Percorrer por autor "Cuk, Ivan"
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- Analysis of over 1 million race records shows runners from East African countries as the fastest in 50-km ultra-marathonsPublication . Weiss, Katja; Valero, David; Villiger, Elias; Thuany, Mabliny; Forte, Pedro; Gajda, Robert; Scheer, Volker; Sreckovic, Sreten; Cuk, Ivan; Nikolaidis, Pantelis Theo; Andrade, Marilia Santos; Knechtle, BeatThe 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.
- Biophysical characterization of the first ultra-cyclist in the world to break the 1,000 km barrier in 24-h non-stop road cycling: a case reportPublication . Knechtle, Beat; Forte, Pedro; Weiss, Katja; Cuk, Ivan; Nikolaidis, Pantelis Theo; Sousa, Caio Victor; Andrade, Marilia Santos; Thuany, MablinyA plethora of factors determine elite cycling performance. Those include training characteristics, pacing strategy, aerodynamics, nutritional habits, psychological traits, physical fitness level, body mass composition, and contextual features; even the slightest changes in any of these factors can be associated with performance improvement or deterioration. The aim of the present case report is to compare the performances of the same ultra-cyclist in achieving two world records (WR) in 24 h cycling. We have analyzed and compared the distance covered and speed for each WR. The 24 h period was split into four-time intervals (0-6 h; > 6-12 h; > 12-18 h; > 18-24 h), and we compared the differences in the distance covered and speed between the two WRs. For both WRs, a strong negative correlation between distance and speed was confirmed (r = -0.85; r = -0.89, for old and new WR, respectively). Differences in speed (km/h) were shown between the two WRs, with the most significant differences in 12-18 h (Delta = 6.50 km/h). For the covered distance in each block, the most significant differences were observed in the last part of the cycling (Delta = 38.54 km). The cyclist effective surface area (ACd) was 0.25 m(2) less and 20% more drag in the new WR. Additionally, the mechanical power was 8%, the power to overcome drag was 31%, and the power-weight ratio was 8% higher in the new WR. The mechanical efficiency of the cyclist was 1% higher in the new WR. Finally, the heart rate (HR) presented significant differences for the first 6 h (Old WR: 145.80 +/- 5.88 bpm; New WR: 139.45 +/- 5.82 bpm) and between the 12 and 18 h time interval (Old WR: 133.19 +/- 3.53 bpm; New WR: 137.63 +/- 2.80 bpm). The marginal gains concept can explain the performance improvement in the new WR, given that the athlete made some improvements in technical specifications after the old WR.
- Case Report: Case study of 100 consecutive IRONMAN®-distance triathlons—impact of race splits and sleep on the performance of an elite athletePublication . Knechtle, Beat; Leite , Luciano Bernardes; Forte, Pedro; Andrade, Marilia Santos; Cuk, Ivan; Nikolaidis, Pantelis Theo; Scheer, Volker; Weiss, Katja; Rosemann, ThomasLong-distance triathletes such as IRONMAN (R) and ultra-triathletes competing in longer race distances continue to extend ultra-endurance limits. While the performance of 60 IRONMAN (R)-distance triathlons in 60 days was the longest described to date, we analysed in the present case study the impact of split disciplines and recovery in one athlete completing 100 IRONMAN (R)-distance triathlons in 100 days. To date, this is the longest self-paced world record attempt for most daily IRONMAN (R)-distance triathlons.To assess the influence of each activity's duration on the total time, the cross-correlation function was calculated for swimming, cycling, running, and sleeping times. The autocorrelation function, which measures the correlation of a time series with itself at different lags, was also employed using NumPy.The moving average for swimming slightly increased in the middle of the period, stabilizing at similar to 1.43 h. Cycling displayed notable fluctuations between similar to 5.5 and 7h, with a downward trend toward the end. The moving average for running remains high, between 5.8 and 7.2 h, showing consistency over the 100 days. The moving average for total time hovered at similar to 15 h, with peaks at the beginning, and slightly declined in the final days. The cross-correlation between swimming time and total time showed relatively low values. Cycling demonstrated a stronger correlation with total time. Running also exhibited a high correlation with total time. The cross-correlation between sleep time and swimming time presented low values. In cycling, the correlation was stronger. For running, a moderate correlation was observed. The correlation with total time was also high. The autocorrelation for swimming showed high values at short lags with a gradual decrease over time. For cycling, the autocorrelation also began strong, decreasing moderately as lags increased. Running displayed high autocorrelation at short lags, indicating a daily dependency in performance, with a gradual decay over time. The total time autocorrelation was high and remained relatively elevated with increasing lags, showing consistent dependency on cumulative efforts across all activities. In a triathlete completing 100 IRONMAN (R)-distance triathlons in 100 days, cycling and running split times have a higher influence on overall times than swimming. Swimming performance is not influenced by sleep quality, whereas cycling performance is. Swimming times slowed faster over days than cycling and running times. Any athlete intending to break this record should focus on cycling and running training in the pre-event preparation.
