Swinging for the Stars: Revisiting Racial Wage
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
More on Defensive Regression (Or Runs) Analysis 7
More on Defensive A Regression (or Runs) Analysis Th is appendix has three primary objectives: fi rst, to disclose aspects of DRA not disclosed in chapter two; second, to address aspects of the model that raise issues related less to baseball per se than to statistical modeling in gen- eral; and third, to drive home the fundamental point that DRA is not an answer, but a method. Included in this appendix are certain alternative models I tried, and suggestions for further improvements, which should provide some sense of the range of alternative approaches that are possible. DRA POST-1951 Overview Th ere are essentially two DRA models: post-1951 and pre-1952. Th e post- 1951 model uses a subset of Retrosheet play-by-play data currently available for seasons aft er 1951, and was almost completely described in chapter two. Th e pre-1952 model must make do with considerably less data, which ren- ders it more primitive for infi elders and unavoidably more complicated for outfi elders. When we fi rst began explaining DRA, we took a ‘bottom-up’ approach, starting from the shortstop position and gradually building up until we had a team model. Here we’ll take a ‘top-down’ approach, revealing the entire post-1951 team model all at once, and then discussing its components. Likewise, we’ll start with a top-down discussion of the pre-1952 model. Th e following page presents the entire post-1951 model on one page, with a glos- sary of defi ned terms on the facing page. 3 AAppendix-A.inddppendix-A.indd 3 22/1/2011/1/2011 22:27:53:27:53 PPMM AAppendix-A.indd 4 p p e n d i x - A . -
Determining the Value of a Baseball Player
the Valu a Samuel Kaufman and Matthew Tennenhouse Samuel Kaufman Matthew Tennenhouse lllinois Mathematics and Science Academy: lllinois Mathematics and Science Academy: Junior (11) Junior (11) 61112012 Samuel Kaufman and Matthew Tennenhouse June 1,2012 Baseball is a game of numbers, and there are many factors that impact how much an individual player contributes to his team's success. Using various statistical databases such as Lahman's Baseball Database (Lahman, 2011) and FanGraphs' publicly available resources, we compiled data and manipulated it to form an overall formula to determine the value of a player for his individual team. To analyze the data, we researched formulas to determine an individual player's hitting, fielding, and pitching production during games. We examined statistics such as hits, walks, and innings played to establish how many runs each player added to their teams' total runs scored, and then used that value to figure how they performed relative to other players. Using these values, we utilized the Pythagorean Expected Wins formula to calculate a coefficient reflecting the number of runs each team in the Major Leagues scored per win. Using our statistic, baseball teams would be able to compare the impact of their players on the team when evaluating talent and determining salary. Our investigation's original focusing question was "How much is an individual player worth to his team?" Over the course of the year, we modified our focusing question to: "What impact does each individual player have on his team's performance over the course of a season?" Though both ask very similar questions, there are significant differences between them. -
COVERING the BASES Milwaukee Brewers Baseball Club June 2020 Alumni Newsletter the Milwaukee Brewers at 50
COVERING THE BASES Milwaukee Brewers Baseball Club June 2020 Alumni Newsletter The Milwaukee Brewers at 50 Special commemorative hardcover book to be released this Spring It’s been 50 years in the making. And for diehard Brewers fans, it may be hard to imagine that their team, their franchise, is celebrating its Golden Anniversary in 2020. Time seems to have passed in a blink of an eye. But the memories of a baseball team so near and dear to many will never fade. Thanks, in part, to MLB.com sportswriter Adam McCalvy, who collaborated with the Brewers and sports publishing leader Triumph Books, there is now a special commemorative hardcover book appropriately entitled, The Milwaukee Brewers at 50: Celebrating a Half-Century of Brewers Baseball. The book retails for $40. Pre-orders are welcomed through Triumphbooks.com and where fine books are sold, including Barnes and Noble and Amazon.com. Fans will relive the passion and excitement of the Brewers' 50-year journey in Major League Baseball through the 256-page treasure chest filled with stories, anecdotes and many never-before published photographs. This official commemorative book rekindles the memories of iconic moments, the legendary players, managers and coaches, and so much more. This is a deluxe coffee table size book every Brewers fan must have. It also includes introductions from former Owner and Commissioner of Baseball Allan H. (Bud) Selig and current Chairman and Principal Owner Mark Attanasio. The foreword is written by Hall of Famer Robin Yount. A New Berlin native, McCalvy graduated from the University of Wisconsin with a journalism degree and interned in the Brewers’ Media Relations Department before working for MLB.com since the online service’s inception in 2001. -
