Pot Limit Omaha poker games offer opportunities for smart players to develop skills which give them a strategic advantage over the average online grinder. You yourself may be an experienced Pot Limit Omaha player or perhaps you’re making the switch to Omaha poker from No Limit Holdem. In this article I’m going to tell you the key skills that you need to play strong Omaha poker and show you that you can best develop those skills within a conceptual framework that describes the fundamental structure of the game.

Hardly a week goes by without some journalist or influencer somewhere announcing that (this time) poker really has been solved. As ever with the shallow, sensationalist world of media there is an awful lot of smoke for very little fire and so rumours of poker’s demise have been greatly exaggerated.

Fortunately, the vast majority of computational research into ‘solving poker’ has been directed into variations of Holdem games. The complexity of Omaha poker games, with 4 or more hole cards makes them less vulnerable to brute force computation and, as a consequence, profitable for intelligent players who can develop skills which cannot be reduced to computation.

This article will explain why it is essential to have a deep conceptual understanding in order to play strong strategies in large, complex games. I will demonstrate that a structural study of a complex game enables us to identify meaningful patterns that permeate the game, and that accurate recognition of those patterns in-game is a fundamental skill.

Pot Limit Omaha Strategy PLO Poker

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So-called ‘Artificial Intelligence’ approaches to poker do not play by using an intelligent understanding of the game. Instead they largely rely on using a self-play algorithm, iterated over vast numbers of hands, to build lookup tables that store near-equilibrium action frequency data for game trees that the algorithm has explored. They then use those lookup tables to generate an output action corresponding to the current state of the game as recognized by an algorithm designed for that purpose.

The problem is that you are not a machine, and so you have no chance of learning lookup tables for games which are not trivially small (Tic-tac-toe you can handle). So, in order for you- a human- to play strong poker, you’re going to need to rely on your understanding of the game and a different set of skills.

So what are the skills required to thrive in large, complex games?

[Note: I am aware that there is more to AI approaches to poker than just self-play. This article is concerned with the skills humans need to compete in PLO poker games, and is not intended as a prelude to a substantive discussion of AI.]

This article will explain the importance of having a strong, meaningful conception of PLO and demonstrate use of structural analysis to derive a fundamental strategic concept. We will see that the power of pattern-recognition heuristics is determined by the relevance of the insights used to develop them.

Pot Limit Omaha has a Greater Variety of Situations

Poker is a game where history matters- both the ‘image’ that your opponents have of you, and the sequence of actions that led to you facing a particular situation should influence any non-trivial decision that you make. In Pot Limit Omaha you will face a far greater variety of preflop action histories to get you to the flop than you do in No-Limit Holdem.

Open-limping is still commonplace, especially in live games, and players tend to call (and overcall) raises wide- alert to the manifold opportunities to hit the flop inherent to holding four hole cards, which are most obvious in double suited starting hands. As a consequence you should expect to play many more multiway pots- which makes naïve solver-imitation approaches less useful than in simpler games like No-Limit Holdem.

The reason for this is simple: solver-imitation approaches rely on modeling the real game tree with a pruned game tree and using a self-play algorithm to derive near-equilibrium strategies for that reduced game tree. The imitator then attempts to substitute mimicry of the machine for a meaningful understanding of the game. This makes such approaches highly effective in games with small stack-to-pot ratios and few, if any multiway post-flop spots because it is possible to build pruned game trees which closely resemble the real game tree.

The table below shows how the number of flop decision nodes for SPR 10 grows as we increase the number of players in the pot on the flop:

# of Players# of Flop Decision Nodes

For this evaluation I only included a single, pot-sized bet or raise as an alternative to checking or calling for each player. In practice, of course, strong play relies on utilising a variety of bet sizes, calibrated for that particular context.

As you can see, the sheer number of decision nodes makes a solver-imitation approach to understanding PLO extremely unwise for anybody seeking to play well in multiway pots at high SPRs- situations which characterize a large fraction of the contexts that you will find yourself in at the table. If you yourself have been starting your PLO analysis with a solver and have been disappointed by your results, then you have first-hand experience of the limitations of a computation-driven approach to a complex game.

That is not to say that solvers are not an extremely useful tool- the Cardquant Research Institute has its own in-house solver to assist my work on strategy development. However I start my analysis with a conceptual approach that places structure ahead of computation and this approach guides any solver work rather than the other way around. (You can read more about causal-conceptual versus statistical-computational approaches in my philosophical writing).

How Weak Pot Limit Omaha Concepts Cause Weak Play

You see players making conceptual errors at the poker table every time that you sit down to play. One