- Case Report: Differences in self-selected pacing in 20, 40, and 60 ironman-distance triathlons: a case studyPublication . Knechtle, Beat; Cuk, Ivan; Andrade, Marilia Santos; Nikolaidis, Pantelis Theo; Weiss, Katja; Forte, Pedro; Thuany, MablinyTriathletes 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.
- Cycling and Running are More Predictive of Overall Race Finish Time than Swimming in IRONMAN® Age Group TriathletesPublication . Knechtle, Beat; Valero, David; Villiger, Elias; Thuany, Mabliny; Cuk, Ivan; Forte, Pedro; Andrade, Marilia Santos; Nikolaidis, Pantelis Theo; Rosemann, Thomas; Weiss, KatjaSeveral studies have evaluated the most predictive discipline (swimming, cycling, and running) of performance in elite IRONMAN® triathletes. However, no study has ever determined the most decisive discipline for IRONMAN® age group triathletes. The present study analyzed the importance of the three disciplines on the overall race times in IRONMAN® age group triathletes, in order to try and determine the most predictive discipline in IRONMAN® for age group triathletes, and whether the importance of the split disciplines changes with increasing age. This cross-sectional study used 687,696 IRONMAN® age group triathletes race records (553,608 from males and 134,088 from females). Age group athletes were divided in 5-year age groups (i.e., 18–24, 25–29, 30–34,…,70–74, and last 75 + years). The relationships between split disciplines (i.e., swimming, cycling, and running) and overall race times were evaluated using Spearman and Pearson correlations. A multi-linear regression model was used to calculate their prediction strength. The overall finish time correlated more with cycling and running times than with swimming times for both male and female IRONMAN® age group triathletes (r = 0.88 and r = 0.89 for females; r = 0.89 and r = 0.90 for males, respectively). All correlation coefficients decreased with increasing age, which was more noticeable for the swimming discipline. Both cycling and running are more predictive than swimming in IRONMAN® age group triathletes, where the correlation between the overall race times and the split times decreased with increasing age more in swimming than in cycling and running. These insights are useful for IRONMAN® age group triathletes and their coaches in planning their IRONMAN® race preparation and concentrating training on the more predictive disciplines.
- Freestyle master’s swimming: Nationality, sex, and performance trends in World Aquatics competitions (1986–2024)Publication . Ahmad, Wais; Wilhelm, Matthias; Moreitz, Sascha; Andrade, Marilia Santos; Forte, Pedro; Stanula, Arkadiusz; Nikolaidis, Pantelis Theo; Cuk, Ivan; Thuany, Mabliny; Weiss, Katja; Rosemann, Thomas; Hill, Lee; Seffrin, Aldo; Knechtle, BeatIn sports science, freestyle swimming has been thoroughly studied for particular performance-related factors. Nonetheless, it is unknown what countries the top freestyle swimmers are from, especially not for age group swimmers. In addition, the existing research on the performance of master freestyle swimmers has yet to confirm that male swimmers achieve faster times than their female counterparts across all age groups and distances. The current study looked into the nationalities and sexes of the top freestyle swimmers in each age group in World Aquatics competitions for the 50m, 100m, 200m, 400m, and 800m events from 1986 to 2024. Methods The data (derived from (www.worldaquatics.com/masters/archives/masters-archives) were presented using mean, standard deviation, maximum and minimum values, and/or confidence intervals. The year of competition, age, age group, stroke, distance, and first and last names of each swimmer were noted. The nations were then divided into six groups: one group comprising all other countries and the top five nations with the greatest number of appearances in the top 10 fastest freestyle swimming times by distance each year. Results In freestyle swimming, most swimmers (30.6%) competed in the 50m event (n = 25,094, 10,909 female and 14,185 male), followed by the 100m event (25.6%, n = 20,961, 8,796 female and 12,165 male), the 200m event (17.4%, n = 14,309, 6,729 female and 7,580 male), the 400m event (13.4%, n = 10,956, 5,363 female and 5,593 male), and the 800m event (12.6%, n = 10,317, 5,179 female and 5,138 male). The results from the generalized linear models indicate that sex, age group, and the interaction between sex and age group all had significant effects on the 50m, 100m, 200m, 400m, and 800m races. Specifically, for the 50m races, the effect of sex was significant (x(2) (1) = 3451.941, p < 0.001), as was the effect of age group (chi(2) (13) = 19,295.169, p < 0.001), and the interaction between sex and age group (chi (2) (13) = 654.671, p < 0.001). Conclusion The USA demonstrates quantitative dominance by contributing the greatest number of top 10 performers. Additionally, the study highlights significant sex-based performance differences, with males generally outperforming females in all age categories. This study comprehensively analyzes the performance trends observed in freestyle master swimming for nearly four decades.
