Poker

A Few Thoughts on Match Poker (and the Match IPL)

A couple of weeks ago, I took part in the Match IPL, playing for Goa Kings. The IPL here stands for Indian Poker League, and it follows a similar franchisee model as cricket’s IPL. I don’t play poker these days, having retired a couple of years ago, but a good friend, who was the mentor of this team, asked me to join, and I thought it would be fun.

Also, one important reason I joined was because this was a new format of poker, and I wanted to set myself the intellectual challenge of understanding it and optimising for it. When I was active, I was mainly a live cash game player, with decent tournament results in the Asian circuit. But this format of poker was different in key ways from both cash games and tournament poker.

The Format

Match Poker is a format played by teams. It’s explained here, but I’ll sum it up briefly. Let’s say there are seven teams with seven players each. They play each other on seven tables, with one player from each team on every table. Also, there’s one player from each team on every seat. So if you are on seat 1 on table 1, all the other tables with have players from other teams on seat 1. So every team will have a player on every table and every seat.

The idea is that the same hand is then dealt across tables. So all teams play the same hand from every position. At the end of every hand, a team’s chips across positions are added. The team with the best chip count gets 7 points, the next team gets 6, and so on down to 1. The chip count is reset, and all teams start the next hand equal in chips. At the end of a certain number of hands – 200 in the case of Match IPL – the team with the highest points (not chips) wins.

The Question of Luck

According to the guys who thought this up, this format ensures that “the luck element in conventional poker via the ‘random draw of cards’ has been removed.” In fact, the Match Poker guys claim that because this removes the element of luck, that makes poker a true sport. They are using this rationale to get Match Poker into the Olympics.

This claim is both moot and false. It is moot because of two reasons, one small and one big. The small reason is that all sports do have an element of luck, and that’s doesn’t make them less of a sport. The big reason is that even though poker has a greater quantum of luck than other sports, it is still a game of skill in the long run. What happens in any one hand is largely luck, but given a large enough sample size, skill will make the difference.

Also, the format doesn’t eliminate luck entirely because there is still much variance in the game. Let’s say you are the best player in the best team. You hit a set, and maximise pot size to get your opponent with top pair all in. No other team manages this. But then your opponent hits a runner-runner full house, as he will 2% of the time. Your team played the best here – but you will come last, and the team will get just one point. This is luck, and it doesn’t matter in the long run because it all evens out. But you need a decent sample size of hands for the skill to show. 200 hands – or even 2000, or perhaps 20,000 – is not enough.

How Match Poker is Different from Poker

Although Match Poker is set up like a deep-stack cash game, it is different in two fundamental ways. One, the unit of measurement here is not chips, but hands won. Two, you are not playing against your table, but against all the other players sitting on your seat (and dealt the same hand) at the other tables.

Let’s start with point one: chips don’t matter. Teams are not ranked according to how many chips they win in a session, but how many hands they win. This is the opposite of regular poker. A study on online sites showed years ago that the players who win the most hands lose the most money. A good cash game player will lose more hands than he wins, but will win more when he wins than when he loses, and be overall profitable.

An illustration of this is set-mining. I will always play 44 preflop, if there is just one raise, and I will hit my set only one in eight times. Seven times I don’t hit the set – but the one time I do, I make enough money to compensate for the times I folded. But in Match Poker, that doesn’t matter. Point two explains why.

Point two: You are not playing against the table, but against other players on your seat. Let me illustrate this with the set-mining example. Let’s say you get 44. You fold preflop, while you know all the other players on your seat will call. Seven out of eight times, they will fold on the flop, and because you saved that preflop call, you are first on your seat. One time you are last. Assuming ceteris paribus (all other teams and players get equal results in other seats), your team gets seven points seven times and 1 point once, for a total of 50 points in eight hands. All other teams get 24.3, splitting the remaining points. Thus, while set-mining with small pairs is profitable in regular poker, folding them preflop is profitable in Match Poker. It is +EV in this format, or as I’d call it, +MPEV.

The same logic holds for speculative hands like suited connectors. If other teams are likely to play those hands, and they lose more than 50% of the time, your profitable move is to fold. Ditto for chasing flush draws on a flop. Remember, pot odds and chip EV don’t matter, because this is not traditional poker. So a lot of moves that +EV in regular poker are -MPEV.

Three Rules

With this thinking in mind, I formulated three thoughts that I wanted my teammates to think before every hand.

1. I am not playing this hand against the table. I am playing it against other players on this seat.

2. What are the players on this seat likely to do with this hand?

3. Will I win this 50% of the time?

If you have a speculative hand that your opponents (the players on your seat) are likely to call, and that hand will lose more than 50% of the time, then it is +MPEV to fold it right away.

The Strategy

So here’s what the points system means. Teams get from seven to one points for every hand. The average is four. It’s all zero-sum, so teams win what other teams lose, and the amount won is equal to the amount lost. You might have one team winning chips on a given hand and six teams losing, in which case the team that lost the least gets six valuable points. You might have one team losing and six winning. But generally, if you fold every hand, you should get around 4 points per hand. (Simulations validate this, FWIW, with the limited data I had from a previous event.) The team that won Match IPL won with 821 points from 200 hands, or 4.1 per hand. Three out of seven teams finished above the mean (800).

Now, obviously, folding every hand does not win you the whole thing. What I considered the optimal strategy was to fold all speculative hands and medium-strength hands, and push all value hands hard, but to define these value hands tightly. Also, profit in poker comes not just from value hands but value spots. Position matters, and there is much value to be had if you can outplay people in a button-vs-blinds dynamic. I’ll come back to this later: our initial strategy was based on not thinking too hard about spots and focussing on hands.

