Poker part 3

This post was written a few months ago, and for some reason I saved it as a draft rather than publishing it. It’s the third in a series of posts exploring poker and, specifically, my experiences of it, following on from My Poker life, part 1, and My Poker life, part 2. There’s a good glossary of poker terms on Wikipedia, if that’s useful. Thanks for reading, and please question and comment!

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In this post, I want to get a bit further into looking at poker as a game, and give, I hope, an idea of why it’s so appealing as an intellectual exercise. Obviously I recognise I’m talking very much from my own perspective here, and a game like poker fits the way my brain works more, I suspect, than it does for many people. Fundamentally, it’s a game with an almost perfect learning curve. Once you understand the rules – memorising the hand ranks and learning how betting works – you can start to play (and you don’t have to play for money, in real life and online) and you can start to get better.

I was also able to print graphs of my winnings – here is April 2007, picked at random as it was the first one I found in my folders:

It demonstrates pretty well the way poker works in terms of success and earnings. I would have made about £200 this particular month overall, but over the course of the month it was a bit of a rollercoaster, albeit with an upward trend. The freefalls that you see at 5.7k hands and 9.2k hands are demonstrations of the concept of “variance” – horrible to go through, but very much a feature of the game . They come hand in hand with the successful runs at 200 hands, 6.5k hands and 10.5k hands – both ups and downs will happen with variance the method through which the laws of probability affect the game. And as long as there is more of the latter than the former, you’re a winning player.

The tricky part, though, is that to win big, you need both strong hands and strong play. It’s possible to lose big with strong hands, played badly, and it’s very very easy to lose big with poor hands played badly. And that’s why poker is a difficult game to master and why, most of all, it needs absolute discipline.

That doesn’t mean taking no risks, or always playing defensively – you can be aggressive and risky as long as you pick y0ur spots, and let reason rather than passion control your actions. This is easier to do when you’re winning than when you’re losing, and maintaining discipline when things aren’t going well is the single most essential quality of any successful poker player, no matter the stakes. It hurts to lose, especially as it’s real money you’re losing, and the moment you let this pain change the way you play, or take away your reason and discipline, is the moment you need to step away from the table and not play again until you’ve calmed down.

This anguish is called “tilt”, and every person who’s ever played the game has stories of multiple buyins lost through tilt, promising situations thrown away, bankrolls decimated and tantrums thrown. Minimising tilt will minimise unnecessary losses. The beauty of poker is that even the best players lose sometimes, and the worst players win – it’s why it’s such a perfectly poised game, and it’s why noobs and idiots will keep flocking to the tables, which is absolutely what you want if you’re a skilled and disciplined player. So there will always be “necessary” losses – the function of playing a game of imperfect information, relying on probability, psychology and other essential variable constructs.  But a good player will negate this types of loss by maintaining focus and allowing their skill, and the flaws of their opponents, to win in the long run.

And the long run is everything in poker. I can’t stress that enough. Literally anything can happen on the turn of one card, one play, one session. But once the hands run into the thousands, the sessions into the hundreds, the powers of probability will make their presence all the more felt, which is why, after twelve thousand hands in the above graph, I was able to be a winning player, despite all the short term fluctuations.

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