For a professional sports team, there are 3 main sources of revenue: media rights deals, ticket sales, and corporate sponsorships. For those franchises that own the stadium they play in, stadium sponsorships are the biggest generator of sponsorship income.

When surveying the landscape of NFL stadium sponsorship income, the first thing that becomes quite clear is that the annual revenue franchises generate through sponsorships varies greatly. The graph below shows a density plot of the annual revenue for the 25 sponsored NFL Stadiums (some stadiums aren’t sponsored, such as Lambeau Field).

The vertical dotted line indicated that the mean annual revenue is $7.8 million. The density curve shows that this data is positively skewed, meaning that the mean is larger than the median due to a few outliers. Two of the more extreme outliers in this case are AT&T Field (~$20 MM / year) and MetLife Stadium (~$18.5 MM / year). The variance in sponsorship revenue shown above poses an interesting question.

At their core, NFL stadiums across the board are relatively homogenous – they provide the opportunity for fans to watch a football game. So why can the Cowboys and Giants charge companies upwards of $20 million a year to sponsor their stadiums, while the Buffalo Bills can only charge a reported ~$6 million a year for their stadium? I decided to build a model to find out.

When thinking about what factors may influence the price a sponsor is willing to pay to name a stadium I considered a few variables:

Market Size –I utilized Nielsen Media Market data associated for the location of each franchise. How much does market size influence sponsorship naming rights, if at all?

Year the stadium was built – Would sponsors be more willing to pay for new “shiny” stadiums that generate buzz and are synonymous with technical innovation?

Age of the stadium upon naming – Is there a premium paid for being the “original” name of a stadium?

2 teams playing in the same stadium – This comes into play more when looking at NBA/NHL arenas, however the Jets and Giants share a stadium. Do more home games correlate to a higher sponsorship price?

Year sponsorship deal was signed –As time goes on, the NFL has become ingrained in the fabric of American culture. Do sponsors recognize this and does sponsorship revenue increase as time goes on?

First year of franchise – This variable was attempting to approximate the “prestige” of a franchise. Would a team like the Cowboys (founded in 1960) be more likely to get higher sponsorship revenue than the Texans (founded in 2002)?

Championships –I measured “Championships before signing” that summed up the number of championships a team won 30 years prior to signing a sponsorship deal. Can an immensely successful franchise such as the Patriots command higher sponsorship revenue than teams such as the Jets and Browns?

Clearly, the length of a sponsorship deal is the most important factor in determining the overall value of a sponsorship deal. To control for this, I regressed the variables listed above against the annual sponsorship revenue. I utilized a “best subsets” regression package in R (statistical software) to find which combination of the variables above most accurately explained the difference in revenue between stadiums.

Ultimately, I came to a model that accurately explained 67% of the difference in revenue between stadiums. What the model found was interesting:

The model is statistically significant (p-value = 0.0004268) and explains 67% of the variance in annual sponsorship revenue between stadiums (R-squared: .669)

The variables that the model included were:

Media Market Size

Year sponsorship deal was signed

If 2 Teams play in the same stadium

Championships

Age of Franchise at Naming

There only three variables that are statically significant in determining annual sponsorship revenue, and two are much more significant:

Media Market Size (significant at .001 level)

Year sponsorship deal was signed (significant at .001 level)

If 2 teams play in the same stadium (significant at .1 level)

MetLife Stadium is such a significant outlier that R removed the data point in determining the best fit.

The graph below shows the residuals created by the model (i.e. what the actual revenue was minus what the model says the revenue would be). Labels are provided for those values where the model missed significantly. For example, the model under predicted the AT&T Stadium deal by approximately $6 million, and over predicted the Raymond James Stadium deal by about $8 million.

However, the model is generally accurate, though clearly not perfect, as the R-squared value of .67 seems to indicate. Where the model seems to stumble is in predicting the more recent naming rights deal, such as Levi’s Field and Hard Rock Stadium.

One factor that may explain this is that naming rights deals include much more than just the right to sponsor a stadium. They include additional tie-ins with the franchise that help drive the price of the deal (think of in-stadium advertisements, giveaways, billboards, etc..). Additionally, the revenue generated by club seats in a stadium may help explain the variance as it could be utilized as a proxy to show how hospitable/attractive a stadium is for corporations. Unfortunately, this data isn’t publicly available.

Ultimately, the best way to maximize annual stadium sponsorship revenue is through simply being at the right place (large market) at the right time (now). Additionally, if you can get two teams to play in your stadium, that helps as well.

Sounds like Stan Kroenke and the LA Rams are in for quite a payday.

For a professional sports team, there are 3 main sources of revenue: media rights deals, ticket sales, and corporate sponsorships. For those franchises that own the stadium they play in, stadium sponsorships are the biggest generator of sponsorship income.

When surveying the landscape of NFL stadium sponsorship income, the first thing that becomes quite clear is that the annual revenue franchises generate through sponsorships varies greatly. The graph below shows a density plot of the annual revenue for the 25 sponsored NFL Stadiums (some stadiums aren’t sponsored, such as Lambeau Field).

The vertical dotted line indicated that the mean annual revenue is $7.8 million. The density curve shows that this data is positively skewed, meaning that the mean is larger than the median due to a few outliers. Two of the more extreme outliers in this case are AT&T Field (~$20 MM / year) and MetLife Stadium (~$18.5 MM / year). The variance in sponsorship revenue shown above poses an interesting question.

At their core, NFL stadiums across the board are relatively homogenous – they provide the opportunity for fans to watch a football game. So why can the Cowboys and Giants charge companies upwards of $20 million a year to sponsor their stadiums, while the Buffalo Bills can only charge a reported ~$6 million a year for their stadium? I decided to build a model to find out.

When thinking about what factors may influence the price a sponsor is willing to pay to name a stadium I considered a few variables:

Clearly, the length of a sponsorship deal is the most important factor in determining the

overallvalue of a sponsorship deal. To control for this, I regressed the variables listed above against theannualsponsorship revenue. I utilized a “best subsets” regression package in R (statistical software) to find which combination of the variables above most accurately explained the difference in revenue between stadiums.Ultimately, I came to a model that accurately explained 67% of the difference in revenue between stadiums. What the model found was interesting:

The graph below shows the residuals created by the model (i.e. what the actual revenue was minus what the model says the revenue would be). Labels are provided for those values where the model missed significantly. For example, the model under predicted the AT&T Stadium deal by approximately $6 million, and over predicted the Raymond James Stadium deal by about $8 million.

However, the model is generally accurate, though clearly not perfect, as the R-squared value of .67 seems to indicate. Where the model seems to stumble is in predicting the more recent naming rights deal, such as Levi’s Field and Hard Rock Stadium.

One factor that may explain this is that naming rights deals include much more than just the right to sponsor a stadium. They include additional tie-ins with the franchise that help drive the price of the deal (think of in-stadium advertisements, giveaways, billboards, etc..). Additionally, the revenue generated by club seats in a stadium may help explain the variance as it could be utilized as a proxy to show how hospitable/attractive a stadium is for corporations. Unfortunately, this data isn’t publicly available.

Ultimately, the best way to maximize annual stadium sponsorship revenue is through simply being at the right place (large market) at the right time (now). Additionally, if you can get two teams to play in your stadium, that helps as well.

Sounds like Stan Kroenke and the LA Rams are in for quite a payday.

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