DOCSLIB.ORG

Estimating Economic Impacts of Tommy John Surgery On

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Estimating Economic Impacts of Tommy John Surgery On ESTIMATING ECONOMIC IMPACTS OF TOMMY JOHN SURGERY ON MAJOR LEAGUE BASEBALL PITCHERS by Finn Ottey McMichael A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science In Applied Economics MONTANA STATE UNIVERSITY Bozeman, Montana April 2018 ©COPYRIGHT by Finn Ottey McMichael 2018 All Rights Reserved ii ACKNOWLEDGEMENTS I would like to thank my thesis committee: chair Dr. Randal Rucker, Dr. Rodney Fort, and Dr. Gregory Gilpin. They provided an immense amount of feedback and guidance throughout this process, and they were essential to the completion of this thesis. I would also like to thank my family for their support and my classmates for making the whole experience more enjoyable. iii TABLE OF CONTENTS 1. INTRODUCTION .......................................................................................................... 1 2. BACKGROUND ............................................................................................................ 3 History of Tommy John Surgery .................................................................................... 3 Wins Above Replacement (WAR) ................................................................................. 4 Baseball-Reference: BR-WAR ................................................................................... 6 FanGraphs: FG-WAR ................................................................................................. 9 Two WAR Measures: Discussion ............................................................................. 12 Chapter Two Figures and Tables .................................................................................. 13 3. RELATED LITERATURE ........................................................................................... 15 Medical Literature ......................................................................................................... 15 Economic Literature on Marginal Revenue Product .................................................... 19 Wins Above Replacement (WAR) Literature ............................................................... 23 Chapter Three Figures and Tables ................................................................................ 27 4. IMPACT OF TOMMY JOHN SURGERY ON PITCHER MARGINAL PRODUCT ............................................................................................. 29 Estimating the Impact of TJS on Pitcher Productivity ................................................. 30 Empirical Specification ............................................................................................. 30 Data ........................................................................................................................... 33 Results ....................................................................................................................... 35 Estimating the Marginal Product of Pitcher WAR on Team Wins ............................... 42 Chapter Four Figures and Tables .................................................................................. 47 5. ESTIMATING MARGINAL REVENUE OF A WIN ................................................. 65 Theory and Empirical Specification ............................................................................. 65 Data ............................................................................................................................... 68 Results ........................................................................................................................... 69 Chapter Five Figures and Tables .................................................................................. 72 iv TABLE OF CONTENTS (CONTINUED) 6. MARGINAL REVENUE PRODUCT CALCULATION ............................................ 78 General Estimate for Average Pitcher .......................................................................... 79 Specific Estimates for Three Hypothetical Pitchers ..................................................... 83 Pitcher A Estimation: ................................................................................................ 84 Pitcher B Estimation: ................................................................................................ 85 Pitcher C Estimation: ................................................................................................ 87 Discussion ..................................................................................................................... 88 7. CONCLUSION ............................................................................................................. 90 REFERENCES CITED ..................................................................................................... 94 APPENDICES .................................................................................................................. 97 A. ADDITIONAL DETAILS ON ESTIMATION OF WAR FOR POSITION PLAYERS ........................................................................................ 98 Appendix A Tables ..................................................................................................... 102 B. ATTEMPT AT REPLICATING MEDICAL STUDY METHODS AND RESULTS ..................................................................................... 103 Appendix B Tables ..................................................................................................... 106 C. ESTIMATION OF MARGINAL REVENUE USING GATE REVENUE INSTEAD OF TOTAL REVENUE ....................................................... 