Note: We are in unprecedented times in terms of the world of sports. Everything cancelled or postponed and society under an indefinite lock down. And fan / brand rankings may not seem important. After all this is just entertainment. But, in another sense, sports are one of the pillars that supports our culture. It also might be a interestingly, enraging piece of content while the games are on hold…
The Report
I have the data collected and numbers crunched for our annual data-based look at Major League Baseball fandom. This is a predictably controversial subject. Fandom is about passion, identity and culture. Claiming that fans in one city are less passionate than fans in another city is almost like a personal attack. In fact, for some fans it is a personal attack. If the local team is a significant part of how the fan thinks about themselves and their community, a “ranking” can be fighting words.
But I can’t ignore that the current societal environment casts a significant shadow on this type of analysis. With the current pandemic, it feels like our focus should be on more serious matters. It is a paradox of being a marketing academic. In some ways, marketing is what really moves the world (political campaigns are little different than marketing campaigns, and marketing often creates the trends that change the culture). However, few things seem as trivial as a discussion about brands.
Hopefully what follows is interesting and fun (with some important business issues in the background). In a follow up post, I’ll go beyond the current rankings to discuss some important macro level events and trends that may affect MLB fandom.
The Rankings
Who has the best fans in Major League Baseball? What are the best brands in MLB? These are simple questions without simple answers.
What makes for a great fan or brand? Fans that show up even when the team is losing? Fans that are willing to pay the highest prices? Fans that are willing to follow a team on social media? These all seem like indicators of fan engagement, but they may also all seem a little bit different from each other.
You might also notice that the preceding list is entirely fan behaviors that can be measured. This is an important distinction. A lot of arguments about fandom take place on an emotional level about which fan base loves their team the most. I have some sympathy for this argument, but to get anywhere, we need to move to the realm of facts. It's also difficult enough to measure fan “love” at the level of the individual. It’s basically impossible to directly measure this kind of unobservable construct across a population.
Even after we agree on the question(s), answering it is also a challenge. How do we adjust for the fact that one team might have gone on a miraculous run that filled the stadium? Or perhaps another team suffered a slew of injuries? How do we compare fan behavior in a market like New York with fans in a place like Milwaukee? What if a team just opened a new stadium? Did the fans stream in to see the building or to see the team? In other words, how do we control for momentary factors such as which team is winning at the moment or permanent factors related to market differences?
For the past few years, I have been studying fandom across professional and college sports. My approach to evaluating fan bases uses data to develop statistical models of fan interest. The key is that these models are used to determine which city’s fans are more willing to spend or follow their teams after controlling for factors like market size and short-term variations in performance.
The “Overall” rankings are based on two sub-rankings – Fan Equity and Social Equity. Fan Equity is a revenue premium based metric that compares team’s box office results with league standards. In other words, Fan Equity assesses how much fans are willing to spend relative to fans across the league. The KEY idea is that we measure this while controlling for team success and market characteristics like incomes and populations.
Fan Equity is a great metric for assessing the CURRENT level of passion or engagement in a local fan base.
Social Equity is focused on teams’ social media followings (Facebook and Twitter). Again, the rankings are based on how a team’s social media results compare across the league after controlling for team success and market characteristics.
The Social Equity metric provides insight into the team’s national and POTENTIAL fan passion.
I determine the overall ranking using a weighted average that puts more emphasis on the Fan Equity measure. Fan Equity gets more weight because it is based on actual spending rather than just an indication of preference. It says more when a fan pays hundreds of dollars to go to a game than when a fan clicks a like or follow button on social media.
A couple more notes:
Models of league economics (revenues) are built using 20 years of data. Models of social media results use 6 years of data.
For the Fan Equity rankings, I focus on the last 3 years of data. This allows for the brand rankings to evolve over time. For the Social rankings, I use the last 2 years of data.
The Winners
Overall, the group of clubs that comprise the Top 6 contains little in the way of surprises. The Red Sox rank number one and are followed by the Giants, Yankees, Cardinals, Cubs and Dodgers. Same group as last year, but slightly different order.
(As an aside — Boston is probably the best sports town in America. I do these rankings across a variety of leagues and Boston teams are almost always in the top 5.)
In general, the clubs at the top of the list share many traits. They are all able to motivate fans to attend and spend as they all possess great attendance numbers and charge relatively high prices. More to the point, these teams are even able to draw well and command price premiums when they are not winning. The Cubs are the best example of this.
It is also interesting that the list includes some historical rivals. While we often think of fandom as about love for a team, hate for a rival helps to create even more passion. Somehow the love for the Red Sox is amplified by hate for the Yankees. Cubs-Cardinals is another passion-filled rivalry.
