Chapter Two Massey’S Method
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Predicting Outcomes of NCAA Basketball Tournament Games
Cripe 1 Predicting Outcomes of NCAA Basketball Tournament Games Aaron Cripe March 12, 2008 Math Senior Project Cripe 2 Introduction All my life I have really enjoyed watching college basketball. When I was younger my favorite month of the year was March, solely because of the fact that the NCAA Division I Basketball Tournament took place in March. I would look forward to filling out a bracket every year to see how well I could predict the winning teams. I quickly realized that I was not an expert on predicting the results of the tournament games. Then I started wondering if anyone was really an expert in terms of predicting the results of the tournament games. For my project I decided to find out, or at least compare some of the top rating systems and see which one is most accurate in predicting the winner of the each game in the Men’s Basketball Division I Tournament. For my project I compared five rating systems (Massey, Pomeroy, Sagarin, RPI) with the actual tournament seedings. I compared these systems by looking at the pre-tournament ratings and the tournament results for 2004 through 2007. The goal of my project was to determine which, if any, of these systems is the best predictor of the winning team in the tournament games. Project Goals Each system that I compared gave a rating to every team in the tournament. For my project I looked at each game and then compared the two team’s ratings. In most cases the two teams had a different rating; however there were a couple of games where the two teams had the same rating which I will address later. -
The Ranking of Football Teams Using Concepts from the Analytic Hierarchy Process
University of Louisville ThinkIR: The University of Louisville's Institutional Repository Electronic Theses and Dissertations 12-2009 The ranking of football teams using concepts from the analytic hierarchy process. Yepeng Sun 1976- University of Louisville Follow this and additional works at: https://ir.library.louisville.edu/etd Recommended Citation Sun, Yepeng 1976-, "The ranking of football teams using concepts from the analytic hierarchy process." (2009). Electronic Theses and Dissertations. Paper 1408. https://doi.org/10.18297/etd/1408 This Master's Thesis is brought to you for free and open access by ThinkIR: The University of Louisville's Institutional Repository. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of ThinkIR: The University of Louisville's Institutional Repository. This title appears here courtesy of the author, who has retained all other copyrights. For more information, please contact [email protected]. THE RANKING OF FOOTBALL TEAMS USING CONCEPTS FROM THE ANALYTIC HIERARCHY PROCESS BY Yepeng Sun Speed Engineering School, 2007 A Thesis Submitted to the Faculty of the Graduate School of the University of Louisville in Partial Fulfillment of the Requirements for the Degree of Master of Engineering Department of Industrial Engineering University of Louisville Louisville, Kentucky December 2009 THE RANKING OF FOOTBALL TEAMS USING CONCEPTS FROM THE ANALYTIC HIERARCHY PROCESS By Yepeng Sun Speed Engineering School, 2007 A Thesis Approved on November 3, 2009 By the following Thesis Committee x Thesis Director x x ii ACKNOWLEDGMENTS I would like to thank my advisor, Dr. Gerald Evans, for his guidance and patience. I attribute the level of my Masters degree to his encouragement and effort and without him this thesis, too, would not have been completed or written. -
Minimizing Game Score Violations in College Football Rankings
University of North Florida UNF Digital Commons Management Faculty Publications Department of Management 2005 Minimizing Game Score Violations in College Football Rankings B. Jay Coleman University of North Florida, [email protected] Follow this and additional works at: https://digitalcommons.unf.edu/bmgt_facpub Part of the Management Sciences and Quantitative Methods Commons Recommended Citation Coleman, B. Jay, "Minimizing Game Score Violations in College Football Rankings" (2005). Management Faculty Publications. 1. https://digitalcommons.unf.edu/bmgt_facpub/1 This Article is brought to you for free and open access by the Department of Management at UNF Digital Commons. It has been accepted for inclusion in Management Faculty Publications by an authorized administrator of UNF Digital Commons. For more information, please contact Digital Projects. © 2005 All Rights Reserved informs ® Vol. 35, No. 6, November-December 2005, pp. 483–496 doi 10.1287/inte.1050.0172 issn 0092-2102 eissn 1526-551X 05 3506 0483 ©2005 INFORMS Minimizing Game Score Violations in College Football Rankings B. Jay Coleman Department of Management, Marketing, and Logistics, Coggin College of Business, University of North Florida, 4567 St. Johns Bluff Road, South, Jacksonville, Florida 32224-2645, [email protected] One metric used to evaluate the myriad ranking systems in college football is retrodictive accuracy. Maximizing retrodictive accuracy is equivalent to minimizing game score violations: the number of times a past game’s winner is ranked behind its loser. None of the roughly 100 current ranking systems achieves this objective. Using a model for minimizing violations that exploits problem characteristics found in college football, I found that all previous ranking systems generated violations that were at least 38 percent higher than the minimum. -
