From ICM to FGS: How Future Game Simulation Changes Tournament Decisions

Once a poker tournament reaches the mid-to-late stages, one of the most important concepts a player can grasp is ICM. At this point, chips can no longer be valued by chip EV alone, because the value of tournament chips depends on the payout structure, the number of players left, and the chip distribution. Players need ICM to weigh the relationship between chip value and prize equity.

ICM has one core limitation, though. It mainly evaluates the chips and prize equity right after the current hand ends, without fully accounting for what can change over the next few hands, such as blind pressure, position shifts, and changes in action order. This is exactly the backdrop against which the FGS model was proposed.

The FGS model is not meant to replace ICM. It is meant to correct ICM for being too static. In most situations, plain ICM is enough. The value of FGS shows up mainly with short stacks, on the bubble, at the final table, or whenever blind pressure weighs heavily on a decision.

What Are the Main Flaws of the ICM Model?

ICM is a very important model for tournament decisions, but it still carries a few clear limitations.

1. It leaves out the dynamics of future hands. It does not evaluate how the next few hands, blind movement, and future position changes affect a player’s tournament equity.
2. It ignores blind and ante pressure. The real cost of the big blind, small blind, and ante matters most to short stacks, where every orbit can sharply compress survival room and shift shoving and calling ranges.
3. It does not account for position relative to the blinds. About to post the big blind versus just past the blinds are very different pressures, but ICM does not reflect that gap.
4. It assumes every player has equal skill. It cannot capture the difference between strong and weak players in realizing tournament equity at the table.

In other words, ICM offers a baseline theoretical valuation, but it cannot fully reflect how player skill, position shifts, and future blind pressure play out at the table. The FGS model discussed next mainly corrects the first three, dynamics-related limitations (future hands, blind pressure, and position relative to the blinds), but the equal-skill assumption is one it shares, because FGS also assumes future hands are played close to theoretically optimal. One study that tested ICM against more than ten thousand tournament events found that it systematically underestimates the performance of big stacks while overestimating that of short stacks.

What Is the FGS Model?

FGS stands for Future Game Simulation, and you can think of it as a tournament decision model built on top of ICM. It evaluates not only the result of the current hand but also simulates what can happen over the next few hands, such as how the blinds move, how player position changes, and the blind costs a short stack is about to face. In this way, FGS can more precisely measure how a decision affects a player’s future tournament equity.

• ICM is a static model. It estimates each player’s tournament equity from current stacks and the payout structure, but it ignores whether you post the big blind next hand, and the immediate pressure of the blinds and ante on a short stack.
• FGS is a dynamic model. It folds the next few hands into the math, especially blind movement, the cost of the small and big blinds plus ante, and your position relative to the blinds. In high blind-pressure spots like short stacks, the bubble, or the final table, FGS usually gives shoving and calling ranges closer to real play than plain ICM.

How Does FGS Change a Decision?

For example, suppose a short stack is sitting in UTG and will soon have to post the big blind. Looking only through traditional ICM, the model might conclude that the player should wait conservatively and avoid risking chips with a marginal hand.

But FGS considers one more thing. If the player folds now, the next hand forces them to pay the big blind and ante, the stack shrinks further, and future fold equity drops too. In other words, a fold that looks safe right now may simply push the player into a worse future spot. So in this case, FGS might conclude that the player should shove a wider range than ICM shows. The reason is not that the hand itself suddenly got stronger, but that once you account for future blind pressure, the overall expected value of acting now can beat waiting.

Example: How Does FGS Open Up the Shoving Range?

Below is a shoving range generated with HRC for a 9-handed SNG bubble spot. The setup: the short stack has 800 chips (about 8bb), every other player has 1,000 chips, the blinds are 50/100, and the ante is 25.

Start with the traditional ICM scenario. The 8bb bubble shove range is 31.1% (22+, Ax, K4s+, KTo+, and so on). What this range fails to account for is the cost of doing nothing. This short stack will soon pay blinds and ante worth about one sixth of its stack. Switch to the FGS ICM model and the picture changes. FGS evaluates not only the ICM result after this hand, but also folds the blind pressure, position shifts, and action order of the next three hands into the calculation.

With FGS, the range is slightly wider (from 31.1% to 37.9%), because the model recognizes that for this player, the cost of posting the big blind next is relatively higher.

There is one more factor for FGS to weigh. Beyond folding in the fact that we are about to post the blinds after this hand, we also have to consider that the blinds may rise once this hand ends. The next range is the result of also accounting for the blinds rising to 100/200 with a 50 ante at the start of the next hand.

Now the range is clearly wider (57.3%). The reason is that the cost this player pays next hand jumps from 15.6% of the stack (the 100 big blind plus the 25 ante, or 125 chips) to 31.25% (the 200 big blind plus the 50 ante, or 250 chips).

The cost of doing nothing has doubled, so the short stack has to take on more risk.

What Is the Core Value of the FGS Model?

The core value of the FGS model is that it pushes a player past the first question and onto the second:

• Is this hand +EV right now?
• If I fold, push, or call now, how will the next few hands change my situation?

In a tournament, chip value is not fixed. All of the following factors shape a player’s true equity:

• The size of the blinds and ante
• Your position relative to the blinds
• Pay jumps
• The chip distribution of the other players
• Your own room to act over the next few hands

By simulating future hands, FGS lets a player understand more precisely how the current decision affects future survival room, fold equity, and prize equity. That is where it stays closer to real play than basic ICM.

What Are the Limitations of FGS?

FGS is not a perfect model. Its biggest limitation is that the model has to make assumptions about how players act over the next few hands, usually assuming they follow Nash equilibrium or a strategy close to theoretically optimal. In real play, different players deviate from theory because of style, skill level, mental pressure, a tendency toward caution, or simple misjudgment. So FGS output should still be treated as a more precise theoretical baseline, not the absolute answer for every hand.

Put another way, FGS is not a universal solution. It is a tool that helps players understand tournament pressure more fully. Its purpose is not to replace a player’s judgment, but to let players make decisions closer to the real game environment when facing short stacks, blind pressure, and pay jumps.

When Should You Use the FGS Model?

By this point you should have a solid grasp of the FGS model, so when should you actually consider using it? Here is a summary from Renji Mao’s video, offering three recommended scenarios.

• The main use case is the final table. When you face a high risk premium and sit on a medium-to-short stack, FGS ranges fit the spot better.
• Late in a tournament, when the table has extreme short stacks or a short stack is about to post the blinds, ICM results drift significantly, and FGS ranges are more accurate here.
• The better a player handles marginal hands, the more extra EV they create, so FGS ranges suit these players more.

Conclusion

Overall, the FGS model is an extension of ICM, not a replacement for it. ICM helps a player understand the relationship between current chips and prize equity. FGS goes further by folding the blind pressure, position shifts, and action order of the next few hands into the picture. So the importance of FGS is not that it overturns ICM, but that it turns ICM from a relatively static chip-valuation tool into a dynamic decision model that sits closer to real play.

ICM tells you what these chips are worth right now. FGS tells you how the current decision will affect your tournament equity over the next few hands. Under the FGS model, a short stack does not always have to play more aggressively. Whether to be aggressive depends on your position at the table.