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ci: add ruff lint, MIT license and CI badges
- Add GitHub Actions (ruff lint/format + smoke compile/import) - Add MIT LICENSE, .editorconfig, .gitattributes and dependabot - Add CI/License badges to README - Fix ruff issues in graph_builder.py
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‎.editorconfig‎

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root = true
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[*]
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charset = utf-8
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end_of_line = lf
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insert_final_newline = true
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trim_trailing_whitespace = true
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[*.{py,pyw}]
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indent_style = space
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indent_size = 4
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[*.{yml,yaml,json,toml}]
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indent_style = space
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indent_size = 2
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‎.gitattributes‎

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# Normalizar saltos de línea a LF en texto, mantener binarios intactos
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* text=auto
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*.png binary
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*.jpg binary
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‎.github/dependabot.yml‎

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version: 2
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updates:
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- package-ecosystem: "pip"
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directory: "/"
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schedule:
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interval: "weekly"
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open-pull-requests-limit: 5
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- package-ecosystem: "github-actions"
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directory: "/"
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schedule:
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interval: "weekly"
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open-pull-requests-limit: 5
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‎.github/workflows/ci.yml‎

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name: CI
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on:
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push:
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branches: [main]
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pull_request:
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concurrency:
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group: ci-${{ github.workflow }}-${{ github.ref }}
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cancel-in-progress: true
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jobs:
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lint:
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name: Ruff (lint + format)
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with:
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python-version: "3.12"
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cache: pip
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install ruff
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- name: Ruff check
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run: ruff check .
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- name: Ruff format check
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run: ruff format --check .
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build:
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name: Smoke (compile + import)
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with:
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python-version: "3.12"
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- name: Compile all Python sources
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run: python -m compileall -q src app.py app_mapas.py 2>/dev/null || python -m compileall -q .
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- name: Import distribution modules
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run: python -c "import sys; sys.path.insert(0, 'src'); import graph_builder; print('graph_builder OK')"

‎LICENSE‎

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MIT License
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Copyright (c) 2026 Álvaro Salinas
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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‎README.md‎

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## Executive Summary & Decision Making
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[![CI](https://github.com/alvarosalinaso/tactical-narrative-graph-analysis/actions/workflows/ci.yml/badge.svg)](https://github.com/alvarosalinaso/tactical-narrative-graph-analysis/actions/workflows/ci.yml) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
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Este proyecto diseña e implementa una infraestructura avanzada de **Ciencia de Redes (Complex Network Analysis - CNA)** y **Teoría de Grafos** aplicada al rendimiento deportivo de élite. Mediante la modelación matemática de un partido de fútbol no como eventos independientes aislados, sino como un **Sistema Complejo Dinámico**, el algoritmo deconstruye las interacciones colectivas del equipo. Esto permite parametrizar de forma cuantitativa la circulación de información (el balón), identificar vulnerabilidades estructurales en la red y optimizar la resiliencia táctica colectiva.
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El análisis de grafos tácticos capacita a Directores Técnicos, Analistas de Rendimiento y Departamentos de Inteligencia Deportiva para tomar **decisiones tácticas de alto nivel**:

