The Mathematic Phenomena Of Sabermetrics

Baseball has always been a game of numbers. Fans of the game have grown up being able to recite them by heart; Ted Williams’.406 batting average, Joe DiMaggio’s 56 game hitting streak, Babe Ruth’s 714 home runs. These numbers hold a special place in the history of the game. Statistics such as batting average, wins, home runs, and runs batted in have always been there to tell us who the best players are. Your favorite player has a .300 batting average? He’s an all-star. He hit 40 home runs and batted in 120 runs? That’s a Most Valuable Player Award candidate. Your favorite team’s best pitcher won 20 games? He’s a Cy Young Award contender. These statistics have been used to evaluate player performance

The book Moneyball by Michael Lewis is about a former major league baseball player who became the manager of the Oakland A’s. It tells the story of how he led the team to success despite their low budget by using computer based analytics to draft players. With the help of Bill James, the Oakland A’s came up with a new plan based on statistics to draft players. He went after players nobody wanted due to their low budget and his new plan. Billy led the Oakland Athletics to a successive win seasons by changing the way he measured players. He abandoned the traditional 5 “tool” the other scouts used and adopted empirical analytics. The abandonment of the traditional assessment of

Baseball statistics are meant to be a representation of a player’s talent. Since baseball’s inception around the mid-19th century, statistics have been used to interpret the talent level of any given player, however, the statistics that have been traditionally used to define talent are often times misleading. At a fundamental level, baseball, like any game, is about winning. To win games, teams have to score runs; to score runs, players have to get on base any way they can. All the while, the pitcher and the defense are supposed to prevent runs from scoring. As simplistic as this view sounds, the statistics being used to evaluate individual players were extremely flawed. In an attempt to develop more

I hope that we can all agree that Baseball is a sport. We can? Great! Now let’s analyze something very similar between the two sports, Baseball and Golf, and that is the swing. Now, what does a good batter’s stance look like? It looks like knees, bent at a 25 degree angle. It looks like a firm grasp on the bat’s handle, gaze permanently affixed on the ball. And the swing, careful, calculated, with a practiced turn at the hips to deliver power in order to send the ball flying. Let’s compare to a good golf stance. It looks like knees, bent at a 25 degree angle. It looks like a firm grasp on the clubs’s handle, gaze permanently affixed on the ball. And the swing, careful, calculated, with a practiced turn at the hips to deliver power in order to send the ball flying. It’s almost as if Baseball is just Air Golfing. Now, let’s look at some numbers, because numbers are fun talk about. “But these two events have no numbers in common,” one might say and to that I say poppycock! Why we can compare the accuracy of the athletes for the respective candidates! But for this comparison we need some subjects, so let's pull two gentleman from history. And of course, they have to be the best in their field, and they are, by the numbers, the best. The two gentlemen being referred to are Tiger Woods and Babe Ruth. If neither of those names ring a bell in your mind, stop reading here, google them and then come back and continue reading. Of course the resource list at the end of the paper might be helpful for that task. Now, Golf is the easy one, they give a percentage staight up, Between the number of fairways hit to the number possible. Tiger wood’s Driving Accuracy Percentage is 55.75% (Tiger). Now Baseball is a bit harder, as a player’s batting average is a decimal. Babe

Saberemtrics is the foundation of Beanes whole organizational philosophy, he tries to get players that take pitches, get on base, walk, and hit for extra base hits. Beane doesn't believe in steals because it's too risky or the sacrifice bunt because it's conceding an out. These beliefs are from Bill James formula "runs created". James measures "runs created" as (hits + walks) X Total Bases / (at bats + walks). This formula proved that conventional wisdom about how to measure offense was wrong because there was not enough emphasis on walks and extra base hits and too much value on expensive but not as important statistics such as batting average and stolen bases. (Lewis pg.77-78) Billy Beane has made a livelihood by concentrating on these important but less expensive statistics as a means of competing against bigger market teams.

