Relative Value of Draft Position in the NBA
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Gether, Regardless Also Note That Rule Changes and Equipment Improve- of Type, Rather Than Having Three Or Four Separate AHP Ments Can Impact Records
Journal of Sports Analytics 2 (2016) 1–18 1 DOI 10.3233/JSA-150007 IOS Press Revisiting the ranking of outstanding professional sports records Matthew J. Liberatorea, Bret R. Myersa,∗, Robert L. Nydicka and Howard J. Weissb aVillanova University, Villanova, PA, USA bTemple University Abstract. Twenty-eight years ago Golden and Wasil (1987) presented the use of the Analytic Hierarchy Process (AHP) for ranking outstanding sports records. Since then much has changed with respect to sports and sports records, the application and theory of the AHP, and the availability of the internet for accessing data. In this paper we revisit the ranking of outstanding sports records and build on past work, focusing on a comprehensive set of records from the four major American professional sports. We interviewed and corresponded with two sports experts and applied an AHP-based approach that features both the traditional pairwise comparison and the AHP rating method to elicit the necessary judgments from these experts. The most outstanding sports records are presented, discussed and compared to Golden and Wasil’s results from a quarter century earlier. Keywords: Sports, analytics, Analytic Hierarchy Process, evaluation and ranking, expert opinion 1. Introduction considered, create a single AHP analysis for differ- ent types of records (career, season, consecutive and In 1987, Golden and Wasil (GW) applied the Ana- game), and harness the opinions of sports experts to lytic Hierarchy Process (AHP) to rank what they adjust the set of criteria and their weights and to drive considered to be “some of the greatest active sports the evaluation process. records” (Golden and Wasil, 1987). -
2020 NFL Draft Scouting Report: QB Joe Burrow, LSU
2020 NFL DRAFT SCOUTING REPORT JANUARY 15, 2020 2020 NFL Draft Scouting Report: QB Joe Burrow, LSU *Our QB grades can and will change as more information comes in from Pro Day workouts, leaked Wonderlic test results, etc. We will update ratings as new info becomes available. Is there anyone speaking out against Joe Burrow as a top QB prospect? Does anyone even doubt that he’s the #1 pick in the draft (not necessarily admitting he’s the best player, just that he will be the #1 pick)? So, what more could I add to the Burrow lovefest of 2020? If I say that I’m very much a Burrow fan for the next level, pro-Burrow as a scout – well then there’s some intrigue taken out of this report already. Just another guy singing Burrow’s praises, big deal! However, despite any desire I might have to surprise you with a controversial take on Burrow, I’m just not finding any reason for a controversial, contrarian call for him as a bust. Not at all. The only real intrigue remaining with Burrow, for scouts and fans – just how good is Joe Burrow? As I was researching background for this report, I was watching some media segments and commentary, post-National Title game. One that caught my eye/ear was Colin Cowherd trying to classify/comp Burrow. He had top CFB analyst Joel Klatt on his show, and Klatt, in full seriousness, compared Burrow to Joe Montana… which makes some sense the more you think of it. The following day, on his show, Cowherd compared Burrow to Tony Romo…a quarterback Colin holds in high regard, so it was a nice comp, even if not quite as glowing as Klatt’s Montana call. -
Joe Montana Reading Comprehension Name______
Joe Montana Reading Comprehension Name_______________________ Joe Montana is known as one of the greatest quarterbacks in the history of the National Football League (NFL). He was born on June 11, 1956, in Pennsylvania. He was offered a basketball scholarship to North Carolina State University, but ended up going to the University of Notre Dame to play football instead. In 1979, he was selected by the San Francisco 49ers in the NFL Draft. Montana played as starting quarterback for the 49ers. The position of quarterback is an important one that requires leadership, quick decision-making skills, and strategy. Montana had all of the above, and he was known for bringing the team to victory even in the last few moments of the game. This earned him the nickname “Joe Cool.” Montana led the 49ers to his first Super Bowl victory in 1982. In the game before the Super Bowl, Montana threw a touchdown pass to wide receiver Dwight Clark with only 51 seconds left in the game, giving them the win. This iconic play is known as “The Catch”. The 49ers won the Super Bowl again in 1985. The following spring, the 49ers drafted Jerry Rice as a wide receiver. Rice and Montana worked well as a pair, and were a star passing combination for six seasons. With Montana on the team, the 49ers won the Super Bowl again in 1989 and 1990. However, Montana suffered an elbow injury that forced him to miss every game but one in the 1991 and 1992 seasons. By the time he had recovered, the 49ers had established a new quarterback: Steve Young. -
Illegal Defense: the Irrational Economics of Banning High School Players from the NBA Draft
