June 5, 2026
Pricing MLB strikeout props
Why I picked pitcher strikeouts, how the projection engine works, and an honest look at 361 bets through the first chunk of the 2026 season.
Record
249-223
Units
+64.70u
ROI
+9.7%
I want to start with the boring truth, because everyone in this space leads with the highlight reel and buries the part that matters: most “models” are just someone’s gut feeling wearing a spreadsheet as a costume. I spent the better part of three months trying to make sure mine wasn’t that. This is the write-up of what I built, why pitcher strikeouts specifically, and what the numbers looked like through the first chunk of the 2026 season.
Why strikeouts
I didn’t pick strikeouts because they were easy. I picked them because they’re the cleanest thing a pitcher does that the market consistently misprices.
A strikeout is mostly a function of the pitcher, the opposing lineup’s tendency to whiff, and how long he’s going to be on the mound. Compare that to something like hits allowed, which gets polluted by defense, ballpark, batted-ball luck and seven other things you can’t model without going insane. Strikeouts are the prop where the signal-to-noise ratio is actually workable.
The other reason is liquidity, or the lack of it. Pitcher strikeout markets, especially alternate lines, don’t get the same attention as game totals or moneylines. The books can be softer than people think. Exchanges and prediction markets are usually extremely sharp, but sharp doesn’t mean unbeatable. If you can produce a projection that’s genuinely better than consensus, or move faster than the market incorporates information, even sharp markets still give you opportunities.
The build
I leaned on Claude heavily through the whole thing: scaffolding the repo, arguing with me about feature selection, catching the dumb bugs that would’ve cost me a week. But the model is mine. I made every call that mattered.
The core is a projection engine. For every probable starter, it produces an expected strikeout total built from three things:
- the pitcher’s own underlying strikeout rate,
- the opponent’s strikeout tendency, adjusted for handedness, and
- an estimate of how deep into the game he’s likely to go.
It then compares that projection to whatever the book or exchange is offering and calculates an edge. If the edge clears the threshold, it fires. If it doesn’t, it sits on its hands, which is the hardest part to respect when you’re staring at a slate and want there to be a play.
The threshold matters more than anything else in the system. A bet is only logged when the model sees at least a 7% edge over the implied line. Everything below that is noise I can’t trust yet.
Staking
Flat-to-edge: roughly $10 of risk per 1% of model edge, capped so a single number never blows up the bankroll, starting from a $10,000 bankroll. It’s deliberately conservative. The point of the experiment was to find out whether the edge was real, not to juice variance into a big number for a screenshot.
What the numbers say
From March 26 through June 4, the model fired 361 qualifying bets.
| Bets | Record | Win rate | Staked | Profit | ROI | Max drawdown |
|---|---|---|---|---|---|---|
| 361 | 190-171 | 52.6% | $51,893 | +$5,305 | +10.2% | −$2,263 |
That took the bankroll from $10,000 to $15,305. It was not a straight line up, and anyone who shows you one is lying.
The win rate looks modest, and it should. At plus-money odds you don’t need to win every bet; you need to win more often than the price implies, across enough volume that it isn’t luck. The goal isn’t to be right a lot. It’s to be right slightly more often than you’re being paid to be.
The edge concentrates where the model disagrees most. The highest-edge bucket carries the lion’s share of the profit:
| Model edge | Bets | Win rate | Staked | Profit |
|---|---|---|---|---|
| 7–9% | 90 | 56.7% | $7,197 | +$834 |
| 9–12% | 80 | 52.5% | $8,324 | +$691 |
| 12%+ | 191 | 50.8% | $36,372 | +$3,780 |
The model leans under, and that lean is profitable. A clear majority of qualifying bets are unders, and they hit at a better clip than the overs. I think that’s structural: casual bettors like overs, and books shade their numbers accordingly. The model doesn’t care what’s fun. It just finds where the number is wrong.
Month to month, it’s unglamorous:
| Month | Bets | Win rate | Staked | Profit |
|---|---|---|---|---|
| March | 34 | 44.1% | $5,358 | −$556 |
| April | 146 | 58.2% | $23,108 | +$6,130 |
| May | 157 | 47.8% | $20,677 | −$604 |
| June (to Jun 4) | 24 | 62.5% | $2,750 | +$336 |
If you only saw April, you’d think I’d cracked the sport. If you only saw May, you’d think the model was dead. The truth is in the aggregate, and an edge has to be ridden through the cold stretches.
What I’m not claiming
I’m not claiming this beats every book on every slate forever. Edges decay, and this one will too. 361 bets is enough to take seriously and not enough to retire on. And I’m absolutely not claiming you should blindly tail anything.
What I am claiming is that the strikeout market is soft enough, and the projection good enough, that the gap between them has been real money, and that I can show every single bet that produced it rather than asking you to take my word for it.
Find the markets nobody’s watching closely, build something genuinely better than the consensus in that one narrow place, and only bet when the gap is big enough to matter.