1 hr 2 min

# Poker Zoo 65: Travis Gets the Grease The Poker Zoo Podcast

• Games

If you play in the OOP deepstack training game, or more abstractly, in any online game, you’ve played a hand with Travis Moss. An increasingly reformed tight aggressive, Travis is a student of the game, with a lot of training sites and study behind him.  On reflection, Travis seems like a good example of a taking up poker successfully in middle age. He’s the squeaky wheel, someone unafraid to ask a lot of questions in order to get there. We get into some of those answers he found, and now, the ones he’s finding on his own: Travis also has some interesting takes on how the solver works and how one should use it.

Here is some of Travis’ mentioned “rant,” from publicly accessible S4Y discord chat, which is really less of rant than than a reminder that in a zero-sum game of turns, your action can be based on what your opponent can or will do, as opposed to just the equilibrium model of the game:

IN the Vlog-cast yesterday a point about GTO was made that exemplifies the wide spread misunderstanding of GTO. Game Theory is a process. It is  not an absolute. Rock paper Scissors… Opponent always throwing Rock. Many assume the GTO way to play RPS would be to randomly play each one 1/3 of the time. As stated in the Vlog this would result in breakeven against the Rock only player. But that’s not Game theory. Game Theory would actually say you always play Paper against this opponent. UNTIL, the opponent changes. When they change, we then change, Eventually both players (if they keep changing to the situation) will BOTH end up at random 1/3 mix of each. It is only now that the 1/3 mix is the optimal way to play. Throughout this process there will be countless iterations of the optimal strategy in a particular moment.

Now lets take RPS one step closer to poker. Rock wins it gets \$3, Paper wins it gets \$2, and Scissors wins it gets \$1. Right away we see that the times  we beat Rock we dont win as much as when Rock wins. So the 1/3 mix is not optimal. The mix would actually be closer to 25% Rock, 35% Paper. and  40% Scissors. Even in this simple example, does anyone think they have the brain power to maintain a randomized mixed that would be balanced in this way over 1000 hands? Not to mention what about all the adjustments along the way that take us from our opponent playing only Rock… to finally playing an EV neutral strategy.

And in Poker the worst possible outcome is to end up EV neutral. That means no one is winning except the house when they take the rake. And now poker is just another casino game.

Here’s that hand Travis mentions in the podcast:

MDLive 5/10 3k cap. effective stack was 9k. Was a friday night and 5/10 had been going for about 6hours. 9 handed game 1 Lag who this was the smallest game he would play, the big games weren’t running that night. 2 2/5 grinders effectively shot taking. 3 2/5- 5/10 good players who would mix it up (I was in this group). 2 lawyers who came every friday and were the VIP whales. The other seat was filled and refilled all night with guys who shouldn’t have been in the game but came to try to double up their paychecks and left broke. At least 4 of us had 15k+ in front of us. I had been running hot and had the table covered with about 19500 and definitely had a target on my b...

If you play in the OOP deepstack training game, or more abstractly, in any online game, you’ve played a hand with Travis Moss. An increasingly reformed tight aggressive, Travis is a student of the game, with a lot of training sites and study behind him.  On reflection, Travis seems like a good example of a taking up poker successfully in middle age. He’s the squeaky wheel, someone unafraid to ask a lot of questions in order to get there. We get into some of those answers he found, and now, the ones he’s finding on his own: Travis also has some interesting takes on how the solver works and how one should use it.

Here is some of Travis’ mentioned “rant,” from publicly accessible S4Y discord chat, which is really less of rant than than a reminder that in a zero-sum game of turns, your action can be based on what your opponent can or will do, as opposed to just the equilibrium model of the game:

IN the Vlog-cast yesterday a point about GTO was made that exemplifies the wide spread misunderstanding of GTO. Game Theory is a process. It is  not an absolute. Rock paper Scissors… Opponent always throwing Rock. Many assume the GTO way to play RPS would be to randomly play each one 1/3 of the time. As stated in the Vlog this would result in breakeven against the Rock only player. But that’s not Game theory. Game Theory would actually say you always play Paper against this opponent. UNTIL, the opponent changes. When they change, we then change, Eventually both players (if they keep changing to the situation) will BOTH end up at random 1/3 mix of each. It is only now that the 1/3 mix is the optimal way to play. Throughout this process there will be countless iterations of the optimal strategy in a particular moment.

Now lets take RPS one step closer to poker. Rock wins it gets \$3, Paper wins it gets \$2, and Scissors wins it gets \$1. Right away we see that the times  we beat Rock we dont win as much as when Rock wins. So the 1/3 mix is not optimal. The mix would actually be closer to 25% Rock, 35% Paper. and  40% Scissors. Even in this simple example, does anyone think they have the brain power to maintain a randomized mixed that would be balanced in this way over 1000 hands? Not to mention what about all the adjustments along the way that take us from our opponent playing only Rock… to finally playing an EV neutral strategy.

And in Poker the worst possible outcome is to end up EV neutral. That means no one is winning except the house when they take the rake. And now poker is just another casino game.

Here’s that hand Travis mentions in the podcast:

MDLive 5/10 3k cap. effective stack was 9k. Was a friday night and 5/10 had been going for about 6hours. 9 handed game 1 Lag who this was the smallest game he would play, the big games weren’t running that night. 2 2/5 grinders effectively shot taking. 3 2/5- 5/10 good players who would mix it up (I was in this group). 2 lawyers who came every friday and were the VIP whales. The other seat was filled and refilled all night with guys who shouldn’t have been in the game but came to try to double up their paychecks and left broke. At least 4 of us had 15k+ in front of us. I had been running hot and had the table covered with about 19500 and definitely had a target on my b...

1 hr 2 min