Instead of playing 20% of hands, as we otherwise might, we decided to play 5%, fold 95% and see how it goes. We would fold all speculative hands, all medium-strength hands (like KJs in early position) and we would also fold strong hands in multiway pots where our chances of winning are less than 50%. (Remember, in this format, pot odds don’t matter. 50% is the magic number.) We even made a hand-chart by position for our players to memorise. This was a new format and we were all beginners here, so that made sense.

We started the tournament disastrously. There were 8 sessions of 25 hands each, and in our first session, we were hit by variance. The problem was Seat 1. Our man in Seat 1 made a series of correct folds, (correct in terms of MPEV), and those hands kept hitting. Sets hit. Random hands hit trips. Connectors hit straights. Hands that would win one in eight or 15 or 25 times kept hitting. And other teams played those hands, and got points for them. We got on the wrong side of variance, which happens. But with just 175 hands left in the session, could we recover?

We remained in the bottom half of teams through that first of two days, though I was topping the individual charts at the end of day 1. In fact, I topped the individual charts at the end of 5 sessions out of the eight, but fell short of winning the MPV at the end of it. And this brings me to the problem of the individual leaderboard.

I assumed that the individual leaderboard would be calculated the same way as the team leaderboard: they’d see how you did against the players on your seat, and assign between 7 to 1 points for each hand. I assumed I led for so long because I was playing optimally. But I later found that this was actually being decided on total chip counts – ie, just like normal poker. (They were assigning differential points based on how you outplayed guys on your seat, but it was still looking at overall chips, not hands won.) Thus, doing well in the individual charts had no correlation to how well you played for your team. One was calculated as per chips, the other was as per hands won.

Value Spots

So given our strategy, how the hell was I leading for so long? Well, this brings me to the issue of value spots. You not only have to play value hands strongly, but also keep your eyes peeled for value spots. When everyone folds to you on the button, that could be a value spot if the blinds are passive. If you are in the small blind against a button open, that could be a value spot if you get him to fold. These are also high-variance spots, but spots you could win more than 50% of the time, so you have to use your judgement. (In this format, btw, I’d define a Value Spot as a spot where you can win the hand more than 50% of the time, regardless of the actual hand you have.)

I took the liberty of searching for such spots against the guy on my left, an excellent cash game reg who would attack my button from his small-blind. I couldn’t simply fold all my buttons, because players on my seat would probably win there a fair bit. So I had to play back at this guy. We had a 3b-4b dynamic going on, and in one hand I stacked him with Q7s against 96. (It went raise-3b-4b-flat, flop came Q96, with 7 on the river. Standard spot.) In another hand on the second day, in another 3b-4b spot, I took an all-in call on the river with A-high, in a spot where he would check-call all showdown hands because I was guaranteed to bluff. His range was polarised, as I thought, but he happened to be bluffing with bottom pair and I lost. The call was correct anyway, because once I have put in enough chips to ensure that I am last on my seat, there is nothing to be lost going all the way. (Ceretis Paribus, again.)

Again, the key rule with value spots is simply whether you’ll win more than 50% of the time, and what your opponents do doesn’t even matter here, because you’ll either get 7 points or 1. You beat the average by winning more than 50%.

So, the optimal strategy is to fold a lot, including all speculative and medium-strength hands, push all value hands hard (if you get coolered, so will everyone on your seat) and use your judgement for value spots. We started off unlucky, some panic set in, and we ended fifth. I slipped off first place in the individual leaderboard, ending 11th, but that was a chip-count thing. (If they calculated that the same way as team points, I’m sure I’d be higher.)

What was worse was that the winning team, after getting lucky on day 1, followed my strategy on day 2 and were thus uncatchable. One of them mentioned that they looked at my hand histories at the end of day 1 as I was leading the individual leaderboard, and their chief strategist is a close friend to whom I had boasted that I had cracked the optimal strategy. They didn’t necessarily get it from me, and this stuff isn’t rocket science to figure out. The fact that they shifted to my strategy on day 2 (one of them folded 24 out of 25 hands in one session, I heard) is intellectual validation that my ideas were correct – though I would have preferred monetary validation. SAD!

(No other team seemed to have figured out the strategy, by the way, with the team that came second playing a LAG style that is perfect for deep-stack cash games but sub-optimal for this format.)

For what it’s worth, I don’t intend to play this again, which is why I am being free with my thoughts here. I expect all the teams to read this, though, thus adding a metagame element, and making their subsequent search for value spots that much more fascinating.

The event was glitzy, with drone cams and so on, so watch it on MTV if you can. I haven’t seen it yet, and I am sure that if I do, I’ll be even more determined to resume my Keto diet.

Also read

The archives of Range Rover, my old poker column for the Economic Times.

The Five Commandments of Poker, an episode of the podcast Mera Kaam Poker that features me.

The Binary Fallacy

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This is an essay I wrote last week for the magazine I edit, Pragati.

1

A few days ago, a friend and I tossed a coin for some reason I don’t remember now. I called Heads. The coin fell Tails.

“It’s Tails,” he said. “You were wrong.”

“No, I wasn’t,” I said.

“Huh? You said Heads, this fell Tails. You were obviously wrong. And I was right.”

“No, I wasn’t. And no you weren’t. Right and wrong are not the only two options. We were both right. And we were both wrong.”

