Happy Holidays to all from our family at Fuller Systems to you and yours. What an exciting year it has been, and in appreciation to everyone for the support we are offering our "Cribbage Pro Online" for Android at a significantly reduced price (just 0.99c USD).
Download now, this price is only going to be out there for a few days!
If your on your Android device now, click here to go directly to the download.
Thanks again to everyone for your support,
Josh
Saturday, December 25, 2010
Thursday, December 2, 2010
New Bonus Program - Help Us Help You With Cribbage Pro Online Bug Reporting
Today, we are announcing a new way that you can help us make Cribbage Pro Online play even better and at the same time get some extra points to brag about. Whenever you encounter any issue in online play, if you report the issue through our online reporting form, and we verify it as an issue on our servers (meaning it was not just someone disconnecting/quiting from the game), then we will award your account with some extra bonus points. Reports verified will result in 15 bonus points awarded to the first reporting player for each incident up to 4 incidents per week and 10 per month up to a life time maximum of 350 points. Reports must be filed within 8 hours of the error to be verified and counted. An email will be sent to the account holder after points have been awarded or with any follow-up or clarification questions as needed. Only the first report of any incident will be counted, and all reports must be complete/valid entries.
Start reporting any issues when they happen by going here:
http://bit.ly/cribBug
We hope you all find this helpful as well, and we look forward to working out any remaining bugs you all may find to help make the game even better for everyone.
Happy Pegging!
-- Josh
P.S. The bonus program is for a limited time only - so start now!
Start reporting any issues when they happen by going here:
http://bit.ly/cribBug
We hope you all find this helpful as well, and we look forward to working out any remaining bugs you all may find to help make the game even better for everyone.
Happy Pegging!
-- Josh
P.S. The bonus program is for a limited time only - so start now!
Tuesday, October 26, 2010
Over 250,000 Downloads And Going Strong!
Thank you all for your support of Cribbage Pro on Android! This morning we were pleased to find out that we had crossed the 250,000 download mark for the game in well under a year of release. All the community feedback and support is what has really enabled us to get here, and honestly we could not have done it without you all.
With that, we think it is about time to start talking about the next release that is coming as we are nearing the final phases of testing now. So here are some things coming in this next release:
With that, we think it is about time to start talking about the next release that is coming as we are nearing the final phases of testing now. So here are some things coming in this next release:
- Much improved network stability and reconnection in online multiplayer
- We heard you here, and we have found even more ways to improve network stability in the flaky world of mobile devices. It's not perfect, as nothing in the mobile data services area ever is, but we really think you will find it to be a vast improvement.
- A new "landscape" horizontal mode of display - rotate your phone and the game flips and rotates too.
- Think of this as the "left handed" players mode. The screen layout changes around quite a bit to "make sense" in a horizontal frame, but we think it works out quite well and are looking for any feedback you all may have if you find anything less intuitive.
- A new "dealer" indicator icon on the avatar of the player who is currently the dealer in the game
- Another common request, and one that frankly we should have delivered sooner. The new "D" dealer icon will show next to the player who is the dealer as yet another indicator of who has the crib and who is "dealing" the cards.
- A new method to selecting the "cut depth" when cutting cards that involves a spread out deck and a tap inside of the spread deck to select the card to cut.
- The old "Tap to set cut depth" thing just was not working for us anymore and many have suggested a simple spread out of the deck to pick a position to cut to. So, that is what this is. The cards are simply laid out left to right with each card in the deck represented. We also enforce the minimum and maximum cut depth rules of the game of cribbage as well - meaning no cutting less then 4 cards and you must leave at least 4 on the bottom.
- We now do a "cut for first deal" - so you will be cutting a card from a "real" deck to pick who is the dealer first (low card wins) to better match a real word scenario and to better comply with the rules of the game.
- Randomly selecting who was the dealer on the first play probably was yielding the same results as this now will, but this new method is actually a rule in the game of cribbage and we think really brings the feel of play into a more complete experience. This change applies to both single player and multiplayer versions.
Aside from all these new things, there are also a few bug fixes and improvements inside in the game and on the multiplayer side of things too. We are really excited to bring this release to the market soon, and hope you will find it as exciting as we do.
Thanks again for your support, and as always leave us comments here or email us with any ideas or questions you have.
-- Josh
Friday, July 16, 2010
Cribbage Pro Shuffling & The Deck
So lately it seems popular to dig on the shuffling and claim that we are stacking the deck in the game to favor the computer player. I'm not going to go into the "Hey, your game cheats" thing again and why people like to think the computer is cheating when they lose, and I'm not even going to go into how the game does it's dealing "every other" and so it could not favor one player over the other. However, I do want to give everyone some actual data points for the discussion since it seems lots of people are excited about it. So, for this post, here is some information you may find interesting.
