DOCSLIB.ORG

Open Peregrim Thesis PDF.Pdf

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Open Peregrim Thesis PDF.Pdf THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE DEPARTMENT OF FINANCE MONEYBALL IN THE NFL: A FINANCIAL MANAGEMENT ANALYSIS OF THE IDEAL NFL SALARY CAP STRUCTURE JOHN PEREGRIM FALL 2019 A thesis submitted in partial fulfillment of the requirements for a baccalaureate degree in Finance with honors in Finance Reviewed and approved* by the following: Robert Novack Associate Professor of Supply Chain Management Thesis Supervisor Brian Davis Professor of Finance Honors Adviser * Signatures are on file in the Schreyer Honors College. i ABSTRACT The paper is meant to determine how the highly acclaimed Major League Baseball strategy Moneyball can be implemented into the National Football League. Overall, the goal is to build a regression model that results in an optimal salary cap allocation for various NFL player position groups based on each groups’ contribution to the team’s average win percentage. All data is sourced from historical National Football League statistics over the last ten seasons, from 2008 to 2017. Players were grouped into eight main position groups, and five key statistics were selected for each position group. Each team’s win percentage was regressed on the forty various player statistics to determine the contribution of each variable towards the team’s win percentage. Once the regression model returned the optimal salary cap allocations per position group, these values were compared to the weighted average salary cap allocation over all teams from 2011 to 2017. It was determined that quarterbacks, running backs, offensive linemen, cornerbacks and safeties, and kickers and punters are underpaid, wide receivers and tight ends and defensive linemen are overpaid, and linebackers are statistically unable to be valued using various player statistics. ii TABLE OF CONTENTS LIST OF FIGURES………………………………………………………………………………iv LIST OF TABLES...………………...…………………………………………………………….v ACKNOWLEDGEMENTS………………………………………………………………………vi Chapter 1 Introduction of Topic.………………………………………………………………….1 NFL: The Business………………………………………………………………………..1 Origins of Moneyball………………………………………………………..……..……...2 Moneyball in the NFL………………………………………………………………...…...3 Paul DePodesta’s Influence on Sports……………………………………………..……...4 Thesis Statement……………………………………………………………………...…...4 Chapter 2 Literature Review………...…………………………………………………………….5 Inconsistencies Between Moneyball in the MLB and in the NFL………………………...5 NFL Salary Cap Breakdown………….……………………………………..……..……...6 Unanswered Questions………...……………………………………………………...…...7 Chapter 3 Data Methodology………………..…………………………………………………….9 Data Collection….………………………………………………………………………...9 Position Groups……….……………………………………………..……..……...9 Quantitative Statistics………………………………………………………...….10 Data Exporting and Sorting……………………………………………………...12 Position Group Regression Model……………..…………………………………..…….14 Position Group Allocations………………………………………………………15 Data Methodology Summary…….…………………………………………………...….16 Chapter 4 Data Analysis…………….…………………………………………………………...17 Regression Model………….…………………………………………………………….17 Model Verification…………………………………………………..……..…….17 Regression Model 1.0…………...…………………………………..……..…….17 Regression Model 2.0…………...…………………………………..……..…….20 Regression Model 3.0…………...…………………………………..……..…….22 Regression Model 4.0…………...…………………………………..……..…….25 Regression Model 5.0…………...…………………………………..……..…….27 Statistical Analysis……………………….…………………………………………...….29 Salary Cap Comparison………………………………………………………...…….….33 iii Chapter 5 Conclusion..…………………………………………………………………………...39 Moneyball’s Potential Impact on NFL Franchises.…………..………………………….39 Topics to Further Explore………………………………………………………………..41 Appendix A Annual Statistics Per Team 2008-2017….…………………………………………44 Appendix B Annual Salary Cap Allocation Per Position Group Per Team 2011-2017…………85 BIBLIOGRAPHY...………...…………………………………………………………………..102 iv LIST OF FIGURES Figure 1: Regression Model 1.0 Output…..……………………………………………………...19 Figure 2: Regression Model 2.0 Output……..…………………………………………………...21 Figure 3: Regression Model 3.0 Output….………………………………………………………24 Figure 4: Regression Model 4.0 Output….………………………………………………………26 Figure 5: Regression Model 5.0 Output….………………………………………………………28 Figure 6: Python 3.7.2 OLS Regression Model 5.0 Output...……………………………………32 Figure 7: Various Salary Cap Breakdowns………………………………………………………33 Figure 8: NFL Salary Cap Simple Average.……..………………………………………………34 Figure 9.1: NFL Salary Cap Weighted Average……………………………………………..…..35 Figure 9.2: NFL Salary Cap Weighted Average………………………………………………....36 Figure 10: NFL Salary Cap Super Bowl Average……………………………………………….36 Figure 11: NFL Franchises Ranked by Total Cap Difference………….………………………..38 v LIST OF TABLES Table 1: Player Statistics Per Position Group..…..