Do Native American Mascots Actually Cost Their Teams Money?

The "face of the franchise" debate in sports is always a curious one, because no matter how popular or talented a player might be, his face is not the one on the team's uniforms or merchandise. That is reserved for the mascot—the unnamed, disembodied heads adorning uniforms, as well as the Philly Phanatics and Mr. Mets roaming the sidelines. And while the neurotic culture of sports is to rank everything it touches, some of the most basic questions about mascots remain unexamined. Here, we will attempt to answer a simple one: Which kind of mascot is the most profitable and, presumably, popular?

We should emphasize that, statistically speaking, this is a "tricky" area. There simply isn't enough variation in the world for us to perfectly identify how a specific or even a type of mascot impacts the fortunes of a given team. For example, in the case of Native American themed mascots our perfect "data" would include examples of teams switching back and forth between Indian and non-Indian mascots. This doesn't mean that it is impossible to study how different types of mascots impact financial performance. It just means that we have to make some assumptions, and we have to make clear how these assumptions limit our results.

We've previously looked at how Native American mascots affect profitability, but that was keyed in on NCAA basketball, and only the Native American mascots. Here, we've gone a little deeper, and examined teams in the MLB and NFL with various types of mascots. That is to say, does being named the Eagles earn more money than being named the Cowboys (or, notably, the Redskins)? The short answer: Historically, teams like the Redskins, Chiefs, and Braves have been far more profitable than those with other types of mascots, but something's changed in recent years. According to our model, they're actually costing their teams money.

Classes of Mascots in Professional Football and Baseball

Earlier this year, we performed a preliminary analysis of the financial impact of Native American themed mascots. (The key result was that switching away from a Native American mascot didn't have a long-term negative effect.) That analysis was based on the simple idea that we could build a statistical model of team box office revenues as a function of team quality (winning percentage, playoff participation, etc.) and market potential (market population, median income, stadium capacity, etc.). During the study, we included a binary (i.e. dummy or indicator) variable in these regressions to indicate if the team had a Native American mascot. We also included an interaction variable between the Native American dummy variable and the year to account for changing consumer preferences.

The most common question coming out of that research was how other types of mascots were affected. This seems simple on the surface, but it was actually far from straightforward. Our first stumbling block was how to determine the different mascot categories. For example, we could have a classification of "human" mascots but then the question arises of if we should differentiate between aggressive humans such as Pirates or Raiders and the gentler Padres or Saints. And then you get to the animals: Should we have a separate category for birds? What about aquatic animals? Or non-threatening animals? Do we need a special category for terrifying lucha libre birds of prey who have come for our children?

To get a handle on these questions, and to avoid simply clumping a few groups together arbitrarily, we created something called a perceptual map. Perceptual maps are used in marketing to visually display the perceptions of customers or potential customers along a number of dimensions (e.g. affordability, social appeal). For our mascot study, the map was based on survey data that asked subjects to rate the similarity between team names. The survey involved 18 team names split between the NFL and MLB. We tried to assemble a cross section of names that included different types of animals (Tigers, Bears, Dolphins), humans (Rangers, Packers, Pirates), miscellaneous names (Rockies, Giants) and a split between baseball and football. The technical term for the procedure is Multidimensional Scaling (MDS).

MDS is great in that we allow subjects the freedom to rate items however it makes sense to them, but this freedom comes with a cost: the data they generate can look like goddamn chaos. That is to say, the perceptual maps generated do not come with labeled dimensions, so we have to translate the associations into what the subjects . We generated a three dimensional perceptual map (using SAS software). Dimension 1 (the horizontal axis in the chart below) seems to roughly correspond to human versus animal mascots. We say roughly because Cardinals are rated more "human" on this axis than Packers—probably because the Cardinals have anthropomorphized, angry bird mascots while the closest the Packers come to a talisman is a hat that looks like Swiss cheese and maybe the letter G. So obviously, a potential issue with our study is that subjects are rating the team names based on factors beyond the literal meaning of the name. This is probably unavoidable given the focal nature of sports teams in American culture.

Do Native American Mascots Actually Cost Their Teams Money?

The second dimension (not displayed) was difficult to interpret. At one extreme we had the Padres and Rockies. At the other, it was the Dodgers and Packers. One thought was that this dimension was about historical success. However, the Steelers were in the middle of the scale. The third dimension (the vertical axis in the chart above) was also difficult to interpret. The Redskins and Indians are at the top of the scale while the Tigers, Cardinals, and Dodgers are at the bottom. While we will not try to name this axis, it is interesting that the two Native American mascots were viewed as extreme on this dimension.

The fundamental point to the MDS exercise was to develop an understanding of how fans perceive different types of mascots. Based on the preceding, we decided to evaluate four mascot types: Human, Native American, Animal, and Other, the last of which serves as the baseline for our analysis.

Animals vs. Humans vs. Um, The Redskins

We conducted statistical analysis separately for the NFL and for MLB. Our logic is that because the games are very different and played at different times of year, the effect of different types of mascots may vary. For each league, we created statistical models of revenue as a function of winning percentage, winning percentage squared, playoff participation, relative payroll, population, population squared, median income, and stadium capacity.

