Meemaw’s Math and Where She Went Wrong
Author: Nicole Olynk Widmar, Associate Head and Professor, Purdue University, Department of Agricultural Economics
A few weeks ago, we tackled the Young Sheldon inspired philosophical question of “Why does everybody knowing something make it right?” Spoiler: It doesn’t, and you should probably be embarrassed you’ve never even slowed down to ask who ‘everybody’ is.
Sheldon did not just advise his father, Coach Cooper, on football plays. He also advised his Meemaw on sports gambling. Sitting in front of the TV with his family he shares … “Statistically always punting on 4th down makes no sense.” And “When the Aggies give up the ball on their own five-yard line, the opposing team has a 92% chance of scoring. When they punt from deep in their own territory, the other team still has a 77% chance of scoring. But since they convert on 4th down, 50% of the time the math says they should never punt again.”
Now, Meemaw had a few debts to a few people she had to get paid back pretty soon, and Meemaw (apparently) liked a good bet (although reading between the lines, she had also participated in a few that went bad). Thus, she goes to tuck young Sheldon in for the night … but also to ask if those statistics he was talking about with his dad could be applied to the game next week and if he could tell her not only who would win but by how much?
Sheldon’s Answer: “I suppose with enough data that I could make a reasonable guess.”
But Meemaw didn’t want to guess — she wanted to know. Thus, she delivers Sheldon the data, who is delayed in his data analytics due to needing to do his homework. After a rather un-grandma-like exchange in which Meemaw insinuates she’s owed gambling tips because she makes him cookies and Sheldon is threatening to tell his mother on his grandmother about her gambling habit — Sheldon tells Meemaw to take the Oilers and gives the points.
Then, the Oilers lost the game. Sheldon shares Meemaw never asked for his help with these sorts of endeavors ever again. But was Sheldon wrong? No, he wasn’t. What we are facing here is a poor outcome from the right decision. It happens, because practically every decision we make is under some degree of uncertainty.
It’s all just math, after all. Taking inspiration from Jeff Ma’s book … imagine you are Ma, sitting at the blackjack table counting cards. You know from a data-driven mathematical perspective what you are supposed to do right here, right now, in front of everyone, but last time you did it, you had a poor outcome. You know that doesn’t matter because last time it was the right decision mathematically, but given the uncertainty, the correct decision does not yield the correct outcome 100% of the time. You know the correct decision using a data-driven process is still the correct decision regardless of how you feel about it. Do you do the right thing?
We previously tackled a lesson in the form of Don’t Eat Random Mushrooms, which included four unfortunate truths.
The unfortunate truth #3 is that you cannot let the fear of a bad outcome stop you from making the right decision. It is entirely possible to make the right decision and experience a bad outcome, and it’s also possible to make the wrong decision and experience a good outcome. However, the outcome is not what we should be worried about in evaluating the decision. Instead, we need to separate the decision from the outcome.
From Jeff Ma: “I’m standing behind you, let’s say next year in Vegas, you have a 16 and the dealer has a 9 up. And you say to me, hey Jeff, what did you say I was supposed to do here. I say, hey, you’re supposed to hit. If you get a 5 to make 21 and win, I’m a genius, but if you get a 6 to get 22 and lose, I’m a moron that they never should have made a book or a movie about. But in both cases the decision was 100% correct; one was just a poor outcome and the other was a great outcome.”
Meemaw tried to use math to win some money betting sports to pay back what were apparently previous gambles that did not go so well. Was she wrong? No, she was finally onto something … she was finally going to be data-driven. But one data-driven decision in she suffered a poor outcome and quit using data. Her mistake, believe it or not, was not getting her young impressionable grandson to help her bet sports. Her mistake was in stopping doing so after one poor outcome from a good decision.