Leveraging Big Data in Baseball
An article by EMC Proven Professional Knowledge Sharing Elite Author A WHOLE NEW BALLGAME: LEVERAGING BIG DATA IN BASEBALL Bruce Yellin Advisory Systems Engineer EMC Corporation [email protected] Table of Contents Introduction – Baseball, Big Data, and Advanced Analytics ...................................... 9 Big Data and Baseball - Players, Coaches, Trainers, and Managers ....................... 13 Sportvision ................................................................................................................ 15 PITCHf/x ................................................................................................................ 16 HITf/x ..................................................................................................................... 25 FIELDf/x ................................................................................................................ 28 Big Data and the Business of Baseball ..................................................................... 33 Player Development .................................................................................................. 36 Revenue From Fans .................................................................................................. 38 Revenue From Media ................................................................................................ 42 Big Data Helps Create Algorithmic Baseball Journalism ......................................... 43 Listen To Your Data - Grady the Goat - The Curse of the Bambino ........................ -
SABR48 Seanforman On
Wins Above Replacement or How I Learned to Stop Worrying and Love deGrom The War Talk to End All War Talks By Sean Forman and Hans Van Slooten, Sports Reference LLC Follow along at: gadel.me/2018-sabr-analytics Hello, I’m sean forman. Originally I vowed to include no WAR-related puns, but as you can see I failed miserably in that task. Sean Hans Hans, who is pictured there, runs baseball-reference on a day-to-day basis. Sean Smith Tom Tango We also relied on some outside experts when developing our WAR framework. Sean Smith developed the original set of equations that we started with in 2009. He originally published under the name rallymonkey (which is why our WAR is sometimes called rWAR). We did a major revamping in 2012 and Tom Tango answered maybe two dozen tedious emails from me during the process. Ways to Measure Value • Wins and Losses • Runs Scored and Runs Allowed • Win Probability Added • Component Measures, WOBA, FIP, DRS, etc. • Launch Angle & Velocity, Catch Probability, Tunneling, Framing, Spin Rate, etc => “Statcast” • Do you care more about: • Did it directly lead to winning outcome? • How likely are they to do this again? or • What is the context-neutral value of what they did? Differing views on what matters leads to many of the arguments over WAR. To some degree, I’m not willing to argue these points. We’ve made our choice and implemented a system based on that. You can make your choice and implement your system based on that. Ways to Measure Value Wins & Losses Skills & Statcast Where you are on the continuum guides your implementation details. -
Framing the Game Through a Sabermetric Lens: Major League
FRAMING THE GAME THROUGH A SABERMETRIC LENS: MAJOR LEAGUE BASEBALL BROADCASTS AND THE DELINEATION OF TRADITIONAL AND NEW FACT METRICS by ZACHARY WILLIAM ARTH ANDREW C. BILLINGS, COMMITTEE CHAIR DARRIN J. GRIFFIN SCOTT PARROTT JAMES D. LEEPER KENON A. BROWN A DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the College of Communication and Information Sciences in the Graduate School of The University of Alabama TUSCALOOSA, ALABAMA 2019 Copyright Zachary William Arth 2019 ALL RIGHTS RESERVED i ABSTRACT This purpose of this dissertation was to first understand how Major League Baseball teams are portraying and discussing statistics within their local broadcasts. From there, the goal was to ascertain how teams differed in their portrayals, with the specific dichotomy of interest being between teams heavy in advanced