Gonzaga Strength of Schedule
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PAT DELANY Assistant Coach
ORLANDO MAGIC MEDIA TOOLS The Magic’s communications department have a few online and social media tools to assist you in your coverage: *@MAGIC_PR ON TWITTER: Please follow @Magic_PR, which will have news, stats, in-game notes, injury updates, press releases and more about the Orlando Magic. *@MAGIC_MEDIAINFO ON TWITTER (MEDIA ONLY-protected): Please follow @ Magic_MediaInfo, which is media only and protected. This is strictly used for updated schedules and media availability times. Orlando Magic on-site communications contacts: Joel Glass Chief Communications Officer (407) 491-4826 (cell) [email protected] Owen Sanborn Communications (602) 505-4432 (cell) [email protected] About the Orlando Magic Orlando’s NBA franchise since 1989, the Magic’s mission is to be world champions on and off the court, delivering legendary moments every step of the way. Under the DeVos family’s ownership, the Magic have seen great success in a relatively short history, winning six division championships (1995, 1996, 2008, 2009, 2010, 2019) with seven 50-plus win seasons and capturing the Eastern Conference title in 1995 and 2009. Off the court, on an annual basis, the Orlando Magic gives more than $2 million to the local community by way of sponsorships of events, donated tickets, autographed merchandise and grants. Orlando Magic community relations programs impact an estimated 100,000 kids each year, while a Magic staff-wide initiative provides more than 7,000 volunteer hours annually. In addition, the Orlando Magic Youth Foundation (OMYF) which serves at-risk youth, has distributed more than $24 million to local nonprofit community organizations over the last 29 years.The Magic’s other entities include the team’s NBA G League affiliate, the Lakeland Magic, which began play in the 2017-18 season in nearby Lakeland, Fla.; the Orlando Solar Bears of the ECHL, which serves as the affiliate to the NHL’s Tampa Bay Lightning; and Magic Gaming is competing in the second season of the NBA 2K League. -
Introduction Predictive Vs. Earned Ranking Methods
An overview of some methods for ranking sports teams Soren P. Sorensen University of Tennessee Knoxville, TN 38996-1200 [email protected] Introduction The purpose of this report is to argue for an open system for ranking sports teams, to review the history of ranking systems, and to document a particular open method for ranking sports teams against each other. In order to do this extensive use of mathematics is used, which might make the text more difficult to read, but ensures the method is well documented and reproducible by others, who might want to use it or derive another ranking method from it. The report is, on the other hand, also more detailed than a ”typical” scientific paper and discusses details, which in a scientific paper intended for publication would be omitted. We will in this report focus on NCAA 1-A football, but the methods described here are very general and can be applied to most other sports with only minor modifications. Predictive vs. Earned Ranking Methods In general most ranking systems fall in one of the following two categories: predictive or earned rankings. The goal of an earned ranking is to rank the teams according to their past performance in the season in order to provide a method for selecting either a champ or a set of teams that should participate in a playoff (or bowl games). The goal of a predictive ranking method, on the other hand, is to provide the best possible prediction of the outcome of a future game between two teams. In an earned system objective and well publicized criteria should be used to rank the teams, like who won or the score difference or a combination of both. -
