Batting, Fielding, Pitching Stats Summary Sheets

Total Page:16

File Type:pdf, Size:1020Kb

Batting, Fielding, Pitching Stats Summary Sheets STATISTICAL SUMMARY SHEET - BATTING AND FIELDING SCORER'S NAME TEAM CHAMPIONSHIP/TOURNAMENT DATE NAME FIELDING FIELDING BATTING BATTING ON BASE OI DI LAST FIRST POS PO A E AVERAGE PA AB H 1B 2B 3B HR R RBI Sac SF BB HPB IBB OTH OE FC SB CS SO OB AVERAGE AVERAGE TEAM TOTAL OI DI POS PO A E PA AB H 1B 2B 3B HR R RBI Sac SF BB HPB IBB OTH OE FC SB CS SO OB NUMBER OF PLAYERS NUMBER OF CHANGES FIELDING BATTING PO NUMBER OFPUT OUTS PA NUMBER OF TIMES IN BATTERS BOX RBI NUMBER OF RUNS BATTED IN OTH NUMBER OF TIMES ON BASE BY KWP A NUMBER OFASSISTS AB NUMBER OF TIMES AT BAT Sac NUMBER OF SACRIFICE BUNTS THAT IS NOT COVERED E NUMBER OF ERRORS H NUMBER OF SAFE HITS SF NUMBER OF SACRIFICE FLYS OE NUMBER OF TIMES ON BASE BY ERROR FIELDING CALCULATED 1B NUMBER OF ONE BASE HITS BB NUMBER OF BASE ON BALLS FC NUMBER OF FIELDER'S CHOICES AVERAGE AVERAGE 2B NUMBER OF TWO BASE HITS HPB NUMBER OF BASE ON BALLS SB NUMBER OF STOLEN BASES 3B NUMBER OF THREE BASE HITS IBB NUMBER OF BASE ON BALLS CS NUMBER OF TIMES CAUGHT STEALING OI OFFENSIVE INNINGS HR NUMBER OF HOME RUN HITS OBS NUMBER OF CATCHERS OBSTRUCTION SO NUMBER OF TIMES STRUCK OUT DI DEFENSIVE INNINGS R NUMBER OFRUNS SCORED OB NUMBER OF TIMES ON BASE If split should be shown as 3 decimal places PLEASE CHECK YOUR MANUAL IN RELATION TO THE CALCULATION OF FIELDING, BATTING AND ON BASE AVERAGES L4 - Batting, Fielding, Pitching Stats Summary Sheets STATISTICAL SUMMARY SHEET - PITCHING SCORER'S NAME TEAM CHAMPIONSHIP/TOURNAMENT VENUE DATE NAME PITCHING PITCH COUNT WIN/LOSS SAVE EARNED RUN WIN LOSS HITS PER LAST FIRST INN H SO BB HPB IBB HR WP R ER BFP IP S B F TOTAL W L S AVERAGE AVERAGE BATTERS FACED TEAM TOTAL INN NUMBER OF INNINGS PITCHED NOTE PART INNS ARE COUNTED AS .333 OR .667 S NUMBER OF STRIKES THROWN (include caught Foul Flies ie FF3) H NUMBER OF SAFE HITS B NUMBER OF BALLS THROWN SO NUMBER OF STRIKE OUTS (include K2,KC,K2-E3,KE2 etc) F NUMBER OF FOULS BB NUMBER OF TIMES ON BASE BY BASE ON BALLS TOTAL TOTAL OF PITCHES THROWN HPB NUMBER OF TIMES ON BASE BY HIT BY PITCHED BALL WIN WINNING PITCHER FOR THE GAME * check Manual for exam IBB NUMBER OF TIMES ON BASE BY INTENTIONAL BASE ON BALL LOSS LOSING PITCHER FOR THE GAME WP NUMBER OF WILD PITCHES THROWN SAVE PITCHER AWARDED SAVE FOR THE GAME R NUMBER OF RUNS SCORED ER NUMBER OF EARNED RUNS SCORED EARNED RUN AVERAGE PLEASE CHECK YOUR MANUAL ON BFP NUMBER OF BATTERS WHO FACED THE PITCHER WIN/LOSS AVERAGE HOW TO CALCULATE IP NUMBER OF ILLEGAL PITCHES THROWN HITS PER BATTER FACED THESE AVERAGES L4 - Batting, Fielding, Pitching Stats Summary Sheets .
