story of me building a League of Legends win interpreter for hard-stuck Silver II…

story of me building a League of Legends win interpreter for hard-stuck Silver II…

League of Legends is a 5v5 team-based game where each player can select a champion (out of 154 options) and role (top, jungle, mid, adc/bot, support/bot). The game’s competitive scene is vibrant and global, with a *definitely not toxic* ranked match-making option where players can compete amongst themselves and climb the ELO ladder to rank up. I wanted to see if a machine learning model could answer the common question players might have about their matches: “what did I do this game that helped or hurt my chances of winning, and how much did each thing that happened matter?”

INTRODUCTION

League of Legends is a 5v5 team-based game where each player can select a champion (out of 154 options) and role (top, jungle, mid, adc/bot, support/bot). The game’s competitive scene is vibrant and global, with a definitely not toxic ranked match-making option where players can compete amongst themselves and climb the ELO ladder to rank up. I wanted to see if a machine learning model could answer the common question players might have about their matches: “what did I do this game that helped or hurt my chances of winning, and how much did each thing that happened matter?”

Before I go any further, please try it out yourself here — note that the algorithms sweat and grind to compute, and the rate limits of the API, so in the beta stages the algo does kind of crash if more than 2 people are using it 😰😰

This blog post will be more 💫journey-oriented💫 and less in the nitty-gritty with 🐍robot snek🐍 (python3, I prefer to refer to it this way), but please feel free to look at all the code here.

flask logistic-regression web-app-development data-science league-of-legends

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