University of New Hampshire University of New Hampshire Scholars' Repository University of New Hampshire – Franklin Pierce Law Faculty Scholarship School of Law 1-1-2004 Illegal Defense: The Irrational Economics of Banning High School Players from the NBA Draft Michael McCann University of New Hampshire School of Law Follow this and additional works at: https://scholars.unh.edu/law_facpub Part of the Antitrust and Trade Regulation Commons, Collective Bargaining Commons, Entertainment, Arts, and Sports Law Commons, Labor and Employment Law Commons, Sports Management Commons, Sports Studies Commons, Strategic Management Policy Commons, and the Unions Commons Recommended Citation Michael McCann, "Illegal Defense: The Irrational Economics of Banning High School Players from the NBA Draft," 3 VA. SPORTS & ENT. L. J.113 (2004). This Article is brought to you for free and open access by the University of New Hampshire – Franklin Pierce School of Law at University of New Hampshire Scholars' Repository. It has been accepted for inclusion in Law Faculty Scholarship by an authorized administrator of University of New Hampshire Scholars' Repository. For more information, please contact [email protected]. +(,121/,1( Citation: 3 Va. Sports & Ent. L.J. 113 2003-2004 Content downloaded/printed from HeinOnline (http://heinonline.org) Mon Aug 10 13:54:45 2015 -- Your use of this HeinOnline PDF indicates your acceptance of HeinOnline's Terms and Conditions of the license agreement available at http://heinonline.org/HOL/License -- The search text of this PDF is generated from uncorrected OCR text. -- To obtain permission to use this article beyond the scope of your HeinOnline license, please use: https://www.copyright.com/ccc/basicSearch.do? &operation=go&searchType=0 &lastSearch=simple&all=on&titleOrStdNo=1556-9799 Article Illegal Defense: The Irrational Economics of Banning High School Players from the NBA Draft Michael A. -
Applied Operational Management Techniques for Sabermetrics
Applied Operational Management Techniques for Sabermetrics An Interactive Qualifying Project Report submitted to the faculty of the WORCESTER POLYTECHNIC INSTITUTE in partial fulfillment of the requirements for the Degree of Bachelor of Science by Rory Fuller ______________________ Kevin Munn ______________________ Ethan Thompson ______________________ May 28, 2005 ______________________ Brigitte Servatius, Advisor Abstract In the growing field of sabermetrics, storage and manipulation of large amounts of statistical data has become a concern. Hence, construction of a cheap and flexible database system would be a boon to the field. This paper aims to briefly introduce sabermetrics, show why it exists, and detail the reasoning behind and creation of such a database. i Acknowledgements We acknowledge first and foremost the great amount of work and inspiration put forth to this project by Pat Malloy. Working alongside us on an attached ISP, Pat’s effort and organization were critical to the success of this project. We also recognize the source of our data, Project Scoresheet from retrosheet.org. The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at 20 Sunset Rd., Newark, DE 19711. We must not forget our advisor, Professor Brigitte Servatius. Several of the ideas and sources employed in this paper came at her suggestion and proved quite valuable to its eventual outcome. ii Table of Contents Title Page Abstract i Acknowledgements ii Table of Contents iii 1. Introduction 1 2. Sabermetrics, Baseball, and Society 3 2.1 Overview of Baseball 3 2.2 Forerunners 4 2.3 What is Sabermetrics? 6 2.3.1 Why Use Sabermetrics? 8 2.3.2 Some Further Financial and Temporal Implications of Baseball 9 3. -
NBA Draft Lottery Probabilities
AMERICAN JOURNAL OF UNDERGRADUATE RESEARCH VOL. 2, NO. 3 (2003) NBA Draft Lottery Probabilities Chad R. Florke and Mark D. Ecker Department of Mathematics University of Northern Iowa Cedar Falls, Iowa 50614-0506 USA Received: November 6, 2003 Accepted: December 1, 2003 ABSTRACT We examine the evolution of the National Basketball Association’s (NBA’s) Draft Lottery by showing how the changes implemented by the Board of Governors have impacted the probabilities of obtaining top picks in the ensuing draft lottery. We explore how these changes have impacted the team with the worst record and also investigate the conditional probabilities of the fourth worst team receiving the third pick. These calculations are conditioned upon two specified teams receiving the first two selections. We show that the probability of the fourth worst team receiving the third pick can be made unconditionally. We calculate the probabilities for the fourth worst team to move up in the draft to receive either the first, second, or third selections, along with its chance of keeping the fourth pick or even dropping in the draft. We find there is a higher chance for the fourth worst team to drop to the fifth, sixth or seventh position than to stay at the fourth position or move up. I. INTRODUCTION the record of finish of the teams, i.e., the number of games won for each team (which a. National Basketball Association (NBA) may also include playoff wins). For example Draft Lottery History and Evolution in football, the team with the worst record (fewest number of wins during the season) In all major professional team will draft first in each round while the best sports, recruiting amateur players from team (the Super Bowl winner) will draft last college, high school, or overseas is vital to in each round. -
