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Phil Rocquemore

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So far Phil Rocquemore has created 13 blog entries.

Why Equity Share is Bullsh*t

In this post I’m sharing a video from the first module of my acclaimed Poker Math 2020 course. I explain why ‘Equity Share’ is a terrible model for making pre-flop decisions and outline a new framework to give you a better understanding of strategic choices Pot-Limit Omaha.

Press play below.

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This is a great opportunity for ambitious low and mid-stakes players who would like to improve their understanding of the game and develop a working understanding of business development.

If you are familiar with my products, can work quickly and effectively with Odds Oracle and Camtasia, and would like to get early access to new material then I invite you to apply.

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By | 2017-04-10T13:22:45+00:00 March 22nd, 2017|

College is a Trap: The Advantages of a Poker Education

“Complacency is a state of mind that exists only in retrospective: it has to be shattered before being ascertained.”
Vladimir Nabokov

Professional online poker is a vocation unique to the early part of the 21st century. It didn’t exist prior to 1998 when Planet Poker opened it’s virtual card room to players with dial-up internet access.

As a child of the new century, online poker feels transient, impermanent, always on the brink of fading away. Whether it be due to the threat of impending government regulation, the perceived dwindling supply of fish or the saturation of knowledge (Sorry guys!) every year players opine that the games are soon going to die.

It is rare to find a player who expects poker to be his primary source of income in even 10 years’ time, let alone 20.

This inherent instability means that the online poker community is consistently concerned with what to do for a living when online poker ‘finally ends’. Many successful poker professionals chose to drop out of college to pursue their chosen lifestyle and, as a consequence, fear that there is something missing from their education.

It is true that there are well-documented benefits to going to college that a college dropout risks missing out on. However, few online players appreciate quite how unique the skill-set required to beat online poker is. Fewer still realize that it is precisely the skills honed by online poker that are vital to succeed in our modern ‘Digital Age’.

Part of my mission here at Cardquant is to enlighten those poker players who are in the dark about the wider utility of the poker skill-set. I want to show my readers exactly what advantages you have over your conventionally educated peers. I also want to open the minds of people outside of the poker community to the benefits of working with a current or former poker pro.

To fully appreciate the benefits of the brutal training that online poker subjects one to, one needs to understand the drawbacks of conventional education. With a couple of degrees from two of the world’s best universities on my CV before I started playing poker professionally, I’m pretty well placed to give a balanced perspective on this issue.

The poker skill-set combines probabilistic and inferential thinking with empiricism and psychology. In this article we will focus on the first of these skills- probabilistic thinking- and contrast it with the binary thinking instilled by a conventional education.

Binary Payoffs and Authority

Conventional education is a two decade period which spans the formative years of your life where you are trained that it is ‘bad to be wrong’. This training is implicit in the teaching methodology; ostensibly you are merely studying the subject matter, be it Mathematics, Geography or Physics. But, long after the facts and methods learned during your education fade, the manner in which they are evaluated does not.

Throughout your schooling you face countless quizzes, examinations and standardized tests, the overwhelming majority of which are graded within a Binary Payoff framework: you get 1 point for being ‘right’ and 0 points for being ‘wrong’.

Furthermore, every point in this Binary Payoff framework is allocated based on whether you got the correct (i.e. same) answer to a question that had already been solved. Even in that erstwhile bastion of tolerance to uncertainty- The Arts- the drive towards testing and standardization has led to students seeking to replicate the perceived ‘model answer’ rather than risk losing points by presenting their own interpretation.

A Binary Payoff framework has at its heart the ever-present figure of AUTHORITY so dear to state-sanctioned, standardized education. I speak here not of the individual professor himself, but rather of the concept that there exists some persistent, knowable objective truth which it is possible to check your responses against.

This educational structure is a Procrustean bed; the same blade that fashions unruly children into compliant cogs ready for The Corporate Machine hacks away at the legs of an independent and adventurous mind.

There are two problems with an approach that venerates educational authority and which evaluates responses by how closely they correspond with those prescribed by that authority.

The first problem is that in life the most interesting and valuable questions are the ones that nobody has solved

1. When we train people merely to recall facts and replicate existing procedures we insulate their minds from experiencing the uncertainty inherent to novel problems.

The standardized educational process generates minds with a strong aversion to uncertainty, unwilling to accept the temporary and contingent nature of all human ‘knowledge’. Such minds are easy to influence, keen to embrace rules and regulations that offer them ready answers to neatly packaged problems.

But this illusion of certainty comes at the cost of imagination and creativity, and stifles the vitality of both the ‘educated’ individual and the wider society in which he participates.

I would like to expand on the problem of authority and its relationship to uncertainty in future articles. For now we’ll direct our attention to the problem generated by rewarding students for giving answers which correspond with an existing model.

When a person is trained within a Binary Payoff framework he tends to overvalue ‘being correct’ in the Fluid Domains of life outside the classroom.

