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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). -
2021 Texas A&M Baseball
2021 TEXAS A&M BASEBALL GAMES 24-26 | GEORGIA | 3/26-28 GAME TIMES: 6:02 / 2:02 / 1:02 P.M. CT | SITE: Olsen Field at Blue Bell Park, Bryan-College Station, Texas Texas A&M Media Relations * www.12thMan.com Baseball Contact: Thomas Dick / E-Mail: [email protected] / C: (512) 784-2153 RESULTS/SCHEDULE TEXAS A&M GEORGIA Date Day Opponent Time (CT) 2/20 SAT XAVIER (DH) S+ L, 6-10 XAVIER (DH) S+ L, 0-2 AGGIES BULLDOGS 2/21 SUN XAVIER S+ W, 15-0 2/23 TUE ABILENE CHRISTIAN S+ L, 5-6 2/24 WED TARLETON STATE S+ (10) W, 8-7 2/26 Fri % vs Baylor Flo W, 12-4 2021 Record 15-8, 0-3 SEC 2021 Record 15-5, 1-2 SEC 2/27 Sat % vs Oklahoma Flo W, 8-1 Ranking - Ranking 12 (CB); 30 (NCBWA) 2/28 Sun % vs Auburn Flo L, 1-6 Streak Lost 4 Streak Won 1 3/2 TUE HOUSTON BAPTIST S+ W, 4-0 Last 5 / Last 10 1-4 / 6-4 Last 5 / Last 10 3-2 / 8-2 3/3 WED INCARNATE WORD S+ W, 6-4 Last Game Mar 23 Last Game Mar 23 3/5 FRI NEW MEXICO STATE S+ W, 4-1 3/6 SAT NEW MEXICO STATE S+ W, 5-0 RICE - L, 1-2 KENNESAW STATE - (10 inn.) W, 3-2 3/7 SUN NEW MEXICO STATE S+ W, 7-1 3/9 TUE A&M-CORPUS CHRISTI S+ W, 7-0 Head Coach Rob Childress (Northwood, ‘90) Head Coach Scott Stricklin (Kent State, ‘95) 3/10 WED PRAIRIE VIEW A&M S+ (7) W, 22-2 Overall 608-317-3 (16th season) Overall 568-354-1 (17th season) 3/12 FRI SAMFORD S+ W, 10-1 at Texas A&M same at Georgia 218-166-1 (8th season) 3/13 SAT SAMFORD (DH) S+ W, 21-4 SUN SAMFORD (DH) S+ W, 5-2 3/16 Tue at Houston E+ W, 9-4 PROBABLE PITCHING MATCHUPS 3/18 Thu * at Florida SEC L, 4-13 • FRIDAY: #37 Dustin Saenz (Sr., LHP, 3-2, 3.07) vs. -
2018 Purdue Baseball
2018 PURDUE BASEBALL P 2012 BIG TEN CHAMPIONS P • PURDUESPORTS.COM • @PURDUEBASEBALL SID Contact Info: Ben Turner // [email protected] // Office: 765-494-3198 // Cell: 217-549-7965 – FEBRUARY (6-1) – – BIG TEN PLAY CONTINUES – 17 ^ vs Western Michigan W 5-1 ^ vs Western Michigan W 5-1 URDUE OILERMAKERS IG EN 18 ^ vs Western Michigan W 10-4 P B (29-16, 13-4 B T ) 23 # vs Saint Louis W 5-2 AT OHIO STATE BUCKEYES (31-16, 11-7 BIG TEN) # vs Incarnate Word W 5-4 (10) 24 # vs #30 Notre Dame L 4-2 3-GAME SERIES • MAY 11-13 • BILL DAVIS STADIUM • COLUMBUS, OHIO 25 # vs #30 Notre Dame W 8-7 Series Opener: Friday, May 11 at 6:30 p.m. ET • BTN Plus on BTN2Go – MARCH (8-9, 3-0 BIG TEN) – Middle Game: Saturday, May 12 at 3 p.m. ET • BTN Plus on BTN2Go 2 + vs Central Michigan W 11-1 Series Finale: Sunday, May 13 at 1 p.m. ET • BTN Plus on BTN2Go 3 + vs Virginia Tech W 12-5 – SERIES HISTORY – 4 + at Stetson L 11-6 All-Time Series: Ohio State leads 146-64-1 • All-Time in Columbus: Ohio State leads 78-25 9 at Tulane L 1-0 10 at Tulane W 12-8 2017: Purdue won 2-of-3 (March 31-April 2 in Columbus) 11 at Tulane L 6-2 First Meeting: Purdue 4, Ohio State 3 (May 1913 in West Lafayette) 13 at Southeastern Louisiana L 4-0 Road team has won the last 8 Purdue-OSU series dating back to 2007 14 at Nicholls State L 4-2 – LIVE COVERAGE – 17 at Saint Louis L 15-1 Video Webcasts: BTN2Go.com & BTN2Go App at Saint Louis L 11-9 Radio: WSHY 104.3 FM & 1410 AM (Games 1 & 3) • Free Online Audio: PurdueSports.com 18 at Saint Louis L 7-3 – PURDUE’S PROBABLE LINEUP – 23 LIPSCOMB L 3-1 LIPSCOMB W 5-2 POS # NAME ..................................... -
A Statistical Study Nicholas Lambrianou 13' Dr. Nicko