Quantifying the Influence of Deviations in Past NFL Standings on the Present
Can Losing Mean Winning in the NFL? Quantifying the Influence of Deviations in Past NFL Standings on the Present The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation MacPhee, William. 2020. Can Losing Mean Winning in the NFL? Quantifying the Influence of Deviations in Past NFL Standings on the Present. Bachelor's thesis, Harvard College. Citable link https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37364661 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Can Losing Mean Winning in the NFL? Quantifying the Influence of Deviations in Past NFL Standings on the Present A thesis presented by William MacPhee to Applied Mathematics in partial fulfillment of the honors requirements for the degree of Bachelor of Arts Harvard College Cambridge, Massachusetts November 15, 2019 Abstract Although plenty of research has studied competitiveness and re-distribution in professional sports leagues from a correlational perspective, the literature fails to provide evidence arguing causal mecha- nisms. This thesis aims to isolate these causal mechanisms within the National Football League (NFL) for four treatments in past seasons: win total, playoff level reached, playoff seed attained, and endowment obtained for the upcoming player selection draft. Causal inference is made possible due to employment of instrumental variables relating to random components of wins (both in the regular season and in the postseason) and the differential impact of tiebreaking metrics on teams in certain ties and teams not in such ties. -
GAME NOTES for In-Game Notes and Updates, Follow Grizzlies PR on Twitter @Grizzliespr
GAME NOTES For in-game notes and updates, follow Grizzlies PR on Twitter @GrizzliesPR GRIZZLIES 2020-21 SCHEDULE/RESULTS Date Opponent Tip-Off/TV • Result 12/23 SAN ANTONIO L 119-131 MEMPHIS GRIZZLIES 12/26 ATLANTA L 112-122 12/28 @ Brooklyn W (OT) 116-111 END OF 2021 POSTSEASON GAME NOTES 12/30 @ Boston L 107-126 1/1 @ Charlotte W 108-93 38-34 1-4 1/3 LA LAKERS L 94-108 Game Notes/Stats Contact: Ross Wooden [email protected] Reg Season Playoffs 1/5 LA LAKERS L 92-94 1/7 CLEVELAND L 90-94 1/8 BROOKLYN W 115-110 MEMPHIS GRIZZLIES STARTING LINEUP 1/11 @ Cleveland W 101-91 1/13 @ Minnesota W 118-107 SF # 1 6-8 ¼ 230 Previous Game 4 PTS 2 REB 5 AST 2 STL 0 BLK 24:11 1/16 PHILADELPHIA W 106-104 Selected 30th overall in the 2015 NBA Draft after two seasons at UCLA. 1/18 PHOENIX W 108-104 First player to compile 10+ steals in any two-game playoff span since Dwyane 1/30 @ San Antonio W 129-112 KYLE ANDERSON 2/1 @ San Antonio W 133-102 Wade during the 2013 NBA Finals. th 2/2 @ Indiana L 116-134 UCLA / USA 7 Season Career-high 94 3PM this season (previous: 24 3PM in 67 games in 2019-20). 2/4 HOUSTON L 103-115 PPG: 8.4 RPG: 5.0 APG: 3.2 2/6 @ New Orleans L 109-118 2/8 TORONTO L 113-128 PF # 13 6-11 242 Previous Game 21 PTS 6 REB 1 AST 1 STL 0 BLK 26:01 2/10 CHARLOTTE W 130-114 Selected fourth overall in 2018 NBA Draft after freshman year at Michigan State. -
UA19/16/2 Basketball Press Releases
Western Kentucky University TopSCHOLAR® WKU Archives Records WKU Archives Fall 2018 UA19/16/2 Basketball Press Releases WKU Athletic Media Relations Follow this and additional works at: https://digitalcommons.wku.edu/dlsc_ua_records Part of the African American Studies Commons, Higher Education Administration Commons, Mass Communication Commons, Public Relations and Advertising Commons, Race and Ethnicity Commons, and the Sports Studies Commons This Article is brought to you for free and open access by TopSCHOLAR®. It has been accepted for inclusion in WKU Archives Records by an authorized administrator of TopSCHOLAR®. For more information, please contact [email protected]. WKU ATHLETIC COMMUNICATIONS / MEDIA RELATIONS FOR