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Scientific Reports
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Nature Publishing Group UK London

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10.1038/s41598-024-71866-6
Article
Europe has the fastest Ironman race courses and the fastest Ironman age group triathletes
http://orcid.org/0000-0002-2412-9103
Knechtle Beat beat.knechtle@hispeed.ch

12
http://orcid.org/0000-0002-6858-1871
Thuany Mabliny 3
http://orcid.org/0000-0003-4133-4843
Valero David 4
http://orcid.org/0000-0001-8371-1390
Villiger Elias 2
http://orcid.org/0000-0001-8030-7122
Nikolaidis Pantelis T. 5
http://orcid.org/0000-0001-7819-4384
Cuk Ivan 6
http://orcid.org/0000-0002-6436-6306
Rosemann Thomas 2
http://orcid.org/0000-0003-1247-6754
Weiss Katja 2
1 grid.491958.8 0000 0004 6354 2931 Medbase St. Gallen Am Vadianplatz, Vadianstrasse 26, 9001 St. Gallen, Switzerland
2 https://ror.org/01462r250 grid.412004.3 0000 0004 0478 9977 Institute of Primary Care, University Hospital Zurich, Zurich, Switzerland
3 https://ror.org/043pwc612 grid.5808.5 0000 0001 1503 7226 Faculty of Sports, University of Porto, Porto, Portugal
4 Ultra Sports Science Foundation, Pierre-Benite, France
5 https://ror.org/00r2r5k05 grid.499377.7 0000 0004 7222 9074 School of Health and Caring Sciences, University of West Attica, Athens, Greece
6 https://ror.org/02qsmb048 grid.7149.b 0000 0001 2166 9385 Faculty of Sport and Physical Education, University of Belgrade, Belgrade, Serbia
8 9 2024
8 9 2024
2024
14 209033 5 2024
2 9 2024
© The Author(s) 2024
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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.

Keywords

Swimming
Cycling
Running
Race prediction
Subject terms

Environmental sciences
Environmental social sciences
issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

The Ironman triathlon is highly popular, attracting a considerable number of participants who primarily consist of age group triathletes, including recreational and master triathletes1–3. An Ironman triathlon is a long-distance triathlon race that comprises a 2.4-mile (3.86 km) swim, a 112-mile (180.260 km) bicycle ride, and a 26.22-mile (42.195 km) run, equivalent to a full marathon. The race is completed in this sequence, covering a total of 140.6 miles (226.3 km) [www.Ironman.com/].

In the case of ‘Ironman Hawaii,’ which serves as the Ironman World Championships, there has been an increase in the participation of master triathletes, while the involvement of younger triathletes has declined2. Notably, over the years, master triathletes have displayed ongoing improvements in both split and overall race times, indicating that they have yet to reach their performance limits1–4.

For Ironman triathletes, it is important to know what influences their race performance4. Previous experiences such as the personal best time in an Ironman race4,5, in a marathon4,6,7, and in an Olympic distance triathlon4,5,7–9 have shown to be predictive for faster Ironman overall race times. Training also plays a significant role4,10–13 with both training volume3,8,9,13 and training intensity4,5,9,12,14 showing varied predictability. Interestingly, personal best times prove to be more predictive of performance than training volume7. Additionally, having a somatotype with low body fat has emerged as a strong predictor of Ironman race performance12,15–17. Furthermore, originating from the USA also seemed predictive18,19 and the best performance age is usually around 30–35 years18,20.

Several studies have aimed to identify the most predictive split discipline in an Ironman triathlon21–23. For professional Ironman triathletes, the bike split appears to be the most predictive split discipline22. However, when analyzing a larger dataset, cycling showed a limited predictability for overall race time, while running emerged as the most predictive split discipline22. This is most likely because the fastest Ironman triathletes were also the fastest in running21. Both cycling and running probably play crucial roles in determining performance in an Ironman race23. In addition, knowing the best pacing strategies to achieve a fast Ironman race time is also essentially24–26.

