Los Angeles Lakers v Brooklyn Nets 2022 NBA Finals in October 2021!
What can we learn from the sports predictions for better decision-making? Using an example of the NBA Finals prediction and previewing Business Games
In October 2021, the Los Angeles Lakers Met the Brooklyn Nets in the 2022 NBA Finals!
At least, such was the overwhelming consensus of people both in the Vegas betting markets and the NBA media—the two groups who should know better, this being a large part of their jobs, after all.
Yes—But Why Do We Care?
Why is any of this related to Business Games, an educational podcast about decision-making and game theory and business, you mean?
Well, as we'll hear in weeks 3 and 5 of our experimental season, predictions are a key part to making good decisions. Having at least an inkling of what is likely to happen in various possible futures is a prerequisite to taking an informed action.
And, as we'll hear from one of the top academics in the field of experimental economics, betting markets are good aggregators of the wisdom of the crowds—provided some conditions are satisfied. That'll be episode two of season one, The Experimental One (👆 the Season's Intro just above 👆)
For now, I'd like to use an example that received a lot of media coverage recently—i.e., the annual media prediction circus, this time before the 2021-2022 NBA season launch—to relate some examples of good predictions / bad predictions.
Yeah, Okay—But, Why the NBA, Exactly?
I find the NBA a nice microcosm of most of the things we care about on this podcast. It has enough complexity to mimic most of the organizations' and market interactions—yet it's contained just enough to have an overview of what's going on.
It's the premier league of the 3rd most-watched and -played world sport (behind football and cricket)—so there are many stories written on it. It's a business and it's a game.
It has plenty uncertainty both on the court and off.
It has marketing, HR, recruitment, strategy, customer satisfaction, leadership—and all sort of other topics that businesspeople care about and from which we can learn. The knowledge that people possess, their moves (who's giving which contract to whom, first?), their…
Honestly, it's an amazing example for almost all our topics.
OK, We Get It, You're a Degenerate NBA Super-Nerd-Fan and You Want to Write and Talk About It—A Lot! Just Say So.
Well, dunno about the "super" part of it—but I've been following the business side of the league for over 20 years now, so… yeah…
Let's Get Back on Point—What's the Deal With These 2022 Finals?
Well, they haven't happened, yet.
And yet all the media had pre-ordained the Nets the Champs, as if it's written in stone, as if there's no uncertainty about it.
And the funny thing is, they're all wrong!
Says statistical likelihood…
Ah, Them's "Blog Boys"!
That's an insider reference nobody will get—and no, exactly the opposite—them Blog Boys got it wrong this time. But not because they used "analytics"—which Kevin Durant doesn't like.
So, Lemme Get This Straight—The Finals Have Not Happened Yet, but You're Saying the Prediction Is Wrong…—How Do We Know This Prediction Is Wrong, Again?
You're right, we don't—at least, not for certain—but certainty and binary know/don't know are very unhelpful for making good decisions.
Even though the Nets and the Lakers both started the season underwhelmingly, each getting trounced in at least one of their first 3 games, they could hypothetically each uncork their own 15-game winning streaks, end up having the top seeds in their respective conferences, march through the playoffs, and indeed meet in the NBA Finals. That's still on the table.
But see, that's exactly why we want to analyse this prediction right this moment—because a lot can happen between now and then, in particular Luck shall cast her dice, and random shit will pop up—yet what happens then has no bearing on the quality of the prediction now, because now ain't then, as especially, we're living in the now and don't know what happens then.
As my buddy JP wrote:
It is striking how few, including the big consultancies, appear to understand that what matters in strategic audits is what was known at the time of the decision, not what has become known after the event.— JP Castlin (@JPCastlin) October 21, 2021
One would have thought it to be analysis 101, but apparently not.
But, How Do You Reconcile This With Experimentation? Isn't Experimentation = Making Decisions, Then Waiting Until the Outcome Reveals Itself to Figure Out Whether It Was the Right Decision? Don't You Have a Podcast Season Dedicated to Business Experimentation? What Did You Call It, Again? "The Experimental One"?
Yes—and!—there's a difference. Here are a few points we need to keep in mind:
First, predictions and experiments work together. Even to set up an experiment, you need to understand what to expect—so, a prediction.
