Between July 24 and July 26, we ran 1,000,000 simulations to produce our 2026 WSOP Main Event prediction.
The results produced a clear statistical favorite.
Lucas Jumalon, who enters the final table with 194 million chips, won 40.4% of our simulations. No other player came close.
But tournament predictions are not always as simple as choosing the player with the most chips or the highest winning percentage.
Before I reveal my official pick, let’s look at the complete results and what they told us about all nine finalists.
Meet the 2026 WSOP Main Event Final Nine
Before running the simulations, we studied each finalist’s background, tournament experience, playing style, strengths, weaknesses, and key hands.
- Lucas Jumalon, Rami Hammoud and Jamie Shaevel
- Greg Mueller, Michael Gagliano and Mario Boos
- Lauri Saaskilahti, Han Feng and Evagoras Evagorou
Our 2026 WSOP Main Event Prediction: Full Simulation Results
Each percentage below represents how often that player finished in each position across 1,000,000 complete final-table simulations.
| Player | 1st | 2nd | 3rd | 4th | 5th | 6th | 7th | 8th | 9th |
|---|---|---|---|---|---|---|---|---|---|
| Lucas Jumalon | 40.4% | 20.4% | 13.1% | 9.0% | 6.4% | 4.6% | 3.1% | 2.0% | 1.0% |
| Rami Hammoud | 13.2% | 15.3% | 14.2% | 13.2% | 11.8% | 10.5% | 8.9% | 7.5% | 5.4% |
| Jamie Shaevel | 11.3% | 13.4% | 13.7% | 13.2% | 12.2% | 11.2% | 10.1% | 8.4% | 6.5% |
| Greg Mueller | 10.9% | 13.5% | 13.5% | 13.0% | 12.4% | 11.3% | 10.1% | 8.6% | 6.7% |
| Michael Gagliano | 7.0% | 10.0% | 11.3% | 11.9% | 12.6% | 12.7% | 12.4% | 11.8% | 10.3% |
| Mario Boos | 6.5% | 9.4% | 10.9% | 11.7% | 12.1% | 12.6% | 12.9% | 12.5% | 11.4% |
| Lauri Saaskilahti | 5.2% | 7.9% | 9.8% | 10.9% | 12.0% | 12.7% | 13.5% | 13.9% | 13.9% |
| Han Feng | 3.0% | 5.3% | 7.2% | 8.9% | 10.6% | 12.5% | 14.4% | 17.3% | 20.8% |
| Evagoras Evagorou | 2.5% | 4.7% | 6.3% | 8.2% | 10.0% | 11.9% | 14.5% | 17.9% | 24.0% |
How Each Player Performed Against ICM Expectations
ICM—or the Independent Chip Model—estimates the dollar value of each player’s stack based on the remaining payouts and the distribution of chips.
We compared that baseline with the average amount each player won in our simulations.
| Player | Seat | Starting Chips | ICM Value | Average Won in Simulations | Difference |
|---|---|---|---|---|---|
| Lucas Jumalon | 9 | 194,000,000 | $6,188,930 | $6,306,953 | +1.91% |
| Rami Hammoud | 7 | 79,000,000 | $4,054,198 | $3,862,553 | -4.73% |
| Jamie Shaevel | 5 | 56,000,000 | $3,414,290 | $3,605,675 | +5.61% |
| Greg Mueller | 4 | 48,500,000 | $3,177,214 | $3,568,945 | +12.33% |
| Michael Gagliano | 2 | 46,500,000 | $3,111,062 | $2,996,098 | -3.70% |
| Mario Boos | 3 | 44,000,000 | $3,026,466 | $2,899,613 | -4.19% |
| Lauri Saaskilahti | 1 | 37,500,000 | $2,795,602 | $2,673,088 | -4.38% |
| Han Feng | 6 | 25,000,000 | $2,296,565 | $2,231,290 | -2.84% |
| Evagoras Evagorou | 8 | 22,500,000 | $2,185,672 | $2,105,788 | -3.65% |
After studying those results, five things stood out to me.
1. Lucas Jumalon’s Chip Lead Is Incredibly Powerful—but Maybe Not as Powerful as You’d Think
It’s no surprise that Lucas Jumalon dominated the simulations.
Holding roughly 35% of all the chips in play, he won the tournament more than 40% of the time.
Why?
Mathematics.
The other players cannot fight back as aggressively because every confrontation carries enormous ICM consequences. Busting one position earlier can cost hundreds of thousands—or even millions—of real dollars.
That gives the chip leader tremendous leverage to steal blinds, apply pressure, and force opponents into uncomfortable decisions.
What surprised me, however, was that all of this translated into only 1.91% more average prize money than his ICM expectation.
I expected the chip leader’s ability to bully the table to create a larger edge than that.
Lucas remains the overwhelming favorite. But the simulations suggest that his enormous stack alone does not make him invincible.
2. Yes, the Chip Leader Really Can Finish Ninth
One number that jumped off the page was Lucas still finishing ninth in approximately 1% of the simulations.
To anyone who thinks that is impossible, welcome to tournament poker.
Just a few weeks ago, I was playing a tournament here in Florida with approximately 450 entrants and 45 players getting paid.
