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.

Number of finishers and performance of age group women and men in long-distance running: comparison among 10km, half-marathon and marathon races in Oslo.

The aim of the present study was to examine the number of finishers and performance trends in 10 km, half-marathon and marathon races in Oslo. Data (total 115,725 finishers; women, n = 50,595; men, n = 65,130) from 10 km, half-marathon and marathon races in Oslo from 2008 to 2018 were analysed considering number, sex, age and running speed of finishers. The total men-to-women ratio was the smallest in the 10 km race (0.60) and the largest in the marathon (3.86) (p < 0.01, φ = 0.28). In both women and men, the slowest running speed was shown in the older age groups (p < 0.01). Based on the findings of the present study, it was concluded that relatively more women finished a 10 km and less a half-marathon and a marathon. Our results indicated that the sex difference in performance was attenuated in the longer race distances and older age groups.

Sex differences in pacing during half-marathon and marathon race.

The main aim of the present study was to examine differences in pacing between half-marathon and marathon in men and women. A total of 17,525 finishers in the marathon (n = 4807 men; n = 1278 women) and half-marathon race (n = 7624 men; n = 3816 women) in Vienna 2017 were considered. Their pacing was assessed through five race segments (0-23.7%, 23.7-47.4%, 47.4-71.1%, 71.1-94.8% and 94.8-100%) of the race. Compared to marathon (where absolute average change of speed [ACS] was 5.46% and 4.12% in men and women, respectively), a more even pacing was observed in half-marathon in both sexes (ACS = 3.60% and 3.36% in men and women, respectively). The more even pacing in women previously observed in marathon races was verified in half-marathon, too. However, the sex difference in pacing was smaller in half-marathon than in marathon. Since men and women endurance runners participate in both races, sport practitioners would have great benefit from these results since they could establish sex-based personalized race strategies and training programmes.

Bone health among indoor female athletes and associated factors; a cross-sectional study.

This study aimed to compare the bone-mineral-density (BMD), vitamin-D (25(OH)D), serum-calcium and serum-phosphorus levels between female athletes engaged in weight-bearing and non-weight-bearing-sport, and to evaluate possible associations among studied variables. Study involved top-level female athletes (age: 16.9 ± 4.4 years) involved in non-weight-bearing- (swimming; n = 14)), and weight-bearing-sport (artistic gymnastic; n = 17). The variables included the BMD of left femoral neck (measured by dual-energy X-ray absorptiometry), 25-hydroxyvitamin D levels (25(OH)D), body height, body mass, body-mass-index, serum-phosphorus, and serum-calcium levels. Sufficiency for 25(OH)D (>75 nmol/L) was observed in 32.3% of the studied athletes (43% and 23% for swimmers and gymnasts, respectively). The studied athletes had appropriate BMD, with higher BMD in gymnasts compared to swimmers (1.24 ± 0.11 and 0.85 ± 0.09 g/cm2; t-test: 10.26, p < 0.01). Serum-phosphorus was higher in gymnasts (1.46 ± 0.17 and 1.27 ± 1.36 mmol/L, t-test: 2.78, p < 0.01. Serum-phosphorus levels were lower in athletes with 25(OH)D sufficiency (t-test: 2.31, p = 0.03). The 25(OH)D was not correlated to BMD (Pearson's r = -0.18, -0.14, -0.28, all p > 0.05, for total sample, gymnasts and swimmers, respectively). Although 25(OH)D was not correlated with BMD, in further investigations other assays of vitamin-D status (i.e. the bioavailability of the vitamin-D) should be included.

Pacing of Women and Men in Half-Marathon and Marathon Races.

Background and objective: Half-marathon is the most popular endurance running race in terms of number of races and runners competing annually; however, no study has compared pacing strategies for this race distance with marathon. The aim of the present study was to profile pacing in half-marathon, compare half-marathon and marathon for pacing, and estimate sex differences in pacing. Materials and methods: A total of 9137 finishers in the half-marathon (n = 7258) and marathon race (n = 1853) in Ljubljana 2017 were considered for their pacing in five race segments (0⁻23.7%, 23.7⁻47.4%, 47.4⁻71.1%, 71.1⁻94.8%, and 94.8⁻100% of the race. Results: Half-marathon runners followed a positive pacing with every segment being slower than its previous one without the presence of an endspurt. Compared to marathon (where the average percent of change in speed (ACS) was 5.71%), a more even pacing was observed in half-marathon (ACS = 4.10%). Moreover, women (ACS = 4.11%) had similar pacing as men (ACS = 4.09%) in half-marathons. Conclusions: In summary, running a half-marathon followed a unique pattern that differentiated this race distance from marathon, with the former showing a more even pacing with an absence of endspurt, and sex difference compared to the latter. Consequently, runners should be advised to adopt a less variable pacing when competing in a half-marathon, regardless of their sex. To the best of our knowledge, the more even pacing in half-marathon, than in marathon, was a novel finding, as it was the first study to compare the two race distances for this characteristic.

