Welcome to what will likely be your most accelerated poker upgrade
yet.
The intention of this guide is to help you become 100% clear on how
money is really made in poker, as understood from the highest level of
data analysis and performance research in the industry.
In just 6 short chapters, we will transform the framework you use to
maximize your earning power in poker so that you can enjoy a long and
robust career.
If this is our first time meeting, I'm Nick Howard, founder of Poker
Detox, a data-driven poker training company that transforms small
stakes players into high stakes crushers.
Over the last 3 years, our Cash and MTT teams have done more than
$10M in profits over massive samples. These are the results of our
contracted players over more than 40M hands of play:
I hope these results help you feel confident that what you are about to
read is sourced from one of the highest performing training operations
1
in the industry, and a worthy investment of your time.
Now let's get into it!
How Money is Really Made in Poker:
You probably already know that GTO play prints money naturally, at
least against opponents who are playing sub-optimally.
What you may not know is that a solver arrives at its solutions by
iterating around maximally exploitative strategies for both players, until
there is no exploitative adjustment left.
At that point, both players are (loosely speaking) playing optimally.
Right about now is when most perfectionists will launch into endless
forum debates around the "exact" definition of GTO.
We're not going to do that here, because I value your time and I only
care about specific knowledge that will make you as much money as
possible.
What we are concerned with here is this:
How does a solver's play compare to population analysis (how humans
actually play), from an overhead perspective?
Identifying the macro differences between human and solver play is the
first step of the transformation we are aiming for in this guide.
Here are three narratives that I've plucked from various poker
discussions over the years. Each narrative builds on the previous one in
order to paint a more complete picture of how money is actually made
in poker:
2
1. Money is made when a player makes a mistake.
Simple enough.
2. Money is made when a player folds a better hand, or calls with
a worse hand.
Better.
3. Money is made when a player either over-pays for, or
under-realizes the equity of their hand.
This one rounds things out nicely.
There's obviously more to it, but we can use these maxims as a strong
foundation. Let's continue building on this framework.
The Importance of the Pot Odds Model
NL Holdem runs on a pot odds model, which incentivizes players to fight
for the preflop blinds along with any future money that goes into the
pot. Without money in the middle to fight for, players would only be
incentivized to play the nuts, and that would neither be a fun game nor
a skilled one.
Here is what you need to know:
The pot odds model allows players to make profitable investments on
each street, without holding the best possible hand.
This is where skill emerges. Through navigating various pot odds
situations and deciding whether the quality of your hand is strong
enough to yield a profitable investment.
3
Over the last decade, poker solvers became mainstream and we gained
the ability to see how computers play near-perfect poker.
The general conclusion from analyzing data from hundreds of millions
of online poker hands and comparing them to solver strategies can be
summed up in one sentence:
A solver fights for the pot much harder than humans do.
Across the board, a solver puts money into the pot at a higher rate than
almost all winning poker professionals.
This observation leads us to our first framework shift:
Playing like a solver requires you to fight for the pot more often
than most people are willing to.
And here's our new intention:
We are interested in exposing the parts of our strategy that are
causing us to give away money, relative to a solver.
Simple enough.
But with population analysis, we can go one step further than that:
We are incentivized to exploit our opponents in zones where we
can statistically prove that they are not playing in alignment with
solver strategies.
Poker Detox has thrived because we outsource large databases of
real poker hands to expert quant analysts, who help us identify the
areas where people play sub-optimally.
4
Spoiler alert: there are a lot of these areas.
From there, we are able to build a clear and concise map of how to
accurately exploit these zones.
The study of population analysis has armed us with a proven,
data-driven strategy.
Using a data-driven strategy still requires a player to study hard and
execute with consistency, but it eliminates most of the doubt and
confusion that arises when players try to create strategies on their own.
Knowing that your system is proven also brings peace of mind when
things aren't going well.
We're almost ready to explore data driven strategies in greater detail.
Before we dive in, here's a motto that you can always come back to
whenever you find yourself in doubt:
If you want to know how a part of your strategy is performing, just
look at how it performs against the population data.
Why?
Because this will show you how that part of your strategy actually
performs against real players, instead of a theoretical model.
