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The Base out Model of Baseball the BOMB
The “Base-Out Model” of Baseball—the BOMB! Barry Codell’s Diamond Metrics (“Diametrics”) seem the perfect mean between misleading traditional statistics and nonsensical sabermetrics--diametrically opposed to each. The Base-Out Model works so beautifully because it is expressive of the game itself: the batter’s ceaseless attempt to accumulate bases into runs while avoiding outs is what we are rooting for constantly (or, of course, from the pitching side, pulling for the hurler’s effort to stop baserunners allowed, before they become runs, by recording outs). Cub fans cheered opening day when Alfonso Soriano led off the game and the season with a home run off the Astros’ Roy Oswalt. Bill James’ Runs Created claimed Alfonso’s homer created four runs. Codell’s Runs Tallied says it tallied one. Let’s, beyond fantasy, get real: that cheering was not because Soriano (or James) somehow “created” 4 runs to make the score 1-0, but for the fact that he tallied 1 run for his team by touching all 4 bases. Even Joe Reichler’s terminally flawed Runs Produced (R + RBI – HR) can reflect that Soriano produced a run. (Even a broken clock . .) Let’s now posit the following, a realistic rally following the tally: after Soriano’s blast, 3 straight singles (the third an RBI) and a 3-run homer, making the score 5-0. Codell computes and chronicles, naturally, 5 Runs Tallied, but James “creates” 11 (as Reichler “produces” 8)! The ever-changing, never-ending algebraic Runs Created formula defies common sense. Its complexity is only exceeded by its inaccuracy. -
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. -
Bill James How His Decimals Deceive
How His Decimals Deceive: James’s Whatnot and What Not to Believe In his new, much heralded Historical Abstract, famous baseball “guru” Bill James proudly introduces us to his attempt at a seemingly succinct (for once!) decimal to determine a batter’s run production, i.e., the Run Average, derived from the following formula: (R+RBI) AB Lesser known Barry Codell’s little known Scoring Average, however, has already established itself as the logical litmus test for an individual’s contribution to team scoring. Since both formulae address exactly the same categories (runs, runs batted in, and at bats) in different ways, let us reconsider the consequences of the two statisticians’ considerations. Codell’s historically radical ratio is derived sequentially from two crucial aspects of his public inventions: the Base-Out Percentage (1979, Baseball Research Journal) and the Runs Tallied, the denominator (AB-H) the groundbreaking Outs Batting calculation from the former, and ½(R+RBI) i.e., Runs Tallied (1990, Baseball Research Journal), the equally revolutionary numerator, culminating in the Scoring Average: ½ (R + RBI) (AB-H) This startling averaging of runs and RBI’s statistically restates a baseball truism: virtually all runs besides homers are contributory tallies created equally and therefore halved into “run-scorer” and “run-plater.” The full run tallied from the solo home run fulfills the reasonable promise of his premise. James’ immodest proposal appears ill conceived from start to finish, and especially when compared to Codell’s precedent. To begin with, what could be the significance of totaling a player’s Runs and RBI’s, an unfortunate and “uncredited homage” to the late Joe Reichler’s “Runs Produced?” The resultant batter’s number cannot be translated to team contribution. -
2011 Ucla Bruins Softball
UCLA SOFTBALL WEEKLY RELEASE • FEBRUARY 22, 2011 • PAGE 1 2011 UCLA BRUINS SOFTBALL SOFTBALL CONTACT: JAMES YBIERNAS • PHONE: (310) 206-8123 • FAX: (310) 825-8664 • E-MAIL: [email protected] • WWW.UCLABRUINS.COM 2011 UCLA SCHEDULE AND RESULTS GAMES 11-16 • AT CAL STATE NORTHRIDGE • AT CATHEDRAL CITY CLASSIC OVERALL: 9-1 PAC-10: 0-0 2/11 UTAH STATE 2, A W, 19-0 (5) Wednesday, Feb. 23 Sunday, Feb. 27 2/11 NORTH DAKOTA STATE 2 W, 7-0 #2/#3 UCLA at Cal State Northridge 2 p.m. #2/#3 UCLA vs. Northwestern 9 a.m. 2/12 NORTH DAKOTA STATE 2, A W, 15-2 (5) #2/#3 UCLA vs. Ohio State 11 a.m. 2/12 UCF 2 W, 10-2 (5) Friday, Feb. 25 #2/#3 UCLA vs. #4/#5 Florida 3:30 p.m. UCLA is in third-base dugout and is designated home team for 2/13 SAN DIEGO STATE 2, A W, 8-2 #2/#3 UCLA vs. #6/#6 Oklahoma 6 p.m. all games but the Tennessee contest 2/16 at Cal State Fullerton Postponed - Rain 2/18 vs. Southern Illinois Edwardsville 3 W, 3-2 (9) Saturday, Feb. 26 Gametracker available for all games at UCLABruins.com 2/18 vs. Arkansas 3 L, 3-4 #2/#3 UCLA vs. #8/#10 Tennessee 3 p.m. All games at Big League Dreams Park Wrigley Field 2/19 vs. Utah 3 W, 6-2 2/19 vs. Portland State 3 W, 3-0 SECOND-RANKED BRUINS PLAY SIX TIMES IN FIVE DAYS Just days before the start of postseason play, a list of 10 fi nalists 2/20 vs. -
