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Thunderfrog



Joined: Jul 25, 2017

Post   Posted: Aug 17, 2017 - 18:25 Reply with quote Back to top

Okay, so I know there's a megathread about the dice and how average they are on the site, but I feel like I gotta ask a bit of a meaty question.

Is there something in place to make sure the dice bias is coming per game or website wide?

Example.

Game 1 finishes and has a range that shows say, 50% of the dice come up 1 or 2's, for whatever reason. Will Game 2 later on adjust to show a bias of more 5's and 6's?

It really seems like the vast majority of games are incredibly streaky. One player always breaks armor. One player always rolls the skulls then rerolls into them again. One player is constantly failing the disturbing presence rolls, etc.

Even on games I win, it feels like it is incredibly lopsided in one persons favor or the other.

So I guess the TLDR question is whether or not Fumbbl has result based randomization, trying to keep the results in balance, or if it tries to truly randomize each digit rolled at the time it is rolled.

If so, how is the 1's and skulls bias so high?
Kondor



Joined: Apr 04, 2008

Post   Posted: Aug 17, 2017 - 18:41 Reply with quote Back to top

It is not results based randomization nor is each digit rolled randomized at the time it is rolled. Take a look at how Random Number Generators function. I will let someone else explain it because I will just confuse the subject.
Rags



Joined: Nov 09, 2008

Post   Posted: Aug 17, 2017 - 18:41 Reply with quote Back to top

Hello,

You're by no means the first to wonder about this. There is no balance feature, every dice roll is, as it should be, stochastic.

With every respect, you've only played 65 games. That's a small sample to work off, and definitely way too small to generalise to the 'vast majority of games'.

Part of getting used to Blod Bowl is realising that hundreds of dice are rolled every game. On a site like fumbbl, with dozens of games each day, hundreds of thousands of dice are rolled every day, and millions in just one week. This means that seemingly unlikely, 1/36, 1/1000, and even 1/1000000 sequences of events will happen all the time. What can seem freakish when you're in the middle of it is actually normal!
MattDakka



Joined: Oct 09, 2007

Post   Posted: Aug 17, 2017 - 19:07 Reply with quote Back to top

Read this page: http://fumbbl.com/help:FFB_RNG


Thunderfrog wrote:

Game 1 finishes and has a range that shows say, 50% of the dice come up 1 or 2's, for whatever reason. Will Game 2 later on adjust to show a bias of more 5's and 6's?

Maybe yes maybe not, but it would be just a fluke, not on purpose.
Every time you roll a die (a theoric flawless die with no weighted sides) the chance of any result is 16.66%.
There is no guarantee that you will roll a 6 if you just rolled a 1, you could roll a streak of 6 or 1 or other results, for the die any result is the same and it has no memory of the past rolls.
We know that the bigger the sample size is, the more likely the die results will match the average, but the single die roll can't be predicted.

In a BB match the sample size is small (and therefore prone to bad distribution of results), so you should not be surprised when wild deviations from the average happen in a single match (or even a streak of matches, it may happen).
10 games can be unlucky but if you play 1000 games the overall dice should be closer to the expected average.
Any single match you play may have bad dice or good dice, but the majority of your matches will have average dice.
Don't get disheartened and keep on playing because FUMBBL dice are actually better than physical dice.

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Last edited by MattDakka on Aug 19, 2017 - 13:54; edited 15 times in total
pythrr



Joined: Mar 07, 2006

Post   Posted: Aug 17, 2017 - 19:21 Reply with quote Back to top

calling Spiro, calling Spiro!

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keggiemckill



Joined: Oct 07, 2004

Post   Posted: Aug 17, 2017 - 20:23 Reply with quote Back to top

Dice are not bias. They hate everyone equally.

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c9805222



Joined: May 11, 2016

Post   Posted: Aug 17, 2017 - 22:37 Reply with quote Back to top

Pseudo-Random Number Generators (PRNGs)
As the word ‘pseudo’ suggests, pseudo-random numbers are not random in the way you might expect, at least not if you're used to dice rolls or lottery tickets. Essentially, PRNGs are algorithms that use mathematical formulae or simply precalculated tables to produce sequences of numbers that appear random. A good example of a PRNG is the linear congruential method. A good deal of research has gone into pseudo-random number theory, and modern algorithms for generating pseudo-random numbers are so good that the numbers look exactly like they were really random.

The basic difference between PRNGs and TRNGs is easy to understand if you compare computer-generated random numbers to rolls of a die. Because PRNGs generate random numbers by using mathematical formulae or precalculated lists, using one corresponds to someone rolling a die many times and writing down the results. Whenever you ask for a die roll, you get the next on the list. Effectively, the numbers appear random, but they are really predetermined. TRNGs work by getting a computer to actually roll the die — or, more commonly, use some other physical phenomenon that is easier to connect to a computer than a die is.