Now that we've established a solid framework for how we should
approach our basic strategy design, let's move on to the next
chapter, where we'll reveal the power of data-driven strategies in
action.
5
We began the first chapter by discussing how GTO play naturally
exploits sub-optimal opponents.
Then, we discussed how accurate exploitative play performs better than
GTO play against sub-optimal opponents.
Here's the clincher:
Exploitative play happens to be much simpler to execute than the
impossible amounts of mixed strategies that GTO overwhelms you
with.
If GTO complexities have stolen many hours from you, I feel your pain
and I hope hearing this comes as a great relief:
Data-driven strategies are both simpler and more effective than GTO
strategies, delivering the best of both worlds.
Let's recall what we learned in Chapter 1 about how a solver arrives at
any solution:
The solver goes through a series of max-exploitative shifts for both
players until there are no exploitative adjustments left.
This is how it arrives at a basic equilibrium, which is what we usually
refer to as GTO.
6
Now, if you lock one side of the solver's strategy to represent an
imbalance present in the player pool, the other side of the solver
exploits HARD.
Here's an example to demonstrate:
In the scenario below, we are looking at a SB 3-bet range vs a BTN open.
The BTN range was locked to represent a common pool imbalance: low
4-bet frequency.
Look at how the solver adjusts the SB 3-bet strategy to counter a BTN
who fails to 4-bet enough:
Quite an aggressive adjustment!
Note that the SB is playing optimally in both grids, because the
technical definition of optimal play is "the strategy that wins the most
money."
7
The optimal strategy is inherently maximally exploitative. All it cares
about is winning the most money.
When you start working with data-driven technology, optimal exploits
begin lighting up all over the game tree.
You discover zones where the population is imbalanced, and hit them
with a statistically proven counter strategy in those zones.
Against tougher opponents, just soften the counter to become less
detectable.
That's it.
● Seek out new zones
● Solve for optimal counters
● Soften the counters against better opponents
Rinse and repeat.
This is what strategy design looks like at the highest level of the
industry.
Let's drive this home to complete the upgrade:
Data-driven strategies are basically a cheat code for poker players who
are willing to execute on statistically proven exploits that out-perform
theoretical models.
By using the guidelines outlined above, you will be able to balance your
strategy against tougher opponents while unleashing a vast exploitative
arsenal against weaker ones.
You will learn to calibrate to any game in a way that will allow you to
make as much money as possible.
8
Take a few moments to integrate what we've covered so far, then move
on to the next chapter.
We're about to look at an example of a data-driven strategy crushing
the competition at the highest level of online poker.
9
Welcome to red-lining 101.
Let's start with a basic question: What is "red-lining"?
The red line is the line on your results graph that indicates your
"non-showdown winnings". This line plots all the money you won and
lost when either you or your opponent folded before the end of the
hand.
Due to the exploitative nature of data-driven strategies, they generally
produce graphs with an unusually high red line.
This is reflective of the additional non-showdown winnings gained from
fighting for more pots against imbalanced opponents.
There are two main ways a player can generate a higher red-line in a
way that increases their overall win rate:
1. Bluffing more in zones where the opponent responds by folding
too frequently.
2. Bluff-catching more in zones where the opponent is bluffing too
frequently.
As we learned in the last chapter, data-driven strategies focus on
exploiting pool imbalances while giving attention to resilience, in order
to remain undetectable.
10
Let's take a look at what a data-driven strategy looks like in action, at
the highest level of poker.
The following graph shows results from a recent heads up match
between two of the top players in the world:
Stefan won the match decisively, boasting a final win-rate of 10bb/100
(adjusted EV), and profiting almost $700,000.
If you aren't familiar with Stefan's style, he's ruthlessly exploitative and
is very comfortable deviating from theory.
11
While we can't know Stefan's exact thought process, it's clear that he
implemented some pretty serious red-line exploits about halfway
through this match.
From that point on, Stefan heavily exploited his way to victory vs
Limitless.
What's fascinating about this match is that Limitless does not usually
get pushed around -- quite the opposite actually. He's very aggressive
and is usually the one setting the pace.
