Code style: black PyPI version
A library and CLI tool for collecting stats from the NHL web API.
Currently, supported data types include event data such as shots / goals / hits / etc, shift information and general scheduling information.
All data is accessible identically through the Python API or command-line tool.
Compatible with Python3.5+.
Use pip:
python3 -m pip install nhlstats
Or from source:
git clone https://github.com/tomplex/nhlstats.git ~/dev/nhlstats python3 -m pip install ~/dev/nhlstats # or python3 ~/dev/nhlstats/setup.py install
This will add a new command to your system, nhl.
This is meant to be a tool to help obtain data about hockey games. I aim to make it easy to download data which people who are more statistically inclined than I can use to make pretty pictures and graphs. I am also trying to, generally, normalize and flatten the data so it easier to use in software which processes tabular data.
It's not meant to be a data model of all of the data available about the NHL. I'm not trying to create models for teams, rosters, players, events, etc. and all the hierarchies therein. If that's what you want, then there is an excellent library that already does that.
The key to NHL stats data is the "gameid", an ID which uniquely identifies every game. It's a 10-digit numeric code which is formatted like so:
2019020565
This tool uses the gameid to obtain data for specific games. You can use the list_games python function or the list-games CLI
command to get game ID's which you can then use to drill down and get information for the games you care about.
Certain derived stats are added to each event. There are three flags - is_shot, is_corsi, is_fenwick, and a field
for shot_distance and shot_angle. The flag fields denote whether the event is a corsi, fenwick, or plain shot event,
and shot_distance and shot_angle describe the distance of the shot from the net in feet, and the angle of the shot
where 0 is head-on with the net.
If you want more derived stats added to the data feel free to let me know.
Let's say you want to write a script which you'll run once a day, which will find all games played on the given day and download all play-by-play data for each game into a CSV file, labelled with the game's ID.
from nhlstats import list_games, list_plays from nhlstats.formatters import csv # List all games today and write all plays from each as a csv file named like the game_id for game in list_games(): # No args will list all games today game_id = game['game_id'] plays = list_plays(game_id) # get plays, normalized with open('{}.csv'.format(game_id), 'w') as f: csv.dump(plays, f)
If you use Pandas, then you can create a dataframe directly from the data which comes back from list_plays or list_shifts:
from nhlstats import list_plays, list_shifts import pandas as pd gameid = "2019020418" plays = pd.DataFrame(list_plays(gameid)) shifts = pd.DataFrame(list_shifts(gameid)) plays.head() shifts.head()
If you use petl, then you can use petl.fromdicts() to create a TableContainer:
from nhlstats import list_plays import petl as etl gameid = "2019020418" pipeline = etl.fromdicts(list_plays(gameid)) print(pipeline)
The formatters package formats data into different types of output, for example CSV, JSON, or a
text-based table. Each formatter has a dump and dumps function which work similarly to Python's json module.
If you want to save your data as JSON, for example:
from nhlstats import list_plays from nhlstats.formatters import json plays = list_plays('gameid') with open('file.json', 'w') as f: json.dump(plays, f)
More detailed examples of the formatters are available below.
$ nhl --help
Usage: nhl [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
list-games List all games from START_DATE to END_DATE.
list-plays List all play events which occurred in the given GAME_ID.
list-shifts List all shifts which occurred in the given GAME_ID.
list-shots List all shot events which occurred in the given GAME_ID.
Use the --output-format option to specify how to display the collected data. This option is available with all commands.
The default is text, which will pretty-print the data as a table. Other options include csv, json,
which will output a nested JSON like {"data": [...]}. The data from these commands will always be printed to stdout.
On Linux, MacOS or Windows you can use the > to redirect stdout to a new file
(will overwrite the contents if it exists), or >> to append to a file, like so:
nhl list-plays 2019020406 --output-format csv > 2019020406.csv # create a new file nhl list-plays 2019020406 --output-format csv >> plays.csv # append result to plays.csv
$ nhl list-games --help Usage: nhl list-games [OPTIONS] [START_DATE] [END_DATE] List all games from START_DATE to END_DATE. Dates should be of the form YYYY-MM-DD. Both date arguments default to "today" by system time, so you can omit the final argument to get a range from the first date to today. Options: --output-format [text|csv|json] --help Show this message and exit.
$ nhl list-plays --help Usage: nhl list-plays [OPTIONS] GAME_ID List all play events which occurred in the given GAME_ID. Options: --output-format [text|csv|json] --help Show this message and exit.
nhl list-shifts --help Usage: nhl list-shifts [OPTIONS] GAME_ID List all shot events which occurred in the given GAME_ID. Options: --output-format [text|csv|json] --help Show this message and exit.
The raw event data from the NHL is highly nested, and doesn't always contain all keys. I flatten, normalize and cull the data a bit to make it easier to display as tabular data and remove bits which didn't initially strike me as important. I could always be convinced to add in more.
