Three origin narratives

Maybe narratives are a sort of storage optimization solution. I can’t remember everything so a narrative is created to remember the thing.

Tom Brady hasn’t always been Tom Brady. “We’ve gone back and retrograded 2006 and 2007,” said Eric Eager, “and when we retrograded ’06, Peyton Manning was something like two standard deviations from anybody else. Tom Brady at the time was just kind of average. It was remarkable to see. We’ve grown accustomed to seeing Tom Brady as this great player and probably one of the best of all-time, and when we turned on the tape it wasn’t quite that way.”

Jack Bogle hasn’t always been Jack Bogle.

Lastly, Native deodorant per Sam Parr, hasn’t always been a mission driven company. Founder Moiz Ali was looking for a business to start and ended up on deodorant thanks to his pregnant sister and unit economics. He wanted a business with repeat purchases and minimal shipping costs. Mattresses didn’t fit the bill but deodorant did.

We are narrative creatures, and these narratives are true but they are not the full story. Maybe the way to think about origin stories is like a movie trailer: it’s a short way to communicate the gist.

Will Novak win the most?

Considering the recency effect of inevitability, practicing with base rates, and reducing the solution space.

In early 2022, the men’s career grand slam standing were tied at twenty wins each between Roger Federer (age 40), Rafael Nadal (35), and Novak Djokovic (34).

For tennis fans, the big question is: Who will end their career with the most? For a while it looked like Novak Djokovic who was the youngest and playing the best. Djokovic had also won three out of the four majors in 2021 and his dominance looked inevitable.

But few things in life are inevitable, no matter how they look in the moment. Sports provides an example for other parts of our life and the Wharton Moneyball crew (in the 1/26/2022 episode) provide a way to think through probabilistic, inevitable, and recency related issues that come in any part of life.

“I was convinced Djokovic was going to end up with the most majors,” Wharton professor Eric Bradlow begins, “but let’s talk about what’s happened in the last six months.” Novak lost the last tournament of 2021 and didn’t play in the first tournament of 2022. Without getting vaccinated, Novak will not play in the French Open (and maybe U.S. Open) either.

But Djokovic not playing also means that someone else, like Nadal can increase his total. Rafael made the semi-finals at WON the Australian and plays best at the French.

“Six months ago I would have said Djokovic would be the top of all time. Now it’s 50/50. I give Nadal a legitimate chance to have the most majors of all time,” Bradlow says.

Tap the brakes, host Cade Massey says, remind us of the base rates. That is, what’s the oldest that someone great tends to win these major tournaments. “Federer hasn’t won one since he was 37,” Eric explains.

Ah. Now we have something to work with. Let’s follow a page from Zeckhauser and simplify.

Djokovic and Nadal each have about 12 chances left. But Novak isn’t vaccinated so subtract the ’22 Australian, ’22 French, and ’22 U.S. Open. “You’ve gone from twelve good chances to nine. And you still have (to play) Nadal at the French!” Bradlow bellows. Plus, it’s not just Nadal and Djokovic but a field of players and like something is always happening, someone unexpected will win.

Rather than overreact to news, we found the base rate (oldest age to win a major) and opportunities left. Though men’s tennis is top heavy, there’s a lot that can happen.

But wait, that’s not all.

“I’m bummed for folks like him (Novak), like Kyrie Irving,” says Massey, “like any high profile athlete, that takes a no vax stance. There is so little room for changing your mind. Once you take that high profile a stance, with the politics of it, it really diminishes the chance of him shifting his position.”

Put another way, a strong public stance creates a restricted action section.

Who will end up with the most majors? We don’t know. But we do know that using base rates, avoiding recency bias, finding simple examples, and not reducing our solution space are all good processes.

For football fans, there’s a section of fervor about the Allen-Mahomes game that speaks to the inevitability and our reactions to recent events.

Underdog or tie game?

It’s important to highlight base rates in the wild as a way to keep them top-of-mind. What’s available is what we think about. Nowhere is this better than sports. We tend to overreact rather than “short the narrative” and base rates are a useful thinking tool to practice.

Talking about the last week of the NFL season and potential playoff situations, otherwise sane Shane Jensen offers up, “I think it’s less likely the Colts lose to the Jaguars than that other game ends in a tie.” Okay, what’s the base rate? Since 2017 there have been 5 games that ended in a tie, and 7 games where a big underdog won.

What’s probably happening is recency bias. The Jaguars lost by forty points to the Patriots, fired their coach mid-season, and maybe aren’t the best run organization. All of those things can be true as well as still being more likely to win than a tie game.