You have now arrived at the most important moment of the guide so
far. Take a deep breath and allow this next sentence to land:
What you're looking at is one of the best players in the world being
systematically dismantled by an opponent who beat him with a
well-calibrated red line strategy.
A data-driven strategy.
Try to imagine how hard it must have been for Limitless to deal with an
opponent who was constantly putting pressure on him, day in and day
out for weeks straight. These two guys probably played more hands
together in this match than you'll play with any one opponent in your
lifetime.
And Limitless, one of the best players in the world, could not deal with
the pressure.
The power of red-lining continues to prove itself year after year, even at
the highest levels of the game. There will never be a style that is more
difficult to play against, and there will never be a style that produces
more edge.
12
Because it is driven by data.
Now the real fun starts.
We're going to take a deep dive into the most common thought
processes that cause players to buckle under pressure.
You're going to get a behind the scenes look into what real players are
thinking in the moments when they make bad decisions.
Decisions that allow us to exploit them.
Keep moving.
13
Over the last decade as a performance coach, I've used various
frameworks to describe the mindset differences that separate the best
poker players from the rest. Recently I settled on a new framework that
categorizes poker mindsets into two groups:
Alpha & Beta.
While I acknowledge the mainstream issues with these labels, I also
believe that the usage of them has become wildly distorted over the
years. The alpha/beta dynamic that I will be exploring here will be
biologically grounded and will serve in furthering our understanding of
performance psychology at the most primal level.
Specifically, we will be examining the connection between performance
quality, and the performer's relationship with the stress response.
Another term for the stress response is the "fight/flight" response, feel
free to use them interchangeably.
Let's begin with some basic credibility for this framework.
Since the beginning of time, the alpha/beta hierarchy has been one of
the most naturally occurring dynamics in virtually every species on the
planet, and the differences between alpha and beta behavior have had
a profound evolutionary impact on our planet.
14
In general, a dominant animal of higher ranking is classified as alpha,
and the submissive lower-ranking animal is classified as beta.
From a performance standpoint, a core distinction of "alpha behavior"
is an animal's ability to accurately orient itself to its environment,
specifically under intense pressure.
In these situations, alphas typically interpret and respond to
information in a clear and decisive manner.
In comparison, betas are less effective under pressure and typically
more submissive.
We can use this framework as a gateway to explore distinctions
between top performing poker players and the rest of the pack.
What we are concerned with is this:
What are the main differences in the way that the alpha and beta
mindset inform the decision-making process during a hand?
Perhaps the most decisive difference between these two types of
mindsets stems from risk tolerance.
The alpha performer typically has a higher risk tolerance than the beta.
A higher risk tolerance usually allows their nervous system to process
stress responses more effectively and remain clear-minded during
execution.
If you don't feel like you have a high risk tolerance, fear not.
You are human!
15
While animals have a limited capacity for introspection, humans can
leverage self-awareness to influence their baseline risk tolerance.
We do this by cultivating perspectives that are more in alignment with
the true incentives of our environment.
There's one key concept integral to risk tolerance that you need to
understand in order to gain leverage over it.
Loss aversion.
This is a core principle of behavioral economics. Here's what it says:
Loss aversion is a cognitive bias that describes why, for individuals, the
pain of losing is psychologically twice as powerful than the pleasure of
gaining.
*https://thedecisionlab.com/biases/loss-aversion
Since this bias is so common, it would be wise to accept that this
principle applies to you in ways that you are not consciously aware of.
Once you accept that, you can establish a new level of awareness over
loss aversion and begin improving your risk tolerance.
We're almost done with the theory part of this chapter, and then we’ll
have some fun.
Ever noticed that the bigger the pot gets, the more difficult it can be to
think clearly?
There's no lion chasing you, but you're still feeling the heat.
In these moments, your risk tolerance is being challenged.
16
As this happens, your stress response kicks in and you are put to
nature's most basic decision: fight or flight.
This stress response is normal and nothing to worry about.
Again, you are lucky to be human and have a unique advantage over
other animals:
You can self-reflect in ways that will aid you in overcoming the stress
response.
We're taking the first big step toward higher performance awareness in
this chapter.