An Investigation of the Relationship Between Junior Girl’S Golf Ratings and Ncaa Division I Women’S Golf Ratings
AN INVESTIGATION OF THE RELATIONSHIP BETWEEN JUNIOR GIRL’S GOLF RATINGS AND NCAA DIVISION I WOMEN’S GOLF RATINGS Patricia Lyn Earley A thesis submitted to the faculty of the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Master of Arts in the Department of Exercise and Sport Science (Sport Administration). Chapel Hill 2011 Approved By: Edgar W. Shields, Jr., Ph.D. Barbara Osborne, Esq. Deborah L. Stroman, Ph.D. ABSTRACT PATRICIA LYN EARLEY: An Investigation of the Relationship Between Junior Girl’s Golf Ratings and NCAA Division I Women’s Golf Ratings (Under the direction of Edgar W. Shields, Jr., Ph.D.) Coaches often depend on rating systems to determine who to recruit. But how reliable are these ratings? Do they help coaches find a player that will contribute to the team’s success? This research examined the degree of importance that should be placed on junior golf ratings and if these ratings will help college coaches predict the impact of each recruit. The research sought to discover the relationship between 207 subjects’ junior golf ratings (Golfweek/Sagarin Junior Girls Golf Ratings) and their freshman, sophomore, junior and senior year golf ratings (NCAA Division I Women’s College Golf Ratings), rate of improvement and number of starter years using simple regression. A significant relationship was found between junior golf ratings and the freshman, sophomore, junior and senior year NCAA Division I women’s college golf ratings. A significant relationship was found between junior golf ratings and starter years but not with the rate of improvement. -
11.24.03 BCS Long Release
The National Football Foundation and College Hall of Fame, Inc. 22 Maple Avenue, Morristown, NJ 07960 FOR RELEASE: Embargoed Until 6:00 PM ET, November 24, 2003 CONTACT: Rick Walls, NFF Director of Operations or Matt Sweeney, Special Projects Coordinator 973-829-1933 2003 BOWL CHAMPIONSHIP SERIES STANDINGS (Games Through Nov . 22, 2003) USA Today/ Poll Anderson Colley New York Comp. Schedule Schedule Loss Sub- Quality AP ESPN Avg. & Hester Billingsley Matrix Massey Times Sagarin Wolfe Avg. Strength Rank Record total Win Total 1. Oklahoma 1 1 1 1 1 1 1 1 1 1 1.00 10 0.40 0 2.40 - 0.5 1.90 2. Southern California 2 2 2 3 2 2 3 3 2 2 2.33 39 1.56 1 6.89 6.89 3. Louisiana State 3 3 3 2 3 4 2 9 3 4 3.00 61 2.44 1 9.44 - 0.4 9.04 4. Michigan 4 4 4 5 4 5 5 4 5 3 4.33 13 0.52 2 10.85 - 0.6 10.25 5. Ohio State 8 7 7.5 6 5 3 4 6 7 5 4.83 6 0.24 2 14.57 14.57 6. Texas 6 6 6 4 10 9 6 2 9 9 6.50 12 0.48 2 14.98 14.98 7. Georgia 5 5 5 8 9 8 7 7.5 6 6 7.08 32 1.28 2 15.36 - 0.3 15.06 8. Tennessee 7 8 7.5 7 6 7 8 7.5 8 8 7.25 33 1.32 2 18.07 - 0.1 17.97 9. -
Using Experts' Opinions in Machine Learning Tasks
Using Experts' Opinions in Machine Learning Tasks Jafar Habibi*^, Amir Fazelinia*, Issa Annamoradnejad* * Department of Computer Engineering, Sharif University of Technology, Tehran, Iran ^ Corresponding Author, Email: [email protected] Abstract— In machine learning tasks, especially in the tasks of This being said, the problems of data collection are out of the prediction, scientists tend to rely solely on available historical data scope of this paper, and our focus is to present a framework for and disregard unproven insights, such as experts’ opinions, polls, building strong ML models using experts’ opinions. The main and betting odds. In this paper, we propose a general three-step contributions of this study are: 1) to propose a general three-step framework for utilizing experts’ insights in machine learning tasks framework to employ experts’ viewpoints, 2) apply that and build four concrete models for a sports game prediction case framework in a case study and build models exclusively based study. For the case study, we have chosen the task of predicting on experts’ opinions, and 3) compare the accuracy of the NCAA Men's Basketball games, which has been the focus of a proposed models with some baseline models. group of Kaggle competitions in recent years. Results highly suggest that the good performance and high scores of the past For the case study analysis, we selected the task of predicting models are a result of chance, and not because of a good- results of NCAA Men's Division I Basketball Tournament, performing and stable model. Furthermore, our proposed models widely known as NCAA March Madness. -
A Simple and Flexible Rating Method for Predicting Success in the NCAA Basketball Tournament∗
