In this paper, we present a machine learning based approach to projecting the success of National Basketball Association (NBA) draft prospects. With the proliferation of data, analytics have increasingly be- come a critical component in the assessment of professional and collegiate basketball players. We leverage player biometric data, college statistics, draft selection order, and positional breakdown as modelling features in our prediction algorithms. We found that a player's draft pick and their college statistics are the best predictors of their longevity in the National Basketball Association.
Kannan, Adarsh; Kolovich, Brian; Lawrence, Brandon; and Rafiqi, Sohail
"Predicting National Basketball Association Success: A Machine Learning Approach,"
SMU Data Science Review: Vol. 1
, Article 7.
Available at: https://scholar.smu.edu/datasciencereview/vol1/iss3/7
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