………………………………………………11 vi ACKNOWLEDGEMENTS I would like to thank my thesis supervisor, Dr. Novack, and my thesis advisor, Dr. Davis, for guiding my vision every step along the way and always being so encouraging about having productive meetings. I would also like to thank my good friend Trent Andraka, a computer engineering major at the Washington University in St. Louis, for teaching me how to use the coding language Python. Finally, and most importantly, I would like to thank my mom for her daily support through this thesis and all of my other Schreyer Honors College experiences. 1 Chapter 1 Introduction of Topic Many sports fans are already familiar with the Major League Baseball (MLB) salary cap management technique, coined the term, Moneyball. The purpose of this thesis is to extend the theory behind Moneyball and implement it into the National Football League. NFL: The Business It is no secret that the National Football League (NFL) is the most popular sports league in the world, both in regard to viewership and revenue. The average NFL game has an audience of 15.472 million viewers which is significantly larger than the second-place league, NASCAR, with an average of 3.332 million viewers (Maglio, 2018). NFL total revenue is currently at $13.68 billion with $1.32 billion coming from sponsorship revenue, proving that the National Football League is a moneymaking juggernaut (Fuller, 2017). It is also key to note that the average NFL franchise has a value of $2.57 billion (Fuller, 2017). The top five most valuable franchises are the Dallas Cowboys, New England Patriots, New York Giants, Los Angeles Rams, and Washington Redskins with five, six, four, one, and three Super Bowl victories respectively (Rovell, 2018). In a sports league, winning is indicative of success, both with viewers, stadium attendance, and overall revenue, and this franchise valuation helps prove that general theory. These various measures prove that the NFL is thriving financially, thanks to a large demographic, however both fans and franchise owners heavily value wins. With league personnel and team owners constantly searching for a formula for success, this thesis is meant to solve this conundrum by playing on the Kairos of the time. 2 Origins of Moneyball In the MLB, salary caps are not consistent amongst teams, proving that older and more revenue driven teams, like the New York Yankees, have a larger budget to acquire players than a newer and less established team, like the Oakland Athletics. This visually unfair playing field gave rise to the highly acclaimed technique called Moneyball. In 2002, the Oakland Athletics began using sabermetric statistics to find undervalued players in an attempt to find talent, while also maximizing their salary cap efficiency. For example, while most MLB teams looked at basic statistics like batting average and field percentage, the Athletics began analyzing statistics like slugging percentage and on-base percentage (Piellucci, 2017). The goal was not just to use statistics to value players but predominantly to find statistics more attributable to wins than were currently being used. At the time, front office assistant Paul DePodesta spearheaded the idea and was involved in the implementation process. DePodesta worked hand in hand with the current Athletics’ General Manager Billy Beane, analyzing statistics, activating player trades, and managing the tight salary cap allocation. By acquiring players with a greater bang-for-your-buck, the Oakland Athletics were able to go from one of the worst team in baseball into a squad that won the American League West division with over one- hundred years, thanks to their iconic twenty-game win streak. Theoretically, the Oakland Athletics were able to catapult themselves into relevancy by having a front office that was able to outsmart their counterparts by using numbers and statistics to value their players more accurately. 3 Moneyball in the NFL NFL coaches have recently begun to rely on statistics more heavily within the last decade, however numbers predominantly play into coaching decisions. The most discussed aspect of using quantitative data in football is in regards to probabilities and whether statistics encourage or discourage certain decisions. For example, coaches have become more inclined to go for it on fourth down, prior to the game being on the line, typically under a certain yardage. This is not because coaches are getting less risk-averse but because going for the first down is statistically favorable. However, when it comes to paying players, general managers are not convinced that statistics are the way to value their talent. Several high name NFL personnel have expressed their skepticism in Moneyball tactics due to the dissimilarities between MLB and NFL statistics. While positions in baseball
Load more

Recommended publications
  • Winning an Unfair Game How a Resource-Constrained Player Uses
