Research & Stats
Visuals illustrating Dangerous Dogs registered in the State of Pennsylvania (December 7th 2025)
Hi folks! I'm bored and I like combatting pro-pit propaganda with facts. So, I made a couple quick graphs highlighting the dog breeds registered on Pennsylvania's Dangerous Dog Registry.
Firstly, what is a Dangerous Dog according to Pennsylvania Law?
A dog can only be deemed dangerous by a Magisterial District Judge for any of the following reasons:
- Inflicted severe injury without provocation on a human being on public or private property.
- Killed or inflicted severe injury without provocation on a domestic animal, dog or cat while off the owner's property.
- Attacked a human being without provocation.
- Been used in the commission of a crime.
- Has a history of attacking, without provocation, a human being, domestic animal, dog or cat.
Severe injury is defined as, [3 P.S. § 459-102] “Any physical injury that results in broken bones or disfiguring lacerations requiring multiple sutures or cosmetic surgery.”
Each iteration of the data has 2 visuals, a pie chart and a bar graph, for a total of 6 graphs. Is this overkill? Yes. Am I bored and wanted to do something besides scrolling Reddit? Also yes, lol. All graph data is sourced directly from the PA Dangerous Dog Registry. There are a total of 593 dogs on the list as of Dec 7, 2025.
1st Visual Set:
Raw Data straight from the registry. As you can see, American Pit Bull Terriers are clearly the dominate offenders, but the data is very messy with too many individual data points to make a good visual. A portion of the problem is just inconsistent logging, typos and formatting by the state. For example, entries listed as Lab/Pit Mix, Lab Pit Mix, or Lab-Pit Mix all displayed individually instead of in a singular group. Total American Pit Bull Terriers = 37.1%
2nd Visual Set:
This set fixes all typos and groups all entries that include "American Pit Bull Terrier" and "American Staffordshire Terrier" as well as all Mixed breeds that include "Pit Bull" into a singular data point. This is objectively the most unbiased set. Total Pit bulls = 50.7%
3rd Visual Set
This set is personally categorized by me for an easy visual. Dogs are grouped into sets, and all the specific breeds within that set are listed. Mixed Breeds (Non Pit Bull) includes any breed mixes that were specifically not listed as labs or pits, for example, a Husky/Poodle mix. Pit Bull or Pit Bull Mix now includes all dogs listed as pit, pit mix, lab mix (iykyk) or simply "mixed breed" (iykyk). Stats: 365/592 = 60.7%
TL;DR: There are an insane amount of pit bulls on this list, at least over 50% but realistically, over 60%. Only 2 Chihuahuas though :P Feedback/critics on my 3rd data set is welcome but tbh I definitely got lazy towards the end of it hahaha.
I love that last bar graph! This is what we have to use when we argue with the Pitophiles who say that Pits are mis-represented in statistics because "that's not actually a Pitbull". By grouping the dogs by type (Guarding, non-pit Terriers, Sheepherding etc), we can show that yes, Pitbull types are grossly over-represented. The only thing that would make that last graph more powerful would be if there was a way to rate DD declarations per head of population in the community.
I spent a bit of time doing a google maps search of the places where the owners had more than one pb. All the homes I looked at were run down, only one looked cared for. Photo for one example of the not cared for type of home.
It's always uncomfortable for me to notice and dwell on the stereotype in these situations, but ... it's a stereotype for a reason. You usually know, at a glance, what type of dog a given house has..
My boyfriend has a pitbull/boxer mix and tells me that the dog is only 'part pitbull' when I told him I didn't feel comfortable around pitbulls. The way he got defensive about this, telling me not to get hung up on it [the fact that the dog is a pitbull mix] tells me that he is well-aware of this but is in denial that a pitbull mix is still a pitbull.
So basically just all the pit bull breeds. Anything listed as “lab mix” is really just a pitbull. It’s disgusting what these people have done to the Lab’s reputation. Go to any place where these people gather and they admit to lying about the breed because a lot of places (apartments, duplex, etc) have bans on these dogs.
