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BurntSushi
nfldb
Python2013–2026
BurntSushi / nfldb

nfldb

A library to manage and update NFL data in a relational database.

Data & DatabasesLibraries & SDKs
Explore on GitHubRead Hacker News discussions
4Distinct people
13Mentions
0Explicit recommenders
1.1KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A library to manage and update NFL data in a relational database.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 5:05:26 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year13 dated mentions

Observed mentions: 2013-08-29 00:55:24 UTC to 2026-06-03 20:09:38 UTC.

02462013-01-01 00:00:00 UTC to 2014-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments20132014-01-01 00:00:00 UTC to 2015-01-01 00:00:00 UTC (exclusive): 6 mentions; 1 submissions, 5 comments2015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20132013-01-01 00:00:00 UTC2014-01-01 00:00:00 UTC211
20142014-01-01 00:00:00 UTC2015-01-01 00:00:00 UTC615
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC110
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC202
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC000
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC101
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

6 sources
burntsushi·May 22, 2014
Author here! That's a pretty nice visualization :-) I thought I'd just squeeze in a few words about nflgame/nfldb. Both offer access to the same stuff: play-by-play data back to 2009. Both can be used with live games so that they are updated in real time (well, at least as frequently as NFL.com). nflgame is responsible for pulling the JSON data and provides some rudimentary searching features. But it's slow. nfldb stores all this data for you in a relational database. It comes with a script that updates the database while games are playing so that you can get access to live data. (It will even migrate the database for you if I've made any changes to the schema.) Here's a quick example that shows how to get all of Julian Edelman's touchdown plays from last season: import nfldb db = nfldb.connect() q = nfldb.Query(db)
Read the rest
q.game(season_year=2013, season_type='Regular') q.player(full_name='Julian Edelman').play(offense_tds=1) for g in q.as_plays(): print g Easy as pie! There's an extensive wiki (almost 20,000 words) with tons of examples and explanation: https://github.com/BurntSushi/nfldb/wiki Other features: aggregating data, player meta data (college, height, weight, etc.) and fuzzy player name matching.
In Open Football Data
burntsushi·Jan 31, 2014
If you're willing to limit yourself to the last five years, you can avoid the pain of parsing those free-form text descriptions with nflgame[1] or nfldb[2]. Disclaimer: I am the author of those tools. We've been slowly building up a small community of people using it. In fact, I'm currently in a small fantasy playoff league that's running off nflgame. They also include live updates while games are playing. I'd be curious to see if you get any substantially different results using structured data (nflgame gets it from NFL.com's JSON feed) as opposed to parsing the text descriptions. [1] - https://github.com/BurntSushi/nflgame [2] - https://github.com/BurntSushi/nfldb
In Dataset: Ten Years of NFL Plays Analyzed, Visualized, Quizzified

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