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NBA_Tutorials Alternatives
Similar projects and alternatives to NBA_Tutorials
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The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better NBA_Tutorials alternative or higher similarity.
NBA_Tutorials reviews and mentions
Posts with mentions or reviews of NBA_Tutorials.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-08-10.
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Our starting lineup and why small sample sizes are deceptive.
The below (text between the horizontal rules) are excerpts from [this](https://www.theringer.com/2023/4/13/23681148/2023-nba-playoff-preview-how-to-use-and-not-use-lineup-data) really great explainer, which was published in April 2023, about the pitfalls of NBA lineup data in small sample sizes. It also serves as an explanation for why data scientist [Andrew Patton](https://www.andrewpatton.org) created the tool ["Should I use this Rating?" ](https://apanalytics.shinyapps.io/should_I_use_this_rating/) (which includes the Warriors starting 5's ORTG, DRTG, and possessions played plugged into it) from which the screenshot in this post was taken. All of the code for the tool as well as an extremely detailed and in depth breakdown of how the tool works and why can be found [here](https://github.com/rd11490/NBA_Tutorials/tree/master/five_man_net_rating).
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[OC] I Hate Traditional Rebounding Stats, So I Made My Own (A.K.A. Who Are the Most Selfless/Selfish Rebounders)
If you use python you can look up nba_scraper and pbpstats packages. Their source code pulls from the NBA API endpoints. Also here is a GitHub repo showing how to find the specific NBA API endpoint (and for other websites)
- [OC] 5 Best Overall 5-Man Lineups this Season
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How learning about a basketball statistic changed how I see the world
His RAPM calculations are also open sourced and available on Github here: https://github.com/rd11490/NBA_Tutorials.
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How do they do it!?
The NBA tracks data, so somewhere in a database they have information on every shot like (x,y) position on the court, closest defender distance, number of dribbles before the shot, etc. As for how they get these labels, they either have a person watch film and track or they use something complicated like computer vision to generate them. But you can do analytics on them yourself if you can get your hands on this data, which you can do by going to the NBA stats website or NBA Shot Charts.com. An NBA team would have direct access to a db, but you can get the data yourself by going to one of those sites, opening your network tab in your browser, and grabbing the data from there and saving it on your machine. Or you can follow a tutorial like this. Once you have the data, you can play around with it in either R or Python (or even Excel). I’m not sure which of R or Python is more in use by teams but I’ve heard you’re supposed to just pick one and learn it well.
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A note from our sponsor - InfluxDB
www.influxdata.com | 2 May 2024
Stats
Basic NBA_Tutorials repo stats
5
151
0.0
over 1 year ago
rd11490/NBA_Tutorials is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of NBA_Tutorials is Python.
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