MLB Analysis – Does Paying for Players Payoff?


Professional Sports Teams have long been present in the United States, and the focus of many economic studies. Although these teams seem to be similar to normal corporations, research has found that they do not always function with the same goals regarding profits. This paper focuses on team’s annual payroll, among other independent variables to examine what factors actually contribute, and are indicators of, the team’s annual revenue. An OLS model is used to examine this relationship. Data is used from all 30 MLB teams, spanning from 1991 – 2011. Using two different models, the independent variables are tested against the dependent variable, annual revenue. Independent variables included win percent, payroll, market size, playoff appearance, World Series appearance and market size among others. Both models suggested a, non-hypothesized, negative relationship between a team’s win percent and revenue. In regards to the variable of interest, all models found that there is a significant positive relationship between payroll and a team’s annual revenue.

I. Introduction:

For over 100 years, Major League Baseball (MLB) has been known as “America’s Favorite Pastime”. The MLB is composed of 30 total teams, 15 in the American League and 15 in the National League. There are MLB teams all over the nation, with some cities even being home to multiple teams. Professional Sports in the US have become one of the highest paying “careers” to have. Professional Athletes are now among some of the wealthiest people in the United States. The amount of money that goes into the creation, managing and running of a team is very large. So how do these teams bring in enough revenue to continue being successful from a financial sense? When discussing issues within the MLB, many times people focus on their favorite team or player statistics and salaries, a team’s winning percentage or championships won. However, what about the other side of a team? What factors significantly affect a MLB team’s annual revenue, and specifically does increases payroll bring in more revenue?

To answer this question I have collected data and information from all MLB teams from 1991 – 2011. Using an Ordinary Least Squares (OLS) I tested how different variables impacted a team’s revenue. First, I included ten different variables that I hypothesized would impact a teams revenue. After running the initial model, I found that a team’s attendance, payroll and ticket price significantly affect the team’s annual revenue.

Surprisingly, I found that some variables I expected to be significant, in reality were not. After deeper analysis, it was found that many of the independent variables were correlated with one another. Following this observation, I ran two more models without significantly correlated variables. In conclusion, I found that increasing a teams payroll does indeed help increase the team’s annual revenue.

II. Background

Since the beginning of the professional sports era, professional sports teams and leagues have been the source of many economic studies and papers. Major League Baseball (MLB) has long been known as America’s Favorite Pastime, however it has also been the base of many economic studies focusing on, productivity, efficiency and competitive balance among others.

In 2004, a study focusing on payroll inequality in relation with production found “a strong, negative relationship between salary inequality and win percentage” (Molina, 128), meaning that a larger inequality in a team’s payroll may result in a lower winning percentage. Additionally, in the same year, it was concluded that “large-market teams were more likely to be inefficient, and large-market teams were also more likely to have higher payrolls (Einolf, 141). Both of these studies focused on the comparison of large and small market teams and their abilities to have an efficient team with several market size and payroll amount disparities. It was found that it was common for large market teams to spend large amounts of money on free-agent players, yet not receiving the individual marginal productivity needed from these players (Einolf, 2004). Molina also touched on the occurrence of smaller market teams increasing efficiency even though they may not have the talent larger markets have (2004). In fact, “results indicate that this is exactly the way some small-market/low revenue teams are able to compete” (Molina, 137).

Although profitability was not the main topic of Molina and Einolf’s studies, both did provide insight on MLB profits and revenues. Specifically, Molina found that although some larger market teams did focus on the acquiring of superstars, which increased payroll but in many cases it did not increase winning percentage, the action may indeed maximize profits (2004). However, Einolf did not come to the same conclusion regarding market size and profitability, stating “small-market teams struggle to turn a profit. Many large-market teams could easily be profitable, but they suffer as well because of high payrolls” (148). In a society primarily focused on profits, the MLB teams are not a typical corporation only looking for profits. “The sports franchise owner faces powerful incentives to report minimal profits or even losses” (Einolf, 131). Einolf says that owners may justify this goal of producing low profits, because it may result in public funding of a new stadium, or other things of that nature, and potentially help in negotiations with player unions.

Because teams owners may focus on other things than running a profit, most studies have focused on team revenues instead. There are many things that contribute to teams revenue, however the two largest factors are gate receipts and media contracts. Market size has the potential to greatly influence these things.