Allow this to resonate:
An alpha poker player with a high risk tolerance has an easier time
aligning with winning exploitative strategies.
The main reason for this is that the alpha is less submissive than the
beta (the alpha is more willing to fight).
I hope it's starting to make sense why cultivating a higher risk tolerance
is so valuable in a game that rewards players who fight hard for pots.
In general, with all other factors held stable:
Alpha risk tolerance = higher performance
Beta risk tolerance = lower performance
17
If it's more intuitive, you can also think of your risk tolerance as your
current susceptibility to loss aversion.
Obviously, there are other considerations, but risk tolerance has
consistently proven to be the best universal indicator of a player's
potential in poker.
We sometimes joke at Poker Detox that a high risk tolerance is perhaps
the only trait we should be looking for when recruiting a new player.
As outrageous as that may sound, there's a lot of wisdom behind it.
Let's continue with some examples that will help us strengthen our risk
tolerance.
How Risk Tolerance Affects Decisions in a Poker Hand:
We've discussed how a player's risk tolerance informs their decisions in
ways that are largely unconscious to them.
Now let's put you in a situation at the table where you can simulate the
stress response, observe how it triggers loss aversion, and ultimately
affects your decision.
Here is the most common example of loss aversion from the real player
case studies I've worked on over the last 10 years:
A player is facing a bet from an opponent and holds a marginal (but still
profitable) hand.
The player thinks one of these two things:
"I'll wait for a better spot."
OR
18
“I’ll call here sometimes, but I’ll fold this time.”
And the player folds.
Both of these statements are really saying the same thing, and they
represent the most common form loss aversion in the industry.
If we look at what happens directly before this type of rationalization
occurs, we gain a key insight into how a beta thought process
unconsciously gravitates towards submission (giving up the pot).
In the moments before submitting, the beta thought process is usually
busy doing one of three things:
1. They are attempting to put the opponent on a hand or range in
an unscientific way. (Ex: Declaring random hands in the
opponent's range)
2. They are attempting to figure out if a GTO strategy would
require them to defend their hand. (Ex: "I think I'm allowed to fold
this hand sometimes in theory..")
3. They are attempting to "level" the opponent. (Ex: “They
wouldn't try to bluff me in this spot/at this time")
Regardless of the exact scenario, the beta usually toggles through one
or more of these narratives in an attempt to arrive at certainty over
their decision.
Unfortunately, none of these narratives produce a stable decision
making process.
But here's what's fascinating:
19
All of these narratives are unconsciously informed by an over-active
flight response that primes the player into submission.
The stress response is literally dictating the beta’s thought process and
causing them to give up pots that they should be fighting for!
All the while, they believe they are using sound reasoning.
We can see similar patterns emerging when a beta mindset is faced
with a decision to bet or check (aggressive options):
"My opponent has too many strong hands in this spot, I can't bluff
here."
"I'm allowed to give up sometimes in theory, I'll pick a different time to
bluff"
"I'm not representing much here, they won't believe me if I bluff."
"They're going to call me this time, they saw me bluff recently." "I
don't want to value bet here, because I'm afraid of getting raised."
Any of these sound familiar?
Here is the key takeaway:
When placed under pressure, the beta mindset will constantly
rationalize in favor of risk aversion.
In comparison, an alpha mindset is generally more aware of its
overarching strategic incentives, and uses a stable decision-making
process to prevent itself from slipping into loss aversion.
20
What follows from this is a fascinating performance paradox:
When making a decision under pressure, the alpha player is typically
able to arrive at greater accuracy than the beta player while consciously
thinking about much less.
The alpha player performs better with a simpler thought process.
Let's show more proof of this.
In Josh Waitzkin's book "The Art of Learning", a similar conclusion was
found when comparing the mindsets of top Grandmasters to weaker
players.
There was an experiment where both types of players were shown the
same position on a chess board and asked to analyze it.
The Grandmaster was able to make more accurate decisions while
consciously thinking about much less than the weaker player.
How is that possible?
Well, the Grandmaster is more integrated and uses a powerful strategic
framework to make decisions under pressure.
