Go back far enough in history, and most poker discussions were about exploitation.

The objective was to put Villain on a range of poker hands and decide on the best play while factoring their tendencies.

Fast forward to the modern era, and poker discussions revolve heavily around what a poker solver would do in each situation.

The idea of exploitation is often forgotten in favour of cold hard poker theory and combos in poker. While this may seem like an upgrade to many, it’s essential not to be overly hasty.

Exploitation remains the heart and soul of poker, and we’ll explain why.

# B**alanced Rock Paper Scissors**

We are presumably all familiar with the game of rock, paper, scissors (RPS).

If we have been living under a rock (excuse the pun) for the last few decades, a quick internet search should yield the easy-to-learn RPS rules.

If a solver were to play RPS, it would always choose each option 33% of the time. This approach is game theory optimal for RPS. Against another perfect player, we’d win half of our games on average.

Let’s pretend that our opponent always picks rock, which paper defeats.

Consider the following 2 options:

**Option 1 (Balanced) **

Continue to choose each option 33% of the time and continue to win 50% of the games on average.

**Option 2 (Exploitative)**

Choose paper 100% of the time and win every single game.

If money were being wagered on this RPS game, Option 2 would be significantly more profitable. Option 2 is an example of an exploitative RPS strategy.

Option 2 does carry some level of risk, though. Our opponent might figure out we are always choosing paper and switch to scissors (or use a poker cheat sheet!)

However, we’ll likely have won a decent number of games by this point.

**RPS vs Poker**

RPS helps us to understand why exploitative play is so valuable, but it’s not a perfect analogy.

For example, a perfect GTO RPS strategy will always break even against our opponent’s strategy. In poker terms, a GTO poker strategy will make profit.

However, the large gap between a GTO and exploitative strategy is true for both RPS and poker.

Any time a poker player deliberately attempts to follow a GTO approach, they miss out on a large amount of potential profit.

**Deriving Exploitative Strategies**

It’s straightforward to derive an exploitative strategy in RPS but much more complex in poker.

So, how are exploitative poker strategies created?

Players use two critical techniques for generating exploitative strategies:

- GTO Analysis
- Primitive Analysis

Let’s take a quick look at both.

**GTO Analysis**

GTO Analysis involves analysing an opponent to see where they are currently deviating from GTO strategies.

For example, say a poker solver folds the flop 40% of the time, but a human folds the flop 50% of the time in a specific situation. That player is likely to be exploitable.

A logical assumption is to increase our flop aggression in that scenario to take advantage of our opponent folding too frequently.

A common industry technique is to employ solver ‘node locking’ (in poker lingo) to compute more precise exploitative strategies. However, such an approach does not result in a pure exploitative strategy.

It’s similar to noting that our opponent always picks rock and then *slightly *increasing the frequency with which we pick paper rather than always choosing paper. It’s undoubtedly an improvement but doesn’t maximise our profits.

While GTO-based exploit analysis is widespread, there is a possible downside. Establishing GTO values for very broad parts of the game without a large amount of computation can be difficult.

## Primitive Analysis

Primitive analysis involves using math to calculate the expected value of various lines without involving a balanced strategy.

Primitive analysis is arguably more potent than GTO-based analysis, although it’s much less common in poker.

A simple example of primitive analysis is looking for spots where bluffs are automatically profitable because of their success frequency.

For example, if a half-pot river bluff succeeds over 33% of the time, it is directly profitable. If we don’t care about balance, we can pull the trigger on a bluff with all our air hands in this spot.

Primitive analysis becomes much more complex when we include multiple streets. Running complex EV calculations with many moving parts is a discipline referred to in the industry as ‘EV modelling’.

While EV modelling generates more precise exploits than solver work, digital tooling is behind in this area.

Poker players must rely on spreadsheets or custom scripts rather than purpose-built commercial software.

**Why Exploitative Play Will Always Win**

It might seem as if all poker players have a choice between a GTO approach or an exploitative approach. This reasoning is misleading, however.

A poker player’s primary objective should always be to play exploitatively.

### A commonly overlooked fact is that GTO poker is a subset of exploitative poker.

GTO poker is the maximum exploit strategy when facing an opponent also playing a perfect GTO game.

If we continue to play a GTO game against everyone, we become like that RPS player who chooses each option 33% of the time, no matter what.

In some senses, the current industry obsession with poker solvers is a step backwards compared to earlier times when players focused purely on exploitation.

That said, poker solvers can be an extremely valuable tool in understanding the theory behind poker.

However, our ultimate goal should be to understand exploitative play, whether we use a solver to help us get there.