In 2007, Gustafson and Hadley found that market size “has a significantly positive impact on local revenues” (260). The authors go farther to state a that “increased population must lead to higher revenue and then that higher revenue must be used to buy player talent that will lead to increased win percent” (251), in short a larger population leads to a higher winning percentage. A 2009 study by Krautmann found similar results, that “teams with above-average payrolls won 98% of all post-season games” (3273).

It has also found that an additional win is actually valued more highly by larger market teams than smaller teams (Hadley & Gustafson 2007, Burger & Walters 2003). With this, they concluded that it is expected that larger market teams are willing to increase payroll to obtain more talent. “The incentive to spend generously to acquire players who performance holds the promise of more victories is great” (Burger 2003). However large market teams have much more capability to do this than smaller market teams.

Although many authors do believe increased payroll does lead to better performance, in 2002 Hall and Szymanksi decided to test the hypothesis of the causality between payroll and performance, in both directions. Immediately they touch on what draws star players to larger market teams, stating that it “may be players are attracted to the largest revenue markets because of the greater opportunities to earn endorsements and promotional income outside the club” (152). They suggest that a player’s rationale does not only depend on the salary offered by the club but also the potential outside salary they might receive in that market. The results of the study found that there is indeed causality in both directions, between performance and payroll, and that restrictive agreements “have made it less likely that teams can fully use their financial muscle to buy success in baseball” (Hall 166). This does not support the claim that large markets with high payrolls have higher winning percentages.

As mentioned earlier, there have been many studies focusing on the competitive balance of the MLB and what actions may truly improve the balance among the league. The NFL now has a revenue sharing agreement and salary cap that is believed to have helped improve the competitive balance in the league, by restricting the larger market teams ability to control most of the market (players) because of their larger payrolls. The MLB does have a revenue sharing agreement however does not have a salary cap, which still enables large market teams to spend great amounts on salaries. Although there is absence of a salary cap, there is a luxury tax that teams must pay if they exceed a certain payroll amount, however it has not deterred teams like the New York Yankees from exceeding the amount and paying the luxury tax. These large teams see greater potential reward than costs in the situation of the luxury tax.

In 2007 Solow and Krautmann focused on the direct effect that revenue sharing had on teams on team operations and the overall competitive balance of the MLB. Just one year later, in 2008, Lewis published s study that too focused on managing the competitive balance in the MLB. Solow and Krautmann found that marginal revenues “were indeed reduced by the redistribution efforts undertaken by the MLB from 1996-2001” (957). However, the authors found that with the revenue sharing, winning percentages stayed essentially where they would have been if there had been no redistribution of revenues. The pair concluded that “redistributive efforts had failed miserably in achieving the goals of moderating payroll disparities and improving competitive balance” (957). Lewis’ study also focused on other negative effects of revenue sharing. Stating that “the current approach to revenue sharing is problematic because these types of plans lessen the incentives for producing local revenues through winning because revenues can also be obtained through revenue-sharing distributions” (547). He believes that instead of improving the competitive balance, revenue sharing will deter teams from winning given that they may be able to receive a larger amount from revenue sharing - and give up a smaller amount of their own revenue. In relation to this, Lewis says that there have been reports that teams with the highest payrolls tend to lose money each season (2008), which may be contrary to some beliefs that come from putting much focus on the link between payroll and winning percentage.

Major League Baseball along with other professional sports both within and outside of the United States have long been studied by economists. There have been many common themes among these papers and journals that include, productivity, competitive balance and efficiency. The topic of profits has not been a common theme, which may be attributed to the fact that professional sports teams are not run the same way many corporations are. Many times there is not as much as an emphasis on profit than on other thins. If a team is not as successful during the season as fans may have hoped yet finished the season with a profit, people may feel as if the owner and coaches should have spent more money on payroll rather than only focusing on profits. Many fans do not focus on their team’s finances, but instead on the success of the team’s season. Owners and managers know this, and see importance on keeping a solid fan base leading to their own focus not solely being on the profitability of the team.

III. Model and Data

Using an OLS model, I tested the significance of ten different variables in relation to a team’s annual revenue. An OLS model calculates the slope coefficient of each variable so that the sum of the squared distance between actual results and predicted results is minimized. In order for the OLS to run correctly, one of the requirements is the absence of multicollinearity between independent variables. The hypothesized OLS model is as follows: REVA = C + βAT + βAWP + βLWP1 +/- βLWP2 + βPA + βPC + βTPA + βWP + βWSC + βMKT

My dependent variable, REVA represents the annual revenue for each team for a given season, adjusted for inflation.

