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@ Real Man Sports
2024-04-30 11:17:34Despite its considerable hype, I have yet to see any tangible benefit from AI. I know others claim to, but for me it simply has no use case. I don’t use it to write code because I’m not a coder, and I certainly don’t want it writing prose in its trademark flowery bullshit style.
That said, I had an idea that might work for fantasy sports:
I’m an old school player. My skill is in understanding what questions to ask in service of sorting out the signal (actionable information) from the noise (trendy jargon, incremental edges oversold, redundant or backward-looking indicators inaccurately marketed as predictive tools.) It is definitely not in crunching data, generating projections or creating UI for people.
To that end, AI might be useful to me in that it can replace a lot of the crunching/stat-nerd work in which I have no interest, but in the end is only as good as the questions of which it is asked. The more AI takes over the mental-menial labor, so to speak, the more the ideas people like me have an edge. The more lazy people like me require mental-menial labor for execution, the more the number-crunching grinders have an edge. I look at AI a bit like I would a chainsaw. It was probably a lazy person who invented it to avoid having to sweat it out swinging an axe
Being an inept, lazy ideas person I don’t really know how to get AI to do what I want it to, so I’d first need someone to integrate it into spread sheets filled with stats and set it up so I can ask it questions.
A few examples off the top of my head: at what number of career at-bats do hitters typically break out (defining “break out” within certain supplied statistical paramters)? At what number of innings pitched do pitchers typically need Tommy John surgery for the first time? Including and excluding minor league innings?
Who are the exceptions? What hitter broke out, but only after 3,000 major league at-bats? Who was good right away? What pitchers never needed surgery? What pitchers needed it right away?
You’re never going to get a list that works for all cases. What you want is to understand the rule and the exceptions. But mostly the exceptions. The outliers are the real signal even though most statisticians discount them as noise. The outliers show you what’s possible, where the limits lie. And the limits often have more explanatory power than observing the pattern itself.
And you can iterate on this, figure out who is an outlier, who is more of a center-of-the-bell-curve rule follower. And how to identify them. You can figure out who to put in the generic projections bucket (center of the bell curve player) and who to put in the hunch one (outlier). And you can develop better heuristics for distinguishing between the two.
The entire game is figuring out the exceptions, where risk and reward are incommensurate, but the market doesn’t yet know. An AI, when prompted with the right questions, won’t hand you the answers, but maybe it can reduce the search space in which you should look.