Skip navigation
Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01nc580q09w
Title: Analysis and Augmentation of Ranking Systems: A Case Study
Authors: Todnem, Trey
Advisors: Daw, Nathaniel
Department: Computer Science
Class Year: 2016
Abstract: The proliferation of online matchmaking and the continued development of gambling in regard to sports makes it more important now than ever to be able to identify the skill levels of players and the likelihood of particular match outcomes. Many good systems use neural networks and support vector machines to predict match outcomes, but these methods are computationally expensive, and it is often impossible or just plain inefficient to compute updated rankings with these systems. We draw our attention to the most popular and computationally inexpensive skill updates systems of chess and online matchmaking: Elo and Microsoft’s TrueSkill. We offer a much needed analysis of how these systems perform in a case study on predicting tennis match outcomes. We analyze the effectiveness of these systems in different situations, and we develop some augmentations and test their effectiveness.
Extent: 38 pages
URI: http://arks.princeton.edu/ark:/88435/dsp01nc580q09w
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Computer Science, 1988-2016

Files in This Item:
File SizeFormat 
Todnem_Trey_2016_Thesis.pdf656.04 kBAdobe PDF    Request a copy


Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.