108 Appendix C Figures and Tables.................................................................................. 110 v LIST OF TABLES Table Page 1. Comparison of Medical Journal Analyses of Tommy John Surgery ................ 27 2. Summary Statistics – Pitchers 1996-2016 ........................................................ 47 3. Summary Statistics – All Pitcher-years 1996-2016 .......................................... 48 4. Summary Statistics – All TJS Pitcher-years 1996-2016 ................................... 48 5. Summary Statistics – Non-TJS Pitcher-years 1996-2016 ................................. 49 6. Impact of TJS on WAR..................................................................................... 51 7. Age Subsamples (Young = Age < 28, Old = Age >= 28) ................................. 52 8. Starter and Reliever Sub-samples ..................................................................... 53 9. Impact of TJS of ERA, WHIP and Innings Pitched for Starting Pitchers ........ 54 10. Impact of TJS of ERA, WHIP and IP for Relief Pitchers ................................ 55 11. Breakdown of WAR into standard pitching statistics ...................................... 56 12. Impact of TJS on Games Pitched, Innings Pitched and ERA for All Pitchers ................................................................................................ 57 13. Impact of TJS on WHIP, Strikeouts, Walks and Homeruns Allowed for All Pitchers ................................................................................................ 58 14. Summary Statistics – Pitchers and Position Player-years ................................ 59 15. Team Wins as a Function of Individual FG-WAR ........................................... 61 16. Team Wins as a Function of Individual BR-WAR .......................................... 62 17. Team Wins as a Function of Team FG-WAR .................................................. 63 18. Team Wins as a Function of Team BR-WAR .................................................. 64 19. Summary Statistics by Team ............................................................................ 74 vi LIST OF TABLES (CONTINUED) Table Page 20. MR of a Win, Quantile Regression with Time Trend and Team Fixed Effects .......................................................................................... 75 21. MR of a Win, Quantile Regression with Team and Year Fixed Effects ......... 76 22. MR of a Win, Quantile Regression with Time Trend and Yankee Dummy ................................................................................................ 77 23. Positional Adjustment Runs ........................................................................... 102 24. Summary Stats for Case (TJS) Group (343 pitchers) ..................................... 106 25. Summary Stats for Control Group (595) ........................................................ 107 26. Comparison of Means for Case and Control Groups ..................................... 107 27. MR of a Win, Quantile Regression with Time Trend and Team Fixed Effects ........................................................................................ 112 28. MR of a Win, Quantile Regression with Team and Year Fixed Effects ................................................................................................... 113 29. MR of aWin, Quantile Regression with Time Trend and Yankee Dummy .............................................................................................. 114 vii LIST OF FIGURES Figure Page 1. Number of Tommy John Surgeries in MLB by Year ....................................... 13 2. Ratio of Players who Return to Same Level after TJS ..................................... 13 3. Average Months to Return to the Same Level after TJS .................................. 14 4. Distribution of FG-WAR Values ...................................................................... 49 5. Distribution
Load more

Recommended publications
  • 2016 Topps Opening Day Baseball Checklist
    BASE OD-1 Mike Trout Angels® OD-2 Noah Syndergaard New York Mets® OD-3 Carlos Santana Cleveland Indians® OD-4 Derek Norris San Diego Padres™ OD-5 Kenley Jansen Los Angeles Dodgers® OD-6 Luke Jackson Texas Rangers® Rookie OD-7 Brian Johnson Boston Red Sox® Rookie OD-8 Russell Martin Toronto Blue Jays® OD-9 Rick Porcello Boston Red Sox® OD-10 Felix Hernandez Seattle Mariners™ OD-11 Danny Salazar Cleveland Indians® OD-12 Dellin Betances New York Yankees® OD-13 Rob Refsnyder New York Yankees® Rookie OD-14 James Shields San Diego Padres™ OD-15 Brandon Crawford San Francisco Giants® OD-16 Tom Murphy Colorado Rockies™ Rookie OD-17 Kris Bryant Chicago Cubs® OD-18 Richie Shaffer Tampa Bay Rays™ Rookie OD-19 Brandon Belt San Francisco Giants® OD-20 Anthony Rizzo Chicago Cubs® OD-21 Mike Moustakas Kansas City Royals® OD-22 Roberto Osuna Toronto Blue Jays® OD-23 Jimmy Nelson Milwaukee Brewers™ OD-24 Luis Severino New York Yankees® Rookie OD-25 Justin Verlander Detroit Tigers® OD-26 Ryan Braun Milwaukee Brewers™ OD-27 Chris Tillman Baltimore Orioles® OD-28 Alex Rodriguez New York Yankees® OD-29 Ichiro Miami Marlins® OD-30 R.A. Dickey Toronto Blue Jays® OD-31 Alex Gordon Kansas City Royals® OD-32 Raul Mondesi Kansas City Royals® Rookie OD-33 Josh Reddick Oakland Athletics™ OD-34 Wilson Ramos Washington Nationals® OD-35 Julio Teheran Atlanta Braves™ OD-36 Colin Rea San Diego Padres™ Rookie OD-37 Stephen Vogt Oakland Athletics™ OD-38 Jon Gray Colorado Rockies™ Rookie OD-39 DJ LeMahieu Colorado Rockies™ OD-40 Michael Taylor Washington Nationals® OD-41 Ketel Marte Seattle Mariners™ Rookie OD-42 Albert Pujols Angels® OD-43 Max Kepler Minnesota Twins® Rookie OD-44 Lorenzo Cain Kansas City Royals® OD-45 Carlos Beltran New York Yankees® OD-46 Carl Edwards Jr.