Age Differences in Pacing in Endurance Running: Comparison between Marathon and Half-MarathonMen and Women.

BACKGROUND AND OBJECTIVE: The increased popularity of marathons and half-marathons has led to a significant increase in the number of master runners worldwide. Since the age-related decrease in performance is dependent on race duration, pacing in long distance running might also vary by race distance in both men and women. Therefore, the main aim of this study was to assess pacing differences between marathon and half-marathon runners with regard to the runners' age group, and independently for men and women. MATERIALS AND METHODS: In total, 17,465 participants in the Vienna City marathon in 2017 were considered for this study (marathon, N = 6081; half-marathon, N = 11,384). Pacing was expressed as two variables (i.e., pace range and end spurt). RESULTS: All runners showed positive pacing strategies (i.e., a fast start with gradual decrease of speed). However, marathon runners showed greater variability in pacing than half-marathon runners. Furthermore, women showed no differences in pace variability in regard to the age group, whereas men younger than 30 years of age, as well as older men (over the age of 60), showed a greater variability in pace than other age groups. Finally, younger half-marathon men and women showed the fastest end spurt compared to older age groups and marathon runners. CONCLUSIONS: The presented findings could help sports and medicine practitioners to create age specific training plans and pacing strategies. This approach could help long distance runners to improve their physical fitness, achieve better race times, reduce the potential risk of musculoskeletal injuries and increase the overall pleasure of long distance running.

Physical function and 25-hydroxyvitamin D in dialysis patients - lessons learned from the Slovenian DIAGIB study.

AIMS: Vitamin D stores in dialysis patients may be associated with their muscle function and physical performance. We analyzed associations of 25-hydroxyvitamin D levels with functional test results in prevalent hemodialysis patients and healthy controls. METHODS: Study sample included 54 dialysis patients and 81 healthy controls who performed a 6-minute walk test, sit-to-stand test, handgrip strength measurement, and self-rated habitual adjusted activity score with Human Activity Profile questionnaire. Adjusted general linear models were used to analyze association of 25-hydroxyvitamin D levels with test results. RESULTS: Serum 25-hydroxyvitamin D concentration was 73.1 ± 35.4 nmol/L in dialysis patients and 64.6 ± 22 nmol/L in controls (p = 0.12). When adjusted for age, sex, body height, spontaneous gait speed, and dialysis dependence, 25-hydroxyitamin D was significantly positively associated with 6-minute walk test result, explaining 5% of variability in walked distance (B = 0.6 m/nmol/L, p = 0.008) and 12% of variability in adjusted activity score (B = 0.1 point/nmol/L, p < 0.001). There was no significant association with handgrip strength or sit-to-stand performance in adjusted models. CONCLUSIONS: Serum 25-hydroxyvitamin D levels are significantly positively associated with submaximal aerobic physical performance and habitual activity level in dialysis patients.
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A simple method for assessment of muscle force, velocity, and power producing capacities from functional movement tasks.

A range of force (F) and velocity (V) data obtained from functional movement tasks (e.g., running, jumping, throwing, lifting, cycling) performed under variety of external loads have typically revealed strong and approximately linear F-V relationships. The regression model parameters reveal the maximum F (F-intercept), V (V-intercept), and power (P) producing capacities of the tested muscles. The aim of the present study was to evaluate the level of agreement between the routinely used "multiple-load model" and a simple "two-load model" based on direct assessment of the F-V relationship from only 2 external loads applied. Twelve participants were tested on the maximum performance vertical jumps, cycling, bench press throws, and bench pull performed against a variety of different loads. All 4 tested tasks revealed both exceptionally strong relationships between the parameters of the 2 models (median R = 0.98) and a lack of meaningful differences between their magnitudes (fixed bias below 3.4%). Therefore, addition of another load to the standard tests of various functional tasks typically conducted under a single set of mechanical conditions could allow for the assessment of the muscle mechanical properties such as the muscle F, V, and P producing capacities.

Generalization of Muscle Strength Capacities as Assessed From Different Variables, Tests, and Muscle Groups.