The list of winners probably raises an issue of “large” market bias. However, keep in mind that the methodology is designed to control for home market effects. The method is explicitly designed to control for differences in market demographics (and team performance). While the “winners” tend to come from the bigger and more lucrative markets, other major-market teams do not fair particularly well (White Sox, Mets, A’s). There is also a more subtle point. The large-market teams likely have the best fan bases because they often have significant histories of success and are often featured in the media.
The last point highlights an important implication of the analysis. How is Fan Equity created? The Cardinals and Yankees highlight the importance of past success. But there is more to the story. At this point, I’ll leave it that the current year results can be used to draw deeper lessons about how fandom is created. But more data and analysis is needed.
The Laggards
The bottom 6 of the list features the White Sox, Diamondbacks, Rays, Athletics, Mets and Marlins. It is interesting that the bottom of the rankings also includes teams from major markets such as the Bay Area, Chicago, New York and Miami.
The bottom of the list includes two types of teams — newish teams and second-tier teams in major markets. This makes sense when we think about how fandom is created and the role fandom plays for many people. From a branding perspective, it is not surprising that we see one dominant brand in the cities with two clubs. Being a sports fan is about being part of a community. Many fans are drawn to the bigger and more dominant community — Yankees, Cubs, Giants or Dodgers rather than the Mets, White Sox, A’s or Angels. Being a Cubs fan in Chicago is about being part of the dominant fan community. Being a White Sox fan is about being part of the outsider group.
The other lesson is related to time in market. Sports fandom is different from customer loyalty in other categories in several respects. For example, the intensity of sports fandom dwarfs the loyalty in any other category. Another distinguishing feature is that sports loyalty may be developed on a generational time frame relative to other categories. While a new consumer product can catch on almost overnight, does real fan loyalty in sports require being part of a community for decades? Maybe this is the reason why loyalty is so much greater in sports. Teams like the Marlins, Diamondbacks and Rays don’t have histories of fandom being passed on from parents and grandparents to children.
Fan Equity vs. Social Equity
It is instructive to consider the two elements of consumer loyalty (brand strength) simultaneously. The figure below shows the Fan Equity and Social Equity Indexes for each team. A longer Orange bar indicates that a team does better in terms of Social Equity and a longer Blue bar indicates that the team has a stronger Fan Equity score.
When teams do well or poorly on both metrics the message is pretty clear. The Giants and Red Sox show great strength in both metrics. These are very healthy fan bases. In contrast, the Mets and the Marlins struggle on both metrics.
But what about the teams with divergent results?
The Astros were 8th in Fan Equity and 29th in Social. This means that the Astros were able to convert their on-field success into full stadiums of paying customers. But the limited social results suggest that the brand did not catch on with the casual or non-Houston audience. Most likely, this is an organization with enough pricing sophistication to cash in on short-term winning but an organization that lacks widespread fandom.
In contrast, the Tigers and Rays have much better Social than Fan Equity results. The Social results suggest some type of recent or potential branding success. There is fan interest in these teams that has not translated to box office success. Maybe the interest is with younger consumers. For these teams, the disparate results suggest a need to dig deeper into the data. Is the team doing better with out-of-market fans? Are prices too low?
Other Thoughts
As always, I hope people enjoy the rankings. I expect a great deal of criticism of my limited understanding of fandom, my methodological deficiencies and general character. I want to end with a couple of comments related to the inevitable feedback.
1. How can the rankings be used?
The rankings are a snapshot of how teams compare at the current moment. The question is how did they get there? The next stage of the analysis would likely be to investigate why some brands score better than others. I’ve done a number of analyses related to the how? I’ve looked at factors ranging from the impact of Heisman awards winners at the college level to types of mascots. I can tell you that the key to developing fandom is pretty simple – histories of success. The interesting empirical question is the value of investing in a winner. There are also interesting questions related to what is possible in a given market.
2. Why do you publish these rankings? Don’t you have better things to do?
I think fans and fan communities are of incredible importance. Fans are people of incredible passion and commitment. And in general, it is the most committed people and groups that “win” or change things. But fandom is a tricky thing to study because it involves elements of psychology, sociology and economic. It also helps to understand modern marketing and to able to analyze data. I also think that the study of fandom helps us to understand elements of the culture beyond sorts such as politics or the importance of iconic brands to society.
3. Your methodology is invalid because it does not consider X, Y or Z?
Trust me, I’ve considered almost everything. Everything that is accessible — meaning that I would love to be able to have more and better data. I use as much publicly available data as I can access. Local and national TV ratings would be great, but they are very expensive. Official revenue numbers would also be great, but I do not have access.
In terms of the statistics, I use relatively simple models. But I have tried a range of specifications and models. For this analysis, I prefer simple.