    [Show full text]
  • A Statistical Study Nicholas Lambrianou 13' Dr. Nicko
    Examining if High-Team Payroll Leads to High-Team Performance in Baseball: A Statistical Study Nicholas Lambrianou 13' B.S. In Mathematics with Minors in English and Economics Dr. Nickolas Kintos Thesis Advisor Thesis submitted to: Honors Program of Saint Peter's University April 2013 Lambrianou 2 Table of Contents Chapter 1: The Study and its Questions 3 An Introduction to the project, its questions, and a breakdown of the chapters that follow Chapter 2: The Baseball Statistics 5 An explanation of the baseball statistics used for the study, including what the statistics measure, how they measure what they do, and their strengths and weaknesses Chapter 3: Statistical Methods and Procedures 16 An introduction to the statistical methods applied to each statistic and an explanation of what the possible results would mean Chapter 4: Results and the Tampa Bay Rays 22 The results of the study, what they mean against the possibilities and other results, and a short analysis of a team that stood out in the study Chapter 5: The Continuing Conclusion 39 A continuation of the results, followed by ideas for future study that continue to project or stem from it for future baseball analysis Appendix 41 References 42 Lambrianou 3 Chapter 1: The Study and its Questions Does high payroll necessarily mean higher performance for all baseball statistics? Major League Baseball (MLB) is a league of different teams in different cities all across the United States, and those locations strongly influence the market of the team and thus the payroll. Year after year, a certain amount of teams, including the usual ones in big markets, choose to spend a great amount on payroll in hopes of improving their team and its player value output, but at times the statistics produced by these teams may not match the difference in payroll with other teams.
    [Show full text]
  • Clips for 7-12-10
    MEDIA CLIPS – Jan. 23, 2019 Walker short in next-to-last year on HOF ballot Former slugger receives 54.6 percent of vote; Helton gets 16.5 percent in first year of eligibility Thomas Harding | MLB.com | Jan. 22, 2019 DENVER -- Former Rockies star Larry Walker introduced himself under a different title during his conference call with Denver media on Tuesday: "Fifty-four-point-six here." That's the percentage of voters who checked Walker in his ninth year of 10 on the Baseball Writers' Association of America Hall of Fame ballot. It's a dramatic jump from his previous high, 34.1 percent last year -- an increase of 88 votes. However, he's going to need an 87-vote leap to reach the requisite 75 percent next year, his final season of eligibility. Jayson Stark of the Athletic noted during MLB Network's telecast that the only player to receive a jump of at least 80 votes in successive years was former Reds shortstop Barry Larkin, who was inducted in 2012. But when publicly revealed ballots had him approaching the mid-60s in percentage, Walker admitted feeling excitement he hadn't experienced in past years. "I haven't tuned in most years because there's been no chance of it really happening," Walker said. "It was nice to see this year, to watch and to have some excitement involved with it. "I was on Twitter and saw the percentages that were getting put out there for me. It made it more interesting. I'm thankful to be able to go as high as I was there before the final announcement." When discussing the vote, one must consider who else is on the ballot.
    [Show full text]
  • Improving the FIP Model
    Project Number: MQP-SDO-204 Improving the FIP Model A Major Qualifying Project Report Submitted to The Faculty of Worcester Polytechnic Institute In partial fulfillment of the requirements for the Degree of Bachelor of Science by Joseph Flanagan April 2014 Approved: Professor Sarah Olson Abstract The goal of this project is to improve the Fielding Independent Pitching (FIP) model for evaluating Major League Baseball starting pitchers. FIP attempts to separate a pitcher's controllable performance from random variation and the performance of his defense. Data from the 2002-2013 seasons will be analyzed and the results will be incorporated into a new metric. The new proposed model will be called jFIP. jFIP adds popups and hit by pitch to the fielding independent stats and also includes adjustments for a pitcher's defense and his efficiency in completing innings. Initial results suggest that the new metric is better than FIP at predicting pitcher ERA. Executive Summary Fielding Independent Pitching (FIP) is a metric created to measure pitcher performance. FIP can trace its roots back to research done by Voros McCracken in pursuit of winning his fantasy baseball league. McCracken discovered that there was little difference in the abilities of pitchers to prevent balls in play from becoming hits. Since individual pitchers can have greatly varying levels of effectiveness, this led him to wonder what pitchers did have control over. He found three that stood apart from the rest: strikeouts, walks, and home runs. Because these events involve only the batter and the pitcher, they are referred to as “fielding independent." FIP takes only strikeouts, walks, home runs, and innings pitched as inputs and it is scaled to earned run average (ERA) to allow for easier and more useful comparisons, as ERA has traditionally been one of the most important statistics for evaluating pitchers.