‎src/graph_builder.py‎

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import pandas as pd
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import os
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import networkx as nx
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import pandas as pd
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from pyvis.network import Network
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import os
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# Simularemos un generador offline primero para asegurar estabilidad inmediata.
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# En producción, usaríamos `from statsbombpy import sb`
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# En producción, usaríamos `from statsbombpy import sb`
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# y haríamos: sb.events(match_id=3750201)
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def build_graph(data: pd.DataFrame) -> nx.DiGraph:
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"""Extrae las conexiones dirigidas de Pases y crea el Grafo Matemático"""
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G = nx.DiGraph()
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# Agregar Nodos y bordes con peso (Weight = N° de Pases)
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for index, row in data.iterrows():
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source = row['Passer']
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target = row['Receiver']
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source = row["Passer"]
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target = row["Receiver"]
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if G.has_edge(source, target):
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G[source][target]['weight'] += 1
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G[source][target]["weight"] += 1
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else:
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G.add_edge(source, target, weight=1)
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return G
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def analyze_and_visualize(G: nx.DiGraph, output_filename="grafo_tactico.html"):
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"""
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Calcula Betweenness Centrality (El dictador o 'broker' táctico del equipo)
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y renderiza el mapa en HTML interactivo
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"""
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# 1. Análisis de centralidad (Matemática Pura)
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centrality = nx.betweenness_centrality(G, weight='weight')
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centrality = nx.betweenness_centrality(G, weight="weight")
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# 2. Configurar motor visual PyVis
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net = Network(height="600px", width="100%", bgcolor="#222222", font_color="white", directed=True)
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net = Network(
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height="600px",
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width="100%",
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bgcolor="#222222",
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font_color="white",
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directed=True,
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)
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# Transformar a formato visual
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for node in G.nodes():
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# El tamaño del jugador en el mapa dependerá de cuánto es el 'puente' táctico
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size = centrality.get(node, 0.01) * 200 + 10
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net.add_node(node, label=node, title=f"Betweenness: {centrality.get(node, 0.01):.2f}", size=size)
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size = centrality.get(node, 0.01) * 200 + 10
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net.add_node(
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node,
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label=node,
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title=f"Betweenness: {centrality.get(node, 0.01):.2f}",
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size=size,
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)
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for source, target, data in G.edges(data=True):
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weight = data['weight']
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weight = data["weight"]
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net.add_edge(source, target, value=weight, title=f"{weight} pases")
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# Guardar en local (Se puede abrir en cualquier navegador web)
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os.makedirs("output", exist_ok=True)
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out_path = os.path.join("output", output_filename)
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net.write_html(out_path)
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print(f"Grafo interactivo renderizado con éxito en: {out_path}")
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if __name__ == "__main__":
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# Generamos un dataset esqueleto con base en tus propias estadísticas de passing.csv del United
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# Esto asegura de que corra a la primera (Plug-and-play)
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mock_data = pd.DataFrame([
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{"Passer": "Onana", "Receiver": "Martinez"},
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{"Passer": "Onana", "Receiver": "Dalot"},
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{"Passer": "Martinez", "Receiver": "Bruno Fernandes"},
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{"Passer": "Martinez", "Receiver": "Mainoo"},
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{"Passer": "Dalot", "Receiver": "Bruno Fernandes"},
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{"Passer": "Mainoo", "Receiver": "Bruno Fernandes"},
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{"Passer": "Bruno Fernandes", "Receiver": "Garnacho"},
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{"Passer": "Bruno Fernandes", "Receiver": "Garnacho"},
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{"Passer": "Bruno Fernandes", "Receiver": "Hojlund"},
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{"Passer": "Garnacho", "Receiver": "Hojlund"},
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])
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mock_data = pd.DataFrame(
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[
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{"Passer": "Onana", "Receiver": "Martinez"},
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{"Passer": "Onana", "Receiver": "Dalot"},
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{"Passer": "Martinez", "Receiver": "Bruno Fernandes"},
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{"Passer": "Martinez", "Receiver": "Mainoo"},
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{"Passer": "Dalot", "Receiver": "Bruno Fernandes"},
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{"Passer": "Mainoo", "Receiver": "Bruno Fernandes"},
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{"Passer": "Bruno Fernandes", "Receiver": "Garnacho"},
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{"Passer": "Bruno Fernandes", "Receiver": "Garnacho"},
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{"Passer": "Bruno Fernandes", "Receiver": "Hojlund"},
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{"Passer": "Garnacho", "Receiver": "Hojlund"},
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]
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)
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grafo = build_graph(mock_data)
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analyze_and_visualize(grafo)

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