Gist 0b62984b5a27dcec2db3a4f234affd91

100%

Gist 0b62984b5a27dcec2db3a4f234affd91

✓ Published0🌍 Public

Last edited Dec 16, 2021

Created on Dec 11, 2021

This example shows an automated pipeline that fetches English Premier League match odds from the Odds API and stores them as timestamped CSV files in Amazon S3. The code first pulls fixture deadlines from the Fantasy Premier League bootstrap API, then filters upcoming matches between the current and next deadline. It extracts William Hill’s home, draw, and away odds for each qualifying game, using the `requests` and `pandas` libraries to process the JSON responses into a DataFrame. Finally, the script writes the odds data to a CSV file named with the current date, demonstrating a serverless data ingestion workflow with `boto3` and AWS Lambda.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 634708b061502ecb288faed61b1b15d4

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.

CConorAspell

85% match

Loading thumbnail…

Gist bc8f07f831e90294024860ced25860a2

This visualization tracks English Premier League betting odds and team probabilities across a gameweek. It combines player and fixture data from the Fantasy Premier League API with head-to-head odds from the Odds API, filtering matches that fall between upcoming deadlines. The code uses pandas DataFrames to merge team names, calculates implied win chances from bookmaker odds (Paddy Power), and uploads the resulting CSV to an S3 bucket via a custom `fpl_utils` helper module. The process runs as an AWS Lambda function, with error handling that triggers SNS notifications on failure.

CConorAspell

84% match

Loading thumbnail…

Gist f284381bb142623d89f9aa34c2a7b15d

This example visualizes the upcoming gameweek’s fixture odds and player selection data, showing the probability of each team winning or drawing as derived from betting odds. The code fetches player and fixture data from the Fantasy Premier League API, merges it with daily odds stored in an S3 bucket, and calculates a “diff” metric comparing home and away win chances. Using pandas for data manipulation and the `requests` library for API calls, it outputs a combined player–fixture dataframe that highlights favorable matchups. The visualization runs as an AWS Lambda function, triggered on demand to refresh the data.

CConorAspell

81% match

Loading thumbnail…

Gist 1da59c7531b92d80b1087f8b51ac9d17

This visualization tracks fantasy Premier League player transfer momentum by calculating weighted "in" and "out" scores from fixture difficulty, player form, and selection chance. It uses a Python AWS Lambda function to fetch live data from the Fantasy Premier League API, merge it with bookmaker odds stored in an S3 bucket, and output a CSV of player weights per gameweek. The code relies on `pandas` for data manipulation, `boto3` for cloud storage access, and `requests` for API calls, with team strengths and fixture probabilities driving the transfer recommendations.

CConorAspell

78% match

Loading thumbnail…

Gist dca53ad15bb437468b52eb951a09f356

This visualization shows the data pipeline for managing Fantasy Premier League player data, fetching gameweek deadlines and team rosters from the official FPL API. It retrieves data using Python's `requests` library, converts JSON responses into DataFrames with `pandas`, and then persists the processed data to Amazon S3 through the `boto3` client. The code also reads configuration parameters from AWS SSM and sends notifications via SNS topics, demonstrating an automated workflow for updating and storing fantasy football statistics.

CConorAspell

78% match

Loading thumbnail…

Gist ab30e8c0c51d8dcb752e7a30cca7edaa

This example presents an automated Fantasy Premier League team optimizer that selects transfers and sets lineups based on live player data and upcoming fixtures. It pulls player statistics and fixture deadlines from the FPL API via `requests`, then processes the data with `pandas` to calculate player weights and enforce squad constraints. The script also reads daily betting odds from an AWS S3 bucket using `boto3` to inform decisions. It logs into the user’s FPL account, submits transfer and team-sheet changes, and prints the chosen starters, substitutes, and captaincy picks. The visualization highlights the workflow’s data-driven decision-making and its integration with external services.

CConorAspell

77% match

Loading thumbnail…

Gist 9d651a8ae89dc0d88db634886a5e5588

This example shows a serverless deadline checker for Fantasy Premier League match deadlines, using a Python AWS Lambda function. It fetches fixture data from the Fantasy Premier League API and determines whether the next upcoming deadline falls within the next 24 hours. The code converts current and future timestamps, filters fixtures by deadline time, and returns `True` if the nearest deadline is tomorrow, otherwise it exits without a response. It relies on the `requests` library for API calls, `pandas` for dataframe filtering, and the `datetime` module for time calculations, with no visual rendering involved.

CConorAspell

75% match

Loading thumbnail…

Gist 9bf3c1df30c761a2f4056979099ea7cd

This example shows an automated Fantasy Premier League workflow that posts a user’s finalized team sheet for a given gameweek. It retrieves the full team JSON from an S3 bucket and submits it to the league’s API via a POST request. The code uses the `requests` library for HTTP communication, `json` for data handling, and custom utility functions from `fpl_utils` to fetch gameweek parameters and storage details. Error handling triggers an SNS notification to a specified topic if the API call fails. The visualization highlights the integration between cloud storage, Lambda execution, and external sports data endpoints.

CConorAspell

74% match