    WINNING AN UNFAIR GAME: HOW A RESOURCE-CONSTRAINED PLAYER USES BRICOLAGE TO MANEUVER FOR ADVANTAGE IN A HIGHLY INSTITUTIONALIZED FIELD Ted Baker, Timothy G. Pollock and Harry J. Sapienza ABSTRACT In this study we examine how resource-constrained organizations can maneuver for competitive advantage in highly institutionalized fields. Unlike studies of institutional entrepreneurship, we investigate competi- tive maneuvering by an organization that is unable to alter either the regulative or normative institutions that characterize its field. Using the ‘‘Moneyball’’ phenomenon and recent changes in Major League Baseball as the basis for an intensive case study of entrepreneurial actions taken by the Oakland A’s, we found that the A’s were able to maneuver for advantage by using bricolage and refusing to enact baseball’s cognitive institutions, and that they continued succeeding despite ongoing resource Entrepreneurial Resourcefulness: Competing with Constraints Advances in Entrepreneurship, Firm Emergence and Growth, Volume 15, 1–41 Copyright r 2013 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: 1074-7540/doi:10.1108/S1074-7540(2013)0000015004 1 2 TED BAKER ET AL. constraints and rapid copying of their actions by other teams. These results contribute to our understanding of competitive maneuvering and change in institutionalized fields. Our findings expand the positioning of bricolage beyond its prior characterization as a tool used primarily by peripheral organizations in less institutionalized fields; our study suggests that bricolage may aid resource constrained participants (including the majority of entrepreneurial firms) to survive in a wider range of circumstances than previously believed. Keywords: Bricolage; Major League Baseball; institutionalized fields; resource-constraints; competitive advantage Images of institutionalized organizational fields evoke expectations of stability and behavioral constraint structured by rules, accepted ways of doing things, and common ways of looking at the world.
    [Show full text]
  • Legal Education and Moneyball: the Art of Winning the Assessment Game
    Legal Education and Moneyball: The Art of Winning the Assessment Game JEREMY POTTER' I. INTRODUCTION Legal education is woefully deficient in the use of data to measure the quality of a law school. In the 1950s educators believed legal education had "two related objectives-the training of lawyers, and the improvement of the law."' These men went on to write that "[t]he latter objective in particular requires not merely that poor law schools be eliminated but that the best law schools be made better."2 While those objectives remain true, legal educators are no longer calling for the closing of poor law schools. One possible reason is the legal profession is unaware which the poor law schools might be. Measuring education, particularly legal education, is difficult. The American Bar Association ("ABA") uses published standards, site visits and accreditation to certify which law schools pass the minimum criteria, but few agencies, if any at all, are using objective metrics to measure the quality of legal education beyond these minimums. Outcome measures and assessments are the tools educators employ to try to measure student proficiency and the quality of education provided by an institution. For example, elementary and secondary educators rely on standardized tests and course grades as a major measure of quality. Even as educators become more sophisticated in understanding educational quality, the nature of education makes objectivity a problem. Legal education has additional challenges because beyond the usual difficulty in educational assessment law students are pursuing diverse careers and require a wide range of intangible skills to achieve proficiency.
    [Show full text]
  • Cleveland Browns 2016 Season Preview
    Several months from now, high school football teams from across Ohio will square off in state champi- onship games. It takes a lot of talent, hard work, and sometimes, a little luck to get there. Getting there is the goal of every team this time of year. Of course, some know they have a better chance than others of making a championship run, but they all want to be the last team standing. With that thought in mind, we are proud to bring you another season of Forecast Magazine, highlight- ing local high school athletes. Inside, you’ll find in-depth analysis, a host of features and schedules you can use all season long. Once again, you’ll hear the games on AM 930 WEOL and www.weol.com. Plus, look for the inside scoop on the Ohio State University Buckeyes and the Cleveland Browns. And remember…your home for OSU football and basketball is AM 930 WEOL! We are proud to cover and support our local teams and appreciate the assistance of high school athletic directors, coaches and staffs who help us with our outstanding broadcast schedule. And we thank our terrific sponsors who make the games and this magazine possible. Friday night lights? You bet! Craig R. Adams Operations Manager/News Director WEOL AM 930 Fall 2016 Forecast Magazine 1 Medina County High School Football Preview.........pg. 3 WEOL FORECAST MAGAZINE Ohio State Football Preview......................................pg. 10 FALL 2016 PREVIEW A Publication of AM930 WEOL Cleveland Browns Preview........................................pg. 15 Volume 3 FOOTBALL SCHEDULES Station Manager Tim Alcorn Tri-C Medina County Match-Ups...............................pg.