Total season attendance at home games in a current season is represented by AT, I expected AT to be significant in the positive direction because of the revenue that will be made by the gate receipts. AWP represents a teams average winning percentage over a three year period, again I expected this coefficient to be positive because a higher average winning percentage will most likely bring in more attendance, more merchandise bought outside of the stadium and higher media contracts among other revenue gaining items.

There are also two lagged winning percent variables that are reflected in the model. A team’s winning percentage is lagged both 1 and 2 years. LWP1 represents the win percent lagged one year. I think that LWP1 will positively affect a teams revenue because, similar to the AWP, a winning season will have a positive impact on the following year (attendance, merchandise etc). However, when it comes to LWP2 I am unsure about the effect, if any it will have on a team’s revenue. I am not sure if a winning season two seasons prior will actually help a team, especially if it is follows an unsuccessful season.

PA represents the teams total Payroll adjusted for inflation. I expect this variable to be positively related to revenue because teams who are able to spend larger amounts on their player salaries are likely to have stronger players on their team. Additionally, highly paid players are likely to be popular players of which fans want to see. Playoff appearance is represented by the dummy variable PC, with 1 meaning that a team attended playoffs in the current year, and 0 meaning they did not. I think this will again be positive because of the popularity and support a team gains when making playoffs. I do have some reservations regarding potential correlation between PC and winning percentage.

TPA is the average ticket price for a team’s home contests, adjusted for inflation. I think that this is again positively correlated. I believe this will be positive because of the factors that provide a team with the ability to increase ticket price are enough to still draw enough of a demand from fans. I think that more successful teams will increase the demand for tickets, which allows the team to increase the ticket price.

The variable WP is the team’s straight winning percent for the current season. I am concerned that it will be correlated with the average winning percentage however I wanted to run it in the model to begin with and see if I will have to omit it. In regards to how it relates to the teams revenue, again I think it will be positive for many of the same reasons listed above regarding AWP and lagged win percent.

There is another dummy variable (WSC) included in the model that accounts for whether or not a team appeared in the World Series Championship in the current year. When a team appears in the World Series the dummy variable is equal to 1, and if they did not it is equal to 0. Again I am concerned about collinearity between World Series appearances and playoff appearances. Similar to many other variables, I expect WSC to have a positive affect on the team’s revenue.

The final variable that was run in the regression was a variable that measured the Metropolitan population in the team’s home city. This is represented by MKT, and I expect the affect to be positive on the team’s annual revenue. I think larger markets are able to increase payrolls as well as attendance, which again, I believe will increase the team’s revenue.

The data set represents all MLB teams, with observations from 1991 to 2011. In 1994 there was not a World Series, so there are not WSC observations for that year. Majority of the data was obtained from Rodney Fort’s Sports Economics (Fort, 2012) website online. Rodney Fort is a known and respected Economists who has published numerous Sports Economic papers. Data for the variable MKT was obtained from Forbes Business of Baseball (Ozanian, 2012), which is a valuation of each team and includes much statistical information for each team. Information regarding both World Series appearances and Playoff appearances was collected from This website is a large portal of many details, facts and statistics pertaining to Major League Baseball.

IV. Results

Model 1

Using Eviews, I ran the first model with all variables included. I did this because I wanted to see what the regression output would be with all variables even though I was worried about potential collinearity. As I assumed, the model was not producing the number of significant variables that I had hoped for. (Appendix 1) With this initial regression, the only significant variables were AT, PA and TPA. The R-squared was 0.715504, which I was pleased to see.

After I ran the initial model, I used a correlation matrix (Appendix 2) to see if there were any independent variables that were highly correlated with one another. After seeing that many variables were correlated with one another, I decided to run an additional two models with two different sets of independent variables.

Model 2

The second model that I ran did not include; average win %, ticket price, World Series appearance or market size. (Appendix 3) There were better results with this model regarding the significance of variables, and the R-squared only dropped slightly.

Win % was significant at the 10% level, in the negative direction. With a coefficient of -58.12, suggests that on average, a .1 increase in wins (10%) will reduce a team’s revenue by $5.812 million dollars. This was not in agreement with my hypothesis, which was the assumption that an increase in Win % would increase a team’s revenue.

