sports-analytics
nba_api
| sports-analytics | nba_api | |
|---|---|---|
| 1 | 55 | |
| 32 | 2,266 | |
| - | - | |
| 2.6 | 7.1 | |
| about 3 years ago | 23 days ago | |
| Jupyter Notebook | Python | |
| - | MIT License |
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sports-analytics
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New NBA dataset on Kaggle! - Every game 60,000+ (1946-2021) w/ box scores, line scores, series info, and more - every player 4500+ w/ draft data, career stats, biometrics, and more - and every team (30 w/ franchise histories, coaches/staffing, and more). Updated daily, with plans for expansion!
The data is from stats.nba.com via the nba_api on GitHub. I compiled the data through an extraction script, and keep it updated daily via a fully automated Kaggle data pipeline. The pipeline is described here, and the project repository is here
nba_api
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An Analysis of How Chris Paul Has Affected His Teams (And How It May Impact the Warriors)
Thanks to the people putting together the open source nba_api, as well as the people at Basketball Reference and the NBA stats page.
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Stat Changes from Regular Season to Playoffs: 2022 - 2023 Season
NBA Stats API
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Scott Foster gets cracked in the face by Lebron. And Foster has his whistle in his mouth when it happens.
Yea ref stuff is hard to acquire. NBA api could b helpful tho and this is prob a good place to start
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NBA Game IDs to Playoff Game Numbers
I think you'll find this useful: https://github.com/swar/nba_api.
- Experiences with the different nba.com APIs? Which one would you recommend for play-by-play data to calculate player-specific ORtg or DRtg in a game?
- Don't have much experience using APIs, I'm trying to use one but do not know how to get it set up properly
- Trying to use an API in Python but I don't know how to set it up properly. Can I get some help?
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[OC] How does playoff basketball differ from the regular season? Analyzing team stats over the past 40 seasons
All data were collected using the nba_api. The dataset consists of per-game stat averages for all teams starting from the 1983-84 season through the 2021-22 season. In total: 1103 regular season teams, 624 of which made the playoffs in their respective years. Traditional stats include: PTS, FGM, FGA, FG_PCT, FG3M, FG3A, FG3_PCT, FTM, FTA, FT_PCT, OREB, DREB, REB, AST, STL, BLK, TOV, PF, PLUS_MINUS. Advanced stats include: NET_RATING, OFF_RATING, DEF_RATING, EFG_PCT, TS_PCT, PACE, OREB_PCT, DREB_PCT, REB_PCT, AST_PCT, AST_TO, AST_RATIO, TM_TOV_PCT. Note: for plus/minus and all of the advanced stats, I could only get data beginning from the 1997-98 season onwards, which resulted in 743 total regular season teams, 400 of which made the playoffs.
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Downloading stats from nba.com/stats
Is it not on the nba api? https://github.com/swar/nba_api/tree/master/docs/nba_api/stats/endpoints
- [Highlight] Poole gets a tech for passing the ball to the ref
What are some alternatives?
nba-sql - :basketball: An application to build an NBA database backed by MySQL, Postgres, or SQLite
nbastatR - NBA Stats API Wrapper and more for R
NBA-attendance-prediction - Attendance prediction tool for NBA games using machine learning. Full pipeline implemented in Python from data ingestion to prediction. Attained mean absolute error of around 800 people (about 5% capacity) on test set.
ImportJSON - Import JSON into Google Sheets, this library adds various ImportJSON functions to your spreadsheet
football_analytics - 📊⚽ A collection of football analytics projects, data, and analysis by Edd Webster (@eddwebster), including a curated list of publicly available resources published by the football analytics community.
nba-stats-analysis - Jupyter Notebooks with Applications of Data Science and Analysis with NBA data, using the information available through the NBA Stats API.
football-crunching - Analysis and datasets about football (soccer)
Python-NSE-Option-Chain-Analyzer - The NSE has a website which displays the option chain in near real-time. This program retrieves this data from the NSE site and then generates useful analysis of the Option Chain for the specified Index or Stock. It also continuously refreshes the Option Chain and visually displays the trend in various indicators useful for Technical Analysis.
td-ameritrade-python-api - Unofficial Python API client library for TD Ameritrade. This library allows for easy access of the Standard API and allows users to build data pipelines for the Streaming API.
pyracing - A complete overhaul of the original ir_webstats; pyracing is an API client/wrapper for iRacing, the leading online simracing service. pyracing handles the queries to iRacing's (known) URL endpoints and maps the returned JSON data into structured objects, allowing for easier access to the data.