Salary Dispersion and Team Performance in the National Basketball Association
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Cleveland Cavaliers (37-22) at Indiana Pacers (23-34)
FRI., FEB. 27, 2015 BANKERS LIFE FIELDHOUSE – INDIANAPOLIS, IN TV: FSO RADIO: WTAM 1100 AM/100.7 WMMS/LA MEGA 87.7 FM 7:00 PM EST CLEVELAND CAVALIERS (37-22) AT INDIANA PACERS (23-34) 2014-15 CLEVELAND CAVALIERS GAME NOTES REGULAR SEASON GAME #60 ROAD GAME #29 PROBABLE STARTERS 2014-15 SCHEDULE POS NO. PLAYER HT. WT. G GS PPG RPG APG FG% MPG 10/30 vs. NYK Lost, 90-95 10/31 @ CHI WON, 114-108* F 23 LEBRON JAMES 6-8 250 14-15: 49 49 26.0 5.8 7.3 .491 36.3 11/4 @ POR Lost, 82-101 11/5 @ UTA Lost, 100-102 11/7 @ DEN WON, 110-101 F 0 KEVIN LOVE 6-10 243 14-15: 56 56 16.9 10.3 2.3 .433 34.6 11/10 vs. NOP WON, 118-111 11/14 @ BOS WON, 122-121 11/15 vs. ATL WON, 127-94 C 20 TIMOFEY MOZGOV 7-1 250 14-15: 58 57 9.3 7.9 0.5 .543 26.0 11/17 vs. DEN Lost, 97-106 11/19 vs. SAS Lost, 90-92 11/21 @ WAS Lost, 78-91 G 5 J.R. SMITH 6-6 225 14-15: 48 29 11.6 2.7 3.0 .410 28.7 11/22 vs. TOR Lost, 93-110 11/24 vs. ORL WON, 106-74 G 8 MATTHEW DELLAVEDOVA 6-4 200 14-15: 44 9 4.3 1.8 2.8 .363 20.0 11/26 vs. WAS WON, 113-87 11/29 vs. -
The Pennsylvania State University Schreyer Honors College
THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE DEPARTMENT OF LABOR AND EMPLOYMENT RELATIONS PLAYERS IN POWER: A HISTORICAL REVIEW OF CONTRACTUALLY BARGAINED AGREEMENTS IN THE NBA INTO THE MODERN AGE AND THEIR LIMITATIONS ERIC PHYTHYON SPRING 2020 A thesis submitted in partial fulfillment of the requirements for baccalaureate degrees in Political Science and Labor and Employment Relations with honors in Labor and Employment Relations Reviewed and approved* by the following: Robert Boland J.D, Adjunct Faculty Labor & Employment Relations, Penn State Law Thesis Advisor Jean Marie Philips Professor of Human Resources Management, Labor and Employment Relations Honors Advisor * Electronic approvals are on file. ii ABSTRACT: This paper analyzes the current bargaining situation between the National Basketball Association (NBA), and the National Basketball Players Association (NBPA) and the changes that have occurred in their bargaining relationship over previous contractually bargained agreements, with specific attention paid to historically significant court cases that molded the league to its current form. The ultimate decision maker for the NBA is the Commissioner, Adam Silver, whose job is to represent the interests of the league and more specifically the team owners, while the ultimate decision maker for the players at the bargaining table is the National Basketball Players Association (NBPA), currently led by Michele Roberts. In the current system of negotiations, the NBA and the NBPA meet to negotiate and make changes to their collective bargaining agreement as it comes close to expiration. This paper will examine the 1976 ABA- NBA merger, and the resulting impact that the joining of these two leagues has had. This paper will utilize language from the current collective bargaining agreement, as well as language from previous iterations agreed upon by both the NBA and NBPA, as well information from other professional sports leagues agreements and accounts from relevant parties involved. -
NBA Court Realty Dan Cervone (New York University), Luke Bornn (Simon Fraser University), and Kirk Goldsberry (University of Texas)