Package 'Mlbstats'
Package ‘mlbstats’ March 16, 2018 Type Package Title Major League Baseball Player Statistics Calculator Version 0.1.0 Author Philip D. Waggoner <[email protected]> Maintainer Philip D. Waggoner <[email protected]> Description Computational functions for player metrics in major league baseball including bat- ting, pitching, fielding, base-running, and overall player statistics. This package is actively main- tained with new metrics being added as they are developed. License MIT + file LICENSE Encoding UTF-8 LazyData true RoxygenNote 6.0.1 NeedsCompilation no Repository CRAN Date/Publication 2018-03-16 09:15:57 UTC R topics documented: ab_hr . .2 aera .............................................3 ba ..............................................4 baa..............................................4 babip . .5 bb9 .............................................6 bb_k.............................................6 BsR .............................................7 dice .............................................7 EqA.............................................8 era..............................................9 erc..............................................9 fip.............................................. 10 fp .............................................. 11 1 2 ab_hr go_ao . 11 gpa.............................................. 12 h9.............................................. 13 iso.............................................. 13 k9.............................................. 14 k_bb............................................ -
OFFICIAL RULES of SOFTBALL (Copyright by the International Softball Federation Playing Rules Committee)
OFFICIAL RULES OF SOFTBALL (Copyright by the International Softball Federation Playing Rules Committee) New Rules and/or changes are bolded and italicized in each section. References to (SP ONLY) include Co-ed Slow Pitch. Wherever “FAST PITCH ONLY (FP ONLY)” appears in the Official Rules, the same rules apply to Modified Pitch with the exception of the pitching rule. "Any reprinting of THE OFFICIAL RULES without the expressed written consent of the International Softball Federation is strictly prohibited." Wherever "he'' or "him" or their related pronouns may appear in this rule book either as words RULE 1 or as parts of words, they have been used for literary purposes and are meant in their generic sense (i.e. To include all humankind, or both male and female sexes). RULE 1. DEFINITIONS. – Sec. 1. ALTERED BAT. Sec. 1/DEFINITIONS/Altered Bat A bat is altered when the physical structure of a legal bat has been changed. Examples of altering a bat are: replacing the handle of a metal bat with a wooden or other type handle, inserting material inside the bat, applying excessive tape (more than two layers) to the bat grip, or painting a bat at the top or bottom for other than identification purposes. Replacing the grip with another legal grip is not considered altering the bat. A "flare" or "cone" grip attached to the bat is considered an altered bat. Engraved “ID” marking on the knob end only of a metal bat is not considered an altered bat. Engraved “ID” marking on the barrel end of a metal bat is considered an altered bat. -
X-Y Scatterplot
Design of Experiments Wizard Revised: 8/11/2019 Summary ......................................................................................................................................... 2 Invoking the DOE Wizard .............................................................................................................. 3 Step 1: Define Responses................................................................................................................ 4 Step 2: Define Experimental Factors .............................................................................................. 7 Step 3: Select Design ...................................................................................................................... 