Do you have to pay to be a winner?
Comparing payrolls and success of MLB teams from 1990-2019
Professional sports leagues have to maintain competitive balance in order to uphold the values of the sport that fans cherish. This is often difficult to do when these sports teams also exist to make a profit and winning can directly impact a team’s bottom line. Three of the major professional sports in the United States, the NFL, NBA, and NHL, have salary caps to enforce how much any team can spend on their roster; Major League Baseball (MLB) does not, resulting in a greater disparity between how much "big-market" and "small-market" teams spend. Take a look at this dataset to determine if there might be evidence to suggest that how much MLB teams spend (their payroll) is related to whether they make the playoffs or even win the World Series.
Variables in the dataset:
Each row in the dataset contains an observation of payroll data.
Year - The year for which the data are collected
Performance - A categorical variable with 3 values: Average Team, Playoff Team, and World Series Team.
Payroll ($M) - The actual average payroll (in millions of $USD) for a single year and level of Performance. It is a numeric variable.
Payroll in 2019 $ - The average payroll (in millions of $USD) for a single year and level of Performance adjusted for inflation by converting into 2019 dollars. It is a numeric variable.
Activity:
Make a graph so you can visualize the distribution of the payrolls before determining if there is a statistically significant difference between the means of each level of Performance. The payroll data also has an additional column to adjust for inflation so we can appreciate how much each of the payrolls are in terms of 2019 dollars. Show "Performance" on the x-axis and "Payroll" on the y-axis and choose the scatter plot icon to make a dot plot. Take a look at what happens when you select the inflation-adjusted payroll for the y-axis. It may help you to visualize the data if you add Descriptive stats by checking the box to the right of the graph.
Questions:
Is there a difference between the average payroll for teams that make the playoffs and the average payroll across all teams? Remember you can exclude all world series teams by clicking the Values button on the column header for Success Level. From there check a box to exclude rows with certain values.
Just like all datasets, there are some anomalies you should address. Look at the data table and try to guess which season was shortened by a player strike.
Is there a difference between the average payroll of the World Series Champions and the payroll for the league as a whole? How about compared to playoff teams as a whole? Adding descriptive stats with the check box at the right will show you averages.
In 2002, the MLB introduced revenue sharing in order to increase competitive balance. Based on the data here, do you think that it had an impact? Pro tip: You can exclude rows from the graph by checking the exclude box when viewing data as a table.
Use the Graph Driven Test button (located just left of the blue “Appearance” button) to carry out an ANOVA test on the payrolls of average, playoff, and World Series teams in 2019$. What do the results of this statistical test suggest?
These data were derived from fueledbysports.com and cross-referenced with data from usatoday.com.
Tags: SEP 4, SEP 5, Sports, Baseball, Statistics, ANOVA, Dot Plot