Both of the Lagged Win % variables were significant at the 5% level. Again, they were significant in the negative direction. The coefficients for lag (1) and (2) were -56.83 and -61.67. This means that on average a .1 increase in win % would result in a decrease in revenue of $5.683 million the following year, and a decrease in revenue of $6.167 million the year following. Again, these were not the results I was expecting for lagged win percent.

The variable payroll was again significant at the 1% level, as it was in Model 1. In Model 2, the coefficient was 1.24; meaning that on average a $1,000,000 increase in payroll would result in a $1,240,000 increase in revenue. This was in the positive direction as I had hypothesized. This result suggests that increasing your payroll will indeed help increase your annual revenue for that year.

Attendance was also significant at the 1% level in the positive direction. A coefficient of 1.46E-05, means that on average, increasing total attendance over the season by 100,000 will increase your revenue by $1,460,000. This makes sense conceptually because it seems that increasing attendance will indeed bring in more revenue because of more tickets being purchased.

The final variable in this model that was significant was World Series appearance, at the 10% level. A positive coefficient of 12.86 did support my hypothesis that attending the World Series as a team would increase the team’s revenue. The model suggests that on average, attending the World Series would increase the team’s annual revenue for that year by $12.86 million dollars.

The R-squared for Model 2 was .658, which was not quite as high as the R-squared in Model 1. However, I was very pleased with this R-squared because of the issues with correlated independent variables present in the initial model.

Model 3

The third model that I ran was again trying to rid of some of the collinearity problems that were present in Model 1. For Model 3, the following variables were used; Average Win %, Ticket Price, Payroll, Market Size, and World Series Appearance. (Appendix 4) The R-squared for the third model was .7002. I was pleased that the third model was accurately able to explain 70% of the variation in the dependent variable.

Four of the five variables used in this model were significant. Average Win % was significant at the 1% level with a coefficient of -77.58. Consistent with the previous two models, it suggests that increases in win percent will decrease a team’s annual revenue. Specifically, model 3 shows that on average, a .1 increase in win percent will lead to a $7.758 million decrease in a team’s revenue in that year.

Ticket Price was also significant at the 1% level, however in the positive direction. This supported my hypothesis that increasing ticket price would result in increased revenue. The variable had a coefficient of 2.69, which means that on average a $1.00 increase in ticket price would increase revenue by $2.69 million dollars.

Payroll was the third variable that was significant at the 1% level. As it was in the previous two models, and supporting my hypothesis, the variable is positively related to annual revenue. In Model 3, the coefficient, of .912, suggests that increasing revenue by $1,000,000 will increase a team’s annual revenue by $912,000.

The fourth and final significant variable was significant at the 5% level. A coefficient of 12.62 shows that on average, a World Series appearance will increase a team’s annual revenue by $12,620,000 in that year.

Variable of Interest

My variable of interest, payroll was significant in all of my models, in the positive direction and at the 1% level of significance. The results of the three models suggest that on increasing payroll by $1,000,000 will lead to an increase in revenue of about $1,000,000. There are many reasons why increasing a teams payroll may lead to an increase in revenue. One reason might be that increasing your revenue will enable you to bring on popular, star players to your roster, who will help increase attendance at games. Additionally these players may help your teams make playoffs, or the World Series, which also helps increase payroll.

V. Conclusion

After many adjustments to the original model and idea, I found that in order to increase a team’s revenue, there are some things that can be done internally however there are also some factors that a team cannot change without actually moving the team. First, a great way for an owner to increase your revenue’s as team is to increase the number of tickets sold at each game. This can be done a number of different ways. There have been many economic journal articles that actually focus directly on this variable, as a dependent variable.

In relation to increasing attendance at games, a team with a higher winning percentage may help increase attendance at games which will help your team gain higher revenues. However, winning games is not something you can just decide to do, there are many outside factors that will affect a team’s winning percentage. As it shows in all three models, payroll does indeed help increase a team’s annual revenue. If a team is able to spend more on payroll and player salaries they may have the ability to sign the highest skilled players, potentially helping the team have a successful post season and while also increasing attendance at games.

Adjusting a team’s ticket price is something that can be done within the internal operations of the team. Each team will have slightly different results to an increase in ticket price, however overall if you increase your ticket prices slightly, you will potentially be able to gain a large amount of additional revenue.

