sportsipy
nba_api
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| sportsipy | nba_api | |
|---|---|---|
| 3 | 55 | |
| 472 | 2,238 | |
| - | - | |
| 0.0 | 7.1 | |
| 5 months ago | 14 days ago | |
| Python | Python | |
| MIT License | MIT License |
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sportsipy
- I’ve been struggling with organizing projects and utilizing classes so I’ve been looking for public projects I can study
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Some data exports to xlsx, other data does not
Unfortunately I don't think you're doing anything wrong, I think the sportsipy package is just a wee bit broken. There's a bunch of related (ongoing) issues on the repo about Boxscore failing to pull data from sports-reference.
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Is there any baseball website that won't get pissed at me scraping it often?
I have used this for my own projects: https://github.com/roclark/sportsipy
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?
lazystream - Easily get LazyMan stream links, output directly or to m3u / xmltv formats. Streams can also be recorded or casted.
nbastatR - NBA Stats API Wrapper and more for R
python-cheatsheet - Comprehensive Python Cheatsheet
ImportJSON - Import JSON into Google Sheets, this library adds various ImportJSON functions to your spreadsheet
hoopR - An R package to quickly obtain clean and tidy men's basketball play by play data.
nba-stats-analysis - Jupyter Notebooks with Applications of Data Science and Analysis with NBA data, using the information available through the NBA Stats API.
Basketball_Analytics - Repository which contains various scripts and work with various basketball statistics
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.
krita-plugin-reference - A temporary Reference Docker for Krita 4.0.0
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.
pydfs-lineup-optimizer - Daily Fantasy Sports lineup optimzer for all popular daily fantasy sports sites
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.