Here are chapters 36–40 of our Easy Game summary:
Chapter 36: Game Theory Optimal Vs. Practically Optimal
In chapter 36, Andrew Seidman contrasts GTO play with practically optimal play. The main mistake he targets is assuming opponents respond the way theory says they should. In real games, emotional bias and human habits often override “correct” frequencies.
His recurring example is the overpair problem: lines that “should” fold out QQ/KK often fail because many players won’t make big disciplined folds. Ego, fear of being bluffed, and attachment to big pairs push them toward calling. The practical lesson is not to build strategies that depend on opponents making perfect laydowns.
The same issue appears with “calling stations.” Theoretical logic might justify barreling because ranges are weak, but if a player simply doesn’t fold marginal pairs, the theoretical plan collapses. Bluffing because someone “should fold” ignores how they actually behave.
Seidman’s model:
-
GTO is a baseline for balanced, rational opponents.
-
Practically optimal asks how this specific opponent deviates and exploits those deviations.
Core takeaway: theory matters, but profit usually comes from targeting predictable human mistakes, not from assuming others will play like solvers.
Chapter 37: Game Dynamics
In chapter 37, Andrew Seidman explains that poker environments evolve. Strategies become popular, get copied, become overused, and then invite counter-adjustments. The best players don’t chase trends—they anticipate when the pool has overcorrected.
Shifts often happen gradually (e.g., light 3-betting spreads, then defenses tighten), but sometimes quickly when a new concept spreads through content or forums.
He recommends staying ahead through:
-
Observation: noticing rising frequencies (3-bets, c-bet raises, cold 4-bets, etc.).
-
Discussion: talking with peers to spot where collective thinking is headed.
Seidman gives a practical adaptation example: when light 3-betting became common, he tightened his 3-betting range. Opponents still assumed he was light and fought back aggressively—allowing him to profit more with strong hands. The edge came from exploiting the trend after it overshot.
Core lesson: dynamics are cyclical. Monitor trends, expect overcorrections, and exploit opponents who are still reacting to yesterday’s meta.
Chapter 38: Creativity, Bet Sizing, and Pseudo-Thin Value
In chapter 38, Andrew Seidman argues that creativity is only profitable when grounded in skill, hand reading, and image. Random “creative” lines from weaker players usually lose money; strong players can deviate because they understand ranges and incentives.
A major tool of structured creativity is unexpected sizing:
Overbetting
-
Overbet for value vs opponents who hero-call or perceive your line as bluffy.
-
Overbet as a bluff when the opponent’s range is capped/weak and normal sizing gets called too often.
-
Overbet for image to shape future perceptions and win bigger later.
Underbetting / Minbetting
Small bets can work when opponents won’t call large bets, won’t raise without strength, or are sitting on missed draws. Tiny sizing can extract value, induce bluffs, or polarize an opponent’s raising range.
Pseudo-Thin Value
Sometimes you have a very strong hand but the opponent’s range is so capped that they can’t call big bets. You must bet small to get paid. It’s not “thin” because your hand is marginal—it’s thin because their range can’t continue.
Core lesson: creativity is controlled deviation. Size based on what the opponent is likely to do, not what “standard” says.
Chapter 39: Advanced Hand-Reading
In chapter 39, Andrew Seidman says advanced hand-reading requires splitting an opponent’s range into two internal parts:
-
Value range (bets to get called by worse)
-
Bluff range (bets to make better fold)
Most players mash these together and make costly mistakes. The process is:
-
Build the bluff range: remove hands unlikely to reach this street; factor in texture and prior action; estimate realistic bluff frequency (possibility isn’t frequency).
-
Build the value range: identify which hands would value-bet here, and whether thin value is plausible in this spot.
-
Weight by frequency: value hands are often bet near 100%; bluffs may be used intermittently.
This leads to both big folds and big calls, depending on whether weighted bluffs outweigh weighted value. The decision isn’t “he’s aggressive” or “he’s strong”—it’s proportions.
Core framework: separate ranges, remove inconsistent hands, estimate bluff frequency, compare weighted value vs bluff portions, then decide.
Chapter 40: The Leveling Ladder
In chapter 40, Andrew Seidman critiques “leveling”—the spiral of thinking about what an opponent thinks about what you think. In practice, higher levels often just flip the decision back and forth (bluff/value), turning the choice into a coin flip without real evidence.
To escape this, he introduces Level Zero: the opponent’s baseline tendency.
Examples of Level Zero biases:
-
Passive players default to calling.
-
Aggressive players default to bluffing.
-
Tight players default to folding.
-
Loose players default to continuing.
Rather than climbing the ladder, Seidman recommends climbing down to observed behavioral defaults. Showdowns and repeated patterns reveal Level Zero, which is usually more reliable than abstract mind-games.
Core lesson: leveling is seductive but often noise. Anchor decisions in baseline tendencies backed by observation.