My friend shot me a bewildered look, and put the coin in his pocket. I later remembered that the coin had been mine.

2

I was hanging with some friends at a birthday party. They were my age. I have never been one for celebrating birthdays, but they seemed happy. At one point, we started talking about the present government of Narendra Modi, and I criticized one of his policies. My infallible logic shut everyone up. The undecided nodded their heads. The devout on the other side, who will be convinced by nothing, shifted uneasily in their chairs. Finally, the Birthday Boy said:

“Amit, You’re such a commie, man. You’re a Lutyens insider. You’re like a courtier of the Gandhi family.”

I sighed. For most of the adult life, I’ve railed against the Gandhis and the Congress, their decades of bad economic policy that kept Indians in enforced poverty, their hypocrisy when it came to liberalism (they were the ones who banned The Satanic Verses), and their pandering to different vote banks. When they were in power, people called me a right-winger, and assumed I must be a Modi supporter. And now that I was criticising Modi, for many of the same reasons, I was suddenly a commie and a Congressi.

I sighed again. Someone handed me a glass of water. I said, “Give me back my coin.”

3

I would, at this point, like to present to you what I call The Binary Fallacy. The term has been used randomly in many other contexts, but never in this specific sense. Here goes:

The Binary Fallacy is the ingrained, mistaken notion that there are just two options in any given situation.

This is a bit like a False Dilemma, but that is a fallacy that is contextual and constructed. It is often a tactic. The Binary Fallacy, I would argue, is an ingrained tendency in us. We have evolved to commit The Binary Fallacy. In fact, it was necessary for our survival.

4

Here’s a common situation evolutionary psychologists often bring up. You are living in prehistoric times. You are in the fields. There are dense bushes near you. You hear a sudden loud sound from the bushes, as if something is moving through them.

It could be a tiger. It could be nothing. You have two options:

a) You get the hell out of there.

b) You investigate what’s in the bushes, as it’s likely to pose no danger given your past experience.

There is no space for nuance here. A data scientist may stop and think, “Ah well, out of a sample size of 641 noises-in-bush heard over the last three years, two turned out to be tigers, which means there’s a .3% chance this is a tiger. In contrast to that, there’s a 13% chance that this is deer, and if so, there is a 54% chance that I will catch it and thus take care of my hunting needs for a week. Plus, I will gain satisfying sex from admiring tribeswomen (70%), and might even be next alpha male (22%). If I attribute a satisfaction score of 80 Happiness Units for hunting needs satisfied, 200 for sexual needs satisfied, 400 for alpha-male status and minus 10,000 for death by tiger, my expected value from exploring the source of the noise is minus 838. I should probably leave.”

Meanwhile, the tiger’s finished his lunch, and your genes aren’t going anywhere.

Here’s the thing: the world is fake news. It’s deeply complex, with millions of events coinciding every moment, sometimes independent, often with chains of connections to each other that the human mind cannot unravel. We cannot deal with all this complexity. If we tried to do so, we would freeze with bewilderment and indecision.

So we tell ourselves simple stories to make a complex world explicable. And over time, decision-making shortcuts, or heuristics, get programmed into our brain as the species evolves. This is necessary for survival. If we didn’t take cognitive shortcuts, the Decision Fatigue alone could kill us, leave alone the tiger.

So here’s the upshot: the guy who runs from the tiger will get chances to propagate his genes. Alternatively, in a safer environment, the guy who catches the deer will get to have more sex, so his genes go forward. The nuanced data scientist will either die by tiger or miss the deer.

5

At one level, The Binary Fallacy is a good thing. We need it to negotiate the world. Also, if you give great importance to outcomes, The Binary Fallacy makes sense. Outcomes are binary. Either something happened, or it didn’t. Either there was a tiger in the bushes, or there wasn’t. You can’t be half-pregnant.

But thinking in terms of outcomes is wrong. I learnt this when I spent a few years as a professional poker player. Poker teaches you to think probabilistically, and to ignore outcomes. For those of you who do not know the rules of poker, I will illustrate this with a coin toss instead of a hand of poker. (The example is taken from this essay I wrote on the subject.)

Let us say I come to you and propose the following bet: we will toss an evenly-weighted coin, chosen or vetted by you. If it falls Heads, I will give you 51 rupees. If it falls Tails, you will give me 49 rupees. You agree, and I flip the coin.

Now, your decision at this moment in time is correct. (In poker terms, it’s a Plus EV decision.) Your expected value from this bet is Rs 1 per toss. (51×50 minus 49×50 divided by 100.) But the outcome is binary. You will either win the toss or lose the toss, win Rs 51 or lose Rs 49. You will never win Rs 1, which is the actual value of the toss to you.

Now, this is a bit of a gamble if you just toss the coin once. But if I offer you unlimited tosses of the coin, it becomes less and less of a gamble. You might get unlucky and have a run of five consecutive tails when we start, but in the long run, you will make money because you made the right decision.

This is what poker players learn, and is also the key insight of the Bhagavada Gita: keep making the right decisions, and don’t worry about the fruits of your actions.

The Binary Fallacy militates against this, though. If your elderly aunt watches you make that bet with me, and the coin comes down Tails, she might be rather upset with you. “You were wrong to make that bet,” she might tell you. “Wrong, wrong, wrong. It’s no surprise that my useless sister has such useless offspring.”

But you weren’t wrong. Your aunt just committed The Binary Fallacy. She is the useless sister.

