Baseball Operations For The Chicago Cubs

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

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

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

The game of baseball has been around for approximately 150 years and throughout its history there have been different genres used to discuss or write about the sport. The two genres are a blog post and a news article about win-loss projections for teams. Each genre will look at the sport and write about the projections in a different way because they have different authors, audiences, and purposes. The two different genres will also differ in the style and context because they have different reasons and goals for their writings. The blog post will attempt to prove how the preseason predictions made by the news article are unreliable. On the other hand, the news article will attempt to show how the predictions are still trustworthy. The genres

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.

Qualitative research plays a huge role in this article because of Rizzo’s decision making this season. This method explains Rizzo’s decision and makes him look a little better than the people persuaded him. He did this simply for security for the future, which makes total sense but most fans live for the now and professional baseball managers have to worry about winning now, but also later in the future and in the nationals situation setting an inning limit on Strasburg was the smart thing to do.

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,

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.

The game of baseball has been argued to be the number one game in America and also around the world. Respectively the game is also known as “America’s pastime” had over 14 million people in the U.S. alone watching the World Series in 20151. Due to the growing popularity of baseball throughout the world the players of Major League Baseball (MLB) have become more diverse. Since 1950 when baseball started to grow in popularity the attendance per game has risen over 40%2.

My study proposes to examine the New York Times sports pages between 1997 and 2017 as a way of testing some ideas about the nature of the changes in the discourse about baseball as that discourse has evolved over the last 20 years. Although these ideas did not necessarily take hold in professional baseball circles until the 21st century, outsiders like Bill James have been promoting non-traditional baseball statistics as more accurate ways of describing the game since the 1970s, while in the 1990s Baseball Prospectus, a publication which debuted the PECOTA predictive baseball model developed by eventual data celebrity Nate Silver, began to spread these ideas to increasingly wide groups of baseball fans. Today, these ideas have widespread popularity, and the yearly Bill James Baseball Abstracts and Baseball Prospectus anthologies both have high circulations, while websites like FanGraphs, which approach baseball journalism from a statistical point of view, have significant daily readership (among them, yours truly).

Accoarding to David Grabiner who wrote the article “The Sabermetric Manifesto” at seanlahman.com, sabermetrics is defined by Bill James as “the search for objective knowledge in baseball.” This is what the sabermatricians use as their key to answer so many questions that plague the game of baseball like “what player for the Kansas City Royals contributed more overall output to their offense?” or “How many homeruns is a certain player projected to have in the future years to come.” These are the questions that all the fans and coaches and managers are asking themselves during the year and during the off season. With all of these questions, this is Graham 2 where the sabermatricians use the stats that are kept during the year to try and debunk

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