This study aimed to examine the racecourses of various Ironman races to determine which ones are the fastest. This information is particularly relevant for age group athletes aiming to qualify for the Ironman World Championship in ‘Ironman Hawaii’ and to attain a fast overall race time. At the moment, athletes have seven different options to qualify for ‘Ironman Hawaii’ with (1) standard qualification where age group athletes have to compete in one of the 47 Ironman races held globally trying to get slot in their age group based on the fastest age group race times, (2) competing in one of the rare Ironman 70.3 events offering a slot in an age group category, (3), achieving an extra women’s slot in one of the 17 Ironman events held globally, (4) the Ironman Legay Program where an athlete must complete 12 full distance Ironman triathlons and never have raced before at the Ironman World Championship, (5) the Ironman Foundation Annual Ironman World Championship Auction, and (6) the Physically Challenged Open/Exhibition Division Drawing, and (7) the Ironman Executive Challenge as last opportunity [www.Ironman.com/news_article/show/1241591].

By identifying the fastest racecourses, athletes can make informed decisions when selecting the most suitable event to improve their chances of qualification for Ironman Hawaii. Since originating from the USA18,19 seems to be predictive of achieving a fast Ironman race time, we hypothesized that a majority of Ironman age group triathletes would come from the USA and preferentially compete in Ironman races held within the USA. In case most of the Ironman age group triathletes would originate from the USA, we also might assume that the fastest Ironman age group triathletes would also be from the USA.

Methods

Data set and data preparation

All athlete data was downloaded from the official Ironman website (www.Ironman.com) using a Python script (www.python.org/). The triathletes’ gender, age, country of origin, event location and year, and times for swimming, running, cycling, and transitioning were thus obtained. We also considered environmental characteristics such as water temperature for swimming and air temperature for both cycling and running. Furthermore, characteristics of the swim course (e.g., swimming in a bay, lake, ocean, reservoir, or river) and both the cycling and running courses (e.g., flat, hilly, or rolling) were included. We considered recreational finishers’ race data of all age groups competing between 2002 and 2022 in all Ironman races recorded on the Ironman website (www.Ironman.com). From an original dataset of 684,656, including professional and age group Ironman triathletes’ records, the age group sub-sample consisted of 677,702 records after all the required data processing. We defined each successful finish of an athlete as a race record.

Statistical analysis

Summary tables and boxplot charts (by age group, event location, racecourse type, etc.) are computed by aggregating records and calculating the statistical parameters of the resulting groups. Descriptive statistics are presented using mean, standard deviation, frequencies, percentages, and min/max values. A two-way ANOVA was used to analyze the differences between countries, locations, and sexes. ML (machine learning) regression models were built, trained, tested, and compared using four different algorithms: Random Forest Regressor, XG Boost Regressor Cat Boot Regressor, and Decision Tree Regressor. These algorithms are popular examples of ML tree-based models. Although mostly applicable to tabular data, they are also widely used in image-related tasks. The Decision Tree is the most basic of the four algorithms. It implements a single large tree, splitting the data into smaller sets by placing conditions on the predicting variables, always seeking to minimize the entropy level. The Random Tree Forest implements several independent decision trees and then averages their outputs to give a final result that is often more accurate than any of the individual trees. CatBoost and XGBoost function similarly to the latter in building several trees. However, it uses the gradient boosting technique during the model training, where each new tree uses the knowledge from earlier ones. In all cases, the models’ target (dependent or predicted variable) is the race Finish Time. The following (independent) variables are used as predictors:numerical variables:Gender

AgeGroup

Country

EventLocation

Water temperature (°C)

Air temperature (°C)

categorical variablesSwim (‘bay’, ‘lake’, ‘ocean’, ‘reservoir’, ‘river’)

Bike (‘flat’, ‘hilly’, ‘rolling’)

Run (‘flat’, ‘hilly’, ‘rolling’)

