Thank you for your interest in Nachos Poker CFP! In this page you will find a summary of our approach to poker, an overview of the team structure, the contract summary and what you can expect from us to help you to achieve your poker goals.
Ever since solvers became widely available around 2016/2017 the common approach for improving at poker has been to study with these tools to gradually improve our play on every node of the game tree. Learning defence thresholds, value betting thresholds, C-betting and barreling range constructions, sizes, frequencies, the list goes on and on. Poker is a very complex game and solver outputs are way too complicated for the average player to learn, and so simplification of outputs and creation of heuristics helps to make sense of the equilibrium play and provides a framework for executing in game.
The only issue with this is that we are studying a model with fixed inputs and looking at the outputs for that particular model. Change the inputs, and the output changes (sometimes significantly) therefore how can we even be sure we are studying the correct model in the first place?
This is where an approach based on Mass Data (MDA) can provide additional context to the puzzle you face whenever the action is on you in game. We dissect a database of (currently) 220 million hands played across various sites and stake levels to see how humans are actually playing on average. Once we create a model with how the average player plays a certain node, the highest EV response can be vastly different to the equilibrium model, so having this information is vital to maximising winrates. More information is always better to inform your decisions.
You may have heard this phrase in your poker studies in the past and it often is part of an explanation of how a solver maximises EV against itself in an equilibrium model range vs range. At Nachos Poker we will show that in reality most of your EV will come from exploiting recreational players for the highest winrate you can, and solvers are pretty ineffective here due to the erratic ranges and postflop decisions that they make. MDA can help paint a picture of the common leaks that recreationals (and regulars) make in game, and how to maximise your EV in these spots.
This is not say that we don’t use solvers, far from it in fact, we simply look at solver outputs, layer on top the additional information we have from studying how regulars and recreationals play, and then come to the highest EV decision we can make in each spot. We just effectively change the inputs to a more accurate representation of reality.
Nachos Poker started in April 2021 and over this time we have expanded from a handful of players to a team of 70+ players. In order to keep the learning environment effective and player focused, you will be entered in a new recruits discord which is for new players entering our team. This will be a smaller sub team that is allocated a dedicated coach and overseen by the team manager and head coach. You will have access to materials in a staggered fashion over time, ensuring you master the foundational strategy before adding in additional complexity.
As you move up stakes and reach certain profit thresholds then you will be eligible for additional content that will help you refine your strategy vs tougher opponents, unlock 1 to 1 coaching and once your recruits phase is over you will join the rest of the team. This approach will enable you to get more individual feedback in the early stages of your contract and enable you to be part of a small class environment, allowing you to form bonds and work with your team mates.