    [Show full text]
  • Performance, Privacy, and the Professional: the Proliferation of Data Analytics in Athletics
    Performance, Privacy, and the Professional: The Proliferation of Data Analytics in Athletics An STS Research Paper presented to the faculty of the School of Engineering and Applied Science University of Virginia by Sarah Nelson May 7, 2020 On my honor as a University student, I have neither given nor received unauthorized aid on this assignment as defined by the Honor Guidelines for Thesis-Related Assignments. Signed: _________________________________________________ Approved: _______________________________________ Date ________________________ Peter Norton, Department of Engineering and Society 1 Performance, Privacy, and the Professional: The Proliferation of Data Analytics in Athletics The explosion of data has transformed many industries. Healthcare, banking, retail, manufacturing, and technology are among the domains impacted by data analytics. Athletics, in particular, has been reshaped by recent technology advancements. Ramon Alonso, holder of the sports telemetry system patent, says that the “gathering, processing, and use of data and statistics are an important aspect of the business, analysis, and appreciation of sports” (Alonso, 2012). The publication of the book Moneyball (Lewis, 2003) sparked a new national appreciation for sports analytics, evident in “Fantasy” leagues, sports betting, and commercial sensors such as Fitbit. Fantasy leagues had a total revenue in 2018 of 7.7 billion dollars, with an annual growth rate of 8 percent over the previous five years (Gambardella, 2018). As for personal health trackers, FitBit posted a 1.51 billion dollar revenue in 2018 (FitBit, 2019), and the Apple Watch posted 5.1 billion dollars in revenue (Miller, 2019). People are more concerned with data than ever, whether it be box statistics for sports betting or biometric data for personal improvement.
    [Show full text]
  • A Good Article
    Coaching Articles : Eric Musselman File For Coaches Jackson makes Hall of Fame on principle By Roscoe Nance, USA TODAY Los Angeles Lakers coach Phil Jackson heads the 2007 Naismith Memorial Basketball Hall of Fame class, which will be inducted Saturday in Springfield, Mass. The group includes University of North Carolina men's coach Roy Williams, Louisiana State University women's coach Van Chancellor, the 1966 Texas Western University men's team, referee Marvin "Mendy" Rudolph and international coaches Pedro Ferrandiz of Spain and Mirko Novosel of Yugoslavia. Jackson, 61, has won nine NBA championships as a coach — six with the Chicago Bulls and three with the Lakers — tying Hall of Famer Red Auerbach's record. He has coached some of the NBA's greatest players, including Michael Jordan and Scottie Pippen with the Bulls and Kobe Bryant and Shaquille O'Neal with the Lakers. Jackson gets players to buy into his coaching philosophy, which is influenced by Native American and Eastern beliefs. "The great thing about Phil is the way he has handled players," Hall of Famer Magic Johnson said. "He has a different style, too, that old Zen thing. I love him because he never gets too high and never gets too low and always wants to help the guys grow as men. He teaches them basketball and also teaches them outside the sport." One of his more renowned strategies is handing out books for players to read during road trips. "He's trying to get players to see that there's more to life than the NBA and your performance today," said NBA TV analyst Steve "Snapper" Jones.
    [Show full text]
  • A's News Clips, Thursday, September 22, 2011 Oakland A's Update
    A’s News Clips, Thursday, September 22, 2011 Oakland A's update: Brandon McCarthy denied victory over Texas Rangers By Joe Stiglich, Oakland Tribune The A's were in line for quite the gratifying victory Wednesday night. That was before Ian Kinsler and Josh Hamilton connected for eighth-inning homers off setup man Grant Balfour, wiping out Oakland's one-run lead and sending the Texas Rangers to a 3-2 victory in front of 19,589 fans at O.co Coliseum. On a day the A's signed interim manager Bob Melvin to a three-year contract, they dropped to 69-86. It's their first time being as low as 17 games under .500 since Sept. 4, 2009, when they were 59-76. They've dropped 12 of their past 13 to the Rangers, who trimmed their magic number to three to clinch the American League West. Balfour (4-2) was disappointed to blow victory No. 10 for A's starter Brandon McCarthy, and the reliever acknowledged a season's worth of frustration against Texas. "I don't know if we're going to make up everything with one game tonight," Balfour said. "It's been a frustrating year. We've had opportunities to beat them. McCarthy pitched great. I take full responsibility for the loss." The A's trailed 1-0 before they rallied for two runs in the fifth off Rangers lefty C.J. Wilson, who drew attention in August for ripping the A's, the Coliseum mound and stating he would never sign with Oakland as a free agent.