Some variables were originally categorical but were encoded as numerical: Gender was encoded as 0 = women, 1 = men, and the age groups were encoded by taking the first two digits of each age group (so “18–24” becomes 18, “25–29” becomes 25, etc.). To encode the country variable, the full set of race records was aggregated by country, and then the resulting set was sorted by number of records (frequency) in descending order. The index of the resulting list (starting at zero) was then used to numerically encode the country names. So, for instance, the USA with ID 0 is at the top of the list. An equivalent process was followed to encode the event location predictor. The fastest racecourses are defined as those race locations where the average finish time is the lowest. Similarly, the fastest countries have the best (lowest) average race times. The two most basic models (the single Decision Tree and the Random Forest) use only the numerical predictors, while Cat Boost and XG Boost use all numerical and categorical variables. We used the full dataset to train and evaluate the models (in-sample training), which, while giving us no hint of our models’ generalization capabilities, allows us to obtain the maximum knowledge from the data available. We calculated the mean absolute error (MAE) and the coefficient of determination (R2) as the accuracy metrics. The MAE represents the mean of the absolute values of the individual prediction errors over all instances in the test set, in which higher values mean higher prediction errors. R2 represents a measure of the model “goodness of fit”. The models’ feature relative importance was also calculated, representing a score for each feature in a specific model. Higher values correspond to features that are of higher importance in predicting the target variable. Due to their mathematical complexity, ML models are often used as black boxes. Their suitability can only be assessed by the accuracy of the results with existing and new samples. To interpret the models’ logic and draw conclusions on the impact of each predictor on the target variable, we used model explainability tools such as the SHAP library. The aggregated SHAP values indicate which predictor is most important for that model and how each predictor influences the model output. A two-way ANOVA was used to analyze the differences between women and men regarding race course characteristics. All data processing and analysis were done using Python (www.python.org/) and related libraries in a Google Colab notebook (https://colab.research.google.com/).

Results

A total of 677,702 Ironman age group finishers´ records (544,963 from men and 132,739 from women) participating in 444 different Ironman events across 66 locations between 2002 and 2022 were analyzed. Men achieved an average race time of 12.64 ± 1.78 h, and women an average race time of 13.53 ± 1.69 h (Fig. 1).Fig. 1 Distributions of overall race times of women and men Ironman age group triathletes.

Table 1 summarizes the number of athletes by age group with their overall race times (mean, SD, min, and max). The variables age group, gender, and their combined effect have a statistically significant effect on overall race time (p < 0.001). The fastest men were in age group 30–34 years, the fastest women in age group 25–29 years.Table 1 Number of athletes by age group with their race times (mean, SD, min, and max) in hours.

Gender	Age group (years)	n	Mean (h)	SD (h)	Min (h)	Max (h)	
Men	18–24	12,796	12.6	1.83	8.4	17.48	
25–29	41,700	12.33	1.79	8.12	17.43	
30–34	77,680	12.26	1.77	8.12	17.73	
35–39	100,036	12.34	1.76	8.21	17.46	
40–44	114,260	12.53	1.73	8.36	17.84	
45–49	92,850	12.74	1.72	8.32	17.55	
50–54	61,150	13.07	1.71	8.06	17.47	
55–59	28,054	13.47	1.68	8.52	17.65	
60–64	11,278	13.9	1.62	9.18	17.55	
65–69	3694	14.36	1.49	10.08	17.24	
70–74	1203	15	1.25	8.54	17.03	
75+	262	15.73	1.08	8.34	17.58	
Women	18–24	3385	13.27	1.67	9.6	17	
25–29	13,458	13.13	1.69	9.01	17.25	
30–34	22,407	13.2	1.7	9.12	17.64	
35–39	24,396	13.33	1.69	9	17.57	
40–44	26,220	13.51	1.66	9.4	17.84	
45–49	20,634	13.73	1.61	9	17.4	
50–54	13,641	14.03	1.57	9.73	17.96	
55–59	5801	14.32	1.47	10.35	17.46	
60–64	2095	14.77	1.36	8.71	17.28	
65–69	573	15.3	1.13	12.05	16.99	
70–74	116	15.72	0.93	13.4	16.99	
75+	13	16.55	0.66	14.69	17.05	

Most of the successful age group triathletes originated from the USA (274,553), followed by triathletes from the United Kingdom (55,410) and Canada (38,264) (Fig. 2).Fig. 2 Top 25 countries by number of race records, and average race time by country.

