铁人三项比赛的大多数参与者都是年龄组运动员。我们对休闲运动员的训练和比赛参与有广泛的了解。尽管如此,铁人年龄组铁人三项运动员必须达到快速的比赛时间才能获得在夏威夷举行的铁人世界锦标赛的资格。他们可以,因此,受益于知道世界上最快的铁人赛马场在哪里。本研究的目的是调查世界上年龄组铁人三项运动员最快的铁人三项赛马场。分析了来自228个国家的677,702个Ironman年龄组完成者的数据(男性544963个,女性132739个),并在2002年至2022年期间参加了66个不同Ironman比赛地点的444项赛事。通过传统的描述性统计和机器学习回归模型对数据进行分析。测试了四种算法(随机森林回归,XGBoostRegressor,CatBootRegressor,和决策树回归器)。模型使用性别,年龄组,原产国,环境因素(平均空气和水温),和事件位置作为独立变量来预测最终的整体比赛时间。尽管大多数成功的铁人三项运动员来自美国(274,553),其次是来自英国(55,410)和加拿大(38,264)的运动员,与速度最快的国家相比,这些国家的平均比赛时间要慢得多。大多数铁人三项运动员都在威斯康星州的铁人比赛(38,545),其次是佛罗里达铁人(38,157)和普莱西德湖铁人(34,341)。在Ironman哥本哈根实现了最快的整体比赛时间(11.68±1.38h),其次是夏威夷铁人(11.72±1.86小时),巴塞罗那铁人(11.78±1.43h),IronmanFlorianópolis(11.80±1.52小时),法兰克福铁人(12.03±1.38小时)和卡尔马铁人(12.08±1.47小时)。最快的运动员来自比利时(11.48±1.47h),其次是来自丹麦的运动员(11.59±1.40h),瑞士(11.62±1.49小时),奥地利(11.68±1.50),芬兰(11.68±1.40h)和德国(11.74±15.1h)。平坦的跑步和自行车路线与更快的整体比赛时间相关。其中三个预测模型将“国家/地区”和“年龄组”变量确定为最重要的预测因子。环境特征对其他变量的影响最小。运动员的起源是最具预测性的变量,而环境特征的影响最小。平坦的自行车和平坦的跑步路线与更快的整体比赛时间相关。最快的整体比赛时间主要在欧洲比赛中实现,例如Ironman哥本哈根,Ironman夏威夷,铁人巴塞罗那,IronmanFlorianópolis,铁人法兰克福和铁人卡尔马。最快的铁人三项运动员来自比利时等欧洲国家,丹麦,瑞士,奥地利,芬兰,和德国。
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.