On May 14th, a mass shooter killed 10 and wounded two in a grocery store in Buffalo, New York. It was a terrible tragedy, but have you worked out if the shooter’s ancestors were from Sub-Saharan Africa? According to Journalist Steve Sailer, you can guess this just from the information I have given you.

In Seve Sailer’s Law of Mass Shooting, he states that:

“If there are more wounded than killed, then the shooters is likely black.

If there are more killed than wounded, then the shooter is likely not black.”

It sounds ridiculous, yet whenever a mass shooting goes viral on Twitter, Sailer’s law gets vindicated, again and again, and again. But what if this is just survivorship bias? Maybe black shooters keep missing vital organs, or maybe we just miss all the mass shootings that don’t conform to Sailer’s Law?

For this fundamental law of the online HBD community, it is essential we know whether it is fact or fiction. So I decided to test it.

Seb Jensen, recommended I look at the Stanford Mass Shootings in America dataset. This was a professional attempt by academics and their assistants to record shootings with 3 or more victims. Note however that the FBI definition of mass shootings requires 4 or more fatalities. It describes itself as a “curated set of spatial and temporal data about mass shootings in America, taken from online media sources”. And as a ‘curated’ dataset, recording only 284 known shootings since 1966, it is not a complete set of all mass shootings and may be biased in which attacks it ends up recording. The project has been suspended since 2016

Using the Stanford dataset, we get the cross-tabulation below. If the number of fatalities is greater than the number of wounded, then there’s a 77% chance the shooter is not black. If the wounded outnumber the killed there is a 43% chance the shooter is black. In other words, if injuries are greater than fatalities, the probability of the shooter being black is 20% higher than in the alternate case. The results show that injuries versus fatalities can help predict race, but they don’t quite agree with Sailer’s law, which requires a +50% chance of the shooter being black if injuries are greater than fatalities.

However, this dataset is ‘curated’ and created by presumably liberal academics so it may be systematically missing mass shootings completed by blacks. In the Stanford dataset, 31% of mass shooters are black, which is odd since we know from FBI crime statistics that most murders are committed by blacks. The Stanford dataset also excludes mass shootings associated with organised crime and drugs. In a dataset which does include such shootings, 75% of the culprits are black.

If we just multiply the number of black mass shootings in the dataset so they are 75% of all shootings, we get the below cross-tabulation. Now if injuries are greater than fatalities, the chance of the shooter being black is 83%! However, if the fatalities outnumber the number of injuries then there is still a 66% chance the shooter is black.

Depending on how many mass shooters are black, Sailer’s Law of Mass Shootings may or may not be fact. Nevertheless, a weaker version of Sailer’s Law is fact - if injuries are greater than fatalities then the chance of the shooter being black increases.

Of course, we have to wonder, why are whites so good at mass shootings compared to black people? I have some speculations, but I’ll let you guess for yourself.

edited Jun 16, 2022Now do one with samples of Asian, Mixed, and White shooters, this might also lend more accuracy towards Sailer's Law. Two hypothesis for the adjusted numbers:

- If fatalities are 2x more than injuries, then the shooter has an 80% chance of not being Black

- If fatalities are more than injuries, then the shooter is 80% likely to be Asian

Thanks for the database! How odd—states with people with guns don’t have as many mass shootings—Montana, Wyoming…..