    [Show full text]
  • New York Mets (36-28) Washington Nationals (32-36) First Place, Nl East, 4.0 Ga Fourth Place, Nl East, 6.0 Gb
    NEW YORK METS (36-28) WASHINGTON NATIONALS (32-36) FIRST PLACE, NL EAST, 4.0 GA FOURTH PLACE, NL EAST, 6.0 GB Sunday, June 20, 2021 • 1:05 P.M. Nationals Park • Washington, D.C. RHP Taijuan Walker (6-2, 2.12) vs. LHP Patrick Corbin (4-5, 5.60) SNY • WCBS 880 AM • WQBU 92.7 FM ROSTER MOVES: Prior to today's game, the Mets selected the contract of RHP The Mets are 56-61 all-time at National Park... Jerad Eickhoff from Triple-A Syracuse...He will wear #43...OF Mason Williams NATIONALS-METS NOTES: has been designated for assignment...Eickoff owns a 21-30 career record with a The Mets have lost five of their six games last D.C. dating to September 26, 4.15 ERA (203 earned runs/440.0 innings) with 126 walks and 396 strikeouts in 2020. 80 games (76 starts)...He made eight starts for Syracuse this year and went 5-1 with a 5.32 ERA (11 earned runs/44.0 innings) with 13 walks and 41 punchouts... 2021: 3-3 2020: 4-6 History: 439-452 He was signed by the Mets as a minor league free agent on December 21, 2020. @NY: 2-1 @NY: 1-2 @NY: 225-227 @WAS: 1-2 @WAS: 2-4 @WAS: 77-68 METSELLANEOUS: The Mets split a doubleheader with the Nationals yesterday, @MON: 135-152 winning the first game, 5-1, and falling in the second game, 6-2...The Mets have @PR: 2-5 been in first place since Saturday, May 8...This is their longest streak atop the NL East since the end of the 2015 season...The Mets are in the nation's capital for four games in three days against the Nationals (1-2) before returning to home on deGOAT: The Mets have won Jacob deGrom’s last six starts, which is the third- Monday for a six-day, eight-game homestand against Atlanta (four games) and longest such streak since his debut in 2014…The Mets won eight consecutive Philadelphia (four games).
    [Show full text]
  • Former Mariner Guardado Takes on Autism | Seattle Mariners Insider - the News Tribune 2/5/12 9:00 AM
    Former Mariner Guardado takes on autism | Seattle Mariners Insider - The News Tribune 2/5/12 9:00 AM MARINERS INSIDER Sunday, February 5, 2012 - Tacoma, WA Web Search powered by YAHOO! SEARCH NEWS SPORTS BUSINESS OPINION SOUNDLIFE ENTERTAINMENT OBITS CLASSIFIEDS JOBS CARS HOMES RENTALS FINDN SAVE Mariners Insider !"#$%#&'(#)*%#&+,(#-(-"&.(/%0&"*&(,.)0$!"#$%#&'(#)*%#&+,(#-(-"&.(/%0&"*&(,.)0$ Post by LarryLarry LarueLarue / The News Tribune on Jan. 13, 2012 at 8:16 am with 11 CommentComment »» When he closed games for the Seattle Mariners from 2004-2006, Ed- die Guardado MARINERS NEWS was serious MARINERS NEWS enough to save 59 games while Mariners reach back, acquire Carlos – off the field – Guillen terrorizing teammates with Mariners sign Carlos Guillen to minor- a penchant for league deal practical jokes. Talented trio waits in wings Now retired, the affable lefty and wife Lisa have MORE MLB NEWS a new cause: autism. It’s one Eddie Guardado Ex-MLB pitcher Penny signs with Softbank that hits close to home, be- Hawks cause their daughter Ava was diagnosed with it in 2007. Wife of Yankees GM Cashman files for divorce Living in Southern California, the Guardados have established a foundation with a more immediate goal than finding a cure. Rangers' Hamilton confirms alcohol relapse “These kids can’t wait 10 years for the research. We have to use what works now … the earlier the better,” Guardado told Teryl Zarnow of the Orange County Register. The Guardados donated $27,000 to buy i-Pads for families with autistic children, and have orga- BLOG SEARCH nized a Stars & Strikes celebrity bowling tournament Jan.
