Classifying teams in the NBA with player behavioral data

I use SecondSpectrum play-by-play data from the 2016-2017 NBA season to assemble behavioral event data for each player. Behavioral data includes propensity to dribble/pass/shoot, and also the resulting quality of shot when players decide to shoot or make another pass. I apply a k-means clustering algorithm to cluster teams based on their starting lineup behavior data; the clusters show different team makeups within the behavioral data collected. In particular, the clustering identified pass-heavy vs dribble-heavy offenses, and good shot-decision making teams.

Name thesis:degree_name

Bachelor

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Poler, Colin(Colin M.)

Advisor dc:contributor.advisor

• Peko Hosoi.

• Mechanical Engineering.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122880

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122880