Part of the Wharton Moneyball podcast is to be entertaining. That’s fine. But part of it is to be analytical, and for that this was a miss.

Words from Wharton

Sometimes we just need the right word to explain an idea which leads to action. Having a name for a thing changes the way we think about it. These are some of my favorites from the Wharton Moneyball podcast.

Let’s be precise. For instance, what does herd immunity mean? Often we are not precise.

What’s the effect size? There are a lot of things we could do, but which matter most?

Short the narrative. In sports there’s a narrative that drives the stories. Often the narrative is overpriced.

The most parsimonious explanation. I can never remember if it’s Hanlon’s or Occam’s razor. This and ‘short the narrative’ pair well together, like the blades of a scissors.

Mean reversion. Outcomes are combinations of skill and luck. Skill mostly persists, luck mostly does not, hence the Madden Curse. But(!!) skill isn’t static. If one player has a great year, the next year they might have a better year because while their luck component contracts, their skill share expands.

Coin flip. Statisticians (and artists) fall in love with their models. The coin flip is a Zeckhauser-esque simplification. How often will a team win three-straight games? One-eighth of the time.

Favorites or the field. We’ve covered this one. (Twice)

Tennis variance. Fewer events means more variance. Tennis events, like the Olympics, may have more upsets. This could also be part-of-the-reason there are more dominant men than women.

Update my priors. To change ones view with information. It’s being Bayesian.


It was fiction that focused the idea of names.

Base rate and mean reversion structure

One decision making suggestion is to start with the base rate, to find a set of comparable circumstances and ask, ‘what typically happens?’

In investing it is to find the price of, say Amazon, and ask how often a company of those characteristics grew at an expected percentage. Rather than start with the idea that Amazon is a great company that does a bunch of things well and so on – we can ask, for companies like this how many have grown at 15% a year? Zero.

Another example is (large, but maybe all) construction. The “base rate” is not good, fast, and cheap but overpromised, over-budget, and underwhelming. You cannot compare this to that people protest. Boloney says Bent Flyvbjerg, the cost overruns and benefit shortfalls are so consistent they are comical.

So starting with the base rate can help. What else?

On Wharton Moneyball the hosts discussed the 2020 Olympics and noted that the United States (men especially) have underperformed compared to years past. Is it culture? Training? Commitment?

“Is the U.S. doing badly or are other countries proportionally better?” – Shane Jensen, Wharton Moneyball, August 2021

Yes, says cohost Cade Massey, “Give Shane credit for the most parsimonious explanation we suggest for most any situation, try regression to the mean on for size and see if that can explain it.”

The idea behind regression to the mean is that performance varies up and down. There are many causal explanations for why this happens, think about talking heads for sports or stocks, but sometimes the mechanism is just old fashioned randomness.

But, ugh, we do no like this. Give me a reason man. So we assign reasons, which may be more comfortable than they are accurate.

We can make easier decisions. Easy decisions are designed. What designing a decision does is it shifts the information people use. Simple starter explanations like, the base rate or mean reversion, both create a decision making structure that will often help people get pointed in the right direction first. Start in the ballpark or base rates and then move towards your unique situation. Start with mean reversion as the mechanism and the adjust for other factors.


Mean reversion, said Cliff Aeneas (Bloomberg) is basically value investing, “they’re almost synonymous.”

Three star recruits

The success equation notes that results are combinations of skill and luck. Some parts of life have a larger luck outcome than others.

So, to improve, we look at the skill slice. Poker’s appeal is this idea. Play more hands with positive expected value. But not every sport is as clean as poker. Football, for instance, is messier.

One way to work around the football outcomes is to focus on the football inputs. For instance, recruiting is important, how many highly rated recruits does a coach get each year? That seems like a nice input as the percent of “Blue Chip” recruits correlates well with wins.

Ah, but incentives are tricky.

“There are stories you hear, anecdotal but I’ve heard more than a few, about how different coaches may have a financial incentive to have higher ranked recruiting classes, and they may be involved in the process of lobbying to get guys higher stars.” – Bruce Feldman, Wharton Moneyball, September 2021

Incentives are like the weather. Just like a bunch of measurable aspects (temperature, pressure, wind, humidity) combine to form ‘the weather’, a bunch of measurable aspects of a job combine to form the incentives. A football coach has incentives to win, but also to pay his daughter’s tuition and take his family on vacation. And it’s not just football coaches, it is all of us.

What to do? Work is a combination of financial and non-financial rewards. Alchemy offers a way to tweak the non-financial rewards. Culture works well too, and that comes from the top.


Here’s a story on auto incentives (thanks Stephen!). Incentives were one of my favorite ideas, get all 62 as a daily email drip on Gumroad. Find them on Amazon too.

Hurdling past covid

One way to think about “adoption” is as series of hurdles. If something is “adopted” it has succeeded by crossing the set of hurdles. There are few food bacteria “adoptions” because of hurdle technology: hot, cold, salt, and acid all make the process harder for food bacteria to survive.

Another metaphor for this approach is Swiss cheese: one layer has multiple holes but if the layers are independent, then stacking one on top of another removes the holes.

Part of the problem with studying, treating, and living with Covid is that it’s hard to figure out what works. There are models, but we’re still kinda guessing. As of August 2021, more than one-fifth of all FDA approved drugs were tried as off label treatment for Covid. Ironically, there’s not enough Covid to study it.

“What should give us reason to be hopeful is that there’s this cumulative effect that if you give the right drug at the right time along the way…there are these 15-30% reductions at each step so if you are someone that gets a monoclonal antibody early on, if you get fluvoxamine, you get remdesivir on admission, you get dexamethasone once you are on oxygen. We should model out where this puts you at.” – David Fajgenbaum, Wharton Moneyball, August 2021

Ah not so fast, Eric Bradlow follows up. How independent are these? Is this like a piece of Swiss cheese? “It’s shocking,” says Fajgenbaum, “they all seem to hit it from a different angle.” That angle appears to be time. Vaccines are like sunscreen, David explains, and that’s the pre-infection prevention. Then it’s one drug to stimulate the body’s immune response, then it’s another to slow that response way down.

Abraham Lincoln is attributed as saying, give me an hour to chop down a tree and I will spend the first fifty minutes sharpening an axe. Rather than trees and axes we can ask: Is our situation a hurdle condition? With Mr. Lincoln and the suggestion of Charlie Munger to invert, always invert (!), we can come up with a simple situational:

For deceleration, we want to create a series of independent hurdles an agent must cross. In the case of covid this might mean that a place mandates masks, vaccines, and social distancing — or maybe just be outside.

For acceleration, we want to create fewer hurdles for an agent. If not possible, we want homogenous hurdles. Smartphones did this for ride sharing: the who (payments), where (location), and when (on-demand) were all integrated into an app. Another way to consolidate hurdles is find the JTBD.


Even 17 months into it still feels early to say these are the treatments. While they may not be this approach still feels okay.

Football ’21 favorites or field?

“Between the Chiefs, Bucs, Packers and Bills there’s a fifty percent chance of one of those four teams winning the Super Bowl. In other words, you can have those four teams or the other twenty-eight in the league.” – Cade Massey, Wharton Moneyball, August 2021

Take the other twenty-eight! This idea holds in both sports and investing.

One potential bit of mental muck is what we can call Visibility. It is easy to imagine a specific thing happening rather than a range of things happening. Visibility is part-of-the-reason counterfactuals and postmortems are difficult to conduct. That three of the four quarterbacks have in fact won a Super Bowl makes this effect more so. I can ‘see’ one of these quarterbacks winning the big one, but that doesn’t provide helpful information.

Not so fast though.

What is ‘the field’? As Tim Harford wrote, it is as often the words as the numbers which cause confusion (life expectancy for instance). In the case of the NFL, it’s the twenty-eight other teams that might win the Super Bowl.

But is that right? Can every other team win the Super Bowl? While on “any given Sunday” any team might win, stringing together a group of wins to be champion is far less likely.

The central point to Zeckhauser’s Maxims is that reframing a situation may cause our conclusions to change. We used this framework to ask: Was the Ohio Vaccine Lotta a Good Idea? What if we reframe the question around Super Bowl favorites then?

Roughly speaking there are three groups: the Favorites (4 teams), the Chasers (X), and the No Chancers (Y). Now how many Chasers and No Chancers there are is questionable, but the framing changes our thinking. If this is the structure then the relevant idea isn’t the field but a subset of the field: the Chasers. If there are 4 Favorites, 4 Chasers, and 24 No Chancers then the choice changes. Take the favorites. What if there are 4 Favorites, 8 Chasers, and 20 No Chancers? The top twelve preseason favorites have won 85% of the previous 20 Super Bowls.

There’s no definite answer to this Favorites, Chasers, and No Chancers structure but the framing does change how we think about it.

Another mental model is the same mindset we used “Tracking Tom“. The idea there was that there’s more downward variance than upward variance. The same idea holds for the Bucs, Chiefs, Bills, and Packers: those teams are more likely to underachieve. Put another way, it’s more likely that something goes wrong (someone’s quarterback underperforms) than right. The Chiefs and Bills, for instance, both notably outperformed their 2020 Pythagorean figures.
“Uncertainty is generally underestimated,” said Adi Wyner, “and that means that the field collectively have a little bit more probability than you might assign to four teams.” The data agrees. Take the field.

A confusing life expectancy calculation

“Statisticians are sometimes dismissed as bean counters. The sneering term is misleading as well as unfair. Most of the concepts that matter in policy are not like beans; they are not merely difficult to count, but difficult to define…the truth is more subtle yet in some ways easier: our confusion often lies less in numbers than in words.” – Tim Harford, The Data Detective, 2021

One of Harford’s goals is to help people understand the world more as it is and less as they wish it. Harford kindly covers ideas like base rates, sampling bias, and algorithm associations.

That last one has some quite funny anecdotes. For instance, one AI system was trained to distinguish healthy skin from cancerous skin. Crunching and comparing are two things computers do really well, so this seemed a good fit. And it was! The AI (read: computer code) categorized correctly. But computer code is like a mango slicer – it has a singular use. In the case of the skin cancer, what the AI “learned” was that if a ruler was present it was cancer.

That’s funny.

But also not. One economic principle that’s going to affect (is affect_ing_) work is the idea that as something gets cheaper it’s used more. LEDs and cameras are two recent examples. Data too, is going to be part of our lives more, and Harford wants us to think about the numbers a bit more. For instance, what does “life expectancy” mean?

“They take the relative risk at every age and they integrate it. They ask, if the relative risk this year stayed constant forever, how long would someone born today live? That’s where we lost a year, but that’s assuming Covid stays and the year we just had gets repeated .” – Adi Wyner, Wharton Moneyball, July 2021

This isn’t the only way to calculate life expectancy, but it was the way that lead to headlines like, “US Life Expectancy in 2020 Saw Biggest Drop Since WWII, With Virus Mostly to Blame”. That’s true, but is that how most people understood it? Does if this previous year repeated forever seem like a good conditional?

Most of what happens, and Harford starts his book on this idea, is that we think fast. But we can use this tendency to being more numerate. Books like Harford’s bump up (Bayesian baby) these ideas. Riddles like: most British men live past the average age help too. A steady dose of numeracy uses the availability heuristic for our own good.


Not into the book thing? Harford has great podcast that cover these ideas. Wharton Moneyball is another with more of a sport’s bent. Gambling podcasts too cover these ideas. As Tyler Cowen said, it’s not that these things are VERY IMPORTANT but that if we see them more we update our mental toolboxes so they are marginally more important.

USA Swimming Analytics

The first breakthrough in swimming was imitation. Like with high jump, seeing a new way to do things helped. The second breakthrough was underwater footage. What’s next?

Adi Wyner asked, is there anything beyond video helping with swimming improvements? It’s a good question. Let’s get some sweet advanced analytical fruit from the random forest up in here!

“We don’t have any tools to calculate instantaneous velocity, which would be the most helpful. It (the tool) also can’t be something that burdens the swimmer because if equipment is hanging off of them it changes how they are interacting with the water.” – Russell Mark, USA Swimming, July 2021

It’s the classic question: how do I know what to do?

It could be that baseball was uniquely suited to analytics: lots of data, one-v.-one matchups, less cultural importance (relatively). Swimming, Mark explained, has a lot of different body types and so there’s less data and fewer answers for “what to do”.

But it’s not completely empty. USA swimming for instance hosts the Olympic trials three weeks before the games. The thinking here, explained by Wyner, is that individuals vary in their performance but not too much during this competition window. If variance runs ‘in chunks’ then a proximate trials-games window makes sense. This theory might work, it is showing some alpha erosion as for the 2020 games Australian swimming has copied this schedule.

Luckily most of life is not the Olympics. The greatest athletes in the world looking for improvements “at the margin” is not the model. Most of life is answering questions like a 15 or 30 year mortgage? Most of life is just choosing from the good options, not finding the best one to the nth degree.


It feels odd writing about luck without mentioning The Success Equation by Michael Mauboussin. There we go, it’s mentioned.