9 Step 4: Specify Model ................................................................................................................... 24 Step 5: Select Runs (Optional) ...................................................................................................... 26 Step 6: Evaluate Design ................................................................................................................ 31 Step 7: Save Experiment ............................................................................................................... 56 Step 8: Analyze Data .................................................................................................................... 57 Step 9: Optimize Responses......................................................................................................... -
Tom Hanrahan
By the Numbers Volume 11, Number 1 The Newsletter of the SABR Statistical Analysis Committee February, 2001 Review “Sabermetric Encyclopedia” – A Good Start Keith Carlson Lee Sinins has developed a Sabermetric Encyclopedia (SE). It is Let me conclude with a summary of what I consider to be the on CDROM, and is described, along with sample pages, at weaknesses of SE: http://members.nbci.com/sabermetricencyclope/. Lee set up that website just for that purpose. There is no need to repeat all of the 1. Park effects. Lee gives several stats as park adjusted (runs details here. Also, you will find pricing and ordering information created above average for hitters and runs saved above there. He offers several packages. average for pitchers, for example). However, he does not give you the park factors. The chief system requirement for the SE is that you have Microsoft Access 2. Fielding stats. installed on your As previously computer. You do mentioned, not need to know In this issue these are not anything about included and running Access; it “Sabermetric Encyclopedia” – A Good Start....................Keith Carlson .................... 1 the position just has to be there Bill James Index...............................................................Stephen Roney ................... 2 listing is in order for SE to be Academic Research – Diversity and incomplete. installed. I found Team Performance.......................................................Charlie Pavitt ..................... 3 True, these are that SE installed POP Extended to Slugging Percentage – all available easily and quickly, Who is Most Definitely Not an Average Slugger? ......Peter Ridges ....................... 4 elsewhere, but using 45 megabytes What Makes a “Clutch” Situation?...................................Tom Hanrahan.................... 7 for the sake of of disc space. -
OFFICIAL GAME INFORMATION Lake County Captains (9-4) at Great Lakes Loons (5-8) Wednesday, May 19Th • 6:05 P.M
High-A Affiliate OFFICIAL GAME INFORMATION Lake County Captains (9-4) at Great Lakes Loons (5-8) Wednesday, May 19th • 6:05 p.m. • Dow Diamond • Broadcast: WJCU.org Game #14 • Road Game #8 • Season Series: 1-0, 23 Games Remaining LHP Logan Allen (2-0, 0.00 ERA) vs. RHP Bobby Miller (0-0, 0.00 ERA) LAST NIGHT: The Captains scored a season-high 10 runs, including seven in the fifth inning, to beat the Great Lakes Loons, 10-6. Lake County. Mike High-A Central League Amditis dealt the big blow in the fifth with a three-run double. Aaron Bracho added insurance when he hit a solo homer in the eighth for his first home run of the season. The Loons nearly came back in the ninth, but Kevin Kelly shut the door. Great Lakes loaded the bases with nobody out on two bit batsmen and a walk by Kellen Rholl. Kelly came in and struck out all three men he faced to end the game and nail down the save. Nic Enright pitched East Division W L GB 2.1 scoreless innings to pick up the win. The Captains’ offense also set a season-high by drawing 10 walks in the game. PITCHING POWERS CAPS: The Captains’ pitching staff leads the High-A Central League in WHIP (1.14). Lake County is second in the league in ERA Dayton (Cincinnati) 9 4 -- (2.84), second in strikeouts (141) and second in opponents’ batting average (.208). RED HOT WEEK: Over the course of last week (May 11-16), the Captains’ pitching staff had the lowest ERA (1.83) and the second-lowest opponent’s LAKE COUNTY (Cleveland) 9 4 -- batting average (.160) in all of High-A. -
BPA Rulebook
