MDA Poker: How to use Mass Data Analysis to crush games
In modern online poker, professional players use advanced tools and techniques to stay ahead of the opponents. One of those techniques is MDA - Mass Data Analysis, which allows you to thoroughly investigate your opponents' game tendencies.
What is poker MDA?
Mass Data Analysis is a way to analyze a game using a large collection of data (that is, hand histories). How it works in simple words: you load a huge number of hands (for example, several million) into your tracker, and then use special functions to visualize the data so that you can work with it and draw important conclusions about your opponents’ game.
What is Range Research?
Another common term you might probably have heard before is Range Research. This used to be the name of a special section in Hand2Note 3 software, where you could implement the "research" of the pool - in other words, MDA.
In the newest Hand2Note 4 version, the architecture has changed slightly, and now there is no separate section for MDA, you can perform it smoothly right from the Statistic or Reports tabs (We’ll explain how later in this article). So the name “Range Research” has been eliminated and no longer relevant. However, due to the already gained popularity, many players continue to call mass data analysis using Hand2Note the term “Range Research”.
Why is it important to include MDA in your regular out-of-table work?
MDA is one of the most powerful techniques that you can use to gain understanding of how different types of players behave on the table in various game scenarios.
You can spend hundreds of hours on solvers, but don’t forget that eventually you play against humans, and all the money in poker is made by exploiting their mistakes. GTO gives you the understanding how to be break-even against GTO opponents, and MDA will show you in what scenarios and to what extent your opponents deviate from the GTO. This is crucial knowledge that will help you to adjust your strategy and exploit your opponents way more efficiently, and as a result, win a lot more money.
Combining GTO learning with MDA will make you unstoppable at the tables.
Getting started with MDA
Alright, now you understand the importance of MDA analysis and you are ready to move on to practical steps. Where to start? Don’t worry, we'll guide you step by step.
First, you need Hand2Note
This is the most advanced poker tracker software on the market, and the only tracker that provides you with MDA features. If you were using a different tracker before, it’s a sign to switch to a more modern software 🙂
So, download and install Hand2Note.
Second, you need good popups to visualize the data
To get started and understand the process, for now you can use default Hand2Note popups - they offer a basic set of stats. But as you go deeper with your research, you’ll need more stats for more specific scenarios - different sizings, board textures, etc. At that point you’ll need professional popups that you can find in our store in the Research category.
Please note that rebuilding a large database might take time, so better install needed popups in Hand2Note, and then proceed with hands importing.
Third, you need a bunch of hand histories
We recommend having at least 300k hands in your database, but the more the better. Collect hand histories of your games and upload them to Hand2Note. If you don’t have enough hands played by yourself, you can buy hand histories online (google "poker mining").
Now the preparation steps are over and you’re ready to perform MDA.
MDA Poker: How it works in practice
Now let’s move to the action. Let's look at 2 specific scenarios in which you can effectively use mass data analysis.
1. Studying recreational players game
Playing against recreational players (or in other words, fish) is one of the most important skills of a professional player. While rake eats up all possible profit from reg wars, fish are the real source of your income in poker. Therefore, the ability to understand the patterns of thinking and behavior of recreational players is the most valuable knowledge that directly affects your win rate.
In most cases, we won’t have a representative sample in your HUD for a specific fish opponent (obviously, because they don’t play as many hands as regular players). That’s where MDA comes to aid - we can combine many similar players from your database and investigate their lines in various spots. By doing it, we’ll be able to make specific conclusions about the whole group of players, and exploit them very efficiently and aggressively.
Let’s jump into Hand2Note and see how it works. Go to Statistics or Reports tab, click on the current player nickname, and then select Multiple players. Then you need to set the parameters to filter out the needed group of players. In our example we set a simple condition VPIP > 40 to filter fish in general, but you can also set more parameters to filter more precisely. Once you done with filters, click Apply.
Once the report is built, you'll see a popup with the combined data of all players who fall under your filters. Now you can investigate how these guys play.
We also recommend reading the article Exploiting Fish: Donkbets lines. It'll show you another exampe of researching fish game with MDA and give you useful insights on various donk bet lines.
2. Analyzing specific hand
Another scenario where you can apply MDA is your regular hand reviews after the session. Imagine you’re reviewing the hand played against a recreational player, where you had a tough decision to make. You know that your opponent is a fish, so it doesn’t make sense to think about your range value/bluff ratio and all those things. The only thing that matters here is pure exploit. So it would be great to know typical range composition for that type of player in that line, right?
Again, we’ll use MDA for that. We’ll go and filter out all similar players from your database and then check how they usually play in that particular spot. The filtering possibilities in Hand2Note are endless, so you can even set a similar board structure and bet sizings. It all just depends on the sample you have. The more specific the filters you set, the larger your database should be so that there is a representative sample of the filtered situations.
Shaping your preflop strategy with MDA
Now let's take a break from studying recreational players and think a little about your game, starting preflop. What are your preflop ranges? How do you construct them? Many players don’t pay much attention to the Preflop, thinking they already know everything about it. They play their ranges semi-automatically Preflop, and only start using their brain on the Flop or Turn.
This is fundamentally the wrong approach. Preflop is the most important street as it shapes your strategy on the following streets. Errors and minor flaws preflop are the least noticeable, but the most important from the point of view of your strategy. It is critical to have a perfectly built preflop in order to use all situations to your advantage and feel confident on subsequent streets.
So let’s get back to your ranges. Most likely you have a common base from solvers, but then you should adjust them for the field you’re playing in. For example, you have a standard open raise range from Cut-Off position, but this range should definitely be wider, if we have a recreational player sitting on a big blind, right? Ok, but how much wider? What hands we can still open to extract additional EV from the fish, and what hands are not worth entering the game even if they look playable? If you don't have a clear answer to this question backed by reliable data, you may be losing a lot of money due to your unstructured preflop decisions.
So, how to structure your preflop play?
First, investigate the pool's preflop ranges using MDA
To build your perfect preflop strategy, you should know how your opponents play preflop. Using this knowledge you'll be able to adjust your ranges for maximum exploit of the pool leaks. You'll be able to effectively expand your ranges from GTO, when there is an opportunity to extract additional EV, and sometimes narrow your ranges, avoiding losing plays with hands that seem worth playing according to the solver.
So we again jump in Hand2Note to see how the opponents construct their ranges preflop. For example, this is how people in early position defend vs 3bet from blinds on average: (Note that these ranges may vary depend on your poker room, game type and stakes, so perform your own research)
Now that you know how people on EP defend on average, you can adjust your 3betting ranges from the blinds, so you have more edge against your opponents postflop.
Second, create your structured preflop strategy based on GTO and MDA data
You can use the Freebetrange tool to create your preflop strategy in a powerful range editor. Take solvers solutions as a base (some are available in the app right after signing up) and add specific adjustments to each range using the knowledge you gained from MDA.
Conclusion
We've looked at MDA and the basic ways to use this powerful tool to improve your game. Now that you have a basic understanding of this technique, be sure to integrate MDA into your poker training routine.