statistics and those heavy in traditional statistics. With advanced baseball statistics still far from being universally accepted among baseball fans, the driving question was whether or not fans that faced greater exposure to advanced statistics would also be more knowledgeable and accepting of them. Thus, based on the results of the content analysis, fans of four of the most advanced teams and four of the most traditional teams were accessed through MLB team subreddits and surveyed. Results initially indicated that there was no difference between fans of teams with advanced versus traditional broadcasts. However, there were clear differences in knowledge based on other factors, such as whether fans had a new school or old school orientation, whether they were high in Schwabism and/or mavenism, and how highly identified they were with the team. -
An Open Source System for Evaluating Overall Player Performance in Major League Baseball
openWAR: An Open Source System for Evaluating Overall Player Performance in Major League Baseball Benjamin S. Baumer Shane T. Jensen Smith College The Wharton School [email protected] University of Pennsylvania [email protected] Gregory J. Matthews Loyola University Chicago [email protected] March 25, 2015 Abstract Within sports analytics, there is substantial interest in comprehensive statistics intended to capture overall player performance. In baseball, one such measure is Wins Above Replace- ment (WAR), which aggregates the contributions of a player in each facet of the game: hitting, pitching, baserunning, and fielding. However, current versions of WAR depend upon propri- etary data, ad hoc methodology, and opaque calculations. We propose a competitive aggregate measure, openW AR, that is based on public data, a methodology with greater rigor and trans- parency, and a principled standard for the nebulous concept of a \replacement" player. Finally, we use simulation-based techniques to provide interval estimates for our openW AR measure that are easily portable to other domains. Keywords: baseball, statistical modeling, simulation, R, reproducibility 1 Introduction In sports analytics, researchers apply statistical methods to game data in order to estimate key quantities of interest. In team sports, arguably the most fundamental challenge is to quantify the contributions of individual players towards the collective performance of their team. In all sports the arXiv:1312.7158v3 [stat.AP] 24 Mar 2015 ultimate goal is winning and so the ultimate measure of player performance is that player's overall contribution to the number of games that his team wins. Although we focus on a particular measure of player contribution, wins above replacement (WAR) in major league baseball, the issues and approaches examined in this paper apply more generally to any endeavor to provide a comprehensive measure of individual player performance in sports. -
WAR and the Hall of Fame
WAR and the Hall of Fame Joshua Bayzick May 5,2015 Abstract Sabermetrics is a term coined by Bill James, and it is the search for objective knowledge about baseball. This mathematical approach to baseball produced a statistic called Wins Above Replacement (WAR), which takes into account batting, baserunning, and fielding to determine how many `wins' that position player was worth. This project looks at how player's WAR values change as they age. We will compare two groups of players, First-ballot Hall of Famers (HOF) and the average player. We know that the HOF group will have higher WAR values because they were well above average players, but the basic question is do they age differently than the average player. 1 Introduction A relatively new statistic called WAR (Wins Above Replacement) was developed by saber- metricians to answer questions such as \Who had the better career?" or \Who had the better year?" For example, in 2014 Alex Gordon had a batting average of .266 with 19 home runs and 74 RBI. In 2014 Yoenis Cespedes had a batting average of .260 with 22 home runs and 100 RBI. Based on these conventional statistics, they appear to have had fairly similar years. One might even argue that Cespedes was slightly better because of the RBI advantage. However, these conventional statistics do not measure everything a player does to help his team win a game. WAR, which takes into account batting, baserunning, and fielding, says that in 2014 Alex Gordon was worth 6.6 WAR for his team, while Ces- pedes was worth 3.3 WAR. -
* Text Features