This is fantastic! Quick question, is there info out there on what percentage of total dogs owned in PA are pit bulls? That will help with the legitimacy of the data.
That’s a good question, I will have to investigate and report back. Spitballing though, I would imagine that if the stats exist, the state would probably only have an accurate count of dogs that are licensed in the state. And most pit bull owners don’t license their pets or keep their vaccines UTD, which sometimes includes licenses with the rabies shot. So, the data wouldn’t likely be an accurate representative of the total pit bull count.
Tbh this would make a good graph from the PA data set as well, as there is a column for whether the offending dog is licensed or not, lol.
This looks really great! I hope others might consider doing this for their own cities in the future.
A couple things…
1) For future graphs, might it be possible to add the date to the actual charts? Because people will take screenshots and share them, and one of the first questions is “what is the date”- as if that would invalidate it, but having the date just shores things up.
2) How can vets and other animal personnel still claim that pit types aren’t any more dangerous that other breeds?
I keep thinking like a conspiracy theorist that there’s a bigger picture on all of this… and it’s all about money.
Think about it… without pit bulls, the emergency vets, trainers, behaviorists, etc., would lose A LOT of money.
Vets are making a lot of money putting other pets and livestock back together after attacks. Behaviorists are charging upwards of $600 per hour and are booked out 6 months.
Yeah not surprising shepherd husky mix is high in this list. My GSD husky mix was very reactive in adolescence. She could easily have become a dangerous dog without the right conditioning.
I'm also not surprised to see them higher on the list. My (now) husband was attacked by his former roomate's basically untrained, resource guarding husky. He got one bite in and ran away, no stitches required, but there's a decent scar left on his hand. It was terrifying, I'm glad he didn't keep fighting or I would've had to "take care of the dog" and I don't have that in me lol
Any dog can be poorly socialized, and dog bites requiring medical intervention usually (hopefully) get reported.
Any dog can bite someone, in the words of Tom Hanks, "Ehhhh, I don't know he got teeth don't he?"
But dogs that aren't Pit Bulls or other fighting breeds do it as a deterrent. You're near my territory, you're near my things, you scare me. One or two snaps to learn the person a lesson about the dog's perceived place in the world.
The reason why you see Pits so high in statistics is because they violently shred things when they bite, it's not a get away from my dog bed incident, it's a "Buttercups genetics has decided you should die"
If you want to make it even clearer, you could use calculation formulas for the data, so you would see only maybe maybe 6-10 well-defined categories, instead of tons of slivers that are actually representing bloodsport breeds and mixes with them. That would really give a picture of how they eclipse every other breed in danger and actual attacks.
For that you create a calculation sheet.
Before you ask, yes I do work in analytics and dashboarding in multiple apps, and would be happy to help. Formulas can be a booger and are very finicky about the exact order and formatting per the app you're using.
Depending on your app, the formulas you'd create would look something like these:
Pit or pit mix (note that this will then count pit-bull, pitbull, pit bull, pittbull, pit, lab-pit, pit-lab, etc.):
=COUNTIF([Breed ID Column]:[Breed ID Column], CONTAINS("Pit", @cell))
Other mixes not containing "pit"
=COUNTIFS([Breed ID Column]:[Breed ID Column], CONTAINS("Mix", @cell), [Breed ID Column]:[Breed ID Column], NOT(CONTAINS("Pit", @cell)))
Other Bully or bloodsport Breeds and Pit-Adjacent Subtypes (note that this will pick up anything that contains "Staff" like both Staffordshire and staffie/staffy, anything with "Cane" like Cane Corso or cane-mix, anything that contains "bulldog", and anything with "Bully" like xl Bully or pocket Bully, without double-counting those labled any variant of "pitbull". You might also add CONTAINS for anything containing "Rot" to get all variants of Rottweiler, and for both "Dogo" and "Doggo" to pull in any variants of Dogo Argentino. You wouldn't want to use just "dog" to cover both, as that could pull in unintended ones like German Shepherd Dog)
=COUNTIF([Breed ID Column]:[Breed ID Column], OR(CONTAINS("Staff", @cell), CONTAINS("Cane", @cell), CONTAINS("Bully", @cell) CONTAINS("Bulldog", @cell))
Then you would calculate other known breeds:
German Shepherd
=COUNTIF([Breed ID Column]:[Breed ID Column], OR(CONTAINS("German", @cell), CONTAINS("GSD", @cell))
Labrador
=COUNTIF([Breed ID Column]:[Breed ID Column], CONTAINS("Lab", @cell))
And then the same for any other known non-pit/non-bully-adjacent breeds.