Everyone has a specific skill set that they are good at. Some people are good with numbers, others have a gift for writing. The same is true in sports. The Buffalo Bills are very good at losing and disappointing their fans, while the Patriots are fantastic at bending the rules and winning Super Bowls. In baseball, each team is built around a specific group of players in order to create the best possible chance to win the World Series. Some of the greatest players have created seemingly unbreakable statistics. Joe DiMaggio had a hit in fifty-six consecutive games. Barry Bonds hit seven hundred and sixty-two home runs over his twenty-two-year career. Statistics like these only come about once in a generation. Teams are comprised of various talent. Some are like Miguel Cabrera. Cabrera always has a batting average over .300 with at least 25 home runs. These types of players are considered great hitters. Other’s, like Stephan Drew, get a hit less than 20% of the time they have an at bat. Players similar to Drew’s skill set have a job because they are very good at defense and their value on defense out ways their value on offense to their specific team. Baseball is sport rich with statistics. Almost anything one can come up with, has a statistic. In the last ten years alone, top executives, such as Billy Bean, have constructed teams based on advanced statistics and have been fairly successful, like the 2002 Oakland Athletics. Other teams, like the Houston Astros,

During the research of this paper there were several discoveries about the inner workings about the great American past time of baseball that was brought to my attention. The realization of how players are selected requires a great deal of thought and ingenuity (Thaler & Sunstein, 2003). As a former ball player (softball player), the remembrance of selecting individuals to play usually meant picking the best hitters and fielder for the team. However, with the tremendous amount of revenue generated in the game of baseball the organization providing the highest salary usually determines the player’s team.

Below is a table and scatter plot displaying David Ortiz’s home runs earned during the past five years with the Boston Red Sox. The data collected is based off of David Ortiz’s home runs earned over the course of that correlating baseball season. The table organizes data into the amount of times David Ortiz was at bat, the amount of earned home runs, as well as the percentage of hits that resulted in home runs. In addition to the table, summary statistics were created to show the mean, variance, standard deviation as well as median of earned home runs. These values show that David Ortiz has been consistent with home runs earned with little variance.

Batting average was the norm adopted by other baseball teams. But training for Oakland was focused on the player’s ability to obtain on-base scoring. The team relied more on selecting players by their on-base percentages. According to Sabermetrics model, teams always win with players having attained high on-base percentages.

Topic: The Billy Beane Way of Business Pattern: Topical Purpose: Inform Thesis: Billy Beane changed the way of the sport of baseball and in doing so changed the technique of business recruitment through the use of sabermetrics. Introduction A.

In determining accurately the effective future value of the home-run of Albert Pujols the following 5 years knowing that it grows at 12 percent per year and that he hit 47 homes run in 2009, one should accurately apply the formula (1) as follows: FVn = PV 〖(1+r)〗^n. It represents the formula (5-1) of Keow, Martin and Petty (2014) at page 145, where the present value (PV) equals 47, the r representing 12% (0.12) and the "n" standing for 5 years. Thus, by running the numbers accurately, the manager could professionally determine the effective future value of Albert Pujols FVap5 as follows: FVap5 = 47 x 〖(1+0.12)〗^5 = 82.83. This number should represent 83 home runs the following 5 years (Keown, Martin & Petty, 2014).

Oakland Athletics’ strategy is using sabermetrics, the sophisticated statistical methodologies, for evaluating players. The Oakland found the undervalued players and hired them with low budget to compete with big-budget teams. Oakland focused “on-base percentage” rather than “batting average”, which had been used by other teams. Major baseball teams rely on experienced baseball men, scouts, to travel the country and to select younger ballplayers. Rather than using naked eyes to evaluate players, Oakland

Sabermetrics is a systematic statistical approach in evaluating teams and players. Based on this science it was found out that this basis for judging the performance of the player should be on-base percentage. The Oakland A’s recruitment would prioritize college players rather than high school players. This was due to the fact that college players have already played more games against better competition. Beane was convinced with the fact that “a young player” is not what he looks like, or what he might become, but what he has done. The bottom line is what the player has produced in college. Beane and DePodesta believed that they could forecast future performance of college players more efficiently than high school ones.

Major League Baseball is known as America’s favorite pastime, and MLB teams spend an extensive amount of money in the excess of a billion dollars with the ultimate goal to win the World Series. This learning team’s focus throughout this descriptive statistics paper is the MLB players’ performances, salaries, salary caps, and winning percentages. Though salaries will by no means be a trade for wins, the goal is to use the less experienced players and pay them a lower salary. Research has been done on whether or not player’s salaries and wins are connected.

and analyzing vast amounts of baseball data. Then came the boom in baseball players’ salaries: this dramatically