As was mentioned in our last post about the latest release, we use a random number generator in single player mode to randomly pick the location of a card in the deck - so as to shuffle it. In multiplayer it is from a truly random source, but in single player we use a "tried and true" pseudo-random source based on the "Mersenne-Twister" algorithm. So what does that really mean though, what is the result of doing that and what does it look like? How fair is it really?
Let's define some terms first. We will call "fair" something that in all measurements possible seems to be as truly random as possible. You may have a different opinion (like it should match a real world shuffle as strictly as possible), but we will not go there for this as there is so much debate about that single point we will simply not solve it here. The second definition is around a "shuffle". For this argument, let's agree to define it as the process used to randomize the deck.
So, without further delay, here is the code that does our "shuffle" in the game today:
rand = new MersenneTwisterRNG(new SecureRandomSeedGenerator());
//Loop through each card giving it a random value
for (int n = 0; n < cards.length; n++) {
cardList.add(new Card(cards[n], rand.nextDouble()));
}
//Sort by that random value
Collections.sort(cardList, new CardSortByKey());
//Store back into the byte array
for (int n = 0; n < cards.length; n++) {
cards[n] = (byte)cardList.get(n).getOrdinal();
}
There you have, it. That is all there is to it and it is a direct copy/paste out of the game with comments and all. If you can't read it, what it is doing is first getting that random number generator I mentioned earlier, the MersenneTwister. It "seeds" it with the same kind of source that is used for security on your phone (that SecureRandomSeedGenerator makes that happen). It then takes random numbers from that and assigns one to each card, and then it simply sorts them by that key and stores each card in that location.
So that is the shuffle, but what does the result look like? Well, one of the best ways we have found to show this is in what is called a scatter/plot graph. Now we could show you every single card and where it landed, but trust me, you could not read that graph/chart. So what we will show you is what we will call "marker cards" in the deck and where they ended up after the shuffle. We chose the "ace" card from each suite for that purpose, but we could just as easily choose anything else, it was just convenient to do that. The data shown here comes from thousands of real games played - not just us running this in some test area - these are real decks used in real games that some of you may have played. We got the data from a set time period and stopped at 3,000 decks. Going anything beyond that and it gets very hard to visualize, but we could of course go to many, many more.
OK, you have been waiting patiently. So here is the first graph:
This graph is for the ace of hearts (AH) and it is showing for 3,000 decks, where that AH was in the deck from position 0 (top of the deck) to position 52 (bottom of the deck). As we progress this forward, what you end up getting is what really is a very random distribution. If there was an inequality, it would stand out as a cluster surrounded by a lot of blank space - and you really just don't see that in the data. Sure, there are some spots that are darker the others and some not as much, but in the end if you zoom in even, you will find that distribution is quite random from one deck to the next. Just for informational purposes and to complete this thought, here are the other cards as mentioned:
So that is the distribution of cards in the deck as given by sample marker cards in the deck. You will see that none of them look the same, this is a good thing, but at the same time don't get too into these and try to read much into them either. I show it here to say simply this - it is clearly not favoring any deck position over any other.
There is one other way that we have found somewhat helpful in determining not just how distributed a card might be from one deck to the next, but generally how "well shuffled" a given deck might be. To do that, you can consider looking at what the relative position of a card is now against the card that is positioned next to it in the deck and it's original position. In other words, two cards may have started as being 1 position away from the other card, but how far apart are they now? So if you give each card a starting point form 0 to 52 and then you shuffle and see how far apart they are from where they started relative to each other you get an interesting picture. Of course the theory here is that the more that the results show some random separation, then it could be considered a good shuffle. There is no "gold standard" we have found in this regard, and maybe we are the only ones looking at this. Anyway, here is our data of a deck we grabbed from one at random:
Here it is averaged over 5 decks:
Now over 10 decks:
Do you see a trend? Here is at the 3,000 mark:
Yeah, we think it looks cool too. It is almost smiling at you. In fact, I think it looks like a really good distribution in a good random shuffle. Things settling towards being equally distributed in the deck on average - meaning each has a decent chance of showing up just about anywhere. If you consider the possible combinations of a deck, the more you approach that factorial, the more this graph will get flat. That is exactly what should happen. I think that is cool.
Feel free to add your interpretations in the comments or ask for other views of the data. If you have some cool graph you would like to see (and you can tell us how to construct it somewhat), we will do what we can to give it to you.
