2015 Sportradar MLB Statistics Summary
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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 -
San Francisco Giants
SAN FRANCISCO GIANTS 2014 GAME NOTES 24 Willie Mays Plaza •San Francisco, CA 94107 •Phone: 415-972-2000 sfgiants.com •sfgigantes.com •sfgiantspressbox.com •@SFGiants •@los_gigantes• @SFG_Stats SAN FRANCISCO GIANTS (67-59) at WASHINGTON NATIONALS (73-53) RHP Tim Hudson (8-9, 3.03) vs. RHP Doug Fister (12-3, 2.20) Game #128/Road Game 64 • Friday, August 22, 2014 • Nationals Park • 4:05 p.m. (PT) • CSN Bay Area UPCOMING PROBABLE STARTING PITCHERS & BROADCAST SCHEDULE: • Saturday, August 23 at Washington (1:05): RHP Tim Lincecum (10-8, 4.48) vs. RHP Jordan Zimmermann (8-5, 2.97) - CSN Bay Area • Sunday, August 24 at Washington (10:35am): RHP Ryan Vogelsong (7-9, 3.73) vs. RHP Stephen Strasburg (10-10, 3.41) - CSN Bay Area Please note all games broadcast on KNBR 680 AM (English radio). All home games and road games in LA, SD and OAK broadcast on 860 AM ESPN Deportes (Spanish radio). THE GIANTS GIANTS ON THIS ROAD TRIP TO GIANTS BY THE NUMBERS • Have won five of their last seven games and enter tonight's CHICAGO AND WASHINGTON game 3.5 games behind the first place Dodgers in the Games . 3 NOTE 2014 National League West . Record . 2-1 Average . .325 (37114) Current Standing in NL West: . 2nd Avg . w/RISP . .237 (9x38) Series Record: . 22-15-3 IN THE STANDINGS Runs Per Game . 4 .7 (14) Series Record, home: . 11-8-3 • San Francisco currently owns the fifth-highest winning Home Runs . 3 Series Record, road: . 11-7-0 percentage in the NL behind Washington (73-53, .579), Stolen Bases . -
Sports Analytics: Maximizing Precision in Predicting Mlb Base Hits
SPORTS ANALYTICS: MAXIMIZING PRECISION IN PREDICTING MLB BASE HITS Pedro Miguel Gonçalves André Alceo Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa SPORTS ANALYTICS: MAXIMIZING PRECISION IN PREDICTING MLB BASE HITS Pedro Miguel Gonçalves André Alceo Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, Specialization Knowledge Management and Business Intelligence Advisor: Roberto André Pereira Henriques February 2019 ACKNOWLEDGEMENTS The completion of this master thesis was the culmination of my work accompanied by the endless support of the people that helped me during this journey. This paper would feel emptier without somewhat expressing my gratitude towards those who were always by my side. Firstly, I would like to thank my supervisor Roberto Henriques for helping me find my passion for data mining and for giving me the guidelines necessary to finish this paper. I would not have chosen this area if not for you teaching data mining during my master’s program. To my parents João and Licínia for always supporting my ideas, being patient and for along my life giving the means to be where I am right now. My sister Rita, her husband João and my nephew Afonso, whose presence was always felt and helped providing good energies throughout this campaign. To my training partner Rodrigo who was always a great company, sometimes an inspiration but in the end always a great friend. My colleagues Bento, Marta and Rita who made my master’s journey unique and very enjoyable. -
League Leaders Batting Top 10 Batter Team Avg G Ab R H
LEAGUE LEADERS BATTING TOP 10 BATTER TEAM AVG G AB R H HR RBI BB SO *Schwartz, JT LAC .378 52 196 40 74 1 35 25 23 *Bottcher, Matt EC .367 57 229 44 84 1 40 32 30 Berkey, Evan ROC .358 57 229 39 82 2 23 24 26 *Thompson, Jake LAK .355 50 186 34 66 2 41 36 15 Michaels, Logan MAD .354 48 192 29 68 0 27 16 23 Frank, Adam WIS .348 56 224 49 78 10 42 17 27 Bigbie, Justice MAD .346 68 283 53 98 12 70 28 58 *Myers, Daryl LAK .341 48 167 36 57 1 28 36 44 *Blazevic, Austin MAD .339 49 186 19 63 4 36 17 26 Dunham, Jake WRP .332 56 190 27 63 8 52 26 25 LEAGUE LEADERS PITCHING TOPTOP 10 PITCHER TEAM G W-L ERA IP H Hoffman, Andrew TVC 12 8-0 1.08 58.1 32 Portela, Polo WIL 10 8-0 1.23 58.1 43 *Koenig, Trevor STC 10 7-1 1.35 60.0 35 *Stroh, Gareth WRP 10 7-1 1.61 61.2 36 Hemmerling, Nathan WRP 12 7-2 2.28 59.1 47 Jones, Kyle TVC 13 6-2 2.73 62.2 68 *Osterberg, Matt WRP 11 6-2 3.10 58.0 61 Husson, Aaron KMO 19 7-1 3.16 68.1 51 *Newberg, Brett MAN 12 5-2 3.22 67.0 67 Traxel, Blaine KMO 18 1-3 3.47 57.0 61 HOME RUNS WINS BATTER TEAM HR PITCHER TEAM W *Holgate, Ryan LAC 13 Portela, Polo WIL 8 Bigbie, Justice MAD 12 Hoffman, Andrew TVC 8 *Elvir, Josh MAN 11 Hemmerling, Nathan WRP 7 Reeves, TJ WIS 10 *Stroh, Gareth WRP 7 Frank, Adam WIS 10 *Koenig, Trevor STC 7 RBI SAVES BATTER TEAM RBI PITCHER TEAM SV Bigbie, Justice MAD 70 Freilich, Jared LAC 13 Morrow, Andrew TVC 58 Denlinger, Theo MAD 12 *Holgate, Ryan LAC 53 Taylor, Keon ROC 12 Dunham, Jake WRP 52 Bonner, Brayden WRP 11 Delano, Garett STC 51 Haass, Joey KMO 10 STOLEN BASES STRIKEOUTS BATTER TEAM SB PITCHER -
Sabermetrics: the Past, the Present, and the Future
Sabermetrics: The Past, the Present, and the Future Jim Albert February 12, 2010 Abstract This article provides an overview of sabermetrics, the science of learn- ing about baseball through objective evidence. Statistics and baseball have always had a strong kinship, as many famous players are known by their famous statistical accomplishments such as Joe Dimaggio’s 56-game hitting streak and Ted Williams’ .406 batting average in the 1941 baseball season. We give an overview of how one measures performance in batting, pitching, and fielding. In baseball, the traditional measures are batting av- erage, slugging percentage, and on-base percentage, but modern measures such as OPS (on-base percentage plus slugging percentage) are better in predicting the number of runs a team will score in a game. Pitching is a harder aspect of performance to measure, since traditional measures such as winning percentage and earned run average are confounded by the abilities of the pitcher teammates. Modern measures of pitching such as DIPS (defense independent pitching statistics) are helpful in isolating the contributions of a pitcher that do not involve his teammates. It is also challenging to measure the quality of a player’s fielding ability, since the standard measure of fielding, the fielding percentage, is not helpful in understanding the range of a player in moving towards a batted ball. New measures of fielding have been developed that are useful in measuring a player’s fielding range. Major League Baseball is measuring the game in new ways, and sabermetrics is using this new data to find better mea- sures of player performance. -
The Psychology of Baseball: How the Mental Game Impacts the Physical Game
University of Connecticut OpenCommons@UConn Honors Scholar Theses Honors Scholar Program Spring 4-26-2018 The syP chology of Baseball: How the Mental Game Impacts the Physical Game Kiera Dalmass [email protected] Follow this and additional works at: https://opencommons.uconn.edu/srhonors_theses Part of the Applied Statistics Commons, Comparative Psychology Commons, and the Design of Experiments and Sample Surveys Commons Recommended Citation Dalmass, Kiera, "The sP ychology of Baseball: How the Mental Game Impacts the Physical Game" (2018). Honors Scholar Theses. 578. https://opencommons.uconn.edu/srhonors_theses/578 Student Researcher: Kiera Dalmass; PI: Haim Bar, Ph.D. Protocol Number: H17-238 The Psychology of Baseball: How the Mental Game Impacts the Physical Game Kiera Dalmass PI: Haim Bar, PhD. University of Connecticut Department of Statistics 1 Student Researcher: Kiera Dalmass; PI: Haim Bar, Ph.D. Protocol Number: H17-238 TABLE OF CONTENTS ACKNOWLEDGEMENTS 3 ABSTRACT 4 LITERATURE REVIEW 5 RESEARCH QUESTIONS AND HYPOTHESIS 14 METHODS 16 PARTICIPANTS 17 MATERIALS 18 PROCEDURE 21 RESULTS 24 STATISTICAL RESULTS SURVEY RESULTS DISCUSSION 58 LIMITATIONS OF STUDY 59 FINDINGS AND FUTURE OF THE STUDY 60 REFERENCES 64 APPENDIX A: DEFINITIONS AND FORMULAS FOR VARIABLES 66 APPENDIX B: SURVEYS 68 APPENDIX C: INSTITUTIONAL REVIEW BOARD FORMS 75 2 Student Researcher: Kiera Dalmass; PI: Haim Bar, Ph.D. Protocol Number: H17-238 ACKNOWLEDGEMENTS I would like to thank my family for always being my support system and helping me achieve my dreams. I would like to give a special thank you to Professor Haim Bar, my research mentor. Without him, none of this project would have been possible. -
2017 Altoona Curve Final Notes
EASTERN LEAGUE CHAMPIONS DIVISION CHAMPIONS PLAYOFF APPEARANCES PLAYERS TO MLB 2010, 2017 2004, 2010, 2017 2003, 2004, 2005, 2006, 144 2010, 2015, 2016, 2017 THE 19TH SEASON OF CURVE BASEBALL: Altoona finished the season eight games over .500 and won the Western 2017 E.L. WESTERN STANDINGS Division by two games over the Baysox. The title marked the franchise's third regular-season division championship in Team W-L PCT GB franchise history, winning the South Division in 2004 and the Western Division in 2010. This year, the Curve led the Altoona 74-66 .529 -- West for 96 of 140 games and took over sole possession of the top spot for good on August 22. The Curve won 10 of Bowie 72-68 .514 2.0 their final 16 games, including a regular-season-best five-game winning streak from August 20-24. Altoona clinched the Western Division regular-season title with a walk-off win over Harrisburg on September 4 and a loss by Bowie at Akron 69-71 .493 5.0 Richmond that afternoon. The Curve advanced to the ELCS with a three-game sweep over the Bowie Baysox in the Erie 65-75 .464 9.0 Western Division Series. In the Championship Series, the Curve took the first two games in Trenton before beating Richmond 63-77 .450 11.0 the Thunder, 4-2, at PNG Field on September 14 to lock up their second league title in franchise history. Including the Harrisburg 60-80 .429 14.0 regular season and the playoffs, the Curve won their final eight games, their best winning streak of the year. -
Understanding Advanced Baseball Stats: Hitting
Understanding Advanced Baseball Stats: Hitting “Baseball is like church. Many attend few understand.” ~ Leo Durocher Durocher, a 17-year major league vet and Hall of Fame manager, sums up the game of baseball quite brilliantly in the above quote, and it’s pretty ridiculous how much fans really don’t understand about the game of baseball that they watch so much. This holds especially true when you start talking about baseball stats. Sure, most people can tell you what a home run is and that batting average is important, but once you get past the basic stats, the rest is really uncharted territory for most fans. But fear not! This is your crash course in advanced baseball stats, explained in plain English, so that even the most rudimentary of fans can become knowledgeable in the mysterious world of baseball analytics, or sabermetrics as it is called in the industry. Because there are so many different stats that can be covered, I’m just going to touch on the hitting stats in this article and we can save the pitching ones for another piece. So without further ado – baseball stats! The Slash Line The baseball “slash line” typically looks like three different numbers rounded to the thousandth decimal place that are separated by forward slashes (hence the name). We’ll use Mike Trout‘s 2014 slash line as an example; this is what a typical slash line looks like: .287/.377/.561 The first of those numbers represents batting average. While most fans know about this stat, I’ll touch on it briefly just to make sure that I have all of my bases covered (baseball pun intended). -
The Rules of Scoring
THE RULES OF SCORING 2011 OFFICIAL BASEBALL RULES WITH CHANGES FROM LITTLE LEAGUE BASEBALL’S “WHAT’S THE SCORE” PUBLICATION INTRODUCTION These “Rules of Scoring” are for the use of those managers and coaches who want to score a Juvenile or Minor League game or wish to know how to correctly score a play or a time at bat during a Juvenile or Minor League game. These “Rules of Scoring” address the recording of individual and team actions, runs batted in, base hits and determining their value, stolen bases and caught stealing, sacrifices, put outs and assists, when to charge or not charge a fielder with an error, wild pitches and passed balls, bases on balls and strikeouts, earned runs, and the winning and losing pitcher. Unlike the Official Baseball Rules used by professional baseball and many amateur leagues, the Little League Playing Rules do not address The Rules of Scoring. However, the Little League Rules of Scoring are similar to the scoring rules used in professional baseball found in Rule 10 of the Official Baseball Rules. Consequently, Rule 10 of the Official Baseball Rules is used as the basis for these Rules of Scoring. However, there are differences (e.g., when to charge or not charge a fielder with an error, runs batted in, winning and losing pitcher). These differences are based on Little League Baseball’s “What’s the Score” booklet. Those additional rules and those modified rules from the “What’s the Score” booklet are in italics. The “What’s the Score” booklet assigns the Official Scorer certain duties under Little League Regulation VI concerning pitching limits which have not implemented by the IAB (see Juvenile League Rule 12.08.08). -
FROM BULLDOGS to SUN DEVILS the EARLY YEARS ASU BASEBALL 1907-1958 Year ...Record
THE TRADITION CONTINUES ASUBASEBALL 2005 2005 SUN DEVIL BASEBALL 2 There comes a time in a little boy’s life when baseball is introduced to him. Thus begins the long journey for those meant to play the game at a higher level, for those who love the game so much they strive to be a part of its history. Sun Devil Baseball! NCAA NATIONAL CHAMPIONS: 1965, 1967, 1969, 1977, 1981 2005 SUN DEVIL BASEBALL 3 ASU AND THE GOLDEN SPIKES AWARD > For the past 26 years, USA Baseball has honored the top amateur baseball player in the country with the Golden Spikes Award. (See winners box.) The award is presented each year to the player who exhibits exceptional athletic ability and exemplary sportsmanship. Past winners of this prestigious award include current Major League Baseball stars J. D. Drew, Pat Burrell, Jason Varitek, Jason Jennings and Mark Prior. > Arizona State’s Bob Horner won the inaugural award in 1978 after hitting .412 with 20 doubles and 25 RBI. Oddibe McDowell (1984) and Mike Kelly (1991) also won the award. > Dustin Pedroia was named one of five finalists for the 2004 Golden Spikes Award. He became the seventh all-time final- ist from ASU, including Horner (1978), McDowell (1984), Kelly (1990), Kelly (1991), Paul Lo Duca (1993) and Jacob Cruz (1994). ODDIBE MCDOWELL > With three Golden Spikes winners, ASU ranks tied for first with Florida State and Cal State Fullerton as the schools with the most players to have earned college baseball’s top honor. BOB HORNER GOLDEN SPIKES AWARD WINNERS 2004 Jered Weaver Long Beach State 2003 Rickie Weeks Southern 2002 Khalil Greene Clemson 2001 Mark Prior Southern California 2000 Kip Bouknight South Carolina 1999 Jason Jennings Baylor 1998 Pat Burrell Miami 1997 J.D. -
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. -
MLB Statistics Feeds
Updated 07.17.17 MLB Statistics Feeds 2017 Season 1 SPORTRADAR MLB STATISTICS FEEDS Updated 07.17.17 Table of Contents Overview ....................................................................................................................... Error! Bookmark not defined. MLB Statistics Feeds.................................................................................................................................................. 3 Coverage Levels........................................................................................................................................................... 4 League Information ..................................................................................................................................................... 5 Team & Staff Information .......................................................................................................................................... 7 Player Information ....................................................................................................................................................... 9 Venue Information .................................................................................................................................................... 13 Injuries & Transactions Information ................................................................................................................... 16 Game & Series Information ..................................................................................................................................