This is the 2nd of 3 blog posts about my process and discoveries working with data from Riot’s online game, League of Legends. This post is a technical writeup of the code I used for my initial ‘baseline’ modeling, and my Data Preparation (and imputation) code. The code I wrote for initial sanity check modelling work can be found here. The feature engineering/Data Prep code is here. The code for my ‘final’ models is here. Background:Summary of the previous post In the 5 v 5 Videogame/Esport two teams of 5 players compete against each other. […]
This is the 1st of 3 blog posts about my process and discoveries working with data from Riot’s online game, League of Legends. The code I wrote to do this can be found here. Background:Project Purpose In the 5 v 5 Videogame/Esport two teams of 5 players compete against each other. Before the ‘Game’ start each player chooses 1 of (currently 133) characters, known as a champion. No two players may play the same champions. In ranked and professional play, players may “ban” a champion and prevent either side from choosing that champion. There are […]
Lol_Scout was a Data Science/ Esports analytics project I pursued to answer questions about how League of Legends players champion choices can be optimized. The current soucecode and readme can be found here. Part 1: Background, Initial Design, Data Gathering, data acquisition lessons learned, ways to pursue the project further in the future. Part 2: Modelling with Neural Net Classifiers with just champion selection. Comparing to XGBoost. Adding Data from Player’s experience at a character. Part 3:Modelling with Neural Nets and Xgboost with more features, conclusions.