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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. -
2021 Topps Tier One Checklist .Xls
AUTOGRAPH TIER ONE AUTOGRAPHS T1A-ABE Adrian Beltre Texas Rangers® T1A-BH Bryce Harper Philadelphia Phillies® T1A-CJ Chipper Jones Atlanta Braves™ T1A-CY Christian Yelich Milwaukee Brewers™ T1A-DJ Derek Jeter New York Yankees® T1A-DS Darryl Strawberry New York Mets® T1A-EJ Eloy Jimenez Chicago White Sox® T1A-EM Edgar Martinez Seattle Mariners™ T1A-FTA Frank Thomas Chicago White Sox® T1A-GM Greg Maddux Chicago Cubs® T1A-I Ichiro Seattle Mariners™ T1A-IR Ivan Rodriguez Florida Marlins™ T1A-JB Johnny Bench Cincinnati Reds® T1A-JMA J.D. Martinez Boston Red Sox® T1A-JS Juan Soto Washington Nationals® T1A-LW Larry Walker Colorado Rockies™ T1A-MC Miguel Cabrera Detroit Tigers® T1A-MR Mariano Rivera New York Yankees® T1A-MS Mike Schmidt Philadelphia Phillies® T1A-MT Mike Trout Angels® T1A-PG Paul Goldschmidt St. Louis Cardinals® T1A-PMO Paul Molitor Minnesota Twins® T1A-RJ Randy Johnson Arizona Diamondbacks® T1A-RJA Reggie Jackson Oakland Athletics™ T1A-SB Shane Bieber Cleveland Indians® T1A-TG Tom Glavine Atlanta Braves™ T1A-WC Will Clark San Francisco Giants® BREAK OUT AUTOGRAPHS BOA-AB Alec Bohm Philadelphia Phillies® Rookie BOA-ABO Alec Bohm Philadelphia Phillies® Rookie BOA-AG Andres Gimenez New York Mets® Rookie BOA-AGI Andres Gimenez New York Mets® Rookie BOA-AK Alex Kirilloff Minnesota Twins® Rookie BOA-AKI Alex Kirilloff Minnesota Twins® Rookie BOA-AN Austin Nola San Diego Padres™ BOA-ANO Austin Nola San Diego Padres™ BOA-AT Anderson Tejeda Texas Rangers® Rookie BOA-ATE Anderson Tejeda Texas Rangers® Rookie BOA-AV Alex Verdugo Boston -
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. -
GOAT01 Challenge Associated Players After Round 25
GOAT01 Challenge Associated Players after Round 25 Rnd Seq Competitor Year PlayerName MLB Roster 14 120 Andrea 2004 Bobby Abreu PHI O4 14 124 Nick 2019 Ronald Acuna ATL O2 24 213 Gary 2000 Antonio Alfonseca FLO P3 4 31 Dave 1999 Roberto Alomar CLE 2B 10 86 Richard 2016 Jose Altuve HOU 2B 16 142 Nick 1996 Brady Anderson BAL O4 15 128 Steve 2016 Nolan Arenado COL 3B 6 52 Nick 2015 Jake Arrieta CHC S3 23 203 Richard 2001 Rich Aurilia SF MI 2 18 Rob 2000 Jeff Bagwell HOU 1B 17 153 Ernie 2018 Trevor Bauer CLE S3 22 192 Andrea 1993 Rod Beck SF P2 5 45 Ernie 1995 Albert Belle CLE O2 21 188 Larry 1987 George Bell TOR U2 19 169 Andrea 2010 Heath Bell SD R2 17 149 Richard 2019 Cody Bellinger LAD O4 23 200 Steve 1999 Jay Bell ARI 2B 6 48 Andrea 2004 Adrian Beltre LAD 3B 13 116 Larry 2004 Carlos Beltran 2Tms O5 22 196 Nick 2004 Armando Benitez FLO R2 22 197 Steve 2006 Lance Berkman HOU U2 13 109 Rob 2018 Mookie Betts BOS U1 12 104 Richard 1996 Dante Bichette COL O1 7 58 Gary 1998 Craig Biggio HOU 2B 11 99 Ernie 2017 Charlie Blackmon COL O3 15 127 Rob 1987 Wade Boggs BOS 3B 1 1 Rob 2001 Barry Bonds SF O1 7 57 Nick 2001 Bret Boone SEA MI 10 84 Andrea 2012 Ryan Braun MIL O2 16 143 Steve 2016 Zack Britton BAL P2 16 139 Dave 1998 Kevin Brown SD P1 21 187 Andrea 2007 Jonathan Broxton LAD P1 20 180 Rob 2015 Madison Bumgarner SF S2 2 15 Gary 1996 Ellis Burks COL O2 6 50 Richard 2012 Miguel Cabrera DET 3B 10 88 Nick 1996 Ken Caminiti SD 3B 22 195 Gary 2010 Robinson Cano NYY U2 2 13 Dave 1988 Jose Canseco OAK O2 25 218 Steve 1985 Gary Carter NYM C2 18 158 -
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. -
2010 Topps Baseball Set Checklist
