Mastering In-Position Raises in 3-Bet Pots: Part II

In the realm of poker strategy, understanding the nuances of in-position play can significantly enhance your performance, particularly in 3-bet pots. In this second installment of our series, we’ll explore how to leverage your opponents’ tendencies and capitalize on their mistakes when raising as the preflop caller. This article builds upon the foundational concepts discussed in Part I, shifting focus toward actionable strategies that can lead to increased profitability.

At a Glance

• Key Focus: Raising in position in 3-bet pots.
• Data Analysis: Insights from GTO solutions and real-world play.
• Strategic Adjustments: How to exploit common player mistakes.

The Importance of Data in Poker Strategy

To effectively analyze in-position raises, we must rely on empirical data. This approach involves establishing a baseline of optimal play, then comparing actual player behavior against this standard. For our analysis, I utilized data from a representative sample of 184 flops, calculated using a Game Theory Optimal (GTO) solver. This data was integrated into my custom-built Hand2Note pop-up tool, which serves as our benchmark for determining optimal play.

The dataset reflects hands played at stakes of 500nl and higher across mainstream poker sites, showcasing a high level of expertise among players. The preflop strategies of these players align closely with GTO frequencies and range compositions, confirming their proficiency in this area.

Understanding Continuation Betting and Responses

Let’s examine how the GTO solver approaches continuation betting and responds to raises. Focusing on relevant data, we find the following:

On Ace-high rainbow flops, the solver’s response to a raise after c-betting is as follows: 30% fold, 50% call, and 20% 3-bet.

When we compare this to the average behavior of expert players, we find significant deviations:

GTO: 28% Fold, 46% Call, 25% 3-bet
Humans: 43% Fold, 42% Call, 16% 3-bet.

These statistics reveal that human players tend to be more risk-averse, folding 15% more often than the solver, while calling and 3-betting less frequently. This raises an important question: Are these tendencies a response to the player pool, or do they represent a leak in strategy?

Examining the Fold and 3-Bet Rates

To further investigate, we can analyze how the solver’s reactions to c-bets compare with those of expert players. In my GTO data, the solver raises only 8% of the time against a c-bet, while the regular population raises 7%. This slight difference raises the question of whether such a discrepancy justifies the higher fold and lower 3-bet rates observed in human play.

Notably, when facing a 3-bet, the solver folds 27% of the time, while humans fold 39%. This suggests a structural bias toward risk aversion among players, which appears consistent across various flop textures.

Identifying Strategic Upgrades

To capitalize on these tendencies, we can implement strategic upgrades that maximize expected value (EV). Let’s consider a specific flop texture: Ace-high rainbow. According to GTO strategy, the solver c-bets at a frequency of 94% on this texture. When faced with a small raise (approximately 2.5x), the solver’s reactions are as follows:

30% fold, 20% 3-bet.

In contrast, high-level regulars under-c-bet by 12%, overfold by 8%, and under-3-bet by 12%. This discrepancy indicates a clear opportunity to exploit their tendencies.

Modeling Deviations

To illustrate how to model these deviations, we can use a specific flop example: As 8d 2h rainbow. The GTO flop c-betting strategy and the reactions against the 2.5x raise are closely aligned with average Ace-high rainbow flops.

Using a node-lock function in our simulation tool, we can adjust for these deviations while maintaining balanced range compositions. The result of these adjustments reveals a significant increase in expected value:

The expected value gained increased from 98.3 chips to 109.8 chips, translating to a net EV gain of 115 bb/100 hands compared to baseline.

Local vs. Global Maximum EV Strategies

It’s essential to differentiate between local and global maximum EV strategies. Local strategies focus on deviations within a specific subtree, recalibrating the rest of the game tree to establish a new Nash equilibrium. This approach allows players to exploit observed tendencies effectively.

Reader Q&A

What are the key takeaways for raising in position?
Focus on recognizing opponents’ tendencies to overfold and under-3-bet, and adjust your strategy accordingly.

How can I use GTO data in my game?
Utilize GTO data to understand optimal play patterns and compare them against your opponents’ behaviors to identify leaks.

Why is risk aversion a common issue among players?
Many players have a natural bias toward avoiding risk, which can lead to overly conservative play and missed opportunities for profit.