This example builds a data pipeline that fetches Fantasy Premier League player and fixture data, merges it with betting odds from an S3 bucket, and uploads the enriched results back to S3 as CSV files. The code uses the `requests` library to query the FPL API, `pandas` for data manipulation, and `boto3` to interact with AWS S3. It calculates a difficulty metric (`diff`) by comparing home and away win probabilities from the odds. The visualization would show how player value or form changes across gameweeks based on this aggregated data, though the gist itself focuses on data preparation rather than rendering.