    [Show full text]
  • Moneyball’ to Medicine: Paul Depodesta on His Time with the Oakland A’S and the Future of Analytics
    Bringing ‘Moneyball’ to Medicine: Paul DePodesta On His Time with the Oakland A’s and the Future of Analytics Written by Jessica Kim Cohen and Molly Gamble | October 23, 2018 As healthcare organizations seek to tap into data for actionable information whatever you like,” Mr. DePodesta continued. “I insights, they are looking outside of healthcare for best started digging deeper and deeper into the data, and I realized practices. One of the world’s most well-respected minds on there was a treasure trove of information. It really astounded the use of data analytics to drive change is someone you may me.” know, but likely have never heard of — Paul DePodesta. Today, he is bringing the Moneyball concepts to football in his Mr. DePodesta was famously profiled in the book and hit movie role as the chief strategy officer for the Cleveland Browns — a Moneyball. This true story of turning the worst team in baseball team that recently made headlines for winning their first game — with lowest payroll — into baseball’s best team revealed in almost two years. the stunning and hidden power of how data and analytics can make a difference. Mr. DePodesta, who is slated to keynote the Strata Decision Summit Oct. 24 in Chicago, is now nationally known and In the movie, the unsung hero is an analyst played by actor sought after expert on applying data to drive changes. At the Jonah Hill — a character based on Mr. DePodesta’s time with Strata Decision Summit, he will discuss how he’s used analytics the Oakland Athletics.
    [Show full text]
  • BILLY BEANE and PAUL Depodesta
    BILLY BEANE and PAUL DePODESTA 0. BILLY BEANE and PAUL DePODESTA - Story Preface 1. BILLY BEANE and PAUL DePODESTA 2. BILL JAMES and SABERMETRICS 3. OAKLAND A'S of 2002 4. 20-GAME WINNING STREAK 5. CORY LIDLE - FATAL ACCIDENT 6. WHERE ARE THEY NOW? Paul DePodesta, when he was the 31-year-old general manager of the Los Angeles Dodgers. Photo by Jon Soohoo, Associated Press. Copyright, Jon Soohoo, all rights reserved. Image used here as fair use for educational purposes. Still in high school, Billy Beane was shocking all the baseball scouts. Possessing all five of the "five tools" a scout looks for in a promising young player, he was destined for greatness in Major League Baseball. He was willing to forgo a scholarship to Stanford (for the football program) to jump-start his dream. But ... Billy wasn’t able to play professional ball. At bat, he couldn’t stay loose. In the field, he couldn’t stay focused. Finally, after six years - including a stint with the Twins - he asked for a different job. In 1990, at the age of 27, he became an advance-scout for the Oakland A's. Paul DePodesta - "DePo" for short (and "Peter Brand" in the film Moneyball) - had a different start. Although he played college football and baseball at Harvard, he wasn't MLB-player material. Despite his cum-laude degree in economics, he signed a contract with the Oakland A's in 1999 - to be a scout - after working with the Cleveland Indians (for three seasons). By 2001, Beane had been the A's general manager four years.
    [Show full text]
  • The Little Book of Behavioral Investing by James Montier
    Table of Contents Little Book Big Profits Series Title Page Copyright Page Dedication Foreword Introduction The Most Important Lesson of All The Power of Star Trek So, Are You Spock or McCoy? X Unchecked Chapter One - In the Heat of the Moment The Perils of Procrastination The Power of Pre-Commitment Chapter Two - Who’s Afraid of the Big Bad Market? Brain Drain and Performance The Cure for Temporary Paralysis Chapter Three - Always Look on the Bright Side of Life Optimism and the X-System Nature versus Nurture Beating Over-Optimism Chapter Four - Why Does Anyone Listen to These Guys? Why Does Anyone Listen to Jim Cramer? The Shocking Dangers of Doing What We Are Told Fund Managers: Weathermen or Doctors? Overconfidence May Be Hazardous to Your Wealth Chapter Five - The Folly of Forecasting So, Why Do We Keep Forecasting? Why Do We Use Forecasts? There’s Got to Be a Better Way Chapter Six - Information Overload Is More Better? When Less Is More From the Emergency Room to the Markets Chapter Seven - Turn off That Bubblevision! Meet Mr. Market Chapter Eight - See No Evil, Hear No Evil The Sorry Tale of Sir Roger Prisoners of Our Preconceptions Kill the Company Chapter Nine - In the Land of the Perma-Bear and the Perma-Bull Hanging onto Your View Sunk Costs at the Root of Conservatism Chapter Ten - The Siren Song of Stories Stock Market Stories Beware of Capitalizing Hope Focus on the Facts Chapter Eleven - This Time Is Different Why Can’t We Time Predictable Surprises? A Beginner’s Guide to Spotting Bubbles Your Edge Over the Pros! Chapter
    [Show full text]
  • Technical Report
    National College of Ireland BSc in Computing 2017/2018 Daniel Murran X13114336 [email protected] Supervisor: Simon Caton Ahead of the Curve, Analysis of the NFL Draft Technical Report 1 | P a g e Contents 1 Table of Figures .................................................................................................. 5 Executive Summary ................................................................................................... 8 2 Introduction ........................................................................................................ 9 Aims ............................................................................................................ 10 Technologies ............................................................................................... 11 3 Background & Literature Review .................................................................... 12 4 Methodology .................................................................................................... 16 Data Selection ............................................................................................. 16 Pre-Processing ............................................................................................ 17 Transformation ........................................................................................... 17 Data Mining ................................................................................................ 18 Interpretation / Evaluation .......................................................................