NBA Court Realty Dan Cervone (New York University), Luke Bornn (Simon Fraser University), and Kirk Goldsberry (University of Texas) The Court is a Real Estate Market Throughout a basketball possession, teams fight to control valuable court space. For example, - being near the basket or in the corner 3 areas leads to high-value shots - having the ball at the top of the arc keeps many pass options open - being open and undefended anywhere on the court eases ball movement and minimizes turnovers. Using only patterns of ball movement such as passes, we are able to infer which regions of the court teams value most, and quantify the effects of controlling such regions. This leads to new spatial characterizations of team/player strategy, and value quantifications of positioning and spacing. For instance, we can compare the value of the space the ballcarrier controls with the value of the space his teammates control to better understand how different lineups manage on- and off-ball resources. Above: Map of NBA court real estate values during 2014-15 season. Just like New York City (right), property values vary Read the full paper: dramatically by neighborhood. The hoop is the Tribeca of the NBA. 30 feet away? That's more like Astoria. The Value of a Player's Court Real Estate Investment Portfolio 3. Valuing property investments based on exchanges: When players pass the ball on offense, the team exchanges property investments. The patterns of these transactions allow us to infer the value of each player's real estate investment porfolio. For instance, in the figure on the left, if a pass between players A and B is equally likely to go A → B as B → A, we'd think A's and B's investment 1. -
Salary Inequality in the NBA: Changing Returns to Skill Or Wider Skill Distributions? Jonah F
Claremont Colleges Scholarship @ Claremont CMC Senior Theses CMC Student Scholarship 2017 Salary Inequality in the NBA: Changing Returns to Skill or Wider Skill Distributions? Jonah F. Breslow Claremont McKenna College Recommended Citation Breslow, Jonah F., "Salary Inequality in the NBA: Changing Returns to Skill or Wider Skill Distributions?" (2017). CMC Senior Theses. 1645. http://scholarship.claremont.edu/cmc_theses/1645 This Open Access Senior Thesis is brought to you by Scholarship@Claremont. It has been accepted for inclusion in this collection by an authorized administrator. For more information, please contact [email protected]. Claremont McKenna College Salary Inequality in the NBA: Changing Returns to Skill or Wider Skill Distributions? submitted to Professor Ricardo Fernholz by Jonah F. Breslow for Senior Thesis Spring 2017 April 24, 2017 Abstract In this paper, I examine trends in salary inequality from the 1985-86 NBA season to the 2015-16 NBA season. Income and wealth inequality have been extremely important issues recently, which motivated me to analyze inequality in the NBA. I investigated if salary inequality trends in the NBA can be explained by either returns to skill or widening skill distributions. I used Pareto exponents to measure inequality levels and tested to see if the levels changed over the sample. Then, I estimated league-wide returns to skill. I found that returns to skill have not significantly changed, but variance in skill has increased. This result explained some of the variation in salary distributions. This could potentially influence future Collective Bargaining Agreements insofar as it provides an explanation for widening NBA salary distributions as opposed to a judgement whether greater levels of inequality is either good or bad for the NBA. -