A Simple and Flexible Rating Method for Predicting Success in the NCAA Basketball Tournament∗ Brady T. West Abstract This paper first presents a brief review of potential rating tools and methods for predicting suc- cess in the NCAA basketball tournament, including those methods (such as the Ratings Percentage Index, or RPI) that receive a great deal of weight in selecting and seeding teams for the tournament. The paper then proposes a simple and flexible rating method based on ordinal logistic regression and expectation (the OLRE method) that is designed to predict success for those teams selected to participate in the NCAA tournament. A simulation based on the parametric Bradley-Terry model for paired comparisons is used to demonstrate the ability of the computationally simple OLRE method to predict success in the tournament, using actual NCAA tournament data. Given that the proposed method can incorporate several different predictors of success in the NCAA tournament when calculating a rating, and has better predictive power than a model-based approach, it should be strongly considered as an alternative to other rating methods currently used to assign seeds and regions to the teams selected to play in the tournament. KEYWORDS: quantitative reasoning, ratings, sports statistics, NCAA basketball ∗The author would like to thank Fred L. Bookstein for the original motivation behind this paper, his colleagues at the Center for Statistical Consultation and Research for their helpful thoughts and suggestions, and freelance writer Dana Mackenzie and four anonymous reviewers for thoughtful and constructive feedback on earlier drafts. West: Predicting Success in the NCAA Basketball Tournament 1. -
March Madness Model Meta-Analysis
March Madness Model Meta-Analysis What determines success in a March Madness model? Ross Steinberg Zane Latif Senior Paper in Economics Professor Gary Smith Spring 2018 Abstract We conducted a meta-analysis of various systems that predict the outcome of games in the NCAA March Madness Tournament. Specifically, we examined the past four years of data from the NCAA seeding system, Nate Silver’s FiveThirtyEight March Madness predictions, Jeff Sagarin’s College Basketball Ratings, Sonny Moore’s Computer Power Ratings, and Ken Pomeroy’s College Basketball Ratings. Each ranking system assigns strength ratings of some variety to each team (i.e. seeding in the case of the NCAA and an overall team strength rating of some kind for the other four systems). Thus, every system should, theoretically, be able to predict the result of each individual game with some accuracy. This paper examined which ranking system best predicted the winner of each game based on the respective strength ratings of the two teams that were facing each other. We then used our findings to build a model that predicted this year’s March Madness tournament. Introduction March Madness is an annual tournament put on by the National College Athletic Association (NCAA) to determine the best D1 college basketball team in the United States. There is one March Madness tournament for men and one for women. We focused on the men’s tournament given the greater amount of analyses and model data available for it. The tournament consists of 68 teams that face each other in single-elimination games until there is only one team left. -
11.29.04 BCS Long Release
FOR RELEASE: Embargoed Until Noon ET, November 29, 2004 The National Football Foundation and College Hall of Fame, Inc. CONTACT: Rick Walls, NFF Director of Operations 22 Maple Avenue, Morristown, NJ 07960 Matt Sweeney, NFF Special Projects Coordinator 973-829-1933 Bob Burda, Assistant Commissioner, Big 12 Conference 214-753-0107 2004 BOWL CHAMPIONSHIP SERIES STANDINGS (Games Through Nov. 27, 2004) USA Today/ESPN Computer Rankings Associated Press Avg. Anderson Colley Computer BCS Rank Points % Rank Points % & Hester Billingsley Matrix Massey Sagarin Wolfe % Rank Average Previous 1. Southern California 1 1610 .9908 1 1509 .9895 24 24 25 25 24 24 .970 2 .9834 1 2. Oklahoma 2 1540 .9477 2 1442 .9456 25 25 24 24 25 25 .990 1 .9611 2 3. Auburn 3 1530 .9415 3 1435 .9410 23 23 23 23 23 23 .920 3 .9342 3 4. California 4 1410 .8677 4 1314 .8616 20 16 20 20 20 20 .800 6 .8431 4 5. Texas 6 1325 .8154 5 1266 .8302 21 22 22 22 22 22 .880 4 .8418 5 6. Utah 5 1342 .8258 6 1222 .8013 22 20 21 21 21 21 .840 5 .8224 6 7. Georgia 8 1094 .6732 7 1100 .7213 17 19 18 18 15 16 .690 8 .6948 8 8. Boise State 11 952 .5858 10 926 .6072 19 21 19 19 19 19 .760 7 .6510 7 9. Louisville 7 1175 .7231 8 1038 .6807 10 10 12 11 18 18 .510 T-12 .6379 10 10. Miami (FL) 9 1037 .6382 9 983 .6446 16 15 16 15 16 15 .620 10 .6342 9 11. -
Big Data: How Data Analytics Is Transforming the World Course Guidebook