Most of the triathletes competed in Ironman Wisconsin (38,545), followed by Ironman Florida (38,157) and Ironman Lake Placid (34,341) (Fig. 3).Fig. 3 Top 25 Ironman event locations by number of race records, and average race time (in hours) by location.

Table 2 gives an overview of the top 25 Ironman event locations by participation (number of unique records), including the number of races, the number of recorded race times, the number of athletes, the overall race times (mean, SD, min, max), the descriptions of the courses for swimming, cycling, and running, and the water and air temperatures. 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) to list the top six races. N-way ANOVA for country and event location indicated statistically significant differences (p < 0.001). Table 3 gives an overview of the top 25 countries by participation (number of unique records), including the number of races, the number of recorded race times, the number of athletes, and the overall race times (mean, SD, min, max). 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) to name the best six nations. N-way ANOVA for country and event location indicated statistically significant differences (p < 0.001).Table 2 Overview of the top 25 Ironman event locations by participation (number of unique records).

Ironman Event location	Number of races	Number of unique records	Number of unique names	Mean race Time (h)	Race time, STD (h)	Race time, min (h)	Race time, max (h)	Swim	Bike	Run	Water (°C)	Air (°C)	
Wisconsin	19	38,545	26,986	13.44	1.67	9.00	17.37	Lake	Rolling	Rolling	21	22	
Florida	19	38,157	29,081	12.96	1.80	8.54	17.47	Ocean	Flat	Flat	22	19	
Lake Placid	17	34,341	24,382	13.35	1.68	8.94	17.17	Lake	Hilly	Rolling	22	17	
Arizona	17	34,246	25,689	13.35	1.75	8.39	17.20	Lake	Rolling	Rolling	16	17	
Hawaii	18	32,156	21,080	11.72	1.86	8.41	17.84	Ocean	Rolling	Rolling	24	27	
Austria	16	30,970	23,560	11.92	1.59	8.56	17.18	Lake	Rolling	Flat	23	27	
France	16	29,302	24,200	12.39	1.53	8.34	16.84	Ocean	Hilly	Flat	24	28	
Canada	15	27,135	19,771	13.25	1.73	9.09	17.10	Lake	Hilly	Rolling	21	26	
Coeur d'Alene	15	24,526	19,575	13.47	1.71	9.09	17.65	Lake	Hilly	Rolling	20	24	
Louisville	10	21,661	17,907	13.36	1.70	8.81	17.61	River	Rolling	Rolling	27	29	
Texas	11	21,137	16,001	13.31	1.93	8.12	17.00	Lake	Flat	Rolling	28	23	
Frankfurt	13	20,057	16,579	12.03	1.38	8.73	15.90	Lake	Rolling	Flat	23	25	
Cozumel	13	17,902	14,470	13.06	1.77	8.12	17.58	Ocean	Flat	Flat	26	26	
Copenhagen	8	16,739	13,344	11.68	1.38	8.32	16.12	Bay	Rolling	Flat	18	21	
New Zealand	18	16,623	11,218	12.85	1.75	8.06	17.64	Lake	Rolling	Flat	18	21	
Mont-Tremblant	8	16,306	12,951	13.06	1.66	8.52	17.13	Lake	Hilly	Flat	17	26	
UK	11	15,221	12,066	13.39	1.61	8.63	17.04	River	Hilly	Rolling	18	22	
Kalmar	8	14,640	9826	12.08	1.47	8.49	16.25	Ocean	Flat	Flat	20	22	
Lanzarote	11	14,530	11,066	13.08	1.67	8.95	17.18	Ocean	Hilly	Rolling	19	24	
Florianopolis	10	14,010	9656	11.80	1.52	8.61	17.54	Ocean	Flat	Flat	20	21	
New South Wales	12	13,668	8454	12.58	1.65	8.65	17.03	River	Rolling	Flat	23	21	
Zurich	9	13,199	11,502	12.36	1.53	8.88	16.03	Lake	Hilly	Flat	19	23	
Wales	8	12,496	9157	13.54	1.56	9.48	17.05	Ocean	Hilly	Hilly	17	16	
Barcelona	6	11,137	10,132	11.78	1.43	8.37	16.02	Lake	Hilly	Rolling	23	21	
Chattanooga	6	10,788	9605	13.46	1.62	8.89	17.09	River	Rolling	Rolling	25	24	

Table 3 Overview of the top 25 countries by participation (number of records).

