How to Predict the Performance of NBA Draft Prospects

37 Pages Posted: 9 Sep 2023 Last revised: 6 Feb 2024

See all articles by Megan Czasonis

Megan Czasonis

State Street Corporate

Mark Kritzman

Windham Capital Management; Massachusetts Institute of Technology (MIT) - Sloan School of Management

Cel Kulasekaran

Windham Capital Management; Cambridge Sports Analytics

David Turkington

State Street Associates

Date Written: September 8, 2023

Abstract

The authors describe a new mathematical system for predicting outcomes of NBA draft prospects based on a statistical concept called relevance, which gives a mathematically precise and theoretically justified measure of the importance of a previously drafted player to a prediction. They also describe fit, which measures a specific prediction’s reliability, thereby offering guidance about how committed a team should be to a draft prospect. And they show how fit, together with asymmetry, identifies the uniquely optimal combination of previously drafted players and predictive variables for each individual prediction task. The authors argue that their new relevance‐based prediction system addresses complexities that are beyond the capacity of conventional prediction models, but in a way that is more transparent, more flexible, and more theoretically justified than widely used machine learning algorithms.

Keywords: Adjusted Fit, Asymmetry, Central Limit Theorem, CKT regression, Codependence, Fit, Gaussian Kernel, Information Theory, Informativeness, Lasso Regression, Machine Learning, Mahalanobis Distance, Model-based Algorithm, Model-free Algorithm, Nearest Neighbor Partial Sample Regression, Relevance

JEL Classification: C00, C01, C02, C10, C13, C18, C45, C50, C51, C55, Z20, Z29

Suggested Citation

Czasonis, Megan and Kritzman, Mark and Kulasekaran, Cel and Turkington, David, How to Predict the Performance of NBA Draft Prospects (September 8, 2023). MIT Sloan Research Paper No. 6955-23, Available at SSRN: https://ssrn.com/abstract=4566449 or http://dx.doi.org/10.2139/ssrn.4566449

Megan Czasonis

State Street Corporate ( email )

1 Lincoln Street
Boston, MA 02111
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Mark Kritzman (Contact Author)

Windham Capital Management ( email )

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6174193900 (Phone)
6172365034 (Fax)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

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Cel Kulasekaran

Windham Capital Management

245 Main Street
2nd Floor
Cambridge, MA 02142
United States

HOME PAGE: http://www.windhamcapital.com

Cambridge Sports Analytics ( email )

245 Main Street
2nd Floor
Cambridge, MA 02142
United States

David Turkington

State Street Associates ( email )

United States

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