Gist dca53ad15bb437468b52eb951a09f356

100%

Gist dca53ad15bb437468b52eb951a09f356

✓ Published0🌍 Public

Last edited Dec 10, 2023

Created on Sep 23, 2023

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.

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

88% match

Loading thumbnail…

Gist a96c926e932dc1beb8054857775aae2d

This example shows a Python script that automatically manages a Fantasy Premier League team by calculating optimal transfers and lineups. It retrieves player data, evaluates potential transfers, and submits changes to the FPL API. The code uses the `requests` library to handle API calls and `pandas` for data manipulation, including pivot tables and filtering based on team constraints. It also demonstrates how to update team sheets and set captaincy through HTTP POST requests. The visualization focuses on the logic and data flow rather than graphical output.

CConorAspell

87% 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

86% match

Loading thumbnail…

Gist 200193de82b0d445df3dd79fd4c9f792

This visualization recommends Fantasy Premier League (FPL) transfers and starting lineups by analyzing player data. It shows a suggested player substitution, optimal starting eleven, captain pick, and bench order based on calculated in/out weights. The code fetches a user’s current team via the FPL API, merges it with precomputed player statistics from a CSV stored in Amazon S3, and applies a greedy selection algorithm. It uses Python with pandas for data manipulation, requests for API calls, and boto3 for S3 retrieval, exposing results through an AWS Lambda function via API Gateway.

CConorAspell

85% match

Loading thumbnail…

Gist a5e694c3852c3b56db4abcb02f364241

This example shows a Fantasy Premier League team update workflow that automatically selects a squad for the upcoming gameweek when the transfer deadline is within 24 hours. The code fetches live player and fixture data from the Fantasy Premier League API, merges team strength ratings with player records, and calculates a difficulty differential for each player based on home or away status. It uses pandas for data manipulation and merging, and the `requests` library to retrieve JSON responses from the API. The visualization is not graphical; instead, it demonstrates a programmatic data-processing pipeline with no charts or rendering, focusing solely on data preparation and conditional logic.

CConorAspell

85% 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

85% 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

84% match

Loading thumbnail…

Gist c444e6c65326dabb5f6b4c25f5a455d6

This visualization demonstrates a Fantasy Premier League (FPL) team selection process, showing how player data is analyzed to determine which player to transfer out each gameweek. The code uses pandas DataFrames to calculate a weighted "out weight" for each player based on their form, difficulty (diff), and position, then randomly selects a player to drop using the `sample()` method with these weights. It leverages the `fpl_utils` library to fetch team data, retrieve player statistics from an S3 bucket, and upload the result as JSON. The underlying data comes from CSV files containing player gameweek statistics, with the output stored for future gameweek planning.

CConorAspell

82% match