In my last post
on the subject, I warned poker rooms against discriminating against winning players. My reasons were that poker should not be branded as a game where winners are not welcome, and that winning players – in addition to providing action at the tables – actually help poker rooms make more money because they are better at converting deposits into rake than recreational players.
In subsequent debates here on twoplustwo
the latter statement was challenged.
In this article, I would like to provide a more detailed explanation as to why my claim is true, namely, that a lower number of strong players would cause a poker room to earn less money per dollar deposited.
Showing this is of key importance for two reasons:
• Most operators are not aware of the true value of winning players, and will make incorrect business decisions as a result of that, by over-adjusting from a "rake based" valuation to a "loss based" valuation.
• Poker players, when conversing with operators, need arguments that cannot be easily dismissed as purely self-serving.
Different views of player value
Fundamentally, there are two ways to look at how a poker rooms generates value for itself. For the sake of simplicity, I will ignore factors such as bonus costs, payment processing fees, etc. in this discussion. Also, let's assume that rake is weighted contributed rake.
The classical view
Value = Rake
Thus, a player that generates $100 in rake and loses $10 is worth ten times more than a player that generates $10 in rake and loses $100
The casino view
Value = Net Loss
Thus, a player that loses $100 and generates $10 in rake is worth ten times more than a player that loses $10 and generates $100 in rake.
How the two views are connected
There is only one way money can enter a poker room: by being deposited and lost by a player. There are two ways it can exit the poker room: by being won then cashed out by a player or by being turned into rake.
On the poker room level:
Rake = Wins – Losses, i.e. Rake = Net Loss
If a poker room switched from the classical valuation to the casino valuation, the sum of value attributed would remain the same - it would just be attributed to different players. The biggest losing players would internally show up as the most valuable players, whereas winning players would internally show up as negative.
Both views can be combined using the following formula and a value of c between 0 and 1:
Rake = Net Loss = c*Rake + (1-c)*Net Loss
Another important way of looking at things is by taking into account the money deposited and lost by weak players. Here, Rake = Gross Losses * Gross Loss to Rake Conversion, with gross loss being defined as the sum of money lost by losing players (without factoring in the corresponding winnings of winning players as is done in the "net loss" calculation).
It is important to note that the gross loss to rake conversion is not static. It is affected by the types of players that play on your tables, and in general, it's fair to say that the value is higher if the average skill difference between those players is low, and vice versa.
In particular, if you get more losing players to your platform, it's not going to help you from a poker room's point of view if none of the extra money lost gets converted into rake. This is a point which – I believe – most poker rooms are not aware of.
The oversight – ignoring the fact that a poker room is an ecosystem
A poker room might be inclined to switch from the classical view to the casino view based on the assumption that if recreational players are at a poker room, high-volume players will find them and join them anyway. This is only partially correct, but for the sake of argument let's assume that it is absolutely true. Therefore, if a poker room switches to the casino view, they will be inclined to act in a manner that scares away a significant amount of their professional players.
Now, for the sake of simplicity, let's assume there are two categories of players: decent players and weak players. A decent player is anyone with some type of game plan, relatively solid pre-flop stats and some experience in the game. A weak player is what some people would call, more or less, a fish.
Based on this, let's assume that 80% of the seats in a poker room are taken by decent players, and 20% are taken by weak players. Let's also assume a simplified model of poker, with only 2 players to a hand and each player entering a hand with the same likelihood. Now, what happens when the player groups get paired? The average skill difference between two "decent" players, given that the group is big, is of course not small (say it is on average 3 BB/100). However, it is totally dwarfed by the average skill difference between a decent and a weak player (say, 10 BB/100).
What is, however, the average skill difference between weak players? It's of course lower than the difference between a decent and a weak player, but it's clearly higher than the difference between two decent players. Why? Well, most of you will know from experience that even between weak players, there can be huge skill difference. Think of somebody playing real-money poker for the first time compared to somebody that has played poker longer, but just likes to chase cards too much and gamble a lot.
Now, what's the impact of the skill difference? The more evenly matched two players are, the more rake they will generate between them as it takes longer for one player to lose his bankroll to the other. If, however, there is a big skill mismatch, the weak player will be out of funds quickly, without much rake having been generated. The metric that is impacted by this is the gross loss to rake conversion, with gross loss being defined as the amount lost by losing players, without deducting wins by winning players.
With the above data, it looks like this:
Pairing / Likelihood / Gross Loss to Rake Conversion
Decent vs Decent / 64% / High (3)
Decent vs Weak / 32% / Low (1)
Weak vs Weak / 4% / Medium (2)
Using the weights (another simplification) above, the gross loss to rake conversion would be 2.28.
Now, what would happen if a poker site focused far more strongly on weak players, such that 50% of seats are taken by them? The table would adjust as follows:
Pairing / Likelihood / Gross Loss to Rake Conversion
Decent vs Decent / 25% / High (3)
Decent vs Weak / 50% / Low (1)
Weak vs Weak / 25% / Medium (2)
Using the weights above, total gross loss to rake conversion would be 1.75.
As a result, focusing more on weak players has significantly decreased the gross loss to rake conversion, and thus, deposits from weak players have to go up significantly in order to make up for the difference!
At the same time, from the players' point of view, those strong players that stay at the site no matter what actually stand to benefit disproportionally from the change.
The implication of the above is that as a poker room, one simply cannot take customer data gathered during a period where the rake view of valuation was used and then estimate what would happen under a casino view. This is because the casino view will change the underlying player composition which will in turn have an impact on gross loss to rake conversion. This impact will reduce both rake and net loss, given that the increased gross loss is over-compensated by the remaining strong players winning much more.
This can be verified easily by checking the stats of the big winners at tough, VIP friendly sites as opposed to VIP unfriendly sites. In the latter case, their win-rates should be much higher, which also fits the example given here
I believe the above argument is by far the strongest one for supporting VIP and winning players in poker, as it is in the poker rooms' very best interest to do so. If you take this in addition to that fact that poker relies on the message that winning is possible and winners are welcome, it's easy to see that the current crusade against strong players is misguided.
The best way to move forward is to make poker more accessible and attractive for recreational players and professional players, with greater focus on recreational players than before, but without interfering with professional players negatively.
As a next step, given that the above article contains quite a few implications, we are working on a detailed model based on real data in order to verify the analysis above.