# devinpleuler/analytics-handbook

Getting started with soccer analytics

Repository: https://github.com/devinpleuler/analytics-handbook
Canonical: https://ross.abutalabs.com/products/analytics-handbook
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2024-05-26T22:14:52+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2340, "days_push": 829, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1695, forks 216 (observed 2026-08-28T04:05:23.698373+00:00)

## What it is
A Jupyter Notebook-based handbook introducing technical skills for soccer analytics, using public data from Statsbomb and Metrica. It covers concepts, code samples, and best practices for working with open soccer data in Python.

## Use cases
- learn soccer analytics from scratch
- analyze soccer match event data with python
- get started with statsbomb open data
- visualize soccer pitch and player data
- career transition into sports analytics
- work with tracking data from metrica sports

## When to choose
- you want a hands-on notebook introduction to soccer analytics
- you prefer learning with pip-installable tools like mplsoccer and kloppy
- you want free open soccer datasets to practice on

## When to avoid
- you need production soccer analytics infrastructure
- you want coverage of sports other than soccer
- you need a maintained software library rather than educational material

## Facets
- artifact type: learning-resource
- maturity: stable
- function: data-science, data-visualization, nlp
- domain: data-science, tutorials, sports
- platform: python, cross-platform
- tags: soccer-analytics, jupyter-notebook, sports-data, tutorial, open-data

## Member repositories
- devinpleuler/analytics-handbook (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:23.698373+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:37:56.146965+00:00, confidence not recorded.
  - readme: https://github.com/devinpleuler/analytics-handbook (fetched 2026-08-28T04:05:23.698373+00:00, sha c536dfc0464c)
- Data as of 2026-08-30T08:39:29.467469+00:00.