Poker Session Win Rate
Hey, what’s your win rate?
A poker winrate calculator based on statistics and probobility, see if you are a genius or you just got lucky. Using your hand sample size, winrate, and std deviation, you can find out 70/95% confidence intervals or your winrate to give you a better idea. Poker win rate standard deviation. Your win rate would be 6.66 bb/100 – that’s $1.66 per 100 hands ($0.25. 6.66). Your hourly rate would depend on how many hands you play per hour – playing 500 hands per hour or more across multiple tables is not uncommon for online poker players. If you’re a tournament or SNG player then you should look at your return-on-investment (ROI).
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It’s another seemingly simple question. But, like most SaaS metrics, when you dig deeper you find it’s not. In this post we’ll take a look at how to calculate win rates and use win rates to introduce the broader concept of milestone vs. flow analysis that applies to conversion rates across the entire sales funnel.
Let’s start with some assumptions. Once an opportunity is accepted by sales (known as a sales-accepted opportunity, or SAL), it eventually will end up in one of three terminal states:
- Won
- Lost
- Other (derailed, no decision)
Some people don’t like “other” and insist that opportunities should be exclusively either won or lost and that other is an unnecessary form of lost which should be tracked with a lost reason code as opposed to its own state. I prefer to keep other, and call it derailed, because a competitive loss is conceptually different from a project cancellation, major delay, loss of sponsor, or a company acquisition that halts the project. Whether you want to call it other, no decision, or derailed, I think having a third terminal state is warranted from first principles. However, it can make things complicated.
For example, you’ll need to calculate win rates two ways:
- Win rate, narrow = wins / (wins + losses)
- Win rate, broad = wins / (wins + losses + derails)
Your narrow win rate tells you how good you are at beating the competition. Your broad rates tells you how good you are at closing deals (that come to a terminal state).
Narrow win rate alone can be misleading. If I told you a company had a 66% win rate, you might be tempted to say “time to add more salespeople and scale this thing up.” If I told you they got the 66% win rate by derailing 94 out of every 100 opportunities it generated, won 4, and lost the other 2, then you’d say “not so fast.” This, of course, would show up in the broad win rate of 4%.
This brings up the important question of timing. Both these win rate calculations ignore deals that push out of a quarter. So another degenerate case is a situation where you win 4, lose 2, derail 4, and push 90 opportunities. In this case, narrow win rate = 66% and broad win rate = 40%. Neither is shining a light on the problem (which, if it happens continuously, I call a rolling hairball problem.)
The issue here is thus far we’ve been performing what I call a milestone analysis. In effect, we put observers by the side of the road at various milestones (created, won, lost, derailed) and ask them to count the number opportunities that pass by each quarter. The issue, especially with companies that have long sales cycles, is that you have no idea of progression. You don’t know if the opportunities that passed “win” this quarter came from the opportunities that passed “created” this quarter, or if they came from last quarter, the quarter before that, or even earlier.
Milestone analysis has two key advantages
- It’s easy — you just need to count opportunities passing milestones
- It’s instant — you don’t have to wait to see how things play out to generate answers
The big disadvantage is it can be misleading, because the opportunities hitting a terminal state this quarter were generated in many different time periods. For a company with an average 9 month sales cycle, the opportunities hitting a terminal state in quarter N, were generated primarily in quarter N-3, but with some coming in quarters N-2 and N-1 and some coming in quarters N-4 and N-5. Across that period very little was constant, for example, marketing programs and messages changed. So a marketing effectiveness analysis would be very difficult when approached this way.
For those sorts of questions, I think it’s far better to do a cohort-based analysis, which I call a flow analysis. Instead of looking at all the opportunities that hit a terminal state in a given time period, you go back in time, grab a cohort of opportunities (e.g., all those generated in 4Q16) and then see how they play out over time. You go with the flow.
For marketing programs effectiveness, this is the only way to do it. Instead of a time-based cohort, you’d take a programs-based cohort (e.g., all the opportunities generated by marketing program X), see how they play out, and then compare various programs in terms of effectiveness.
