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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 -
Trevor Bauer
TREVOR BAUER’S CAREER APPEARANCES Trevor Bauer (47) 2009 – Freshman (9-3, 2.99 ERA, 20 games, 10 starts) JUNIOR – RHP – 6-2, 185 – R/R Date Opponent IP H R ER BB SO W/L SV ERA Valencia, Calif. (Hart HS) 2/21 UC Davis* 1.0 0 0 0 0 2 --- 1 0.00 2/22 UC Davis* 4.1 7 3 3 2 6 L 0 5.06 CAREER ACCOLADES 2/27 vs. Rice* 2.2 3 2 1 4 3 L 0 4.50 • 2011 National Player of the Year, Collegiate Baseball • 2011 Pac-10 Pitcher of the Year 3/1 UC Irvine* 2.1 1 0 0 0 0 --- 0 3.48 • 2011, 2010, 2009 All-Pac-10 selection 3/3 Pepperdine* 1.1 1 1 1 1 2 L 0 3.86 • 2010 Baseball America All-America (second team) 3/7 at Oklahoma* 0.2 1 0 0 0 0 --- 0 3.65 • 2010 Collegiate Baseball All-America (second team) 3/11 San Diego State 6.0 2 1 1 3 4 --- 0 2.95 • 2009 Louisville Slugger Freshman Pitcher of the Year 3/11 at East Carolina* 3.2 2 0 0 0 5 W 0 2.45 • 2009 Collegiate Baseball Freshman All-America 3/21 at USC* 4.0 4 2 1 0 3 --- 1 2.42 • 2009 NCBWA Freshman All-America (first team) 3/25 at Pepperdine 8.0 6 2 2 1 8 W 0 2.38 • 2009 Pac-10 Freshman of the Year 3/29 Arizona* 5.1 4 0 0 1 4 W 0 2.06 • Posted a 34-8 career record (32-5 as a starter) 4/3 at Washington State* 0.1 1 2 1 0 0 --- 0 2.27 • 1st on UCLA’s career strikeouts list (460) 4/5 at Washington State 6.2 9 4 4 0 7 W 0 2.72 • 1st on UCLA’s career wins list (34) 4/10 at Stanford 6.0 8 5 4 0 5 W 0 3.10 • 1st on UCLA’s career innings list (373.1) 4/18 Washington 9.0 1 0 0 2 9 W 0 2.64 • 2nd on Pac-10’s career strikeouts list (460) 4/25 Oregon State 8.0 7 2 2 1 7 W 0 2.60 • 2nd on UCLA’s career complete games list (15) 5/2 at Oregon 9.0 6 2 2 4 4 W 0 2.53 • 8th on UCLA’s career ERA list (2.36) • 1st on Pac-10’s single-season strikeouts list (203 in 2011) 5/9 California 9.0 8 4 4 1 10 W 0 2.68 • 8th on Pac-10’s single-season strikeouts list (165 in 2010) 5/16 Cal State Fullerton 9.0 8 5 5 2 8 --- 0 2.90 • 1st on UCLA’s single-season strikeouts list (203 in 2011) 5/23 at Arizona State 9.0 6 4 4 5 5 W 0 2.99 • 2nd on UCLA’s single-season strikeouts list (165 in 2010) TOTAL 20 app. -
Iscore Baseball | Training
| Follow us Login Baseball Basketball Football Soccer To view a completed Scorebook (2004 ALCS Game 7), click the image to the right. NOTE: You must have a PDF Viewer to view the sample. Play Description Scorebook Box Picture / Details Typical batter making an out. Strike boxes will be white for strike looking, yellow for foul balls, and red for swinging strikes. Typical batter getting a hit and going on to score Ways for Batter to make an out Scorebook Out Type Additional Comments Scorebook Out Type Additional Comments Box Strikeout Count was full, 3rd out of inning Looking Strikeout Count full, swinging strikeout, 2nd out of inning Swinging Fly Out Fly out to left field, 1st out of inning Ground Out Ground out to shortstop, 1-0 count, 2nd out of inning Unassisted Unassisted ground out to first baseman, ending the inning Ground Out Double Play Batter hit into a 1-6-3 double play (DP1-6-3) Batter hit into a triple play. In this case, a line drive to short stop, he stepped on Triple Play bag at second and threw to first. Line Drive Out Line drive out to shortstop (just shows position number). First out of inning. Infield Fly Rule Infield Fly Rule. Second out of inning. Batter tried for a bunt base hit, but was thrown out by catcher to first base (2- Bunt Out 3). Sacrifice fly to center field. One RBI (blue dot), 2nd out of inning. Three foul Sacrifice Fly balls during at bat - really worked for it. Sacrifice Bunt Sacrifice bunt to advance a runner. -
Baseball/Softball
