Poker Sample Size
I recently passed a milestone in my quest to beat 100NL online. That milestone was playing over 500,000 hands of online cash game poker. The truth is that I’ve played more than 500,000 hands of cash online in my life but these 500,000 make up the vast majority of my online cash game experience. I decided to dive into this data and write up as detailed a post as possible to help share some of the hard facts (data doesn’t lie) about grinding micro and low stakes cash game poker online.
The post has dozens of insights backed up with data but I’m sure there are things that can be included in my analysis. If you would like me to discuss something specific which doesn’t appear in the post then please comment below or email me at justin@thegreatgrind.com.
- So depending on the poker games that you play, you will have to adjust these WTSD numbers. For a much deeper discussion of calling ranges on the river versus various player types in cash games, tournaments and sit and gos, check out The Upswing Poker Lab. WTSD% and Sample Size.
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Understanding sample sizes when using your poker HUD is absolutely crucial. A lot of people make all kinds of mistakes by drawing incorrect conclusions from insufficient data. Hopefully this little guide will prevent you from making sample size errors with your poker. 100k is probably your first good sample size So, if we want to track live, and we want a sufficient sample size, we need a lot of hours. Probably 800-1000 hours before we get a 'this might be sort of correct'.
Distribution of the hands played AKA the high level view of the data
As you can see from the table below the 500k hands were played across all buy-in levels from 2NL (1c/2c) all the way to 100NL (50c/$1). Since February last year when I made the decision to move to cash games I started with only $80 in my account with the aim of building a bankroll. My plan for 2013 was to start at 1c/2c and build up a roll and move through the levels until I was comfortably beating 50NL. It is for this reason that 40% of the hands in the 500k sample were played at 50NL and the buy-in levels with the fewest hands are the very first levels. Since I was able to beat 50NL at around the 400k hands mark, my focus has been on moving up in stakes and beating the 100NL level. As you can see in the 100NL row I still have some work to do.
The 500k hands were played over 699 hours which comes to around 715 hands per hour. On average I see around 65 hands per table at full ring which means that on average I’ve been playing 11 tables simultaneously which sounds about right. Initially I played 4-6 tables and April was the first full month that I played 24 tables. I have since gone down to 12 and even 6 tables while trying to beat 100NL.
The table below shows the distribution of the 500k sample by day of the week. You can clearly see that I play the vast majority of my hands over Friday and Saturday (my weekend).
These 500k hands were racked up in 201 different sessions. During these 201 sessions I lost money 75 times (37%) with my longest loosing streak being 6 sessions. My longest winning streak was 15 sessions (unfortunately this was at 2NL:)). The lesson here is that even winning players loss one in 3 sessions on average. During these sessions I’ve paid $10,012 in rake. Unfortunately I haven’t recorded the money I’ve earned from bonuses.
The law of diminishing win rates
One of the most obvious concepts of poker is that the game gets tougher to beat the higher you go up in stakes. An interesting question though is by how much does one’s win rate go down the higher he climbs in stakes. Before we look at my data to get some sense of the drop, it is important to understand that the shape of the graph of someone’s win rate as you go up in stakes will be very different on a player-by-player basis. The main reason for this is because most players don’t stick to a level they can beat long enough to determine their true win rate. This effect can be catastrophic to some players who move up in stakes too quickly after running hot at a new buy-in level. Ask two poker players how many hands you should play to determine your true win rate and you will get three answers. Later in my analysis you will see that the real number is much higher than what most people think.
Do you notice the different between by actual win rate and my EV adjusted winrate at 2NL and 5NL? The reason for this is because my sample sizes for these buy-in levels are small which means I was running very hot (at 5NL) and very cold (at 2NL). In the long run your actual winrate and EV adjusted winrate will match.
The plight of the break-even periods
Below are three graphs of periods within my 500k hands sample which almost drove me mentally insane. Let’s look at each one on their own and lets see what we can learn from the horror.
