Analyzing NBA basketball data with R. For about three years now, telemetry has been gathered for professional basketball games in the US by SportVU for the NBA. Six cameras track the on-court position of the players and the ball, with a resolution of 25 samples per second. Combine this movement data with NBA play-by-play data (players, plays, fouls, and points scored — data sadly no longer made available by the NBA), and you have a rich data set for analysis.
The series will go through all the major steps in a data analytic pipeline, such as obtaining, cleaning, exploring and analyzing data, with a rich set of statistics for NBA players. In this post, we will learn how to scrape relevant data from basketball-reference and how to turn the data into a clean usable data frame. #used packages.
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September 27, 2015 March 26, 2017 Ed Maia Sport, Visualisation Basketball, NBA, R A while ago I found this fantastic post about NBA shot charts built in Python. Since my Python skills are quite basic I decided to reproduce such charts in R using data scraped from the internet and ggplot2 .
ncaahoopR is an R package for working with NCAA Basketball Play-by-Play Data. It scrapes play-by-play data and returns it to the user in a tidy format, allowing the user to explore the data with assist networks, shot charts, and in-game win-probability charts. For pre-scraped schedules, rosters, box scores, and play-by-play data, check out the ncaahoopR_data repository.
NBA Play By Play Data By Season (CSV) Download a historically accurate NBA play by play dataset – with information for each team in the league, and for every season since the 2000/2001 season. NBA Season. Play By Play CSV File. 2000-2001. 2000-01_pbp.csv. 2001-2002.
Dataset. As mentioned above, the SpaceJam Basketball Action Dataset was used to train the R(2+1)D CNN model for video/action classification of basketball actions. The Repo contains two datasets (clips->.mp4 files and joints -> .npy files) of basketball single-player actions. The size of the two final annotated datasets is about 32,560 examples.
However, in my internet searching I didn’t come across any free easy-to-use datasets. The website Basketball-Reference.com is an excellent compendium of all the data I would want, but it was embedded within the webpage, not made available in an analysis-ready format. (Or at least, I couldn’t find it, or it wasn’t free.)
The pull initially contained 52 rows of missing data. The gaps have been manually filled using data from Basketball Reference. I am not aware of any other data quality issues. Analysis Ideas. The data set can be used to explore how age/height/weight tendencies have changed over time due to changes in game philosophy and player development ...