6

Here’s an example of what this means in contemporary terms. Let us look at classical liberals who supported Narendra Modi in the 2014 elections. Assume that they wanted economic reforms but were wary of social unrest caused by the Hindutva fringe. So how would Modi govern if he came to power? I’d say that there were many possibilities.

X percent of the time he’d carry out economic reforms and keep his Hindutva warriors in check on the social front. Y percent of the time he would carry out zero reforms and unleash communal forces. Z percent of the time he would carry out both reforms and a communal agenda. And so on, with many permutations and combinations.

Now, no one can say what those numbers would be, but X, Y and Z are definitely all more than zero percent. If Y happened, someone who hoped for X would not be proved ‘wrong.’ (And vice versa, of course.) His thinking may have been correct, even if the outcome went the other way.

This holds for almost any historical event. The recent US presidential election was so close that anyone who said Hillary Clinton would win was both wrong and right, just as anyone who bet on Donald Trump was both right and wrong. (Unless they exuded certainty, in which case they were both wrong.) Ditto Brexit or Macron or Goriaghaat.

This brings me to The Hindsight Bias, another tool in the brainkit natural selection gave us to build simple narratives for a complex world. The Hindsight Bias is our tendency to believe that a) whatever happened in the past was inevitable and b) that we knew it would happen. Therefore, someone who makes a fallacious prediction or carries out an action that leads to a bad outcome was… wrong. After all, he wasn’t right, and what other options are there?

(By the way, there were no elections at Goriaghaat. I just made that up to see if you were paying attention.)

7

Let’s take a mild deviation here from our main subject, and muse about both The Hindsight Bias and probabilistic thinking. Consider what would have happened – and this is a fascinating counterfactual – if Sanjay Gandhi hadn’t died in an air crash in 1980.

I think it’s fair to say that Indian history would have been very different. I’d also add that we couldn’t say in what direction, though I’d wager that we would probably be worse off. But the thing to note here is that the history we take for granted is a confluence of unlikely events that just happen to happen. When Gandhi flew off that June morning, he wasn’t guaranteed to die, for there is no such thing as destiny. (‘Destiny’ itself is a consequence of our urge for narrative and comfort, and yes, The Hindsight Bias.) There was a very small chance that the plane would crash, and he got unlucky. If there were a million parallel universes that diverged at the moment, he’s still alive in most of them.

8

The Binary Fallacy has poisoned our political discourse. Part of this is the nature of our times. Our senses are bombarded by more information than ever before. We need to simplify. Who has time for nuanced thinking?

Also, we have evolved in prehistoric times to think in terms of tribes, Our People vs The Other. Culture has gone a long way towards fighting off biology – and culture itself is a consequence of biology, for we have contradictory impulses – but our instincts are what they are. We form teams. And we take everything personally.

I hardly need to elaborate on this binarification. (I wrote a post about it a year ago.) All political discourse has become a matter of you are for us or against us. All arguments have only two sides. If I am against Modi, I am an AAPtard, Fiberal Congressi. If I am against Rahul Gandhi, I am a Sanghi who hates Muslims.

Once I protested at the violence carried out by gaurakshaks, and was asked why I didn’t protest when ISIS killed people in Syria. I have had Whataboutery thrown at me when I have criticized the stifling of free speech by this government, and been asked where I was when Muslims were the one doing the muzzling. Naively, I once produced links to pieces I’d written supporting the brave cartoonists at Charlie Hebdo, the Danish cartoonists, and Salman Rushdie (in the context of The Satanic Verses). But to reply to Whataboutery is foolish and futile.

The Binary Fallacy is ingrained in human nature. It is the nature of the beast. We are the beast; and we must also fight the beast. It is not simple.

Two Essays

It’s been a long time since I wrote something substantive on sport, so here are two recent essays I’ve written that scratched my itch. The first, published in The Cricket Monthly, is a 4000-word longread titled ‘What Cricket Can Learn From Poker’. It basically talks about the importance of probabilistic thinking, not just in poker and cricket but also in life in general. In what is a cricket magazine, I get in thoughts on poker, probability, football, the free-will-vs-determinism debate and even the Bhagawad Gita. An excerpt:

One way to think about probability is to imagine parallel universes. You flip an evenly weighted coin, and instantly the world splits into 1000 parallel worlds, and the coin falls heads in 500 of them and tails in the other 500. You flip again and these universes are split into units of 250, each showing sequences of HH, HT, TH and TT. You keep flipping.

This is true for everything that happens. Every single thing that happens in this world (or may happen) has a probability attached to it. These probabilities change at every instant, affected by all other events to some degree or the other. So imagine, in every single moment, for every single event, the parallel universes multiplying. You can increase or decrease the number of hypothetical parallel universes depending on how granular you wish to make the thought experiment, but there are basically infinite parallel universes, each of them containing unique outcomes. And the world that you are in right now is just one of trillions of trillions of freakin’ gazillions. Imagine the level of randomness, then, of this world being what it is.

*

My other essay, ‘The Tamilian Gentleman Who Took On The World’, was part of ESPN.in’s series of The Top 20 Moments in Indian Sport. Vishy Anand winning the undisputed chess world championship in 2007 was ranked No. 4 by ESPN, though I would place it at the top. Being a chess lover, I’m obviously biased, but I’d hope that after reading my piece, which is about the context of Anand’s remarkable achievement, you will agree with me!

Why I Loved and Left Poker

This is the 42nd and last installment of my fortnightly poker column in the Economic Times, Range Rover.

