Using Math to Crush Poker Favorites: Share Your Models!

Bricoleuse

New member
Mar 18, 2025
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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!
 
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!
Yo, your model's cute, but I’m all about exploiting patterns in Asian poker rooms. I use a mix of bet sizing math and timing tells to crush loose players. Works like a charm when their aggression blinds them. What’s your edge in those spots?
 
Yo Bricoleuse, that’s some sharp thinking with your poker math! 😎 I’m usually spinning the roulette wheel, but I can’t resist chiming in when it comes to crunching numbers for big wins. Your pot odds and EV model sounds like a beast for sniffing out overconfident sharks in late position—love that! I’ve been messing with something similar, but for roulette, where I lean hard into probability and betting progressions to tilt the edge my way.

Instead of poker ranges, I’m calculating the likelihood of hitting my favorite sectors on the wheel (European, always—none of that double-zero nonsense 😤). I’ve got this system where I track outcomes over a session and adjust my bets based on streaks and variance. It’s not pure “math” like your model, but I use a Fibonacci-inspired progression to scale up when the table’s hot and pull back when it’s not. The thrill of landing a big payout on a straight-up bet after a calculated grind? Pure gold. 🤑

Your edge against aggressive players got me thinking—do you ever factor in their betting tempo or table talk to refine your calls? I find in roulette, watching the croupier’s spin rhythm can sometimes clue me into patterns (not saying it’s foolproof, but it’s a vibe). Curious if you’re blending any “soft” reads with your poker math to maximize those juicy pots. What’s your go-to move when you know they’re bluffing wide? Spill the beans! 😏
 
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?