You had a killer year. You open the P&L breakdown and one oversized winner towers over everything else.

The lesson feels obvious: "I'm great at sizing. Next year I go bigger on my best ideas." 🐬

It smells like skill. Giuseppe Paleologo, who ran enterprise risk at Millennium and risk at HRT, says it's mostly statistics talking. He explained why on Flirting with Models (season 7, episode 11).

The heavy-tail trap

In an ordinary year, profit spreads fairly evenly across the names in a book. Nothing dominates.

In a very big year, up or down, the picture flips. A handful of names, often one or two, deliver most of the result. That's the defining feature of a heavy-tailed distribution: when the sum is big, it comes mostly from the biggest term.

It holds across a whole platform too. When a multi-manager fund has a huge year, "for some deep statistical reason" one business often contributes 30 to 40% of the total.

So your big year will almost always show one oversized hero. That's the shape of the distribution. Skill would have to show up in the ordinary years too.

The bitter part is his verdict on the urge to size up: "a little bit of a behavioral bias." True, but only conditional on a big year. Most years, most managers don't make that kind of money, and they still believe they have sizing skill. Most of the result comes from being on the right side of the bet, not from being extra large or extra small in it.

The cost of getting this wrong is quiet at first. You size up on a lesson luck taught you, and the next big year arrives on the wrong side of the tail.

Why it bites harder when you sell options 🐯

Selling premium makes the trap worse. Wins are small and frequent. Losses are rare and large. A few trades decide the year on the downside too.

In my own Income Portfolio the worst months weren't caused by one terrible idea. They came from several positions that turned out to be the same bet, sized as if each one were independent. Conviction made it feel safe to size up. Then one move hit them all at once. The market didn't care about my conviction.

The 4-step test for sizing skill

Grab your own trade log. You can run this over a weekend, no model needed.

1. Take your closed trades for a meaningful period (I'm using 644 of mine).
2. Replay them at equal risk per trade. Same entries, same exits, every position risking the same amount. In a spreadsheet that's one extra column: what this trade would have made at the standard size.
3. Compare. If your real sizing beats the equal-risk version across periods, not only in one hot month, you have real sizing skill, the rare kind. Keep it.
4. If not, size close to equal risk, with at most two or three conviction tiers and a hard cap per name and per group of names that move together.

Paleologo adds a fair counterpoint: quant people also take time to accept that oversizing is sometimes right, in certain conditions. The test tells you whether you're one of those cases. Your memory of your best year can't.

Picture the end of next year. One column on your screen tells you, in black and white, whether your sizing earned its money. No arguing with your memory. 🌴

Size by risk, not by count

One more rule from the same conversation. Ranking stocks and buying the top names at the same size ignores how different their risks are. In his words, holding Nvidia and IBM the same way "is a crime against humanity."

Equal risk means the wild stock gets a smaller position and the quiet one a bigger one. If one name swings three times as hard as another, it gets roughly a third of the size. It sounds basic. Most books still aren't built that way.

The year you remember best is the one most likely to teach you the wrong lesson. Let the equal-risk replay decide. Then you keep a process you can trust in the next big year, up or down. 🐬

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Happy hunting.

Simon & The Sprinters 🐬⚡️🐆