This is the 42nd and final installment of Range Rover, and I end this column at an appropriate time: after around five years of being a professional poker player, I have stopped playing fulltime, and am getting back to writing books. I am the first winning player i know to walk away from this game – but more than the money, I cherish the life lessons that poker has given me. As I sign off, let me share two of them: the first accounts, to some extent, for my love of the game; the second is the reason I am leaving it.

Poker is a game centred around the long term. The public image of poker is based around hands we see in movies or YouTube videos, and the beginner fantasizes about specific events, spectacular hands in which he pulls off a big bluff or deceives someone into stacking off to him. But once you go deeper into the game, you learn that short-term outcomes are largely determined by luck, and your skill only manifests itself in the long run. You learn to not be results-oriented but process-oriented, to just make the optimal move at every opportunity and ignore immediate outcomes. You learn, viscerally, for much money and pride is involved, the same lesson that the Bhagavad Gita teaches: Don’t worry about the fruits of your action, just do the right thing.

Needless to say, this applies to life as well. Luck plays a far bigger part in our lives than we realise: the very fact that you are literate enough to read this, presumably on a device you own, means you have already won the lottery of life. Much of what happens to us and around us is outside our control, and we would be foolish to ascribe meaning to these, or to let them affect us. Too many players I know let short-term wins and losses affect them, and become either arrogant or angry. This is folly. Equanimity is the key to being profitable in poker – and happy in life.

Why am I leaving a game that has given me so much? There are many reasons: Poker is all-consuming, and impacts one’s health and lifestyle; my real calling is to write, and I am pregnant with books that demand labour; but one key reason is that poker is a zero-sum game.

In life, you benefit when others do too. When two people transact a business deal, they do so because both gain value from it. When lovers kiss, the net happiness of both goes up. Life is a positive-sum game. But poker is not. You can only win if someone else loses, and the main skill in poker is exploiting the mistakes of others.

Now, all sport is zero-sum and consenting adults play this game, so this should not be a problem—except for the fact that poker lies on the intersection of sport and gambling. Gambling addiction destroys lives and families just as drug or alcohol addiction do, and i have seen this happen to people around me. I can sit at a poker table and calculate equities and figure out game-theoretically optimal ways of playing—but where is the nobility in this when my opponent is not doing likewise, but is a mindless slave to the dopamine rushes in his head? In the live games I played, I sometimes felt that there was no difference between me and a drug dealer: we were both exploiting someone else’s addiction.

When I write books, i have a shot at enriching myself by enriching others. This can never happen in poker. And so, my friends, goodbye.

*  *  *

Addendum: You can read all the archives of my column on the Range Rover homepage. Here, briefly, are some I enjoyed writing.

My first column, The Bookshop Romeo, talks about the importance of thinking in terms of ranges, and its applicability to life. The Numbers Game and The Answer is 42 are about the importance of mathematics in poker. Make no Mistake, Finding Your Edge, The Colors of Money, The Cigarette Case and The Importance of Profiling deal with some basics of exploitive poker, while The Balancing Act, Miller’s Pyramid and Imagine You’re a Computer  talk about game-theory optimal (GTO) poker. Om Namah Volume is about the importance of putting volume.

I had great fun writing this series of pieces of probability, randomness and the nature of luck in poker and in life: Unlikely is Inevitable; Black Cats at the Poker Table; Running Good. I fed into my interest in cognitive psychology and behavioural economics for those pieces, as I did for these: The Interpreter; Poker at Lake Wobegon; Keep Calm and Carry On; The Endowment Effect; Steve Jobs and his Black Turtleneck.

Beast vs Human and The Zen Master Speaks deal with temperamental aspects of poker. The Game Outside the Game is about the politics of access, and Raking Bad about the ill effects of excessive rake. Sweet Dopamine talks about poker as an addiction, and The Dark Game and The Second Game of Dice expand upon this subject using personal experience.

Steve Jobs and his Black Turtleneck

This is the 41st installment of my fortnightly poker column in the Economic Times, Range Rover.

The next time you are sitting at a poker table, faced with a big decision for a lot of money, take a few seconds off and think of Steve Jobs, naked after his morning shower, walking to his closet to pick out the clothes he will wear for the day. Does he tank over what to wear? No, he doesn’t. He just takes out a black turtleneck, a pair of jeans, and that’s his outfit for the day. Through the last years of his life, in fact, that was his outfit for every day.

Jobs wasn’t lazy or devoid of imagination. He had just cottoned on to a phenomenon called Decision Fatigue. Basically, neurologists have found that every decision you take tires you out a little bit, and robs you of energy. Through the day, Decision Fatigue accumulates, as you get more and more pooped. So if you want to use your energy optimally, the smart thing to do is to automate all trivial decisions, or get them over with quickly, so you can bring all your powers to bear on the big decisions that really matter. Basically, don’t sweat the small stuff.

Jobs did this by wearing the same outfit every day, as does Mark Zuckerberg, thus eliminating one early decision at the start of the day. You could do this by having the same breakfast every day (or letting someone else decide for you), parking in the first available spot instead of searching for the perfect one, and so on. One way to deal with Decision Fatigue is by Satisficing. When I shop, for example, I don’t look for the perfect item to buy, but pick the first adequate one. This is Satisficing: making quick and easy decisions instead of perfect ones. If I’m buying a TV or a T-shirt or a portable hard drive, I won’t agonise for hours over all the different models available, but just pick the first one that seems satisfactory. I’ll devote more scrutiny to big ticket items that really matter—like buying a house, for example.

