Coaches Handbook 2021
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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. -
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......................................................................................................... -
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 -
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. -
Season Other Metrics
Lamar University Other Metrics for Lamar (2007) (All games Sorted by Player Name) Runs Created Base Runs Extrapolated Runs Onbase + Slugging Total Average Batter RC RC/9 BSR BSR/9 XR XR/9 ob% slug% OPS GPA bases outs TA BABIP 33 Ambort, Michael 49.43 11.51 43.82 10.20 41.83 9.74 . 4 2 8 . 6 5 0 1.078 . 3 5 5 135 181 . 7 4 6 . 3 9 3 7 Baker, Ryan 34.52 6.61 33.69 6.45 33.23 6.36 . 3 9 2 . 4 3 3 . 8 2 5 . 2 8 5 109 199 . 5 4 8 . 3 3 2 6 Colvin, Michael 0.00 0.00 0.00 0.00 -0.47 -2.56 . 0 0 0 . 0 0 0 . 0 0 0 . 0 0 0 0 5 . 0 0 0 . 0 0 0 24 DeLome, Collin 48.42 9.90 44.39 9.08 42.49 8.69 . 4 0 4 . 6 1 3 1.016 . 3 3 5 141 195 . 7 2 3 . 3 7 5 28 Desmond, Wes 0.06 0.11 0.06 0.10 -0.89 -1.60 . 0 6 3 . 0 6 3 . 1 2 5 . 0 4 4 1 16 . 0 6 2 . 0 9 1 14 Dunson, Travis 16.42 3.66 16.32 3.64 16.29 3.64 . 3 4 0 . 3 4 5 . 6 8 4 . 2 3 9 70 154 . 4 5 5 . 3 0 2 27 Hernandez, Dan 21.04 7.78 20.75 7.68 19.02 7.04 . -
2021 Texas A&M Baseball
2021 TEXAS A&M BASEBALL GAMES 36-38 | @ #1 ARKANSAS | APRIL 16-18 GAME TIMES: 6:32 / 6:32 / 2:02 P.M. CT | SITE: Baum Stadium, Fayetteville, Arkansas Texas A&M Media Relations * www.12thMan.com Baseball Contact: Thomas Dick / E-Mail: [email protected] / C: (512) 784-2153 RESULTS/SCHEDULE TEXAS A&M 1 ARKANSAS Date Day Opponent Time (CT) # 2/20 SAT XAVIER (DH) S+ L, 6-10 XAVIER (DH) S+ L, 0-2 AGGIES RAZORBACKS 2/21 SUN XAVIER S+ W, 15-0 2/23 TUE ABILENE CHRISTIAN S+ L, 5-6 2/24 WED TARLETON STATE S+ (10) W, 8-7 2/26 Fri % vs Baylor Flo W, 12-4 2021 Record 20-15, 3-9 SEC 2021 Record 28-5, 9-3 SEC 2/27 Sat % vs Oklahoma Flo W, 8-1 Ranking - Ranking 1 (USAT, BA, NCBWA, CB, D1B) 2/28 Sun % vs Auburn Flo L, 1-6 Streak Won 1 Streak Won 3 3/2 TUE HOUSTON BAPTIST S+ W, 4-0 Last 5 / Last 10 1-4 / 3-7 Last 5 / Last 10 4-1 / 8-2 3/3 WED INCARNATE WORD S+ W, 6-4 Last Game April 13 Last Game April 14 3/5 FRI NEW MEXICO STATE S+ W, 4-1 3/6 SAT NEW MEXICO STATE S+ W, 5-0 at Texas State - W, 8-4 ARKANSAS-PINE BLUFF - W, 26-1 (7 inn) 3/7 SUN NEW MEXICO STATE S+ W, 7-1 3/9 TUE A&M-CORPUS CHRISTI S+ W, 7-0 Head Coach Rob Childress (Northwood, ‘90) Head Coach Dave Van Horn (Arkansas, ‘88) 3/10 WED PRAIRIE VIEW A&M S+ (7) W, 22-2 Overall 613-324-3 (16th season) Overall 1,099-562 (28th season) 3/12 FRI SAMFORD S+ W, 10-1 at Texas A&M same at Arkansas 728-394 (19th season 3/13 SAT SAMFORD (DH) S+ W, 21-4 SUN SAMFORD (DH) S+ W, 5-2 3/16 Tue at Houston E+ W, 9-4 PROBABLE PITCHING MATCHUPS 3/18 Thu * at #5 Florida SEC L, 4-13 • FRIDAY: #37 Dustin Saenz (Sr., LHP, 5-3, 3.19) vs. -
WAR and the Hall of Fame
WAR and the Hall of Fame Joshua Bayzick May 5,2015 Abstract Sabermetrics is a term coined by Bill James, and it is the search for objective knowledge about baseball. This mathematical approach to baseball produced a statistic called Wins Above Replacement (WAR), which takes into account batting, baserunning, and fielding to determine how many `wins' that position player was worth. This project looks at how player's WAR values change as they age. We will compare two groups of players, First-ballot Hall of Famers (HOF) and the average player. We know that the HOF group will have higher WAR values because they were well above average players, but the basic question is do they age differently than the average player. 1 Introduction A relatively new statistic called WAR (Wins Above Replacement) was developed by saber- metricians to answer questions such as \Who had the better career?" or \Who had the better year?" For example, in 2014 Alex Gordon had a batting average of .266 with 19 home runs and 74 RBI. In 2014 Yoenis Cespedes had a batting average of .260 with 22 home runs and 100 RBI. Based on these conventional statistics, they appear to have had fairly similar years. One might even argue that Cespedes was slightly better because of the RBI advantage. However, these conventional statistics do not measure everything a player does to help his team win a game. WAR, which takes into account batting, baserunning, and fielding, says that in 2014 Alex Gordon was worth 6.6 WAR for his team, while Ces- pedes was worth 3.3 WAR. -