Recommended publications
  • NCAA Division I Baseball Records
    Division I Baseball Records Individual Records .................................................................. 2 Individual Leaders .................................................................. 4 Annual Individual Champions .......................................... 14 Team Records ........................................................................... 22 Team Leaders ............................................................................ 24 Annual Team Champions .................................................... 32 All-Time Winningest Teams ................................................ 38 Collegiate Baseball Division I Final Polls ....................... 42 Baseball America Division I Final Polls ........................... 45 USA Today Baseball Weekly/ESPN/ American Baseball Coaches Association Division I Final Polls ............................................................ 46 National Collegiate Baseball Writers Association Division I Final Polls ............................................................ 48 Statistical Trends ...................................................................... 49 No-Hitters and Perfect Games by Year .......................... 50 2 NCAA BASEBALL DIVISION I RECORDS THROUGH 2011 Official NCAA Division I baseball records began Season Career with the 1957 season and are based on informa- 39—Jason Krizan, Dallas Baptist, 2011 (62 games) 346—Jeff Ledbetter, Florida St., 1979-82 (262 games) tion submitted to the NCAA statistics service by Career RUNS BATTED IN PER GAME institutions
    [Show full text]
  • OFFICIAL GAME INFORMATION Lake County Captains (14-15) Vs
    High-A Affiliate OFFICIAL GAME INFORMATION Lake County Captains (14-15) vs. Dayton Dragons (16-13) Sunday, June 6th • 1:30 p.m. • Classic Park • Broadcast: WJCU.org Game #30 • Home Game #12 • Season Series: 3-2, 19 Games Remaining RHP Mason Hickman (1-2, 3.45 ERA) vs. RHP Spencer Stockton (2-0, 3.57 ERA) YESTERDAY: The Captains’ three-game winning streak ended with a 15-4 loss to Dayton on Saturday night. Kevin Coulter surrendered seven runs on 10 hits over 1.2 innings to take the loss in a spot start. Dragons centerfielder Quin Cotton hit two home runs and drove in six High-A Central League runs to lead the Dayton offense. Dragons starter Graham Ashcraft earned the win with seven strong innings, in which he allowed just one run on two hits and struck out nine. East Division W L GB COMING ALIVE: After scoring just 12 runs and suffering a six-game sweep last week at West Michigan, the Captains have already scored 29 runs in the first five games of this series against Dayton. Will Brennan has gone 7-for-18 (.389) with two home runs, two doubles, 10 RBI and West Michigan (Detroit) 16 12 -- a 1.254 OPS. Joe Naranjo has gone 3-for-10 with a team-leading five walks for a .533 on-base percentage. Dayton (Cincinnati) 16 13 0.5 BRENNAN BASHING: Captains OF Will Brennan leads the High-A Central League (HAC) lead in doubles (11). He is second in batting average (.326), fourth in wRC+ (154), fifth in on-base percentage (.410), sixth in OPS (.920), sixth in extra-base hits (13) and ninth in slugging Great Lakes (Los Angeles - NL) 15 14 1.5 percentage (.511).
    [Show full text]
  • Washington, Dc and the Mlb All-Star Game
    TEAM UP FEBRUARY TOUCH BASE 2021 WASHINGTON, DC AND THE MLB ALL-STAR GAME The Major League Baseball All-Star Game is also known as the “Midsummer Classic.” The game features the best players in the National League (NL) playing against the best players in the American League (AL). Fans choose the starting lineups; and a combination of players, coaches, and managers choose the rest of the players on the All-Star rosters. The game is played every year, usually on the second or third Tuesday in July. The very first All-Star Game was on July 6, 1933, at the home of the Chicago White Sox. Only two times since then has the game not been played — in 1945 due to World War II travel restrictions, and 2020 due to the coronavirus pandemic. Nationals Park and Washington, DC were at the center of the baseball universe in July 2018, serving as host of the 89th Major League Baseball All-Star Game. Remember all those festivities? This may come as a surprise, but that was actually the fifth time the All-Star Game was played in DC. Here is a little bit about each of the All-Star Games played in the Nation’s Capital. JULY 7, 1937 The 1937 Midsummer Classic, which was the fifth Major League Baseball All-Star Game, was played on July 7, at Griffith Stadium. President Franklin D. Roosevelt was in attendance, making this the first All-Star Game to be played in front of a current President. The American League won the game 8-3, improving to 4 wins and 1 loss in the five games.
