How many buy-ins do I need for MTTs?

The number of buy-ins depends on your ROI, field size, and risk tolerance. At a 5% risk of ruin, a player with 30% ROI in 1,000-player fields typically needs 80-120 buy-ins. Lower ROI or larger fields increase the requirement substantially. Run your specific numbers in the simulator above to get a precise figure for your tournament mix.

What ROI is realistic for tournament poker?

For online MTTs, 5-15% ROI is a strong result for a regular player at mid-stakes. World-class players may sustain 20%+ in softer fields, but those numbers are rare and often unsustainable as competition improves. ROI also depends heavily on field size and structure. Turbo formats and large-field events have thinner margins. Anything above 0% after rake puts you ahead of most of the field.

How does field size affect bankroll needs?

Larger fields increase variance because payout structures become more top-heavy. A 100-player tournament pays roughly 15% of the field with a relatively flat distribution. A 10,000-player tournament also pays about 15%, but the winner takes a much larger share of the prize pool. This top-heaviness means longer stretches between significant cashes and deeper potential downswings, requiring a proportionally larger bankroll.

Should I play PKO or freezeout for bankroll stability?

PKO tournaments reduce variance by approximately 60% compared to equivalent freezeouts because bounty payouts create a more consistent income stream. If bankroll preservation is a priority, PKO formats require significantly fewer buy-ins at the same risk of ruin. However, mystery bounty tournaments add envelope variance and typically require more buy-ins than standard freezeouts. Use our PKO Variance Calculator to compare formats directly.

Tournament Bankroll Simulator

Bankroll paths across a full tournament series at your stakes and ROI.

Bankroll Simulation Parameters

Buy-in ($)

Your ROI (%)

Field Size

Starting Bankroll (buy-ins)

Tournaments to Simulate

Running 5,000 Monte Carlo tournament series...

Risk of Ruin

The percentage of simulated series where your bankroll hit zero before completing the full tournament schedule. Lower is better.

Monte Carlo risk of ruin	--
Probability of profit	--

Target RoR Level	Required Buy-ins

Profit Distribution

How your bankroll performed across 5,000 simulated series. The median is a better guide than the mean because tournament distributions are heavily right-skewed.

Expected profit	--
Median outcome	--
Worst case (p5)	--

Percentile	Profit / Loss

Your Bankroll

High risk

Thin

Solid

Conservative

YOU

Your bankroll	--
Recommended (5% RoR)Standard for serious grinders	--
Surplus	--
Status	--

How This Simulator Works

Payout Structures

Uses actual payout structures from PrimeDope's tournament variance engine, auto-selected based on field size. The same validated payout distributions used by our Tournament Variance Calculator.

Monte Carlo Method

The simulator runs 5,000 independent tournament series, each consisting of your specified number of tournaments. Each tournament within a series produces a random finish position weighted by your ROI, and the corresponding payout (or loss of buy-in) is applied to your running bankroll. A series is marked as "ruin" if the bankroll hits zero at any point.

ROI Distribution

Your ROI is applied as a shift to the finishing position distribution. This is a simplification. In reality, a skilled player's edge is concentrated in deep runs and final tables rather than uniformly distributed across all finishing positions. The model is conservative in the sense that it slightly underestimates downswing severity for high-ROI players.

Limitations

Simplified ROI distribution (uniform shift rather than position-dependent edge)
Does not account for shot-taking, moving down stakes, or bankroll management adjustments during the series
Assumes a single tournament type throughout the series (use Tournament Variance Calculator for mixed schedules)
Does not model PKO or mystery bounty formats (use PKO Variance Calculator for those)