Monday, January 22, 2007 Quicken Loans
THURSDAY, OCT. 8, 2015 WELLS FARGO CENTER – PHILADELPHIA, PA TV: FSO RADIO: WTAM 1100 AM / LA MEGA 87.7 FM 7:00 PM EST CLEVELAND CAVALIERS (0-1) AT PHILADELPHIA 76ERS (0-1) 2015-16 CLEVELAND CAVALIERS GAME NOTES PRESEASON GAME #2 LAST GAME’S STARTERS 2015-16 SCHEDULE POS NO. PLAYER HT. WT. G GS PPG RPG APG FG% MPG 10/27 @ CHI 8:00 PM TNT F 23 LEBRON JAMES 6-8 250 14-15: 69 69 25.3 6.0 7.4 .488 36.1 10/28 @ MEM 8:00 PM FSO 10/30 vs. MIA 7:00PM ESPN/FSO PRE: 1 1 8.0 7.0 5.0 .231 22.0 11/2 @ PHI 7:00 PM FSO F 1 JAMES JONES 6-8 218 14-15: 57 2 4.4 1.1 0.4 .368 11.7 11/4 vs. NYK 8:00 PM ESPN/FSO PRE: 1 1 9.0 1.0 2.0 .333 22.0 11/6 vs. PHI 7:30 PM FSO 11/8 vs. IND 3:30 PM FSO C 13 TIMOFEY MOZGOV 7-1 275 14-15: 81 80 9.7 7.3 0.7 .555 25.3 11/10 vs. UTA 7:00 PM FSO PRE: 1 1 13.0 5.0 1.0 .857 21.0 11/13 @ NYK 7:30 PM FSO G 3 J.R. SMITH 6-6 225 14-15: 70 51 12.1 3.2 2.8 .417 29.7 11/14 @ MIL 8:30 PM FSO 11/17 @ DET 7:30 PM FSO PRE: 1 1 15.0 4.0 4.0 .600 22.0 11/19 vs. -
(Wtam, Fsn Oh) 7:00 Pm
CLEVELAND CAVALIERS (1-0) AT TORONTO RAPTORS (1-0) FRI., OCT. 28, 2016 AIR CANADA CENTRE – TORONTO, ON 7:00 PM EST TV: FSO/ESPN RADIO: 100.7 WMMS/LA MEGA 87.7 FM 2016-17 CLEVELAND CAVALIERS GAME NOTES OVERALL GAME # 2 ROAD GAME # 1 PROBABLE STARTERS 2016-17 SCHEDULE All games can be heard on WTAM/La Mega 87.7 FM POS NO. PLAYER HT. WT. G GS PPG RPG APG FG% MPG 10/25 vs. NYK WON, 117-88 10/28 @ TOR 7:00 p.m. ESPN/FSO 10/29 vs. ORL 7:30 p.m. FSO F 23 LEBRON JAMES 6-8 250 16-17: 1 1 19.0 11.0 14.0 .643 32.4 11/1 vs. HOU 7:00 p.m. NBATV/FSO 11/3 vs. BOS 8:00 p.m. TNT 11/5 @ PHI 7:00 p.m. FSO F 0 KEVIN LOVE 6-10 251 16-17: 1 1 23.0 12.0 2.0 .400 25.0 11/8 vs. ATL 7:00 p.m. NBATV/FSO 11/11 @ WAS 7:00 p.m. FSO C 13 TRISTAN THOMPSON 6-10 238 16-17: 1 1 0.0 6.0 0.0 .000 22.4 11/13 vs. CHA 3:30 p.m. FSO 11/15 vs. TOR 7:00 p.m. NBATV/FSO 11/16 @ IND 7:00 p.m. FSO G 5 J.R. SMITH 6-6 225 16-17: 1 1 8.0 3.0 2.0 .231 25.2 11/18 vs. DET 7:30 p.m. -
Cleveland Cavaliers (51-20) at Brooklyn Nets (19-51)
THUR., MARCH 24, 2016 BARCLAYS CENTER – BROOKLYN, NY TV: FSO RADIO: WTAM 1100 AM/100.7 WMMS/LA MEGA 87.7 FM 7:30 PM EST CLEVELAND CAVALIERS (51-20) AT BROOKLYN NETS (19-51) 2015-16 CLEVELAND CAVALIERS GAME NOTES OVERALL GAME # 72 ROAD GAME # 36 PROBABLE STARTERS 2015-16 SCHEDULE POS NO. PLAYER HT. WT. G GS PPG RPG APG FG% MPG 10/27 @ CHI Lost, 95-97 10/28 @ MEM WON, 106-76 F 23 LEBRON JAMES 6-8 250 15-16: 68 68 *24.9 7.4 >6.6 .506 35.6 10/30 vs. MIA WON, 102-92 11/2 @ PHI WON, 107-100 11/4 vs. NYK WON, 96-86 F 0 KEVIN LOVE 6-10 251 15-16: 67 67 15.7 ^9.9 2.4 .414 31.7 11/6 vs. PHI WON, 108-102 11/8 vs. IND WON, 101-97 11/10 vs. UTA WON, 118-114 C 20 TIMOFEY MOZGOV 7-1 275 15-16: 66 40 6.4 4.3 0.4 .567 17.4 11/13 @ NYK WON, 90-84 11/14 @ MIL Lost, 105-108** 11/17 @ DET Lost, 99-104 G 5 J.R. SMITH 6-6 225 15-16: 67 67 12.3 2.8 1.6 .411 30.6 11/19 vs. MIL WON, 115-100 11/21 vs. ATL WON, 109-97 G 2 KYRIE IRVING 6-3 193 15-16: 45 45 19.6 2.9 4.5 .462 30.9 11/23 vs. ORL WON, 117-103 11/25 @ TOR Lost, 99-103 11/27 @ CHA WON, 95-90 * Ranks 6th in NBA > Ranks 9th in NBA ^ Ranks 11th in NBA 11/28 vs. -
Cleveland Cavaliers (22-20) Vs
