Race

@misc{coon1962the1,
    author = "Coon, C. S",
    title = "The Origin of Races",
    year = "1962",
    howpublished = "New York, Knopf, 724 p",
    note = "talkorigins\_source = {true}; raw\_reference = {Coon, C. S., 1962, The Origin of Races: New York, Knopf, 724 p.}"
}

@misc{mackay1984the2,
    author = "Mackay, J",
    title = "The origin of races",
    year = "1984",
    howpublished = "Ex Nihilo, v. 6, no. 4, p. 6-12",
    note = "talkorigins\_source = {true}; raw\_reference = {Mackay, J., 1984, The origin of races: Ex Nihilo, v. 6, no. 4, p. 6-12.}"
}

BACKGROUND 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. OBJECTIVE To determine the fastest IM racecourse for age group IM triathletes using descriptive and predictive statistical methods. 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. RESULTS 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. CONCLUSIONS 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.

@article{knechtle2024the,
    author = "Knechtle, Beat and Valero, David and Villiger, Elias and Thuany, Mabliny and Nikolaidis, Pantelis T and Cuk, Ivan and Andrade, Marilia Santos and Forte, Pedro and Braschler, Lorin and Rosemann, Thomas and Weiss, Katja",
    title = "The Influence of Origin and Race Location on Performance in IRONMAN® Age Group Triathletes",
    year = "2024",
    publisher = "Public Library of Science (PLoS)",
    abstract = "BACKGROUND 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. OBJECTIVE To determine the fastest IM racecourse for age group IM triathletes using descriptive and predictive statistical methods. 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. RESULTS 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. CONCLUSIONS 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.",
    url = "https://www.zora.uzh.ch/handle/20.500.14742/224394",
    doi = "10.5167/uzh-266421"
}

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 ofwhere 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.

@article{thuany2024a,
    author = "Thuany, Mabliny and Valero, David and Villiger, Elias and Andrade, Marília S and Weiss, Katja and Nikolaidis, Pantelis T and Vancini, Rodrigo Luiz and Rosemann, Thomas and Knechtle, Beat",
    title = "A Descriptive Analysis of the Fastest Race Courses for Triathletes",
    year = "2024",
    publisher = "Lithuanian Sports University",
    abstract = "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 ofwhere 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.",
    url = "https://www.zora.uzh.ch/handle/20.500.14742/220170",
    doi = "10.5167/uzh-260757"
}

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.

@article{knechtle2025race,
    author = "Knechtle, Beat and Valero, David and Villiger, Elias and Weiss, Katja and Nikolaidis, Pantelis T and Braschler, Lorin and Vancini, Rodrigo Luiz and Andrade, Marilia Santos and Cuk, Ivan and Rosemann, Thomas and Thuany, Mabliny",
    title = "Race course characteristics are the most important predictors in 48 h ultramarathon running",
    year = "2025",
    publisher = "Nature Publishing Group",
    abstract = "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 \textasciitilde 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.",
    url = "https://www.zora.uzh.ch/handle/20.500.14742/230814",
    doi = "10.5167/uzh-277805"
}