TAG & RCM Present: Pro Sports Team Valuations – Building a Factor-Driven Model

This piece is part of a series and is based on a quantitative study put together in collaboration by The Audible Group, RCM-X, and the University of Illinois at Urbana-Champaign’s Masters in Financial Engineering Program.

Josh Moscot - June 8th, 2020

The valuations of professional sports team franchises have been growing at a tremendous clip over the past 20 years. Across all of the major North American sports leagues, every franchise has seen its valuation climb higher as a result of both GP and LP transactions occurring in and across leagues. The NFL’s Dallas Cowboys top the charts at an estimated $5.5B and the NBA recently saw all of its teams pass the $1B valuation mark. MLB valuations remain high while the NHL and MLS are experiencing rapid growth in their own numbers. In the first weeks of 2020, we here at TAG and our friends at RCM-X sat down and asked the question on many folks’ minds: Just what is it exactly that’s driving this widespread valuation growth?

It’s no secret that ownership of a professional team in any capacity is a highly-coveted asset. It’s also no secret that these transactions are very private, with the information that goes into them being even more private still. However, despite the private nature of sports teams’ financials, they remain highly-public entities, performing (before the pandemic at least) on a sometimes daily basis in front of crowds numbering in the tens of thousands. Because of this public-private dichotomy, we launched an effort in early 2020 to build a factor-driven model based on publicly-available data to see if we could uncover some clarity as to the question of where sports teams truly derive their value. We’re glad we did. As the global pandemic continues to drive uncertainty in all markets, our quantitative approach to valuations has offered interesting insights about valuations today and where they might be headed in the future.

Not surprisingly, the construction of our factor model is all about data. Pulling from a wide base of public sources, we aggregated large amounts of information for the franchises of the major North American sports leagues, looking at everything from historical valuations going back over a decade to non-financial datapoints including social media engagement, live attendance, and even on-field performance. We then used this data to generate various types of regression models, our favorite being a linear regression that breaks down value into three main categories: Team Revenues from the League, Team Revenues from the Local Market, and Non-Financial Data. This model has proved to be not only highly accurate, it also let’s us gain insight into the relative importance of each type of “value” as it relates to the final valuation of a team. Pretty cool, especially when those valuations look to be set for change in the short-term.

Many findings emerged from our analysis, but one realization really shone through: at its most basic level, a team’s success depends upon its fan base. But that’s a two-way relationship. On the one hand, teams create and distribute content and various avenues for fan engagement; on the other hand, fans engage with teams in-person, on social media, and through merchandise and sponsorship consumption. Understanding the relationships between these various engagement channels and the critical fact that a large part of a fan’s engagement is non-monetary led us to develop what we like to call our “X-Factor”.

A few interesting findings from our factor model:

• When looking across leagues without breaking out revenues by league and franchise-contributions, the total revenue is close to 3x more important to value than other factors

• When looking across leagues with a breakout of revenues, the “league revenue” is nearly 1.5x more important to value than “franchise revenue” (defined as “non-League revenue”)

• In both cases, a team’s “Player Expenses” exercise a negative effect on a team’s value in the range of 0.5-2x a team’s total player expenditure

• Depending on the league, anywhere from 5% to 20% of a team’s value can be attributed to our “X-Factor”

• Within the “X-Factor” analysis:

  • The three most important contributors to a team’s valuation were how expensive it is for each team victory on-field, whether or not that team won a championship in a given year, and the aggregate value of a team’s popularity across Google and social media.

The “X-Factor” is our unique way of quantifying the impact of “fan engagement” on a team’s valuation. Using a principal component analysis methodology, our team narrowed down a host of non-financial data points to arrive at six critical factors that are grouped together under this heading (for each year): number of star players on a given team, live attendance, whether or not a championship was won, the ratio between player expenses and “on-field” performance, an aggregation of social media engagement (Google Trend Scores), and the population of the team’s home city. Combining these factors into a single “X-Factor” allows us to fine-tune our model to better approximate sports team valuations and capture that elusive delta of non-financial value that can be missed when using a pure income-stream approach. Further, when we couple this factor back into our two financial variables, we arrive at a more complete view of where team value is sourced from.

So, what’s all this mean?

With greater than 90% accuracy in estimating team valuations at any given point in time, our model is a pretty incredible piece of intelligence in a sports landscape that is set for some major changes in the short-term. It also shows us that, despite most teams not being priced for transactions according to an income stream or asset valuation approach, our model is unique in being able to inform the discussions around where each component of a team’s value might be headed and how they interact with each other.  Perhaps even more important to us here at TAG, the model demonstrates quite clearly that teams are complex entities relying on at least three different primary sources of value creation. We couldn’t be more excited to bring this intelligence into our work with professional sports teams to help create value down the road.

Stay tuned for more on our Factor-Driven Model series. Upcoming pieces will be looking at the impact of the pandemic on sports team valuations, the importance of player health, and the similarities and differences between sports teams and other private off-market assets, to name just a few.

Brought to you by TAG and RCM-X