IMMEDIATE RELEASE / August 7, 2018 Men’s Basketball / Contact: Zach Greenwell, WKU Athletic Communications/Media Relations Darius Thompson headshot is attached. THOMPSON SIGNS FIRST PRO CONTRACT IN THE NETHERLANDS BOWLING GREEN, Ky. — Former WKU Basketball guard Darius Thompson has inked his first professional contract, signing with Zorg en Zekerheid Basketball Leiden in the Netherlands. ZZ Leiden competes in the Dutch Basketball League. The team has won the national championship three times, the NBB Cup twice and the Dutch Basketball Supercup twice. Thompson, a 6-foot-4 guard from Murfreesboro, Tenn., joined the Hilltoppers for the 2017-18 season as a graduate transfer from Virginia. He left his mark on the program in just one year, starting all 38 games, averaging 35.6 minutes per contest and earning a spot on the All-Conference USA Second Team. Thompson averaged 13.6 points, 4.8 assists and 4.2 rebounds for WKU, while netting a team- high 54 made 3-pointers. -
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. -
Open Evan Bittner Thesis.Pdf
THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE DEPARTMENT OF STATISTICS PREDICTING MAJOR LEAGUE BASEBALL PLAYOFF PROBABILITIES USING LOGISTIC REGRESSION EVAN J. BITTNER FALL 2015 A thesis submitted in partial fulfillment of the requirements for a baccalaureate degree in Statistics with honors in Statistics Reviewed and approved* by the following: Andrew Wiesner Lecturer of Statistics Thesis Supervisor Murali Haran Associate Professor of Statistics Honors Adviser * Signatures are on file in the Schreyer Honors College. i ABSTRACT Major League Baseball teams are constantly assessing whether or not they think their teams will make the playoffs. Many sources publish playoff probabilities or odds throughout the season using advanced statistical methods. These methods are somewhat secretive and typically advanced and difficult to understand. The goal of this work is to determine a way to calculate playoff probabilities midseason that can easily be understood and applied. The goal is to develop a method and compare its predictive accuracy to the current methods published by statistical baseball sources such as Baseball Prospectus and Fangraphs. ii TABLE OF CONTENTS List of Figures .............................................................................................................. iii List of Tables ............................................................................................................... iv Acknowledgements ..................................................................................................... -
Texas Tech Season Stats
Texas Tech Basketball TEXAS TECHTexas Tech SEASON Combined Team Statistics STATS (as of Apr 07, 2019) All games Overall record: 31-6 Conf: 14-4 Home: 17-1 Away: 6-3 Neutral: 8-2 Total 3-Point F-Throw Rebounds ## Player gp-gs min avg fg-fga fg% 3fg-fga 3fg% ft-fta ft% off def tot avg pf dq a to blk stl pts avg 23 Culver,Jarrett 37-37 1193 32.2 249-529 .4 7 1 49-155 .3 1 6 142-202 .7 0 3 49 185 234 6.3 70 0 137 100 20 55 689 18.6 25 Moretti,Davide 37-37 1159 31.3 129-259 .4 9 8 70-153 .4 5 8 95-103 .9 2 2 3 74 77 2.1 81 2 91 49 0 40 423 11.4 13 Mooney,Matt 37-37 1136 30.7 152-357 .4 2 6 47-121 .3 8 8 68-87 .7 8 2 7 110 117 3.2 71 1 123 93 4 68 419 11.3 11 Owens,Tariq 37-36 941 25.4 128-208 .6 1 5 8-31 .2 5 8 63-80 .7 8 8 79 135 214 5.8 106 4 28 37 91 18 327 8.8 01 Francis,Brandone 37-1 876 23.7 82-227 .3 6 1 41-125 .3 2 8 24-36 .6 6 7 12 74 86 2.3 80 1 53 33 6 29 229 6.2 00 Edwards,Kyler 37-0 655 17.7 67-167 .4 0 1 29-66 .4 3 9 33-51 .6 4 7 10 72 82 2.2 57 1 41 33 8 23 196 5.3 03 Corprew,Deshawn 36-1 495 13.8 75-133 .5 6 4 20-49 .4 0 8 14-21 .6 6 7 36 87 123 3.4 54 0 16 28 10 13 184 5.1 32 Odiase,Norense 36-36 627 17.4 55-94 .5 8 5 0-0 .0 0 0 39-63 .6 1 9 64 128 