Sabermetrics Over Time: Persuasion and Symbolic Convergence Across a Diffusion of Innovations
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Online Fantasy Football Draft Spreadsheet
Online Fantasy Football Draft Spreadsheet idolizesStupendous her zoogeography and reply-paid crushingly, Rutledge elucidating she canalizes her newspeakit pliably. Wylie deprecated is red-figure: while Deaneshe inlay retrieving glowingly some and variole sharks unusefully. her unguis. Harrold Likelihood a fantasy football draft spreadsheets now an online score prediction path to beat the service workers are property of stuff longer able to. How do you keep six of fantasy football draft? Instead I'm here to point head toward a handful are free online tools that can puff you land for publish draft - and manage her team throughout. Own fantasy draft board using spreadsheet software like Google Sheets. Jazz in order the dynamics of favoring bass before the best tools and virus free tools based on the number of pulling down a member? Fantasy Draft Day Kit Download Rankings Cheat Sheets. 2020 Fantasy Football Cheat Sheet Download Free Lineups. Identify were still not only later rounds at fantasy footballers to spreadsheets and other useful jupyter notebook extensions for their rankings and weaknesses as online on top. Arsenal of tools to help you conclude before try and hamper your fantasy draft. As a cattle station in mind. A Fantasy Football Draft Optimizer Powered by Opalytics. This spreadsheet program designed to spreadsheets is also important to view the online drafts are drafting is also avoid exceeding budgets and body contacts that. FREE Online Fantasy Draft Board for american draft parties or online drafts Project the board require a TV and draft following your rugged tablet or computer. It in online quickly reference as draft spreadsheets is one year? He is fantasy football squares pool spreadsheet? Fantasy rank generator. -
LNCS 7215, Pp
ACoreCalculusforProvenance Umut A. Acar1,AmalAhmed2,JamesCheney3,andRolyPerera1 1 Max Planck Institute for Software Systems umut,rolyp @mpi-sws.org { 2 Indiana} University [email protected] 3 University of Edinburgh [email protected] Abstract. Provenance is an increasing concern due to the revolution in sharing and processing scientific data on the Web and in other computer systems. It is proposed that many computer systems will need to become provenance-aware in order to provide satisfactory accountability, reproducibility,andtrustforscien- tific or other high-value data. To date, there is not a consensus concerning ap- propriate formal models or security properties for provenance. In previous work, we introduced a formal framework for provenance security and proposed formal definitions of properties called disclosure and obfuscation. This paper develops a core calculus for provenance in programming languages. Whereas previous models of provenance have focused on special-purpose languages such as workflows and database queries, we consider a higher-order, functional language with sums, products, and recursive types and functions. We explore the ramifications of using traces based on operational derivations for the purpose of comparing other forms of provenance. We design a rich class of prove- nance views over traces. Finally, we prove relationships among provenance views and develop some solutions to the disclosure and obfuscation problems. 1Introduction Provenance, or meta-information about the origin, history, or derivation of an object, is now recognized as a central challenge in establishing trust and providing security in computer systems, particularly on the Web. Essentially, provenance management in- volves instrumenting a system with detailed monitoring or logging of auditable records that help explain how results depend on inputs or other (sometimes untrustworthy) sources. -
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. -
Picking Winners Using Integer Programming
Picking Winners Using Integer Programming David Scott Hunter Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139, [email protected] Juan Pablo Vielma Department of Operations Research, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02139, [email protected] Tauhid Zaman Department of Operations Management, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02139, [email protected] We consider the problem of selecting a portfolio of entries of fixed cardinality for a winner take all contest such that the probability of at least one entry winning is maximized. This framework is very general and can be used to model a variety of problems, such as movie studios selecting movies to produce, drug companies choosing drugs to develop, or venture capital firms picking start-up companies in which to invest. We model this as a combinatorial optimization problem with a submodular objective function, which is the probability of winning. We then show that the objective function can be approximated using only pairwise marginal probabilities of the entries winning when there is a certain structure on their joint distribution. We consider a model where the entries are jointly Gaussian random variables and present a closed form approximation to the objective function. We then consider a model where the entries are given by sums of constrained resources and present a greedy integer programming formulation to construct the entries. Our formulation uses three principles to construct entries: maximize the expected score of an entry, lower bound its variance, and upper bound its correlation with previously constructed entries. To demonstrate the effectiveness of our greedy integer programming formulation, we apply it to daily fantasy sports contests that have top heavy payoff structures (i.e. -
Not Even Past NOT EVEN PAST
The past is never dead. It's not even past NOT EVEN PAST Search the site ... Film Review – Baseball by the Numbers: Moneyball (2011) Like 0 Tweet by Tolga Ozyurtcu Although its subject is one of the more interesting moments in recent sports history, Moneyball offers surprisingly little of that history. The lm opens with the disappointing end of the Oakland Athletics’ 2001 season, followed by General Manager Billy Beane’s (Brad Pitt) novel offseason rebuilding efforts and the team’s unexpected success in the 2002 season. The novelty at hand was Beane’s decision to abandon most of the traditional measures by which baseball scouts evaluated talent, replacing an old-guard of “lifer” baseball scouts and their obsession with traditional statistics, with economics-inspired, statistical models designed to nd hidden value in baseball’s talent market. Beane’s shift to the new approach was driven by the inability of his small media market franchise to offer salaries to ballplayers that could compete with the big money, large market teams, like the New York Yankees. While all of this is communicated reasonably well in Bennett Miller’s lm, the casual viewer may be misled to think that Beane’s number-crunching approach was a twenty- rst century innovation. What the lm does not adequately address is the history of Sabermetrics, the name given to the general approach to baseball statistics that Beane and Paul DePodesta (or Peter Brand, as he was rechristened in the lm, played by Jonah Hill) employed in revolutionizing the Oakland team and all of baseball. Sabermetrics are the brainchild of Bill James, a baseball historian, writer, and statistician who has been publishing on the subject since 1977. -
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 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. -
Does Sabermetrics Have a Place in Amateur Baseball?