Consider the implications of this for poker. Poker players typically play sessions that last for many hours, sometimes upwards of 15, which is tiring in itself. They have to stay focussed, observe the action even when they’re not in the hand, and in live games, where such things matter, interact with others for the sake of conviviality. Add to this Decision Fatigue. In any session, you will face dozens of decisions, some of them big ones, increasing the likelihood of your getting exhausted as the session goes on, and thus more prone to errors. So what is one to do?

The obvious answer is to automate. At a beginner level, if you have a starting hand chart for every position, at least those preflop decisions won’t consume energy. As you grow into the game, you can have default decisions for more and more situations. But there is one huge problem with playing like this: you run the risk of becoming predictable, and therefore, exploitable. As you rise up the stakes in poker, you need to start balancing your ranges. This involves a huge amount of work off the table, so that decisions are easy while actually playing. I think of it as akin to a batsman spending thousands of hours in the nets till it becomes reflexive for him, in a match, to lean into an elegant cover-drive against a half volley outside off. Test cricketers don’t actually make a decision on every ball when they are batting; they just follow their reflexes. They have to hone their second nature.

This is why online grinders, whether they are playing cash games or tourneys, multi-table with such ease. Most decisions are automated. Of course, since most of us are playing exploitive poker instead of GTO, we also have to be observant for mistakes to exploit—but even this becomes second nature with practice and hard work off the tables. So here’s my takeaway from this: to reduce decision fatigue at the tables, and to become a better player overall, you need to put in lots of work off the tables. If you do that, there’ll be many black turtlenecks and jeans ahead of you, and Steve Jobs won’t be naked no more.

The Zen Master Speaks

This is the 40th installment of my fortnightly poker column in the Economic Times, Range Rover.

Once upon a time, a poker player went to a Zen master in the hills, Quiet River, and prostrated himself at his feet. ‘Sensei Quiet River,’ he said, ‘I have something I need to ask you. I am a poker player. But I am not as good as I can be, despite studying both the mathematical intricacies of the game and the psychological tendencies of others. Something is missing. I need you tell me what it is?’

Sensei Quiet River just looked into his eyes.

‘Here,’ said the poker player, whipping out his smartphone. ‘I have all my hand histories here. Let me play them for you. Please tell me my leaks.’ He switched on the hand replayer on his phone and held it up in front of the Sensei. But the Sensei ignored it and kept staring into the player’s eyes. Many seconds passed. Finally, the player understood.

‘I get it now,’ he says. ‘The problem is not in the math or the psychology. The problem is me.’

Sensei Quiet River smiled.

In the last installment of Range Rover I wrote, ‘We lose money in poker not because we think too little but because we feel too much.’ I promised to elaborate on it this week, so here goes.

Poker is a challenging game not because of mathematical complexity but because of human frailty. You can master it in a technical sense: you can understand equities, put people on ranges accurately, balance your own ranges, and so on. You will never be perfect at this, but you can easily be adequate for the games you play. But technique is half the story; temperament is the other half.

Even if you know all the right moves to make, you still need to have the discipline to detach yourself from the short-term outcomes of hands or sessions and play correctly. It’s hard to do this: we are all emotional creatures, casting a veneer of rationality on our reptile brains. We get tired, upset, elated, impatient; we give in to greed, sloth, arrogance, and, most of all, anger. Every poker player is familiar with a phenomenon called ‘Tilt’? What is tilt? The sports psychologist Jared Tendler, writer of a brilliant book called The Mental Game of Poker, describes it as “anger+bad play.” We get angry, so we play bad. And why do we get angry?

In his book, Tendler identifies different kinds of tilt. There’s Injustice Tilt, where you feel you are getting unluckier than others, and it’s just not fair. There’s Revenge Tilt, where you take things personally against certain other players at the table (maybe they gave you a bad beat, or they 3b you frequently). There’s Entitlement Tilt, where you feel you deserve to win more than you are, because you’re better dammit. And so on.

Our emotional condition at any point in time can cause us to play sub-optimally, even when we know what the optimal play is. This is most likely to happen at times of stress, and poker is an incredibly stressful activity, because there is always lots of money involved – not to mention ego. We often equate our sense of self and our well-being with the money we have – though we shouldn’t – and having it taken from us can destroy our emotional equalibrium. It isn’t easy, as that saying goes, to keep calm and carry on.

Let me now end this column with a tip. The next time you are at a poker table, facing a difficult decision, buffeted by emotions, here’s what I want you to do: Imagine that Sensei Quiet River is standing by your side. What would he do in your place? Do exactly that, and see him smile.

Imagine You Are A Computer

This is the 39th installment of my fortnightly poker column in the Economic Times, Range Rover.

Being human sucks in many ways, but one of its great advantages is that little thing called the imagination. We can imagine away our frailties and pretend to rise above our cognitive limitations. We are all Walter Mitty and Mungerilal, so this following thought experiment should appeal to you. Imagine that you are not a human being, but a computer designed to play poker perfectly and take the money of puny humans. Now tell me: what would change in the way you play the game? (Pause and think about this before you go to the next para, please.)

If you were God, you would know what cards your opponents held and the rundowns of all future boards. But as a computer, you wouldn’t need that information. You would play game-theory optimal (GTO) poker, with a strategy guaranteed not to lose in the long run regardless of the hands others might have or what they might do with them. Most of us humans, on the other hand, play exploitive poker, for which the hands and tendencies of others do matter. Let me illustrate the difference.

