The association of origin and environmental conditions with performance in professional IRONMAN triathletes.

We have (i) little knowledge about where the fastest professional IRONMAN triathletes originate from and where the fastest races take place and (ii) we have no knowledge of the optimal weather conditions for an IRONMAN triathlon. The aims of the present study were, therefore, (i) to investigate the origin and the fastest IRONMAN race courses for professional triathletes and (ii) to evaluate the best environmental conditions (i.e. water and air temperatures and type of race course) for the fastest IRONMAN race times in professional IRONMAN triathletes. Data of all professional female and male IRONMAN triathletes competing between 2002 and 2022 in all IRONMAN races held worldwide were collected. A total of 6,943 finishers´ records (4,162 from men and 2,781 from women) from 58 different countries participating in 54 different event locations between 2002 and 2022 were considered. Data was analyzed using descriptive statistics and machine learning (ML) regression models. The models considered gender, country of origin, event location, water, and air temperature as independent variables to predict the final race time. Three different ML models were built and evaluated, based on three algorithms, in order of growing complexity and predictive power: Decision Tree Regressor, Random Forest Regressor, and XG Boost Regressor. Most of the athletes originated from the USA (1786), followed by athletes from Germany (674), Canada (426), Australia (396), United Kingdom (342), France (325), and Switzerland (276). Most of the athletes competed in IRONMAN Hawaii (925), IRONMAN Florida (563), IRONMAN Austria (452), IRONMAN France (354), IRONMAN Wisconsin (330), IRONMAN Lanzarote (322) and IRONMAN Texas (313). The Decision Tree and the XG Boost models were the best performing models (r2 = 0.48) and rated the relative feature importances in the order gender, country of origin, water temperature, air temperature and event location. Men were on average ~ 0.8 h faster than women. Switzerland had the fastest and Japan and Slovakia the slowest athletes. IRONMAN Brazil Florianopolis, IRONMAN Barcelona, and IRONMAN Louisville hold the fastest races. Optimal water temperature was over 22 °C and optimal air temperature between 19 and 26 °C. Between 2002 and 2022, most professional IRONMAN triathletes originated from the USA, and most professional IRONMAN triathletes competed in IRONMAN Hawaii. The fastest athletes originated from Switzerland, the fastest race courses were IRONMAN Brazil Florianopolis, IRONMAN Barcelona, and IRONMAN Louisville. The fastest race times were achieved in water temperature warmer than 22 °C and air temperature between 19 and 26 °C.

Knechtle B ，Thuany M ，Valero D ，Villiger E ，Nikolaidis PT ，Andrade MS ，Cuk I ，Rosemann T ，Weiss K ... -  《Scientific Reports》

The influence of origin and race location on performance in IRONMAN® age group triathletes.

The IRONMAN® (IM) triathlon is a popular multi-sport, where age group athletes often strive to qualify for the IM World Championship in Hawaii. The aim of the present study was to investigate the location of the fastest IM racecourses for age group IM triathletes. This knowledge will help IM age group triathletes find the best racecourse, considering their strengths and weaknesses, to qualify. To determine the fastest IM racecourse for age group IM triathletes using descriptive and predictive statistical methods. We collected and analyzed 677,702 age group IM finishers' records from 228 countries participating in 444 IM competitions held between 2002 and 2022 across 66 event locations. Locations were ranked by average race speed (performance), and countries were sorted by number of records in the sample (participation). A predictive model was built with race finish time as the predicted variable and the triathlete's gender, age group, country of origin, event location, average air, and water temperatures in each location as predictors. The model was trained with 75% of the available data and was validated against the remaining 25%. Several model interpretability tools were used to explore how each predictor contributed to the model's predictive power, from which we intended to infer whether one or more predictors were more important than the others. The average race speed ranking showed IM Vitoria-Gasteiz (1 race only), IM Copenhagen (8 races), IM Hawaii (18 races), IM Tallinn (4 races) and IM Regensburg (2 races) in the first five positions. The XG Boost Regressor model analysis indicated that the IM Hawaii course was the fastest race course and that male athletes aged 35 years and younger were the fastest. Most of the finishers were competing in IM triathlons held in the US, such as IM Wisconsin, IM Florida, IM Lake Placid, IM Arizona, and IM Hawaii, where the IM World Championship took place. However, the fastest average times were achieved in IM Vitoria-Gasteiz, IM Copenhagen, IM Hawaii, IM Tallin, IM Regensburg, IM Brazil Florianopolis, IM Barcelona, or IM Austria with the absolutely fastest race time in IM Hawaii. Most of the successful IM finishers originated from the US, followed by athletes from the UK, Canada, Australia, Germany, and France. The best mean IM race times were achieved by athletes from Austria, Germany, Belgium, Switzerland, Finland, and Denmark. Regarding environmental conditions, the best IM race times were achieved at an air temperature of ∼27°C and a water temperature of ∼24°C. IM age group athletes who intend to qualify for IM World Championship in IM Hawaii are encouraged to participate in IM Austria, IM Copenhagen, IM Brazil Florianopolis, and/or IM Barcelona in order to achieve a fast race time to qualify for the IM World Championship in IM Hawaii where the top race times were achieved. Most likely these races offer the best ambient temperatures for a fast race time.