BaseballGB Full Article Does sabermetrics have a place in amateur baseball? Joe Gray 7 March 2009 he term “sabermetrics” is one of the many The term sabermetrics combines SABR (the acronym creations of Bill James, the great baseball for the Society for American Baseball Research) and theoretician (for details of the term’s metrics (numerical measurements). The extra “e” T was presumably added to avoid the difficult-to- derivation and usage see Box 1). Several tight pronounce sequence of letters “brm”. An alternative definitions exist for the term, but I feel that rather exists without the “e”, but in this the first four than presenting one or more of these it is more letters are capitalized to show that it is a word to valuable to offer an alternative, looser definition: which normal rules of pronunciation do not apply. sabermetrics is a tree of knowledge with its roots in It is a singular noun despite the “s” at the end (that is, you would say “sabermetrics is growing in the philosophy of answering baseball questions in as popularity” rather than “sabermetrics are growing in accurate, objective, and meaningful a fashion as popularity”). possible. The philosophy is an alternative to The adjective sabermetric has been back-derived from the term and is exemplified by “a sabermetric accepting traditional thinking without question. tool”, or its plural “sabermetric tools”. The adverb sabermetrically, built on that back- Branches of the sabermetric tree derived adjective, is illustrated in the phrase “she The metaphor of sabermetrics as a tree extends to approached the problem sabermetrically”. describing the various broad concepts and themes of The noun sabermetrician can be used to describe any practitioner of sabermetrics, although to some research as branches. -
Girls' Elite 2 0 2 0 - 2 1 S E a S O N by the Numbers
GIRLS' ELITE 2 0 2 0 - 2 1 S E A S O N BY THE NUMBERS COMPARING NORMAL SEASON TO 2020-21 NORMAL 2020-21 SEASON SEASON SEASON LENGTH SEASON LENGTH 6.5 Months; Dec - Jun 6.5 Months, Split Season The 2020-21 Season will be split into two segments running from mid-September through mid-February, taking a break for the IHSA season, and then returning May through mid- June. The season length is virtually the exact same amount of time as previous years. TRAINING PROGRAM TRAINING PROGRAM 25 Weeks; 157 Hours 25 Weeks; 156 Hours The training hours for the 2020-21 season are nearly exact to last season's plan. The training hours do not include 16 additional in-house scrimmage hours on the weekends Sep-Dec. Courtney DeBolt-Slinko returns as our Technical Director. 4 new courts this season. STRENGTH PROGRAM STRENGTH PROGRAM 3 Days/Week; 72 Hours 3 Days/Week; 76 Hours Similar to the Training Time, the 2020-21 schedule will actually allow for a 4 additional hours at Oak Strength in our Sparta Science Strength & Conditioning program. These hours are in addition to the volleyball-specific Training Time. Oak Strength is expanding by 8,800 sq. ft. RECRUITING SUPPORT RECRUITING SUPPORT Full Season Enhanced Full Season In response to the recruiting challenges created by the pandemic, we are ADDING livestreaming/recording of scrimmages and scheduled in-person visits from Lauren, Mikaela or Peter. This is in addition to our normal support services throughout the season. TOURNAMENT DATES TOURNAMENT DATES 24-28 Dates; 10-12 Events TBD Dates; TBD Events We are preparing for 15 Dates/6 Events Dec-Feb. -
"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). -
Making It Pay to Be a Fan: the Political Economy of Digital Sports Fandom and the Sports Media Industry
City University of New York (CUNY) CUNY Academic Works All Dissertations, Theses, and Capstone Projects Dissertations, Theses, and Capstone Projects 9-2018 Making It Pay to be a Fan: The Political Economy of Digital Sports Fandom and the Sports Media Industry Andrew McKinney The Graduate Center, City University of New York How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/gc_etds/2800 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] MAKING IT PAY TO BE A FAN: THE POLITICAL ECONOMY OF DIGITAL SPORTS FANDOM AND THE SPORTS MEDIA INDUSTRY by Andrew G McKinney A dissertation submitted to the Graduate Faculty in Sociology in partial fulfillment of the requirements for the degree of Doctor of Philosophy, The City University of New York 2018 ©2018 ANDREW G MCKINNEY All Rights Reserved ii Making it Pay to be a Fan: The Political Economy of Digital Sport Fandom and the Sports Media Industry by Andrew G McKinney This manuscript has been read and accepted for the Graduate Faculty in Sociology in satisfaction of the dissertation requirement for the degree of Doctor of Philosophy. Date William Kornblum Chair of Examining Committee Date Lynn Chancer Executive Officer Supervisory Committee: William Kornblum Stanley Aronowitz Lynn Chancer THE CITY UNIVERSITY OF NEW YORK I iii ABSTRACT Making it Pay to be a Fan: The Political Economy of Digital Sport Fandom and the Sports Media Industry by Andrew G McKinney Advisor: William Kornblum This dissertation is a series of case studies and sociological examinations of the role that the sports media industry and mediated sport fandom plays in the political economy of the Internet.