You are heads up in a hand, and on the river make a pot-size bet. Your opponent is getting 2 to 1 to call, and needs to be right one in three times to break even. Now, the aim of GTO poker is to make your opponent indifferent to calling or folding. You will do this by having what is known as a ‘balanced’ range jn this spot. Because you are offering him 2 to 1, a balanced range here would have 1/3 bluffs and 2/3 value hands. (Note that the composition of a balanced range depends on bet sizing, or the odds you give the opponent. If you bet half-pot, giving him 3 to 1, a balanced range would have 75% value hands.) Being balanced in any spot means that your opponent has to play perfectly to break even—and if he calls too much or folds too much, you make money. Basically, you cannot lose, and are thus likely to win.

Unless you’re playing high stakes online cash games, you’re unlikely to ever actually need to play GTO. The cash-game poker I play is exploitive poker. I try to identify mistakes my opponents tend to make and exploit them. In the above example, if my opponent tends to give up too often on the river, I will increase the number of bluffs in my range. If he is a calling machine and never folds, I will have 100% value bets in my range. While this is exploitive, this is also exploitable. By deviating from GTO to exploit his mistakes, I offer him (or someone else) a chance to exploit me. If i start bluffing more because he folds too much, he, or another player, could increase their calling frequency against me.

A computer would aim to play GTO poker, and it would do this by building balanced ranges for every spot, starting from preflop, across streets and board textures. This is incredibly complicated, and humans can just come to an approximation of this. This is useful, for understanding balanced ranges help us understand our own mistakes, and those of others, even if we don’t actually intend to play GTO poker. But my question at the start of this piece was not supposed to turn into a lecture on game theory. Indeed, my own answer to that question has nothing to do with game theory or exploiting others.

In any game I play, I tend to assume, correctly so far, that I can acquire the technical knowledge to beat the game. My big leaks are temperamental ones. If i was a computer, I would not feel any emotion, and would thus avoid all the pitfalls of being human at a poker table. We lose money in poker not because we think too little but because we feel too much. I shall elaborate on this in my next column.

*

Also read:

The Balancing Act
Miller’s Pyramid

The Answer is 42

This is the 38th installment of my fortnightly poker column in the Economic Times, Range Rover.

The answer to the Ultimate Question of Life, the Universe and Everything, according to The Hitchhiker’s Guide to the Galaxy, is 42. The computer that came up with this took 7.5 million years to calculate it, though the question for this answer wasn’t known. Well, I have a guess as to what it was.

My guess is that Douglas Adams was a keen connoisseur of Pot Limit Omaha, and he got into the following hand with his friend Richard Dawkins. Adams had T985ds, spades and diamonds, and the flop came K67, one spade and two diamonds, giving him a humongous wrap, a flush draw and a backdoor flush draw. Dawkins potted, Adams repotted, Dawkins jammed, Adams called. Dawkins had AAKKds, clubs and hearts, for top set. ‘Ha,’ he exclaimed, ‘I have the nuts. Take a hitchhike, my friend!’

‘Now, now, calm down,’ said Adams. ‘It is in your genes to be excitable, I know, but I must inform you that your top set is not the best hand here. Indeed, I am actually the favourite to win here.’

‘You’re kidding me,’ said Dawkins, as he looked at Adams’s cards in growing horror. ‘So what percent of the time do I win this hand?’

And that’s the question, dear reader, to which the answer is 42.

As it happens, the turn gave Adams a straight flush, at which point Dawkins became a militant atheist, as indeed am I, but that is not a matter on which I shall dwell today. Instead, I wish to bring up the role of numbers in poker. I have written before on how poker is a numbers game, and to master the game, you must master the math. In my last column, I wrote about the hard work involved in teaching yourself the game, much of which involved number-crunching. In response, my friend Rajat, a keen player with a recent live tournament win under his belt, tweeted: ‘I’m an old-school player, terrified of numbers. What advice for me?’ This is a reaction many people would have, so here’s what I have to say.

The mathematical laws that govern poker, and indeed, the universe, are not ‘new-school’ inventions. Just as an old-school physicist before the time of Newton was subject to the laws of gravity, so is poker subject to mathematical laws, rewarding those who master them. Indeed, ‘old-school’ players knew their math, as you will note from the vintage of David Sklansky’s The Theory of Poker (1983), and the musings of Doyle Brunson, a man who knew his fold equity, in Super System (1979). Since the internet boom in poker, the math behind the game has been far better understood, to the extent that a talented player who ignores the numbers is like a prodigious swimmer trying to cross an ocean but just refusing to get on a bloody boat.

All decisions in poker come down to the math of estimating pot equity and fold equity and making the best decision possible. You may use your ‘reads’ and psychological insights to get a better sense of your opponent’s range, and how likely he might be to act in a particular way, but all these merely help you come up with the right inputs. The answer, in the end, lies in the math. And here’s the thing: if you ignore the math, that doesn’t mean the math goes away. No, it’s working away in the background, like the laws of nature, ensuring the survival of the fittest – or those who adapt the best, as Dawkins would say.

If you have been winning at poker without caring too much about the math, it is either because you’re playing really soft games, or you’ve been lucky. The way the game is growing in India, both of these are bound to change. So here’s a thought for you: It is a truism in poker we must not be results-oriented, and should just focus on making the right decisions so that we show a profit in the long run. But how do we know what the right decisions are? The answer lies in asking the right questions – as Dawkins did to Adams.

Don’t Maro Ratta

This is the 37th installment of my fortnightly poker column in the Economic Times, Range Rover.