    [Show full text]
  • NFCA Home Plate: ATEC: Beyond the Basics of Scoring Fastpitch Softball
    NFCA Home Plate: ATEC: Beyond the Basics of Scoring Fastpitch Softball by Jeri Findlay Published by National Fastpitch Coaches Association Copyright 1999. All Right Reserved Introduction Basic Guidelines and Scorer Responsibilities Proving A Box Score Percentages and Averages Cumulative Performance Records Called and Forfeited Games Offense: Statistics Offense: Hits Offense: Extra Base Hits Offense: Game Ending Hits Offense: Fielder's Choice Offense: Sacrifices Offense: Runs Batted In (RBI) Offense: Batting Out of Order Offense: Strikeouts Offense: Stolen Bases Offense: Caught Stealing (Unsuccessful Attempt) Defense: Statistics Defense: Errors Defense: Putouts Defense: Assists Defense: Double Play/Triple Play Defense: Throw Outs Pitching: Statistics Pitching: Earned Runs Pitching: Charging Runs Scored (When Relief Pitchers Are Used) Pitching: Strikeouts Pitching: Bases On Balls Pitching: Wild Pitches/Passed Balls Pitching: Winning and Losing Pitcher Pitching: Saves Scoring The Tie-Breaker Some images Copyright www.arttoday.com Web design by Ray Foster. Reproduction of material from any NFCA Home Plate pages without written permission is strictly prohibited. Copyright ©1999 National Fastpitch Coaches Association. NFCA, 409 Vandiver Drive, Suite 5-202, Columbia, MO 65202 TELEPHONE (573) 875-3033 | FAX (573) 875-2924 | EMAIL http://www.nfca.org/indexscoringfp.lasso [1/27/2002 2:21:41 AM] NFCA Homeplate: ATEC: Beyond The Basics of Scoring Fastpitch Softball TABLE OF CONTENTS Introduction Introduction Basic Guidelines and Scorer - - - - - - - - - - - - - - - - - - - - - - - Responsibilities Proving A Box Score Published by: National Softball Coaches Association Percentages and Averages Written by Jeri Findlay, Head Softball Coach, Ball State University Cumulative Performance Records Introduction Called and Forfeited Games Scoring in the game of fastpitch softball seems to be as diversified as the people Offense: Statistics playing it.
    [Show full text]
  • 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............................................
    [Show full text]
  • Investigating Major League Baseball Pitchers and Quality of Contact Through Cluster Analysis
    Grand Valley State University ScholarWorks@GVSU Honors Projects Undergraduate Research and Creative Practice 4-2020 Investigating Major League Baseball Pitchers and Quality of Contact through Cluster Analysis Charlie Marcou Grand Valley State University Follow this and additional works at: https://scholarworks.gvsu.edu/honorsprojects Part of the Sports Sciences Commons, and the Statistics and Probability Commons ScholarWorks Citation Marcou, Charlie, "Investigating Major League Baseball Pitchers and Quality of Contact through Cluster Analysis" (2020). Honors Projects. 765. https://scholarworks.gvsu.edu/honorsprojects/765 This Open Access is brought to you for free and open access by the Undergraduate Research and Creative Practice at ScholarWorks@GVSU. It has been accepted for inclusion in Honors Projects by an authorized administrator of ScholarWorks@GVSU. For more information, please contact [email protected]. Investigating Major League Baseball Pitchers and Quality of Contact through Cluster Analysis Charlie Marcou Introduction The rise of sabermetrics, the quantitative analysis of baseball, has changed how baseball front offices operate, how prospects are evaluated and developed, and how baseball is played on the field. Stolen bases are on the decline, while strikeouts, walks, and homeruns have steadily increased. Hitters care more and more about their launch angle and pitchers have started using high speed cameras to analyze their movement. Despite these changes, there are still many areas that need investigation. This paper seeks to investigate the quality of contact that a pitcher allows. Not much is currently known about quality of contact, but if factors determining quality of contact could be determined it could assist teams in identifying and developing pitching talent.
