Sabermetrics is the empirical analysis of baseball, especially baseball statistics that measure in-game activity.
Sabermetricians collect and summarize the relevant data from this in-game activity to answer specific questions. The term is derived from the acronym SABR, which stands for the Society for American Baseball Research, founded in 1971. The term "sabermetrics" was coined by Bill James, who is one of its pioneers and is often considered its most prominent advocate and public face.
1. Early history
Henry Chadwick, a sportswriter in New York, developed the box score in 1858. This was the first way statisticians were able to describe the sport of baseball by numerically tracking various aspects of game play. The creation of the box score has given baseball statisticians a summary of the individual and team performances for a given game.
Sabermetrics research began in the middle of the 20th century with the writings of Earnshaw Cook, one of the earliest sabermetricians. Cooks 1964 book, Percentage Baseball, was one of the first of its kind. At first, the book was widely criticized and considered hocus pocus by organized baseball. The idea of a science of baseball statistics was not considered legitimate until Bill James began releasing his annual compendium of baseball data, Baseball Abstracts, in 1977.
Bill James believed that people misunderstood how the game of baseball was played, claiming that it is actually defined by the conditions under which the sport is played. Sabermetricians, sometimes considered baseball statisticians, began trying to replace the longtime favorite statistic known as the batting average. It has been claimed that team batting average provides a relatively poor fit for team runs scored. Sabermetric reasoning would say that runs win ballgames, and that a good measure of a players worth his ability to help his team score more runs than the opposing team.
Before Bill James made the concept of sabermetrics known, Davey Johnson used an IBM System/360 at team owner Jerold Hoffbergers brewery to write a FORTRAN baseball computer simulation while playing for the Baltimore Orioles in the early 1970s. He used his results in an unsuccessful attempt to promote the idea that he should bat second in the lineup to his manager Earl Weaver. He wrote IBM BASIC programs to help him manage the Tidewater Tides, and after becoming manager of the New York Mets in 1984, he arranged for a team employee to write a dBASE II application to compile and store advanced metrics on team statistics. Craig R. Wright was another employee in Major League Baseball, working with the Texas Rangers in the early 1980s. During his time with the Rangers, he became known as the first front office employee in MLB history to work under the title Sabermetrician.
David Smith founded Retrosheet in 1989, with the objective of computerizing the box score of every major league baseball game ever played, in order to more accurately collect and compare the statistics of the game.
The Oakland Athletics began to use a more quantitative approach to baseball by focusing on sabermetric principles in the 1990s. This initially began with Sandy Alderson as the former general manager of the team when he used the principles toward obtaining relatively undervalued players. His ideas were continued when Billy Beane took over as general manager in 1997, a job he held until 2015, and hired his assistant Paul DePodesta. Through the statistical analysis done by Beane and DePodesta in the 2002 season, the Oakland As went on to win 20 games in a row. This was a historic moment for the franchise, in which the 20th game was played at the Alameda County Coliseum. His approaches to baseball soon gained national recognition when Michael Lewis published Moneyball: The Art of Winning an Unfair Game in 2003 to detail Beanes use of Sabermetrics. In 2011, a film based on Lewis book also called Moneyball was released to further provide insight into the techniques used in the Oakland Athletics front office.
2. Traditional measurements
Sabermetrics was created in an attempt for baseball fans to learn about the sport through objective evidence. This is performed by evaluating players in every aspect of the game, specifically batting, pitching, and fielding. These evaluation measures are usually phrased in terms of either runs or team wins as older statistics were deemed ineffective.
2.1. Traditional measurements Batting measurements
The traditional measure of batting performance is considered to be hits divided by the total number of at-bats. Bill James, along with other fathers of sabermetrics, found this measure to be flawed, as it ignores any other way a batter can reach base besides a hit. This led to the creation of the On-base percentage, which takes walks and hit-by-pitches into consideration. To calculate the On-Base percentage, the total number of hits + bases on balls + hit by pitch are divided by at bats + bases on balls + hit by pitch + sacrifice flies.