Think of it this way:
Alpha vs beta is basically like putting a Ferrari next to a Volkswagen in a
street race.
The engine power and steering precision of the two vehicles is so
different that the Volkswagen doesn't stand a chance.
Bringing this analogy back to a real poker hand:
21
The alpha player remains stable, scientific, and calm when placed
under decision-making pressure.
The beta player becomes scattered, distorted, and neurotic when
placed under decision-making pressure.
Let's look at some exceptions.
Over the years, I've encountered some players who have had unusually
high risk tolerances, but were strategically raw on account of not
studying with data-driven technology.
I came to understand these players as "naive alphas".
They fought for pots, but they never held themselves accountable to
zone-by-zone statistical review.
They had calibration issues in areas of the game where their high risk
tolerance was actually hurting them.
Plus they were more prone to explosive forms of tilt.
Generally, this type of player is easy to train if they are willing to commit
to population analysis.
But if they are stubborn, they will never learn how to patiently navigate
the zones of the game where the data shows that they would truly
benefit from surrendering their hand.
22
Recap:
We've now covered the main differences between the alpha and beta
poker mindset, as they relate to decision-making under pressure.
Before we move on, there is one more major weakness of the beta
mindset that is worth noting.
Most betas believe they can exploit aggressive players by waiting
for strong hands.
The first hand example of this chapter gave us a glimpse into how this
belief manifests during a hand: "I'll fold this time and wait for a better
spot."
This is the type of rationalization that the data-driven alpha preys upon.
While the beta player thinks he's trapping an aggressive player by
waiting for a stronger hand, the aggressive player is dismantling him in
non-showdown winnings and running up a red line edge.
The beta just won’t get dealt enough strong hands to put up a sufficient
defense.
And what's even crazier?
Most of them don't even realize they're being exploited.
In the next chapter, we're going to lock in our transformation with some
final perspective shifts that will elevate your mindset, increase your risk
tolerance, and maximize your earning power.
Vamos!
23
Welcome to the transformation.
The first 4 sections of this guide have equipped you with an essential
overview of how data-driven strategies work.
Now it's time to get you into alignment with what it feels like to execute
on them.
Since playing better poker will likely require you to increase your risk
tolerance, we need to discuss some mindset strategies that will help
you do just that.
Let's start with an important performance truth:
Any strategic upgrade that requires you to take on greater risk will also
require you to work with your fears around executing it.
It's one thing to find new exploits, but it's another thing to get into
emotional agreement with them.
Emotional agreement basically means that you have accepted the
changes that you'll be making to your strategy, and no longer feel inner
conflict with those changes during execution.
Resolving inner conflict around your strategy is what will allow you to
authorize the right decision without collapsing back into old, submissive
ways.
24
Let's begin with a simple overview of the 3 mindset dimensions
of poker.
Virtually all of the stress you feel at the poker table can be explained
by one or more of the following:
1. An unstable decision-making process.
2. Playing in games too big for your bankroll.
3. An oppositional relationship with variance/time.
We'll address these in order.
In the previous chapter, we detailed the importance of cultivating a
stable decision-making process.
You saw various examples of how the beta mindset collapses into loss
aversion with little or no awareness over what’s happening.
On the flip side, we saw how the alpha mindset is generally more calm
under pressure and uses a stable decision making process to overcome
rationalizations.
What we haven't pointed out yet is this:
A big reason why alphas are able to remain more calm is that they
have a greater conviction in their edge.
They know they are working with proven strategic frameworks, and this
naturally relaxes their nervous system.
Adopting a data-driven strategy is one of the most logical steps toward
improving your decision-making stability, because higher technical
confidence helps you navigate the hand in a clearer and more relaxed
way.
25
Let's take a deeper look at #2 on the list above:
Playing in games too big for your bankroll.
One of the simplest ways to resolve performance stress is by
committing to safer bankroll management.
In the early stages of adopting a data-driven strategy (or any new
strategy), it makes a lot of sense to drop down in limits as you train
yourself into alignment with the new style of play.
Dropping limits will protect you from large downswings, which will
make it easier for you to confidently execute on new strategic
upgrades.
In the early stages of implementation, you want to pick a limit where
you can execute decisively without being phased by the results.