Examining if High-Team Payroll Leads to High-Team Performance in Baseball: A Statistical Study Nicholas Lambrianou 13' B.S. In Mathematics with Minors in English and Economics Dr. Nickolas Kintos Thesis Advisor Thesis submitted to: Honors Program of Saint Peter's University April 2013 Lambrianou 2 Table of Contents Chapter 1: The Study and its Questions 3 An Introduction to the project, its questions, and a breakdown of the chapters that follow Chapter 2: The Baseball Statistics 5 An explanation of the baseball statistics used for the study, including what the statistics measure, how they measure what they do, and their strengths and weaknesses Chapter 3: Statistical Methods and Procedures 16 An introduction to the statistical methods applied to each statistic and an explanation of what the possible results would mean Chapter 4: Results and the Tampa Bay Rays 22 The results of the study, what they mean against the possibilities and other results, and a short analysis of a team that stood out in the study Chapter 5: The Continuing Conclusion 39 A continuation of the results, followed by ideas for future study that continue to project or stem from it for future baseball analysis Appendix 41 References 42 Lambrianou 3 Chapter 1: The Study and its Questions Does high payroll necessarily mean higher performance for all baseball statistics? Major League Baseball (MLB) is a league of different teams in different cities all across the United States, and those locations strongly influence the market of the team and thus the payroll. Year after year, a certain amount of teams, including the usual ones in big markets, choose to spend a great amount on payroll in hopes of improving their team and its player value output, but at times the statistics produced by these teams may not match the difference in payroll with other teams. -
2019 GOPHER BASEBALL UNIVERSITY of MINNESOTA 2019 > SCHEDULE & RESULTS WEEK 2 > DALLAS, TEXAS FEBRUARY College Baseball Classic (Feb
2019 GOPHER BASEBALL UNIVERSITY OF MINNESOTA 2019 > SCHEDULE & RESULTS WEEK 2 > DALLAS, TEXAS FEBRUARY College Baseball Classic (Feb. 15-18) 15 Gonzaga Surprise, Ariz. W 8-5 16 New Mexico Surprise, Ariz. L 11-1 17 Oregon State Surprise, Ariz. L 13-1 18 Gonzaga Surprise, Ariz. L 6-5 DALLAS BAPTIST 22 Dallas Baptist Dallas, Texas 6:30pm >> VS. >> PATRIOTS 23 Dallas Baptist Dallas, Texas 2pm 3-1 (0-0 Missouri Valley) 24 Dallas Baptist Dallas, Texas 1pm MINNESOTA ALL-TIME SERIES: MARCH GOLDEN GOPHERS Minnesota leads 3-1 1 NC State Raleigh, NC 5pm ET 1-3 (0-0 Big Ten) 2 NC State Raleigh, NC 4pm ET LAST MEETING 3 NC State Raleigh, NC 3pm ET W 11-9, March 21, 2009 Seattle Baseball Showcase (March 8-10) Dallas, Texas 8 Oregon State Seattle, Wash. 3pm PT 9 San Diego Seattle, Wash. 11am PT PROBABLE STARTERS 10 Washington Seattle, Wash. 7pm PT POS. NO. NAME YR. B/T 2019 STATISTICS 11 Seattle Seattle, Wash. 11:30am PT C 4 Eli Wilson So. R/R 4 GP-4 GS, .357/.400/.500, 2 2B, 3 RBI, 3 R 15 Long Beach State Long Beach, Calif. 6pm PT 1B 10 Cole McDevitt Jr. R/R 4 GP-4 GS, .286/.333/.357, 1 2B, RBI, 2 R 16 Long Beach State Long Beach, Calif. 2pm PT 2B 18 Riley Smith Sr. R/R 4 GP-4 GS, .143/.250/.143, 1-2 SB 17 Long Beach State Long Beach, Calif. 1pm PT SS 7 Jordan Kozicky R-Jr. R/R 4 GP-4 GS, .077/.250/.077, 1-1 SB, 1 R 19 Pepperdine Malibu, Calif. -
"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. -
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. -
Salary Correlations with Batting Performance