The data fit our model well for the NFL (the R-squared is 0.51 with the dummy models; and a reminder here that full statistical wonkiness is available at the original post here). The coefficients associated with each class of mascot are provided in the table below. The model suggests that over this time period, having a Native American mascot had a significant positive revenue impact relative to the "other" category of mascots. Animal mascots had a negative impact. (The human model doesn't pass the significance hurdle, but we've included it along with its "P-Value." A figure is generally considered significant if its P-Value falls under 0.05, or in other words, has a 95 percent chance of not being random. So here, there's a 40.9 percent chance that the figure for humans is off, but just a 1.3 percent chance that the one for animals is.)

Mascot TypeValue
P-Value
Native American$12,117,107.2
<.0001
Human$1,353,243.8
0.409
Animal-$3,567,963.7
0.013

However, as we noted above, our analysis includes some strong implicit assumptions. In the case of the NFL results above, the Native American variable is associated with just two cities: Kansas City and DC. The danger is that this variable may be picking up some common trait of the two cities other than the mascot. An additional concern is that the preceding model treats the mascot issue as static. It seems more likely that opinions change over time. To account for these issues we next re-estimated the model but now included interactions between time and the mascot indicators. This model yields an R-squared of 0.55. Again all of the control variables (win percent, population, etc.) are of the expected signs.

This model is the most instructive of the two models as it allows for both dynamic effects and lessens the concern about a shared latent factor between Kansas City and Washington DC. The key result is that there seems to be a shift in preferences. In particular, the Native American mascots seem to be becoming less popular over time. Historically, the Chiefs and Redskins have been strong franchises so it makes sense that the static Native American indicator would be positive. Given the increased scrutiny applied to Native American mascots it also makes sense that we observe a negative long-term trend.

Mascot TypeValue
P-Value
Native American$21,861,806.2
<.0001
Human-$2,924,904.4
0.234
Animal-$6,616,731.1
0.001
Native American*YR-$1,636,981.4
0.010
Human*YR$722,698.9
0.021
Animal*YR$508,348.0
0.032

In the preceding model the dependent variable is box office revenues (in constant 2008 dollars). The interaction between time and the Native American dummy variable suggests that the value of having a Native American mascot is dropping by about $1.6 million per year. Again, we fully admit that this is a messy statistical problem and readers may be able to construct alternative explanations for the findings. But the KEY point is that we have intentionally performed a simple analysis in an effort to just let the data speak. The data seems to be saying that considering mascot type significantly improves model fit and that Native American mascots are becoming less valuable brand assets over time.

In the case of MLB we executed a similar procedure. The baseline revenue model for MLB used the same variables as the NFL analysis. The R-squared of the baseline model was 0.627. In the second analysis, we added dummy variables for the three classes of mascots: Native American, Human and Animal Mascots. In this case, the improvement in the model is minimal as the R-Squared increases to just 0.631. None of the mascot dummies are significant.

Mascot TypeValue
P-Value
Native American-$8,494.4
0.1015
Human-$2,822.0
0.360
Animal$3,782.2
0.224

However, adding the interactions between time and mascot type produces an interesting set of results. In particular, we find the same pattern of results for the Native American mascot terms. In both leagues these mascots have positive coefficient associated with the static dummy variable but a negative interaction between the dummy for Native American mascot and time. Which means the clock is ticking.

Mascot TypeValue
P-Value
Native American$24,567,815.9
0.0196
Human$697,834.0
0.909
Animal$22,957,750.4
0.001
Native American*YR-$2,675,563.5
0.000
Human*YR-$260,405.6
0.555
Animal*YR-$1,523,533.9
0.001

In the case of MLB, the model results suggest that having a Native American is also driving lower box office revenues over time. The effect is bit higher in MLB with the trend being a loss of about $2.6 million per year.

Despite the limitations inherent to our analyses, the consistency between the NFL and MLB findings is in accordance with a trend of growing opposition to these mascots. However, we do acknowledge that our claim of a trend of "growing opposition" is based largely on anecdotal data such as retirements of prominent Native American mascots in college sports, journalists dropping the use of "offensive" nicknames, and politicians beginning to weigh in on the issue. Our results imply that fans are also becoming less enthusiastic about these mascots.

To be blunt, the implication is that the trends suggest that keeping a Native American mascot is reducing financial performance and harming team brand equity. And if that continues, it will likely become an encroaching factor on any debate of whether or not to change a given team's name. Which means, essentially, Keith Olbermann is probably right.


Leave any comments or criticisms in the discussion below.

Mike Lewis is an Associate Professor of Marketing at Goizueta Business School, Emory University

Manish Tripathi is an Assistant Professor of Marketing at Goizueta Business School, Emory University

The Emory Sports Marketing Analytics website provides an outlet for research on how sports entities (Leagues, Teams, & Players) create and maintain valuable marketing assets. You can find them on Twitter @sportsmktprof.