Second, experiments are there when you've hit a wall with predictions—so, they are exactly for those situations when you just don't know, but you want to know, and more importantly, you are OK with failing while trying stuff out.
In essence, with predictions you want to figure out what's the likeliest outcome—while with experimentation, you go, "Duck it! Lemme just see what happens!"
Except—except!—this "just trying stuff" is actually super structured and you still need to set things up in such a way that your result is not driven by inherent randomness, else you'd be in a situation where you're always reacting to luck without any rhyme or reason… something that poker players call "resulting".
Wait, Did You Just Say, "Duck It!"?
Yes—but to be fair, I meant something else. The podcast is rated E for Explicit—but I try not to over-use it.
Will There Be More on This? Ahem—I Mean, Experimentation and Resulting and Such?
Absolutely! All of it!
Lemme Guess… Premium Content?
You got it! Once we've covered predictions and experiments in episode 4, I then dive a bit deeper into this very topic on the premium-members-only follow-up episode.
We do have lots of free content on this, too. I mean, we've got a whole season on experiments with half of it available for free to the public, but… Daddy's gotta eat!
That's a Disgusting Saying
Yeah, I'm not going to use it again.
Can We Get to the Story, Finally?
Yes. So, there's this guy, Ric Bucher. On the podcast On The Ball with Ric Bucher, Ric quite deftly analysed not only why most media is wrong, but also gave thoughtful reasoning behind his own predictions. Have a listen between the minutes 2:23 and 22:30 here—it's a master-class in analysis. I'll wait. It's relatively short and Ric asks not to aggregate.
Wait, What? Are You Kidding? Do We Have To?
You don't "have to" have to. Look, not everyone is a—what did you call me?—degenerate NBA nerd fan. As I say, it's a master-class in analysis, and I would listen to it because it's relevant and also, well, because I'm me.
But for our purposes right here, I can continue even without it.
Feel free to skip it if you don't want an amazing analysis master-class in NBA predictions—and just read on about how it relates to making better decisions.
So, that one was before the opening night. Then, the opening night happened, the Nets were summarily trounced by the current NBA Champions, the Milwaukee Bucks, who played free and with joy and selflessness—and the Lakers fell to the Golden State Warriors. In fact, the present incarnation of the Lakers had at that point yet to win a game together, after going 0-6 in the pre-season. They also lost game 2 of the season to the Suns, nearly had an on-the-court inner-team fight, and, well, looked out of sorts.
Ric did a follow-up here—play between the minutes 2:15 and 26:15:
Fine, I already motivated why you should care. This story foreshadows a few topics we'll start discussing from the very beginning of Season One, namely predictions in and off themselves, as well as betting markets for predictions, and ultimately how it all relates to making better decisions.
(Very Monty Python Booming Sketch Voice) Get On With It!
So, the first learning: the Lakers v Nets 2022 NBA Finals prediction might not be wrong. And if it's neither right nor wrong, how would we deal analysing this Schrödinger's prediction?
I personally suggest at least these three considerations when looking into the future—and keep in mind, we'll get into this problem in-depth later on in Business Games. Luckily, in the end, this "game" only has 2 possible outcomes: the Lakers and the Nets either meet in the Finals—or they don't; there's no in-between. So, these simplified considerations suffice for now:
Find all plausible scenarios and work backward from the end-state of the scenarios (Meet in the Finals / Don't Meet in the Finals) to understand what would have to be true for each scenario to happen—i.e., if you're in one of these scenarios, how did you get there?
Critically, what is the likelihood of all of those things happening for one scenario versus the other? On balance, what's more likely?
If there's something riding on your getting this right, or signalling this right, or indeed interpreting other people's signals correctly—would you rather get hurt by betting A and ending up in B—or by betting B and ending up in A?
If you've listened to Ric above, he gives a lot of considered arguments exactly of the kind highlighted in points 1. & 2. above. I won't repeat them here, because you just listened to his pods—suffice it to say, I'm convinced that on balance, the likelihood of the Lakers and the Nets not meeting in the Finals is significantly (in my opinion) higher than the likelihood of them meeting in the 2022 Finals.
Now, it's not 100%—but it doesn't have to be! I'm not going to place probabilities, it could be 60-40, could be 70-30, could be 67.81% for them not meeting—the actual numbers aren't important, I'm just confident that them meeting in the Finals is not the prohibitively favourite outcome most pundits took it to be.
That's the trick with the real world predictions and actions based on them—it's not just about likelihood but also impact. That's exactly why I snuck in that 3rd consideration up there—which side would you rather err on? Because when I say it's 60-40, there's still 4 futures out of 10 that these teams do meet in the finals—but is betting this way, on a 40% chance, the right thing to do? It might be, depending on what you want to achieve. But that's a different blog post.
For now, I'm convinced that the pundits and the betting markets are wrong, and Ric is right: Nets–Lakers is ex-ante not the most likely outcome for 2022.
Now, as we'll see in a moment, we can apply the 3rd consideration from the above list in a meta sense, when looking at other people's predictions. That is, if somebody says something, are they speaking the actual beliefs on the actual probabilities, or are they bringing in some impact consideration? Even if it's a subconscious impact on their career or the sense of self. That is, is it an objective belief, or wishful thinking, or something else?
OK, So… Why Do People Get This Wrong?
In what follows, I'm combining Ric's and my own thinking. Let's look at it in sections.
The Situation: Two Groups Who Got This Wrong
The 2021-22 NBA Entrance Survey on The Ringer places the Nets into the 2022 Finals 9 out of 9 times, and The Ringer's pundits have the Lakers meet the Nets there 6 times out of those 9 times.
The ESPN experts give the Nets a whooping 78.9% of the first-place votes to win the East, and the Lakers and equally-whooping 78.9% first-place votes to win the West. That's 8 out of 10 times the Nets win the East and the Lakers win the West. According to the same experts, the Nets win the Finals 78.9% of the time (first-place votes)—which means, whoever took the Nets to come out of the East also 10 out of 10 times backed them to win it all. 10 out of 10. Think about it. 100%. 🤯 (mind, blown!)
The other media outlets dedicated to following the NBA have similar predictions.
Why would this be the case? And hey, at least 80% is not 100%, so at least some experts are giving somebody else a chance, but still.
To answer "Why?" I'd like to point out to the final point on my 3-step consideration list: what's the price for getting things wrong? And here's the rub: for NBA punditry? A-B-S-O-L-U-T-E-L-Y NOTHING.
Economists like to say, "talk is cheap"—you can say whatever you want, if you don't stand to gain or lose anything. And given that humans are typically risk-averse, gaining means relatively less, so it's more "if you don't stand to lose much".
What do pundits stand to lose? Now, granted, these predictions are enshrined into digital "stone", which could lead to… what, a split-second ridicule on social media, which will dissipate as fast as it came over? Especially given that after the full 82 games + the playoffs, who's even going to care about checking the past predictions of an ESPN staffer?
While right this very moment? Ah, right this very moment, if you don't pick the Nets versus the Lakers—only the teams of super-duper-stars, what with the combined 101 All-Star appearances and all between them—why, right this very moment you'd have the ire of the joint (and rabid) fan bases of LeBron James, Kevin Durant, Kyrie Irving, James Harden, Russell Westbrook, Anthony Davis, Carmelo Anthony (assuming he still has a rabid fan-base), not to mention the fan-base of the LA ducking Lakers, possibly the most rabid and delusional fan-base of them all!
To sum up: there is no downside to being wrong, but there is downside to not picking Lakers–Nets as finalists.
And I'm not even saying it's conscious—I don't know these people personally, and it's not nice to accuse the journalists of lacking journalistic integrity—so I'm very explicitly not saying this.
These pundits being people, and visible people at that, the vast majority of them huge NBA fans themselves, who interact with the players and their fans on a daily basis… It doesn't have to be conscious to be a bias. We know enough about unconscious bias to accept the possibility of this hypothesis.
Hell, when making predictions about sports, I know I get swayed by liking a team or a player—and hey, we're all only humans, even the professional journalists.
But there's another aspect.
As Ric points out in his pod, the punditry is as much performative as it is informative—from this point of view, too, it makes a lot of sense to take a loud if indefensible position and die on that proverbial hill—because the sports pundits are in the business of info-tainment, with possibly more and more skewing the latter, i.e., the "-tainment" half. At least, this feels like a trend over the past 20 years, but anyway…
The Betting Markets
So, now let us look at the people whose livelihoods depend on getting the right decisions: the betting markets. You'd think they'd get the odds right—right?
Well—and here Ric also nicely explains, to which I might add only very little—the betting markets are made up of several actors:
The markets themselves—the people who run the market.
The bettors, of which there are probably 2 sub-categories of interest to us:
2.1 The fans and micro-bettors, and
2.2 The professional gamblers
OK. The markets themselves have one incentive and one incentive only: minimise actual pay-outs, thus maximizing their profits. They don't actually care about being right—they care about making money based on whatever the participants are betting. You could say, rather than guessing what the actual outcome would be, they need to set the odds in such a way, that they make money based on the actual betting behaviour of the participants. In effect, they are playing a completely different game: they are right when they guess how the participants would bet, rather than guessing what the final outcome would be (though this, too, influences their pay-outs).
Because the actual outcome is a product of both the probability and the size of the bet. That's our 3rd consideration from the above list, again.
So, for example, if Los Angeles and New York fans bet a certain way, the size of the bet and therefore the bookmakers' exposure would be such as to be a large enough risk even at a smaller probability.
And we're talking probabilities large enough, right? Remember, in my example, say I believe that the Nets' likelihood of making the Finals is 40% rather than 80%—it's still 40% and not 0.05%, right?
Taking our consideration No.3: setting the opening odds "correctly" and then ex-post actually having the Lakers in the Finals, with the size of all of LA fandom betting this way, might still be worse than skewing the odds away from this more mathematically "correct" starting point—exactly because you expect all of the LA fandom betting this way. Ditto for the Brooklyn fandom.
Here, figuring out what bites more is really important: would you rather err on this side and get it wrong—or would you rather err on the opposite side and get that wrong?
This means that even if you think that Milwaukee is more likely to reach the Finals than the Nets, you might still downplay this knowledge, because there are much fewer people in Milwaukee compared to NY, period, and thus also there are fewer people who are likely to bet on Milwaukee as compared to Brooklyn.
Because that group in the middle of my betting markets' participant list—the casual fans—are large enough to have its betting moves skew the market.
So, Why Is Ric Right? What's the Other Actual Learning Here?
We just looked at how the existing prediction markets are not (what economists call) incentive compatible when it comes to wanting to draw an actual prediction from them regarding who will likely meet in the 2022 NBA Finals. And I'm including the pundits' predictions into the wider scope of prediction markets. In Season One, Episode Two (available in a few weeks after this blog post), we discuss these concepts in detail with one of the top-published Professors of Experimental Economics. This will be free content and available wherever you get your podcasts, FYI— 👈 you can subscribe to the public podcast by clicking on this link here 👈 😉
Ooh 👆 Promotional Content and a Call To Action… Subtle
OK, Mr Hyde—you're working for this company, too, y'know!
Anyway, unlike my opinion of the NBA punditry and the bookmakers, I trust Ric for a couple of reasons. In my opinion, these same reasons make for a good list of things to think about when looking at other people's analysis:
- Ric has no horse in the performative game—at least, not on his podcast. He does plenty of it on TV in his other roles (I guess?). He's playing a different game: an informative one. He sounds trustworthy, because in this setting, why wouldn't he be? He also admits many times on his pod why he was wrong on things—he does even that thoughtfully, analysing what he'd discounted and why and why he shouldn't have.
- Ric's analysis scope is wide. He considers the situation from all sorts of angles—in fact, I rarely heard any NBA pundit consider anywhere close to the amount of angles that Ric considers on his podcast. Players' health histories, peak performance trajectories, age, personalities, individual games, team fit, teams' off-season moves, the starters versus the bench, knowledge of "having been there", the "culture", the way people in this or that organization make decisions, etc. etc. etc.
- Ric's analysis process is sound. As a specific point, Ric knows how to handle uncertainty. He's also one of the few who explicitly consider only the factors that are available now, and also mentions how we should update our predictions based on the factors as they appear throughout the season—in the second pod above, he even talks about exactly the value of one observation—the Opening Night—in the overall prediction game, what we've learnt and what we haven't, and why. I like how he considers things out loud and motivates his thinking on the value of this one additional observation.
So, um, "that about does it for this episode"—to play Ric a final homage.
Any Famous Last Words?
I guess, these…: While the Brooklyn Nets and the LA Lakers might end up in the 2022 NBA Finals, the real value of this exercise is to realize that the actual outcome will be neither here nor there when it comes to evaluating the quality of the analysis and the prediction. For at this very moment, we must evaluate the information that we have at this very moment.
Hopefully, this article showed that this is both relevant and doable.
Still Feels Like We Should Just Let It Play Out, Before We Can Know Who's Right and Who's Wrong…
From the pure basketball point of view? Absolutely—that's what the game is for! And by the way, no entertainment happens without uncertainty. Though there's some comfort in the known, there's more fun in the (f)un-known!
Argh, That's a Terrible Dad Near-Pun!
Yeah, not my best one.
Anyway, there's more fun in the twists—and sports offer exactly those kinds of twists.
From the point of view of analysing decisions, though? …
Get yourself a Decision Diary or a Prediction Diary and always go back to what you knew at the time in order to analyse any individual decision. Over time, incorporate the results as a group if you see patterns—but avoid resulting, that is being overly influenced / swung by a singular random event that made the coin come up heads or tails that one time (for repetitive events).
Hindsight is 20/20, as they say—but I'm pretty sure this is said in irony. Here's what I consider a better quote on hindsight, this one by Helen Reddy:
Hindsight is wonderful. It's always very easy to second guess after the fact.
Does this work as a sign-off?
Maybe… Anyway, Glad We're Done Here! Thanks for Writing! Next Time, Make It Shorter.
It's my pleasure! … Um … I guess? 🤷♂️
And Now… Footnotes
These episodes will be dropping on the public feed available on all the major platforms in the coming weeks and even earlier on the private RSS feeds available to Premium subscribers. Plus: paid subscribers will get transcripts and extra episodes with my own analysis. Read more about the Premium Subscription here. ↩︎
I use the word occasionally and generally know what it means—but just to make sure, I looked it up in the Oxford English Dictionary and found this particular definition and fell in love with it even more! Especially, the way it uses complexity, something we'll be coming back to over and over in this series, starting with the very first episode dropping next week. OED defines microcosm as: "Any complex entity, esp. a community, regarded as forming a self-contained or self-regulating world or universe. In early use frequently one which encapsulates in miniature the characteristics of a larger." ↩︎
I refuse to call football "soccer" because it's "football" and needs no qualifiers because it's the #1 world sport and I was born in Europe and I shall have no arguments on this even though my source calls it "soccer": Top 10 Most Watched Sports in the World Today. Whatever. You know I'm right. ↩︎
Well, mostly all wrong, but "mostly" sounds too subtle 😜 ↩︎
For those who do not follow the NBA but do know LeBron James, KD is like LBJ, but better at this stage of their respective careers. ↩︎
Well, one of them could make it without the other one. For illustration purposes, don't care about this finer detail. Does not detract from the overall thinking—but to analyse the complete set of possibilities, there are in fact 4 scenarios: LAL in the Finals no BKN, BKN in the Finals no LAL, both in the Finals, none in the Finals. ↩︎
NBA predictions: Our experts' picks for MVP and Finals winners ↩︎
Two of the top-3 NBA teams ever, according to Los Angeles Lakers and Brooklyn Nets stacking All-Star talent at a historic level ↩︎
This last one is not even my feeling, though it's also my feeling. As the Dothraki say, "it is known". ↩︎
This is called "pricing up the market" and you can read more here: How Do Bookmakers Set Odds? ↩︎
See, I'm even using my 3-step here: for me to be wrong on Ric, what would need to be true, and which way would I rather err? On balance, I trust Ric for the reasons I gave. ↩︎
What makes me an authority on the NBA punditry? Well, a 20-year obsessive reading and trying to somehow combine the NBA knowledge with my day-to-day job, first writing my PhD thesis, then strategy consulting and running my own company (yes, we did talk of starting a Big-3 in my own firm with my first employees) would turn one into some form of half-knowledgeable something. As I said, I think we can learn some things from how NBA teams run themselves off-the-court. Case studies on leadership, culture, complexity, and yes, decision-making under uncertainty… But I repeat myself. Anyway… ↩︎