I’ve been often struck by the unoriginality of most sports team names, and as an ecologist, by the seeming over-representation of teams named after predatory birds and mammals. As an ambivalent sports fan who usually decides which team to support based on how much I personally like their mascot (Panthers v. Timberwolves, Orioles v. Bluejays), I decided one day I would conduct a full classification and analysis of sports team names. Because why not.
I originally planned to compile information on all U.S. and Canadian professional sports teams. However, it turns out that in the NBA, NFL, MLB, and NHL combined, there were (at the time I did this in 2016) only 122 total teams, which was not nearly the sample size I was looking for. So I turned instead to U.S. colleges, for which Wikipedia had sports team data on over 1,500 different schools. Also according to Wikipedia at the time, there were 4,726 total colleges in the U.S., not all of which have sports teams, so I considered this a representative sample of the schools that do.
After getting the data into a useable format, I put each team into 5 coarse categories: Animals (which included all real, non-human animals), Mythic Creatures (all mythical animals or people), Plants (any reference to vegetation), People (any group or type of humans), or Other (everything else). I then sub-categorized and sub-sub-categorized as seemed subjectively appropriate. See the end of this post for more information on my methodology and to download the data yourself.
Over half of the teams were named after non-human animals. Trophically, 57% of those animals were carnivores, 28% were omnivores, and 15% were herbivores. Taxonomically, mammals were the most common, among which felines were most common. There were a large number of big cats (family Pantheridae, including Lions, Tigers, Leopards, Jaguars, and Panthers), decent numbers of small cats (family Felinidae, including Bobcats, Lynx, and Ocelots), cougars (also in the family Felinidae, but I separated them out), and other cats that weren’t taxonomically classifiable (e.g. Wildcats). Bears and various breeds of dogs were also very common. Among the birds, birds of prey (Eagles, Hawks, Falcons) were the clear winners in terms of numbers, followed by the ubiquitous Cardinal. Next in line were Grouse (various cocks and hens), Owls, and Roadrunners. I am happy to report that states with teams named after Cardinals are states within the range of the Northern Cardinal. Likewise with Blue Jays. Roadrunners on the other hand...
I originally planned to compile information on all U.S. and Canadian professional sports teams. However, it turns out that in the NBA, NFL, MLB, and NHL combined, there were (at the time I did this in 2016) only 122 total teams, which was not nearly the sample size I was looking for. So I turned instead to U.S. colleges, for which Wikipedia had sports team data on over 1,500 different schools. Also according to Wikipedia at the time, there were 4,726 total colleges in the U.S., not all of which have sports teams, so I considered this a representative sample of the schools that do.
After getting the data into a useable format, I put each team into 5 coarse categories: Animals (which included all real, non-human animals), Mythic Creatures (all mythical animals or people), Plants (any reference to vegetation), People (any group or type of humans), or Other (everything else). I then sub-categorized and sub-sub-categorized as seemed subjectively appropriate. See the end of this post for more information on my methodology and to download the data yourself.
Over half of the teams were named after non-human animals. Trophically, 57% of those animals were carnivores, 28% were omnivores, and 15% were herbivores. Taxonomically, mammals were the most common, among which felines were most common. There were a large number of big cats (family Pantheridae, including Lions, Tigers, Leopards, Jaguars, and Panthers), decent numbers of small cats (family Felinidae, including Bobcats, Lynx, and Ocelots), cougars (also in the family Felinidae, but I separated them out), and other cats that weren’t taxonomically classifiable (e.g. Wildcats). Bears and various breeds of dogs were also very common. Among the birds, birds of prey (Eagles, Hawks, Falcons) were the clear winners in terms of numbers, followed by the ubiquitous Cardinal. Next in line were Grouse (various cocks and hens), Owls, and Roadrunners. I am happy to report that states with teams named after Cardinals are states within the range of the Northern Cardinal. Likewise with Blue Jays. Roadrunners on the other hand...
Continuing with the predator theme, War/Military related names (Warriors, Cavaliers, Colonels, Lancers, etc.) were the most common sub-category of People, and Pirate variants (Pirates, Raiders, Buccaneers, Marauders, Privateers, etc.) the third most common. On the whole, I was impressed by the wide range of different occupations represented in the People category. I was curious if there were any geographic patterns in these names, perhaps reflecting regional cultural differences. No major patterns jumped out when I mapped them, although that’s not to say there aren’t any.
Next, in honor of my botanist friends, it is my pleasure to give a shout out to the 11 teams named after Plants. GO PLANTS!
Ohio State: Buckeyes
Lubbock Christian: Chaparrals
Arkansas - Monticello: Cotton Blossoms
Delta State University: Fighting Okra
North Carolina School of the Arts: Fighting Pickles
Goshen: Maple Leafs
SUNY Environmental Science and Forestry: Mighty Oaks
Menlo College: Oaks
Chaminade: Silverswords
Indiana State: Sycamores
And of course I must give a shout out to the only 2 teams named after non-Arthropod invertebrates. GO MOLLUSCS!
UC Santa Cruz: Banana Slugs
Evergreen State: Geoducks
There is certainly more to explore with regard to taxonomic diversity, for instance, and I am sure a social scientist could find something interesting in here. Perhaps unsurprisingly, there were no teams named after unicellular organisms or members of any but 2 invertebrate phyla, which I think is a lot of untapped potential. If there ever were a team named the Tardigrades or Sea Nettles, they would totally have my support.
