Poker has elements of luck, but there is a significant amount of skill in making correct decisions. The invention improves poker analysis and training, with embodiments running in an Internet browser. Some embodiments incorporate “dynamic analyses” that examine the user’s long-term strengths and weaknesses.
Prior to the invention of this technology, poker analysis was primarily accomplished by static analysis. Static analysis is a method of analyzing a specific decision point, and giving advice to end users on what they should do at that moment. This can be useful but is not particularly valuable for an average poker player who has to make a number of decisions throughout a whole session.
On the other hand dynamic analysis analyses a set of complete decisions that occurred during the course of the poker session. This is a more powerful way of analyzing a player’s performances. Dynamic analysis can also detect errors that static analysis cannot.
The idea behind dynamic analysis is that you can use a collection of statistics gathered during the poker session to rank a player’s performance across various categories, such as post-flop play or bluffing. The average or median score for all users in the same poker session is then used to compare the performance of an end-user.
One approach to this problem has been to use a neural network that attempts to develop GTO (Generalized Optimal) solutions for different types of poker hands. This requires massive computing power, however, and it is not practical for many situations. Other approaches have focused on tree building software that evaluates the expected value of different game trees. While this has improved over time, it is still a relatively labor intensive process that does not produce an overall poker skill score or ranking as described above.
The present invention makes use of a combination between dynamic and stat analysis to improve the training and coaching of poker players in a way that is easy to understand for end-users and that can be implemented on a massive scale. Some embodiments are also available in Internet browsers, making them more accessible to all players. The inventive analysis engines are also more flexible than previous systems which have relied on static rules or huge lookup tables. This makes them more adaptable to a wider range of poker games and situations. The algorithms of the present invention provide a useful and comprehensive picture about a poker player’s strengths and weakness. These information will allow players to make better decisions, both short-term and long-term. Ultimately, all poker players will benefit from improved winning rates.