Originally Posted by unowme View Post
Contrary to what the OP might think there isn't a single type of 'Math' that might apply to all gambling situations. There are branches of Math and indeed branches of probability theory. I sincerely doubt that KJ really understands 'The Math' but just goes with the math that others have worked out based on traditional statistical analysis. And that's ok.

I just wanted to point out another Mathematical possibility here. I've always thought that Bayesian Inference might be an alternative way to look at some gambling scenarios. This analysis is based on Bayes Theorem first posed by Mathematician Thomas Bayes in 1763. Bayesian analysis allows one to update conditional probabilities based on observed outcomes and then predict future outcomes with confidence levels based on those observations.

Here is Bayes Theorem in all its glory.


How can this be applied to gambling? Traditional analysis strictly based on probability theory makes a number of assumptions that may not be true. Like the roulette wheel is really random with no flaws or that the cards dealt are also random or that the random number generator in a machine is really random. But what if that's not true? Or what if you don't know all of the variables in the game? Bayesian inference might allow APs to adjust their play based on observation of outcomes by uncovering a lack of true randomness in the game itself or by overcoming a lack of knowledge on the inner workings of the game.

Now, is Spikes 'outcome-based method' steeped in a deep understanding of this alternative math? Unlikely. But that's not to say there might not be some basis for his outcome-based approach. What I do know with certainty is that Bayesian Inference and analysis has enjoyed a resurgence with some very high-end machine learning projects and it's becoming a key part of some new AI systems. And that's no fantasy.
Probably the best post in this thread. Spike, are you ready to reveal your method?