Project Proposal : Simple Linear Regression Project

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.

There are a lot of aspects that should be taken into account when determining what makes an athlete successful. One could argue that natural ability is one of the determining factors which leads to success. For example, if an athlete has dreams of playing in the NBA then height makes a difference. There aren’t many successful players in the league under six feet tall. One could argue that level of competition plays a role in the development of an athlete. This could include the skill level of the opponent or outside factors such as playing conditions or game circumstances. These types of arguments play a role in athlete success, but will not be used in this paper because they are factors that are out of the athlete’s control. This paper

Iterations of analysis eliminated data points that were listed as “unusual observations,” or any data point with a large standardized residual. After 5 iterations, the analysis showed improved residual plots. Randomness in the versus fits and versus order plots means that the linear regression model is appropriate for the data; a straight line in the normal probability plot illustrates the linearity of the data, and a bell shaped curve in the histogram illustrates the normality of the data.

In this project, many different statistics were used to try and predict the winners of the NCAA March Madness tournament. To do this, statistics were tested from the previous year to see if they moderately correlated to winning games. When a stat is moderately correlated, that means it relates to winning. Using a scatter plot, a graph that gives a visual of whether or not a stat is correlated, correlation coefficients were found for each stat. The correlation coefficient is a decimal that shows if a set of numbers is moderately correlated. After finding stats that were the closest to being moderately correlated, a metric was put together that used the best stats to determine which teams will win games. The stat that was the closest to being moderately correlated was the turnover ratio of the teams. Another stat that was almost moderately correlated was RPI, or Rating Percentage Index. This stat uses a team’s win percentage, their opponents’ win percentage, and their opponents’ opponents’ win percentage to rank teams. Also, missed field goal percentage was a stat that was used.

When thinking of the National Basketball Association (NBA), the players who come to mind are usually Michael Jordan and LeBron James. Many different opinions exist between the different generations of each player, with the older generation usually liking the Jordan era and vice versa with LeBron James. While the debate about who the greatest player of all time may never end, there are a few ways that the best can be determined; through looking at the player’s childhood to adulthood, statistics, and achievements on and off of the court, one can decide for themselves.

6. Why is the black line so much more variable than the red line? What 's the difference between the data they show?

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.

“I aspire to be a GM for a sports team,” this comment would surprise my classmates who know me better as a musician and thespian. However, my passion for researching statistics, assessing talent, and discussing the business behind sports provides the impetus for my career choice. Possibly, I will be the next innovator to Major League Baseball, like GM Billy Beane who pioneered the use of applied statistical analysis to player evaluations and changed the whole concept of recruiting.

Statistics in baseball have always existed on a player and team basis, with some common statistics being hits, runs, earned runs, runs batted in, home runs, etc. Since the publication of Bill James’ abstract (1986), however, there has been large growth of interest in baseball data and analytics, and, more specifically, a large growth of interest in a branch of baseball statistics known as sabermetrics. This branch of statistics has been further spurred on by the publication of Moneyball (Lewis, 2003), and even more so by the movie debut of Moneyball in 2011. Sabermetrics uses baseball’s common statistics and creates a statistic that more efficiently determines the performance of baseball players.However, there is still some

To start off, I made a spread sheet full of statistics in the program Excel. I listed ten of the leagues top players to calculate their averages and standard deviations in points, assists, offensive and defensive rebounds. To do this I just used the Excel function. The players I used were Kevin Durant, Lebron James, Carmelo Anthony, Stephen Curry, Kobe Bryant, Derrick Rose, Russell Westbrook, Chris Paul, James Harden, and Anthony Davis. I chose these specific players based on numbers, age, position, contract, salary and popularity. From there in each category in the

All variables are yearly team totals. The dependent variable, W, represents wins. My regressions will estimate the effects of the following independent variables on wins.

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.

With the advent of technology in baseball expanding with instant replay and newfound statistics to measure player success the MLB and their respective clubs are trying to find new ways to answer the question: How do measure one players worth from another? Is there one statistic by which we can measure performance or player worth across a specific league? There is a strong idea in baseball today that with Sabermetrics someone can only be held responsible if what they make a difference to their team (WAR: It Works | FanGraphs Baseball."). There are two specific ways to judge the extent to which a person made a difference to their team. The first way is to make an action-based contrast and compare the actual action a person took with alternative actions she could have taken (Gerstenberg). In the following modeling experiment the researcher will attempt to answer these questions and model the Sabermetrics, Wins Above Replacement, statistic to measure its effectiveness and compare to its monetary worth. This theoretical model of sabermetrics will give insight to how teams prepare for a players injury, a player who leaves the team or leaves via Free Agency and which players are underperforming.

The use of computerized statistical analysis has developed into a vital tool in the world of sports. Using data analytics to project player abilities, indicative of their potential future performance, is now an important part of team decision-making. This analysis can provide information that makes the difference between adding a quality player, as opposed to one who does not perform up to expectations.

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.