Methodological Details:
I complied and merged data from this page and this page ensuring that there were no repeats. This took longer than I thought it would. If a school had both an old and new team name, I only used the current name. If the men’s and women’s teams had different team names, but the names would be classified equivalently under my scheme (e.g. Cowboys and Cowgirls), I merged them into one entry for that school. I allowed repeats entries for only 7 schools, where either the men’s and women’s teams had different names and would be classified differently under my scheme, or a school had both an official and unofficial (but still widely used) team name that would be classified differently. I ended up with 1,517 data points representing 1,510 U.S. colleges, including private, public, community, 2-year, 4-year, religious, and military colleges.
To clarify, I used the team name for each college, not the team mascot, which is the character who runs around. Sometimes they are the same, sometimes not (e.g. Stanford’s team name is the Cardinal, referring to the color, not the bird or high Catholic priest, while their mascot is a coniferous tree). While this same analysis could be done with mascots instead of team names, this information was not as readily available, and I was not about to go look up information on the appearance and taxonomic disposition of hundreds of obscure college mascots. You are more than welcome to though, if you feel so inclined.
When classifying, I pooled teams that differed only in adjectives or that were synonyms (e.g. Red Wolves, Seawolves, and Timberwolves were all classified as Wolves; Cougars, Mountain Lions, Pumas, and Catamounts all as Cougars). With all of the teams, I did my best to look up the exact meaning of ambiguous names. For example, Hawks, Redhawks, Seahawks, and Skyhawks were all classified as Hawks, but Warhawks were not, as this seemed generally to refer to a war plane, not a bird. Particularly in the People and Other category, I’ll admit that some of the sub-categories are subjective and arbitrary (e.g. I put Mountaineers, Pioneers, Rangers, Trailblazers, and similar names into a “Frontier” category because they seemed related in my mind), and some of them are best guesses (e.g. the School-Name-Derived teams).
The entire dataset is posted on my Github if you want to check it out, come up with your own visualization, or look for errors. I am most definitely not an expert on sports teams, and can’t guarantee there aren’t misclassifications or mistakes. If you find any, and I am happy to make corrections.
I made the visualization at the top using Inkscape. I made the maps using ggplot2 in R.
Next, in honor of my botanist friends, it is my pleasure to give a shout out to the 11 teams named after Plants. GO PLANTS!
Ohio State: Buckeyes
Lubbock Christian: Chaparrals
Arkansas - Monticello: Cotton Blossoms
Delta State University: Fighting Okra
North Carolina School of the Arts: Fighting Pickles
Goshen: Maple Leafs
SUNY Environmental Science and Forestry: Mighty Oaks
Menlo College: Oaks
Chaminade: Silverswords
Indiana State: Sycamores
And of course I must give a shout out to the only 2 teams named after non-Arthropod invertebrates. GO MOLLUSCS!
UC Santa Cruz: Banana Slugs
Evergreen State: Geoducks
There is certainly more to explore with regard to taxonomic diversity, for instance, and I am sure a social scientist could find something interesting in here. Perhaps unsurprisingly, there were no teams named after unicellular organisms or members of any but 2 invertebrate phyla, which I think is a lot of untapped potential. If there ever were a team named the Tardigrades or Sea Nettles, they would totally have my support.
Methodological Details:
I complied and merged data from this page and this page ensuring that there were no repeats. This took longer than I thought it would. If a school had both an old and new team name, I only used the current name. If the men’s and women’s teams had different team names, but the names would be classified equivalently under my scheme (e.g. Cowboys and Cowgirls), I merged them into one entry for that school. I allowed repeats entries for only 7 schools, where either the men’s and women’s teams had different names and would be classified differently under my scheme, or a school had both an official and unofficial (but still widely used) team name that would be classified differently. I ended up with 1,517 data points representing 1,510 U.S. colleges, including private, public, community, 2-year, 4-year, religious, and military colleges.
To clarify, I used the team name for each college, not the team mascot, which is the character who runs around. Sometimes they are the same, sometimes not (e.g. Stanford’s team name is the Cardinal, referring to the color, not the bird or high Catholic priest, while their mascot is a coniferous tree). While this same analysis could be done with mascots instead of team names, this information was not as readily available, and I was not about to go look up information on the appearance and taxonomic disposition of hundreds of obscure college mascots. You are more than welcome to though, if you feel so inclined.
When classifying, I pooled teams that differed only in adjectives or that were synonyms (e.g. Red Wolves, Seawolves, and Timberwolves were all classified as Wolves; Cougars, Mountain Lions, Pumas, and Catamounts all as Cougars). With all of the teams, I did my best to look up the exact meaning of ambiguous names. For example, Hawks, Redhawks, Seahawks, and Skyhawks were all classified as Hawks, but Warhawks were not, as this seemed generally to refer to a war plane, not a bird. Particularly in the People and Other category, I’ll admit that some of the sub-categories are subjective and arbitrary (e.g. I put Mountaineers, Pioneers, Rangers, Trailblazers, and similar names into a “Frontier” category because they seemed related in my mind), and some of them are best guesses (e.g. the School-Name-Derived teams).
The entire dataset is posted on my Github if you want to check it out, come up with your own visualization, or look for errors. I am most definitely not an expert on sports teams, and can’t guarantee there aren’t misclassifications or mistakes. If you find any, and I am happy to make corrections.
I made the visualization at the top using Inkscape. I made the maps using ggplot2 in R.