In summary, we think this shows two very good perspectives on the shuffle and distribution in the game. We hope you found it interesting and informative. We think it fairly clearly shows the unbiased nature of the game and the random nature of it's results. I'm sure some will disagree, but if you do disagree, please let us know why and what you may find more compelling or if there are parts we did not discuss that you would be interested in knowing more about.
-- Josh
As was mentioned in our last post about the latest release, we use a random number generator in single player mode to randomly pick the location of a card in the deck - so as to shuffle it. In multiplayer it is from a truly random source, but in single player we use a "tried and true" pseudo-random source based on the "Mersenne-Twister" algorithm. So what does that really mean though, what is the result of doing that and what does it look like? How fair is it really?
Let's define some terms first. We will call "fair" something that in all measurements possible seems to be as truly random as possible. You may have a different opinion (like it should match a real world shuffle as strictly as possible), but we will not go there for this as there is so much debate about that single point we will simply not solve it here. The second definition is around a "shuffle". For this argument, let's agree to define it as the process used to randomize the deck.
So, without further delay, here is the code that does our "shuffle" in the game today:
rand = new MersenneTwisterRNG(new SecureRandomSeedGenerator());
//Loop through each card giving it a random value
for (int n = 0; n < cards.length; n++) {
cardList.add(new Card(cards[n], rand.nextDouble()));
}
//Sort by that random value
Collections.sort(cardList, new CardSortByKey());
//Store back into the byte array
for (int n = 0; n < cards.length; n++) {
cards[n] = (byte)cardList.get(n).getOrdinal();
}
There you have, it. That is all there is to it and it is a direct copy/paste out of the game with comments and all. If you can't read it, what it is doing is first getting that random number generator I mentioned earlier, the MersenneTwister. It "seeds" it with the same kind of source that is used for security on your phone (that SecureRandomSeedGenerator makes that happen). It then takes random numbers from that and assigns one to each card, and then it simply sorts them by that key and stores each card in that location.
So that is the shuffle, but what does the result look like? Well, one of the best ways we have found to show this is in what is called a scatter/plot graph. Now we could show you every single card and where it landed, but trust me, you could not read that graph/chart. So what we will show you is what we will call "marker cards" in the deck and where they ended up after the shuffle. We chose the "ace" card from each suite for that purpose, but we could just as easily choose anything else, it was just convenient to do that. The data shown here comes from thousands of real games played - not just us running this in some test area - these are real decks used in real games that some of you may have played. We got the data from a set time period and stopped at 3,000 decks. Going anything beyond that and it gets very hard to visualize, but we could of course go to many, many more.
OK, you have been waiting patiently. So here is the first graph:
So that is the distribution of cards in the deck as given by sample marker cards in the deck. You will see that none of them look the same, this is a good thing, but at the same time don't get too into these and try to read much into them either. I show it here to say simply this - it is clearly not favoring any deck position over any other.
There is one other way that we have found somewhat helpful in determining not just how distributed a card might be from one deck to the next, but generally how "well shuffled" a given deck might be. To do that, you can consider looking at what the relative position of a card is now against the card that is positioned next to it in the deck and it's original position. In other words, two cards may have started as being 1 position away from the other card, but how far apart are they now? So if you give each card a starting point form 0 to 52 and then you shuffle and see how far apart they are from where they started relative to each other you get an interesting picture. Of course the theory here is that the more that the results show some random separation, then it could be considered a good shuffle. There is no "gold standard" we have found in this regard, and maybe we are the only ones looking at this. Anyway, here is our data of a deck we grabbed from one at random:
Here it is averaged over 5 decks:
Now over 10 decks:
Do you see a trend? Here is at the 3,000 mark:
Yeah, we think it looks cool too. It is almost smiling at you. In fact, I think it looks like a really good distribution in a good random shuffle. Things settling towards being equally distributed in the deck on average - meaning each has a decent chance of showing up just about anywhere. If you consider the possible combinations of a deck, the more you approach that factorial, the more this graph will get flat. That is exactly what should happen. I think that is cool.
Feel free to add your interpretations in the comments or ask for other views of the data. If you have some cool graph you would like to see (and you can tell us how to construct it somewhat), we will do what we can to give it to you.
In summary, we think this shows two very good perspectives on the shuffle and distribution in the game. We hope you found it interesting and informative. We think it fairly clearly shows the unbiased nature of the game and the random nature of it's results. I'm sure some will disagree, but if you do disagree, please let us know why and what you may find more compelling or if there are parts we did not discuss that you would be interested in knowing more about.
-- Josh
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