2010 TOPPS BASEBALL SET CHECKLIST 1 Prince Fielder 2 Buster Posey RC 3 Derrek Lee 4 Hanley Ramirez / Pablo Sandoval / Albert Pujols LL 5 Texas Rangers TC 6 Chicago White Sox FH 7 Mickey Mantle 8 Joe Mauer / Ichiro / Derek Jeter LL 9 Tim Lincecum NL CY 10 Clayton Kershaw 11 Orlando Cabrera 12 Doug Davis 13 Melvin Mora 14 Ted Lilly 15 Bobby Abreu 16 Johnny Cueto 17 Dexter Fowler 18 Tim Stauffer 19 Felipe Lopez 20 Tommy Hanson 21 Cristian Guzman 22 Anthony Swarzak 23 Shane Victorino 24 John Maine 25 Adam Jones 26 Zach Duke 27 Lance Berkman / Mike Hampton CC 28 Jonathan Sanchez 29 Aubrey Huff 30 Victor Martinez 31 Jason Grilli 32 Cincinnati Reds TC 33 Adam Moore RC 34 Michael Dunn RC 35 Rick Porcello 36 Tobi Stoner RC 37 Garret Anderson 38 Houston Astros TC 39 Jeff Baker 40 Josh Johnson 41 Los Angeles Dodgers FH 42 Prince Fielder / Ryan Howard / Albert Pujols LL Compliments of BaseballCardBinders.com© 2019 1 43 Marco Scutaro 44 Howie Kendrick 45 David Hernandez 46 Chad Tracy 47 Brad Penny 48 Joey Votto 49 Jorge De La Rosa 50 Zack Greinke 51 Eric Young Jr 52 Billy Butler 53 Craig Counsell 54 John Lackey 55 Manny Ramirez 56 Andy Pettitte 57 CC Sabathia 58 Kyle Blanks 59 Kevin Gregg 60 David Wright 61 Skip Schumaker 62 Kevin Millwood 63 Josh Bard 64 Drew Stubbs RC 65 Nick Swisher 66 Kyle Phillips RC 67 Matt LaPorta 68 Brandon Inge 69 Kansas City Royals TC 70 Cole Hamels 71 Mike Hampton 72 Milwaukee Brewers FH 73 Adam Wainwright / Chris Carpenter / Jorge De La Ro LL 74 Casey Blake 75 Adrian Gonzalez 76 Joe Saunders 77 Kenshin Kawakami 78 Cesar Izturis 79 Francisco Cordero 80 Tim Lincecum 81 Ryan Theroit 82 Jason Marquis 83 Mark Teahen 84 Nate Robertson 85 Ken Griffey, Jr. -
"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. -
Basic Combinatorics
Basic Combinatorics Carl G. Wagner Department of Mathematics The University of Tennessee Knoxville, TN 37996-1300 Contents List of Figures iv List of Tables v 1 The Fibonacci Numbers From a Combinatorial Perspective 1 1.1 A Simple Counting Problem . 1 1.2 A Closed Form Expression for f(n) . 2 1.3 The Method of Generating Functions . 3 1.4 Approximation of f(n) . 4 2 Functions, Sequences, Words, and Distributions 5 2.1 Multisets and sets . 5 2.2 Functions . 6 2.3 Sequences and words . 7 2.4 Distributions . 7 2.5 The cardinality of a set . 8 2.6 The addition and multiplication rules . 9 2.7 Useful counting strategies . 11 2.8 The pigeonhole principle . 13 2.9 Functions with empty domain and/or codomain . 14 3 Subsets with Prescribed Cardinality 17 3.1 The power set of a set . 17 3.2 Binomial coefficients . 17 4 Sequences of Two Sorts of Things with Prescribed Frequency 23 4.1 A special sequence counting problem . 23 4.2 The binomial theorem . 24 4.3 Counting lattice paths in the plane . 26 5 Sequences of Integers with Prescribed Sum 28 5.1 Urn problems with indistinguishable balls . 28 5.2 The family of all compositions of n . 30 5.3 Upper bounds on the terms of sequences with prescribed sum . 31 i CONTENTS 6 Sequences of k Sorts of Things with Prescribed Frequency 33 6.1 Trinomial Coefficients . 33 6.2 The trinomial theorem . 35 6.3 Multinomial coefficients and the multinomial theorem . 37 7 Combinatorics and Probability 39 7.1 The Multinomial Distribution . -
Applied Operational Management Techniques for Sabermetrics
Applied Operational Management Techniques for Sabermetrics An Interactive Qualifying Project Report submitted to the faculty of the WORCESTER POLYTECHNIC INSTITUTE in partial fulfillment of the requirements for the Degree of Bachelor of Science by Rory Fuller ______________________ Kevin Munn ______________________ Ethan Thompson ______________________ May 28, 2005 ______________________ Brigitte Servatius, Advisor Abstract In the growing field of sabermetrics, storage and manipulation of large amounts of statistical data has become a concern. Hence, construction of a cheap and flexible database system would be a boon to the field. This paper aims to briefly introduce sabermetrics, show why it exists, and detail the reasoning behind and creation of such a database. i Acknowledgements We acknowledge first and foremost the great amount of work and inspiration put forth to this project by Pat Malloy. Working alongside us on an attached ISP, Pat’s effort and organization were critical to the success of this project. We also recognize the source of our data, Project Scoresheet from retrosheet.org. The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at 20 Sunset Rd., Newark, DE 19711. We must not forget our advisor, Professor Brigitte Servatius. Several of the ideas and sources employed in this paper came at her suggestion and proved quite valuable to its eventual outcome. ii Table of Contents Title Page Abstract i Acknowledgements ii Table of Contents iii 1. Introduction 1 2. Sabermetrics, Baseball, and Society 3 2.1 Overview of Baseball 3 2.2 Forerunners 4 2.3 What is Sabermetrics? 6 2.3.1 Why Use Sabermetrics? 8 2.3.2 Some Further Financial and Temporal Implications of Baseball 9 3. -