www.PlayBPA.com BPA ~ NSA NATIONAL SPONSORS DUDLEY SPORTS GREATER CHATTANOOGA SPORTS & EVENTS COMMITTEE POLK COUNTY TOURISM & SPORTS MARKETING For information on becoming a National Sponsor, contact the National Office at 859-887-4114 BPA INSURANCE PROGRAM Youth teams are required to be covered by BPA Westpoint Insurance. The team can either purchase a yearly BPA Westpoint policy or the Tournament Director is required to purchase tournament insurance offered through Westpoint Insurance. Proper insurance is a concern of all BPA Teams, Leagues and Field Owners who host BPA sanctioned competitions. $100,000 Accident Medical Coverage – Excess Accidents happen, and with today’s soaring medical costs, they can ruin an injured player financially. The BPA Program offers $100,000 of excess accident medical insurance for each covered injury which pays the bills left unpaid by other collectable insurance or health plans after a $100 deductible. To learn more about the BPA / Westpoint Insurance Program, please visit our website at www.PlayBPA.com You may also call the Westpoint Office at 1-800-318-7709 or Email – [email protected] Membership and Coverage begins with receipt of your full payment and enrollment request. Baseball Players Association Official Baseball Rules Editor Baseball– Don Players Snopek Association / BPA National Office Official Baseball Rules Editor – Don Snopek / BPA National Office Table of Contents RULE TableDESCRIPTION of Contents PAGES Rule RULE 1 DefinitiDESCRIPTIONons of Terms 1PAGES-‐10 Rule Rule 1 2 Playing -
Major Qualifying Project: Advanced Baseball Statistics
Major Qualifying Project: Advanced Baseball Statistics Matthew Boros, Elijah Ellis, Leah Mitchell Advisors: Jon Abraham and Barry Posterro April 30, 2020 Contents 1 Background 5 1.1 The History of Baseball . .5 1.2 Key Historical Figures . .7 1.2.1 Jerome Holtzman . .7 1.2.2 Bill James . .7 1.2.3 Nate Silver . .8 1.2.4 Joe Peta . .8 1.3 Explanation of Baseball Statistics . .9 1.3.1 Save . .9 1.3.2 OBP,SLG,ISO . 10 1.3.3 Earned Run Estimators . 10 1.3.4 Probability Based Statistics . 11 1.3.5 wOBA . 12 1.3.6 WAR . 12 1.3.7 Projection Systems . 13 2 Aggregated Baseball Database 15 2.1 Data Sources . 16 2.1.1 Retrosheet . 16 2.1.2 MLB.com . 17 2.1.3 PECOTA . 17 2.1.4 CBS Sports . 17 2.2 Table Structure . 17 2.2.1 Game Logs . 17 2.2.2 Play-by-Play . 17 2.2.3 Starting Lineups . 18 2.2.4 Team Schedules . 18 2.2.5 General Team Information . 18 2.2.6 Player - Game Participation . 18 2.2.7 Roster by Game . 18 2.2.8 Seasonal Rosters . 18 2.2.9 General Team Statistics . 18 2.2.10 Player and Team Specific Statistics Tables . 19 2.2.11 PECOTA Batting and Pitching . 20 2.2.12 Game State Counts by Year . 20 2.2.13 Game State Counts . 20 1 CONTENTS 2 2.3 Conclusion . 20 3 Cluster Luck 21 3.1 Quantifying Cluster Luck . 22 3.2 Circumventing Cluster Luck with Total Bases . -
Investigating Discrimination in Major League Baseball: Not So Perfect Competition
Bard College Bard Digital Commons Masters of Science in Economic Theory and Policy Levy Economics Institute of Bard College Spring 2020 Investigating Discrimination in Major League Baseball: Not So Perfect Competition Esteban Rivera Follow this and additional works at: https://digitalcommons.bard.edu/levy_ms Part of the Labor Economics Commons Investigating Discrimination in Major League Baseball: Not So Perfect Competition Thesis Submitted to Levy Economics Institute of Bard College by Esteban Rivera Annandale‐on‐Hudson, New York May 2020 0 Acknowledgements First, I would like to thank Martha Tepepa and the rest of the Spring 2019 Economics 508 class for supporting me in my decision to pursue this issue. Without your support, I simply would not have investigated this, leaving these players with one less person fighting for them. Next, I am beyond grateful for the help from my thesis advisor, Fernando Rios-Avila. His critiques and teachings made the work in this thesis possible. To Jan Kregel and my fellow 2020 M.S. peers, thank you for your valued input throughout this journey. I would also like to thank all of the faculty members at the Levy Economics Institute for pushing me to become the best possible student and learner that I can be. Special thanks go to my coaches and teammates for their support in these last few years on and off the field. Lastly, I want to take this opportunity to thank my family and girlfriend. Emily, thank you for listening to my rants on this topic, and many others. You have been my rock through all of this.