How do you measure poker bot strategy performance and success?

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Poker bots have changed online poker play. Not only are they great learning tools, but the best poker bots can play entire games for you, and with some of the latest ones, even play multiple tables at once, unattended.

This sounds like a great way to earn money from poker, and it definitely can be, but only if the poker bot wins over the long term for you. So how do you know how good a poker bot is and whether it will be a success for you?

How effective can an online poker bot be?

For a long time, poker bots were very good at playing poker, but not so good at winning against real players. The introduction of AI changed all that though, going from basic mathematical formulas to intelligent systems that adapt to solve the game as it develops. As a result, the best bots today can generate reliable profits over time. No single poker bot is going to turn you into a multimillionaire overnight, but they can generate regular profits, and when playing multiple tables against the right kind of opponent, that can be a substantial amount. The ability to beat professional players transformed the bot experience and from there set new expectations for them to challenge.

When we consider that most studies approximate that  around 90% of all online poker players lose money over time, a poker bot that can show a profit is more effective than you might think, but it is still important to know exactly how well a poker bot is performing.

Importance of knowing how to calculate poker bot performance

How can you know whether you are achieving success or not if you don’t measure performance? This is the logic at the heart of the need to measure poker bot performance, but there are several more reasons why it matters.

Poker bots are not just a ‘fit and forget’ tool, they learn as they play, and most have different profiles that adopt slightly different approaches to the games. By measuring success, you can see where the poker bot is doing well, and just as importantly, perhaps spot areas where things could be improved.

A bot is more than just a game solver. Where traditional solvers simply looks at the probability of outcome, machine learning bots apply game theory strategies, considering many variables that make them truly capable players. But they are not superhuman, nor are they impossible to beat. The longer they are in use, the larger the database, the more of an edge they develop. 

The holy grail is of course to employ GTO strategies that make the chance of losing so low it is practically impossible, but to do that the bot must adjust to players and situations as they come up. There is no single solution to becoming a poker champion, to be a winner the bot must make the right choices every time. From the buy in through the pre-flop to the flop and beyond, spotting exploitable situations is always crucial, and that often needs refining to achieve.

Without measuring performance, you cannot know where things can be improved, or where things are going wrong.

What is Success in the Context of Poker Bots

Defining a poker bot’s performance really depends on what you are doing with it. A poker bot being used as a trainer might be a success if it strengthens your game in a specific area, or helps you understand pre-flop betting better for instance.

However, for most of us, a poker bot is there to make money. In this context, success is a profit over time. Now, we all wish that there was a magic button we could press that meant a poker bot wins us millions of dollars a week, but while the technology is incredible, we do have to be realistic.

For a poker bot operating in a way that doesn’t attract attention, consistent wins across multiple tables in lower entry games can generate a reasonable income, and that would be considered a success. However, we should look at that profitability over the long term, because in the short-term performance on an individual table can be affected by outside factors.

A Guide to Factors Influencing Bot Performance

The key with poker bot success is to evaluate performance over time. Taken in isolation, a player who knows how to recognize a bot and apply an appropriate strategy could decimate win rates for a few hours at a table. But does that reflect overall performance? No, and we should not fall into the trap of thinking these small samples ever do. These changes in performance in the short term are known as variance, but our goal when assessing a bot is to find a benchmark level that they can maintain over time.

Poker bots can be affected by most of the things human players are, from simply better players at the table to just a streak of bad cards that leave little opportunity to win. As they build their database, bots are in effect learning the game too, so can have periods where play is not as optimized as it could be here as well.

However, conversely, a poker bot never gets tired or loses focus, so they do have advantages over human players that can make a difference.

Key Metrics for Evaluating Bot Success

Understanding poker bot performance means asking the right questions, and that begins with the metrics you need to be looking at to give the best understanding of what’s going on. The most obvious number to look at is win rate, which is usually expressed as the number of big blinds won per 100 hands of poker.

However, while win rate seems like it offers all the answers, that is not strictly true. You can have a great win rate and still not make much money, and if we are running poker bots to make money, then it is profitability that matters even more. Of course, you need a good win rate to achieve profitability, so the two are connected, but profitability gives you a better idea of how good a poker bot is at making money.

There are other metrics to consider too, hands played, VPIP, PFR, hand strength and aggression can all provide good insight into the kind of game a bot is playing and where it is delivering optimal strategy choices. To assess success though, win rate and profitability are key.

Calculate Win Rate and Profitability

Working out a win rate is relatively easy. As an example, if your bot plays 500 hands and comes away with $50, at a table where the big blind is $1, then they have win a total of 50 big blinds in 500 games. Divide that total by 5 to get the BBs per 100 games, and you get 10BBs per 100 games win rate. The formula for that looks like this:

Win rate in bb/100 = [Profit in bbs  / Number of hands] * 100

Anything above 0 is, you may be surprised, above average, but if your poker bot is operating at between 10 and 30 BBs/100 games, it is performing exceptionally well.

Profitability is different, and this quantifies how much you are making, the amount of return on your investment. You can calculate this by dividing your overall profits from the poker bot by the amount spent on buy ins, expressed as a percentage. That formula is as follows:

[winnings – buy-ins] / buy-ins * 100 = The return-on-investment percentage

If your poker bot is generating above 10% ROI profitability you can consider it performing well.

Use Poker Bot Hand Strength and Aggression Metrics

Effective hand strength metrics are really useful when evaluating performance as they allow you to place your actual results in context. Think about this, your bot plays 100 hands at table and wins 40BBs. That looks like a highly impressive performance.

But what if hand strength metrics show that out of those 100 hands, the poker bot had the best hand in 70 of them. Is 40 wins a good performance then? It might still be, but it could mean that even that level of win rate is a bad return. That is why context matters, and so we always need to look beyond the outcomes themselves to understand what is going on.

It is a similar story with Aggression Factor, the metric that measures the ratio of aggressive actions to calls in the bots overall game. If the AF is 2, that would mean that the bot bet or raised twice as many times as it called. For tournament players, it is generally accepted that this ratios should be at or close to 3, however it really does depend on the tables and players to what is an optimal value. If your bot is more than 2, then it is working well and looking to maximize card advantage where it can.

Performance Against Different Opponents and Player Types

Perhaps the most important context of understanding bot performance is in who they are playing. Against newer or inexperienced players bots can be much mor effective than an opponent who understands bot strategies and knows how to adapt to their game. Deploying bots in the right games and tables is a key part of finding success with poker bots.


What are the main metrics used to measure the success of a poker bot?

Win rate, profitability, Effective Hand Strength and aggression metrics all allow accurate assessment of overall performance.

How do poker bots adapt to different opponent strategies, and how is this measured?

AI algorithms constantly learn and adapt to different scenarios and strategies, and we can see this through aggression metrics and performance in different games and player types.

How do poker bot developers test and evaluate the performance of their creations before deployment?

They play 1000s of hands either in a controlled environment such as using historical game data, or even against other bots to build a database and assess performance before use on live games.


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