The Boston Red Sox Thursday, April 2, 2020 * The Boston Globe Why a shortened baseball season works in the Red Sox’ favor Peter Abraham Thursday was supposed to be the home opener at Fenway Park, a 2:05 p.m. tilt against Yoan Moncada and the newly competitive Chicago White Sox. The sellout crowd would have been eager to see the Red Sox regardless of how they played in the first seven games of the season. For one day, just being back in Fenway and seeing those crisp white home uniforms would have been enough. But if there is a home opener this year, it’s not going to be for a long time. Major League Baseball has officially pushed the season back until at least mid-May because of the COVID-19 pandemic, and a more realistic appraisal is sometime in June or July. The idea of a brief spring training in June and then a July 4 Opening Day is something MLB officials privately believe is plausible. Their goal is to start the season when fans would be allowed at ballparks, but the idea of playing in empty stadiums has been discussed. It’s impossible to say how it will turn out, given how quickly the news about the pandemic changes. But as eager as we all are to see baseball, playing an abbreviated season could be an advantage to the Red Sox. They’ll never acknowledge that, but it’s true. Fewer games would mean less exposure for a shaky rotation that was further weakened Monday when Chris Sale had Tommy John surgery. -
Defensive Ability and Salary Determination in Major League Baseball
Colby College Digital Commons @ Colby Honors Theses Student Research 2016 Defensive Ability and Salary Determination in Major League Baseball Christopher B. Shorey Follow this and additional works at: https://digitalcommons.colby.edu/honorstheses Part of the Management Sciences and Quantitative Methods Commons Colby College theses are protected by copyright. They may be viewed or downloaded from this site for the purposes of research and scholarship. Reproduction or distribution for commercial purposes is prohibited without written permission of the author. Recommended Citation Shorey, Christopher B., "Defensive Ability and Salary Determination in Major League Baseball" (2016). Honors Theses. Paper 940. https://digitalcommons.colby.edu/honorstheses/940 This Honors Thesis (Open Access) is brought to you for free and open access by the Student Research at Digital Commons @ Colby. It has been accepted for inclusion in Honors Theses by an authorized administrator of Digital Commons @ Colby. Defensive Ability and Salary Determination in Major League Baseball Chris Shorey Readers: Professor Dave Findlay and Professor Tim Hubbard Abstract The process of salary determination in Major League Baseball (MLB) includes multiple levels of bargaining power and performance determinants. Previous studies of MLB salary determination have used a variety of measures of player performance. This paper examines the effect defensive ability has on salary determination for arbitration eligible players and for free agent players. Specifically, it will analyze player salary/contract data negotiated during the 2012- 2015 period along with performance data from past seasons to examine the extent to which fielding percentage, errors, and the more recently developed measures of defensive ability affect player salary. Particular attention is paid to matching the negotiated contract/salary data to previous seasons’ performance data in order to replicate the informational conditions known to both the team and the player at the time of negotiation. -
8 17.5% 20 24
Monday, June 14, 2021 Game #68 (32-35) T-Mobile Park SEATTLE MARINERS (32-35) vs. MINNESOTA TWINS (26-39) Home #32 (17-14) TONIGHT’S GAME INFORMATION Starting Pitchers: LHP Marco Gonzales (1-4, 5.45) vs. RHP Kenta Maeda (2-2, 5.27) First Pitch: 7:10 pm PT • Radio: 710 ESPN / Mariners.com • TV: ROOT SPORTS NW Day Date Opp. Time (PT) Mariners Pitcher Opposing Pitcher RADIO Tuesday June 15 vs. MIN 7:10 pm RHP Chris Flexen (5-3, 4.68) vs. LHP J.A. Happ (3-2, 5.75) 710 ESPN Wednesday June 16 vs. MIN 7:10 pm LHP Justus Sheffield (5-5, 4.91) vs. RHP Bailey Ober (0-0, 4.85) 710 ESPN All games televised on ROOT SPORTS with home games featuring Spanish Audio via TV SAP TONIGHT’S TILT…the Mariners return to T-Mobile Park tonight to begin a 9-day, 8-game homestand ON BASE % LEADERS vs. Minnesota (3 G, 6/14-6/16), Tampa Bay (4 G, 6/17-6/20) and Colorado (2 G, 6/22-6/23)...following the (MIN. 60 PA) conclusion of the homestand, will embark on a 7-day, 6-game road trip vs. Chicago-AL (3 G) and Toronto Rk. Player OBP (3 G)...tonight’s game will be televised on ROOT SPORTS, as well as broadcast on 710 ESPN Seattle and 1. Mike Trout-LAA .................... .466 the Mariners Radio Network. 2. Jake Fraley-SEA ................. .464 3. Vladimir Guererro Jr.-TOR .... .450 ODDS AND ENDS…the Mariners won yesterday by a 6-2 score...have won 11 of their last 20...are 14-9 4.