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BurntSushi
nflgame
Python2013–2018
BurntSushi / nflgame

nflgame

An API to retrieve and read NFL Game Center JSON data. It can work with real-time data, which can be used for fantasy football.

Games & EmulationData & Databases
Explore on GitHubRead Hacker News discussions
4Distinct people
9Mentions
0Explicit recommenders
1.3KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

An API to retrieve and read NFL Game Center JSON data. It can work with real-time data, which can be used for fantasy football.

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 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 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 year9 dated mentions

Observed mentions: 2013-01-03 23:06:01 UTC to 2018-11-13 21:12:30 UTC.

012342013-01-01 00:00:00 UTC to 2014-01-01 00:00:00 UTC (exclusive): 4 mentions; 0 submissions, 4 comments20132014-01-01 00:00:00 UTC to 2015-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20152016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 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): 2 mentions; 0 submissions, 2 comments2018

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 UTC404
20142014-01-01 00:00:00 UTC2015-01-01 00:00:00 UTC202
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC101
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC000
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 UTC202

What Hacker News says

9 sources
burntsushi·Nov 13, 2018
I'm not an NFL insider. I don't know what they do internally. I only know that 1) the undocumented NFL GameCenter JSON is not 100% accurate and that 2) any user of a fantasy league would notice these inaccuracies. I did a test a while back by comparing GameCenter data with Yahoo's data. Kickers tend to have the most inaccuracies: https://github.com/BurntSushi/nflgame/blob/master/test-data/... QB stats are more solid for example, but there are still minor problems: https://github.com/BurntSushi/nflgame/blob/master/test-data/... From those observations, you can't really make any solid conclusions. But if you think about it for a bit, you might be able to reason your way to some guesses. For example, one possibility is that the GameCenter data is NFL's own construction that's only used for their GameCenter interfaces, where as places that "o
Read the rest
fficial" data is needed might be powered by Elias[1]. Why the discrepancy? Again, I don't know. It could be legacy software related. It could be contract/legal related. Or it could jus tbe plain old bugs. e.g., Maybe GameCenter hooks into an initial lossy but fast feed that is updated during the game, but never receives updates from a slower but more accurate feed later. Or maybe the NFL purposely inserts data canaries because they know this JSON feed is unprotected, and they intend on using those data canaries to detect folks using their data in an unlicensed fashion. I'm pretty sure IMDb does this, for example. Or maybe they just insert errors purposely to make it too costly for anyone to use this data in situations that require 100% accuracy (like fantasy football leagues). My guess is some innocuous blend of legal and legacy software reasons. [1] - http://www.esb.com/
In Fun with NFL Stats, Bokeh, and Pandas
bmelton·Mar 5, 2015
The NFL game feed is a combination of a couple of feeds. There's http://www.nfl.com/liveupdate/scorestrip -- which will show you the events and IDs that are currently going active. Right now, in the off-season, it will undoubtedly be blank. You can then take those values and plug them into this one: http://www.nfl.com/liveupdate/game-center/%s/%s_gtd.json % (year, game_id) For something to illustrate how they work together, you can check out this repository: https://github.com/BurntSushi/nflgame I don't remember the one for the NBA, as I've never used it, but I saw an article on reddit a few weeks ago about how to get the data from the NBA using their RSS feed, so I have a good faith belief that it exists.
In Show HN: Hooks – Get Custom Notifications for Everything Important to You
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
burntsushi·Mar 12, 2013
A related, but different project is my own nflgame [1]. Related in that it provides play-by-play data, but different in that it can be used to get it in real time. Also different in that the data only goes back to 2009, so it can't really be used to do the cool kinds of statistical analysis being done in the OP. The source is JSON data from NFL's GameCenter pages. It contains its fair share of errors, but it is at least structured. [1] - https://github.com/BurntSushi/nflgame
In NFL Play-by-Play Data—Analyzed, Visualized, and Quizzified

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