    [Show full text]
  • A Bayesian Variable Selection Approach to Major League
    A Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics∗ Blakeley B. McShane, Alexander Braunstein, James Piette, and Shane T. Jensen Department of Statistics The Wharton School University of Pennsylvania Abstract Numerous statistics have been proposed for the measure of offensive ability in major league baseball. While some of these measures may offer moderate predictive power in certain situations, it is unclear which simple offensive metrics are the most reliable or consistent. We address this issue with a Bayesian hierarchical model for variable selection to capture which offensive metrics are most predictive within players across time. Our sophisticated methodology allows for full estimation of the posterior distri- butions for our parameters and automatically adjusts for multiple testing, providing a distinct advantage over alternative approaches. We implement our model on a set of 50 different offensive metrics and discuss our results in the context of comparison to other variable selection techniques. We find that 33/50 metrics demonstrate signal. However, these metrics are highly correlated with one another and related to traditional notions of performance (e.g., plate discipline, power, and ability to make contact). Keywords: Baseball, Bayesian models, entropy, mixture models, random effects arXiv:0911.4503v1 [stat.AP] 23 Nov 2009 October 22, 2018 ∗Blake McShane, Alex Braunstein, and James Piette are doctoral candidates and Shane Jensen is an As- sistant Professor, all in the Department of Statistics at the Wharton School of the University of Pennsylvania. All correspondence on this manuscript should be sent to Blake McShane, [email protected], 400 Jon M. Huntsman Hall, 3730 Walnut Street, Philadelphia, PA 19104.
    [Show full text]
  • A Hierarchical Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics
    University of Pennsylvania ScholarlyCommons Statistics Papers Wharton Faculty Research 10-2011 A Hierarchical Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics Blakeley B. McShane University of Pennsylvania Alexander Braunstein James M. Piette III University of Pennsylvania Shane T. Jensen University of Pennsylvania Follow this and additional works at: https://repository.upenn.edu/statistics_papers Part of the Statistics and Probability Commons Recommended Citation McShane, B. B., Braunstein, A., Piette, J. M., & Jensen, S. T. (2011). A Hierarchical Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics. Journal of Quantitative Analysis in Sports, 7 (4), http://dx.doi.org/10.2202/1559-0410.1323 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/statistics_papers/442 For more information, please contact [email protected]. A Hierarchical Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics Abstract Numerous statistics have been proposed to measure offensive ability in Major League Baseball. While some of these measures may offer moderate predictive power in certain situations, it is unclear which simple offensive metrics are the most reliable or consistent. We address this issue by using a hierarchical Bayesian variable selection model to determine which offensive metrics are most predictive within players across time. Our sophisticated methodology allows for full estimation of the posterior distributions for our parameters and automatically adjusts for multiple testing, providing a distinct advantage over alternative approaches. We implement our model on a set of fifty different offensive metrics and discuss our results in the context of comparison to other variable selection techniques. We find that a large number of metrics demonstrate signal.
    [Show full text]
  • 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 .
    [Show full text]
  • Fixed It! Baseball Scorebook Help File Overview
    Fixed It! Baseball ScoreBook Help File Overview Thank you for your interest in Fixed It! Baseball ScoreBook . We hope the information provided in our help file will answer most of your questions about our product. In many cases you may find using the Tab and Alt keys easier and quicker than using a mouse or other pointing device: - Use the Tab key to jump from one field to another. - Also, whenever you see a button that has a letter underlined, you may use the Alt key in combination with that letter to select that button. For example, the Print button may be chosen by selecting Alt-P. In addition, several accelerators are also available for common functions: F1 - Help file Alt-X - Exit the program Control-S - Save the game Control-W - Make a substitution Control-B - Courtesy batter Control-R - Courtesy runner Control-Alt-L - Load game as it was last saved We are constantly looking for ways to improve our products. If you have any questions, comments, or suggestions, feel free to e-mail us at [email protected] . Table Of Contents Schedules 3 Teams & Rosters 4 Lineups 6 Games 7 Game Play 7 Adding / Modifying Players During Games 8 Undo Substitutions 8 Inning Editing Features 9 At Bats 10 Editing At Bat Details 12 Scoring Legend 13 Example Scoring Situations 14 Statistics 15 Batting Statistics 17 Running Statistics 19 Fielding Statistics 20 Pitcher Statistics 21 Scatter Plots 22 Pitch Plots 22 Current Pitcher / Inning Statistics 22 Box Scores / Game Summary 23 Printing 23 PDA Utilities 24 Options 26 Using FTP Functions to Send Stats and Box Scores 28 Using FTP Functions to Send Game and Team files 29 Accessing ScoreBook Home Page 29 Removing the Program 29 Registration 30 2 Schedules Schedules can provide the user with a means to track games by Date and Time and an alternate means for launching games.
