Hey everyone, been diving deep into poker math lately and wanted to share a quick thought. I’ve been tweaking a model based on pot odds and expected value to decide when to push against aggressive players. It’s helped me spot moments where their range is wider than they think, especially in late position. Anyone else playing around with similar calculations? Curious to hear what’s working for you!
Interesting stuff on the poker math front. I’m coming at this from a slightly different angle, though, since my focus is usually on roulette systems. That said, your post about exploiting aggressive players’ wide ranges got me thinking about how psychology ties into betting decisions, whether it’s poker or spinning the wheel.
In roulette, I’ve been testing systems like Martingale and D’Alembert, but what’s really stood out in my experiments is how much player behavior—mine and others’—screws with the math. You’d think a system based on doubling bets after losses is pure numbers, but the second you’re in a real game, your head starts playing tricks. You hesitate, you second-guess, or you get cocky after a win streak. It’s like poker players tilting after a bad beat. Your model on pot odds and EV is solid because it leans on cold, hard logic, but I bet you’ve seen moments where even the best calculations go out the window when someone’s ego gets involved.
I ran a few simulations recently, tracking not just outcomes but how my own decision-making shifted under pressure. For example, with a flat-betting system on red/black, I stuck to the plan 95% of the time in a low-stakes setting. But when I upped the bet size to mimic a high-pressure game, I started deviating—chasing losses or pulling back too early. It’s not unlike poker players who overplay a marginal hand because they’re emotionally invested in “owning” the table. I’m curious if you’ve factored psychology into your model at all. Like, do you adjust your EV calculations based on how tilted or overconfident you think your opponent is?
One thing I’ve been toying with is a hybrid approach—using strict math but adding a layer for “human error” probability. In roulette, I assign a small percentage chance that I’ll break my own rules based on past behavior. It’s not perfect, but it’s helped me stay disciplined. Maybe something similar could work in poker, like estimating how likely an aggressive player is to bluff too far based on their recent losses. Anyway, love hearing about your model. What’s the biggest leak you’ve plugged with it so far?