chickenstats 1.7.8

A Python package for scraping & analyzing sports statistics

chickenstats

---

About

chickenstats is a Python package for scraping & analyzing sports data. With just a few lines of code:

• Scrape & manipulate data from various NHL endpoints, leveraging chickenstats.chicken_nhl, which includes a proprietary xG model for shot quality metrics
• Augment play-by-play data & generate custom aggregations from raw csv files downloaded from Evolving-Hockey (subscription required) with chickenstats.evolving_hockey

For more in-depth explanations, tutorials, & detailed reference materials, consult the Documentation.

---

Compatibility

chickenstats requires Python 3.10 or greater & runs on the latest stable versions of Linux, MacOS, & Windows operating systems.

---

Installation

Very simple - install using PyPi. Best practice is to develop in an isolated virtual environment (conda or otherwise), but who's a chicken to judge?

pip install chickenstats

To confirm installation & confirm the latest version (1.7.8):

pip show chickenstats

---

Usage

chickenstats is structured as three underlying modules, each used with different data sources:

• chickenstats.chicken_nhl
• chickenstats.evolving_hockey

The package is under active development - features will be added or modified in the coming weeks & months.

chicken_nhl

The chickenstats.chicken_nhl module scrapes & manipulates data directly from various NHL endpoints, with outputs including schedule & game results, rosters, & play-by-play data.

The below example scrapes the schedule for the Nashville Predators, extracts the game IDs, then scrapes play-by-play data for the first ten regular season games.

from chickenstats.chicken_nhl import Season, Scraper

# Create a Season object for the current season
season = Season(2023)

# Download the Nashville schedule & filter for regular season games
nsh_schedule = season.schedule('NSH')
nsh_schedule_reg = nsh_schedule.loc[nsh_schedule.session == 2].reset_index(drop=True)

# Extract game IDs, excluding pre-season games
game_ids = nsh_schedule_reg.game_id.tolist()[:10]

# Create a scraper object using the game IDs
scraper = Scraper(game_ids)

# Scrape play-by-play data
play_by_play = scraper.play_by_play

evolving_hockey

The chickenstats.evolving_hockey module manipulates raw csv files downloaded from Evolving-Hockey. Using their original shifts & play-by-play data, adds additional information & aggregate for individual & on-ice statistics, including high-danger shooting events, xG & adjusted xG, faceoffs, & changes.

import pandas as pd
from chickenstats.evolving_hockey import prep_pbp, prep_stats, prep_lines

# The prep_pbp function takes the raw event and shifts dataframes
raw_shifts = pd.read_csv('./raw_shifts.csv')
raw_pbp = pd.read_csv('./raw_pbp.csv')

play_by_play = prep_pbp(raw_pbp, raw_shifts)

# You can use the play_by_play dataframe in various aggregations
# These are individual game statistics, including on-ice & usage,
# accounting for teammates & opposition on-ice
individual_game = prep_stats(play_by_play, level='game', teammates=True, opposition=True)

# These are game statistics for forward-line combinations, accounting for opponents on-ice
forward_lines = prep_lines(play_by_play, position='f', opposition=True)

---

Acknowledgements

This project wouldn't be possible without the support & efforts of countless others. I am obviously extremely grateful, even if there are too many of you to thank individually. However, this chicken will do his best.

First & foremost is my wife - the lovely Mrs. Chicken has been patient, understanding, & supportive throughout the countless hours of development, sometimes to her detriment.

Sincere apologies to the friends & family that have put up with me since my entry into Python, programming, & data analysis in January 2021. Thank you for being excited for me & with me throughout all of this, especially when you've had to fake it...

Speaking of which, thank you to the hockey analytics community on (the artist formerly known as) Twitter. You're producing & reacting to cutting-edge statistical analyses, while providing a supportive, welcoming environment for newcomers. This is by no means exhaustive, but a few people worth calling out specifically: