Load F1 Fantasy data to DuckDB

F1 Fantasy is a platform providing access to Formula 1 fantasy game data including teams, players, and user-specific league statistics. Everything needed to build a working F1 Fantasy → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your F1 Fantasy to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from F1 Fantasy to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the F1 Fantasy API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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F1 Fantasy API at a glance

These values come from the F1 Fantasy API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the F1 Fantasy API?

Authentication requires a session token obtained from the official website, which must be included in the Authorization header as a Bearer token.

1. Get your credentials

The F1 Fantasy API does not provide a traditional API key setup via a developer dashboard. Instead, you must authenticate using a session token obtained through the official web interface: 1. Navigate to the official F1 Fantasy website (https://fantasy.formula1.com) and log in to your account. 2. Open your browser's developer tools (typically F12 or Right-Click > Inspect) and navigate to the Network tab. 3. Perform a login or refresh your page. 4. Locate the network request made to the /services/session/login or similar authentication endpoint. 5. Inspect the response body to copy your session token (often used as a Bearer token) and potentially your user_guid if required by specific client implementations.

2. Add them to .dlt/secrets.toml

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[sources.f1_fantasy_source]
token = "REPLACE_ME"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What F1 Fantasy data can I load into DuckDB?

These are the F1 Fantasy endpoints dlt can load into DuckDB:

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How do I load only new F1 Fantasy records?

The F1 Fantasy API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "players", "endpoint": {
    "path": "2022/players",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated F1 Fantasy pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading players and teams from the F1 Fantasy API into DuckDB:

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

@dlt.source
def f1_fantasy_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://fantasy-api.formula1.com/partner_games/f1",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "players", "endpoint": {"path": "2022/players", "data_selector": "players"}},
            {"name": "teams", "endpoint": {"path": "2022/teams", "data_selector": "teams"}}
        ],
    }
    yield from rest_api_resources(config)


def load_f1_fantasy_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="f1_fantasy_pipeline",
        destination="duckdb",
        dataset_name="f1_fantasy_data",
    )
    load_info = pipeline.run(f1_fantasy_source())
    print(load_info)


if __name__ == "__main__":
    load_f1_fantasy_to_duckdb()

Run it with python f1_fantasy_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query F1 Fantasy data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("f1_fantasy_pipeline").dataset()
df = data.players.df()
print(df.head())

SQL:

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SELECT * FROM f1_fantasy_data.players LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the F1 Fantasy to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw F1 Fantasy loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load F1 Fantasy data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack