A Regression Model Using Common Baseball Statistics to Project Offensive and Defensive Efficiency
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India's Take on Sports Analytics
PSYCHOLOGY AND EDUCATION (2020) 57(9): 5817-5827 ISSN: 00333077 India’s Take on Sports Analytics Rohan Mehta1, Dr.Shilpa Parkhi2 Student, Symbiosis Institute of Operations Mangement, Nashik, India Deputy Director, Symbiosis Institute of Operations Mangement, Nashik, India Email Id: [email protected] ABSTRACT Purpose – The aim of this paper is to study what is sports analytics, what are the different roles in this field, which sports are prominently using this, how big data has impacted this field, how this field is shaping up in Indian context. Also, the aim is to study the growth of job opportunities in this field, how B-schools are shaping up in this aspect and what are the interests and expectations of the B-school grads from this sector. Keywords Sports analytics, Sabermetrics, Moneyball, Technologies, Team sports, IOT, Cloud Article Received: 10 August 2020, Revised: 25 October 2020, Accepted: 18 November 2020 Design Approach analysis, he had done on approximately 10000 deliveries. Another writer, for one of the US The paper starts by explaining about the origin of magazines, F.C Lane was of the opinion that the sports analytics, the most naïve form of it, then batting average of the individual doesn’t reflect moves towards explaining the evolution of it over the complete picture of the individual’s the years (from emergence of sabermetrics to the performance. There were other significant efforts most advanced applications), how it has spread made by other statisticians or writers such as across different sports and how the applications of George Lindsey, Allan Roth, Earnshaw Cook till it has increased with the advent of different 1969. -
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 -
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. -
Peter Gammons: the Cleveland Indians, Best Run Team in Professional Sports March 5, 2018 by Peter Gammons 7 Comments PHOENIX—T
Peter Gammons: The Cleveland Indians, best run team in professional sports March 5, 2018 by Peter Gammons 7 Comments PHOENIX—The Cleveland Indians have won 454 games the last five years, 22 more than the runner-up Boston Red Sox. In those years, the Indians spent $414M less in payroll than Boston, which at the start speaks volumes about how well the Indians have been run. Two years ago, they got to the tenth inning of an incredible World Series game 7, in a rain delay. Last October they lost an agonizing 5th game of the ALDS to the Yankees, with Corey Kluber, the best pitcher in the American League hurt. They had a 22 game winning streak that ran until September 15, their +254 run differential was 56 runs better than the next best American League team (Houston), they won 102 games, they led the league in earned run average, their starters were 81-38 and they had four players hit between 29 and 38 homers, including 29 apiece from the left side of their infield, Francisco Lindor and Jose Ramirez. And they even drew 2.05M (22nd in MLB) to the ballpark formerly known as The Jake, the only time in this five year run they drew more than 1.6M or were higher than 28th in the majors. That is the reality they live with. One could argue that in terms of talent and human player development, the growth of young front office talent (6 current general managers and three club presidents), they are presently the best run organization in the sport, especially given their financial restraints. -
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). -
Incorporating the Effects of Designated Hitters in the Pythagorean Expectation
Abstract The Pythagorean Expectation is widely used in the field of sabermetrics to estimate a baseball team’s overall season winning percentage based on the number of runs scored and allowed in its games thus far. Bill James devised the simplest version RS 2 p q of the formula through empirical observation as W inning P ercentage RS 2 RA 2 “ p q `p q where RS and RA are runs scored and allowed, respectively. Statisticians later found 1.83 to be a more accurate exponent, estimating overall season wins within 3-4 games per season. Steven Miller provided a theoretical justification for the Pythagorean Expectation by modeling runs scored and allowed as independent continuous random variables drawn from Weibull distributions. This paper aims to first explain Miller’s methodology using recent data and then build upon Miller’s work by incorporating the e↵ects of designated hitters, specifically on the distribution of runs scored by a team. Past studies have attempted to include other e↵ects on run production such as ballpark factor, game state, and pitching power. The results indicate that incorporating information on designated hitters does not improve the error of the Pythagorean Expectation to better than 3-4 games per season. ii Contents Abstract ii Acknowledgements vi 1 Background 1 1.1 Empirical Derivation ........................... 