Predicting 2000-Meter Indoor Rowing Performance Using Accessible Machine Learning Models
Arihant Singh Jaggi, Hiren Dandia
JADS
Journal of Applied Data Sciences
publishes peer-reviewed applied data science research. Published by Bright Publisher (ISSN 2723-6471). View DOI
ABSTRACT
The 2000-meter ergometer test is widely used to measure athlete's strength, skill and efficiency in competitive rowing. Traditional tests like 500m or 1000m rowing can be too physical and elaborate for beginners or younger rowers. This study aimed to create a simpler and data driven approach to predict 2000m rowing times using basic information like age, gender and weights. Predictions were made using machine … [view more]
Keywords: Rowing; 2000-Meter Ergometer; AI Prediction; Machine Learning; Xgboost
Awards & Recognition
Peer-Reviewed Publication
Journal of Applied Data Sciences · Vol 7, No 2
Accepted after peer review and published in JADS — an open-access journal for applied data science research under Bright Publisher (ISSN 2723-6471).
Open-Access DOI
10.47738/jads.v7i2.999
Assigned a permanent Digital Object Identifier and hosted in Bright Publisher’s open-access program for lasting academic citation.