Country	Number of races	Number of records	Number of unique names	Mean race time (h)	Race time, STD (h)	Race time, min (h)	Race time, max (h)	
United States	442	274,553	124,646	13.36	1.75	8.29	17.96	
United Kingdom	435	55,410	29,852	13.04	1.65	8.53	17.08	
Canada	428	38,264	18,074	13.03	1.73	8.4	17.84	
Australia	409	37,571	16,836	12.26	1.67	8.65	17.06	
Germany	427	32,662	16,240	11.74	1.51	8.21	17.59	
France	415	27,873	16,293	12.11	1.54	8.32	17.03	
Spain	357	17,009	9788	12.07	1.58	8.12	16.96	
Sweden	345	14,640	7057	12.09	1.48	8.52	17.84	
Brazil	356	14,408	7911	11.86	1.59	8.5	17.54	
Austria	321	12,792	6267	11.68	1.50	8.62	16.94	
Italy	368	12,702	6236	12.09	1.48	8.6	16.84	
Mexico	374	11,941	6211	13.23	1.65	8.61	17.09	
Denmark	317	11,874	6469	11.59	1.40	8.38	17.13	
New Zealand	323	11,092	5538	12.70	1.75	8.06	17.64	
Japan	380	10,249	4990	13.46	1.82	8.34	17.01	
South Africa	328	9675	5732	13.29	1.65	8.7	16.97	
Belgium	344	8444	3896	11.48	1.47	8.27	17	
Switzerland	374	7570	3381	11.62	1.49	8.4	16.89	
Ireland	355	6337	3625	12.54	1.65	8.43	17.04	
Argentina	331	5470	2679	12.10	1.59	8.56	17.32	
Netherlands	345	4750	2841	11.83	1.50	8.28	16.99	
Finland	241	3497	1785	11.68	1.40	8.54	16.59	
Poland	290	3438	2005	12.01	1.52	8.78	16.75	
Russia	255	3163	1953	11.91	1.50	8.37	17.05	
Israel	256	2952	1884	12.74	1.59	8.67	16.89	

With respect to the ML models, Table 4 summarizes the evaluation results, with all performing very similarly.Table 4 Summary of ML models set up and performance.

Model	Main parameters	MAE (h)	R2	
Random Forest Regressor	n_estimators = 200	1.22	0.30	
XG Boost	n_estimators = 2500	1.23	0.29	
learning_rate = 0.1	
cat_features_enabled	
Cat Boost	n_estimators = 2000	1.25	0.28	
learning_rate = 0.15	
cat_features_enabled	
Decision Tree	max_depth = 15	1.24	0.28	

Figure 4 shows the feature relative importance for the first predictive model (Random Forest Regressor). The athlete’s country was the most important variable (0.46), followed by the athlete’s age group (0.18), event location (0.13), gender (0.09), air temperature (0.08), and water temperature (0.06).Fig. 4 Random Forest Regressor features relative importance.

Figure 5 shows the features’ relative importance for the second predictive model (XG Boost Regressor). Gender (0.18) was the most important variable, followed by the split discipline running (0.16), the split discipline cycling (0.14), athlete’s country (0.13), athlete’s age group (0.08), air temperature (0.08), the split discipline swimming (0.08), event location (0.07), and water temperature (0.07).Fig. 5 XG Boost Regressor features relative importances.

Figure 6 shows the features’ relative importance for the third predictive model (CatBoost Regressor). The athlete’s country (0.38) was the most predictive variable, followed by the athlete's age group (0.15), event location (0.11), gender (0.09), air temperature (0.09), water temperature (0.06), and split disciplines cycling (0.05), swimming (0.04), and running (0.04).Fig. 6 CatBoost Regressor features relative importances.

