Sabermetrics Essay

This project investigates how salary and performance of offensive players in Major League Baseball are linked. We believe this is an interesting problem because it is traditionally believed that professional athletes play with hopes of earning a high salary, yet it often seems a batter’s performance is not linked to their salary (Jensen). Therefore, it seems as if the link between a player’s performance and their salary is different than their true performance. Performing a statistical analysis of this conundrum will give us great insight as to if it is accurate to say that performance changes salary drastically. Studies that prior statisticians have done differ from this study because their studies focus on salary and team performance rather than on the performance of individual players (Jane). Our study focuses on salary and individual performance in the current season. While there is extensive data on both game performances in the MLB and salaries, we can contribute to the statistical community by comparing how salaries are affected by different performance indicators for randomly selected individual players. Essentially, our hypothesis is an examination into how a batter 's game performance affects salary. We expect that the better a player’s statistics are, the higher their salary will be.

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

Throughout the history of major league baseball we have seen countless astonishing hitters come pass through, some putting up record breaking numbers, at times too good to be true. Barry Bonds, known as the “homerun king” was able to put up an amazing total of 73 home runs in 2001. The very next season, he continued to present his skill with a .370 batting average. Between 1999 and 2007, Bonds’ stats were shocking, hitting 351 home runs, winning four national league MVP awards, and racking up 780 RBI’s, 69 stolen bases, and just over one thousand hits. Alex Rodriguez, better known as “A-rod” is another huge name when talking about the greatest hitters in baseball. For nine seasons out of Rodriguez’s twenty two season career, 8.5% of the balls he would put into play were home runs. By the end of his lengthy career, he was able to collect 698 home runs, over two thousand RBI’s and runs scored, with over three thousand hits. He was also named All-Star fourteen times. With numbers like these, it is hard to

The new approach helped the Oakland A’s succeed because it was ethical. Billy Beane used numbers to evaluate the players. Numbers matter but can be misleading. By looking closely and understanding what he was doing Billy made good decisions based on numbers. The old approach was unethical because it misjudged the players. In the “old fashion statistics of the players some key important factors were left out. For instance the old statistics did not mention the number of walks a batter earned. This left out information misleads coach’s judgments and resulted in scouts undervaluing players.

MLB teams are finding new innovative ways to use analytics, one way is to use the statistics for a more effective way to evaluate free agents. “Astros employed an analysis based on the TrackMan system to acquire an unaccomplished pitcher called Collin McHugh, because of his fast-spinning curveball” (“Every Step They”). “They then told him to throw that pitch far more often during the next season, and he blossomed into a star” (“Every Step They”). General Managers and coaches can use these analytics to scout areas they have never scouted before. Analytics gives teams more resources and more assistance to evaluate players. “For example, by comparing the number of strikes called for a catcher in relation to every other catcher in the league, the data can illustrate how good any one catcher is at getting umpires to call strikes” (Nadler). “Additionally, general managers and scouts can use

(1) A study of the number of cars sold looked at the number of cars sold at 500

He wanted to keep track how many total bases each player touched and where they hit the ball. “(Rickey) hired the first statistician in baseball and used mathematical formulas to predict a team’s success in offensive and defensive categories. ”(“Wesley Branch Rickey” Bio in Context) By calculating what kind of team he had, whether it was a fast team that liked to run or a slower team that had the capabilities to hit a ball out of the park, Rickey could find players that fit into his team’s style.

In baseball a common factor for determining the quality of a player is batting average. (# of hits/# of bats) This statistic helps to show a player's ability to successfully make contact with the ball, which leads to RBI’s (runs batted

Since a comparison between homerun numbers has no merit because of the multiple variables existing between the eras in which Cobb and Williams played, it should be thrown out as a criterion for a great hitter. Therefore, the qualities that exemplify great hitting would be: batting average, batting titles, hits, doubles, triples, Runs Batted In, and the opinions of other baseball players and analysts.

The study of sports analytics is a common strategy and performance tool for players and fans. Many professional athletes will study analytics to help improve their game. Not only do professional athletes use this to their advantage but fans and workers in the sports world can use it to study the game and make wise predictions. Sports analytics also provides an in-depth evaluation of how numbers can track a particular sport and helps us follow along to a closer degree. Sports analytics provides a strategy for studying player’s habits and the selected sport as well as digging deep into the root of the game and understanding it from an analytic point of view.

Analytical data includes different psychological aspects of a player’s mentality. Stopwatches record times, tape measures calculate distances and heights, plus video gives scouts and coaches a visual look at player performance. However, for all their usefulness, they do little more than categorize athletes based on a series of numbers.

c.)Find a 95% confidence interval for the difference between the above obtained mean starting salaries.

I heard that there are two types of Sports Statisticians and It is academic and recorder. Academic Sports Statisticians use data to look for trends. For example, A hockey player named Robert Schutz recently analyzed overtime. The National Hockey league Record book provided with all the overtime games over the past ten years, he explains. They use the data to decide the value of overtime and to determine how long is a overtime period. Sports Statisticians usually have a master’s degree or very intelligent in Mathematics and Statistics. They are often university professors. They pursue Sporting Statistics as an interest.

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