For isolated decisions where payoffs can take extreme positive and/or negative values Binary Thinking is catastrophic. The Binary thinker inhibits his ability to experience unusually high returns whilst simultaneously exposing himself to excessive downside risk. For iterated decisions the Binary thinker suffers from poor performance despite (or rather because of) his tendency to be consistently accurate. We explore this problem further in the example below.

The Power of Probabilistic Thinking

To illustrate how conventional education trains people to make poor decisions in Fluid Domains, I present a simple poker scenario:

Consider a one-street poker game where our opponent makes a pot-sized bet with a range composed of either the nuts or a pure bluff (perfectly polarized). We may choose either to call his bet and go to showdown, or fold and surrender the pot to our opponent.

We face this same decision 10 times and our opponent constructs his range with a mix of 60% value hands and 40% bluffs.

If we use the Binary Payoff model so dear to an educational system that emphasizes how bad it is to be wrong then we will fold every time. Our result? We get ‘the correct answer’ 6/10 times and make $0.

The professional poker player incorporates pay-offs into his model and so calls every time, not flinching at being ‘wrong’ more often than not. His result? By thinking probabilistically he gets ‘the correct answer’ only 4/10 times and yet profits 6*-1+4*+2 = $2.

Our lesson is this: The Correct Answer is NOT always the Best Response.

COLLEGE CONSTRUCT: Get the right answer to as many questions as you can. Payoffs are binary.
POKER PERSPECTIVE: The best response depends on the associated payoffs; there are worse things in life than being wrong.

Players who fold too much in these situations (nits) do so because their Binary Payoff worldview renders them overly attached to being ‘right’. This worldview leads to a miscalibrated, loss-averse mentality because the emotional payoff associated with being ‘wrong’ is excessive.

In those Fluid Domains where there are very large potential payoffs, both positive and negative, a bias towards getting ‘the correct answer’ means that the Binary individual makes bad decisions consistently and with increasing frustration due to his poor returns.

Since the Domains essential to life are almost all Fluid this Binary Payoff bias cripples a man everywhere outside of his academic or corporate cocoon. This bias is so pervasive that it deserves its own name; I call it “The Meta-stupidity of Geeks”.

The Meta-stupidity of Geeks

In his classic personal development book, ‘The Magic of Thinking Big’, David Schwartz relates an anecdote about Henry Ford, Founder of the Ford Motor Company:

“One time Henry Ford was involved in a libel suit with the Chicago Tribune. The Tribune had called Ford an ignoramus, and Ford said, in effect, ‘Prove it’.
Tribune asked him scores of simple questions such as ‘Who was Benedict Arnold?’ ‘When was the Revolutionary War fought?” and others, most of which Ford, who had little formal education, could not answer.

Finally he became quite exasperated and said, ‘I don’t know the answers to those questions, but I could find a man in five minutes who does.'”

In an era where we almost always have access to Google, Ford’s perspective becomes even more relevant: those who use their mind as a garage for facts will find success in artificial ‘examination’ environments but little in the real world.

We all knew fellow students in school with an encyclopaedic knowledge of trivia who were apparently incapable of getting to grips (for want of a better term) with members of the opposite sex.

Over the course of any given day, these students would have been faced with perhaps 100 questions on some academic matter or other and perhaps 1-2 interactions with a potential mate. Day-in, day-out they would score perhaps 95% on their academics and a big, fat zero in their sexual life.

Under the Binary Payoff model they were doing very well- still scoring over 90% on aggregate each day- yet a few years down the line they would regret not focusing their attention on the weightier questions of life2. Eventually their complacency catches up with them.

The Meta-stupidity of Geeks is this:
They are consistently right about everything that doesn’t matter.


QUESTION(S) OF THE WEEK: Where in your own life has using a Binary Payoff model in a Fluid Domain held you back? If you are comfortable sharing I would love to hear your stories in the comments below. On a lighter note, what opportunities has playing online poker given you that you would have missed out on if you had stayed in college?

For my blog readers I am offering £100 off of the price of my new course, “Poker Math 2020: Pre-flop Principles” until midnight UK time on Friday 9th December.

Click here to view the Pre-flop Principles Course and testimonials from High Stakes Winners!

Show 2 footnotes

  1. I originally wrote “The problem is that in life the most interesting and valuable questions are the ones that nobody has solved yet.” For now I’ll leave it to the reader to consider the change in meaning that comes from truncating this sentence.
  2. I have used the question of finding a mate as an example and yet the same argument can be applied to other weighty domains. The majority of people also delay the task of building a meaningful life until crisis forces it upon them. A young man who experiences crisis early holds a tremendous advantage over those peers of his who enjoy a balmier Spring. It just takes a while for him to realise it.
By | 2017-04-21T18:38:47+00:00 December 5th, 2016|

Camouflaged Ranges in the Small Blind

“No enterprise is more likely to succeed than one concealed from the enemy until it is ripe for execution.”
Niccolo Machiavelli

Whilst a lot of professional poker players pride themselves on their ‘hand-reading abilities’, they tend to assume that their own betting lines are hard for other players to read.

