Number of finishers by age group for women and men with the overall race times for each age group (HH:MM:SS). by Beat Knechtle (5504402)

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The performance difference between the sexes (in percent) across age groups for split disciplines and overall race times. by Beat Knechtle (5504402)

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Participants by event status and sex. by Beat Knechtle (5504402)

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S1 Data - by Beat Knechtle (5504402)

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Sex difference in swimming by split disciplines by the type of swim, bike, and run course. by Beat Knechtle (5504402)

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Average split and overall race times (HH:MM:SS). by Beat Knechtle (5504402)

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Split and overall race times for women and by age group. by Beat Knechtle (5504402)

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Trends of average and best race times for IRONMAN^® age group triathletes. by Beat Knechtle (5504402)

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S2 Data - by Beat Knechtle (5504402)

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Number of finishers by sex and men-to-women ratio over the years between 2002 and 2022. by Beat Knechtle (5504402)

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Split and overall IRONMAN^® race times histograms for women and men. by Beat Knechtle (5504402)

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Men-to-women ratio across age groups. by Beat Knechtle (5504402)

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Trend of the sex difference across years in overall race time. by Beat Knechtle (5504402)

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Minimal data set. by Ikuo Mikoshiba (9312007)

SHAP dependence plots with interaction coloring. by Wentao Yang (205781)

“…The resampled data were split 7:3 into a training set (n = 29,309) and a test set (n = 12,561).Ten machine learning models were built, with discrimination, calibration, and clinical utility evaluated to identify the optimal model. …”

Screening process diagram. by Wentao Yang (205781)

“…The resampled data were split 7:3 into a training set (n = 29,309) and a test set (n = 12,561).Ten machine learning models were built, with discrimination, calibration, and clinical utility evaluated to identify the optimal model. …”

SHAP waterfall plot. by Wentao Yang (205781)

“…The resampled data were split 7:3 into a training set (n = 29,309) and a test set (n = 12,561).Ten machine learning models were built, with discrimination, calibration, and clinical utility evaluated to identify the optimal model. …”

SHAP decision plot. by Wentao Yang (205781)

“…The resampled data were split 7:3 into a training set (n = 29,309) and a test set (n = 12,561).Ten machine learning models were built, with discrimination, calibration, and clinical utility evaluated to identify the optimal model. …”

LASSO regression visualization plot. by Wentao Yang (205781)

“…The resampled data were split 7:3 into a training set (n = 29,309) and a test set (n = 12,561).Ten machine learning models were built, with discrimination, calibration, and clinical utility evaluated to identify the optimal model. …”

SHAP dependence plots. by Wentao Yang (205781)

“…The resampled data were split 7:3 into a training set (n = 29,309) and a test set (n = 12,561).Ten machine learning models were built, with discrimination, calibration, and clinical utility evaluated to identify the optimal model. …”