Cuk, I, Prebeg, G, Sreckovic, S, Mirkov, DM, and Jaric, S. Generalization of muscle strength capacities as assessed from different variables, tests, and muscle groups. J Strength Cond Res 31(2): 305-312, 2017-The muscle strength capacities to exert force under various movement conditions have been indiscriminately assessed from various strength tests and variables applied on different muscles. We tested the hypotheses that the distinctive strength capacities would be revealed (H1) through different strength tests, and (H2) through different strength variables. Alternatively, (H3) all strength variables independent of the selected test could depict the same strength capacity of the tested muscle. Sixty subjects performed both the standard strength test and the test of alternating contractions of 6 pairs of antagonistic muscles acting in different leg and arm joints. The dependent variables obtained from each test and muscle were the maximum isometric force and the rate of force development. A confirmatory principle component analysis set to 2 factors explained 31.9% of the total variance. The factor loadings discerned between the tested arm and leg muscles, but not between the strength tests and variables. An exploratory analysis applied on the same data revealed 6 factors that explained 60.1% of the total variance. Again, the individual factors were mainly loaded by different tests and variables obtained from the same pair of antagonistic muscles. Therefore, a comprehensive assessment of the muscle strength capacity of the tested individual should be based on a single strength test and variable obtained from a number of different muscles, than on a single muscle tested through different tests and variables. The selected muscles should act in different limbs and joints, while the maximum isometric force should be the variable of choice.

Evaluation of force-velocity and power-velocity relationship of arm muscles.

PURPOSE: A number of recent studies have revealed an approximately linear force-velocity (F-V) and, consequently, a parabolic power-velocity (P-V) relationship of multi-joint tasks. However, the measurement characteristics of their parameters have been neglected, particularly those regarding arm muscles, which could be a problem for using the linear F-V model in both research and routine testing. Therefore, the aims of the present study were to evaluate the strength, shape, reliability, and concurrent validity of the F-V relationship of arm muscles. METHODS: Twelve healthy participants performed maximum bench press throws against loads ranging from 20 to 70 % of their maximum strength, and linear regression model was applied on the obtained range of F and V data. One-repetition maximum bench press and medicine ball throw tests were also conducted. RESULTS: The observed individual F-V relationships were exceptionally strong (r = 0.96-0.99; all P < 0.05) and fairly linear, although it remains unresolved whether a polynomial fit could provide even stronger relationships. The reliability of parameters obtained from the linear F-V regressions proved to be mainly high (ICC > 0.80), while their concurrent validity regarding directly measured F, P, and V ranged from high (for maximum F) to medium-to-low (for maximum P and V). CONCLUSIONS: The findings add to the evidence that the linear F-V and, consequently, parabolic P-V models could be used to study the mechanical properties of muscular systems, as well as to design a relatively simple, reliable, and ecologically valid routine test of the muscle ability of force, power, and velocity production.

Force-velocity relationship of leg extensors obtained from loaded and unloaded vertical jumps.

PURPOSE: Resent research has suggested that loaded multi-joint movements could reveal a linear force-velocity (F-V) relationship. The aim of the present study was to evaluate the F-V relationship both across different types of vertical jumps and across different F and V variables. METHODS: Ten healthy subjects performed maximum various vertical jumps that were either loaded or unloaded by constant external forces of up to 30 % of their body weight. Both the maximum and averaged F and V data were recorded. RESULTS: The observed F-V relationships proved to be strong (median correlation coefficients ranged 0.78-0.93) and quasi-linear. Their F- and V-intercepts and the calculated maximum power (P) were highly reliable (0.85 < ICC < 0.98), while their concurrent validity with respect to their directly measured values was on average moderate-to-large. The obtained F-V relationships also revealed that (1) the assessment of maximum F and P could be somewhat more reliable and valid than the assessment of maximum V, (2) natural countermovement jumps should be employed rather than the jumps performed from a fixed squat position, while (3) both maximum and averaged F and V variables could be used despite revealing markedly different regression parameters. CONCLUSIONS: The data generally reveal a reliable, valid, strong and quasi-linear F-V relationship across variety of vertical jumps and the recorded F and V variables. Therefore, we conclude that the loaded vertical jumps could be developed into a routine method for testing the force, velocity, and power generating capacity of leg extensors.

Comparison of Global Navigation Satellite System Devices on Speed Tracking in Road (Tran)SPORT Applications.

