Gist 99774fc136a57a61654393793e16488e

Gist a41979f68ef67d82ce9476f23d63d1bb

This example shows a serverless recommendation system that suggests a single player transfer for a Fantasy Premier League team, identifying one player to sell and one to buy. It displays the result as a simple text message, such as "PlayerName -> PlayerName". The code uses AWS Lambda with Python, retrieving player data from an S3 bucket and fetching current team picks from the Fantasy Premier League API. It calculates transfer suggestions by filtering potential replacements based on position, budget, and team constraints, then sorts players by pre-computed in/out weight columns in the CSV data. The implementation relies on the pandas library for data manipulation and the requests library for API calls.

84% match

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.

83% match

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.

82% match

Gist b515456174db6bc9128368273472fee1

This example shows a fantasy Premier League transfer suggestion tool that selects one player to swap out and one to bring in for a given team. It calculates a budget based on the current squad’s bank and the outgoing player’s cost, then filters potential replacements by position and team restrictions. The code uses the FPL API endpoints for bootstrap-static and entry event picks, with pandas for data manipulation and weighting logic. It randomly picks an outgoing player weighted by `out_weight` and an incoming player weighted by `in_weight`, then returns the transfer as a JSON response via a lambda function.

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.

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.

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.

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.