A Comparison Between the Mets and the Yankees Many baseball fans criticize the New York Yankees for “buying” the best players in Major League Baseball.

Slides:



Advertisements
Similar presentations
Sta220 - Statistics Mr. Smith Room 310 Class #14.
Advertisements

Baseball Pay and Performance By: Mikhail Averbukh Scott Brown Brian Chase.
10-3 Inferences.
Baseball Statistics By Krishna Hajari Faraz Hyder William Walker.
Overview Motivation Data and Sources Methods Results Summary.
CSE 219 COMPUTER SCIENCE III PROJECT INTRODUCTION: A FANTASY BASEBALL DRAFT KIT.
Chapter 10: Hypothesis Testing
By Drew Meek and Will Seidel. Why These Players We chose Pettitte and Santo because neither are, at this time, in the Hall of Fame for different reasons.
Comparing Two Population Means The Two-Sample T-Test and T-Interval.
Babe Ruth. Background Born February 6 th, 1895 in Baltimore, Maryland Died August 16, 1948 Real name was George Herman Ruth.
CHAPTER 11 Inference for Distributions of Categorical Data
MARE 250 Dr. Jason Turner Hypothesis Testing II To ASSUME is to make an… Four assumptions for t-test hypothesis testing: 1. Random Samples 2. Independent.
S TATISTICS Part IIIB. Hypothesis Testing. 3B.2 The observed  : the P-value n S’pose H O : μ ≤ 140 H A : μ > 140 α =.05 And sample results yield a Z.
Sample Size Determination In the Context of Hypothesis Testing
Ch. 9 Fundamental of Hypothesis Testing
Lecture 6. Hypothesis tests for the population mean  Similar arguments to those used to develop the idea of a confidence interval allow us to test the.
2014 Milwaukee Brewers Franchise/Roster Evaluation By: Philip Sophinos, Professor: James Wible 2014 Milwaukee Brewers Franchise/Roster Evaluation By: Philip.
McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. Business Statistics: Communicating with Numbers By Sanjiv Jaggia.
Psy B07 Chapter 1Slide 1 ANALYSIS OF VARIANCE. Psy B07 Chapter 1Slide 2 t-test refresher  In chapter 7 we talked about analyses that could be conducted.
Two Sample Project Example 5/6/2013 Ms. Browne made this up Saber metrics: TX Rangers vs. SF Giants.
Comparing Systems Using Sample Data Andy Wang CIS Computer Systems Performance Analysis.
8.1 Inference for a Single Proportion
Introduction to Statistical Inference Probability & Statistics April 2014.
14 Elements of Nonparametric Statistics
One Sample Inf-1 If sample came from a normal distribution, t has a t-distribution with n-1 degrees of freedom. 1)Symmetric about 0. 2)Looks like a standard.
Chapter 8 Standardized Scores and Normal Distributions
6.1 - One Sample One Sample  Mean μ, Variance σ 2, Proportion π Two Samples Two Samples  Means, Variances, Proportions μ 1 vs. μ 2.
Major League Baseball has been around since 1869 and is considered America’s Pastime because of this. Baseball is also the only major sport in America.
10.2 Tests of Significance Use confidence intervals when the goal is to estimate the population parameter If the goal is to.
Aluminum bats VS Wooden bats!
Confidence intervals are one of the two most common types of statistical inference. Use a confidence interval when your goal is to estimate a population.
AP Statistics Chapter 20 Notes
Chapter 5 Comparing Two Means or Two Medians
Normal Distr Practice Major League baseball attendance in 2011 averaged 30,000 with a standard deviation of 6,000. i. What percentage of teams had between.
The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers CHAPTER 11 Inference for Distributions of Categorical.
Chapter 11: Inference for Distributions of Categorical Data Section 11.1 Chi-Square Goodness-of-Fit Tests.
6/4/2016Slide 1 The one sample t-test compares two values for the population mean of a single variable. The two-sample t-test of population means (aka.
Significance Test A claim is made. Is the claim true? Is the claim false?
Hypothesis Testing. The 2 nd type of formal statistical inference Our goal is to assess the evidence provided by data from a sample about some claim concerning.
