{funspotr} -- #rstats function spotter -- is now compatible with quarto documents and is available on CRAN. Rstudio conf 2022 pres: https://t.co/co42z34sRz package on github: https://t.co/GEeOFz2dy0

I'm referenced here:

Reading "Everything is Predictable," is interesting how some of the origins of probability came about by identifying "fair" ways to split a gambling pot part way through a game. Could be interesting to calculate in the context of a game of pick-up... applying pascals' triangle

Gave a lightning talk at @cascadiarconf on how prediction intervals are helpful both in informing intermediary agents and in setting the bounds within which you want those agents to operate : https://t.co/PR9kV85nj5 #rstats

When playing make-it-take-it pickup basketball the expected value of shots can be approximated as a geometric series: https://t.co/0PZVQRioje

#rstats #sportsanalytics post on expected values under different possession and scoring systems in pickup basketball. One takeaway is that, once you leave "balanced" FG % conditions, shooting incentives in make-it-take-it can get out-of-whack pretty quickly.

1s and 2s full court is a scourge that must be defeated but if you think you "get it" yet are playing make-it-take-it by 2s and 3s, you are missing something. https://t.co/0PZVQRioje

If you're curious why and what you should do with this information, I wrote a short blog post on the topic:

When you work it out under these conditions, it turns out 1s and 2s likely offers more balanced incentives than 2s and 3s.

However 2s and 3s isn't always better. If you're playing "make-it-take-it"--where scoring comes with retaining possession--expected points are not just {expected points of shot} but also {expected points of future shots you may get if you score and therefore retain possession}.

When I play pickup basketball I like to go by 2s and 3s because it gives more similar expected values to a wider range of shots compared to the more commonly played 1s and 2s (where taking long range shots often becomes the dominant strategy).

Inconsistent rules are like plot holes in a movie, you get pulled out of the experience... and then, what's the point? Why play? (... yeah, yeah, time with friends / family, blah blah blah...). If I have a say in the rules, I like to choose rules that make for a balanced game.

I don't like the phrase "it's just a game." Yes, the stakes may be low but the zest of life comes from *getting into it.* I tend to be a stickler on rules because rules facilitate balanced games. I'm fine with house rules but be consistent.

A lot of armchair AI psychologists out there...

Pascal's triangle is awesome:

Very good visual for understanding epistemic and aleatoric uncertainty ( https://t.co/ejKva2yPXY).

posit::conf is a highlight each year. I'm excited it's in Seattle for 2024. I'll be presenting on the first day, Tuesday 8/13, in the ML and Statistical Modeling session. My talk is on uncertainty in prediction ( https://t.co/m1hrBfIQf8). If you're around come say "Hi."

#TidyTuesday A look at @drob's #R scripts' most common functions. As expected, #tidyverse and #ggplot2 dominate! Data: {funspotr} by @brshallo Code🔗 https://t.co/5y9eZnYzaN Tools: #rstats, #ggraph, #ggtext, #packcircles by @ptr_menzel and @R_Graph_Gallery

Incredible lineup of⚡️ talks! We heard about #TidyModels, Posit Public Package Manager, #QuartoPub blogs, #Shiny, #RStats pkg development, & GenTwoArmsTrialSize! 👏🏼 for Bryan Shalloway, Joe Roberts, Randi Bolt, Lovekumar Patel, Cameron Ashton, and Mohsen Soltanifar!

There are plenty of weaknesses and questions regarding the "Intermittent Fasting linked to 91% higher risk of heart death" study that was grabbing headlines in March... but in this post I walk through problems with some of @nntaleb 's lines of criticism: https://t.co/7G8y0HuZrn

@brshallo will be presenting his talk titled “Prediction Intervals in Tidymodels” where he will overview tidymodels ecosystem and different methods to produce prediction intervals. Check out his blog posts for a primer: https://t.co/2fHn5uiL53