The first graph shows a period of roughly 166,000 hands where I broke even at 25NL and 50NL. Before embarking on my challenge to beat 50NL I knew I could beat 10NL but always got stuck at 25NL so when I finally reached 25NL I was ready for the challenge. About 2 month into playing 25NL I took my first shot at 50NL and it went badly. After dropping $1,000 dollars I went back down to 25NL expecting to continue to beat the level like I was doing before taking the shot at 50NL. Not only did I not sustain my earlier win rate at the level but my confidence took a big knock and my volume suffered. I knew I could beat the level and that my failed shot at 50NL was mostly as a result of run bad (more on this later) so I held firm and pushed through it, 40,000 hands later I saw the light and continued to build the mountain (you know, the shape of my graph:)).
The second break-even period is a bit different from the first. The main difference here is that instead of experiencing an extended period of time where I failed to maintain a positive win rate, I ended up wiping out 5 months of positive results in a mere 33 hours. My first shot at 100NL was so devastating financially in comparison to my positive results over the months that in a mere 12,784 hands I wrote off the winnings I had accumulated over the first 7 months of 2013. The way I got through this blow was by understanding the big picture and that if I can wipe out so much money so quickly, the opposite is also true when positive variance is on your side. Losing $500-$1000 or 5-10 buy-ins over a period of 12k hands isn’t unheard of. It is very important to keep things in proportion and to remember the final goal.
The final break-even graph shows all the hands I played at 50NL in the 500k sample. Do you notice what happened during the first 80,000 hands? The first 80k hands was a special type of hell but when I finally got through it and I broke even at the level something happened, I started to beat the level and haven’t looked back. I think a very important lesson can be learnt from this graph. 80k hands is a hell of a lot of hands for someone who plays this game on the side and I know there are many players out there who couldn’t handle going through so many hands without showing a profit for their work. The reality is that if I decided to call it quits after 80k hands at 50NL I would have cost myself thousands of dollars and perhaps even tens of thousands of dollars if I successfully beat 100NL. The lesson to be learnt here is that it can take you tens of thousands of hands to beat a level so keep your head up and don’t quit.
Your aces will break 10 percent of the time and you should fold 9 2
Did you know there are 169 different hands you can be dealt in a Texas Holdem hand? I know what you’re thinking, there are a lot more than 169 different combinations of hands that someone can be dealt. You’re right, there are actually 1,326 combinations but if you had to group those into suited combinations and unsuited combinations you would get 169 different combinations. Don’t believe me, just look at the table below. 13 hands per row, times 13 rows equals 169.
Now that we have determined that there are 169 different hand strength combinations that you can be dealt, lets have a look at how each one fairs.
There are a number of very interesting things we can gather from this table:
- Even the strongest hand in poker loses 1 in 10 times.
- You will flop a set 11.7% of the time (1 in 8.5).
- On average you will be flipping (50-50) when all in pre-flop.
- Even though 500k hands is a large sample size, when you are breaking it down by 169 different hand combos, there will still be variance on a hand per hand basis. This explains why pocket 3s is my biggest losing hand when looking at dollars won and lost and why I’ve lost more money with QJs vs 82o.
- I’ve mis-click folded AA or timed out when I was dealt them 5 times (whoops!!).
One stat which I find very interesting is the fact that I’ve lost money with 130 of the 169 hand combinations shown in the table above, that’s a majority of 77%. I’m beating 50NL for over 3bb/100 and I’m still losing money with 77% of my hands. If you are struggling to beat this game then most likely you are still playing a number of unprofitable hands on a constant basis. Understand that there are hands which can’t make you money in the long run and that you need to cut them out of your game.
How tight is the competition?
Something which was extremely obvious to me when I was moving up in stakes was the increase in the number of regs at my tables. The first level where I noticed this was at 25NL which seems ridiculously reg infested (50 and 100NL aren’t much better). I decided to dive into my data and see the difference in the tightness level for each buy-in level. Check out the pie charts below which show the percentage of players with certain VPIP ranges per buy-in level.
I was surprised by these results because from the 4 pie charts above it seems 10NL is tighter than 25 and 50NL which wasn’t my experience. One of the issues with this data is that we don’t necessarily know the true VPIP of many of the players in the sample. Check out the table below to see the average number of hands for players based on their VPIP. You can clearly see that the tighter players have a much higher average number of hands which makes sense because these are the regulars which are multi-tabling and putting in a lot of hours. We can also clearly see that players which play above 25% of their hands don’t last.
