PLO variance and downswings
Published bankroll advice for PLO runs from 10 buy-ins to 100 with no math attached. These tables are the math: 50,000 Monte Carlo trials per cell, heavy-tailed increments, the same engine as the variance calculator.
The short answer
A 5 bb/100 winner in PLO 6-max (SD 140) should expect a worst downswing around 34 buy-ins over the next 100,000 hands, and 1 run in 20 goes deeper than 67. The same winner is still losing after 100k hands 13% of the time. Over 500,000 hands the median worst stretch reaches 59 buy-ins. A bankroll of 57 buy-ins keeps the bust risk of a million-hand run near 5% (89 buy-ins for 1%). Smaller win rates need far more: every table below spans 2-8 bb/100 and SD 120-160, so read your own cell, then run your exact numbers in the calculator.
Worst downswing, next 100,000 hands
Deepest peak-to-trough drop per run, in 100bb buy-ins. Each cell: median, then the 95th percentile (1 run in 20 is worse).
| Win rate | SD 120 | SD 140 | SD 160 |
|---|---|---|---|
| 2 bb/100 | 35 · 70 | 42 · 84 | 48 · 97 |
| 5 bb/100 | 28 · 54 | 34 · 67 | 40 · 80 |
| 8 bb/100 | 24 · 43 | 29 · 55 | 35 · 66 |
SD columns follow the calculator's game presets: 120 = PLO full ring, 140 = PLO 6-max, 160 = PLO heads-up. The win rate barely moves the depth at this horizon; the SD owns it.
Worst downswing, next 500,000 hands
Same grid over a serious year-plus of volume. Median · 95th percentile, in buy-ins.
| Win rate | SD 120 | SD 140 | SD 160 |
|---|---|---|---|
| 2 bb/100 | 67 · 127 | 82 · 156 | 96 · 186 |
| 5 bb/100 | 47 · 80 | 59 · 102 | 71 · 126 |
| 8 bb/100 | 37 · 59 | 46 · 76 | 57 · 96 |
More volume means deeper worst stretches, not shallower: the longer you play, the worse your worst run gets. Volume buys income, not safety.
How long they last, and how often you're still stuck
Left: longest below-peak stretch over 500k hands (median · 95th percentile, in hands). Right: share of runs still losing money after 100k hands.
| Win rate | SD 120 | SD 140 | SD 160 |
|---|---|---|---|
| 2 bb/100 | 181k · 440k | 196k · 458k | 207k · 469k |
| 5 bb/100 | 92k · 222k | 107k · 263k | 119k · 300k |
| 8 bb/100 | 56k · 120k | 67k · 149k | 77k · 179k |
| Win rate | SD 120 | SD 140 | SD 160 |
|---|---|---|---|
| 2 bb/100 | 30% | 33% | 35% |
| 5 bb/100 | 9% | 13% | 16% |
| 8 bb/100 | 2% | 4% | 6% |
A 2 bb/100 winner at SD 140 spends a median of 196k hands of a 500k sample below a previous peak, and is still down money after 100k hands 33% of the time. That is what a real, positive win rate feels like from inside.
The bankroll table
Buy-ins so that only 5% (or 1%) of simulated 1,000,000-hand runs ever go broke. Sim-derived: the 5%/1% quantile of each run's worst point, not a formula.
| Win rate | SD 120 | SD 140 | SD 160 |
|---|---|---|---|
| 2 bb/100 | 102 · 155 | 135 · 202 | 170 · 249 |
| 5 bb/100 | 42 · 64 | 57 · 89 | 75 · 117 |
| 8 bb/100 | 26 · 41 | 36 · 56 | 46 · 74 |
| 10 bb/100 | 21 · 33 | 28 · 45 | 37 · 59 |
Cell format: 5% risk · 1% risk. This settles the forum argument honestly: the popular "50 buy-ins" runs a little light for a 5 bb/100 PLO 6-max winner at 5% risk (57 buy-ins), badly light for a 2 bb/100 winner (135), and comfortable for an 8 bb/100 one (36). The bigger danger is the win-rate assumption itself: bankroll requirements scale like 1 over the win rate, so being half as good as you think doubles every number here. The calculator's Bayesian tab exists for exactly that doubt.
PLO vs NLHE, same win rate
Hold skill constant at 5 bb/100 and switch games. The NLHE 6-max model (SD 100) gives a median worst 100k-hand downswing of 22 buy-ins; PLO 6-max (SD 140) gives 34. Over 500k hands: 36 vs 59. Still losing after 100k hands: 6% vs 13%. PLO's reputation for brutal swings is not culture, it is the standard deviation, and it also means PLO bankroll advice copied from NLHE is systematically too small.
Hands before your win rate means anything
Hands needed before a 95% confidence interval on your measured win rate excludes zero, by true win rate and SD. Closed form, no simulation.
| True win rate | SD 120 | SD 140 | SD 160 |
|---|---|---|---|
| 2 bb/100 | 1.4M | 1.9M | 2.5M |
| 5 bb/100 | 221k | 301k | 393k |
| 8 bb/100 | 86k | 118k | 154k |
| 10 bb/100 | 55k | 75k | 98k |
A 2 bb/100 winner at SD 140 needs 1.9M hands before the sample alone proves they win at all; even a 5 bb/100 winner needs 301k. Grade your play with review tools, not your graph.
