Master_Capstone League of Legend match result analysis

Introduction

League of Legends, commonly referred to as League, is a 2009 multiplayer online battle arena video game developed and published by Riot Games. Inspired by Defense of the Ancients, a custom map for Warcraft III, Riot's founders sought to develop a stand-alone game in the same genre.

In this project, we wonder how to predict the result of a single match, what makes the team win. How to help those people who don't understand this game can understand the game by data analysis. And throughout current data, we can get the win rate.

Decision tree

A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements. https://en.wikipedia.org/wiki/Decision_tree

Processes

• 0_information:

  This folder contains four regions usernames; those data are collect from opgg (https://na.op.gg/). Then I use stratified sampling to sampling username by rank information, player's rank distribution usually like this:

  • Iron – 7.1% of players
  • Bronze – 22% of players
  • Silver – 35% of players
  • Gold – 23% of players
  • Platinum – 7.9% of players
  • Diamond – 2.5% of players
  • Master – 0.032% of players
  • Grandmaster – 0.040% of players
  • Challenger – 0.017% of players

  Based on the demographic of the players, we hierarchically extracted the corresponding proportions of players in different regions.

• 1_processing

  In this folder, we generate information from Riot Developer Portal. Based on user's region and username, we get their PUUID, summoner IDs, account IDs, and PUUIDs. Summoner and account IDs are only unique per region, and PUUIDs are unique globally. Because the PUUID is globally unique, when a player transfers regions their PUUID will not change. This allows you to track summoners that have transferred regions. After we got each user's PUUIDs, we search their recent match history by their PUUIDs. We got 2 match information named MatchID, and we can find match information based on MatchID.

• 2_analysing

  Once we got the match information we can start doing data analysing.

• 3_classifier

  Based on the data analysing, we will doing data classifier in this project.