Final thoughts
There is an almost unlimited number of analyses I could add to this post but I’ve decided to stop here. I think there is enough here for now and I hope that even the most seasoned professional will find something in this post which will help them improve their game. I would love to hear your thoughts on this post and what you would like me to include in my analysis.
Poker Sample Size
If you enjoyed this post and want to learn how to analyze your own poker stats then check out this detailed guide on the poker stats which matter at 2NL. You can also subscribe to this blog or follow me on Twitter by clicking on the follow button below.
Good luck at the tables.
What is Planning Poker?
Planning Poker is an agile estimating and planning technique that is consensus based. To start a poker planning session, the product owner or customer reads an agile user story or describes a feature to the estimators.
Each estimator is holding a deck of Planning Poker cards with values like 0, 1, 2, 3, 5, 8, 13, 20, 40 and 100, which is the sequence we recommend. The values represent the number of story points, ideal days, or other units in which the team estimates.
The estimators discuss the feature, asking questions of the product owner as needed. When the feature has been fully discussed, each estimator privately selects one card to represent his or her estimate. All cards are then revealed at the same time.
If all estimators selected the same value, that becomes the estimate. If not, the estimators discuss their estimates. The high and low estimators should especially share their reasons. After further discussion, each estimator reselects an estimate card, and all cards are again revealed at the same time.
The poker planning process is repeated until consensus is achieved or until the estimators decide that agile estimating and planning of a particular item needs to be deferred until additional information can be acquired.
When should we engage in Planning Poker?
Most teams will hold a Planning Poker session shortly after an initial product backlog is written. This session (which may be spread over multiple days) is used to create initial estimates useful in scoping or sizing the project.
Because product backlog items (usually in the form of user stories) will continue to be added throughout the project, most teams will find it helpful to conduct subsequent agile estimating and planning sessions once per iteration. Usually this is done a few days before the end of the iteration and immediately following a daily standup, since the whole team is together at that time anyway.
How does poker planning work with a distributed team?
Simple: go to PlanningPoker.com. Mountain Goat Software helped develop that website to offer it as a free resource to the agile community. A product owner, ScrumMaster or agile coach can log in and preload a set of items to be estimated. A private URL can then be shared with estimators who log in and join a conference call or Skype session. Agile estimating and planning then proceeds as it would in person.
Does Planning Poker work?
Absolutely. Teams estimating with Planning Poker consistently report that they arrive at more accurate estimates than with any technique they'd used before.
One reason Planning Poker leads to better estimates is because it brings together multiple expert opinions. Because these experts form a cross-functional team from all disciplines on a software project, they are better suited to the estimation task than anyone else.
After completing a thorough review of the literature on software estimation, Magne Jørgensen, Ph.D., of the Simula Research Lab concluded that “the people most competent in solving the task should estimate it.”
Second, a lively dialogue ensues during poker planning, and estimators are called upon by their peers to justify their estimates. Researchers have found that this improves estimate accuracy, especially on items with a lot of uncertainty as we find on most software projects.
Further, being asked to justify estimates has also been shown to result in estimates that better compensate for missing information. This is important on an agile project because the user stories being estimated are often intentionally vague.
Additionally, studies have shown that averaging individual estimates during agile estimating and planning leads to better results as do group discussions of estimates.
How can I get Planning Poker cards?
Planning Poker cards are available in the Mountain Goat Software store. Mountain Goat Software's branded Planning Poker cards are sold at cost as a courtesy to the agile community.
Our full-color cards are the absolute highest-quality cards available anywhere. They are manufactured by the same company that prints many of the world's most popular playing card brands, including Bicycle, Bee, and the World Poker Tour.
We also offer royalty-free licenses to organizations that wish to produce their own cards. The license is available here: https://www.mountaingoatsoftware.com/agile/planning-poker/license
Recommended Resources Related To Planning Poker
- How Can We Get the Best Estimates of Story Size?
- The Best Way to Establish a Baseline When Playing Planning Poker
- Don’t Average During Planning Poker
- Agile Estimating
Live Poker Sample Size
Courses Related To Planning Poker
Scrum Foundations Video Series
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