The final variable that contributes to an increase in team revenue is the team’s market size. This is something that cannot directly be changed by the team each season. If a team is serious about increasing revenues, they may want to look into moving into a larger market. However, there are many costs that will be incurred when a team is moved and one must weigh both these costs and the benefits of moving a team.

When completing this project and paper there have been issues that I was not expecting and was unable to solve. First, there were many independent variables that were correlated with other variables so I omitted these variables the model, and ran two additional models. Secondly, there seemed to be some results from the model that did not agree with theory or past publications. This was specifically, the variable Average Winning Percent. The results that were produced from the model seemed a bit surprising. One would expect that a winning season would increase a team’s revenue for more reason than one (more attendance, more merchandise sales, playoffs, World Series). I would like to try and continue changing and running this model until I am able to find results that are more similar to other publications. I think there are still some variables that are not included in the model. For example, I think that it would be interesting to see how much of an effect media contracts have a team’s revenue. I think this will end up greatly affecting revenues. In future research I would like to include this variable and also try and find other variables that do indeed significantly affect a team’s revenue.

Appendix 1

Descriptive statistics of the variable

Variable Description Mean Std Dev Min Max Dep. Variable REVA Total annual revenue adj. for inflation 146.97 62.41 36.85 463.05 Ind. Variables AT Total Attendance during Season 2,335,761 741846.5 642745 448335 AWP Average Win % (3 Seasons) 0.50001 0.055628 0.350333 0.637667 LWP1 Lagged Win % 1 yr. 0.500228 0.070025 0.265 0.716 LWP2 Lagged Win % 2 yr. 0.500215 0.070259 0.265 0.716 PA Total payroll adj. for inflation 71.77053 35.05380 12.69880 239.5528 PC Dummy: Playoff appearance, current yr. 0.253425 0.435345 0 1 TPA Average ticket price adj. for inflation 21.43616 8.030978 11.25 76.61850 WP Win % current season 0.500079 0.070021 0.265000 0.716000 WSC Dummy: World Series appearance current season 0.068493 0.252807 0 1 MKT Metropolitan population in home city 5.924346 4.609772 1.6 19.1 Total 998 908 90

Appendix 2

Ordinary Least Squares regression results – Model 1

Variable Estimated Coefficient t- Statistic

Constant 82.46972 4.988531 Attendancea 1.39E-05 4.571233 Avg Win % -97.02389 -0.355513 Lagged Win % (1) -14.94123 -0.157160 Lagged Win % (2) -30.0532 -.0319499 Payrolla .752788 9.606746 Playoff Current 7.228872 1.542027 Ticket Pricea 2.695918 10.10743 Win % -22.47304 -0.232597 World Series Current 8.359844 1.30645 Market Size 0.588189 1.603768 Model Statistics Adj. R-Squared 0.709958 F – Statistic 129.0186 Number of Obs. 524 Total 998 908

        a indicate significance at the .01 level
        b indicate significance at the .05 level
        c indicate significance at the .10 level

Appendix 3

Ordinary Least Squares regression results – Model 2

Variable Estimated Coefficient t- Statistic

Constant 112.0734 6.302581 Attendancea 1.46E-05 4.465783 Lagged Win % (1)b -56.83042 -1.958865 Lagged Win % (2)a -61.76384 -2.270315 Payrolla 1.241642 18.48844 Playoff Current 6.023217 1.175769 Win %c -58.13299 -1.7552258 World Series Currentc 12.86570 1.850069 Market Size 0.269080 0.690742 Model Statistics Adj. R-Squared 0.658657 F – Statistic 124.2181 Number of Obs. 524 Total 998 908

        a indicate significance at the .01 level
        b indicate significance at the .05 level
        c indicate significance at the .10 level

Appendix 4

Ordinary Least Squares regression results – Model 3

Variable Estimated Coefficient t- Statistic

Constant 62.11199 4.258405 Avg Win %a -77.58242 -2.459120 Payrolla 0.911677 12.64998 Ticket Pricea 2.693839 9.942185 World Series Currentb 12.62176 2.071270 Market Size 0.497880 1.368806 Model Statistics Adj. R-Squared 0.7002800 F – Statistic 242.0563 Number of Obs. 524 Total 998 908

        a indicate significance at the .01 level
        b indicate significance at the .05 level
        c indicate significance at the .10 level


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