Medbase St. Gallen Am Vadianplatz, St. Gallen, Switzerland.
Institute of Primary Care, University of Zurich, Zurich, Switzerland.
PLoS One. 2024 Aug 22;19(8):e0303960. doi: 10.1371/journal.pone.0303960. eCollection 2024.
The present study intended to determine the nationality of the fastest 100-mile ultra-marathoners and the country/events where the fastest 100-mile races are held. A machine learning model based on the XG Boost algorithm was built to predict the running speed from the athlete's age (Age group), gender (Gender), country of origin (Athlete country) and where the race occurred (Event country). Model explainability tools were then used to investigate how each independent variable influenced the predicted running speed. A total of 172,110 race records from 65,392 unique runners from 68 different countries participating in races held in 44 different countries were used for analyses. The model rates Event country (0.53) as the most important predictor (based on data entropy reduction), followed by Athlete country (0.21), Age group (0.14), and Gender (0.13). In terms of participation, the United States leads by far, followed by Great Britain, Canada, South Africa, and Japan, in both athlete and event counts. The fastest 100-mile races are held in Romania, Israel, Switzerland, Finland, Russia, the Netherlands, France, Denmark, Czechia, and Taiwan. The fastest athletes come mostly from Eastern European countries (Lithuania, Latvia, Ukraine, Finland, Russia, Hungary, Slovakia) and also Israel. In contrast, the slowest athletes come from Asian countries like China, Thailand, Vietnam, Indonesia, Malaysia, and Brunei. The difference among male and female predictions is relatively small at about 0.25 km/h. The fastest age group is 25-29 years, but the average speeds of groups 20-24 and 30-34 years are close. Participation, however, peaks for the age group 40-44 years. The model predicts the event location (country of event) as the most important predictor for a fast 100-mile race time. The fastest race courses were occurred in Romania, Israel, Switzerland, Finland, Russia, the Netherlands, France, Denmark, Czechia, and Taiwan. Athletes and coaches can use these findings for their race preparation to find the most appropriate racecourse for a fast 100-mile race time.
本研究旨在确定 100 英里超级马拉松最快的运动员的国籍以及举办最快 100 英里比赛的国家/地区。我们构建了一个基于 XG Boost 算法的机器学习模型，用于根据运动员的年龄（年龄组）、性别（性别）、原籍国（运动员原籍国）和比赛地点（赛事国）预测跑步速度。然后使用模型解释性工具来研究每个自变量如何影响预测的跑步速度。该分析共使用了来自 68 个不同国家/地区的 65392 名独特跑步者的 172110 条比赛记录，这些比赛分别在 44 个不同的国家举行。该模型将赛事国（0.53）评为最重要的预测因素（基于数据信息熵减少），其次是运动员原籍国（0.21）、年龄组（0.14）和性别（0.13）。就参与人数而言，美国遥遥领先，其次是英国、加拿大、南非和日本，无论是运动员人数还是赛事数量都是如此。最快的 100 英里比赛在罗马尼亚、以色列、瑞士、芬兰、俄罗斯、荷兰、法国、丹麦、捷克和中国台湾举行。最快的运动员大多来自东欧国家（立陶宛、拉脱维亚、乌克兰、芬兰、俄罗斯、匈牙利、斯洛伐克）和以色列。相比之下，速度最慢的运动员来自中国、泰国、越南、印度尼西亚、马来西亚和文莱等亚洲国家。男女预测结果的差异相对较小，约为 0.25km/h。最快的年龄组是 25-29 岁，但 20-24 岁和 30-34 岁组的平均速度接近。然而，年龄组 40-44 岁的参与度达到峰值。该模型预测赛事地点（赛事国）是预测 100 英里比赛成绩的最重要因素。最快的比赛路线发生在罗马尼亚、以色列、瑞士、芬兰、俄罗斯、荷兰、法国、丹麦、捷克和中国台湾。运动员和教练可以利用这些发现为比赛做准备，为获得最快的 100 英里比赛成绩找到最合适的比赛场地。
