Defensive Ability and Salary Determination in Major League Baseball
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Gether, Regardless Also Note That Rule Changes and Equipment Improve- of Type, Rather Than Having Three Or Four Separate AHP Ments Can Impact Records
Journal of Sports Analytics 2 (2016) 1–18 1 DOI 10.3233/JSA-150007 IOS Press Revisiting the ranking of outstanding professional sports records Matthew J. Liberatorea, Bret R. Myersa,∗, Robert L. Nydicka and Howard J. Weissb aVillanova University, Villanova, PA, USA bTemple University Abstract. Twenty-eight years ago Golden and Wasil (1987) presented the use of the Analytic Hierarchy Process (AHP) for ranking outstanding sports records. Since then much has changed with respect to sports and sports records, the application and theory of the AHP, and the availability of the internet for accessing data. In this paper we revisit the ranking of outstanding sports records and build on past work, focusing on a comprehensive set of records from the four major American professional sports. We interviewed and corresponded with two sports experts and applied an AHP-based approach that features both the traditional pairwise comparison and the AHP rating method to elicit the necessary judgments from these experts. The most outstanding sports records are presented, discussed and compared to Golden and Wasil’s results from a quarter century earlier. Keywords: Sports, analytics, Analytic Hierarchy Process, evaluation and ranking, expert opinion 1. Introduction considered, create a single AHP analysis for differ- ent types of records (career, season, consecutive and In 1987, Golden and Wasil (GW) applied the Ana- game), and harness the opinions of sports experts to lytic Hierarchy Process (AHP) to rank what they adjust the set of criteria and their weights and to drive considered to be “some of the greatest active sports the evaluation process. records” (Golden and Wasil, 1987). -
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. -
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 .................................................................................................................................. -
Baseball U Maryland Defensive Philosophy
Baseball U Maryland Defensive Philosophy Defense LONG TOSS AS MUCH AS POSSIBLE Individual skill work is a priority GET BETTER everyday Team Defensive Concepts: We will be fundamentally sound defensively- both physically and mentally. 1. Win the Free Base War : BB,HBP, errors, extra bases, mental mistakes 2. Prevent the Big Inning : 3 or more runs 3. Keep Double Play a pitch away (keep runners at 1B) 4. Communicate, Communicate, Communicate Goal: .970 fielding percentage (relates to 1 per game) - majority of errors should be IF play (3b,SS, 2b)- 1B, C, P, and OF cannot attribute to errors. Catching a. Catcher needs to be the most athletic guy on the field…VERY TOUGH b. Catcher has to be a LEADER…MUST BE VOCAL…confidence is key a. MUST be constantly communicating the defense b. MUST know each pitcher and situation c. Before the 1st pitch is thrown, get to know the umpire-“Good Morning, my name is …..” d. Calling Pitches: a. When giving signs, they must be hidden b. Be consistent when calling pitches (i.e. 1-FB, 2 CB, etc.)(Be consistent with a runner on base (i.e. 2nd sign, etc.) c. Know what the Pitchers BEST pitch is THAT DAY d. Avoid tendencies (i.e. every 0-2 count, throw CB) e. Defensive tenets: a. Receiving (80%) (1) be on time, (2) manipulate the ball, (3) keep strikes strikes i. 1 knee vs. 2 knee stances…comfort, try it all in bullpens for best results b. Blocking (15%) (1) know the pitcher, (2) anticipate block, (3) control the baseball i. -
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. -
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. -
October, 1998
By the Numbers Volume 8, Number 1 The Newsletter of the SABR Statistical Analysis Committee October, 1998 RSVP Phil Birnbaum, Editor Well, here we go again: if you want to continue to receive By the If you already replied to my September e-mail, you don’t need to Numbers, you’ll have to drop me a line to let me know. We’ve reply again. If you didn’t receive my September e-mail, it means asked this before, and I apologize if you’re getting tired of it, but that the committee has no e-mail address for you. If you do have there’s a good reason for it: our committee budget. an e-mail address but we don’t know about it, please let Neal Traven (our committee chair – see his remarks later this issue) Our budget is $500 per year. know, so that we can communicate with you more easily. Giving us your e-mail address does not register you to receive BTN by e- Our current committee member list numbers about 200. Of the mail. Unless you explicitly request that, I’ll continue to send 200 of us, 50 have agreed to accept delivery of this newsletter by BTN by regular mail. e-mail. That leaves 150 readers who need physical copies of BTN. At four issues a year, that’s 600 mailings, and there’s no As our 1998 budget has not been touched until now, we have way to do 600 photocopyings and mailings for $500. sufficient funds left over for one more full issue this year. -