Tournaments
Same engine, tournament model: finish distributions derived from ROI against real payout shapes. Downswings in buy-ins (median · 95th percentile) plus the odds you're still down after the sample.
| Format | Sample | Worst downswing (BI) | Still losing |
|---|---|---|---|
| Reg MTT ($109), 10% ROI, 500-player | 1,000 | 136 · 257 | 30% |
| Reg MTT ($109), 10% ROI, 500-player | 5,000 | 272 · 508 | 10% |
| Sunday Major ($530), 30% ROI, 2000-player | 1,000 | 271 · 486 | 34% |
| Sunday Major ($530), 30% ROI, 2000-player | 5,000 | 603 · 1129 | 10% |
Large steep-payout fields are a different sport: a winning Sunday-major reg is still down after a thousand of them 34% of the time, and the median worst downswing across five thousand runs past 603 buy-ins. MTT bankrolls are counted in hundreds of buy-ins for structural reasons, not cowardice.
How to use these numbers
Two practical conclusions. First, size your roll for the variance you'll actually face, because the alternative taxes your play: short money makes you avoid thin +EV spots, and that self-inflicted win-rate cut costs more than the variance itself. Second, treat your win-rate estimate as the soft spot in every table here. The depth of your downswings you cannot control; which column you're really in, you can study your way into. When a downswing hits, the question worth asking is never "when does it end" but "am I in the cell I think I'm in" - and that is a review question, not a variance question.
Methodology
Everything on this page runs the same code as the variance calculator (src/lib/variance-calc): cumulative profit is simulated in 100-hand steps with Student-t increments, not normal ones, using the calculator's game presets for tail weight (SD 120/df 6, SD 140/df 5, SD 160/df 4, NLHE SD 100/df 10). Fat tails make deep downswings meaningfully more likely than the normal-model numbers most calculators print.
Downswing cells: 50,000 trials each. Worst downswing = the deepest peak-to-trough drop anywhere in a run; duration = the longest stretch below a prior peak; a buy-in is 100bb. Bankroll cells: 40,000 trials of 1M hands; the 5%/1% figures are the matching quantiles of each run's worst point, so they are finite-horizon numbers. The play-forever closed form (Malmuth) sits above them: for 5 bb/100 at SD 140 it gives 59 and 90 buy-ins vs the sim's 57. A second-seed re-run of the anchor cell moves the 5% figure by 0.1 buy-ins or less. Fixed seed 20260718, generated 2026-07-17.
The honest limitation: SD is a modeled input. No public PLO database publishes
measured population SDs, so the columns follow the ranges tracking-software
populations show, and every table spans 120-160 rather than pretending one number
is the truth. Tournament rows use the calculator's preset ROIs and payout shapes;
your ROI assumption dominates those numbers. Nothing here models moving down in
stakes, rake changes, or getting better. Regenerate with node scripts/study-guides/gen-variance-tables.mjs.
SolvePLO, "PLO Variance: Downswing and Bankroll Numbers", solveplo.app/plo-variance-downswings, updated July 2026. Questions
- How many buy-ins do I need for PLO?
- Depends on the risk you accept, and the honest range is wider than any rule of thumb. From the simulation: a 5 bb/100 winner in PLO 6-max (SD 140) needs about 57 buy-ins so that only 5% of million-hand runs ever go bust, and about 89 for 1%. A 2 bb/100 winner needs 135 and 202. The popular "50 buy-ins" answer corresponds to a real but double-digit bust risk for small winners.
- How big do PLO downswings get?
- For a 5 bb/100 winner at SD 140, the median worst downswing over 100,000 hands is 34 buy-ins, and 1 run in 20 exceeds 67. Over 500,000 hands the median worst stretch deepens to 59 buy-ins (95th percentile 102). Winning does not exempt you: 13% of that winner's 100k-hand runs end in the red.
- How long do downswings last?
- Longer than they feel. Over 500,000 hands, the same 5 bb/100 winner's longest below-peak stretch has a median of 107k hands, and 5% of runs contain one longer than 263k hands. A downswing here means the whole time between setting a bankroll peak and beating it.
- Is PLO higher variance than NLHE?
- Yes, materially. At the same 5 bb/100 win rate, moving from the NLHE 6-max model (SD 100) to PLO 6-max (SD 140) takes the median worst 100k-hand downswing from 22 to 34 buy-ins and the still-losing-after-100k odds from 6% to 13%. Same skill, deeper swings: that is the SD doing it.
- What standard deviation should I use for PLO?
- Nobody publishes measured population SDs, so treat SD as a modeled input: tracking-software populations put PLO 6-max around 140 bb/100, full ring nearer 120, heads-up 160 and up. Every table on this page spans 120-160 so you can read your own column, and the calculator accepts any value.