Salary correlations with batting performance By: Jaime Craig, Avery Heilbron, Kasey Kirschner, Luke Rector, Will Kunin Introduction Many teams pay very high prices to acquire the players needed to make that team the best it can be. While it often seems that high budget teams like the New York Yankees are often very successful, is the high price tag worth the improvement in performance? We compared many statistics including batting average, on base percentage, slugging, on base plus slugging, home runs, strike outs, stolen bases, runs created, and BABIP (batting average for balls in play) to salaries. We predicted that higher salaries will correlate to better batting performances. We also divided players into three groups by salary range, with the low salary range going up to $1 million per year, the mid-range salaries from $1 million to $10 million per year, and the high salaries greater than $10 million per year. We expected a stronger correlation between batting performance and salaries for players in the higher salary range than the correlation in the lower salary ranges. Low Salary Below $1 million In figure 1 is a correlation plot between salary and batting statistics. This correlation plot is for players that are making below $1 million. We see in all of the plots that there is not a significant correlation between salary and batting statistics. It is , however, evident that players earning the lowest salaries show the lowest correlations. The overall trend for low salary players--which would be expected--is a negative correlation between salary and batting performance. This negative correlation is likely a result of players getting paid according to their specific performance, or the data are reflecting underpaid rookies who have not bloomed in the major leagues yet. -
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. -
Package 'Mlbstats'
Package ‘mlbstats’ March 16, 2018 Type Package Title Major League Baseball Player Statistics Calculator Version 0.1.0 Author Philip D. Waggoner <[email protected]> Maintainer Philip D. Waggoner <[email protected]> Description Computational functions for player metrics in major league baseball including bat- ting, pitching, fielding, base-running, and overall player statistics. This package is actively main- tained with new metrics being added as they are developed. License MIT + file LICENSE Encoding UTF-8 LazyData true RoxygenNote 6.0.1 NeedsCompilation no Repository CRAN Date/Publication 2018-03-16 09:15:57 UTC R topics documented: ab_hr . .2 aera .............................................3 ba ..............................................4 baa..............................................4 babip . .5 bb9 .............................................6 bb_k.............................................6 BsR .............................................7 dice .............................................7 EqA.............................................8 era..............................................9 erc..............................................9 fip.............................................. 10 fp .............................................. 11 1 2 ab_hr go_ao . 11 gpa.............................................. 12 h9.............................................. 13 iso.............................................. 13 k9.............................................. 14 k_bb............................................