The initial events, as received from the NHL, look like this:
{
"players": [
{
"player": {
"id": 8474189,
"fullName": "Lars Eller",
"link": "/api/v1/people/8474189"
},
"playerType": "Winner"
},
{
"player": {
"id": 8470144,
"fullName": "Frans Nielsen",
"link": "/api/v1/people/8470144"
},
"playerType": "Loser"
}
],
"result": {
"event": "Faceoff",
"eventCode": "DET52",
"eventTypeId": "FACEOFF",
"description": "Lars Eller faceoff won against Frans Nielsen"
},
"about": {
"eventIdx": 3,
"eventId": 52,
"period": 1,
"periodType": "REGULAR",
"ordinalNum": "1st",
"periodTime": "00:00",
"periodTimeRemaining": "20:00",
"dateTime": "2019年12月01日T00:08:26Z",
"goals": {
"away": 0,
"home": 0
}
},
"coordinates": {
"x": 0,
"y": 0
},
"team": {
"id": 15,
"name": "Washington Capitals",
"link": "/api/v1/teams/15",
"triCode": "WSH"
}
}the same event, "normalized", looks like this:
{
"datetime": "2019年12月01日T00:08:26Z",
"period": 1,
"period_time": "00:00",
"period_time_remaining": "20:00",
"period_type": "REGULAR",
"x": 0.0,
"y": 0.0,
"event_type": "FACEOFF",
"event_secondary_type": null,
"event_description": "Lars Eller faceoff won against Frans Nielsen",
"team_for": "WSH",
"player_1": "Lars Eller",
"player_1_type": "Winner",
"player_1_id": 8474189,
"player_2": "Frans Nielsen",
"player_2_type": "Loser",
"player_2_id": 8470144
}The currently available formatters are csv, json, and text.
Using the text output format, we get a pretty-printed table with the data:
datetime period period_time period_time_remaining period_type x y event_type event_secondary_type event_description team_for player_1 player_1_type player_1_id player_2 player_2_type player_2_id player_3 player_3_type player_3_id player_4 player_4_type player_4_id
-------------------- -------- ------------- ----------------------- ------------- --- --- --------------- ----------------------- -------------------------------------------------------------------------------- ---------- ------------------- --------------- ------------- ------------------- --------------- ------------- ---------------- --------------- ------------- ---------------- --------------- -------------
2019年11月30日T23:00:31Z 1 00:00 20:00 REGULAR GAME_SCHEDULED Game Scheduled
2019年12月01日T00:08:21Z 1 00:00 20:00 REGULAR PERIOD_READY Period Ready
2019年12月01日T00:08:26Z 1 00:00 20:00 REGULAR PERIOD_START Period Start
2019年12月01日T00:08:26Z 1 00:00 20:00 REGULAR 0 0 FACEOFF Lars Eller faceoff won against Frans Nielsen WSH Lars Eller Winner 8474189 Frans Nielsen Loser 8470144
2019年12月01日T00:09:03Z 1 00:20 19:40 REGULAR 80 8 SHOT Tip-In T.J. Oshie Tip-In saved by Jonathan Bernier WSH T.J. Oshie Shooter 8471698 Jonathan Bernier Goalie 8473541
2019年12月01日T00:09:09Z 1 00:26 19:34 REGULAR 78 -34 HIT T.J. Oshie hit Darren Helm WSH T.J. Oshie Hitter 8471698 Darren Helm Hittee 8471794
2019年12月01日T00:09:45Z 1 01:02 18:58 REGULAR -88 29 TAKEAWAY Takeaway by Michal Kempny WSH Michal Kempny PlayerID 8479482
Using the csv formatter, we get csv-like output:
datetime,period,period_time,period_time_remaining,period_type,x,y,event_type,event_secondary_type,event_description,team_for,player_1,player_1_type,player_1_id,player_2,player_2_type,player_2_id,player_3,player_3_type,player_3_id,player_4,player_4_type,player_4_id
2019年12月02日T01:42:03Z,1,00:00,20:00,REGULAR,,,GAME_SCHEDULED,,Game Scheduled,,,,,,,,,,,,,
2019年12月02日T03:07:47Z,1,00:00,20:00,REGULAR,,,PERIOD_READY,,Period Ready,,,,,,,,,,,,,
2019年12月02日T03:07:53Z,1,00:00,20:00,REGULAR,,,PERIOD_START,,Period Start,,,,,,,,,,,,,
2019年12月02日T03:07:53Z,1,00:00,20:00,REGULAR,0.0,0.0,FACEOFF,,Leon Draisaitl faceoff won against Bo Horvat,EDM,Leon Draisaitl,Winner,8477934,Bo Horvat,Loser,8477500,,,,,,
2019年12月02日T03:08:27Z,1,00:12,19:48,REGULAR,97.0,-19.0,HIT,,Bo Horvat hit Leon Draisaitl,VAN,Bo Horvat,Hitter,8477500,Leon Draisaitl,Hittee,8477934,,,,,,
2019年12月02日T03:08:45Z,1,00:30,19:30,REGULAR,51.0,-36.0,TAKEAWAY,,Takeaway by Jordie Benn,VAN,Jordie Benn,PlayerID,8474818,,,,,,,,,
2019年12月02日T03:09:16Z,1,01:01,18:59,REGULAR,-58.0,0.0,BLOCKED_SHOT,,Elias Pettersson shot blocked shot by Darnell Nurse,EDM,Darnell Nurse,Blocker,8477498,Elias Pettersson,Shooter,8480012,,,,,,
2019年12月02日T03:11:13Z,1,02:58,17:02,REGULAR,76.0,-17.0,SHOT,Backhand,Joakim Nygard Backhand saved by Jacob Markstrom,EDM,Joakim Nygard,Shooter,8481638,Jacob Markstrom,Goalie,8474593,,,,,,
2019年12月02日T03:11:24Z,1,03:09,16:51,REGULAR,7.0,-3.0,TAKEAWAY,,Takeaway by Tanner Pearson,VAN,Tanner Pearson,PlayerID,8476871,,,,,,,,,
the json formatter returns JSON identical to the normalized event above.