Bias warning, My wife and I can work from home, my kids kinda like homeschool (but really miss their friends) and I wiped down the groceries in the garage.
It’s always helpful to ask, has someone faced my situation before? The answer is often yes. Rory Sutherland thrives at this.
On recent podcasts from Deep Dive (#249) and Wharton Moneyball (April 1, 2020) there were two very good steps to understanding anything with uncertainty.
Wharton Moneyball takes its name from Michael Lewis’s Moneyball. That book shed light on using advanced statistics to find other ways to win baseball games, that walking to first after a full count was actually better than hitting a single to first on the first pitched ball. Moneyball thinking has extended to new areas like basketball, movies, and Jeopardy.
On Wharton Moneyball, Adi Wyner spoke with Alan Salzberg who mentioned that he’s starting looking at CoVid19 deaths rather than cases. The former takes longer to materialize in number form but is better than the former which is mostly a product of testing. It’s trading a sampling error for a time lag.
“It was what we would generally call ‘garbage data’. A confirmed case might me it was confirmed because someone came to the hospital and was already sick.” Alan Salzberg
Ok, good so far.
We need good data (walks instead of hits) but then Alan goes too far. The virus is mostly airborne and mostly won’t bother someone if it lands on a surface someone might touch and then finds a path into their body. That’s a lot of ifs. “Is that enough,” Salzberg wonders, “It stays for a little while, but in my mind I don’t think that should be a worry. I think you should wash your hands, and I’ve been doing that and I try not to touch my face a lot. But I think being ridiculously uptight about it is kind of crazy.”
Ok, that’s fine if we had better data.
But we don’t. Instead of six feet we might heed caution and stand at least twenty-seven feet apart. What’s the R0? How long is someone infected and asymptotic?
Ok, those are good questions.
There’s a lot of unknowns here and on Deep Dive, Matt (@PlusEVAnalytics) talked through what we can do when there are so many unknowns.
Think of Tom Brady’s 2020 over-under passing line of 4,256 passing yards, or 266 yards per game. His last four years totaled; 4057, 4355, 4577, and 3554. But with Tampa Bay he’s got better receivers. And he wants to prove to everyone that he’s still got it! And he wants to do it without Belichick!! Yeah!!!
But how much do those things count for? Like how much we know about CoVid19, we don’t know. Matt gives us a guide though. Do the things we don’t know make one outcome more likely? With age, ambiguity, competition, injury and so on, the unknown makes the under much more likely.
Matt credits much of this thinking to Taleb but the concept of sports and gambling make it clear. It seems like the unknown parts of the CoVid19 pandemic tilt the outcomes in favor of what’s much worse. Good data is a necessary start but ambiguity must be considered too.
Latest book: Idea Trails, 50 ideas from blogging the last four years.