In actual fact, there are many situations in PLO where the majority of players have transparent ranges. With multiple streets left to play, transparent ranges Pre-flop do a great deal of damage to your win-rate.

When raising first in, camouflaging our ranges is usually a minor consideration. We seek to play the best hand possible and, since the alternative to raising is folding, we have only one playable range to consider. But sometimes we have two playable ranges to consider and that is when camouflage comes to the fore.

In this article we began to address the most complex opening scenario in PLO,

“What is the optimal pre-flop strategy in a 6-max game when the action is folded to us in the Small Blind?”

That analysis led us to the conclusion that we should use both a raising range and a limping range in this situation. Yet many of you will find, when you examine your database, that your limping range performs quite poorly.

The investigation that follows will show you a problem that arises when you construct a limping range purely out of hands, “not good enough to open raise.”

How a Capped Range Interacts with a High Card Flop

Let’s assume we want to play 60% of all starting hands from the Small Blind and distribute those hands between a limping range and a raising range. One of the worst ways to do this is to ‘split’ our range: raise the best hands and limp the weaker hands in a linear fashion.

And yet this is exactly how most players proceed!

If we choose to raise the top 35% of all starting hands and limp hands ranked between 36%-60% the limping range becomes weak on many high card textures. In the example below we use Pokerjuice to examine the flop interaction for a linearly split limping range on an AK♠2 flop:


Flop interaction for a 36%-60% pre-flop range

Flop interaction for a 36%-60% pre-flop range

Compare the flop interaction for this limping range with two alternative pre-flop constructions: a linear ‘raising range’ of 35% and an alternative limping range of 60% without a split:

Flop interaction for a 35% (L) and 60% (R) Pre-flop range

Flop interaction for a 35% (L) and 60% (R) Pre-flop range

Why Splitting your Pre-flop range is a Mistake

It is not surprising that the raising range hits this high-card board much harder, with 50% of the range interacting well with the board- twice the frequency of the 36%-60% limping range above. Almost 1/5th of the range is either a set or top two pair (Group A) and it is thus clear that a range so constructed can be aggressive on this texture.

However it will surprise you that diluting the pre-flop range by appending the weaker 36%-60% hands to the top 35% still leaves us with a defensible distribution post-flop. We have fully 5 times as many top two pair and sets in the complete 60% range as we do in the 36%-60% range. In fact the weakest range can only muster {2nd pair + a gutter} or better 1/4 of the time whereas the complete range manages this 38% of the time.

This pattern repeats itself on many high card textures whenever you compare a split range with a complete range. Since these textures occur with a high frequency (36% of all flops are unpaired and contain two cards ranked 9 or higher) it is essential that we build a limping range that can defend them.

Lesson: A limping range constructed using a ‘linear split’ is at the mercy of an opponent who can represent strong hands on boards which you cannot where he has three streets in position left to play.

Not only does he need not fear a check-raise from you on boards where your range is capped on the flop, but he can also represent high card run-outs which you cannot.

Pouring oil on the fire: The limp re-raise

Another problem we face when we retain both a raising range and a limping range in the Small Blind is that some players in the Big Blind will attack our limps, perceiving them as weak. As we saw in the previous article, an opponent who raises our limp 60% of the time makes us pay an average price of 1.7bb to see the flop whenever we do limp:

f(Raise) Average Price to See Flop
20% 0.9bb
30% 1.1bb
40% 1.3bb
50% 1.5bb
60% 1.7bb


Since we benefit from our opponent raising our limps less frequently we would like to modify our strategy to dissuade him from raising aggressively. One response that players experiment with is to start limp-raising with some strong hands. Unfortunately this action exacerbates the very problem we are attempting to solve. Why?

Because if after limping we raise when we have a strong hand our limp-call range is still very weak on precisely those boards which we need to protect our range!

Limp-raising discourages our opponent from raising our limp with a very high frequency but it does nothing for us when we play post-flop after limp-calling.

In fact what it usually achieves is a further range split, compounding our problems in the small blind by assigning some strong hands to limp-raising which would otherwise be in our open raising range. This enables our opponent to attack both our open-raising and our limping ranges more frequently!

No wonder so many players find it frustrating to play against aggressive players when they are in the Small Blind: they find themselves confronted by aggression at every turn.

Moving Beyond the Linear Split

I have shown conclusively that the simple linear split is an ineffective way to play our range from the Small Blind. Thus if we wish to retain both a raising range and a limping range then we have to find a better way of distributing our starting hands between the two ranges.

In my next article on this subject I will introduce the reader to weighted ranges. I will demonstrate that effective use of weighting leaves our pre-flop ranges opaque and reduces our opponent’s range advantage on high-card boards.

If you enjoyed this pre-flop analysis and would like access to the best theoretical work on Pre-flop play in Pot-Limit Omaha then you should check out POKER MATH 2020.

The first module of POKER MATH 2020, Pre-flop Principles is on pre-sale to my readers now; click here to read the syllabus and rave reviews of my work!

By | 2017-04-10T13:22:46+00:00 October 28th, 2016|