Figure 7 shows the set of features’ importances for the fourth and last model (Decision Tree Regressor). The athlete’s country (0.47) was the most predictive variable, followed by the athlete's age group (0.18), event location (0.12), gender (0.10), air temperature (0.09), and water temperature (0.05).Fig. 7 Decision Tree Regressor features relative importances.

Figure 8 shows the swim times by the type of the swim course by gender. Differences between genders and race conditions were statistically significant.Fig. 8 Swimming times by the type of swimming course.

Figure 9 shows the cycling times by the type of cycling course by gender. Differences between genders and race conditions were statistically significant.Fig. 9 Cycling times by the type of cycling course.

Figure 10 shows the running times by the type of the running course by gender. Differences between genders and race conditions were statistically significant.Fig. 10 Running times by the type of running course.

Discussion

This study aimed to investigate the locations of the fastest Ironman racecourses globally, focusing on age group triathletes participating in all Ironman races. The hypothesis was that the USA would host the fastest Ironman racecourses, given the considerable number of Ironman age group triathletes from the USA who actively compete in races held within the USA. The most important findings were (i) a majority of successful Ironman age group triathletes originated from the USA, followed by athletes from the United Kingdom and Canada, where these countries exhibited average overall race times that were significantly slower compared to the fastest countries, (ii) most of the age group triathletes completed Ironman races held in the USA such as Ironman Wisconsin, Ironman Florida and Ironman Lake Placid, (iii) the fastest Ironman race times were achieved by athletes aged 35 years or younger (fastest men in age group 30–34 years, fastest women in age group 25–29 years), (iv) the fastest age group Ironman triathletes originated from Belgium, Denmark, Switzerland, Austria, Finland and Germany, and (iv) the fastest overall race times were recorded in the Ironman races held in Copenhagen, Hawaii, Barcelona, Florianópolis, Frankfurt and Kalmar. Further important findings were that three of the four predictive models identified the country of origin and the age group of the athletes as the most important predictors. Regarding environmental conditions such as weather (i.e. water and air temperatures) and course characteristics, these variables showed the lowest influence on performance compared to the other variables. Flat cycling and flat running courses were associated with faster overall race times.

Most successful Ironman finishers originated from the USA

Our hypothesis was confirmed by the initial significant finding, which indicated that the most successful age group of Ironman triathletes originated from the USA, followed by athletes from the United Kingdom and from Canada. Moreover, our hypothesis was further confirmed as most age group triathletes completed Ironman races held in the USA. These outcomes can be explained by the distribution of Ironman race locations, with the first five races being situated in the USA (Ironman Wisconsin, Ironman Florida, Ironman Lake Placid, Ironman Arizona, and Ironman Hawaii), followed by the Ironman races held in Austria and France. A study investigating the origin and age group of the fastest Ironman age group triathletes competing in Ironman Hawaii between 2003 and 2019 showed that North American athletes were the most performant and the most frequent participants in Ironman Hawaii27.

The annual schedule of the Ironman circuit includes numerous races (www.Ironman.com/races). Examining the North American region for 2023 (www.Ironman.com/im-north-america), one Ironman race is scheduled in Mexico, two in Canada, and 12 races are set to take place in the USA. In contrast, the European race calendar for 2023 (www.Ironman.com/im-europe) reveals a larger number of 20 planned Ironman races. While one might assume that the popularity of the Ironman triathlon is higher in North America, given its origin as a US–American invention and the larger number of athletes competing there, it is noteworthy that fewer Ironman races are offered in North America compared to Europe (www.Ironman.com/races). Hence, the geographical location of an Ironman race within a country or a continent may significantly influence participation26–28. A study analyzing the participation and performance trends in Ironman Switzerland from 1995 to 2011 showed that 90% of the triathletes originated from Europe, with 31.9% from Switzerland and 18.9% from Germany28.