Another issue with the traditional measure of the batting average is that it does not distinguish between hits and gives each hit equal value. Thus, a measure that differentiates between these four hit outcomes, the slugging percentage, was created. To calculate the slugging percentage, the total number of bases of all hits is divided by the total numbers of time at bat. Stephen Jay Gould proposed that the disappearance of.400 batting average is actually a sign of general improvement in batting. This is because, in the modern era, players are becoming more focused on hitting for power than for average. Therefore, it has become more valuable to compare players using the slugging percentage and on-base percentage over the batting average.
These two improved sabermetric measures are important skills to measure in a batter and have been combined to create the modern statistic OPS. On-base plus slugging is the sum of the on-base percentage and the slugging percentage. This modern statistic has become useful in comparing players and is a powerful method of predicting runs scored from a certain player.
Some of the other statistics that sabermetricians use to evaluate batting performance are weighted on-base average, secondary average, runs created, and equivalent average.
2.2. Traditional measurements Pitching measurements
The traditional measure of pitching performance is considered to be the earned run average. It is calculated by dividing the number of earned runs allowed by the number of innings pitched and multiplying by nine because of the nine innings. This statistic provides the number of runs that a pitcher allows per game. It has proven to be flawed as it does not separate the ability of the pitcher from the abilities of the fielders that he plays with. Another classic measure for pitching is a pitchers winning percentage. Winning percentage is calculated by dividing wins by the number of decisions wins plus losses. This statistic can also be flawed as it is dependent on the pitchers teammates performances at the plate and in the field.
Sabermetricians have attempted to find different measures of pitching performance that does not include the performances of the fielders involved. One of the earliest developed, and one of the most popular in use, is walks plus hits per inning pitched WHIP, which while not completely defense-independent, tends to indicate how many times a pitcher is likely to put a player on base and thus how effective batters are against a particular pitcher in reaching base. A more recent development is the creation of defense independent pitching statistics DIPS system. Voros McCracken has been credited with the development of this system in 1999. Through his research, McCracken was able to show that there is little to no difference between pitchers in the number of hits they allow, regardless of their skill level. Some examples of these statistics are defense-independent ERA, fielding independent pitching, and defense-independent component ERA. Other sabermetricians have furthered the work in DIPS, such as Tom Tango who runs the Tango on Baseball sabermetrics website.
Baseball Prospectus created another statistics called the peripheral ERA. This measure of a pitchers performance takes hits, walks, home runs allowed, and strikeouts while adjusting for ballpark factors. Each ballpark has different dimensions when it comes to the outfield wall so a pitcher should not be measured the same for each of these parks.
Batting average on balls in play BABIP is another useful measurement for determining pitchers performance. When a pitcher has a high BABIP, they will often show improvements in the following season, while a pitcher with low BABIP will often show a decline in the following season. This is based on the statistical concept of regression to the mean. Others have created various means of attempting to quantify individual pitches based on characteristics of the pitch, as opposed to runs earned or balls hit.
3. Higher mathematics
Value over replacement player VORP is considered a popular sabermetric statistic. This statistic demonstrates how much a player contributes to his team in comparison to a fake replacement player that performs below average. This measurement was founded by Keith Woolner, a former writer for the sabermetric group/website Baseball Prospectus.
Wins above replacement WAR is another popular sabermetric statistic that will evaluate a players contributions to his team. Similar to VORP, WAR compares a certain player to a replacement-level player in order to determine the number of additional wins the player has provided to his team. WAR values vary with hitting positions and are largely determined by a players successful performance and their amount of playing time.
3.1. Higher mathematics Quantitative analysis in baseball
Many traditional and modern statistics, such as ERA and Wins Shared, dont give a full understanding of what is taking place on the field. Simple ratios are not sufficient to understand the statistical data of baseball. Structured quantitative analysis is capable of explaining many aspects of the game, for example, to examine how often a team should attempt to steal.
3.2. Higher mathematics Related rates in baseball
Related rates can be used in baseball to give exact calculations of different plays in a game. For example, if a runner is being sent home from third, related rates can be used to show if a throw from the outfield would have been on time or if it was correctly cut off before the plate. Related rates also can aid in determining how fast a player can get around the bases after a batted ball, information that helps in the development of scouting reports and individual player development.