192 5.3 87 1 10 30 34 16 149 4.1 10 Ondigo,Malik 18-0 106 5.9 9-23 .3 9 1 0-5 .0 0 0 5-9 .5 5 6 12 10 22 1.2 15 0 1 9 3 0 23 1.3 20 Hicks,Parker 16-0 50 3.1 5-15 .3 3 3 2-10 .2 0 0 4-5 .8 0 0 3 9 12 0.8 2 0 0 3 2 1 16 1.0 35 Mballa,Josh 17-0 57 3.4 4-9 .4 4 4 0-1 .0 0 0 5-14 .3 5 7 2 3 5 0.3 6 0 2 3 2 2 13 0.8 24 Benson,Avery 20-0 70 3.5 5-13 .3 8 5 0-4 .0 0 0 1-5 .2 0 0 4 8 12 0.6 9 0 5 6 3 5 11 0.6 30 Sorrells,Andrew 17-0 57 3.4 3-13 .2 3 1 1-9 .1 1 1 2-3 .6 6 7 1 2 3 0.2 7 0 2 1 0 2 9 0.5 21 Moore,Khavon 1-0 2 2.0 0-0 .0 0 0 0-0 .0 0 0 0-0 .0 0 0 0 0 0 0.0 0 0 0 0 0 0 0 0.0 Team 34 50 84 24 Total......... -
2020-21 Panini Contenders Draft Basketball Checklist
2020-21 Contenders Draft Basketball Checklist - By College 81 Colleges/Teams; 62 with Autographs; 19 without Autographs Player Set Card # College Kira Lewis Jr. Auto - Prospect Ticket RPS 80 Alabama Crimson Tide Kira Lewis Jr. Insert - Front-Row Seats 23 Alabama Crimson Tide Kira Lewis Jr. Insert - Playing the Numbers Game 23 Alabama Crimson Tide Kira Lewis Jr. Insert - School Colors 23 Alabama Crimson Tide James Harden Base - Prospect Ticket 2 Arizona State Sun Devils James Harden Insert - Campus Legends 33 Arizona State Sun Devils Sparky The Sun Devil Insert - Mascots 10 Arizona State Sun Devils Josh Green Auto - Prospect Ticket RPS 65 Arizona Wildcats Nico Mannion Auto - Game Day Prospect Ticket Stickers 9 Arizona Wildcats Nico Mannion Auto - Prospect Ticket RPS 59 Arizona Wildcats Zeke Nnaji Auto - Prospect Ticket Stickers 84 Arizona Wildcats Deandre Ayton Base - Prospect Ticket 21 Arizona Wildcats Josh Green Insert - Campus ID 20 Arizona Wildcats Josh Green Insert - Front-Row Seats 1 Arizona Wildcats Josh Green Insert - Playing the Numbers Game 30 Arizona Wildcats Josh Green Insert - School Colors 1 Arizona Wildcats Lauri Markkanen Base - Prospect Ticket 26 Arizona Wildcats Mike Bibby Insert - Winning Tickets 23 Arizona Wildcats Nico Mannion Insert - Campus ID 7 Arizona Wildcats Nico Mannion Insert - Front-Row Seats 18 Arizona Wildcats Nico Mannion Insert - Playing the Numbers Game 18 Arizona Wildcats Nico Mannion Insert - School Colors 18 Arizona Wildcats Zeke Nnaji Insert - School Colors 28 Arizona Wildcats Mason Jones Auto - Prospect -
THE BASKETBALL GOLD SHEET the Only Basketball Publication
THE BASKETBALL GOLD SHEET Published Weekly by: Nation-Wide Sports Publications 4717 Van Nuys Blvd., Suite 101 Sherman Oaks, CA 91403 (800)798-GOLD (4653) e-mail: [email protected] In cyberspace: http://www.goldsheet.com $9 The Only Basketball Publication of its kind! Call 1-800-798-4653 or visit www.goldsheet.com to Subscribe!! NATION-WIDE SPORTS PUBLICATIONS COPYRIGHT © 2015 VOLUME 59 NOVEMBER 13-19, 2015 NO. 4 IT’S TIP-OFF TIME! TGS COLLEGE HOOPS PRESEASON TOP 40! by Bruce Marshall, Goldsheet.com Editor Now, the fun really begins! The college basketball season swings into action this weekend, and we’re prepared with predicted margins and select writeups (including our usual featured Key Release selections) for the first week, all found in our College Forecast. In the meantime, to get a bit better prepared for the unfolding drama, we present our TGS Preseason Top 40. Hang on for the ride... 1-WICHITA STATE...They’re b-a-a-a-c-k! We’re talking about Shocker senior Gs Ron Baker and Fred VanVleet, holdovers from the 2013 Final Four team and having flirted with the NBA Draft last spring before deciding to come back for one more go at Koch Arena. As did top-tier HC Gregg Marshall, who continues to resist moves to bigger-name programs (Texas the latest high- profile suitor to be rejected). Baker, VanVleet, hard-nosed senior Evan Wessel, and Kansas transfer Connor Frankamp (eligible mid-December) gives Marshall arguably the top backcourt in the country, and the addition of Cleveland State graduate transfer PF Anton Grady (All-Horizon League LY when scoring 14.2 ppg and grabbing 7.9 rpg) fortifies the frontline. -