The Effect Alternate Player Efficiency Rating Has on NBA Franchises Regarding Winning and Individual Value to an Organization
St. John Fisher College Fisher Digital Publications Sport Management Undergraduate Sport Management Department Spring 2012 The Effect Alternate Player Efficiency Rating Has on NBA Franchises Regarding Winning and Individual Value to an Organization Anthony Van Curen St. John Fisher College Follow this and additional works at: https://fisherpub.sjfc.edu/sport_undergrad Part of the Sports Management Commons How has open access to Fisher Digital Publications benefited ou?y Recommended Citation Van Curen, Anthony, "The Effect Alternate Player Efficiency Rating Has on NBAr F anchises Regarding Winning and Individual Value to an Organization" (2012). Sport Management Undergraduate. Paper 35. Please note that the Recommended Citation provides general citation information and may not be appropriate for your discipline. To receive help in creating a citation based on your discipline, please visit http://libguides.sjfc.edu/citations. This document is posted at https://fisherpub.sjfc.edu/sport_undergrad/35 and is brought to you for free and open access by Fisher Digital Publications at St. John Fisher College. For more information, please contact [email protected]. The Effect Alternate Player Efficiency Rating Has on NBAr F anchises Regarding Winning and Individual Value to an Organization Abstract For NBA organizations, it can be argued that success is measured in terms of wins and championships. There are major emphases placed on the demand for “superstar” players and the ability to score. Both of which are assumed to be a player’s value to their respective organization. However, this study will attempt to show that scoring alone cannot measure success. The research uses statistics from the 2008-2011 seasons that can be used to measure success through aspects such as efficiency, productivity, value and wins a player contributes to their organization. -
Player Benchmarking and Outcomes: a Behavioral Science Approach
Player Benchmarking and Outcomes: A Behavioral Science Approach Ambra Mazzelli (Asia School of Business & MIT) Robert Nason (Concordia University) Track: Basketball Paper ID: 1548699 1. Introduction Benchmarking is important to monitor and provide feedback to players on their performance. (i.e. are they under or overperforming). Behavioral science research, especially that grounded in social psychology, indicates that performance feedback has a strong impact on human behavior including: individual motivation (Deci, 1972; DeNisi, Randolph, & Blencoe, 1982; Pavett, 1983), risk-taking (Kacperczyk et al., 2015; Krueger Jr & Dickson, 1994; March & Shapira, 1992), and performance (Sehunk, 1984; Smither, London, & Reilly, 2005). As a result, performance feedback that is provided to players has the potential to induce changes in player performance. For example, Hall of Famers Shaquille O'Neal and Charles Barkley recently gave voice to negative performance feedback regarding Philadelphia 76ers Center Joel Embiid by openly criticizing him on TNT’s Inside the NBA. Early in the 2019 season, Joel Embiid’s player efficiency rating (PER) currently ranks an impressive 11th in the league (24.73), but is under his own past performance (2018/2019 PER = 26.21)1. Shaq and Charles suggested that Embiid’s recent relative underperformance was due to a lack of motivation and made a point of comparing Embiid to centers on other teams with higher PERs, such as Giannis Antetokounmpo and Anthony Davis. In the next game following Shaq and Charles comments (December 12, 2019 vs. the Boston Celtics) Embiid posted his best game of the season with a season high 38 points and a plus minus of +21 (in a game that the Sixers only won by 6 points). -
PFF WAR: Modeling Player Value in American Football
PFF WAR: Modeling Player Value in American Football Submission ID: 1548728 Track: Other Sports Player valuation is one of the most important problems in all of team sports. In this paper we use Pro Football Focus (PFF) player grades and participation data to develop a wins above replacement (WAR) model for the National Football League. We find that PFF WAR at the player level is more stable than traditional, or even advanced, measures of performance, and yields dramatic insiGhts into the value of different positions. The number of wins a team accrues throuGh its players’ WAR is more stable than other means of measurinG team success, such as PythaGorean win totals. We finish the paper with a discussion of the value of each position in the NFL draft and add nuance to the research suggesting that tradinG up in the draft is a negative-expected-value move. 1. Introduction Player valuation is one of the most important problems in all of team sports. While this problem has been addressed at various levels of satisfaction in baseball [1], [23], basketball [2] and hockey [24], [6], [13], it has larGely been unsolved in football, due to unavailable data for positions like offensive line and substantial variations in the relative values of different positions. Yurko et al. [25] used publicly available play-by-play data to estimate wins above replacement (WAR) values for quarterbacks, receivers and running backs. Estimates for punters [4] have also been computed, and the most-recent work of ESPN Sports Analytics [9] has modified the +/- approach in basketball to college football beginning in the fall of 2019. -