3.3. Higher mathematics Momentum and force
Momentum and force is a similar application of calculus in baseball. Particularly, the average force on a bat while hitting a ball can be calculated by combining different concepts within applied calculus. First, the change in the balls momentum by the external force Ft must be calculated. The momentum can be found by multiplying the mass and velocity. The external force Ft is a continuous function of time.
Sabermetrics can be used for multiple purposes, but the most common are evaluating past performance and predicting future performance to determine a players contributions to his team. These may be useful when determining who should win end-of-the-season awards such as MVP and when determining the value of making a certain trade.
Most baseball players tend to play a few years in the minor leagues before they are called up to the major league. The competitive differences coupled with ballpark effects make the exact comparison of a players statistics a problem. Sabermetricians have been able to clear this problem by adjusting the players minor league statistics, also known as the Minor-League Equivalency. Through these adjustments, teams are able to look at a players performance in both AA and AAA to determine if he is fit to be called up to the majors.
4.1. Applications Applied statistics
Sabermetrics methods are generally used for three purposes:
- To provide prediction of future performance of a given player or a team. When past data is available about the performance of a team or a specific player, Sabermetrics can be used to predict the average future performances for the next season. Thus, a prediction can be made with a certain probability about the number of wins and losses.
- To compare key performances among certain specific players under realistic data conditions. The evaluation of past performance of a player enables an analytic overview. The comparison of this data between players can help one understand key points such as their market values. In that way, the role and the salary that should be given to that player can be defined.
- To provide a useful function of the players contributions to his team. When analyzing data, one is able to understand the contributions a player makes to the success/failure of his team. Given that correlation, we can sign or release players with certain characteristics.
4.2. Applications Machine learning for predicting game outcome
A machine learning model can be built using data sets available at sources such as baseball-reference. This model will give probability estimates for the outcome of specific games or the performance of particular players. These estimates are increasingly accurate when applied to a large number of events over a long term. The game outcome win/lose is treated as having a binomial distribution.
Predictions can be made using a logistic regression model with explanatory variables including: opponents runs scored, runs scored, shutouts time at bat, winning rate, and pitcher whip.
5. Recent advances
Many sabermetricians are still working hard to contribute to the field through creating new measures and asking new questions. Bill James two Historical Baseball Abstract editions and Win Shares book have continued to advance the field of sabermetrics, 25 years after he helped start the movement. His former assistant Rob Neyer, who is now a senior writer at ESPN.com and national baseball editor of SBNation, also worked on popularizing sabermetrics since the mid-1980s.
Nate Silver, a former writer and managing partner of Baseball Prospectus, invented PECOTA. This acronym stands for Player Empirical Comparison and Optimization Test Algorithm, and is a sabermetric system for forecasting Major League Baseball player performance. Simply put, it assumes that the players careers will follow a similar trajectory to players that they are similar to now. This system has been owned by Baseball Prospectus since 2003 and helps the websites authors invent or improve widely relied upon sabermetric measures and techniques.
Beginning in the 2007 baseball season, the MLB started looking at technology to record detailed information regarding each pitch that is thrown in a game. This became known as the PITCHf/x system which is able to record the speed of the pitch, at its release point and as it crossed the plate, as well as the location and angle of the break of certain pitches through video cameras. FanGraphs is a website that favors this system as well as the analysis of play-by-play data. The website also specializes in publishing advanced baseball statistics as well as graphics that evaluate and track the performance of players and teams.
6. In popular culture
- "MoneyBART", the third episode of The Simpsons 22nd season, in which Lisa utilizes sabermetrics to coach Barts Little League Baseball team.
- The season 3 Numb3rs episode "Hardball" focuses on sabermetrics, and the season 1 episode "Sacrifice" also covers the subject.
- Moneyball, the 2011 film about Billy Beanes use of sabermetrics to build the Oakland Athletics. The film is based on Michael Lewis book of the same name.
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