Data-Driven Approaches to NBA Team Evaluation and Building
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No. of downloads: 342
Date
2020Author
Chun, Hyunsoo
Creegan, David
Majedi, Olivia
Smolyak, Daniel
Valcarcel, Brian
Advisor
Bjarnadottir, Margret
DRUM DOI
Metadata
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In the National Basketball Association (NBA), it has historically been difficult to
build and sustain a team that can consistently compete for championships. Given
this challenge, we have developed a series of analyses to support NBA teams in
making data-driven decisions. Relying on a variety of datasets, we examined
several facets related to the construction of NBA rosters and their performance. In
our analysis of on-court performance, we have used clustering algorithms to
classify teams in terms of play style, and determined which play styles tend to
lead to success. In our analysis of roster construction and transactions, we have
investigated the relative value of draft picks and the impact of trades involving
draft picks, as well as the effect of roster continuity (i.e. maintaining the same
players across seasons) on team success. Additionally, we have developed a
model for predicting player contract values and performance versus contract
value, which will help teams in identifying the most cost-effective players to
acquire. Ultimately, this assembly of analyses, in conjunction, can be used to
inform any NBA team’s decisions in its pursuit of success.
Notes
Gemstone Team PROCESS