Fantastical Python API Docs | dltHub

Fantastical is the Yahoo Fantasy Sports REST API that provides access to fantasy game, league, team, player, roster, transaction and user data for Yahoo Fantasy Sports (NFL, MLB, NBA, NHL). The REST API base URL is https://fantasysports.yahooapis.com/fantasy/v2 and OAuth (3-legged for user/private data; 2-legged for public read-only) using OAuth1/OAuth2 flows..

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 Fantastical data in under 10 minutes.

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What data can I load from Fantastical?

Here are some of the endpoints you can load from Fantastical:

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How do I authenticate with the Fantastical API?

Register an app on Yahoo Developer Network to obtain consumer key and secret. Use OAuth 3-legged flow to obtain access/refresh tokens for user-scoped requests (Fantasy private data). For purely public data 2-legged OAuth (consumer key/secret as token) is supported. Include OAuth Authorization header (or signed request) per Yahoo OAuth docs; JSON endpoints accept Bearer-style tokens when using OAuth2 libraries.

1. Get your credentials

1. Go to https://developer.yahoo.com/apps/create/ and create a new application. 2) Specify Fantasy Sports API permission and select Read or Read/Write. 3) Provide callback URL for 3-legged OAuth. 4) Save the app to obtain consumer key and consumer secret. 5) For user access, perform OAuth 3-legged authorization flow to get access_token (and refresh_token if OAuth2).

2. Add them to .dlt/secrets.toml

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[sources.fantastical_source]
consumer_key = "your_consumer_key_here"
consumer_secret = "your_consumer_secret_here"
access_token = "user_access_token_if_applicable"
refresh_token = "user_refresh_token_if_applicable"

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 Fantastical 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 fantastical_pipeline.py

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

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Pipeline fantastical_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fantastical_data
The duckdb destination used duckdb:/fantastical.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 fantastical_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 leagues and teams from the Fantastical 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 fantastical_source(oauth_credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://fantasysports.yahooapis.com/fantasy/v2",
            "auth": {
                "type": "oauth",
                "consumer_key_and_secret": oauth_credentials,
            },
        },
        "resources": [
            {"name": "leagues", "endpoint": {"path": "league/{league_key}", "data_selector": "fantasy_content.league"}},
            {"name": "teams", "endpoint": {"path": "team/{team_key}", "data_selector": "fantasy_content.team"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fantastical_pipeline",
        destination="duckdb",
        dataset_name="fantastical_data",
    )
    load_info = pipeline.run(fantastical_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("fantastical_pipeline").dataset()
sessions_df = data.leagues.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM fantastical_data.leagues LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fantastical_pipeline").dataset()
data.leagues.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 Fantastical 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 you get 401/403, ensure your consumer_key and consumer_secret are correct and that the OAuth 3-legged flow was completed to obtain a valid access token for private league/team data. For public-only requests, use 2-legged OAuth (consumer key/secret). Verify callback URL matches app registration.

Rate limiting and timeouts

The API documentation does not publish strict rate limits; handle 429 responses gracefully with exponential backoff and retry. Monitor request timing from response headers and back off on repeated failures.

Pagination and XML-to-JSON quirks

Responses are nested under <fantasy_content> and collections appear under specific child elements (e.g., , ). JSON output is a direct XML-to-JSON translation where arrays may be keyed by positional indexes and objects appear in varying positions; code should locate objects by property names rather than fixed array indices.

Common errors

• 400 Bad Request: malformed URI or invalid parameters.
• 401 Unauthorized / 403 Forbidden: invalid/expired tokens or insufficient app permissions (need Fantasy Sports scope).
• 404 Not Found: invalid game/league/team/player keys.
• 429 Too Many Requests: rate limit exceeded — backoff and retry.

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