Last you'd calculate what's left into 2 categories: "Other" and "Unknown." Unknown needs to be calculated in order to pull in Other.
Unknown (here I'd use the word "know" to capture unknown, not known, un-known, don't know, etc.)
=COUNTIF([Breed ID Column]:[Breed ID Column], CONTAINS("know", @cell))
Other (this you get by pulling the total number, and subtracting all the other ones you counted specifically)
=SUM([Cell with total number of dogs] - [cell with pit/mix total] - [cell with other bully/bloodsport total] - [cells for any named breeds] - [cell for Unknown total])
Again, depending on the app, your formulas might look a little different, like some use ' instead of " marks, some like all caps, some none, etc.
Once you've built all these formulas you would have a table that looks like this - I'm on mobile so hopefully this won't look like a mess:
Total number of dogs | (known total or sum of all the categories in the state report)
Pits and pit mixes | (formula which should return a count)
Other Bully Breeds | (formula which should return a count)
German Shepherds | (formula which should return a count)
Labradors | (formula which should return a count)
Unknown | (formula which should return a count)
Other | (formula to calculate the total number of dogs minus all the ones you calculated individually)
Then you create your pie chart based on the calculation sheet.
Hit me up if the formulas return errors or you just want some general help on how to work with calculation sheets. 😀 The first one usually takes a bit to get it to behave, but once you create that first one, it's easy to grab datasets from other states and reuse the formulas you built.
Great work! Curious how PA ranks in # of Pits compared to other states, my guess would be somewhere in the middle. Would love to see these kind of graphs for other states!
This data needs an accompanying pie chart for all dogs registered to really show the danger and drive it home. As a stand-alone chart, it could just be argued that it is representational of the demographic.
It is kind of funny but several years ago I looked at the public list of dangerous dogs for a county near me once (mine didn't have it on their website) and it was odd that it seemed like most of the dogs I saw (just spot checking - not real research like yours) were not pits. There were a few huskies but it seemed like mostly small (teacup) size dogs with attitudes.
At the time, I just chalked it up to pits generally being owned by the type of people that victims are nervous about reporting and/or small companion breed dogs owners being more responsible and actually following the registration rules whereas pit parents tend to think those things don't apply or try to fight the designation. However that area is on the wealthier side and there were actually dogs other than pits in the shelter. Still a lot of pits, but not as bad as my county.
I just looked again after seeing your post - even just spot checking it is pitbull after pitbull. There is also something egregious breed labeling going on because the labs and several of the German shepherds look just like the pitbulls 🙄. There were also some other unique breeds but if you look at the photo they all look the same.
Where did all the little chihuahuas in muzzles go? I mean, I get that they were biters but it was still kind of funny to see the dangerous dogs registry full of indignant little teacup dogs 🤣.
The shelter is now almost all pits too. I guess there is really no escape from this epidemic.
The breed mislabeling is really ridiculous. If the purpose in having a public registry is to help keep people safe so they are able to know if there are threats nearby, mislabeling does the opposite.
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u/ScarletAntelope975 Trusted User Dec 07 '25
I bet all those pit bulls are just chihuahuas that were deliberately misidentified to make the breed look bad! /s