Knechtle B ，Valero D ，Villiger E ，Thuany M ，Nikolaidis PT ，Cuk I ，Andrade MS ，Forte P ，Braschler L ，Rosemann T ，Weiss K ... -  《PLoS One》

The fastest nonprofessional age group IRONMAN triathletes in the world originate from Europe.

It is well known that elite athletes of specific ethnicities and/or nationalities dominate certain sports disciplines (e.g., East Africans in marathon running). However, we do not know the nationalities of the fastest non-professional IRONMAN triathletes. Therefore, this study intended to identify the fastest athletes by country of origin competing in IRONMAN triathlon events, focusing on non-professional age group triathletes. Data from all IRONMAN age group athletes competing worldwide between 2002 and 2022 in all official IRONMAN races were collected. Sex, age group, country of origin of the athletes, location and year of the event, split times, overall race times, and transition times were obtained. Additionally, the dataset was augmented with specific data (i.e. event characteristics such as temperatures for water and air and course characteristics for all three split disciplines) related to the different race locations. We limited the analysis to the top 150 countries by participation (i.e. countries with at least 13 successful finishers records in the sample). A total of 677,320 records of IRONMAN age group triathletes originating from 150 different countries and participating in 443 races over 65 different locations were analyzed. European countries such as Germany, Austria, Denmark, Belgium, Switzerland, Norway, Czechia, Estonia, and Slovenia have the fastest IRONMAN age group athletes. IRONMAN Hawaii, IRONMAN Vitoria-Gasteiz and IRONMAN Hamburg are the fastest races. Hilly running and cycling race courses led to slower race times, while flat surfaces, rolling cycling and ocean swimming led to faster race times. Optimal water temperatures were found at 23-25 °C and optimal air temperature ranged between 19-21 and 25-28 °C. The fastest IRONMAN age group triathletes from European countries such as Germany, Austria, Denmark, Belgium, Switzerland, Norway, Czechia, Estonia, and Slovenia. With the presented results for optimal air and water temperatures and description of the optimal cycling and running course characteristics, IRONMAN age group athletes might be able to select an IRONMAN race with the best conditions in order to achieve a fast IRONMAN race time.

Knechtle B ，Villiger E ，Weiss K ，Valero D ，Rosemann T ，Nikolaidis PT ，Vancini RL ，Andrade MS ，Thuany M ... -  《Scientific Reports》

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.

Knechtle B ，Thuany M ，Valero D ，Villiger E ，Nikolaidis PT ，Cuk I ，Rosemann T ，Weiss K ... -  《Scientific Reports》

Race course characteristics are the most important predictors in 48 h ultramarathon running.

Ultra-marathon running - where races are held in distance-limited (50 km, 50 miles, 100 km, 100 miles, etc.), time-limited (6 h, 12 h, 24 h, 48 h, 72 h, etc.), and multi-stage races - is gaining in popularity. However, we have no knowledge of where the fastest 48-hour runners originate and where the fastest 48-hour races are held. This study tried to determine the origin of the fastest 48-hour runners and the predictor factors associated with 48-hour ultra-marathon performance, such as age, gender, event country, country of origin and race course specific characteristics. A machine learning (ML) model based on the XG Boost algorithm was built to predict running speed from the athlete´s age, gender, country of origin, where the race occurs and race course characteristic such as elevation (flat or hilly) and surface (asphalt, cement, granite, grass, gravel, sand, track, or trail). Model explainability tools were then used to investigate how each independent variable would influence the predicted result. A sample of 16,233 race records from 7,075 unique runners originating from 60 different countries and participating in races held in 36 different countries between 1980 and 2022 was analyzed. Participation was spread across many countries, with USA, France, Germany, and Australia at the top of the participants' rankings. Athletes from Japan, Israel, and Iceland achieved the fastest average running speed. The fastest races were held in Japan, France, Great Britain, Netherlands, and Egypt. The XG Boost model showed that elevation of the course (flat course) and the running surface (track) were the variables that had a larger influence on the running speed. The country of origin of the athlete and the country where the event was hold were the most important features by the SHAP analysis, yielding the broader range of model outputs. Men were ~ 0.5 km/h faster than women. Most finishers were 45-49 years old, and runners in this age group achieved the fastest running speeds. In summary, elevation of the course (flat course) and the running surface (track) were the most important variables for fast 48-hour races, whilst the country of origin of the athlete and the country where the event was hold would lead to the broadest difference in the predicted running speed range. Athletes from Japan, Israel, and Iceland achieved the fastest average running speed. The fastest races were held in Japan, France, Great Britain, Netherlands, and Egypt. Any athlete intending to achieve a personal best performance in this race format can benefit from these findings by selecting the most appropriate race course.

Knechtle B ，Valero D ，Villiger E ，Weiss K ，Nikolaidis PT ，Braschler L ，Vancini RL ，Andrade MS ，Cuk I ，Rosemann T ，Thuany M ... -  《Scientific Reports》