Topic Science Subtopic & Mathematics Mathematics Big Data: How Data Analytics Is Transforming the World Course Guidebook Professor Tim Chartier Daavidson College PUBLISHED BY: THE GREAT COURSES Corporate Headquarters 4840 Westfields Boulevard, Suite 500 Chantilly, Virginia 20151-2299 Phone: 1-800-832-2412 Fax: 703-378-3819 www.thegreatcourses.com Copyright © The Teaching Company, 2014 Printed in the United States of America This book is in copyright. All rights reserved. Without limiting the rights under copyright reserved above, no part of this publication may be reproduced, stored in or introduced into a retrieval system, or transmitted, in any form, or by any means (electronic, mechanical, photocopying, recording, or otherwise), without the prior written permission of The Teaching Company. Tim Chartier, Ph.D. Associate Professor of Mathematics and Computer Science Davidson College rofessor Tim Chartier is an Associate Professor of Mathematics and Computer PScience at Davidson College. He holds a B.S. in Applied Mathematics and an M.S. in Computational Mathematics, both from Western Michigan University. Professor Chartier received his Ph.D. in Applied Mathematics from the University of Colorado Boulder. From 2001 to 2003, at the University of Washington, he held a postdoctoral position supported by VIGRE, a prestigious program of the National Science Foundation that focuses on innovation in mathematics education. Professor Chartier is a recipient of a national teaching award from the Mathematical Association of America (MAA). He is the author of Math Bytes: Google Bombs, Chocolate-Covered Pi, and Other Cool Bits in Computing and coauthor (with Anne Greenbaum) of Numerical Methods: Design, Analysis, and Computer Implementation of Algorithms. -
Extending the Colley Method to Generate Predictive Football Rankings R
Extending the Colley Method to Generate Predictive Football Rankings R. Drew Pasteur Abstract. Among the many mathematical ranking systems published in college football, the method used by Wes Colley is notable for its elegance. It involves setting up a matrix system in a relatively simple way, then solving it to determine a ranking. However, the Colley rankings are not particularly strong at predicting the outcomes of future games. We discuss the reasons why ranking college football teams is difficult, namely weak connections (as 120 teams each play 11-14 games) and divergent strengths-of- schedule. Then, we attempt to extend this method to improve the predictive quality, partially by applying margin-of-victory and home-field advantage in a logical manner. Each team's games are weighted unequally, to emphasize the outcome of the most informative games. This extension of the Colley method is developed in detail, and its predictive accuracy during a recent season is assessed. Many algorithmic ranking systems in collegiate American football publish their results online each season. Kenneth Massey compares the results of over one hundred such systems (see [9]), and David Wilson’s site [14] lists many rankings by category. A variety of methods are used, and some are dependent on complex tools from statistics or mathematics. For example, Massey’s ratings [10] use maximum likelihood estimation. A few methods, including those of Richard Billingsley [3], are computed recursively, so that each week’s ratings are a function of the previous week’s ratings and new results. Some high-profile rankings, such as those of USA Today oddsmaker Jeff Sagarin [12], use methods that are not fully disclosed, for proprietary reasons. -
The Rhetoric of the Bowl Championship Series: an Analysis of the Persuasive Methods Underlying an "Objective" System
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Supervised Undergraduate Student Research Chancellor’s Honors Program Projects and Creative Work Spring 5-2007 The Rhetoric of the Bowl Championship Series: An Analysis of the Persuasive Methods Underlying an "Objective" System Thomas Marion Gray University of Tennessee - Knoxville Follow this and additional works at: https://trace.tennessee.edu/utk_chanhonoproj Recommended Citation Gray, Thomas Marion, "The Rhetoric of the Bowl Championship Series: An Analysis of the Persuasive Methods Underlying an "Objective" System" (2007). Chancellor’s Honors Program Projects. https://trace.tennessee.edu/utk_chanhonoproj/1074 This is brought to you for free and open access by the Supervised Undergraduate Student Research and Creative Work at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Chancellor’s Honors Program Projects by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. Thomas M. Gray, III Bachelor of Arts The Rhetoric of the Bowl Championship Series: An Analysis of the Persuasive Methods Underlying an "Objective" System By ThomGray Ul1iversity of Tennessee Chancellor's Honors Program 2007 Graduate in English (Rhetoric and Writing) Introduction Since I began my formal education in 1990, I have been caught in the middle of the "technological revolution." I have watched the computer grow from a tool for playing The Oregon Trail between lessons to a vital part of every student's life from middle school to the university. I have watched the Internet encourage worldwide communication among diverse cultures and help us spread and gather information faster than ever before.