July2006 ?fe Aatuated ScowS& For Basebatt/Softbatt Quick Keys: Batter keywords: Press this: To perform this menu function: Keyword: Situation: Keyword: Situation: a.Lt*s Balancescoresheet IB Single SAC Sacrificebunt ALT+D Show defense 2B Double SF Sacrifice fly eLt*B Edit plays 3B Triple RBI# # Runs batted in RLt*n Savea gamefile to disk HR Home run DP Hit into doubleplay crnl*n Load a gamefile from disk BB Walk GDP Groundedinto doubleplay alr*I Inning-by-inning summary IBB Intentionalwalk TP Hit into triple play nlr*r Lineupcards HP Hit by pitch PB Reachedon passedball crRL*t List substitutions FC Fielder'schoice WP Reachedon wild pitch alr*o Optionswindow CI Catcher interference E# Reachon error by # ALT+N Gamenotes window BI Batter interference BU,GR Bunt, ground-ruledouble nll*p Playswindow E# Reachedon error by DF Droppedfoul ball ALr*g Quit the program F# Flied out to # + Advanced I base alr*n Rosterwindow P# Poppedup to # -r-r Advanced2 bases CTRL+R Rosterwindow (edit profiles) L# Lined out to # +++ Advanced3 bases a,lr*s Statisticswindow FF# Fouledout to # +T Advancedon throw 4 J-l eLt*:t Turn the scoresheetpage tt- tt Groundedout # to # +E Advanced on effor l+1+1+ .ALr*u Updatestat counts trtrft Out with assists A# Assistto # p4 Sendbox score(to remotedisplay) #UA Unassistedputout O:# Setouts to # Ff, Edit defensivelineup K Struck out B:# Set batter to # F6 Pitchingchange KS Struck out swinging R:#,b Placebatter # on baseb r7 Pinchhitter KL Struck out looking t# Infield fly to # p8 Edit offensivelineup r9 Print the currentwindow alr*n1 Displayquick keyslist Runner keywords: nlr*p2 Displaymenu keys list Keyword: Situation: Keyword: Situation: SB Stolenbase + Adv one base Hit locations: PB Adv on passedball ++ Adv two bases WP Adv on wild pitch +++ Adv threebases Ke1+vord: Description: BK Adv on balk +E Adv on error 1..9 PositionsI thru 9 (p thru rf) CS Caughtstealing +E# Adv on error by # P. -
Here Comes the Strikeout
LEVEL 2.0 7573 HERE COMES THE STRIKEOUT BY LEONARD KESSLER In the spring the birds sing. The grass is green. Boys and girls run to play BASEBALL. Bobby plays baseball too. He can run the bases fast. He can slide. He can catch the ball. But he cannot hit the ball. He has never hit the ball. “Twenty times at bat and twenty strikeouts,” said Bobby. “I am in a bad slump.” “Next time try my good-luck bat,” said Willie. “Thank you,” said Bobby. “I hope it will help me get a hit.” “Boo, Bobby,” yelled the other team. “Easy out. Easy out. Here comes the strikeout.” “He can’t hit.” “Give him the fast ball.” Bobby stood at home plate and waited. The first pitch was a fast ball. “Strike one.” The next pitch was slow. Bobby swung hard, but he missed. “Strike two.” “Boo!” Strike him out!” “I will hit it this time,” said Bobby. He stepped out of the batter’s box. He tapped the lucky bat on the ground. He stepped back into the batter’s box. He waited for the pitch. It was fast ball right over the plate. Bobby swung. “STRIKE TRHEE! You are OUT!” The game was over. Bobby’s team had lost the game. “I did it again,” said Bobby. “Twenty –one time at bat. Twenty-one strikeouts. Take back your lucky bat, Willie. It was not lucky for me.” It was not a good day for Bobby. He had missed two fly balls. One dropped out of his glove. -
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. -
Stolen Base Physics David Kagan
Stolen Base Physics David Kagan Citation: Phys. Teach. 51, 269 (2013); doi: 10.1119/1.4801351 View online: http://dx.doi.org/10.1119/1.4801351 View Table of Contents: http://tpt.aapt.org/resource/1/PHTEAH/v51/i5 Published by the American Association of Physics Teachers Additional information on Phys. Teach. Journal Homepage: http://tpt.aapt.org/ Journal Information: http://tpt.aapt.org/about/about_the_journal Top downloads: http://tpt.aapt.org/most_downloaded Information for Authors: http://www.aapt.org/publications/tptauthors.cfm Downloaded 11 Apr 2013 to 128.174.13.178. Redistribution subject to AAPT license or copyright; see http://tpt.aapt.org/authors/copyright_permission Stolen Base Physics David Kagan, California State University, Chico, Chico, CA ew plays in baseball are as consistently close and excit- ing as the stolen base.1 While there are several studies 2-4 v of sprinting, the art of base stealing is much more slope = a Fnuanced. This article describes the motion of the base- stealing runner using a very basic kinematic model. The mod- - vf + el will be compared to some data from a Major League game. Velocity The predictions of the model show consistency with the skills slope = a needed for effective base stealing. The basic kinematic model Let’s just consider a steal of second base as opposed to Time third or home. The goal of the runner is to minimize the time required to get there. The basic kinematic model breaks the Fig. 1. The velocity-time graph for the kinematic model. It is the shape of the curve that describes the kinematic model, so units total distance between the bases (D = 90.0 ft) into four parts. -
