![]() Penalty minutes and hits are among those that many consider to be fantasy hockey trash stats - scoring categories that are frowned upon as superfluous and inconsequential. ,1,03:09,16:51,REGULAR,7.0,-3.Beyond goals and assists, differentiating the necessary "treasured" fantasy hockey stats from the "trash" stats can get murky. ,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, 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 1 01:02 18:58 REGULAR -88 29 TAKEAWAY Takeaway by Michal Kempny WSH Michal Kempny PlayerID 8479482 Oshie Hitter 8471698 Darren Helm Hittee 8471794 Oshie Shooter 8471698 Jonathan Bernier Goalie 8473541 Oshie Tip-In saved by Jonathan Bernier WSH T.J. ![]() 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 1 00:00 20:00 REGULAR PERIOD_START Period Start 1 00:00 20:00 REGULAR PERIOD_READY Period Ready 1 00:00 20:00 REGULAR GAME_SCHEDULED Game Scheduled 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 ![]() The currently available formatters are csv, json, and text. 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 plays = list_plays ( game_id ) # get plays, normalized with open ( ' Formatters 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. You can use the list_games python function or the list-games CLIĬommand to get game ID's which you can then use to drill down and get information for the games you care about. This tool uses the gameid to obtain data for specific games. It's a 10-digit numeric code which is formatted like so: 2019020565 The key to NHL stats data is the "gameid", an ID which uniquely identifies every game. If that's what you want, then there is an excellent It's not meant to be a data model of all of the data available about the NHL. Normalize and flatten the data so it easier to use in software which processes tabular data. Who are more statistically inclined than I can use to make pretty pictures and graphs. I aim to make it easy to download data which people This is meant to be a tool to help obtain data about hockey games. This will add a new command to your system, nhl. A library and CLI tool for collecting stats from the NHL web API.Ĭurrently, supported data types include event data such as shots / goals / hits / etc, shift information and general scheduling information.Īll data is accessible identically through the Python API or command-line tool.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |