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Should Mlb Teams Have Price Caps

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Submitted By vin1291
Words 1464
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Business Problem
The goal for every firm is to increase earnings, and it is no different for Major League Baseball (MLB) teams. A Major League Baseball team can reasonably expect to make the most money when they win more games due to more advertising sales, merchandise sales, etc. as a team becomes more popular. However, the way in which to increase winning percentage of a team has always been highly debated. Some of the most popular opinions on how to do this have been to: 1) increase a team’s payroll, 2) decrease the age of the team by signing younger players, and 3) increase attendance at baseball games.
First, since MLB does not have a salary cap, some teams focus on the aspect of having a large team payroll to acquire more talented players. Many people, such as economist Dr. Gerald Scully, believe this to be one reason why large market teams such as the LA Dodgers and NY Yankees always perform above average. He states, “How good a professional sports team is depends, of course, on the quality of its players. Because teams compete for better players by offering higher salaries, the quality of a team depends largely on how strong it is financially. The financially stronger teams will, on average, be the better teams. And they will also, on average, be the ones in bigger cities, because more revenues can be made in bigger cities.” (Scully) Second, some teams will acquire younger, up-and-coming players, in the belief that these players are in their prime and may be less prone to injury and better suited to playing a long season. “Out of the top ten offensive WAR leaders this past season four were 25 or younger while just ten years ago, in 2002, there was just one such player in the top ten.” (Age) Third, some teams will attempt to increase attendance at home games through the use of promotions in order to garner support for their team.
The research question that we will investigate is a MLB team’s regular season winning percentage, and how it is affected by the team’s player payroll, average age, and the percentage of attendance per home game.

Motivation The motivation for our study is that we are all baseball fans. One day we were all watching the movie “Moneyball” which is about how statistics can directly correlate with getting wins in Major League Baseball. We were very interested in the actual fact that in the movie, the Oakland Athletics were a team that did not have a high team payroll. One of the biggest reasons why the Oakland Athletics are faced with the problem of getting wins is because they cannot afford the higher valued athletes because there is no salary cap in baseball. This means teams with more money can afford these kinds of players. Throughout the movie, the motivation to build the team was through statistics that would prove to get a higher winning percentage. This concept caught our attention and that is how the idea was born for us to research and test if there was a correlation between certain statistics that affect the game of professional baseball. Another motivation is that we were are very curious how the other 4 major professional sports in the United States all have some sort of salary cap that was put into place so the amount of spending per team was on a even playing field. Usually the way people think of getting a higher winning percentage is by having the best players but we wanted to think there was another way. All over baseball websites and sporting news channels, baseball is always explained through statistics. Our group decided to research more into this and started to collect data to try and prove our motivations.
Data collection method
We calculated the winning percentage based on games won and games played taken from ESPN.go.com, for the Teams Payroll we used the sites Deadspin.com and Gammonsdaily.com, we used Baseball-reference.com for the Average age of players, we calculated the Percentage Attendance by gathering Average people attended per game from ESPN.com and the Total stadium capacity was taken from MLB.com
Team Payroll -- http://deadspin.com/2013-payrolls-and-salaries-for-every-mlb-team-462765594 -- http://www.gammonsdaily.com/2014-mlb-opening-day-payrolls/
Winning Percentage -- http://espn.go.com/mlb/standings/_/year/2013
Percentage Attendance -- http://www.baseball-reference.com/leagues/MLB/2014-misc.shtml
Average Age -- http://www.baseball-reference.com/leagues/MLB/2013.shtml

Numerical Highlights
There was a lot of data collected during this group project. While collecting all the data, we noticed some data that stood out for us. The wide range of the team payroll was a huge standout because LA Dodgers had over a 200 million dollar payroll compared to the Houston Astros who had a between 24 and 44 million in payroll for the past 2 years. Another number that stuck out to us was the increase of payroll from 2013 to 2014 for a majority of teams. The LA Dodgers went from 216 million to 235 million dollars. The Houston Astros jumped from 24 million dollars in 2013 to 44 million dollars in 2014.

Hypothesis testing: have a multiple regression model here with the winning percentage as the y, team payroll as X1, Average age of players as the X2 and the percentage attendance as X3 to check if any of the variables relate to the winning percentage at a level of significance of 10%. The equation for the model would be “Y = β1X2 + β2X2 + β3X3”
H0: β1 = β2 = β3 = 0
Ha: Any of the β ≠ 0
Conducting the multiple regression analysis on the data we get that the Significance F = 0.0154 < 0.1, so we can conclude that the overall model is significant
But if we see the variables p-values, we find that only the variable “Average age of players” is significant so we eliminate the other two variables and conduct a regression analysis for the winning percentage and the average age of the players.
We can get a model that is significant as the P-value < 0.1 and therefore the equation will be
Winning Percentage = -0.2419 + 0.0262 * Average Age of Players i.e. β2 ≠ 0
We have enough evidence to show that the winning percentage depends on at least one if the variables, but because the model is only 13.7% good we can infer that there might be many other variables that the winning percentage is dependent on other than Average age of players.
When we performed a singular regression test, we found each of the 3 variables we tested (Team Payroll, AVG age of players, and percentage of attendance) all are correlated with the winning percentage. The models are good for only 10% for the percentage attendance and 9% for the team payroll.
Winning Percentage = 0.3933 + 0.1527 * Percentage Attendance
Winning Percentage = 0.449 + 4.6*10^-10 * Team Payroll

Here the type 1 error would be when we assume that the winning percentage is dependent on at least one of the variable when it is not.
Type 2 errors would be when we assume that we don’t have enough evidence to prove that the winning percentage is independent of all the three variables when it is actually dependent on at least one variable listed
Shortcomings
There are three main shortcomings with our experiment that need to be addressed. One shortcoming of the research is the number of years for which we gathered data. The only data that was gathered and inputted into the regression model was for the last two regular seasons of MLB baseball. More defined trends may have been noticed if data had been gathered for more seasons. Another factor that could have been added into the research was coaching staff experience. The coach can have a large effect on game strategy, as well as on the players throughout the season, both in physically helping them to prepare, and mentally by making sure players remain as competitive as possible. A trend may arise that a more experienced coach can lead a team to a higher winning percentage. A third factor that could have had an effect on winning percentage is the number of high-paid players (outliers) on a particular team. A team with a few very high-paid players may skew the results of the regression. For example, the 2013 SF Giants had three players who combine for 44.37% of the salary, yet they only had a winning percentage of .469. Perhaps taking another financial measurement such as median team salary or removing the outliers from the team salary may have been more correlated.

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