Additionally, the importance of a race might also influence the participation of the athletes. In the Powerman Duathlon World Championship held in Switzerland from 2002 to 2011, most of the finishers were from Switzerland, followed by participants from European countries (i.e., Germany, France, Italy, Belgium, Spain, Great Britain, the Netherlands, and Denmark)29. Analyzing Ironman Hawaii as the Ironman World Championship and its qualifying races, it was observed that American triathletes dominated both participation and performance in both Ironman Hawaii and its qualifiers30. An analysis of 39,706 finishers from 124 countries who competed in Ironman Hawaii between 1985 and 2012 showed that most finishers originated from the USA, followed by triathletes from Germany, Japan, Australia, Canada, Switzerland, France, Great Britain, New Zealand, and Austria31. Although fewer races are offered in North America than in Europe, more US–Americans compete in Ironman races. Future studies should explore potential explanations for this difference in comparing the motivations of North American and European Ironman triathletes.

The fastest Ironman race courses are in Europe

A further important finding was that the fastest Ironman race times were recorded in the Ironman race held in Copenhagen, Hawaii, Barcelona, Florianópolis, Frankfurt and Kalmar. One might expect that the fastest race times would be achieved in US–American races since most athletes were from the USA. However, it is noteworthy that despite the USA having the largest group of Ironman age group triathletes, they did not produce the fastest. Surprisingly, the second group (United Kingdom) and the third group (Canada) of triathletes also did not yield the fastest participants in terms of race performance, which was unexpected given their participation rates. The observation that triathletes originating from the USA were not the fastest is based on the average race speed. This should be considered a limitation of this study since a high distribution of lower-performing athletes will result in a lower average value. Ironman Hawaii is the only race among the fastest races outside of Europe and located in the USA. This is simply explained by the fact that Ironman Hawaii is the Ironman World Championship.

The fastest Ironman triathletes are from Europe

We found that the fastest Ironman age group triathletes were from Europe, especially from Belgium, Denmark, Switzerland, Austria, Finland, and Germany. A study investigating 302,535 Ironman triathletes competing between 2002 and 2015 in 253 different Ironman races explored the impact of nationality on pacing30. The findings showed that Germans (both women and men), had the fastest performance, closely followed by Australian, Austrian, and Brazilian triathletes. US–Americans did not rank among the fastest nationalities30.

The dominance of a particular country in a specific sports discipline can be attributed to a combination of environmental and individual factors31. However, the available evidence mainly pertains to long-distance running events, where factors such as altitude, lifestyle, natural surroundings, historical background, and genetic characteristics have been linked to the emergence of athletes32,33. Similarly, in the context of running events, there is a need for future studies to explore the environmental factors associated with athletes’ place of origin, including aspects such as social, training culture, socio-economic aspects, and political support. These studies should also consider an analysis of lower levels, considering cities of origin.

The best Ironman race times were achieved at the age 35 years or younger

A further important finding was that the fastest Ironman race times were achieved by men triathletes aged 45 years or younger. Previous studies have shown that the peak performance age for the Ironman triathlon is around 32–33 years for both women and men34. However, over the years, the age of peak performance in elite women and men triathletes has shifted to approximately 34–35 years7,35–37. The variations observed in the studies can be attributed to differences in the time periods analyzed and the specific age groups considered. As a result, considering the growing popularity of Ironman events among non-professional athletes and the increasing commitment of participants38, individuals aiming to qualify for ‘Ironman Hawaii’ should consider getting involved in the sport at a younger age18.

The present study's findings align with an analysis of Olympic track and field data, which demonstrated that the age of peak performance increases with the distance of the foot race, with women generally achieving peak performance at younger ages39. An analysis of the World Championships or Olympics triathlon between 2008 and 2012 found that the age of peak total performance was at ~ 28 years40. Considering the mode of exercise and duration of endurance events, the age of peak performance increases with the event duration, ranging from ~ 20 years (swimming, ~ 2–15 min) to ~ 39 years (ultra-distance cycling, ~ 27–29 h) with minimal difference between men and women41.