    [Show full text]
  • A Case Study of the Actor-Network Effects of Moneyball on Major League Baseball
    A Case Study of the Actor-Network Effects of Moneyball on Major League Baseball A Research Paper submitted to the Department of Engineering and Society Presented to the Faculty of the School of Engineering and Applied Science University of Virginia • Charlottesville, Virginia In Partial Fulfillment of the Requirements for the Degree Bachelor of Science, School of Engineering Corey Nolan Spring 2020 On my honor as a University Student, I have neither given nor received unauthorized aid on this assignment as defined by the Honor Guidelines for Thesis-Related Assignments Advisor Sean M. Ferguson, Department of Engineering and Society Introduction Entering the 2002 Major League Baseball (MLB) season, the Oakland Athletics found themselves in a tough spot. As one of the poorest organizations in the MLB, they were unable to re-sign three star players Jason Giambi, Johnny Damon, and Jason Isringhausen as the New York Yankees, Boston Red Sox, and St. Louis Cardinals, three large-market, powerhouse organizations, signed the free agents to deals the Athletics could not match. Desperate to keep his team competitive but unable to afford the exorbitant price tag of high-profile athletes, Billy -minded assistant Paul DePodesta to find a way their team could compete with larger market teams with only a third of their salary cap. DePodesta was long a student to find a way the Athletics could compete (A Guide to Sabermetric Research, 2020). Sharply departing from tradition scouting theory, the theory DePodesta and Beane discovered did not emphasize how an athlete looks or their physical tools; instead, it asked if a player could get on base or hit, meaning players with higher on-base (OBP) and slugging percentages (SLG).
    [Show full text]
  • Gene Kinasewich ‘64 NEWS & VIEWS of Harvard Sports Page 4
    FEATURE STORY: Harvard Varsity Club Gene Kinasewich ‘64 NEWS & VIEWS of Harvard Sports Page 4 Vol. 46, No. 7 February 27, 2004 Tennis Teams Continue To Rise In National Rankings by Chuck Sullivan grams, but realistically, Director of Athletic Communications a far cry from conten- tion for a national It’s difficult to pinpoint the exact time that Harvard’s varsity championship. Harvard tennis programs achieved its current status as bona fide national moved up to the No. 56 powers. spot following the 2001- After all, the Crimson’s men’s and women’s teams were both 02 season and contin- well established as powers in the Northeast for some time. Harvard’s ued to climb the ladder women have won 14 Ivy League championships— twice as many through all of last year, as any other school— while the men have captured 25 Ivy crowns— culminating in a final again, far more than any of the Crimson’s Ancient Eight counter- 2002-03 ranking of No. parts. 14. But when exactly did David Fish ’72’s men’s team and Gordon Harvard has con- Graham’s women’s squad move into the echelon of the nation’s best? tinued to inch its way Why does toward the top this sea- Harvard now find son, as the Crimson itself interspersed came into the year with schools from ranked No. 13 in the the Southeastern preseason, climbed a Conference, the spot to No. 12 at the end Atlantic Coast of January, and cur- Conference and rently sits at No. 11— the Pacific-10 Con- the highest national Susanna Lingman ‘05 ference in the na- ranking in school his- tional rankings? tory.
    [Show full text]