The big downside of flow analysis is you end up analyzing ancient history. For example, if you have a 9 month average sales cycle with a wide distribution around the mean, you may need to wait 15-18 months before the vast majority of the opportunities hit a terminal state. If you analyze too early, too many opportunities are still open. But if you put off analysis then you may get important information, but too late.
You can compress the time window by analyzing programs effectiveness not to sales outcomes but to important steps along the funnel. That way you could compare two programs on the basis of their ability to generate MQLs or SALs, but you still wouldn’t know whether and at what relative rate they generate actual customers. So you could end up doubling down on a program that generates a lot of interest, but not a lot of deals.
Back to our original topic, the same concept comes up in analyzing win rates. Regardless of which win rate you’re calculating, at most companies you’re calculating it on a milestone basis. I find milestone-based win rates more volatile and less accurate that a flow-based SAL-to-close rate. For example, if I were building a marketing funnel to determine how many deals I need to hit next year’s number, I’d want to use a SAL-to-close rate, not a win rate, to do so. Why? SAL-to-close rates:
- Are less volatile because they’re damped by using long periods of time.
- Are more accurate because they actually tracking what you care about — if I get 100 opportunities, how many close within a given time period.
- Automatically factor in derails and slips (the former are ignored in the narrow win rate and the latter ignored in both the narrow and broad win rates).
Let’s look at an example. Here’s a chart that tracks 20 opportunities, 10 generated in 1Q17 and 10 generated in 2Q17, through their entire lifetime to a terminal stage.
In reality things are a lot more complicated than this picture because you have opportunities still being generated in 3Q17 through 4Q18 and you’ll have opportunities that are still in play generated in numerous quarters before 1Q17. But to keep things simple, let’s just analyze this little slice of the world. Let’s do a milestone-based win/loss analysis.
First, you can see the milestone-based win/loss rates bounce around a lot. Here it’s due in part due to law of small numbers, but I do see similar volatility in real life — in my experience win rates bounce within a fairly broad zone — so I think it’s a real issue. Regardless of that, what’s indisputable is that in this example, this is how things will look to the milestone-based win/loss analyzer. Not a very clear picture — and a lot to panic about in 4Q17.
Poker Session Win Rate Formula
Let’s look at what a flow-based cohort analysis produces.
In this case, we analyze the cohort of opportunities generated in the year-ago quarter. Since we only generate opportunities in two quarters, 1Q17 and 2Q17, we only have two cohorts to analyze, and we get only two sets of numbers. The thin blue box shows in opportunity tracking chart shows the data summarized in the 1Q18 column and the thin orange box shows the data for the 2Q18 column. Both boxes depict how 3 opportunities in each cohort are still open at the end of the analysis period (imagine you did the 1Q18 analysis in 1Q18) and haven’t come to final resolution. The cohorts both produce a 50% narrow win rate, a 43% vs. 29% broad win rate, and a 30% vs. 20% close rate. How good are these numbers?
Well, in our example, we have the luxury of finding the true rates by letting the six open opportunities close out over time. By doing a flow-based analysis in 4Q18 of the 1H17 cohort, we can see that our true narrow win rate is 57%, our true broad win rate is 40%, and our close rate is also 40% (which, once everything has arrived at a terminal state, is definitionally identical to the broad win rate).
Hopefully this post has helped you think about your funnel differently by introducing the concept of milestone- vs. flow-based analysis and by demonstrating how the same business situation results in a very different rates depending on both the choice of win rate and analysis type.
Please note that the math in this example backed me into a 40% close rate which is about double what I believe is the benchmark in enterprise software — I think 20 to 25% is a more normal range.
Online Poker » Poker Strategy » Math » ROI
It is very important for all players to look at poker as a life long session. All the plays you make, beats you take and money won simply does not mean much of anything in a short span of time. What will matter is how these stats look after a player has played thousands and thousands of hands or games.
One important stat that is essential to understand and calculate is poker ROI. ROI stands for 'return on investment' which really translates to how much money are you making in comparison to what you are spending to play.