Imagine you’re seven years old and you’re sitting in a classroom where an old professor who scratches his ass constantly is teaching you poker. ‘Now children,’ he intones , as if a robotic app is speaking from inside his body and not an actual human, ‘Imagine you have 13 big blinds in the cutoff and the action folds to you. What is the bottom of your shoving range?’

Someone titters from the back benches. The teacher ignores him and continues: ‘I will tell you what your shoving range is. Now everybody write down what I am putting on the blackboard, and learn by heart for exams. Okay? Learn. By. Heart.’ He turns to the board and starts writing with his right hand and scratching his ass with his left. Someone throws a paper plane at him.

Wouldn’t you hate to learn poker like that? I bet it would kill your interest in the game forever. Maybe you could have been a recreational shark, emptying people’s bank balances in your spare time. But no, classroom happens to you, and you take up stamp collecting as a hobby instead, for the sheer adrenalin rush that gives you. What a shame.

Poker has never been taught in a classroom, of course. It is a recent science, and if anyone wants to learn poker, they have to put in the hard yards and learn it on their own. There’s no university course, no poker diploma you can get, no MOOCs on Coursera, and most instructional books are outdated. Friends may help you, you could even get some online coaching, but if you want to be really good, you’ll have to do most of the hard work yourself. And before you learn how to play poker, you’ll first have to learn how to learn.

One of the problems with Indian education is the emphasis it places on ratta maroing – or learning by heart. When I was a student, I would spend all night before an exam mugging up facts from a guidebook, only to forget them the day after the tests. I believe that every time I did this, a small part of my brain died. And I didn’t learn anything about the subject in question.

Why do I bring this up in the context of poker? It is because too many beginning players indulge in their old habits of ratta maaroing when it comes to learning this game. I know tournament players who will know their push-fold ranges quite precisely, but have never, ever, even once calculated the equity of a particular move. Indeed, some tournament coaches begin and end by teaching ranges for different spots, and while this is useful, it would make more sense to teach a beginner to figure out those ranges for himself. Get the calculator out, figure out fold equity against players left to act, pot equity against calling ranges, and so on.  It’s a lot of work, but at the end of it, such knowledge will be deeper than just mugged-up push-fold charts – and as the game evolves, you will have the tools to do so as well.

In cash games as well, where stacks are deeper, you need to work hard at understanding how to play in different spots. Poker is, ultimately, about nothing more than maximising EV. If you don’t spend lots of time fiddling around with tools like Odds Oracle by ProPokerTools, which helps you figure out equities against weighted ranges, and Flopzilla, which helps you understand how different ranges connect with different board textures, then you can’t improve beyond a certain point.

Let me sum it up: inside your brain there is an old teacher who scratches his ass and encourages you to take shortcuts and ratta maro. Expel him.

We Are All Sharks

This is the 36th installment of my fortnightly poker column in the Economic Times, Range Rover.

A few days ago, I was shooting the breeze with a friend of mine when he told me about a couple of business ventures he was planning, and the investors he’d lined up for them. ‘You won’t believe how gullible they are,’ he said. ‘If there’s one thing I’ve learned from poker, it’s how to find fish and exploit them. And there are so many fish in the business world.’

It’s a good thing I was sipping lemonade at the time and not my usual hot Americano, or I’d have singed myself. Having recovered from the shock of his statement, I shook my head sadly. Poker is a beautiful game, and it can teach you a lot about life. But the lesson my friend had learned was entirely the wrong one.

Poker is a zero-sum game. (A negative-sum game, in fact, if you’re playing a raked game.) The only way you can win money is if someone else loses it. So it’s natural that the key skill in poker lies in exploiting the mistakes of others, sometimes after inducing those mistakes in the first place. It is a mathematical exercise that plays on the frailties of human nature. The game is played by consenting adults, and as your opponents are also trying to exploit you and take your money, they’re fair game. But the real world works differently.

Life is a positive-sum game. This is most eloquently illustrated by what the libertarian writer John Stossel once described, in an old column, as the Double Thank You Moment. When you buy a cup of coffee at a café, you say ‘thank you’ when you are handed the coffee, and the person behind the counter says ‘thank you’ on receiving your money. Both of you are better off. Indeed, the vast majority of human transaction, including all business transactions, are like this. Both people benefit – or they wouldn’t be transacting in the first place.

This amazing phenomenon, which we take for granted, is responsible for the remarkable economic and technological progress of the last three centuries. The economies of nations across the world have grown in consonance with the rise of free markets within them. Think about it: if every transaction leads to both parties benefiting, and a consequent increase of value in the world, then the more people are free to transact, in whatever form, the more we progress as an economy and a society. This is why libertarians such as myself consider it a crime to clamp down on any kind of freedom, be it economic or social.

The positive-sumness of things is unintuitive, and many people reflexively speak of the world in zero-sum terms. For example, socialists, with all their talk of ‘exploitation’, the rich getting richer at the expense of the poor and the need for redistribution. But that is not how the world works; it is not a game of poker. Just as in poker there is no possibility of a Double Thank You Moment, in life, we can all be sharks.

So much for learning the wrong lesson from poker. What does poker teach us about life that is useful to us? Well, the most important lesson I have learnt from poker is not to be results-oriented. Luck plays a huge role in the short term, you only get what you deserve in the long run, so just focus on doing the right thing and don’t worry about the fruits of your actions. The Bhagawad Gita teaches the exact same lesson. Lord Krishna would have crushed the games.