Show Me the Facts

Sabermetrics is the advanced statistical analysis of baseball. Its numerical analysis is used to forecast baseball players career and future state of play as well as build successful teams solely based on player’s statistics. Multi-million dollar professional baseball organizations began straying away from traditional scouting reports and implementing “sabermetrics” to trade, acquire, and draft lesser-known players to have a higher on-base percentage, leading to a higher win percentage. After reading Michael Lewis’s novel Moneyball, I became intrigued with the idea of using statistics as an advantage in a traditionally traditional game. Sabermetrics is more than just numbers used to predict the performance of players, it is the risky reality of some Major League front offices. I chose to research the use of sabermetrics in baseball due to its mysterious nature of using mathematics to potentially alter the outcome of a sporting event. Throughout publications surrounding sabermetrics, a common theme of using the data collected to” project rather than predict” was prominently displayed. The controversy between using sabermetrics as opposed to taking the traditional route of using scouting reports was largely accounted for through the various texts. This literature review will evaluate the common trends of analyzing raw data, errors in the system, and usage of sabermetrics in Major League clubs as well as sources coverage of controversial ‘tradition versus new age’ debate found in currently available sources surrounding the topic of sabermetrics in Major League Baseball.

This scene in the popular film, Moneyball, reenacts the conflict that arose when the Oakland Athletics’ General Manager, Billy Beane, wanted to transition his scouting department from traditional scouting to sabermetric-based scouting.

In this clip from Moneyball, Billy Beane attempts to persuade his scouts that sabermetrics is the new wave of scouting. He asks his assistant, Paul DePodesta to show them the math with hundreds of player statistic cards accompanied by his mathematics that show each players value to the team.

Manipulating Raw Data

Extracting data from a live baseball game to utilize it as evidence in a case for signing the next biggest baseball star is risky business. What if there are mistakes in calculations? What if a player trips on the sidewalk, leading to a career-ending injury? What if the statistics are nonsense? The Society for American Baseball Research (more commonly known as SABR), Lindy’s Sports Baseball 2018 Preview publication, as well as mathematicians, Gary Talsma and Jim Albert tackle the issue regarding raw data usage in sabermetrics. SABR is the main source for sabermetric information on the statistical analysis of baseball. Using sources such as, MLB.com, Baseball Reference, The Lahman Database, and Retrosheet.org, SABR reviews how these professional companies use data collected from baseball games, place them into sabermetric equations created by Bill James and other mathematicians to create data that could be used to compare players. Lahman created a database source that allows users to input their own raw data to achieve the output of sabermetric data. Baseball Reference and The Lahman Database use the method of displaying the equations and explaining how to input the raw data collected to encourage users to try sabermetrics at home. While Lindy’s Sports Baseball 2018 Preview, creates a 150-page magazine publication that displays the sabermetric information and analyzes it to create predictions for the upcoming season. The trend of teaching readers how to input data and manipulate it to achieve the desired outcome is apparent throughout these texts and elaborates by using the data to create predictions.

Errors in the System

 Mathematics and statistics are susceptible to errors and errors are apparent even in the precise nature of sabermetrics. Stephen Marche of The New Yorker expands on the importance of the error statistic column in Major League Baseball in relation to the importance of errors in mathematics. A roundtable discussion on the question of sabermetrics’ impact on sports was featured in The Atlantic. The discussion surrounding the validity is apparent between both The New Yorker and The Atlantic. The Atlantic and The New Yorker articles stormed past explaining the functionalities of sabermetrics and jumped immediately into the nitty-gritty: discussing the reality of error within the mathematics. There is no case studies or graphs within these two articles, just sentences flowing with opinions. The discussion is important while determining the functionality of a new statistical advancement, discussing its errors, potential positive (and negative) impacts, and real-life function within the Major League setting. Open dialogue discussion surrounding the topic of sabermetrics and all of its flaws is an apparent trait in sabermetric-centered articles.

The Usage of Sabermetrics in Major League Organizations

 Sabermetrics became popular in 2003 when Michael Lewis’s book Moneyball reporting on the 2001 Oakland Athletics success with using sabermetrics, dropped in bookstores across the United States. Ever since Paul DePodesta teamed up with the Oakland Athletics using Bill James’s sabermetric equations, teams around Major League Baseball have been grandfathering sabermetrics into front office decisions. Sources such as ESPN.com, Steve Dilbeck from The Los Angeles Times, and R.J Anderson from CBS Sports all report on similar instances revolving Major League teams utilizing sabermetrics in the front office. ESPN published an entire website analyzing the 2015 season in the four major sports (MLB, NBA, NHL, and NFL) on how much they utilize sabermetrics and how successful they were in the 2015 season. Their studies show that teams who utilized sabermetrics more than others were more likely to be more successful in postseason play than those who did not utilize sabermetrics as much as the other. As opposed to ESPN the two other sources reported in a more journalistic fashion on how the teams were using sabermetrics to their advantage during preseason play and drafting future professionals. Many sources took that same journalistic standpoint as The Los Angeles Times and CBS Sports tackled sabermetrics by, reporting what they witnessed and how the team’s actions affected the team’s success. The main theme overall 20 sources were the theme of how Major League Baseball teams used sabermetrics to their advantages, viewed at from a journalistic viewpoint.

The Baseball Almanac, and Baseball Prospectus introduce their publications with a disclaimer stating that the information in their magazine is not intended for predictions related to trading or gambling, rather their statistics are projections of how the statistics project that player or team should perform in the future. A similar warning arose in Lindy’s Sports Baseball 2018 Preview. It was explaining to users the precautions that should be taken before making any decisions based on advanced statistics. In the film (based on the novel) Moneyball, General Manager Billy Beane faced backlash from older scouts when he attempts to transition the club to a primarily sabermetrically driven front office. The scouts argue that baseball was created to be a traditional sport and it should be left in its traditional setting, definitely not risk becoming infiltrated by new age statistics. The argument about tradition versus new age statistics comes into play in The Atlantic’s roundtable discussion with half the table agreeing to continue traditional baseball while the other half is in support of sabermetrics advancements. The discussion continues and the table is open to anyone who opposes or supports sabermetrics, as most platforms allow for discussion within the comments that may later be translated into their own opinions. The controversy is there and the debate floor is open.

Overall, the literature presented on the topic of sabermetrics spans from journalism-style reporting to mathematical research papers on the functionality of sabermetrics in Major League Baseball but the information remains constant. Sabermetrics works in moderation and sabermetrics is controversial. Throughout my research I read from mathematical and business baseball professionals, further research may be conducting from the viewpoint of Major League Baseball players; the ones who are producing the raw data. Major agreements within my research included sabermetrics functionality and purpose to project and not predict. No major disagreements were present other than analysts taking the side of traditionality in baseball rather than use sabermetrics. In conclusion, the published research surrounding sabermetrics is advance and hold depth in discussing the functionality, errors, application, and workings of sabermetrics as well as holding sabermetrics accountable for further discussion.