    [Show full text]
  • More on Defensive Regression (Or Runs) Analysis 7
    More on Defensive A Regression (or Runs) Analysis Th is appendix has three primary objectives: fi rst, to disclose aspects of DRA not disclosed in chapter two; second, to address aspects of the model that raise issues related less to baseball per se than to statistical modeling in gen- eral; and third, to drive home the fundamental point that DRA is not an answer, but a method. Included in this appendix are certain alternative models I tried, and suggestions for further improvements, which should provide some sense of the range of alternative approaches that are possible. DRA POST-1951 Overview Th ere are essentially two DRA models: post-1951 and pre-1952. Th e post- 1951 model uses a subset of Retrosheet play-by-play data currently available for seasons aft er 1951, and was almost completely described in chapter two. Th e pre-1952 model must make do with considerably less data, which ren- ders it more primitive for infi elders and unavoidably more complicated for outfi elders. When we fi rst began explaining DRA, we took a ‘bottom-up’ approach, starting from the shortstop position and gradually building up until we had a team model. Here we’ll take a ‘top-down’ approach, revealing the entire post-1951 team model all at once, and then discussing its components. Likewise, we’ll start with a top-down discussion of the pre-1952 model. Th e following page presents the entire post-1951 model on one page, with a glos- sary of defi ned terms on the facing page. 3 AAppendix-A.inddppendix-A.indd 3 22/1/2011/1/2011 22:27:53:27:53 PPMM AAppendix-A.indd 4 p p e n d i x - A .
    [Show full text]
  • Chicago Cubs (38-27) New York Mets (32-25) Tied for First Place, Nl Central, -- First Place, Nl East, 3.0 Ga
    CHICAGO CUBS (38-27) NEW YORK METS (32-25) TIED FOR FIRST PLACE, NL CENTRAL, -- FIRST PLACE, NL EAST, 3.0 GA Monday, June 14, 2021 • 7:10 P.M. Citi Field • Flushing, NY RHP Jake Arrieta (5-6, 4.97) vs. LHP David Peterson (1-5, 6.32) SNY • ESPN • WCBS 880 AM • WQBU 92.7 FM FATHER'S DAY FLASH SALE: Today through Friday, the Mets are offering a 25% METS-CUBS NOTES: The Mets have lost seven straight to the Cubs dating back to discount on tickets in select vaccinated seating locations for all games excluding June 23, 2019...The Mets have also lost seven straight at home to the Cubs, dating the Subway Series...The sale is available only by entering promo code "Dad" back to May 31, 2018. when checking out at Mets.com...For more information, please visit Mets.com/ FathersDay. 2021: 0-3 2019: 2-5 History: 362-386-2 @NY: 0-0 @NY: 0-3 @NY: 189-178-1 METSELLANEOUS: The Mets fell to the Padres, 7-3, yesterday...The Mets have @CHC: 0-3 @CHC: 2-2 @CHC: 173-208-1 won five of their last seven games...The Mets have been in first place since Saturday, May 8...This is their longest streak atop the NL East since the end The Mets have caught 14 runners attempting to steal a base, of the 2015 season...Today, the team continues a seven game homestand (2-1) ACCESS DENIED: including 10 catcher caught stealings...The team's 14 runners caught stealing is against the Padres (2-1) and Cubs (four games)...Following this series, the Mets fourth in the majors...The team's 10 catcher caught stealings is tied for fourth in head to the Capital for four games in three days against the Nationals.
    [Show full text]
  • Combining Radar and Optical Sensor Data to Measure Player Value in Baseball
    sensors Article Combining Radar and Optical Sensor Data to Measure Player Value in Baseball Glenn Healey Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92617, USA; [email protected] Abstract: Evaluating a player’s talent level based on batted balls is one of the most important and difficult tasks facing baseball analysts. An array of sensors has been installed in Major League Baseball stadiums that capture seven terabytes of data during each game. These data increase interest among spectators, but also can be used to quantify the performances of players on the field. The weighted on base average cube model has been used to generate reliable estimates of batter performance using measured batted-ball parameters, but research has shown that running speed is also a determinant of batted-ball performance. In this work, we used machine learning methods to combine a three-dimensional batted-ball vector measured by Doppler radar with running speed measurements generated by stereoscopic optical sensors. We show that this process leads to an improved model for the batted-ball performances of players. Keywords: Bayesian; baseball analytics; machine learning; radar; intrinsic values; forecasting; sensors; batted ball; statistics; wOBA cube 1. Introduction The expanded presence of sensor systems at sporting events has enhanced the enjoy- ment of fans and supported a number of new applications [1–4]. Measuring skill on batted balls is of fundamental importance in quantifying player value in baseball. Traditional measures for batted-ball skill have been based on outcomes, but these measures have a low Citation: Healey, G. Combining repeatability due to the dependence of outcomes on variables such as the defense, the ball- Radar and Optical Sensor Data to park dimensions, and the atmospheric conditions [5,6].