PRNGs are efficient, meaning they can produce many numbers in a short time, and deterministic, meaning that a given sequence of numbers can be reproduced at a later date if the starting point in the sequence is known. Efficiency is a nice characteristic if your application needs many numbers, and determinism is handy if you need to replay the same sequence of numbers again at a later stage. PRNGs are typically also periodic, which means that the sequence will eventually repeat itself. While periodicity is hardly ever a desirable characteristic, modern PRNGs have a period that is so long that it can be ignored for most practical purposes.

These characteristics make PRNGs suitable for applications where many numbers are required and where it is useful that the same sequence can be replayed easily. Popular examples of such applications are simulation and modeling applications. PRNGs are not suitable for applications where it is important that the numbers are really unpredictable, such as data encryption and gambling.

It should be noted that even though good PRNG algorithms exist, they aren't always used, and it's easy to get nasty surprises. Take the example of the popular web programming language PHP. If you use PHP for GNU/Linux, chances are you will be perfectly happy with your random numbers. However, if you use PHP for Microsoft Windows, you will probably find that your random numbers aren't quite up to scratch as shown in this visual analysis from 2008. Another example dates back to 2002 when one researcher reported that the PRNG on MacOS was not good enough for scientific simulation of virus infections. The bottom line is that even if a PRNG will serve your application's needs, you still need to be careful about which one you use.

True Random Number Generators (TRNGs)
In comparison with PRNGs, TRNGs extract randomness from physical phenomena and introduce it into a computer. You can imagine this as a die connected to a computer, but typically people use a physical phenomenon that is easier to connect to a computer than a die is. The physical phenomenon can be very simple, like the little variations in somebody's mouse movements or in the amount of time between keystrokes. In practice, however, you have to be careful about which source you choose. For example, it can be tricky to use keystrokes in this fashion, because keystrokes are often buffered by the computer's operating system, meaning that several keystrokes are collected before they are sent to the program waiting for them. To a program waiting for the keystrokes, it will seem as though the keys were pressed almost simultaneously, and there may not be a lot of randomness there after all.

However, there are many other ways to get true randomness into your computer. A really good physical phenomenon to use is a radioactive source. The points in time at which a radioactive source decays are completely unpredictable, and they can quite easily be detected and fed into a computer, avoiding any buffering mechanisms in the operating system. The HotBits service at Fourmilab in Switzerland is an excellent example of a random number generator that uses this technique. Another suitable physical phenomenon is atmospheric noise, which is quite easy to pick up with a normal radio. This is the approach used by RANDOM.ORG. You could also use background noise from an office or laboratory, but you'll have to watch out for patterns. The fan from your computer might contribute to the background noise, and since the fan is a rotating device, chances are the noise it produces won't be as random as atmospheric noise.

Thunderstorm over Denver, USA
Thunderstorms generate atmospheric noise

As long as you are careful, the possibilities are endless. Undoubtedly the visually coolest approach was the lavarand generator, which was built by Silicon Graphics and used snapshots of lava lamps to generate true random numbers. Unfortunately, lavarand is no longer operational, but one of its inventors is carrying on the work (without the lava lamps) at the LavaRnd web site. Yet another approach is the Java EntropyPool, which gathers random bits from a variety of sources including HotBits and RANDOM.ORG, but also from web page hits received by the EntropyPool's own web server.

Regardless of which physical phenomenon is used, the process of generating true random numbers involves identifying little, unpredictable changes in the data. For example, HotBits uses little variations in the delay between occurrences of radioactive decay, and RANDOM.ORG uses little variations in the amplitude of atmospheric noise.

The characteristics of TRNGs are quite different from PRNGs. First, TRNGs are generally rather inefficient compared to PRNGs, taking considerably longer time to produce numbers. They are also nondeterministic, meaning that a given sequence of numbers cannot be reproduced, although the same sequence may of course occur several times by chance. TRNGs have no period.

Comparison of PRNGs and TRNGs
The table below sums up the characteristics of the two types of random number generators.