Pythagoras at the Bat 3 to Be 2, Which Is the Source of the Name As the Formula Is Reminiscent of the Sum of Squares from the Pythagorean Theorem
Pythagoras at the Bat Steven J Miller, Taylor Corcoran, Jennifer Gossels, Victor Luo and Jaclyn Porfilio The Pythagorean formula is one of the most popular ways to measure the true abil- ity of a team. It is very easy to use, estimating a team’s winning percentage from the runs they score and allow. This data is readily available on standings pages; no computationally intensive simulations are needed. Normally accurate to within a few games per season, it allows teams to determine how much a run is worth in different situations. This determination helps solve some of the most important eco- nomic decisions a team faces: How much is a player worth, which players should be pursued, and how much should they be offered. We discuss the formula and these applications in detail, and provide a theoretical justification, both for the formula as well as simpler linear estimators of a team’s winning percentage. The calculations and modeling are discussed in detail, and when possible multiple proofs are given. We analyze the 2012 season in detail, and see that the data for that and other recent years support our modeling conjectures. We conclude with a discussion of work in progress to generalize the formula and increase its predictive power without needing expensive simulations, though at the cost of requiring play-by-play data. Steven J Miller Williams College, Williamstown, MA 01267, e-mail: [email protected],[email protected] arXiv:1406.0758v1 [math.HO] 29 May 2014 Taylor Corcoran The University of Arizona, Tucson, AZ 85721 Jennifer Gossels Princeton University, Princeton, NJ 08544 Victor Luo Williams College, Williamstown, MA 01267 Jaclyn Porfilio Williams College, Williamstown, MA 01267 1 2 Steven J Miller, Taylor Corcoran, Jennifer Gossels, Victor Luo and Jaclyn Porfilio 1 Introduction In the classic movie Other People's Money, New England Wire and Cable is a firm whose parts are worth more than the whole. -
Sports Analytics from a to Z
i Table of Contents About Victor Holman .................................................................................................................................... 1 About This Book ............................................................................................................................................ 2 Introduction to Analytic Methods................................................................................................................. 3 Sports Analytics Maturity Model .................................................................................................................. 4 Sports Analytics Maturity Model Phases .................................................................................................. 4 Sports Analytics Key Success Areas ........................................................................................................... 5 Allocative and Dynamic Efficiency ................................................................................................................ 7 Optimal Strategy in Basketball .................................................................................................................. 7 Backwards Selection Regression ................................................................................................................... 9 Competition between Sports Hurts TV Ratings: How to Shift League Calendars to Optimize Viewership .................................................................................................................................................................