Aws work by what I name "controlled randomness", meaning that it's only the software which decides where the ball will most likely land for every spin.
In the effort to give the most random outcomes, we may suppose that only in very rare circumstances the software will set the same previous launching parameters for the next spin.
Indeed the amount of number repeats vs the human tables is lower, surely lower than 1/38 or 1/37 probability.
Moreover tha ball will interact with the same environment for every spin: temperature, humidity, uniformed force applied on the ball surface (no spin effect or constant spin effect), ball and slots cleanliness are costant. No employee sweat, no dust, I mean.
In a sense we might infer that the software knows at the start where the ball will land every hand, so our worries should be focused about the different air forces applied to the ball and about the "interfering agents" acting thereafter.
We know that the ball speed decays up to its falling point at the same velocity independently of the launching speed. So the only variable now is the position of the rotor in relation of the ball's fall.
In a word, a ball may make 35, 25 or 10 revolutions before falling but its falling speed remains a constant value.
Some manufacturers like to give the rotor different speeds or alternate clockwise and counterclockwise revolutions, but the point remains the same: software indirectly knows where the ball will most likely land.
At least without a more or less impact of the interfering agents.
The interfering agents are: deflectors, slots edge, ball weight/diameter and rotor speed.
Deflectors were originally inserted to amplify the random effect, actually and also according to L. Scott they tend to reduce randomness.
Slots edge plays a major "random" role as low edges tend to enlarge the bouncing and splattering effect but we'll see that even wheels presenting very low slot edges can be very profitable to play in.
The same considerations could be made about ball weight/diameter, but aws cannot utilize low ball weights and low ball diameters for obvious procedural reasons.
High rotor speeds increase the bouncing effect as the ball before its immediate fall will encounter a dynamic propelling object. But again this feature could be easily disregarded as such bias tend to equalize itlr.
In our long study we have considered many aw brands and good news is that everyone of them is perfectly beatable (providing different strategies acting in relation of the actual wheel).
Now let's consider the most sophisticated aw ever built. It's an east european product.
This wheel has low edge slots, a quite low weight/diameter ball, the rotor alternatively changes its direction clockwise and counterclockwise, the rotor speed is quite high or very high, there are 16 deflectors and the space between rotor and wheel edge is almost double than many other products.
Should this be a perfect random machine, right?
It is.
However is quite interesting to notice that even in this very sophisticated machine the number of repeats is lower than what the probability laws dictate.
In our study we have even examined wheels having each four or five different launching points, naturally chosen randomly by the software.
And guess what? In this case too we got a lower number of repeats than expected.
Obviously the number of repeats is just one of the parameters taken into account, it can't be a value to build a strategy around.
as.
In the effort to give the most random outcomes, we may suppose that only in very rare circumstances the software will set the same previous launching parameters for the next spin.
Indeed the amount of number repeats vs the human tables is lower, surely lower than 1/38 or 1/37 probability.
Moreover tha ball will interact with the same environment for every spin: temperature, humidity, uniformed force applied on the ball surface (no spin effect or constant spin effect), ball and slots cleanliness are costant. No employee sweat, no dust, I mean.
In a sense we might infer that the software knows at the start where the ball will land every hand, so our worries should be focused about the different air forces applied to the ball and about the "interfering agents" acting thereafter.
We know that the ball speed decays up to its falling point at the same velocity independently of the launching speed. So the only variable now is the position of the rotor in relation of the ball's fall.
In a word, a ball may make 35, 25 or 10 revolutions before falling but its falling speed remains a constant value.
Some manufacturers like to give the rotor different speeds or alternate clockwise and counterclockwise revolutions, but the point remains the same: software indirectly knows where the ball will most likely land.
At least without a more or less impact of the interfering agents.
The interfering agents are: deflectors, slots edge, ball weight/diameter and rotor speed.
Deflectors were originally inserted to amplify the random effect, actually and also according to L. Scott they tend to reduce randomness.
Slots edge plays a major "random" role as low edges tend to enlarge the bouncing and splattering effect but we'll see that even wheels presenting very low slot edges can be very profitable to play in.
The same considerations could be made about ball weight/diameter, but aws cannot utilize low ball weights and low ball diameters for obvious procedural reasons.
High rotor speeds increase the bouncing effect as the ball before its immediate fall will encounter a dynamic propelling object. But again this feature could be easily disregarded as such bias tend to equalize itlr.
In our long study we have considered many aw brands and good news is that everyone of them is perfectly beatable (providing different strategies acting in relation of the actual wheel).
Now let's consider the most sophisticated aw ever built. It's an east european product.
This wheel has low edge slots, a quite low weight/diameter ball, the rotor alternatively changes its direction clockwise and counterclockwise, the rotor speed is quite high or very high, there are 16 deflectors and the space between rotor and wheel edge is almost double than many other products.
Should this be a perfect random machine, right?
It is.
However is quite interesting to notice that even in this very sophisticated machine the number of repeats is lower than what the probability laws dictate.
In our study we have even examined wheels having each four or five different launching points, naturally chosen randomly by the software.
And guess what? In this case too we got a lower number of repeats than expected.
Obviously the number of repeats is just one of the parameters taken into account, it can't be a value to build a strategy around.
as.
Just teasing....