Football Data Python API Docs | dltHub

Football-Data API is a REST API exposing football (soccer) domain data—competitions, matches, teams, persons, standings, scorers and related subresources. The REST API base URL is https://api.football-data.org/v4 and all requests require an X-Auth-Token header (API token)..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Football Data data in under 10 minutes.

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What data can I load from Football Data?

Here are some of the endpoints you can load from Football Data:

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How do I authenticate with the Football Data API?

Authentication is performed by including your API token in the X-Auth-Token HTTP header for every request (e.g., X-Auth-Token: YOUR_TOKEN). Some example client response headers also include X-Authenticated-Client, X-API-Version and rate-limit related headers.

1. Get your credentials

1. Register / sign in at https://www.football-data.org/client/register or "Get started" on the site. 2) Create or view your API client / token on the dashboard. 3) Copy the issued API token. 4) Use the token in the X-Auth-Token header for requests.

2. Add them to .dlt/secrets.toml

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[sources.football_data_source]
api_key = "your_api_token_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Football Data API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python football_data_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline football_data_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset football_data_data
The duckdb destination used duckdb:/football_data.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline football_data_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads matches and competitions from the Football Data API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def football_data_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.football-data.org/v4",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "matches", "endpoint": {"path": "matches", "data_selector": "matches"}},
            {"name": "competitions", "endpoint": {"path": "competitions", "data_selector": "competitions"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="football_data_pipeline",
        destination="duckdb",
        dataset_name="football_data_data",
    )
    load_info = pipeline.run(football_data_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("football_data_pipeline").dataset()
sessions_df = data.matches.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM football_data_data.matches LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("football_data_pipeline").dataset()
data.matches.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Football Data data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If the X-Auth-Token header is missing or invalid the API returns 403 (Restricted Resource) or a JSON error object describing the problem. Ensure X-Auth-Token: YOUR_TOKEN is included for all non-public endpoints.

Rate limits / Too Many Requests

The API is rate-limited. Unauthenticated clients: 100 requests/24h (very limited). Registered free-tier clients: typically 10 requests/minute; Standard: 30/min; higher tiers up to 60/min. Exceeding limits returns HTTP 429 Too Many Requests. Response headers to monitor include X-Requests-Available-Minute and X-RequestCounter-Reset.

Pagination and result wrappers

List resources include meta nodes such as filters and resultSet and then the list under a resource-specific key (e.g., 'matches', 'competitions', 'teams', 'scorers'). Use limit and offset query parameters to paginate (limit default and max vary per resource). Some list endpoints may return top-level count or resultSet.count; check the resource-specific response.

Common HTTP errors

• 400 Bad Request — malformed request or invalid filter value.
• 403 Restricted Resource — missing/invalid auth or resource restricted by plan.
• 404 Not Found — resource id does not exist.
• 429 Too Many Requests — rate limit exceeded.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack