For 20 years I’ve felt like a complete idiot when it comes to understanding the evolution of baseball. That changed over the past couple of years the more into the weeds I got trying to make sense out of analytics. I’m still really quite dumb here and everywhere in life, but I feel a whole lot less terrible about it because although I never quite grasped the fundamentals of analysis, I feel like I learned a lot more about why it has revolutionized the sport.
Yes, there’s the cost savings factor of it all — that’s really the main reason why baseball teams are now managed like a stock — but at its core, it’s about our innate need to find order in chaos. Computers help us manage the chaos a little bit better, and thanks to science, we might be getting closer to understanding the way our cognitive biases affect our judgment.
That’s where Thinking, Fast and Slow comes in. Professor Daniel Kahneman won the Nobel Prize in Economic Sciences back in 2002 because there isn’t a Nobel Prize for Psychology, his primary field. In 2011, he published this book which was an extension of his years of research (that he had initially began with Amos Tversky).
Farhan Zaidi admitted in a KNBR interview last year that he gifts this book to all his coaches and team execs. That caught my interest, mainly because the book sounds like an obvious extension of his area of expertise: behavioral economics.
It’s tough not to be cynical about that interest, however. At their cores, behavioral economics and research on decision-making tend to boil down to figuring out what people are willing to spend their money on so that companies can do a better job of offering their goods and services. A less cynical take can be found in this overview of the book and Kahneman’s research.
Full disclosure: I brought this book with me on a three-week trip to Greece thinking I’d have plenty of free time to finish it. I was right about my free time but wrong about my ability to finish it. The book isn’t poorly written. It’s simply very dry; so, I only read about a third of it.
A good chunk of this book review comes from a video talk you can watch below, because he encapsulates the book’s ideas very clearly. But, if you can power through or approach it from a scholarly perspective, I believe you’ll be rewarded. You might not figure out what the Giants have in store for the rest of this rebuild, but you’ll at least have some experience with the front office’s reference material.
On the other hand, it’s a safe bet that most of the people who’ve received this as a gift from Farhan Zaidi have not read it.
Here’s the video I mentioned:
Kahneman characterizes the brain as being a multi-system entity.
System 1 — automatic operations
Intuitive thinking: it comes from somewhere, and we are not the author of it.
System 2 — deliberate operations
Effortful thinking. Self-control and controlling your attention and deliberate exertion of effort are impaired by other activities. For example ... if somebody is asked to retain seven digits in their head and then given a choice between sinful chocolate cake and virtuous fruit salad, they’re more likely to choose the chocolate cake then they would if they did not have seven digits in their head. It takes some effort to control your impulses.
Essentially, our minds passively or actively react to external stimuli, with and our active reaction is based on our system knowledge learned over time.
If you don’t feel like sitting through an hour lecture — it’s not dry! I swear! — I’ll reveal that Kahneman gives away the entire game — at least in terms of where Farhan Zaidi’s coming from — within the first twenty minutes, and the book and this talk are so obviously crucial to understanding the future of the Giants that I really feel it’s important to have this on the site.
Beyond the initial premise setup of Systems 1 & 2, Kahneman divulges two other key points that are entirely relevant to an analytics-driven front office and player development system:
“Intuition... is simply recognition.”
Once you think about it this way, this really demystifies intuition to a considerable extent, and it also leads you to sort of a solution to the problem that Gary Kline and I were trying to solve: when can you trust intuition and when can’t you? And then it becomes an issue of ‘is the world regular enough so that you can learn to recognize things?’ or — and then — ‘did that particular individual have an opportunity to learn the irregularities of the world’?
So, the world of chess players is highly regular; and, statistically, even the world of poker players is regular, so there can be an element of chance — but there are rules; and the mind is so set that if there are rules in the environment and we’re exposed to them fo ra long time, and we get immediate feedback on what is right and wrong — or fairly immediate feedback — we will acquire those rules. So, all of us have expert intuitions...
... that is part of the answer about intuitive expertise. We don’t need to disagree about that, because we know — pretty much — when intuitive expertise is likely to develop. And as I said, that means intuitive expertise is not going to develop in a chaotic universe, or a chaotic universe.
“When there are marginal situations where there is some predictability... formulas do better than individuals.”
That is the domain where formulas beat individuals regularly, is the domain of fairly low predictability. Because when there are weak cues, people are not very good at picking them up and are not good at using them consistently. But formulas can be generated on the basis of experience, and they will do a better job than individual judgment.
The book elaborates on this further. I swear I don’t remember reading as far as page 223, but there’s my bookmark, and within the first two pages of Chapter 21 — “Intuitions vs. Formulas” lays bare Kahneman’s (and, the entire thrust of baseball analytics) substantiated belief:
The number of studies reporting comparisons of clinical and statistical predictions has increased to roughly two hundred, but the score in the contest between algorithms and humans has not changed. About 60% of the studies have shown significantly better accuracy for the algorithms. The other comparisons scored a draw in accuracy, but a tie it tantamount to a win for the statistical rules, which are normally much less expensive to use than expert judgment. No exception has been convincingly documented.
This means that Bruce Bochy absolutely can be replaced by a computer, and the only function of baseball coaches now is to train the System 2s of players so that the analytics knowledge and practices can be learned deeply and subsumed by System 1, making analytics intuitive; or, simply put, recognizable.
For the purpose of this review, I skipped to the end of the book, just before the final conclusions, and found this wonderfully simple semi-final thought (p.407):
During the last ten years we have learned many new facts about happiness. But we have also learned that the word happiness does not have a simple meaning and should not be used as if it does. Sometimes scientific progress leaves us more puzzled than we were before.
Essentially, intuition has limits. That’s a powerful message to inject into an organization that has relied on it for more than a generation.