Characteristic Pseudo-Random Number Generators True Random Number Generators
Efficiency Excellent Poor
Determinism Determinstic Nondeterministic
Periodicity Periodic Aperiodic
These characteristics make TRNGs suitable for roughly the set of applications that PRNGs are unsuitable for, such as data encryption, games and gambling. Conversely, the poor efficiency and nondeterministic nature of TRNGs make them less suitable for simulation and modeling applications, which often require more data than it's feasible to generate with a TRNG. The following table contains a summary of which applications are best served by which type of generator:

Application Most Suitable Generator
Lotteries and Draws TRNG
Games and Gambling TRNG
Random Sampling (e.g., drug screening) TRNG
Simulation and Modelling PRNG
Security (e.g., generation of data encryption keys) TRNG
The Arts Varies
c9805222



Joined: May 11, 2016

Post   Posted: Aug 17, 2017 - 22:39 Reply with quote Back to top

reference: https://www.random.org/randomness/

I'm not sure but I think that fumbbl uses a PRNG.
Christer



Joined: Aug 02, 2003

Post   Posted: Aug 17, 2017 - 23:32
FUMBBL Staff
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FUMBBL uses a hybrid TRNG/PRNG. The TRNG part is entropy gathering from players playing the game (specifically from mouse movements). This entropy is pushed through a set of streaming hash functions (SHA) to generate an encryption key. The encryption key is used as input to an AES encryption block, running in counter mode.

The net effect of this is that the TRNG entropy from people playing the client is used as a base, and the SHA and AES blocks very effectively remove any type of bias in the entropy data. They also allow expansion of the true randomness to roll more dice than the entropy on its own would be able to provide.

Edit: Also, the RNG used is shared between all players playing the game. As a real-world analogy, everyone is using the same dice. If there was a problem in terms of random distribution, everyone would end up rolling equally good/bad.

The RNG code itself doesn't know which coach is making the game; nor does it even know which match the roll is for. So saying that the dice rolls are streaky and uneven in terms of one player having consistently better dice than the other is simply a silly notion.

The human brain has a hard time dealing with proper randomness, and is highly trained in identifying patterns. Faced with true randomness, we tend to pick up on very short sequences and decide there is one. When the real world (e.g. the dice rolls) do not match this pattern, the brain filters out the roll as an anomaly and forgets about it; whereas a roll that enforces the pattern is taken as evidence of its existence. This is called confirmation bias, and is very much something that affects most if not all humans.

The harsh truth is that most people who complain about bad dice are simply facing stronger players than themselves. I honestly believe that the greatest factor that differentiates an above average coach from a below average one is that the stronger coaches have a tendency to play the game in a safer way. Knowing when to make the risky rolls, and when to not; and positioning players in a way that minimizes the consequences of a failed roll.

You can say what you will about how luck is a huge factor Blood Bowl, but raw numbers and statistics imply that the coach playing the game is a much much more significant factor. This is why newcomers to the site have a tendency to start out with losing streaks rather than having a 50% record straight away which would be the case if the dice were the major factor deciding games.

Anyway, I tend to be a bit long-winded when talking about RNGs. It's a topic that genuinely interests me and I don't hesitate to try to straighten out misconceptions about RNGs and what we use here.
Thunderfrog



Joined: Jul 25, 2017

Post   Posted: Aug 18, 2017 - 05:28 Reply with quote Back to top

Interesting stuff! I'll keep an eye on these better coaches then, and realize that half of the game seems to be limiting it's inherent randomness.
mrt1212



Joined: Feb 26, 2013

Post   Posted: Aug 18, 2017 - 05:54 Reply with quote Back to top

Thunderfrog wrote:
Interesting stuff! I'll keep an eye on these better coaches then, and realize that half of the game seems to be limiting it's inherent randomness.


Oh absolutely.
JackassRampant



Joined: Feb 26, 2011

Post   Posted: Aug 18, 2017 - 06:52 Reply with quote Back to top

And the other half is maximizing the game's randomness for the other guy. That's the fun part, if you ask me.

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Grod



Joined: Sep 30, 2003

Post   Posted: Aug 18, 2017 - 09:35 Reply with quote Back to top

We could use this beast instead:

http://gamesbyemail.com/News/DiceOMatic

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steelers_wales



Joined: Jun 28, 2017

Post   Posted: Aug 18, 2017 - 10:34 Reply with quote Back to top

Christer wrote:
The human brain has a hard time dealing with proper randomness, and is highly trained in identifying patterns. Faced with true randomness, we tend to pick up on very short sequences and decide there is one


This explains every thread I've ever seen about Blood Bolw dice... ever

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mike467



Joined: Feb 21, 2013

Post   Posted: Aug 18, 2017 - 10:59 Reply with quote Back to top

I remember reading that Apple had to reprogram its random feature on iTunes as people had a hard time accepting it. They actually had to make it more calculated and less random for people to be ok with it, basically people couldn't accept that out of your 60000 tracks randomness could result in 3 tracks being played from the same album in a row (for example). I haven't verified this but this seems to fit in with my experience of people and their ability to process randomness when dealing with a machine.
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