- A machine learning approach to finding the fastest race course for professional athletes competing in Ironman® 70.3 races between 2004 and 2020Publication . Thuany, Mabliny; Valero, David; Villiger, Elias; Forte, Pedro; Weiss, Katja; Nikolaidis, Pantelis Theo; Andrade, Marilia Santos; Cuk, Ivan; Sousa, Caio Victor; Knechtle, BeatOur purpose was to find the fastest race courses for elite Ironman® 70.3 athletes, using machine learning (ML) algorithms. We collected the data of all professional triathletes competing between 2004 and 2020 in Ironman 70.3 races held worldwide. A sample of 16,611 professional athletes originating from 97 different countries and competing in 163 different races was thus obtained. Four different ML regression models were built, with gender, country of origin, and event location considered as independent variables to predict the final race time. For all the models, gender was the most important variable in predicting finish times. Attending to the single decision tree model, the fastest race times in the Ironman® 70.3 World Championship of around ~4 h 03 min would be achieved by men from Austria, Australia, Belgium, Brazil, Switzerland, Germany, France, the United Kingdom, South Africa, Canada, and New Zealand. Considering the World Championship is the target event for most professional athletes, it is expected that training is planned so that they attain their best performance in this event.
- Modeling Performance in IRONMAN® 70.3 Age Group TriathletesPublication . Thuany, Mabliny; Valero, David; Villiger, Elias; Forte, Pedro; Weiss, Katja; Andrade, Marilia Santos; Nikolaidis, Pantelis Theo; Cuk, Ivan; Rosemann, Thomas; Knechtle, BeatIndividual factors related to performance in age group triathletes competing in different race distances have been explored in scientific literature. However, only a few studies have been conducted using machine learning (ML) predictive models to explore the importance of those individual factors. This study intended to build and analyze machine learning regression models that predict the performance of IRONMAN (R) 70.3 age group triathletes, considering sex, age, country of origin, and event location as predictive factors. A total of 823,464 finishers records (625,398 men and 198,066 women) of IRONMAN (R) 70.3 age group triathletes participating in 197 different events in 183 different locations between 2004 and 2020 were analyzed. The triathletes' sex, age, country of origin, event location and year, and race finish times were thus obtained and considered for the study. Four different ML regression models were built to predict the triathletes' race times from their age, sex, country of origin, and race location. The model with the best performance was then selected and further analyzed using model-agnostic interpretability tools to understand which factors would contribute most to the model predictions.ResultsThe Random Forest Regressor model obtained the best predictive score. This model's partial dependence plots indicated that men under 30 years, from Switzerland or Denmark, competing in IRONMAN (R) 70.3 Austria/St. Polten, IRONMAN (R) 70.3 Switzerland, IRONMAN (R) 70.3 Sunshine Coast, and IRONMAN (R) 70.3 Busselton presented the best performance.ConclusionsOur results prove that ML models can be used to examine the complex, non-linear interactions between the factors that influence performance and gain insights that can help IRONMAN (R) 70.3 age group triathletes better plan their races.
- Pacing of the first and only female finisher in the world's longest triathlon: The 2024 Triple Deca ultra triathlonPublication . Duric, Sasa; Andrade, Marilia Santos; Leite, Luciano Bernardes; Forte, Pedro; Nikolaidis, Pantelis Theo; Cuk, Ivan; Weiss, Katja; Rosemann, Thomas; Knechtle, BeatPacing in triathlon has been analyzed for distances up to 60 long-distance triathlons in 60 days in men. However, no study has examined pacing in a female ultra-endurance triathlete in a multi-day triathlon exceeding 10 days. Thus, this case study analyzes the pacing of the first and only woman to complete 30 long-distance triathlons in 30 days. Methods: Lap times for swimming, cycling, and running, including transitions, were collected from race results. The athlete tracked each discipline daily using a Fenix 7 Sapphire Solar, recording average and maximum heart rates and energy expenditure. The coefficient of variation and second-order polynomial regression were calculated for average pace, split, and total times. Repeated measures ANOVA tested interactions in pace performance across 10-day phases and intra-discipline daily pacing variations. Multivariate regression examined physiological parameters' impact on pacing. Results: The female triathlete maintained a relatively even pacing strategy throughout the race, with a decrease in cycling speed and an increase in running speed. Cycling showed the strongest and significant correlation with total race time (r = 0.810; p < 0.001), while running (r = 0.347; p = 0.119) and swimming (r = -0.312; p = 0.165) displayed non-significant associations. The pace varied within the disciplines, with cycling becoming slower and running faster in the last quarter of the race. Energy expenditure, maximum and average heart rate were significant predictors for cycling (R-2 = 0.538; p < 0.001), while only average heart rate was the best predictor for running performance (R-2 = 0.450; p < 0.001). Conclusions: Tactical considerations most likely influenced pacing, particularly in cycling and running. Future research should further explore pacing strategies in ultra-endurance events.
- Performance and pacing of professional IRONMAN triathletes: the fastest IRONMAN World Championship ever - IRONMAN Hawaii 2022Publication . Knechtle, Beat; Cuk, Ivan; Villiger, Elias; Forte, Pedro; Thuany, Mabliny; Andrade, Marilia Santos; Nikolaidis, Pantelis Theo; Weiss, KatjaPacing 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.