Global Navigation Satellite Systems (GNSS) are, in addition to being most widely used vehicle navigation method, becoming popular in sport-related tests. There is a lack of knowledge regarding tracking speed using GNSS, therefore the aims of this study were to examine under dynamic conditions: (1) how accurate technologically different GNSS measure speed and (2) how large is latency in speed measurements in real time applications. Five GNSSs were tested. They were fixed to a car's roof-rack: a  smart phone, a wrist watch, a handheld device, a professional system for testing vehicles and a high-end Real Time Kinematics (RTK) GNSS. The speed data were recorded and analyzed during rapid acceleration and deceleration as well as at steady speed. The study produced four main findings. Higher frequency and high quality GNSS receivers track speed at least at comparable accuracy to a vehicle speedometer. All GNSS systems measured maximum speed and movement at a constant speed well. Acceleration and deceleration have different level of error at different speeds. Low cost GNSS receivers operating at 1 Hz sampling rate had high latency (up to 2.16 s) and are not appropriate for tracking speed in real time, especially during dynamic movements.

Case Report: Differences in self-selected pacing in 20, 40, and 60 ironman-distance triathlons: a case study.

Background: Triathletes are pushing their limits in multi-stage Ironman-distance triathlons. In the present case study, we investigated the pacing during 20, 40, and 60 Ironman-distance triathlons in 20, 40, and 60 days, respectively, of one professional IRONMAN® triathlete. Case study: Event 1 (20 Ironman-distance triathlons in 20 days), Event 2 (40 Ironman-distance triathlons in 40 days), and Event 3 (60 Ironman-distance triathlons in 60 days) were analyzed by discipline (swimming, cycling, running, and overall event time), by Deca intervals (10 days of consecutive Ironman-distance triathlons) and additional data (sleep duration, body mass, heart rate in cycling and running). To test differences between Events and Deca intervals within the same discipline, T-tests (2 groups) or one-way ANOVAs (3 or more groups) were used. Results: Swimming splits were fastest in Event 1, (ii) cycling and running splits were fastest in both Event 2 and 3, (iii) overall speed was fastest in Event 3, (iv) sleep duration increased during Event 2 but decreased in Event 3, (v) body mass decreased in Event 2, but increased in Event 3 and (vi) heart rate during cycling was similar in both Event 2 and 3. In contrast, heart rate during running was greater in Event 3. Conclusion: In a professional IRONMAN® triathlete finishing 20, 40, and 60 Ironman-distance triathlons in 20, 40, and 60 days, respectively, split performances and both anthropometrical and physiological changes such as body mass and heart rate differed depending upon the duration of the events.

Europe has the fastest Ironman race courses and the fastest Ironman age group triathletes.

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.

Sex difference in IRONMAN age group triathletes.

BACKGROUND: The sex difference in athletic performance has been thoroughly investigated in single sport disciplines such as swimming, cycling, and running. In contrast, only small samples of long-distance triathlons, such as the IRONMAN® triathlon, have been investigated so far. AIM: The aim of the study was to examine potential sex differences in the three split disciplines by age groups in 5-year intervals in a very large data set of IRONMAN® age group triathletes. METHODS: Data from 687,696 (553,608 men and 134,088 women) IRONMAN® age group triathletes (in 5-year intervals from 18-24 to 75+ years) finishing successfully between 2002 and 2022 an official IRONMAN® race worldwide were analyzed. The differences in performance between women and men were determined for each split discipline and for the overall race distance. RESULTS: Most finishers were in the age group 40-44 years. The fastest women were in the age group 25-29 years, and the fastest men were in the age group 30-34 years. For all split disciplines and overall race time, men were always faster than women in all groups. The performance difference between the sexes was more pronounced in cycling compared to swimming and running. From the age group 35-39 years until 60-64 years, the sex differences were nearly identical in swimming and running. For both women and men, the smallest sex difference was least significant in age group 18-24 years for all split disciplines and increased in a U-shaped manner until age group 70-74 years. For age groups 75 years and older, the sex difference decreased in swimming and cycling but increased in running. Considering the different characteristics of the race courses, the smallest performance gaps between men and women were found in river swimming, flat surface cycling and rolling running courses. CONCLUSIONS: The sex difference in the IRONMAN® triathlon was least significant in age group 18-24 years for all split disciplines and increased in a U-shaped manner until age group 70-74 years. For 75 years and older, the sex difference decreased in swimming and cycling but increased in running.

Using machine learning to determine the nationalities of the fastest 100-mile ultra-marathoners and identify top racing events.

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.

Analysis of over 1 million race records shows runners from East African countries as the fastest in 50-km ultra-marathons.

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.