This Day in Hornets History
THIS DAY IN HORNETS HISTORY January 1, 2005 – Emeka Okafor records his 19th straight double-double, the longest double-double streak by a rookie since 12-time NBA All-Star Elvin Hayes registered 60 straight during the 1968-69 season. January 2, 1998 – Glen Rice scores 42 points, including a franchise-record-tying 28 in the second half, in a 99-88 overtime win over Miami. January 3, 1992 – Larry Johnson becomes the first Hornets player to be named NBA Rookie of the Month, winning the award for the month of December. January 3, 2002 – Baron Davis records his third career triple-double in a 114-102 win over Golden State. January 3, 2005 – For the second time in as many months, Emeka Okafor earns the Eastern Conference Rookie of the Month award for the month of December 2004. January 6, 1997 – After being named NBA Player of the Week earlier in the day, Glen Rice scores 39 points to lead the Hornets to a 109-101 win at Golden State. January 7, 1995 – Alonzo Mourning tallies 33 points and 13 rebounds to lead the Hornets to the 200th win in franchise history, a 106-98 triumph over the Boston Celtics at the Hive. January 7, 1998 – David Wesley steals the ball and hits a jumper with 2.2 seconds left to lift the Hornets to a 91-89 win over Portland. January 7, 2002 – P.J. Brown grabs a career-high 22 rebounds in a 94-80 win over Denver. January 8, 1994 – The Hornets beat the Knicks for the second time in six days, erasing a 20-2 first quarter deficit en route to a 102-99 win. -
NBA Draft Pick Valuation
University of Pennsylvania ScholarlyCommons Summer Program for Undergraduate Research (SPUR) Wharton Undergraduate Research 2018 NBA Draft Pick Valuation Corey Parker University of Pennsylvania Follow this and additional works at: https://repository.upenn.edu/spur Part of the Business Commons Recommended Citation Parker, C. (2018). "NBA Draft Pick Valuation," Summer Program for Undergraduate Research (SPUR). Available at https://repository.upenn.edu/spur/25 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/spur/25 For more information, please contact [email protected]. NBA Draft Pick Valuation Abstract In this research paper, the discussion is targeted towards understanding the statistical analyses associated with the determining of draft pick success in the National Basketball Association. The three methodologies in question are Player Efficiency Rating and standard statistical categories, Roland Beech’s Rating System, and Win Shares. Through the discussion of these three separate methods, the research aims to give a holistic assessment of which study is the most likely to predict a player’s success post-draft. Through the paper, the ideas presented before, during, and after research are discussed with an emphasis on educating the reader on the field of predictive analytics and the role it plays in sports business. In its conclusion, the paper summarizes not that one methodology is superior to the others, but that the most conclusive method is one where all three approaches are analyzed and combined. Keywords draft, NBA, predictive analytics, sports statistics Disciplines Business This working paper is available at ScholarlyCommons: https://repository.upenn.edu/spur/25 Corey A. Parker SPUR 2018 NBA Draft Pick Valuation Abstract In this research paper, the discussion is targeted towards understanding the statistical analyses associated with the determining of draft pick success in the National Basketball Association. -
Forecasting Most Valuable Players of the National Basketball Association
FORECASTING MOST VALUABLE PLAYERS OF THE NATIONAL BASKETBALL ASSOCIATION by Jordan Malik McCorey A thesis submitted to the faculty of The University of North Carolina at Charlotte in partial fulfillment of the requirements for the degree of Master of Science in Engineering Management Charlotte 2021 Approved by: _______________________________ Dr. Tao Hong _______________________________ Dr. Linquan Bai _______________________________ Dr. Pu Wang ii ©2021 Jordan Malik McCorey ALL RIGHTS RESERVED iii ABSTRACT JORDAN MALIK MCCOREY. Forecasting Most Valuable Players of the National Basketball Association. (Under the direction of DR. TAO HONG) This thesis aims at developing models that would accurately forecast the Most Valuable Player (MVP) of the National Basketball Association (NBA). R programming language was used in this study to implement different techniques, such as Artificial Neural Networks (ANN), K- Nearest Neighbors (KNN), and Linear Regression Models (LRM). NBA statistics were extracted from all of the past MVP recipients and the top five runner-up MVP candidates from the last ten seasons (2009-2019). The objective is to forecast the Point Total Ratio (PTR) for MVP during the regular season. Seven different underlying models were created and applied to the three techniques in order to produce potential outputs for the 2018-19 season. The best models were then selected and optimized to form the MVP forecasting algorithm, which was validated by predicting the MVP of the 2019-20 season. Ultimately, two underlying models were most robust under the LRM framework, which is considered the champion approach. As a result, two combination models were constructed based on the champion approach and proved to be most efficient.