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 . -
PFF WAR: Modeling Player Value in American Football
PFF WAR: Modeling Player Value in American Football Submission ID: 1548728 Track: Other Sports Player valuation is one of the most important problems in all of team sports. In this paper we use Pro Football Focus (PFF) player grades and participation data to develop a wins above replacement (WAR) model for the National Football League. We find that PFF WAR at the player level is more stable than traditional, or even advanced, measures of performance, and yields dramatic insiGhts into the value of different positions. The number of wins a team accrues throuGh its players’ WAR is more stable than other means of measurinG team success, such as PythaGorean win totals. We finish the paper with a discussion of the value of each position in the NFL draft and add nuance to the research suggesting that tradinG up in the draft is a negative-expected-value move. 1. Introduction Player valuation is one of the most important problems in all of team sports. While this problem has been addressed at various levels of satisfaction in baseball [1], [23], basketball [2] and hockey [24], [6], [13], it has larGely been unsolved in football, due to unavailable data for positions like offensive line and substantial variations in the relative values of different positions. Yurko et al. [25] used publicly available play-by-play data to estimate wins above replacement (WAR) values for quarterbacks, receivers and running backs. Estimates for punters [4] have also been computed, and the most-recent work of ESPN Sports Analytics [9] has modified the +/- approach in basketball to college football beginning in the fall of 2019. -
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. -
How to Do Stats
EXPLANATION OF STATS IN SCORE BOOK FIELDING STATISTICS COLUMNS DO - Defensive Outs The number of put outs the team participated in while each player was in the line-up. Defensive outs are used in National Championships as a qualification rule. PO - Put out (10.09) A putout shall be credited to each fielder who (1) Catches a fly ball or a line drive, whether fair or foul. (2) Catches a thrown ball, which puts out a batter or a runner. (3) Tags a runner when the runner is off the base to which he is legally entitled. A – Assist (10.10) Any fielder who throws or deflects a battered or thrown ball in such a way that a putout results or would have except for a subsequent error, will be credited with an Assist. E – Error (10.12) An error is scored against any fielder who by any misplay (fumble, muff or wild throw) prolongs the life of the batter or runner or enables a runner to advance. BATTING STATISTICS COLUMNS PA - Plate Appearance Every time the batter completes his time at bat he is credited with a PA. Note: if the third out is made in the field he does not get a PA but is first to bat in the next innings. AB - At Bat (10.02(a)(1)) When a batter has reached 1st base without the aid of an ‘unofficial time at bat’. i.e. do not include Base on Balls, Hit by a Pitched Ball, Sacrifice flies/Bunts and Catches Interference. R – Runs (2.66) every time the runner crosses home plate scoring a run. -
Stattrak for Baseball/Softball Statistics Quick Reference
StatTrak for Baseball/Softball Statistics Quick Reference Batting PA Plate Appearances HP Hit by Pitch R Runs CO Catcher's Obstruction H Hits SO Strike Outs 2B Doubles SH Sacrifice Hit (sacrifice bunt) 3B Triples SF Sacrifice Fly HR Home Runs DP Double Plays Hit Into OE Reaching On-Error SB Stolen Bases FC Fielder’s Choice CS Caught Stealing BB Walks RBI Runs Batted In B1, B2, B3, B4, B5 Name Your Own Categories (renamable) BS1, BS2, BS3, BS4, BS5 Create Your Own Statistics (renamable) G Games = Number of Batting records in database AB At Bats = PA - BB - HP - SH - SF - CO 1B Singles = H - 2B - 3B - HR TB Total Bases = H + 2B + (2 x 3B) + (3 x HR) SLG Slugging Percentage = TB / AB OBP On-Base Percentage = (H + BB + HP) / (AB + BB + HP + SF) <=== Major League Method or (H + BB + HP + OE) / (AB + BB + HP + SF) <=== Include OE or (H + BB + HP + FC) / (AB + BB + HP + SF) <=== Include FC or (H + BB + HP + OE + FC) / (AB + BB + HP + SF) <=== Include OE and FC BA Batting Average = H / AB RC Runs Created = ((H + BB) x TB) / (AB + BB) TA Total Average = (TB + SB + BB + HP) / (AB - H + CS + DP) PP Pure Power = SLG - BA SBA Stolen Base Average = SB / (SB + CS) CHS Current Hitting Streak LHS Longest Hitting Streak Pitching IP Innings Pitched SF Sacrifice Fly R Runs WP Wild Pitch ER Earned-Runs Bk Balks BF Batters Faced PO Pick Offs H Hits B Balls 2B Doubles S Strikes 3B Triples GS Games Started HR Home Runs GF Games Finished BB Walks CG Complete Games HB Hit Batter W Wins CO Catcher's Obstruction L Losses SO Strike Outs Sv Saves SH Sacrifice Hit