Continue viewing this PowerPoint to read all about the 27 Time World Series Champions! Mason Siegel Presented by: Mason Siegel January 10, 2012.
1 Objective Compare of two population variances using two samples from each population. Hypothesis Tests and Confidence Intervals of two variances use.
Logic and Vocabulary of Hypothesis Tests Chapter 13.
Welcome to MM570 Psychological Statistics
Business Statistics, A First Course (4e) © 2006 Prentice-Hall, Inc. Chap 9-1 Chapter 9 Fundamentals of Hypothesis Testing: One-Sample Tests Business Statistics,
AP Statistics Section 11.1 B More on Significance Tests.
CHAPTER 27: One-Way Analysis of Variance: Comparing Several Means
CHAPTER 27: One-Way Analysis of Variance: Comparing Several Means
The New York Yankees Learn fast facts and stats about the best team in the East, before you strike out! Presented by: Allegra Jacobs Project #12: My Favorite.
Created By Stephanie and Claire. What Will You Learn? His background & childhood His surprising adoption Origin of his nickname “Babe” His career and.
Comparing Systems Using Sample Data Andy Wang CIS Computer Systems Performance Analysis.
Example 10.4 Measuring the Effects of Traditional and New Styles of Soft-Drink Cans Hypothesis Test for Other Parameters.
Review: Stages in Research Process Formulate Problem Determine Research Design Determine Data Collection Method Design Data Collection Forms Design Sample.
1-1 Copyright © 2015, 2010, 2007 Pearson Education, Inc. Chapter 23, Slide 1 Chapter 24 Comparing Means.
CHAPTER 15: Tests of Significance The Basics ESSENTIAL STATISTICS Second Edition David S. Moore, William I. Notz, and Michael A. Fligner Lecture Presentation.
Section 9.1 First Day The idea of a significance test What is a p-value?
Chapter 14 Inference for Distribution of Categorical Variables: Chi-Squared Procedures.
Does higher club payroll in Major League Soccer directly relate to success? In US professional sports the big market teams or the teams that have the most.
Copyright © 2009 Pearson Education, Inc t LEARNING GOAL Understand when it is appropriate to use the Student t distribution rather than the normal.
Statistical Decision Making. Almost all problems in statistics can be formulated as a problem of making a decision. That is given some data observed from.
The Differences in Ticket Prices for Broadway Shows By Courtney Snow I wanted to find out whether there was a significant difference in the price of musicals,
Team Payrolls... Yay, or nay?. Our Question We were curious: Do teams with one player occupying a large percentage of payroll win more games than other.
Copyright © Cengage Learning. All rights reserved. Hypothesis Testing 9.
At Bats Hits Runs Doubles Triples Home Runs RBI’s Walks Batting Average Strikeouts.
Unit 5: Hypothesis Testing
Displaying and Describing Categorical Data
Significance Tests: The Basics
Chapter 7: The Normality Assumption and Inference with OLS
Science Fair – Baseball
Sample Presentation – Mr. Linden
Presentation transcript:

A Comparison Between the Mets and the Yankees Many baseball fans criticize the New York Yankees for “buying” the best players in Major League Baseball and starting their own sort of regime. Baseball fans often accuse George Steinbrenner, the owner of the team, of simply wanting to dominate all of baseball by throwing as much money as the franchise can afford at the most desirable players. Do this mean that money controls the system and players are no longer playing for JUST “the love of the game”? For comparison’s sake, let’s use the New York Mets as an example. The graph to the right is an overlying histogram comparing the different salaries for the two teams during the 2002 season. The blue lines are the Yankee salaries and the red are the Mets. The mean salary average for the Yankees was $4,818,792, while the Mets averaged $3,639,754. So, because the Yankees make more money (on average), does this mean they will be a statistically better team? One way to try to figure out an answer is by doing multiple confidence intervals in the different aspects of the two teams, i.e. pitching, home runs, RBIs. ClemensWellsMussinaPettitteHernandez LeiterAstacioTrachselEstesD’Amico Lets first look at pitching. What constitutes a good pitcher? There are a variety of different factors, but among them, for the purpose of this project, we will examine ERA, home runs given up and strike outs. I have selected the top five pitchers from both teams and have created a table, which is on the left. The null hypothesis is that there is NO difference in stats between the two teams and that they are equal in standings. By doing a 95% confidence interval for each of the categories, we can find out whether or not there is statistical evidence in favor of the null. Throughout these CI’s, if 0 falls within the interval, then we have evidence in favor of the null. Using Mini-Tab, I performed a two-sample t-test for the two pitching squads ERA. The computations gave me a CI of (-1.345, 0.517) with a P-value of.318. Because 0 DOES fall within the CI, I have my first piece of evidence to keep the null. Because the P-value is so high is also another sign to keep accepting the null. The same results apply for strike-outs. Using the same two-sample test, the confidence interval that Mini-tab gave me was (-38.1, 77.7) with a P-value of.445, even stronger than the first test. For the final test ERAStrike outsHome runs given up R. Clemens (Y) D. Wells (Y) M. Mussina (Y) A. Pettitte (Y) O. Hernandez (Y) A. Leiter (M) P. Astacio (M) S. Trachsel (M) S. Estes (M) J. D’Amico I conducted, my results were (almost not surprisingly) even more in favor of the null than the first two. My 95% CI was (-14.22, 8.62) with a P-value of.580. Therefore, amongst the pitching staff, we have evidence in favor of the null in that the Yankees do not simply buy up all the best players. The Yankee franchise has always been criticized for finding the best batters and offering them large salaries in order to come play for the team. Does this mean that the Yankee batters will WilliamsGiambiJeterPosadaSorianoAlomarVaughnPiazzaAlfonzoCedeno always do a better job simply because they are being paid more? Looking at the batting statistics for both teams, I decided that the two most logical categories to test on would be RBI’s (runs batted in) and total number of hits for each player. Observe the table to the right to see the top five batter’s for each teams statistics. Looking at the numbers for the Mets (M) and Yankees (Y) at first indicate that there might be Total hitsRBIs J. Giambi (Y) D. Jeter (Y)19175 A. Soriano (Y) J. Posada (Y)13799 B. Williams (Y) M. Vaughn (M)12672 R. Cedono (M)13341 M. Piazza (M)13498 R. Alomar (M)15753 E. Alfonzo (M)15156 some evidence against the null hypothesis, but only further testing will confirm this fact for us. Lets go back to the Two-sample T-test and confidence intervals. First, lets look at the total number of hits. Using Mini-tab, the confidence interval I got was (-79.7, -6.7) and a P-value of These results are very much different than the results obtained from the pitching statistics, as these new computations provide evidence AGAINST the null hypothesis. To confirm our finding, let’s look at RBI’s to see if the pattern follows. Using Mini-tab again, I got very similar results as the total hits test. The confidence interval is (-65.2, -6.8) with a P-Vale of So once again, we have evidence against the null hypothesis and in favor of the alternative hypothesis, in that players might be more attracted to a particular franchise, in this case, the Yankees, due to offers of high salaries. By looking at the results from the various T-tests that I have made, we can draw several conclusions. It would seem that the pitching squads are not as concerned with salaries as the batters are. As you can see from the pitching table above, many of the numbers are very similar and there is not a huge difference in statistics between the two teams. This is despite the fact that the mean salary for the five Yankee pitchers in 2002 was $7.5 million, while the mean salary for the Mets was $5,958,000, a difference of about $1.5 million. The Yankee franchise, however, seems to be much more concerned with getting better hitters, as you can see from the striking difference in total hits and RBI’s. The mean salary for the five Yankee batters was $7,526,000 and the average for the Mets was $7.12 million. At first, one might question why there is a larger difference for the pitching staff rather than the batters, when clearly the Yankee roster had better stats than the Mets. This difference is due to a lurking variable in Alfonso Soriano’s salary, which is only $630,000, while Williams, Giambi, Jeter and Posada each make $12 million, $8 million, $13million, and $4 million, respectively. The reason for this drastic difference in salary is because the 2002 season was only Soriano’s second year playing professional baseball. In summary, it would seem that the Yankee franchise is very much concerned with recruiting excellent batters, and those batters are more than willing to play for them, especially for a team that has won multiple world series’ and is considered to be the best franchise is Major League Baseball history. Eric RaicovichQuantitative ReasoningProf. B. HartlaubMay 6, 2003