For example, if a player were to buy into a $6.50 9-man sit n go, this player would stand to win $29.25 for first, $17.55 for second and $11.70 for third respectively. If this player were to win first place, the ROI would be calculated into a percentage based on his or her winnings minus the difference of his or her initial buy-in.
This may seem confusing at first, but is much easier to understand once you know how to calculate ROI.
How to Calculate Your ROI
Calculating poker ROI is very simple to do using the following formula:
* ($ Won - Buy In) / Buy In x 100 = ROI %
So using our example of a $6.50 9-man sit n go, let us assume that our player won first place. What would this player's ROI be? Well, let's plug in the numbers.
* ($29.25 - $6.50) / $6.50 x 100 = 350%
Wow! This is an incredible ROI as it shows that this player has earned 3.5 times his initial investment.
However, this is actually very deceiving. The reason that it is deceiving is because as we mentioned above, poker is a life long session and should not be based on one game. In other words, would you rather have an excellent ROI after one game and be a life long loser or lose one game and be a winning player over the course of your poker career?
I would lose one game and prefer to be a life long winner.
It is important that since poker is a life long session that it is understood that there are variances that will undoubtedly take place. That's why a ROI of 350% is unreasonable as it only shows one game and not a life long session of ups and downs.
For example, let's assume that our player played 1,000 games at the same stakes and won 1st place 77 times, 2nd place 186 times and third place 103 times. What is this player's ROI now?
First, we need to know how much this player won total. To do that, we will do the following:
* 1st Place - 77 x $29.25 = $2252.25
* 2nd Place - 186 x $17.55 = $3264.30
* 3rd Place - 103 x $11.70 = $1205.10
* Total Won = $6721.65
Now, you need to know how much your investment was:
* 1,000 games x $6.50/game = $6,500
To figure out our ROI all we need to do now is plug in our numbers into our handy formula:
* ($6721.65 - $6,500) / $6,500 x 100 = 3.41%
As you can see, that is quite a significant difference from a 350% ROI. This player here has played a standard sample size which should have had several variances, up and down, and has proved to be slightly profitable over time.
So, what is a solid ROI? Well, opinions will vary but the most common answer found will be around 10% or so. It is possible to be much higher, but if a player is looking at an ROI of 20%, 30%, or more, than it may be in their best interest to move up in stakes even if it means dropping the ROI a little bit. This is simply because even though the ROI will drop due to competition and such, there will be so much more money to be made playing at higher stakes. As long as a player is around 5 to 10%, they should be just fine.
Since we have tournament and sit n go ROI out of the way, it is time to now focus on all of you cash game players. Sadly, no, the ROI formula will not work for you.
Cash Game ROI
The ROI formula described above will not work for cash games because there are just too many different variables with no consistencies. Cash game players can sit down, buy in for the minimum up to the maximum, play for 5 minutes, then get up, and leave whenever they want. There is no set buy-in and worse yet it is much more difficult to use the ROI formula because there is no set prize pool either.
Poker Session
If a player wants to figure out how well they are doing at cash games, they will want to figure out how much money they are making per hour, similar to a job.
For example, if a player were to go online and buy in for $100 at a $1/$2 game and at the end of 1 hour they had $225 in front of them, they would subtract their buy-in from their total to see how much they profited.
* Total - Buy In = Profit
* Profit/Amount of Hours = $ Won / Hour
So, in our case, this is how much this player made per hour:
* $225 - $100 = $125
* $125/1 = $125 per hour
If he had the same amount after 8 hours, it would look like this:
* $225 - $100 = $125
* $125/8 = $15.63 per hour
This method of calculating an hourly rate can actually be used to figure out your hourly average over the course of a week, month or even a year as long as you keep track of how much you buy-in for each session, what you walk away with and how many hours you played each session. From here, you can determine how profitable you are at cash games.
Calculating Poker ROI
Poker Session Win Rate Chart
Hopefully it is apparent by now that calculating poker ROI is actually very easy to do. Not to mention that it is extremely important to learn simply because everyone has improvements to make in their game, whether good or bad, and knowing your ROI will let you know the extent of those improvements. Making improvements and adjustments is what poker is all about and solid poker players know this.