With 48 players remaining, I was the chip leader at my table. I was putting pressure on the shorter stacks, just as the mathematics says I should.
Then three brutal hands happened in quick succession.
A few minutes later, I was walking to my car after finishing 46th—one position outside the money.
Tournament poker is brutally high variance. Even when you are making good decisions, sometimes the deck simply has other ideas.
Lucas has a huge advantage, but no tournament stack is immune to variance.
3. Why Did Rami Hammoud Underperform?
The biggest disappointment in the simulations was Rami Hammoud.
His average simulated winnings came in 4.73% below his ICM value, the largest negative difference among the nine finalists.
There are several possible explanations.
First, while Hammoud is an excellent player, he is still technically an amateur. Several of the other finalists have spent considerably more time competing at the highest levels of poker.
Second, our scouting suggested that he can be somewhat overaggressive.
That style famously worked for Qui Nguyen in 2016, but today’s players are generally better prepared to defend against relentless aggression than they were a decade ago.
The biggest factor, however, may simply be the seat draw.
Hammoud has the unfortunate position of having chip leader Lucas Jumalon two seats to his left.
Whenever Hammoud reaches the cutoff or button—the two most natural positions from which to attack the blinds—Jumalon is waiting with a massive stack and maximum leverage.
That is about as awkward a seating arrangement as you could draw.
4. Jamie Shaevel and Greg Mueller Look Like the Sleepers
The two biggest overperformers were Jamie Shaevel and, especially, Greg Mueller.
Both have favorable characteristics for this particular final table: reasonable table position, extensive experience, and the patience to wait for profitable opportunities.
Shaevel has eight Main Event cashes and is a respected Los Angeles cash-game regular. He appears to be a thoughtful, disciplined player with the experience to capitalize when the right opportunities arise.
His average simulated winnings were 5.61% higher than his ICM value.
Mueller’s results were even more impressive.
He owns three WSOP bracelets, has accumulated dozens of WSOP cashes, and has been competing at this level since before some of his final-table opponents were born.
His average simulated winnings came in 12.33% above his ICM expectation—by far the largest positive difference at the table.
Mueller is also a former professional hockey player. Competing under pressure and performing in front of large crowds are not new experiences for him.
Experience matters at a final table this big, and both players appear well positioned to outperform their starting stacks.
5. Life Is Brutal for the Shorter Stacks
The five shortest stacks all underperformed their ICM expectations.
That is not especially surprising.
Every pay jump represents life-changing money, making it extremely difficult to find profitable opportunities without taking significant risks.
The shorter stacks must constantly weigh the value of preserving their tournament lives against the need to accumulate chips before the blinds consume them.
Wait too long, and your stack loses its ability to threaten anyone.
Take a risk too early, and you could lose hundreds of thousands of dollars in expected prize money.
It is an incredibly difficult balancing act, and the simulations show just how unforgiving that environment can be.
My Official 2026 WSOP Main Event Prediction
Nobody is going to accuse me of making the safe pick.
If you are choosing purely by the numbers, Lucas Jumalon is the clear favorite. He starts with more than one-third of the chips, and our simulations had him winning more than 40% of the time.
Picking anyone else is, mathematically speaking, swimming upstream.
But where is the fun in simply predicting the favorite?
The player who kept jumping off the page in my simulations was Greg Mueller.
Time after time, he found a way to outperform the value of his chip stack—and there is a reason for that.
Experience matters, especially under the brightest lights in poker. Mueller has been cashing WSOP events since before some of his opponents were born. Last year, Michael Mizrachi reminded everyone that experience is not merely an impressive line on a résumé. It can become a genuine edge when the pressure reaches its highest point.
Then there is the Greg Mueller factor.
Before poker, he made his living as a professional hockey player, so performing under pressure is not exactly new territory. By all accounts, he is also one of the genuinely good guys in poker—charismatic, approachable, and easy for fans to support.
Would I be shocked if Lucas Jumalon wins?
Of course not. The numbers say he is considerably more likely to win than anyone else.
But if I am planting my flag on one player to beat the odds, I am going with the veteran who quietly crushed his ICM expectations throughout our simulations.
So here is my official prediction: Greg Mueller for the win, baby!
How We Built the Final-Table Simulations
These results did not come from assigning every finalist a generic poker strategy.
We studied each player’s experience, background, playing tendencies, key hands, approach to risk, and response to tournament pressure. We then used those observations to create nine distinct computerized poker personalities.
We recreated the actual final-table conditions—including the seat draw, starting stacks, blinds, antes, and time remaining—and played the tournament out repeatedly until we had completed one million simulations.
No simulation can know which cards will be dealt, how a player will feel under the lights, or whether someone will change their strategy before play resumes.
This is not certainty.
It is a data-informed prediction based on one million possible versions of what could happen.
Now we get to find out what happens in the version that counts.
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Practice against different playing styles, tendencies, strengths, weaknesses, and real-world mistakes—without waiting for your next live game.
Also Featured on PokerNews
PokerNews published its own coverage of our one-million-simulation project, including my official prediction for the 2026 WSOP Main Event winner.
