SMG Studio

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It may also be worth noting that they said they now have a system in place that records highly unlikely outcomes so they can keep track of how often they are occurring in actual gameplay. This means they are at least not blind to the results their new algorithm is producing and should hopefully be able to asses things and adjust accordingly.

Good news guys. I have been in contact with support about the heavily biased 1's issue and they have acknowledged that this was a glitch in the 1.9.36 release. They say they have remedied it as of the latest release that came out today, so hopefully this should no longer be a concern.

The issue is that SMG changed the algorithm (hence this thread) and they should explain how and why, and open-source the code. I have actual data demonstrating that the "fix" does indeed produce unrealistic results:

• Prior to the update (v1.9.36) I had 162 games on record, and the in-game statistics showed an even 17% distribution among dice rolls, as one would expect with such a large data-set.
  
• I have played 76 post-update games and despite those games representing less than ⅓ of total games played on my account, they have already impressively skewed my roll statistics which now report 19% for 1's, and an even 16% for all other numbers.
  
• This is irrefutable empirical evidence that the "fix" is flawed. We are talking about a sample size of hundreds of thousands of rolls, so people can shut up about freak improbabilities.Furthermore, the fact that the latter ⅓ of post-update games so heavily skewed what was previously an even distribution among pre-update games, and that the skew was entirely toward only a single number (1 in this case), proves beyond any reasonable uncertainty that the new algorithm biases rolls specifically toward 1 for some reason, and does so in a very consistent manner.
  
• There are also several anecdotal accounts in this thread further suggesting a bias specifically towards rolling 1's since the update.
  

I agree with Glenn's observation that the "fix" is likely a modification that intentionally disadvantages rolls for small armies being attacked by dramatically larger ones, much more so than the attacker's advantage inherent in the natural probabilities. When I have seen large armies attack post update they almost always win, and usually by a gigantic margin. You would expect to see large armies have a completely failed attack on occasion, even to much smaller forces.

This was probably done to shut-up people complaining about the algorithm being broken in the first place (which my data suggests was never the case, as the statistics were perfectly even pre-update) just because they had large armies destroyed by blitz-attacking smaller ones as on a few occasions. 

Also, any programmer knows that the pseudo-random generation of numbers is a function built into the framework of the programming language itself. Therefore, the "roll algorithm" is a very simple feat of building out the 1/6 per die probabilities, there should be absolutely no way for such a simple thing to be flawed in the first place. Ergo, no "fix" should have been required, suggesting that the "fix" was actually a modification that made the probabilities far LESS realistic so that people wouldn't get as upset when chance did not favor them.

So there you have it, I have presented very strong empirical, logical, and anecdotal evidence that the algorithm was never flawed to begin with, and is now flawed as of the "fix." People should stop arguing about probabilities and demand that SMG explain what they have done, why they have done it, and make that portion of the code open source already so people can stop with the asinine complaining.

The only idiots here are the ones "explaining" that probabilities don't preclude long-tail scenarios… You also going to tell us the sky is blue and poop smells bad?

Thanks for the life lesson Justin.

My post was in response to Richard's post immediately before mine.  Has nothing to do with your demand to see the algorithm. There is more than one issue here.  But I appreciate the feedback.