WED., JAN. 21, 2015 QUICKEN LOANS ARENA – CLEVELAND, OH TV: FSO RADIO: WTAM 1100 AM/100.7 WMMS/LA MEGA 87.7 FM 7:00 PM EST CLEVELAND CAVALIERS (22-20) VS. UTAH JAZZ (14-27) 2014-15 CLEVELAND CAVALIERS GAME NOTES REGULAR SEASON GAME #43 HOME GAME #22 PROBABLE STARTERS 2014-15 SCHEDULE POS NO. PLAYER HT. WT. G GS PPG RPG APG FG% MPG 10/30 vs. NYK Lost, 90-95 10/31 @ CHI WON, 114-108* F 23 LEBRON JAMES 6-8 250 14-15: 33 33 26.0 5.5 7.3 .493 37.6 11/4 @ POR Lost, 82-101 11/5 @ UTA Lost, 100-102 11/7 @ DEN WON, 110-101 F 0 KEVIN LOVE 6-10 243 14-15: 40 40 17.6 10.4 2.3 .429 35.6 11/10 vs. NOP WON, 118-111 11/14 @ BOS WON, 122-121 11/15 vs. ATL WON, 127-94 C 20 TIMOFEY MOZGOV 7-1 250 14-15: 41 40 8.7 8.0 0.4 .509 25.7 11/17 vs. DEN Lost, 97-106 11/19 vs. SAS Lost, 90-92 11/21 @ WAS Lost, 78-91 G 5 J.R. SMITH 6-6 225 14-15: 31 12 11.7 2.5 3.0 .412 27.4 11/22 vs. TOR Lost, 93-110 11/24 vs. ORL WON, 106-74 G 2 KYRIE IRVING 6-3 193 14-15: 39 39 21.1 3.1 5.3 .459 38.3 11/26 vs. WAS WON, 113-87 11/29 vs. -
Monday, January 22, 2007 Quicken Loans
MON., MAY 4, 2015 QUICKEN LOANS ARENA – CLEVELAND, OH TV: TNT RADIO: WTAM 1100/100.7 WMMS/LA MEGA 87.7 FM 7:00 PM EST CLEVELAND CAVALIERS (4-0, 53-29) VS. CHICAGO BULLS (4-2, 50-32) 2014-15 CLEVELAND CAVALIERS GAME NOTES PLAYOFF GAME #5 HOME GAME #3 PROBABLE STARTERS 2015 NBA PLAYOFFS QUARTERFINALS POS NO. PLAYER HT. WT. G GS PPG RPG APG FG% MPG F 23 LEBRON JAMES 6-8 250 14-15: 69 69 25.3 6.0 7.4 .488 36.1 # 2 Cleveland vs. # 7 Boston CAVS won series 4-0 PLAYOFFS: 4 4 27.0 9.0 6.5 .467 43.0 TBD Game 1 at Cleveland Sunday, April 19 C 20 TIMOFEY MOZGOV 7-1 250 14-15: 81 80 9.7 7.3 0.7 .555 25.3 CAVS 113, Celtics 100 PLAYOFFS: 4 4 9.8 8.0 0.0 .538 24.5 Game 2 at Cleveland TBD Tuesday, April 21 CAVS 99, Celtics 91 G 2 KYRIE IRVING 6-3 193 14-15: 75 75 21.7 3.2 5.2 .468 36.4 PLAYOFFS: 4 4 23.3 5.0 4.3 .435 40.6 Game 3 at Boston Thursday, April 23 CAVS QUICK FACTS CAVS 103, Celtics 95 th The Cleveland Cavaliers continue their 19 overall postseason appearance versus the Chicago Bulls in the 2015 Eastern Conference Semifinals. It Game 4 at Boston will mark the 7th time the teams have faced each other in the playoffs, which is the Cavs’ most against one single opponent in postseason history. -
DYNASTY Rulebook 2004 Season
NEW! 2020 Edition The Official Rulebook And How—To—Play Guide “Cieslinski developed the board game Pursue the Pennant, which was an amazingly lifelike representation of baseball. DYNASTY League Baseball, which is available as both a board game and a computer game, is even better.” Michael Bauman — Milwaukee Journal Sentinel EDITED BY Michael Cieslinski 2020 Edition Edited by Michael Cieslinski DYNASTY League Baseball A Design Depot Book / October 2020 All rights reserved Copyright © 2020 by Design Depot Inc. This book may not be reproduced in whole or in part by any means without express written consent from Design Depot Inc. First Edition: March, 1997 Printed in the United States of America ISBN 0-9670323-2-6 Official Rulebook DYNASTY League Baseball © 2020 Spring Training: Learning The Fundamentals Building Your Own Baseball Dynasty DYNASTY League Baseball includes two types of player cards: hitters and pitchers. Take a look at the 1982 Paul Molitor and Dennis Martinez player cards. "As far as I can tell, there's only one tried and true way If #168 was rolled, (dice are read in the order red, to build yourself a modern dynasty in sports. You find white and blue), check Molitor's card (#0-499 are yourself one guy who knows the sport inside out, and always found on the hitter's card) and look down the top to bottom, and you put him in charge. You let him "vs. Right" column (he's vs. the right-handed Marichal). run the show totally." - Whitey Herzog. You'll find the result is a ground single into right field. -
