I think it’s about time we talk about the real nature of variance when playing 6-max ZOOM. As games get tougher, even on regular 6-max tables, the swings become more noticeable and win rates inevitably go down.
This is one of the reasons why some players have chosen ZOOM over regular tables, since they’re more concerned with BB per hour rather than BB/100 win rates. At ZOOM, you can play a ton more hands than you would at regular tables, which provides the opportunity to really put in a ton of volume.
Because of this, we’re willing to embrace a lower BB/100 win rate for a higher hourly rate. We’re also willing to accept that variance is going to be much higher and therefore more difficult mentally to keep our head in the game.
When playing ZOOM, you can easily play 5k hand sessions if you’re good at fast folding. And sometimes you may lose like 10 buy-ins during that stretch. This is a far cry from a typical session at regular tables where you play maybe 1k hands and could be up or down a single buy-in.
So what we’re going to look at in this article is the kind of variance we can expect playing 6-max ZOOM so we can wrap our head around what’s to come and not fly off the handle when things go poorly.
If you pull up your graph after 50k hands, 100k hands, etc. you should have a positive win rate but it’s almost a guarantee that you will have experienced massive downswings and upswings alike. It’s inevitable.
So if you’re the type of player who may be a bit shortsighted, meaning thinking you’re a crusher when you’re on the upswing and a donkey on a downswing, you need to have a much better appreciation of the variance these games can bring.
To look at how much variance we can expect over time, we can take a look at our standard deviation of bb/100 stat in our tracking program. Now I’m not going to go too much into what this stat is, so if you need to brush up on standard deviation, google is your friend.
Generally, the higher the number, the more variance we can expect. And at 6-max ZOOM, this stat is typically higher than what you’d experience at regular 6-max tables.
Let’s say we have a win rate of 2.5bb/100 with a standard deviation of 85bb/100 over 100k hands played at 6-max ZOOM. Now let’s take this data and plug it into our poker variance calculator.
Basically what this data shows is how often we should run better or worse than our observed win rate over a given sample.
In the graph below, our true win rate is the median line in black. Everything above and below show the good and bad fluctuations that can happen over the sample.
That’s right. With this win rate, we can fall anywhere within this graph and it would be completely standard. Now with that said, the chances of us running on the bad side of things is much less than coming out on top.
In fact, our probability of loss over 100k hands is only 17.6%, whereas the probability of running at or above the observed win rate is 82.4%. Additionally, our expected winnings over this sample size is 2500BB.
So while we’re much more likely to come out ahead after 100k hands, there’s still a 17.6% chance we finish under our true win rate of 2.5BB/100.
What this boils down to is that we’re never as good as we think we are and we’re never as bad either. Things can always be better but they could always be worse.
The only thing we can control is leaks in our game and making +EV plays, over and over. The absolute last thing we need is to let short term variance affect us. And yes, 100k hands is short term in the grand scheme of things.
The above graph represents a detailed sample with downswings. Winnings in BB on right axis, current downswing in BB on left axis.
Depending on the number of hands displayed, the extent and number of downswings may be underrepresented due to the resolution of the graph.
For this analysis, a downswing is defined as any period where the current total winnings are below the maximum previous total winnings. Any downswing is considered to last until the total winnings at least equal the previous peak.
The red area shows for any given point, how much the sample is currently away from its previous peak, meaning it tracks downswings.
This chart uses two vertical axes. While the sample winnings have their scale on the right axis, the downswing tracker has its scale on the left axis.
In this example, the simulation ended up with winnings over 25,000 big blinds after 2.5 million hands but had to deal with a nasty downswing of almost 10,000 big blinds between hand 1.2 million and hand 2 million.
Shit happens. Variance happens. Focus on what we can control and move on.