    [Show full text]
  • Syndergaard Ancla Una Rotación Presente Y Futura
    202 NEW YORK METS (NL EAST) (NL EAST) NEW YORK METS 203 MLB MLB NEW YORK LOS PRONÓSTICOS DEL SANEDRÍN DE @MLB_AS GAME DIVISIÓN CAMPEONATO PLAYOFFS DE DE MUNDIALES DE CARD AMPEÓN SERIES SERIES WILD FUERA C SERIES METS FERNANDO DÍAZ X X X X X X PEPE RODRÍGUEZ X X X X X X LEÓN FELIPE GIRÓN X X X X X X SYNDERGAARD ANCLA UNA DANI GARCÍA X X X X X X CARLOS SAIZ X X X X X X CARLOS COELLO X X X X X X EDUARDO DE PAZ X X X X X X ROTACIÓN PRESENTE Y FUTURA ANGEL LLUIS CARRILLO X X X X X X PEPE LATORRE X X X X X X Los Mets cuentan CARLOS PARRA X X X X X X con todo el LAS TRES CLAVES DEL EQUIPO o es fácil nacer y vivir siem- potencial para John. Si todo eso se conjuga, el cie- Npre a la sombra de un her- sacar la cara lo es el límite en Queens. Salud, salud, salud. La temporada pasada alcan- mano mayor como los Yankees, por Nueva York, A ello hay que agregarle que una zaron un Wild Card pese a perder por lesión por pero esas son las cartas que le to- ofensiva que se ubicó en el último 1 tiempo prolongado a tres de sus cinco abridores, caron y deben aprender a jugar con pero para ello tercio del ‘Viejo Circuito’ en bateo así como a su cátcher, primera base, segunda ellas. Y ahora, tienen la posibilidad necesitarán que el colectivo, también recupera de cos- base y tercera base.
    [Show full text]
  • Determining the Value of a Baseball Player
    the Valu a Samuel Kaufman and Matthew Tennenhouse Samuel Kaufman Matthew Tennenhouse lllinois Mathematics and Science Academy: lllinois Mathematics and Science Academy: Junior (11) Junior (11) 61112012 Samuel Kaufman and Matthew Tennenhouse June 1,2012 Baseball is a game of numbers, and there are many factors that impact how much an individual player contributes to his team's success. Using various statistical databases such as Lahman's Baseball Database (Lahman, 2011) and FanGraphs' publicly available resources, we compiled data and manipulated it to form an overall formula to determine the value of a player for his individual team. To analyze the data, we researched formulas to determine an individual player's hitting, fielding, and pitching production during games. We examined statistics such as hits, walks, and innings played to establish how many runs each player added to their teams' total runs scored, and then used that value to figure how they performed relative to other players. Using these values, we utilized the Pythagorean Expected Wins formula to calculate a coefficient reflecting the number of runs each team in the Major Leagues scored per win. Using our statistic, baseball teams would be able to compare the impact of their players on the team when evaluating talent and determining salary. Our investigation's original focusing question was "How much is an individual player worth to his team?" Over the course of the year, we modified our focusing question to: "What impact does each individual player have on his team's performance over the course of a season?" Though both ask very similar questions, there are significant differences between them.
    [Show full text]
  • Lugnuts Media Guide & Record Book
    Lugnuts Media Guide & Record Book Table of Contents Lugnuts Media Guide Staff Directory ..................................................................................................................................................................................................................3 Executive Profiles ............................................................................................................................................................................................................4 The Midwest League Midwest League Map and Affiliation History .................................................................................................................................................................... 6 Bowling Green Hot Rods / Dayton Dragons ................................................................................................................................................................... 7 Fort Wayne TinCaps / Great Lakes Loons ...................................................................................................................................................................... 8 Lake County Captains / South Bend Cubs ...................................................................................................................................................................... 9 West Michigan Whitecaps ............................................................................................................................................................................................
    [Show full text]