2 1.2 Weibull Distribution ........................... 2 1.3 Application to Other Sports ....................... 4 2 Miller’s Model 5 2.1 Model Assumptions ............................ 5 2.1.1 Continuity of the Data ...................... 6 2.1.2 Independence of Runs Scored and Allowed ........... 7 2.2 Pythagorean Won-Loss Formula .................... -
The Numbers Game: Baseball's Lifelong Fascination with Statistics (Book Review)
The Numbers Game: Baseball's Lifelong Fascination with Statistics (Book Review) The American Statistician May 1, 2005 | Cochran, James J. The Numbers Game: Baseball's Lifelong Fascination with Statistics. Alan SCHWARZ. New York: Thomas Dunne Books, 2004, xv + 270 pp. $24.95 (H), ISBN: 0‐312‐322222‐4. I am amazed at how frequently I walk into a colleague's office for the first time and spot a copy of The Baseball Encyclopedia or Total Baseball on a bookshelf. How many statisticians developed and nurtured their interest in probability and statistics by playing Strat‐O‐ Matic[R] and/or APBA[R] baseball board games; reading publications like the annual The Bill James Baseball Abstract; or scanning countless box scores and current summary statistics in the Sporting News or in the sports section of the local newspaper, looking for an edge in a rotisserie baseball league? For many of us, baseball is what initially opened our eyes to the power of probability and statistics, and the sport continues to be an integral part of our lives (for evidence of this, attend the sessions or business meeting of the ASA's Statistics in Sports section at the next Joint Statistical Meetings). This is why so many probabilists and statisticians will enjoy reading Alan Schwarz's The Numbers Game: Baseball's Lifelong Fascination with Statistics. In this book, Schwarz effectively chronicles the ongoing association between baseball and statistics by describing the evolution of the sport from the mid‐nineteenth century through the beginning of the twenty‐first century, and looking at several of its most influential characters. -
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 .................................................................................................................................. -
Fans Don't Boo Nobodies: Image Repair Strategies of High-Profile Baseball Players During the Steroid Era
Brigham Young University BYU ScholarsArchive Theses and Dissertations 2011-09-23 Fans Don't Boo Nobodies: Image Repair Strategies of High-Profile Baseball Players During the Steroid Era Kevin R. Nielsen Brigham Young University - Provo Follow this and additional works at: https://scholarsarchive.byu.edu/etd Part of the Communication Commons BYU ScholarsArchive Citation Nielsen, Kevin R., "Fans Don't Boo Nobodies: Image Repair Strategies of High-Profile Baseball Players During the Steroid Era" (2011). Theses and Dissertations. 2876. https://scholarsarchive.byu.edu/etd/2876 This Thesis is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of BYU ScholarsArchive. For more information, please contact [email protected], [email protected]. Fans don't boo nobodies: Image repair strategies of high-profile baseball players during the Steroid Era Kevin Nielsen A thesis submitted to the faculty of Brigham Young University in partial fulfillment of the requirements for the degree of Master of Arts Steve Thomsen, Chair Kenneth Plowman Tom Robinson Department of Communications Brigham Young University December 2011 Copyright © 2011 Kevin Nielsen All Rights Reserved Fans don't boo nobodies: Image repair strategies of high-profile baseball players during the Steroid Era Kevin Nielsen Department of Communications, BYU Master of Arts Baseball's Steroid Era put many different high-profile athletes under pressure to explain steroid allegations that were made against them. This thesis used textual analysis of news reports and media portrayals of the athletes, along with analysis of their image repair strategies to combat those allegations, to determine how successful the athletes were in changing public opinion as evidenced through the media. -
Name of the Game: Do Statistics Confirm the Labels of Professional Baseball Eras?