    [Show full text]
  • NEC BASEBALLBASEBALLBASEBALL NN EWSEWS && NN OTESOTES 200 Cottontail Lane, Vantage Court North, Somerset, NJ 08873 Baseball Contact: Ralph Ventre, Asst
    20082008 NECNECNEC BASEBALLBASEBALLBASEBALL NN EWSEWS && NN OTESOTES 200 Cottontail Lane, Vantage Court North, Somerset, NJ 08873 Baseball Contact: Ralph Ventre, Asst. Dir. Comm. Phone: 732-469-0440/Fax: 732-469-0744 E-Mail: [email protected] Central Connecticut State • Fairleigh Dickinson • Long Island • Monmouth • Mount St. Mary’s Quinnipiac • Robert Morris • Sacred Heart • St. Francis (NY) • St. Francis (PA) • Wagner NEC Baseball Northeast Conference Baseball Facts & Figures (Week 7 - April 7, 2008) Latest Results School NEC Pct. Overall Pct. Streak Home Away Neutral Tuesday, April 1 Saturday, April 5 1. Monmouth 4-0 1.000 13-8 .619 W11 5-0 6-6 2-2 MONMOUTH 11, Princeton 8 WAGNER 16, LONG ISLAND 5 Wagner 4-0 1.000 11-12 .478 W4 7-3 4-9 0-0 QUINNIPIAC at Siena, cancelled WAGNER 3, LONG ISLAND 0 URI at CENTRAL CONN ST. , ppd MOUNT 7, FDU 3 Central Conn. St. 4-0 1.000 8-11 .421 W5 4-0 2-4 2-7 MONMOUTH 12, SACRED HEART 0 4. Mount St. Mary's 2-1 .667 3-18 .143 L1 1-8 2-4 0-6 Wednesday, April 2 MONMOUTH 12, SACRED HEART 0 MONMOUTH 6, Rutgers 5 CENTRAL CONN ST. 3, QUINNIPIAC 2 5. Fairleigh Dickinson 1-2 .333 5-20 .200 W1 3-5 2-5 0-10 Yale 19, QUINNIPIAC 13 CENTRAL CONN ST. 5, QUINNIPIAC 2 6. Long Island 0-4 .000 8-17 .320 L5 2-1 2-9 4-7 St. Peter’s 17, LONG ISLAND 11 Quinnipiac 0-4 .000 5-15 .250 L6 1-3 3-9 1-3 SACRED HEART 14, Iona 10 Sunday, April 6 Seaton Hall 9, FDU 4 CENTRAL CONN ST.
    [Show full text]
  • Simulation-Based Projections for Baseball Statistics
    Simulation-Based Projections for Baseball Statistics A Thesis Presented to the Faculty of California State Polytechnic University, Pomona In Partial Fulfillment Of the Requirements for the Degree Master of Science In Computer Science By Daniel Adam Acevedo 2018 SIGNATURE PAGE THESIS: SIMULATION-BASED PROJECTIONS FOR BASEBALL STATISTICS AUTHOR: DANIEL ADAM ACEVEDO TERM SUBMITTED: Spring 2018 Computer Science Department Dr. Yu Sun ____________________________________ Thesis Committee Chair Department of Computer Science Dr. Abdelfattah Amamra ____________________________________ Department of Computer Science Dr. Sampath Jayarathna ____________________________________ Department of Computer Science ii ACKNOWLEDGEMENTS I would like to thank my family for their love, support, and for all of the sacrifices they’ve made so that I could get an education, resulting in a Master’s Degree. I would like to thank my advisor, Dr. Yu Sun, for his guidance and help throughout my time conducting this thesis. I would also like to thank my Dr. Sampath Jayarathna and Dr. Abdelfattah Amamra for being members of my committee. iii ABSTRACT Baseball is an unpredictable sport. The introduction of sabermetrics established an opening for the application of computer science methods within the game’s evaluation. Every Major League Baseball organization has developed their own method of measuring players’ results and making predictions as to what they should expect from a player entering a season. While most industry models use their own statistical analysis to perform predictions, this thesis introduces a new model that uses simulations in addition to statistical analysis in order to make predictions. The results of this thesis show that this model is comparable to some of the best projection systems available.
    [Show full text]