Guide to Softball Rules and Basics
Guide to Softball Rules and Basics History Softball was created by George Hancock in Chicago in 1887. The game originated as an indoor variation of baseball and was eventually converted to an outdoor game. The popularity of softball has grown considerably, both at the recreational and competitive levels. In fact, not only is women’s fast pitch softball a popular high school and college sport, it was recognized as an Olympic sport in 1996. Object of the Game To score more runs than the opposing team. The team with the most runs at the end of the game wins. Offense & Defense The primary objective of the offense is to score runs and avoid outs. The primary objective of the defense is to prevent runs and create outs. Offensive strategy A run is scored every time a base runner touches all four bases, in the sequence of 1st, 2nd, 3rd, and home. To score a run, a batter must hit the ball into play and then run to circle the bases, counterclockwise. On offense, each time a player is at-bat, she attempts to get on base via hit or walk. A hit occurs when she hits the ball into the field of play and reaches 1st base before the defense throws the ball to the base, or gets an extra base (2nd, 3rd, or home) before being tagged out. A walk occurs when the pitcher throws four balls. It is rare that a hitter can round all the bases during her own at-bat; therefore, her strategy is often to get “on base” and advance during the next at-bat. -
The Stolen Base Is an Integral Part of the Game of Baseball
THE STOLEN BASE by Lindsay S. Parr A thesis submitted to the Faculty and the Board of Trustees of the Colorado School of Mines in partial fulfillment of the requirements for the degree of Master of Science (Applied Mathematics and Statistics). Golden, Colorado Date Signed: Lindsay S. Parr Signed: Dr. William C. Navidi Thesis Advisor Golden, Colorado Date Signed: Dr. Willy A. Hereman Professor and Head Department of Applied Mathematics and Statistics ii ABSTRACT The stolen base is an integral part of the game of baseball. As it is frequent that a player is in a situation where he could attempt to steal a base, it is important to determine when he should try to steal in order to obtain more wins per season for his team. I used a sample of games during the 2012 and 2013 Major League Baseball seasons to see how often players stole in given scenarios based on number of outs, pickoff attempts, runs until the end of the inning, left or right-handed batter/pitcher, run differential, and inning. New stolen base strategies were created using the percentage of opportunities attempted and the percentage of successful attempts for each scenario in the sample, a formula introduced by Bill James for batter/pitcher match-up, and run expectancy. After writing a program in R to simulate baseball games with the ability to change the stolen base strategy, I compared new strategies to the current strategy used to see if they would increase each Major League Baseball team’s average number of wins per season. I found that when using a strategy where a team steals 80% of the time it increases its run expectancy and 20% of the time that it does not, the average number of wins per season increases for a vast majority of teams over using the current strategy. -
Pitching Safety & Performance Management
PITCHING SAFETY & PERFORMANCE MANAGEMENT o To accurately, and comprehensively, track safety-driven pitching restrictions: • Athletes pitch year-round across multiple teams • No centralized mechanism in THE NEED place for calculating & reporting mandated/recommended rest o To provide baseball organizations with a simple-to-use platform that allows real-time tracking of pitch loads and performance to enhance player safety and improve success THE SOLUTION THE SOLUTION: ChangeUp o Comprehensive player-centric tracking of an athlete’s pitch load across unlimited teams and seasons o Automated reporting and compliance tracking, with Preconfigured support for: o Little League® o USA Baseball/MLB Pitch Smart o National Federation of High School Associations (NFHS) o Powerful