Fracking by the Numbers
Fracking by the Numbers The Damage to Our Water, Land and Climate from a Decade of Dirty Drilling Fracking by the Numbers The Damage to Our Water, Land and Climate from a Decade of Dirty Drilling Written by: Elizabeth Ridlington and Kim Norman Frontier Group Rachel Richardson Environment America Research & Policy Center April 2016 Acknowledgments Environment America Research & Policy Center sincerely thanks Amy Mall, Senior Policy Analyst, Land & Wildlife Program, Natural Resources Defense Council; Courtney Bernhardt, Senior Research Analyst, Environmental Integrity Project; and Professor Anthony Ingraffea of Cornell University for their review of drafts of this document, as well as their insights and suggestions. Frontier Group interns Dana Bradley and Danielle Elefritz provided valuable research assistance. Our appreciation goes to Jeff Inglis for data assistance. Thanks also to Tony Dutzik and Gideon Weissman of Frontier Group for editorial help. We also are grateful to the many state agency staff who answered our numerous questions and requests for data. Many of them are listed by name in the methodology. The authors bear responsibility for any factual errors. The recommendations are those of Environment America Research & Policy Center. The views expressed in this report are those of the authors and do not necessarily reflect the views of our funders or those who provided review. 2016 Environment America Research & Policy Center. Some Rights Reserved. This work is licensed under a Creative Commons Attribution Non-Commercial No Derivatives 3.0 Unported License. To view the terms of this license, visit creativecommons.org/licenses/by-nc-nd/3.0. Environment America Research & Policy Center is a 501(c)(3) organization. -
Bats 3 Post-Expansion
BATS 3 POST-EXPANSION (1961-to the present) 30 teams 31 players per team 930 total players Names in red are Hall of Famers MVP Most Valuable Player league award ROY Rookie of the Year; league award. CY Cy Young winner league award; CY(M) Cy Young winner when only awarded to best pitcher in the majors NATIONAL LEAGUE MILWAUKEE-ATLANTA BRAVES ARIZONA DIAMONDBACKS CHICAGO CUBS CINCINNATI REDS Hank Aaron – 1971 Jay Bell – 1999 Javier Baez – 2017 Johnny Bench – 1970 MVP Felipe Alou – 1966 Eric Byrnes – 2007 Ernie Banks – 1961 Leo Cardenas – 1966 Jeff Blauser – 1997 Alex Cintron – 2003 Michael Barrett – 2006 Sean Casey – 1999 Rico Carty – 1970 Craig Counsell – 2002 Glenn Beckert – 1971 Dave Concepcion – 1978 Del Crandall – 1962 Stephen Drew – 2008 Kris Bryant – 2016 MVP Eric Davis – 1987 Darrell Evans – 1973 Steve Finley – 2000 Jody Davis – 1983 Adam Dunn – 2004 Freddie Freeman – 2017 Paul Goldschmidt – 2015 Andre Dawson – 1987 MVP George Foster – 1977 MVP Rafael Furcal – 2003 Luis Gonzalez – 2001 Shawon Dunston – 1995 Ken Griffey, Sr. - 1976 Ralph Garr – 1974 Orlando Hudson – 2008 Leon Durham – 1982 Barry Larkin – 1996 Andruw Jones – 2005 Conor Jackson – 2006 Mark Grace – 1995 Lee May – 1969 Chipper Jones – 2008 Jake Lamb – 2016 Jim Hickman – 1970 Devin Mesoraco – 2014 David Justice – 1994 Damian Miller – 2001 Dave Kingman – 1979 Joe Morgan – 1976 MVP Javier Lopez – 2003 Miguel Montero – 2009 Derrek Lee – 2005 Tony Perez – 1970 Brian McCann – 2006 David Peralta – 2015 Anthony Rizzo – 2016 Brandon Phillips – 2007 Fred McGriff – 1994 A.J. Pollock