If you haven't already, we'd recommend reading Part 1 about Edge before reading about opponent selection here!
Back of the Napkin #7: Know Your Edge
Part 2: Opponent Selection
In any game, choosing who you compete against often gets overlooked relative to improving your own skill. Let’s say you’re the best poker player in the world – the gap between you and the second best player in the world may be as small as 1%, but the gap between you and the 50th best player could be 50%. It may be extremely hard to improve your own skills by 50%, so if your goal is simply to win, choosing to compete against weaker players may make a much bigger difference than trying to grind out small incremental skill improvements.
If you know what your edge is, you can choose to compete in games where your edge generates the most advantage. For example, if your edge is access to capital, you can choose to compete in markets where you are able to throw your weight around in order to win (think Softbank circa 2015). But if your edge is deep technical knowledge of GPTs, you might be better suited to founding an AI startup.
This sounds obvious, and in a way it should be – figure out your strengths, and play to them. The challenge in probabilistic games like investing or poker or managing your career (where we have imperfect information and the outcomes aren’t guaranteed) is that we don’t know. We lack a large sample size (one we can’t really get until time, money, etc. is put in), so we’re left approximating. Famed venture capitalist David Hornik often says it took him ten years to know if he was lucky and another ten to know if he was good!
Even years of data may not be enough. Excelling in college basketball doesn’t necessarily translate to being good in professional basketball. If a college star, who’s a ball-dominant guard and the default scorer, joins an NBA team with a better ball-dominant guard, he’ll need to adjust to playing off-ball. Inability to adjust means that they may not excel at the NBA level. Because you can’t play college basketball forever, what you thought was your edge may end up a commodity.
As an investor, understanding your edge helps you know when you’ve lost your edge and build new ones. As interest rates change, investors who only understood how to invest into a zero interest rate regime struggle to adapt to the new environment. Those investors might have thought their edge was the quality of their network, or company selection skills, when in reality due to an environment of cheap money, their edge was actually speed and access to capital. If you don’t understand where your value comes from, it’s hard to adjust to operating in a new environment.
Knowing your edge isn’t only for investors and poker sharks - it can help with career decisions too: If you’re phenomenal with small teams, your talent may be wasted (and you might be frustrated) working at a large company. If you’re an expert manager of managers, that skill will be underutilized at a startup. You can think of applying for a job as another imperfect information probabilistic game. To give yourself the best chances, make sure you’re selecting the right table to sit down at.
Ultimately, edge might boil down to the idea “what makes you unique, and how can you profit from it?” We use the word “profit” here, but it’s not limited to money – doing something you’re great at is more likely to leave you fulfilled, and successful by whatever metrics you choose.
The truth is most people go through life without ever seriously thinking about this, just working hard and hoping for the best. But in the random walk of life, it pays to know your edge.
We discussed whether 50% was a ludicrously high gap to put here. But it turns out, if you look at the Elos of the #1 (Magnus Carlsen at 2830) vs #50 (Alexey Sarana at 2685) ranked Chess players, and put them into an Elo win calculator, the #50 player only has a 9.2% chance of winning compared to the #1 player with 48% odds (the remaining 42% of outcomes are predicted as a draw). And in a best-of-5 format, the #1 player is favored to win 84% of the time, with the #50 player winning only 5%! ↩︎
One of the biggest reasons why a player like JJ Reddick had such a great and long NBA career was his ability to adjust from a ball-dominant college player to the NBA by accepting that there was a talent gap between him and the ball-handling scoring abilities of NBA stars, and also realizing his edge came from shooting 3 pointers (and doubling down on that edge). ↩︎
In practice this is hard to do in probabilistic games because you could just be on a streak of bad luck – there’s an art to identifying whether you’re just on a bad streak, or if the regime has changed and your edge has decayed. The number of crypto traders who were blown out and unable to make money once better quantitative traders from top firms came in is… large. ↩︎
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