Machine Learning Outperforms Regression Analysis to Predict Next
Henry Ford Health System Henry Ford Health System Scholarly Commons Orthopaedics Articles Orthopaedics / Bone and Joint Center 11-1-2020 Machine Learning Outperforms Regression Analysis to Predict Next-Season Major League Baseball Player Injuries: Epidemiology and Validation of 13,982 Player-Years From Performance and Injury Profile rT ends, 2000-2017 Jaret M. Karnuta Bryan C. Luu Heather S. Haeberle Paul M. Saluan Salvatore J. Frangiamore See next page for additional authors Follow this and additional works at: https://scholarlycommons.henryford.com/orthopaedics_articles Authors Jaret M. Karnuta, Bryan C. Luu, Heather S. Haeberle, Paul M. Saluan, Salvatore J. Frangiamore, Kim L. Stearns, Lutul D. Farrow, Benedict U. Nwachukwu, Nikhil N. Verma, Eric C. Makhni, Mark S. Schickendantz, and Prem N. Ramkumar Original Research Machine Learning Outperforms Regression Analysis to Predict Next-Season Major League Baseball Player Injuries Epidemiology and Validation of 13,982 Player-Years From Performance and Injury Profile Trends, 2000-2017 Jaret M. Karnuta,* MS, Bryan C. Luu,† BS, Heather S. Haeberle,*† MD, Paul M. Saluan,* MD, Salvatore J. Frangiamore,* MD, Kim L. Stearns,* MD, Lutul D. Farrow,* MD, Benedict U. Nwachukwu,‡ MD, Nikhil N. Verma,§ MD, Eric C. Makhni,k MD, MBA, Mark S. Schickendantz,* MD, and Prem N. Ramkumar,*{ MD, MBA Investigation performed at the Cleveland Clinic, Cleveland, Ohio, USA Background: Machine learning (ML) allows for the development of a predictive algorithm capable of imbibing historical data on a Major League Baseball (MLB) player to accurately project the player’s future availability. Purpose: To determine the validity of an ML model in predicting the next-season injury risk and anatomic injury location for both position players and pitchers in the MLB. -
IBAF Scorers' Manual
IBAF Scorers’ Manual INTERNATIONAL BASEBALL FEDERATION FEDERACION INTERNACIONAL DE BEISBOL REVISED IN 2008 2 CONTENTS The Scorekeeper 5 Preface 6 Chapter 1 – The Official Score-sheet 7 Symbols and abbreviations 9 The official score-sheet 10 Headings, first sheet 10 Headings, second sheet 11 Line-up (batting order) 11 Central section 11 Defense information 13 Offense information 13 Pitching performance 14 Catching performance 15 Score-sheet test 15 Completing the tables 15 Final score-sheet test 16 Substitutions 18 Internal Changes 18 Changes in the batting order 19 Pitcher replaces DH in offense 19 Substitution of runners 20 Substitution of pitchers 20 Substitution of a batter before he has completed his turn 20 Substitution of a pitcher with a batter in the box 21 Insufficient space on score-sheet 21 Chapter 2 – Defense 23 Putouts 25 Putting out a batter 25 Putting out a runner 26 Double and triple plays 26 Automatic putouts or Out by Rule (“OBR”) 29 Appeal plays 32 Appeal plays against batters who have hit doubles, triples or home runs 33 Out by infield fly 36 Batting out of turn 38 Assists 40 Errors 41 Decisive errors 42 Extra base errors 42 Interference and obstruction 43 Exempted errors 49 Chapter 3 – Offense 51 Safe hits 53 Determining the value of safe hits 56 Game-ending hits 57 Final conclusions on safe hits 58 Sacrifices 58 Sacrifice hit 58 Sacrifice fly 60 Free arrivals on first base 62 Bases on balls 62 3 Intentional base on balls 63 Hit by pitch 63 Defensive interference 63 Obstruction 64 Advance to first base on the ball