gabridego/football-dangerousity-tables

Collect football match statistics using Math&Sport APIs

Football dangerousity collector

Introduction

Leveraging the APIs provided by Math&Sport, produce two csv tables containing the average maneuvre dangerousity in each match of a football season. Values are also stored, together with other KPIs, in MongoDB collections.

Username and password are required to access a Math&Sport authentication endpoint.

usage: main.py [-h] -a AUTH_ENDPOINT -u AUTH_USERNAME -p AUTH_PASSWORD -m MATCH_ENDPOINT [-d [MONGODB_URI]] [-c [CALENDAR_PATH]] [-o [HOME_OUTPUT_PATH]] [-w [AWAY_OUTPUT_PATH]]

Team dangerousity collector

optional arguments:
  -h, --help            show this help message and exit
  -a AUTH_ENDPOINT, --auth-endpoint AUTH_ENDPOINT
                        URL of authentication endpoint
  -u AUTH_USERNAME, --auth-username AUTH_USERNAME
                        Username to access authentication endpoint
  -p AUTH_PASSWORD, --auth-password AUTH_PASSWORD
                        Password to access authentication endpoint
  -m MATCH_ENDPOINT, --match-endpoint MATCH_ENDPOINT
                        URL of endpoint of match statistics
  -d [MONGODB_URI], --mongodb-uri [MONGODB_URI]
                        URI of mongodb host
  -c [CALENDAR_PATH], --calendar-path [CALENDAR_PATH]
                        Path to JSON file containing match information
  -o [HOME_OUTPUT_PATH], --home-output-path [HOME_OUTPUT_PATH]
                        Output path for the file containing dangerousity in home matches
  -w [AWAY_OUTPUT_PATH], --away-output-path [AWAY_OUTPUT_PATH]
                        Output path for the file containing dangerousity in away matches

Project organization

This repository is organized as follows:

.
├── data
│   └── calendar.json
├── docker-compose.yml
├── Dockerfile
├── main.py
├── out
│   ├── away_dangerousity.csv
│   └── home_dangerousity.csv
├── README.md
└── requirements.txt

data/calendar.json is provided by Math&Sport and contains informations about matches of the Italian Serie A, year 2021/22, including match ids that are required to contact their API.

Table representation

The two produced tables are related to the first and return round of a regular football season. They contain one value for each match and are organized as follows:

• rows correspond to home teams
• collumns correspond to away teams
• cell (i,j) contains average dangerousity of home team i versus away team j (in the match i vs. j)

Some cells might be empty, meaning that no statistics was collected for that match.

Dependencies

Docker can be installed following the official guide.

Python requirements are listed in requirements.txt and can be installed running pip install -r requirements.txt.

Instructions

Run using docker compose

Python command line arguments are hardcoded in docker-compose.yml. Please update it with the API endpoints and with your credentials for the authentication API.

docker compose up -d mongo
docker compose up python

To move the generated tables from the container to the host machine, retrieve the python container id using docker ps -a and copy the csv files to the host:

docker cp <container_id>:/usr/src/app/out/home_dangerousity.csv .
docker cp <container_id>:/usr/src/app/out/away_dangerousity.csv .

Run as python script

Run mongo image:

docker run -itd -p 27017:27017 --name mongo mongo

Run python script:

python3 main.py -a https://url/to/auth/api -u username -p password -m https://url/to/match/{id}/api -d mongodb://localhost:27017/

License

Data and APIs are provided by Math&Sport, please contact them for information and to access the APIs. All rights reserved.