Skip to content
Clustering NBA statistical data to identify position of player
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.vscode
averages
clustering
helpers
nbaStats
player
.gitattributes
.gitignore
ColumnGroupKey.csv
NBA Clustering Dashboard.pbix
README.md
assignment_output_all.csv
clustering_all.csv
dashboard.pbix
find_polarity.py
getBoxScore.py
getNBAdata.py
hierarchical_output.csv
hierarchical_output2.csv
log.txt
newClusterNBAdata.py
oldClusterNBAdata.py
organizeNBAdata.py
playerDocs.txt
polarity_output.csv
shotChartValues.txt
twitterStream.py

README.md

NBA-Positional-Clustering

Clustering NBA statistical data to identify position of player

Player positions in the NBA have become a rather fluid concept. Teams like the Warriors with their "Lineup of Death" have shaken the traditional mindset of the basketball world.

We wanted to be able to build out a clustering model that used a player's statistics to identify the player's "true position."

When we say "true position," we mean the position the player plays most alike. While LeBron James could be listed at just about any position on the floor, we wanted to know what his stats told us.

By creating an unsupervised clustering model, players would be grouped together with other players of a similar statistical model.

For a full explanation of this process, please see our final report

You can’t perform that action at this time.