analytics focused on safety, durability, effectiveness, and other key performance metrics o Real-time, systematic reconciliation with opposing teams o Detailed historical player profiles enabling: o Current coaches to best deploy their athletes o Prospective coaches to evaluate recruits o Medical professionals to better understand athletes’ performance thresholds and injury trends o Governing organizations to use real data to evaluate existing and new regulations furthering the goal of player safety o Official Pitch Smart certified application THE SOLUTION: Player-Centric Tracking o The only Player-Centric solution in the marketplace o All pitching, regardless of team represented, tracked at the player level o For multi-team athletes, availability is properly reflected across -
Using Pitchf/X to Model the Dependence of Strikeout Rate on the Predictability of Pitch Sequences
Journal of Sports Analytics 3 (2017) 93–101 93 DOI 10.3233/JSA-170103 IOS Press Using PITCHf/x to model the dependence of strikeout rate on the predictability of pitch sequences Glenn Healey∗ and Shiyuan Zhao Electrical Engineering and Computer Science, University of California, Irvine, CA, USA Abstract. We develop a model for pitch sequencing in baseball that is defined by pitch-to-pitch correlation in location, velocity, and movement. The correlations quantify the average similarity of consecutive pitches and provide a measure of the batter’s ability to predict the properties of the upcoming pitch. We examine the characteristics of the model for a set of major league pitchers using PITCHf/x data for nearly three million pitches thrown over seven major league seasons. After partitioning the data according to batter handedness, we show that a pitcher’s correlations for velocity and movement are persistent from year-to-year. We also show that pitch-to-pitch correlations are significant in a model for pitcher strikeout rate and that a higher correlation, other factors being equal, is predictive of fewer strikeouts. This finding is consistent with experiments showing that swing errors by experienced batters tend to increase as the differences between the properties of consecutive pitches increase. We provide examples that demonstrate the role of pitch-to-pitch correlation in the strikeout rate model. Keywords: Baseball, pitch sequencing, strikeout rate, PITCHf/x, correlation 1. Introduction ball which alters its trajectory. Given the difficulty of the hitting task, batters can benefit from being The act of hitting a pitch in major league base- able to predict the characteristics of an upcoming ball places extraordinary demands on the batter’s pitch. -
John Taff, “Double No-Hitters for Pinehot” ©Diamondsinthedusk.Com
John Taff, “Double No-Hitters for Pinehot” ©DiamondsintheDusk.com Only July 15, 1910, Brownsville Brownie rookie pitcher July 15, 1910 John Taff pitches a no-hitter in the first game of a South- John Taff No-Hitter west Texas (D) League doubleheader against the Beeville Brownsville, Texas Orange Growers. Following the 90-minute, 5-0 win against Beeville, Taff gains a measure of national attention as it is the second no-hit, no-run game that talented right-hander has turned in within a three week span, having also no-hit the Corpus Christi Pelicans on June 27. Over his brief six-year professional career, the Austin, Texas, native and son of Bickler public school prinicipal J.J. Taff, is referred to in the press as either “John Taff” or “Bill Taff.” In addition to his multiple first names, Taff acquires sever- al interesting nicknames during his playing days such as John Taff “Possum Bill”, “Pinehot”, “Waco Bill” and “Elongated John.” 1913 Baltimore Orioles A 19-year-old Taff begins his organized baseball career in 1909 with the Browns- ville Brownies, one of South Texas’ top amateur teams. The pitcher/first baseman performs so well for the local nine that when the city joins the six-team Southwest Texas League the following year he is signed to a minor league contract and placed on the team’s opening day roster by manager S.H. Bell. Tabbed by Bell to be the Brownies’ opening day starter on April 21, Taff make his professional debut one to remember, pitching a 10-inning complete game 3-2 win over visiting Corpus Christi.