Dataset: Injury Prediction For Competitive Runners

About this Dataset

Injury Prediction For Competitive Runners

Context🏃

Running is a great form of exercise, recreation, and sport participation for adults, adolescents, and children. Whether alone or in a team environment, running, when done properly, can enhance physical fitness, coordination, sense of accomplishment, and physical and emotional development. However, running under adverse conditions or with inadequate clothing and equipment can cause a variety of injuries and physical stress.

Content🏃

The data set consists of a detailed training log from a Dutch high-level running team over a period of seven years (2012-2019). We included the middle and long-distance runners of the team, that is, those competing on distances between the 800 meters and the marathon. This design decision is motivated by the fact that these groups have strong endurance-based components in their training, making their training regimes comparable. The head coach of the team did not change during the years of data collection.

The data set contains samples from 74 runners, of whom 27 are women and 47 are men. At the moment of data collection, they had been in the team for an average of 3.7 years. Most athletes competed on a national level, and some also on an international level. The study was conducted according to the requirements of the Declaration of Helsinki and was approved by the ethics committee of the second author’s institution

Acknowledgements🏃

S. Lovdal, Ruud J. R. Den Hartigh, G. Azzopardi, "Injury Prediction in Competitive Runners with Machine Learning", International Journal of Sports Physiology and Performance, 2020, in press.

author = {Lovdal, Sofie and den Hartigh, Ruud and Azzopardi, George},
publisher = {DataverseNL},
title = {{Replication Data for: Injury Prediction In Competitive Runners With Machine Learning}},
year = {2021},
version = {V1},
doi = {10.34894/UWU9PV},
url = {https://doi.org/10.34894/UWU9PV}
}
Source of Data-set --> here.

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Tables

Day Approach Maskedid Timeseries

@kaggle.shashwatwork_injury_prediction_for_competitive_runners.day_approach_maskedid_timeseries

1.47 MB
42766 rows
73 columns


CREATE TABLE day_approach_maskedid_timeseries (
  "nr_sessions" DOUBLE,
  "total_km" DOUBLE,
  "km_z3_4" DOUBLE,
  "km_z5_t1_t2" DOUBLE,
  "km_sprinting" DOUBLE,
  "strength_training" DOUBLE,
  "hours_alternative" DOUBLE,
  "perceived_exertion" DOUBLE,
  "perceived_trainingsuccess" DOUBLE,
  "perceived_recovery" DOUBLE,
  "nr_sessions_1" DOUBLE,
  "total_km_1" DOUBLE,
  "km_z3_4_1" DOUBLE,
  "km_z5_t1_t2_1" DOUBLE,
  "km_sprinting_1" DOUBLE,
  "strength_training_1" DOUBLE,
  "hours_alternative_1" DOUBLE,
  "perceived_exertion_1" DOUBLE,
  "perceived_trainingsuccess_1" DOUBLE,
  "perceived_recovery_1" DOUBLE,
  "nr_sessions_2" DOUBLE,
  "total_km_2" DOUBLE,
  "km_z3_4_2" DOUBLE,
  "km_z5_t1_t2_2" DOUBLE,
  "km_sprinting_2" DOUBLE,
  "strength_training_2" DOUBLE,
  "hours_alternative_2" DOUBLE,
  "perceived_exertion_2" DOUBLE,
  "perceived_trainingsuccess_2" DOUBLE,
  "perceived_recovery_2" DOUBLE,
  "nr_sessions_3" DOUBLE,
  "total_km_3" DOUBLE,
  "km_z3_4_3" DOUBLE,
  "km_z5_t1_t2_3" DOUBLE,
  "km_sprinting_3" DOUBLE,
  "strength_training_3" DOUBLE,
  "hours_alternative_3" DOUBLE,
  "perceived_exertion_3" DOUBLE,
  "perceived_trainingsuccess_3" DOUBLE,
  "perceived_recovery_3" DOUBLE,
  "nr_sessions_4" DOUBLE,
  "total_km_4" DOUBLE,
  "km_z3_4_4" DOUBLE,
  "km_z5_t1_t2_4" DOUBLE,
  "km_sprinting_4" DOUBLE,
  "strength_training_4" DOUBLE,
  "hours_alternative_4" DOUBLE,
  "perceived_exertion_4" DOUBLE,
  "perceived_trainingsuccess_4" DOUBLE,
  "perceived_recovery_4" DOUBLE,
  "nr_sessions_5" DOUBLE,
  "total_km_5" DOUBLE,
  "km_z3_4_5" DOUBLE,
  "km_z5_t1_t2_5" DOUBLE,
  "km_sprinting_5" DOUBLE,
  "strength_training_5" DOUBLE,
  "hours_alternative_5" DOUBLE,
  "perceived_exertion_5" DOUBLE,
  "perceived_trainingsuccess_5" DOUBLE,
  "perceived_recovery_5" DOUBLE,
  "nr_sessions_6" DOUBLE,
  "total_km_6" DOUBLE,
  "km_z3_4_6" DOUBLE,
  "km_z5_t1_t2_6" DOUBLE,
  "km_sprinting_6" DOUBLE,
  "strength_training_6" DOUBLE,
  "hours_alternative_6" DOUBLE,
  "perceived_exertion_6" DOUBLE,
  "perceived_trainingsuccess_6" DOUBLE,
  "perceived_recovery_6" DOUBLE,
  "athlete_id" BIGINT,
  "injury" BIGINT,
  "date" BIGINT
);