NAME OF THE GAME: DO STATISTICS CONFIRM THE LABELS OF PROFESSIONAL BASEBALL ERAS? by Mitchell T. Woltring A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Leisure and Sport Management Middle Tennessee State University May 2013 Thesis Committee: Dr. Colby Jubenville Dr. Steven Estes ACKNOWLEDGEMENTS I would not be where I am if not for support I have received from many important people. First and foremost, I would like thank my wife, Sarah Woltring, for believing in me and supporting me in an incalculable manner. I would like to thank my parents, Tom and Julie Woltring, for always supporting and encouraging me to make myself a better person. I would be remiss to not personally thank Dr. Colby Jubenville and the entire Department at Middle Tennessee State University. Without Dr. Jubenville convincing me that MTSU was the place where I needed to come in order to thrive, I would not be in the position I am now. Furthermore, thank you to Dr. Elroy Sullivan for helping me run and understand the statistical analyses. Without your help I would not have been able to undertake the study at hand. Last, but certainly not least, thank you to all my family and friends, which are far too many to name. You have all helped shape me into the person I am and have played an integral role in my life. ii ABSTRACT A game defined and measured by hitting and pitching performances, baseball exists as the most statistical of all sports (Albert, 2003, p. -
"What Raw Statistics Have the Greatest Effect on Wrc+ in Major League Baseball in 2017?" Gavin D
1 "What raw statistics have the greatest effect on wRC+ in Major League Baseball in 2017?" Gavin D. Sanford University of Minnesota Duluth Honors Capstone Project 2 Abstract Major League Baseball has different statistics for hitters, fielders, and pitchers. The game has followed the same rules for over a century and this has allowed for statistical comparison. As technology grows, so does the game of baseball as there is more areas of the game that people can monitor and track including pitch speed, spin rates, launch angle, exit velocity and directional break. The website QOPBaseball.com is a newer website that attempts to correctly track every pitches horizontal and vertical break and grade it based on these factors (Wilson, 2016). Fangraphs has statistics on the direction players hit the ball and what percentage of the time. The game of baseball is all about quantifying players and being able give a value to their contributions. Sabermetrics have given us the ability to do this in far more depth. Weighted Runs Created Plus (wRC+) is an offensive stat which is attempted to quantify a player’s total offensive value (wRC and wRC+, Fangraphs). It is Era and park adjusted, meaning that the park and year can be compared without altering the statistic further. In this paper, we look at what 2018 statistics have the greatest effect on an individual player’s wRC+. Keywords: Sabermetrics, Econometrics, Spin Rates, Baseball, Introduction Major League Baseball has been around for over a century has given awards out for almost 100 years. The way that these awards are given out is based on statistics accumulated over the season. -
Machine Learning Applications in Baseball: a Systematic Literature Review
This is an Accepted Manuscript of an article published by Taylor & Francis in Applied Artificial Intelligence on February 26 2018, available online: https://doi.org/10.1080/08839514.2018.1442991 Machine Learning Applications in Baseball: A Systematic Literature Review Kaan Koseler ([email protected]) and Matthew Stephan* ([email protected]) Miami University Department of Computer Science and Software Engineering 205 Benton Hall 510 E. High St. Oxford, OH 45056 Abstract Statistical analysis of baseball has long been popular, albeit only in limited capacity until relatively recently. In particular, analysts can now apply machine learning algorithms to large baseball data sets to derive meaningful insights into player and team performance. In the interest of stimulating new research and serving as a go-to resource for academic and industrial analysts, we perform a systematic literature review of machine learning applications in baseball analytics. The approaches employed in literature fall mainly under three problem class umbrellas: Regression, Binary Classification, and Multiclass Classification. We categorize these approaches, provide our insights on possible future ap- plications, and conclude with a summary our findings. We find two algorithms dominate the literature: 1) Support Vector Machines for classification problems and 2) k-Nearest Neighbors for both classification and Regression problems. We postulate that recent pro- liferation of neural networks in general machine learning research will soon carry over into baseball analytics. keywords: baseball, machine learning, systematic literature review, classification, regres- sion 1 Introduction Baseball analytics has experienced tremendous growth in the past two decades. Often referred to as \sabermetrics", a term popularized by Bill James, it has become a critical part of professional baseball leagues worldwide (Costa, Huber, and Saccoman 2007; James 1987).