Dick jokes aren’t typically intricate works of genius. There’s not a whole lot of substance or depth to them. They’re probably stereotypically associated with a drunk, meat-headed, college dude, “bro-ing down” with his friends. As a reasonably intelligent human being, you could probably come up with a quick equation as to what really makes a dick joke funny. If I had to build that equation off the top of my head right now, it would probably look something like this:
pervertedness + vulgarity = X level of comedy
Makes sense. Simple enough, right? Well not if you’re the guys in the Pied Piper crew from HBO’s series, Silicon Valley. Their dick joke is bigger than the dicks within the joke. It’s about the mathematical logistics of said dicks in correlation with the punchline of the dick joke. I could set the scene for you, but I’ll leave it up to our pal, TJ Miller, and his band of geeky cohorts to lay it all out, real legit like.
Well there you have it. The Pied Piper guys sounded pretty thorough about their approach to finding the most efficient way to… you know what they’re up to. But every team of people running complex numbers needs another team to check those numbers for accuracy. In this case, they would be dick joke auditors of sorts. Who better to check the numeric and systematic accuracy of a bunch of computer geek geniuses and their dick joke theories than researchers from Stanford University? Maybe an Andrologist or an Urologist with a second PhD in math? Irrelevant. We have Stanford University researchers and they say the math… STANDS UP!!! Get it??? This is what they have to say about the dick joke math.
A probabilistic model is introduced for the problem of stimulating a large male audience.
Double jerking is considered, in which two shafts may be stimulated with a single hand.
Both tip-to-tip and shaft-to-shaft configurations of audience members are analyzed. We demonstrate that pre-sorting members of the audience according to both shaft girth and leg length allows for more
efficient stimulation. Simulations establish steady rates of stimulation even as the variance of certain parameters is allowed to grow, whereas naive unsorted schemes have increasingly flaccid performance.
And that’s not all. They also provided us with a 12 page pdf thesis of all the numbers behind their findings. Hit the link below to check it out.
I don’t know what’s funnier at this point. The actual dick joke and the guys on Silicon Valley taking it so seriously that they used ridiculously advanced math to figure out the most efficient solution to the punchline, or the fact that researchers from Stanford University took the time to check the show’s math and confirm its correctness? You tell me. They’re both hilarious.
Ok. I’d say it’s safe to wrap this up. **WINK WINK** – Have you been watching Silicon Valley at all? What do you think about this season now that it’s come to a close? What do you think about Stanford University’s priorities now? Do we even want to approach how you feel about the unspoken result of the Tip-To-Tip model for every participant involved??? Let me know down in the comments and I’ll see you down there.
By: Eli Rebich