The aspect of environmental characteristics

The algorithms showed that the origin of the athlete was the most predictive variable whereas environmental characteristics showed the lowest influence on overall race time. Little is known regarding the influence of environmental conditions such as ambient temperature in Ironman triathlon. In Ironman Hawaii, body core temperature increased during the race and correlated negatively with the position in the age group42. We also found that flat cycling and flat running courses were associated with faster overall race times. A case study investigated the pacing strategy of a female winner regarding elevation changes. The authors found that velocity varied with changes in elevation, but the athlete minimized fluctuations in heart rate and watts43. Furthermore, Ironman triathletes maintaining faster relative speed in the downhill segments were more successful regarding their estimated final race time44. Future studies need to investigate more deeply the influence of environmental conditions on Ironman race performance.

Limitations, strength, practical applications, and implications for future research

A limitation of this study is that it focuses specifically on the distinguishing characteristics of the Ironman triathlon. Therefore, caution should be exercised when generalizing the findings to other triathlon formats, such as the sprint triathlon, Olympic distance triathlon, and Half-Ironman Ironman 70.3, as these formats have shorter durations. Another limitation to consider is the nature of using averages as seen in the results for the origin of the fastest age group athletes. As seen for the athletes originating from the USA, a high distribution of lower-performing athletes will result in a lower average value. The R2 of the best model shows that our models are limited. For future studies, more variables (e.g. altitude, humidity, experience of the athletes, training regimes) should be included. Unmeasured confounding variables might also have an influence on race times. The ML models used are limited in terms of their interpretability and potential biases. A further limitation is cross-validation and testing on a separate validation set was not performed. This could ensure the generalizability of the results. The strength of the study was in its innovative data analysis approach, which incorporates various machine learning regression models. A further strength was the inclusion of environmental characteristics, which showed that these variables have no major influence on overall race performance. These findings provide valuable insights to age group triathletes and coaches. It highlights the importance of race location, suggesting that participating in Ironman races held in the USA may offer better performance opportunities and, by extension, a better chance to qualify for the Ironman World Championship. For governmental bodies involved in sports and event planning these findings indicate the significance of race location and its impact on participation and performance trends. Government agencies that promote sports and organize events can use this information to make informed decisions about hosting Ironman races. Researchers and academics can use these findings to further investigate the factors influencing performance in Ironman triathlons. They can delve deeper into the environmental and individual characteristics that contribute to the success of athletes from specific countries. Additionally, researchers can explore motivations and psychological factors that differ between North American and European triathletes. However, future studies need to investigate more deeply the influence of environmental conditions on Ironman race performance.

Conclusions

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. Most successful Ironman age group triathletes originating from the USA, the United Kingdom, and Canada, but the fastest athletes originated from European countries such as Belgium, Denmark, Switzerland, Austria, Finland, and Germany (11.74 ± 15.1 h). The fastest overall race times were achieved in Ironman Copenhagen, Ironman Hawaii, Ironman Barcelona, Ironman Florianópolis, Ironman Frankfurt and Ironman Kalmar. For any Ironman age group triathlete aiming to achieve a fast Ironman race time and to qualify for Ironman Hawaii, it is advisable to consider participating in an Ironman race held in Europe, preferably before reaching the age of 35 years.

Abbreviations

Ada Boost Regressor Adaptive Boosting

MAE Mean absolute error

ML Machine learning

XG Boost Regressor EXtreme Gradient Boosting

Acknowledgements

Not applicable.

Author contributions

B.K., M.T., and K.W. conceptualized the study. B.K., M.T. and D.V. designed the methodology. The formal analysis was done by D.V. The data was curated by E.V. The original draft was prepared by B.K., M.T., and K.W. P.T.N., I.C., and T.R. helped in drafting the manuscript. All authors have read and agreed to the published version of the manuscript.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study was approved by the Institutional Review Board of Kanton St. Gallen, Switzerland, with a waiver of the requirement for informed consent of the participants as the study involved the analysis of publicly available data (EKSG 01/06/2010). The study was conducted in accordance with recognized ethical standards according to the Declaration of Helsinki adopted in 1964 and revised in 2013.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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