Analysis of the 50-mile ultramarathon distance using a predictive XGBoost model
Abstract Although the 50-mile ultramarathon is one of the most common race distances, it has received little scientific attention. The objective of this study was to assess how an athlete’s age group, sex, nationality, and the race location, affect race speed. Utilizing a dataset with ultramarathon...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-92581-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850040179975258112 |
|---|---|
| author | Jonas Turnwald David Valero Pedro Forte Katja Weiss Elias Villiger Mabliny Thuany Volker Scheer Matthias Wilhelm Marilia Andrade Ivan Cuk Pantelis T. Nikolaidis Beat Knechtle |
| author_facet | Jonas Turnwald David Valero Pedro Forte Katja Weiss Elias Villiger Mabliny Thuany Volker Scheer Matthias Wilhelm Marilia Andrade Ivan Cuk Pantelis T. Nikolaidis Beat Knechtle |
| author_sort | Jonas Turnwald |
| collection | DOAJ |
| description | Abstract Although the 50-mile ultramarathon is one of the most common race distances, it has received little scientific attention. The objective of this study was to assess how an athlete’s age group, sex, nationality, and the race location, affect race speed. Utilizing a dataset with ultramarathon races from 1863 to 2022, a machine learning model based on the XGBoost algorithm was developed to predict the race speed based on the aforementioned variables. Model explainability tools, including model features relative importances and prediction distribution plots were then used to investigate how each feature affects the predicted race speed. The most important features, with respect to the predictive power of the XGBoost model, were the location of the race and the athlete’s gender. The top 3 countries with the fastest predicted median race speeds were Slovenia, New Zealand, and Bulgaria for nationality and New Zealand, Croatia, and Serbia for the race location. The fastest median race speed was predicted for the age group 20–24 years, but a marked age-related performance decline only became apparent from the age group 40–44 years onward. Model predictions for male athletes were faster than for female athletes. This study offers insights into factors influencing race speed in 50-mile ultramarathons, which may be beneficial for athletes, coaches, and race organizers. The identification of nationalities and event countries with fast race speeds provides a foundation for further exploration in the field of ultramarathon events. |
| format | Article |
| id | doaj-art-a7a8f33ae8624d51a0f3efc4ba5418a7 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a7a8f33ae8624d51a0f3efc4ba5418a72025-08-20T02:56:09ZengNature PortfolioScientific Reports2045-23222025-03-0115111110.1038/s41598-025-92581-wAnalysis of the 50-mile ultramarathon distance using a predictive XGBoost modelJonas Turnwald0David Valero1Pedro Forte2Katja Weiss3Elias Villiger4Mabliny Thuany5Volker Scheer6Matthias Wilhelm7Marilia Andrade8Ivan Cuk9Pantelis T. Nikolaidis10Beat Knechtle11Centre for Rehabilitation and Sports Medicine, University Hospital Bern, Inselspital Bern, University of BernUltra Sports Science FoundationHigher Institute of Educational Sciences of the DouroInstitute of Primary Care, University of ZurichInstitute of Primary Care, University of ZurichFaculty of Sports, University of PortoUltra Sports Science FoundationCentre for Rehabilitation and Sports Medicine, University Hospital Bern, Inselspital Bern, University of BernPhysiology Department, Federal University of Sao PauloFaculty of Sport and Physical Education, University of BelgradeSchool of Health and Caring Sciences, University of West AtticaInstitute of Primary Care, University of ZurichAbstract Although the 50-mile ultramarathon is one of the most common race distances, it has received little scientific attention. The objective of this study was to assess how an athlete’s age group, sex, nationality, and the race location, affect race speed. Utilizing a dataset with ultramarathon races from 1863 to 2022, a machine learning model based on the XGBoost algorithm was developed to predict the race speed based on the aforementioned variables. Model explainability tools, including model features relative importances and prediction distribution plots were then used to investigate how each feature affects the predicted race speed. The most important features, with respect to the predictive power of the XGBoost model, were the location of the race and the athlete’s gender. The top 3 countries with the fastest predicted median race speeds were Slovenia, New Zealand, and Bulgaria for nationality and New Zealand, Croatia, and Serbia for the race location. The fastest median race speed was predicted for the age group 20–24 years, but a marked age-related performance decline only became apparent from the age group 40–44 years onward. Model predictions for male athletes were faster than for female athletes. This study offers insights into factors influencing race speed in 50-mile ultramarathons, which may be beneficial for athletes, coaches, and race organizers. The identification of nationalities and event countries with fast race speeds provides a foundation for further exploration in the field of ultramarathon events.https://doi.org/10.1038/s41598-025-92581-w |
| spellingShingle | Jonas Turnwald David Valero Pedro Forte Katja Weiss Elias Villiger Mabliny Thuany Volker Scheer Matthias Wilhelm Marilia Andrade Ivan Cuk Pantelis T. Nikolaidis Beat Knechtle Analysis of the 50-mile ultramarathon distance using a predictive XGBoost model Scientific Reports |
| title | Analysis of the 50-mile ultramarathon distance using a predictive XGBoost model |
| title_full | Analysis of the 50-mile ultramarathon distance using a predictive XGBoost model |
| title_fullStr | Analysis of the 50-mile ultramarathon distance using a predictive XGBoost model |
| title_full_unstemmed | Analysis of the 50-mile ultramarathon distance using a predictive XGBoost model |
| title_short | Analysis of the 50-mile ultramarathon distance using a predictive XGBoost model |
| title_sort | analysis of the 50 mile ultramarathon distance using a predictive xgboost model |
| url | https://doi.org/10.1038/s41598-025-92581-w |
| work_keys_str_mv | AT jonasturnwald analysisofthe50mileultramarathondistanceusingapredictivexgboostmodel AT davidvalero analysisofthe50mileultramarathondistanceusingapredictivexgboostmodel AT pedroforte analysisofthe50mileultramarathondistanceusingapredictivexgboostmodel AT katjaweiss analysisofthe50mileultramarathondistanceusingapredictivexgboostmodel AT eliasvilliger analysisofthe50mileultramarathondistanceusingapredictivexgboostmodel AT mablinythuany analysisofthe50mileultramarathondistanceusingapredictivexgboostmodel AT volkerscheer analysisofthe50mileultramarathondistanceusingapredictivexgboostmodel AT matthiaswilhelm analysisofthe50mileultramarathondistanceusingapredictivexgboostmodel AT mariliaandrade analysisofthe50mileultramarathondistanceusingapredictivexgboostmodel AT ivancuk analysisofthe50mileultramarathondistanceusingapredictivexgboostmodel AT pantelistnikolaidis analysisofthe50mileultramarathondistanceusingapredictivexgboostmodel AT beatknechtle analysisofthe50mileultramarathondistanceusingapredictivexgboostmodel |