Working with data makes this easier, because for the first time you have
real confidence that your strategy is proven to win.
From there, it's really just about playing enough hands to realize your
edge.
As for what your exact bankroll management strategy should be, this
is always player dependent.
You should decide what makes sense for you based on a combination of
3 things:
1. Your current risk tolerance
2. Your savings
3. Your expenses
For example, a timid player with a mortgage and a family cannot usually
justify the same bankroll strategy as a young 20-something with nothing
to lose.
26
Consider your situation through the lens of the 3 italicized zones above,
and adjust your plan accordingly.
If daily swings start putting too much pressure on you, never be afraid
to drop down.
You have plenty of time.
That brings us to #3, my all-time favorite topic.
A poker player’s stress is often caused by a dysfunctional
relationship with variance/time.
A lot of players tend to feel like they're introducing more variance into
their strategy when they start playing more aggressively.
While this adjustment can feel uncomfortable in the short term, it's not
actually a riskier approach in the long term.
Let's explore why.
When shifting to a data-driven strategy, your win rate will increase
alongside your “standard deviation” (a metric of volatility).
As win rate and standard deviation increase, they have a counteracting
effect on variance.
Technically your volatility increases as your standard deviation goes up,
which means your results may swing a bit more in the short term.
But since your win rate is also increasing, there is minimal effect on
your variance in the long run.
In fact, it's not uncommon for a data-driven player's win rate to increase
so much relative to their old strategy that they actually lower their long
run variance!
27
Understanding the science behind this usually helps players prepare for
the journey ahead, by helping them relax into the long term safety of
the data-driven strategy.
Recall what we discussed in the last chapter:
Any shift in perspective that helps you alleviate stress will result in
clearer decision-making and a higher baseline risk tolerance.
Without statistical proof that your strategy works, you are far more
likely to make destructive changes in an attempt to escape
variance.
As you've probably experienced, the urge to re-establish control over
results can become very intense on a downswing.
Players typically resort to changing their strategy when they start
feeling desperate to recoup short term losses.
The problem with this approach is that it is unscientific, and as a result
you end up hastily replacing good parts of your strategy with bad parts.
But when you commit to a statistically proven strategy, all you need to
do is continue executing on it until variance presses out.
Trying to escape the hands of time is one of the biggest pitfalls in poker,
specifically because the feedback loops are slower than virtually all
other skill games.
You cannot reliably know what is wrong with your poker strategy by
looking at your results in the short (or even medium) term, which is why
you need data-driven technology to help you measure your
performance.
Grounded statistical reviews allow you to shorten feedback loops and
prevent costly, irrational changes to your strategy.
28
In games with slow feedback loops, data helps you keep your
sanity.
So, if you’re not going to fall into the trap of making bad strategy
adjustments when we hit a bad stretch of variance, what other form of
control do you have?
You have control over your relationship to variance.
And deeper than this, you have control over your relationship with
time.
Stay with me.
Variance is basically a function time.
If you truly understand the law of large numbers, then your logical mind
should believe this statement:
"I trust that over the long-run, my results will have very little to do with
luck."
But when the downswing hits, you tend to lose faith that things are
going to turn around.
If your logical mind is on board with variance, how come you’re
still shaken by downswings?
There must be something else creating an inner conflict.
A hidden belief that creates a toxic relationship with variance -particularly, the amount of time it's taking to overcome
variance.
29
If we keep approaching this logically, we could prove that our need for
speed is an unrealistic expectation. After all, variance is allowed to "take
its time", and there's nothing objectively unfair about that.
But this logical approach doesn't help soothe our frustration.
We need a different fix.
So what is it?
What we need to do is repair our relationship with time, so that we
can approach our career in harmony with it.
Here's the million dollar question:
How do we get ourselves to play professional levels of volume, in
harmony with time, while weathering the inevitable storms?
And here's the answer:
We make time our ally, by re-framing it as a new dimension of edge.
The old version of you thought time was an obstacle.
Most of your opponents feel this way too.
But time is not an obstacle, it is the very thing that provides you with a
consistent edge over opponents who have a negative relationship with
it.
