I used Riot Games, Inc.'s public API to model player behavior and outcomes in the game League of Legends. The first project was to teach myself some Python to access Riot's public API to compile a data set of 22 variables from the past 400 or so games of a given player's match history. I used these data sets to build models to assess which variables may be useful for predicting whether the player will win or lose a match. Thank you to Riot for making the public API available.
Table of Contents:
- Python-API-Data-Compiler - My first python program - teaching myself from scratch to access Riot's API to compile a data set to use for building models.
- Player T Model - Contains files related to the model built around Player T
- Report for Player T.md - A report about a classification model I built using data compiled with the above Python program.
- Player T Analysis.R - Here you can look at the code I wrote in R to analyze the data set and build the model discussed in this report.
- Executive Summary - Player T.md - An example of a summary for executives that have less need for details and less backgrounds knowledge in statistics.
- Player A Model - Contains files related to the model built around Player A
- Report for Player A.md - A follow up report focusing on a different style of player to compare classification models.
- Player A Analysis.R - The code I wrote in R to analyze the data set and build the model for this report.
- Executive Summary - Player A.md - An example of a summary for executives that have less need for details and less backgrounds knowledge in statistics.
This project isn’t endorsed by Riot Games and doesn’t reflect the views or opinions of Riot Games or anyone officially involved in producing or managing League of Legends. League of Legends and Riot Games are trademarks or registered trademarks of Riot Games, Inc. League of Legends © Riot Games, Inc.