Week Approach Maskedid Timeseries

@kaggle.shashwatwork_injury_prediction_for_competitive_runners.week_approach_maskedid_timeseries

2.5 MB
42798 rows
72 columns


CREATE TABLE week_approach_maskedid_timeseries (
  "nr_sessions" DOUBLE,
  "nr_rest_days" DOUBLE,
  "total_kms" DOUBLE,
  "max_km_one_day" DOUBLE,
  "total_km_z3_z4_z5_t1_t2" DOUBLE,
  "nr_tough_sessions_effort_in_z5_t1_or_t2" DOUBLE,
  "nr_days_with_interval_session" DOUBLE,
  "total_km_z3_4" DOUBLE,
  "max_km_z3_4_one_day" DOUBLE,
  "total_km_z5_t1_t2" DOUBLE,
  "max_km_z5_t1_t2_one_day" DOUBLE,
  "total_hours_alternative_training" DOUBLE,
  "nr_strength_trainings" DOUBLE,
  "avg_exertion" DOUBLE,
  "min_exertion" DOUBLE,
  "max_exertion" DOUBLE,
  "avg_training_success" DOUBLE,
  "min_training_success" DOUBLE,
  "max_training_success" DOUBLE,
  "avg_recovery" DOUBLE,
  "min_recovery" DOUBLE,
  "max_recovery" DOUBLE,
  "nr_sessions_1" DOUBLE,
  "nr_rest_days_1" DOUBLE,
  "total_kms_1" DOUBLE,
  "max_km_one_day_1" DOUBLE,
  "total_km_z3_z4_z5_t1_t2_1" DOUBLE,
  "nr_tough_sessions_effort_in_z5_t1_or_t2_1" DOUBLE,
  "nr_days_with_interval_session_1" DOUBLE,
  "total_km_z3_4_1" DOUBLE,
  "max_km_z3_4_one_day_1" DOUBLE,
  "total_km_z5_t1_t2_1" DOUBLE,
  "max_km_z5_t1_t2_one_day_1" DOUBLE,
  "total_hours_alternative_training_1" DOUBLE,
  "nr_strength_trainings_1" DOUBLE,
  "avg_exertion_1" DOUBLE,
  "min_exertion_1" DOUBLE,
  "max_exertion_1" DOUBLE,
  "avg_training_success_1" DOUBLE,
  "min_training_success_1" DOUBLE,
  "max_training_success_1" DOUBLE,
  "avg_recovery_1" DOUBLE,
  "min_recovery_1" DOUBLE,
  "max_recovery_1" DOUBLE,
  "nr_sessions_2" DOUBLE,
  "nr_rest_days_2" DOUBLE,
  "total_kms_2" DOUBLE,
  "max_km_one_day_2" DOUBLE,
  "total_km_z3_z4_z5_t1_t2_2" DOUBLE,
  "nr_tough_sessions_effort_in_z5_t1_or_t2_2" DOUBLE,
  "nr_days_with_interval_session_2" DOUBLE,
  "total_km_z3_4_2" DOUBLE,
  "max_km_z3_4_one_day_2" DOUBLE,
  "total_km_z5_t1_t2_2" DOUBLE,
  "max_km_z5_t1_t2_one_day_2" DOUBLE,
  "total_hours_alternative_training_2" DOUBLE,
  "nr_strength_trainings_2" DOUBLE,
  "avg_exertion_2" DOUBLE,
  "min_exertion_2" DOUBLE,
  "max_exertion_2" DOUBLE,
  "avg_training_success_2" DOUBLE,
  "min_training_success_2" DOUBLE,
  "max_training_success_2" DOUBLE,
  "avg_recovery_2" DOUBLE,
  "min_recovery_2" DOUBLE,
  "max_recovery_2" DOUBLE,
  "athlete_id" BIGINT,
  "injury" BIGINT,
  "rel_total_kms_week_0_1" DOUBLE,
  "rel_total_kms_week_0_2" DOUBLE,
  "rel_total_kms_week_1_2" DOUBLE,
  "date" BIGINT
);