30
Opponents who don't relate to time as part of their edge.
Instead of seeing time as an obstacle and feeling like the Poker Gods are
punishing you, you can flip the script and make it a lethal weapon in
your arsenal.
By swimming downstream with time, you will begin to leverage it
against opponents who are wasting valuable energy fighting upstream
against it.
Can you see how this causes them to self sabotage?
When a player is in conflict with time, there are three main outcomes:
1. The player will desperately look for ways to adjust their strategy,
and usually replace good parts for bad ones.
2. The player will begin to drift into submissive, risk-averse behavior
at the table (with occasional tilt explosions).
3. The player will become so overwhelmed that they literally cannot
show up to play.
This wide-angle view over how your competition self sabotages is what
gives you a new understanding of the long-run edges available in poker.
Edges that can only be gained by transforming your relationship with
time.
To recap, here are the 3 mindset principles that will put you in
alignment with a healthy, robust poker career.
31
1. A stable, data-driven decision-making process.
2. Solid bankroll management
3. A cooperative relationship with time.
Aligning with these three principles will transform your earning power
as a poker player.
By mastering your relationship with each principle, you will strengthen
your conviction in the outcome of your career and begin to feel at ease
on your journey.
You're headed to the top, and that's not negotiable.
Variance cannot stop you.
Time cannot stop you.
They will become your allies, and you will relate to them as assets that
can only strengthen your edge.
Guess what?
You’re crushing the guide.
Just one last chapter to bring it all together!
32
Congratulations on reaching the last chapter of this guide and reviving
your poker career in the process.
In less than an hour of reading, you have been introduced to a powerful
strategic framework that will take you as high as you wish to fly.
You now understand these key concepts:
● How a data-driven strategy is designed
● The type of playing style that it produces
● Why it's so effective against the competition (even at the highest
levels)
● Key mindset shifts that will allow you to increase your risk tolerance
● How variance and time operate as assets to your long-run edge
With your framework transformed, you're ready to set out on a path to
high stakes.
A couple of quick words of advice on how to proceed from here:
33
I mentioned earlier that when I built Poker Detox, I outsourced all of the
strategy work to gifted data engineers who were far more capable than
I was.
I did that because I valued my time, and I wanted my strategy to be as
accurate as possible.
I also trusted that in the long run, it would pay for itself many times
over.
What I'm trying to say is that it is possible for you to build a data-driven
strategy from scratch, but it is also extremely tedious and
time-consuming.
Especially if you're not technologically gifted.
I have seen many players try, and many who either failed or got it
wrong.
In my case I was lucky.
I had the funds to pay qualified experts to build the strategies.
But what always stuck with me was this:
What the hell would I have done if I couldn't afford help?
The things I felt from considering that question were what drove me to
create the coaching-for-profits division of Poker Detox (CFP).
I wanted to create a format of training that could save others from the
confusion and overwhelm that nearly destroyed me.
I was extremely lucky to have found help at a time when I had access to
funds to pay for it.
But I also know that many people don't have that luxury.
34
What my team and I have created at Detox is a training program that
allows students to access our full strategy and community support
channels for zero money down.
That's a structure that a younger, more desperate version of me would
have been able to grab onto as a lifeline, even with nothing left to
spend.
Solving this problem for our industry has brought me more fulfillment
than any achievement, and I would love to share it with you in a more
personal way.
If you found this success guide helpful, I can’t stress enough how much
it has only scratched the surface. The Poker Detox 30-Day Training Camp
is a new cohort-based boot camp and the entry point for anyone who
wants to become a successful pro. We run this camp at the beginning of
each new month and it's available to the public.
If you've made more than $50,000 in the last 12 months and are ready
for personalized high stakes coaching with unlimited 1-on-1 support,
you can head over to this link and fill out an application.
35
Thank you for reading this entire guide. I understand how valuable your
time is, and I don't take that lightly.
Wherever your journey leads you from here, I hope you get what you
want out of it.
You are worthy of success beyond your wildest dreams, and with the
right attitude, there is truly nothing that can stop you.
Now is the time to decide if poker is the dream you wish to pursue!
Strength and honor,
36