STATISTICAL ANALYSIS AND MACHINE LEARNING TO IMPROVE LEAGUE CHAMPIONSHIP SERIES TEAMS

Aug 4, 2026

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Advanced analytics in esports can enhance performance evaluation and prediction. This culminating project developed and implemented descriptive and predictive performance analytics techniques using League of Legends as the subject, guided by questions about how champions, players, and in-game variables affect match outcomes, and how machine learning can best leverage analytics for prediction. Results produced metrics such as CMV, CPF, and OPR, and a machine learning model that used variable correlation and weighting to estimate win probability with confidence levels.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages51

Reading Time2 h 9 min

AccessFree to download

CHAPTER ONE: INTRODUCTION

Overview

Problem Statement

Study Limitations

Purpose of Study

CHAPTER TWO: LITERATURE REVIEW

Scope of Review

Data Source

Sports and Esports

California State University, San Bernardino CSUSB ScholarWorks | Electronic Theses, Projects, and Dissertations | Office of Graduate Studies | | --- | --- | | 8-2023
STATISTICAL ANALYSIS AND MACHINE LEARNING TO IMPROVE LEAGUE CHAMPIONSHIP SERIES TEAMS
Alexander Gilles
Follow this and additional works at: [https://scholarworks.lib.csusb.edu/etd](https://scholarworks.lib.csusb.edu/etd)
Part of the Sports Management Commons | | Recommended Citation Gilles, Alexander, "STATISTICAL ANALYSIS AND MACHINE LEARNING TO IMPROVE LEAGUE CHAMPIONSHIP SERIES TEAMS" (2023) . Electronic Theses, Projects, and Dissertations. 1793. [https://scholarworks.lib.csusb.edu/etd/1793](https://scholarworks.lib.csusb.edu/etd/1793) This Project is brought to you for free and open access by the Office of Graduate Studies at CSUSB ScholarWorks. It has been accepted for inclusion in Electronic Theses, Projects, and Dissertations by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [scholarworks@csusb.edu](scholarworks@csusb.edu). STATISTICAL ANALYSIS AND MACHINE LEARNING TO IMPROVE LEAGUE CHAMPIONSHIP SERIES TEAMS A Project Presented to the Faculty of California State University, San Bernardino In Partial Fulfillment of the Requirements for the Degree Master of Science in Information Systems Technology by Alexander L. Gilles August 2023 STATISTICAL ANALYSIS AND MACHINE LEARNING TO IMPROVE LEAGUE CHAMPIONSHIP SERIES TEAMS A Project Presented to the Faculty of California State University, San Bernardino by Alexander L. Gilles August 2023 Approved by: Dr. Conrad Shayo, Committee Chair & Department Chair, Information and Decision Sciences Dr. Frank Lin, Member, Reader © 2023 Alexander L. Gilles ABSTRACT One area for further study in Esports is the use of advanced analytics from a performance standpoint. This culminating experience project sought to find and implement effective performance analytics techniques, using the most popular Esport (League of Legends) as its subject. The research questions asked are (Q1) How do champions, players, and their associated in-game variables impact the results of League of Legends matches? (Q2) How can machine learning algorithms be implemented to utilize descriptive and predictive analytics for League of Legends most effectively? Additionally, while not an element of the analysis and machine learning model, it is important to discern the importance and scope of the data collected. The findings are: (Q1) In game variables can be utilized to create descriptive analytics metrics like Champion Matchup Value (CMV), Composition Pace Factor (CPF) , or Overall Pace Rating (OPR), and (Q2) . The results from the machine learning model focused on correlation and weighting variables , in conjunction with the metrics formulated from answering Q2 can effectively determine a team’s chance of winning with an associated confidence rating for that prediction. Additionally, the data collected from OraclesElixir presented abroad set of variables and a substantial number of observations that opened the path to more meaningful analysis than in prior studies. The machine learning model, when fed professional matches of League of Legends saw nearly a 70% accuracy rating, with a confidence band to determine the likelihood of outcome in each match. Breaking down the basic statistics into more refined metrics, like CPF or CMV, provides an additional vector from which the game can be analyzed. While some studies aim to use the in-game statistics as they are found, emulating the process of sprots could greatly benefit the world of esports. Creation of advanced analytics allows for a heightened look into how these stats impact games. Additionally, factoring these advanced statistics into a machine learning model which can intake raw in-game statistics, calculate these stats, and utilize them to predict a winner of a match is also beneficial. Many

FAQs

What research questions drive the project?

The project asks how champions, players, and in-game variables influence League of Legends match results, and how machine learning can use descriptive and predictive analytics to improve effectiveness.

What descriptive analytics metrics were created?

It uses in-game variables to build metrics including Champion Matchup Value (CMV), Composition Pace Factor (CPF), and Overall Pace Rating (OPR).

How does the machine learning model predict outcomes?

The model focuses on correlation and weighting of variables and, together with the derived metrics, estimates a team’s chance of winning with an associated confidence rating.

Aug 4, 2026

Upload by Anda