The 2011 Collective Bargaining Agreement's Impact
The New Landscape of the NBA: The 2011 Collective Bargaining Agreement’s Impact on Competitive Balance and Players’ Salaries Nicholas Yam Professor Peter Arcidiacono, Faculty Advisor Duke University Durham, North Carolina 2015 Nicholas graduated from Duke University with a Bachelor of Science in Economics. Following graduation, he will begin working at Citigroup as an Investment Banking Analyst. He can be reached at [email protected] with any questions. Table of Contents I. Introduction ............................................................................................................................................ 4 II. Literature Review ............................................................................................................................... 10 III. Competitive Balance ........................................................................................................................... 16 A. Theoretical Framework .................................................................................................................................... 16 B. Data ........................................................................................................................................................................ 18 C. Empirical Specification .................................................................................................................................... 20 IV. Player Salaries .................................................................................................................................... -
Parity and Predictability of Competitions
Parity and Predictability of Competitions E. Ben-Naim,1, ¤ F. Vazquez,1, 2, y and S. Redner1, 2, z 1Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545 2Department of Physics, Boston University, Boston, Massachusetts 02215 We present an extensive statistical analysis of the results of all sports competitions in ¯ve major sports leagues in England and the United States. We characterize the parity among teams by the variance in the winning fraction from season-end standings data and quantify the predictability of games by the frequency of upsets from game results data. We introduce a novel mathematical model in which the underdog team wins with a ¯xed upset probability. This model quantitatively relates the parity among teams with the predictability of the games, and it can be used to estimate the upset frequency from standings data. What is the most competitive sports league? We 1 answer this question via an extensive statistical survey (a) of game results in ¯ve major sports. Previous stud- 0.8 ies have separately characterized parity (Fort 1995) and predictability (Stern 1997, Wesson 2002, Lundh 2006) of 0.6 sports competitions. In this investigation, we relate par- F(x) 0.4 NFL ity with predictability using a novel theoretical model in NBA NHL which the underdog wins with a ¯xed upset probability. MLB Our results provide further evidence that the likelihood 0.2 of upsets is a useful measure of competitiveness in a given 0 sport (Wesson 2002, Lundh 2006). This characterization 0 0.2 0.4 0.6 0.8 1 complements the myriad of available statistics on the out- x comes of sports events (Albert 2005, Stern 1991, Gembris FIG.