ArticlePDF Available

# Predicting the outcome of roulette

Authors:

## Abstract and Figures

There have been several popular reports of various groups exploiting the deterministic nature of the game of roulette for profit. Moreover, through its history, the inherent determinism in the game of roulette has attracted the attention of many luminaries of chaos theory. In this paper, we provide a short review of that history and then set out to determine to what extent that determinism can really be exploited for profit. To do this, we provide a very simple model for the motion of a roulette wheel and ball and demonstrate that knowledge of initial position, velocity, and acceleration is sufficient to predict the outcome with adequate certainty to achieve a positive expected return. We describe two physically realizable systems to obtain this knowledge both incognito and in situ. The first system relies only on a mechanical count of rotation of the ball and the wheel to measure the relevant parameters. By applying these techniques to a standard casino-grade European roulette wheel, we demonstrate an expected return of at least 18%, well above the -2.7% expected of a random bet. With a more sophisticated, albeit more intrusive, system (mounting a digital camera above the wheel), we demonstrate a range of systematic and statistically significant biases which can be exploited to provide an improved guess of the outcome. Finally, our analysis demonstrates that even a very slight slant in the roulette table leads to a very pronounced bias which could be further exploited to substantially enhance returns.
Content may be subject to copyright.
Predicting the outcome of roulette
Michael Small and Chi Kong Tse
Citation: Chaos 22, 033150 (2012); doi: 10.1063/1.4753920
View online: http://dx.doi.org/10.1063/1.4753920
Related Articles
Stability of discrete breathers in nonlinear Klein-Gordon type lattices with pure anharmonic couplings
J. Math. Phys. 53, 102701 (2012)
Secondary nontwist phenomena in area-preserving maps
Chaos 22, 033142 (2012)
Delay induced bifurcation of dominant transition pathways
Chaos 22, 033141 (2012)
Clocking convergence to a stable limit cycle of a periodically driven nonlinear pendulum
Chaos 22, 033138 (2012)
Characterizing the dynamics of higher dimensional nonintegrable conservative systems
Chaos 22, 033137 (2012)
Journal Homepage: http://chaos.aip.org/
Information for Authors: http://chaos.aip.org/authors
Predicting the outcome of roulette
Michael Small
1,2,a)
and Chi Kong Tse
2
1
School of Mathematics and Statistics, The University of Western Australia, Perth, Australia
2
Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong
(Received 30 April 2012; accepted 4 September 2012; published online 26 September 2012)
There have been several popular reports of various groups exploiting the deterministic nature of the
game of roulette for proﬁt. Moreover, through its history, the inherent determinism in the game of
roulette has attracted the attention of many luminaries of chaos theory. In this paper, we provide a
short review of that history and then set out to determine to what extent that determinism can really
be exploited for proﬁt. To do this, we provide a very simple model for the motion of a roulette
wheel and ball and demonstrate that knowledge of initial position, velocity, and acceleration is
sufﬁcient to predict the outcome with adequate certainty to achieve a positive expected return. We
describe two physically realizable systems to obtain this knowledge both incognito and in situ. The
ﬁrst system relies only on a mechanical count of rotation of the ball and the wheel to measure the
relevant parameters. By applying these techniques to a standard casino-grade European roulette
wheel, we demonstrate an expected return of at least 18%, well above the 2.7% expected of a
random bet. With a more sophisticated, albeit more intrusive, system (mounting a digital camera
above the wheel), we demonstrate a range of systematic and statistically signiﬁcant biases which
can be exploited to provide an improved guess of the outcome. Finally, our analysis demonstrates
that even a very slight slant in the roulette table leads to a very pronounced bias which
could be further exploited to substantially enhance returns. V
C2012 American Institute of Physics.
[http://dx.doi.org/10.1063/1.4753920]
“No one can possibly win at roulette unless he steals
money from the table when the croupier isn’t
looking” (Attributed to Albert Einstein in Ref. 1)
Among the various gaming systems, both current
and historical, roulette is uniquely deterministic. Rela-
tively simple laws of motion allow one, in principle, to
forecast the path of the ball on the roulette wheel and to
its ﬁnal destination. Perhaps because of this appealing
deterministic nature, many notable ﬁgures from the early
development of chaos theory have leant their hand to
exploiting this determinism and undermining the pre-
sumed randomness of the outcome. In this paper, we aim
only to establish whether the determinism in this system
really can be proﬁtably exploited. We ﬁnd that this is def-
initely possible and propose several systems which could
be used to gain an edge over the house in a game of rou-
lette. While none of these systems are optimal, they all
demonstrate positive expected return.
I. A HISTORY OF ROULETTE
The game of roulette has a long, glamorous, inglorious
history, and has been connected with several notable men of
science. The origin of the game has been attributed,
2
perhaps
erroneously,
1
to the mathematician Blaise Pascal.
3
Despite
the roulette wheel becoming a staple of probability theory,
the alleged motivation for Pascal’s interest in the device was
not solely to torment undergraduate students, but rather as
part of a vain search for perpetual motion. Alternative stories
have attributed the origin of the game to the ancient Chinese,
a French monk or an Italian mathematician.
2,4
In any case,
the device was introduced to Parisian gamblers in the mid-
eighteenth century to provide a fairer game than those cur-
rently in circulation. By the turn of the century, the game
was popular and wide-spread. Its popularity bolstered by its
apparent randomness and inherent (perceived) honesty.
The game of roulette consists of a heavy wheel,
machined and balanced to have very low friction, and
designed to spin for a relatively long time with a slowly
decaying angular velocity. The wheel is spun in one direc-
tion, while a small ball is spun in the opposite direction on
the rim of a ﬁxed circularly inclined surface surrounding and
abutting the wheel. As the ball loses momentum, it drops to-
ward the wheel and eventually will come to rest in one of 37
numbered pockets arranged around the outer edge of the
spinning wheel. Various wagers can be made on which
pocket, or group of pockets, the ball will eventually fall into.
It is accepted practice that, on a successful wager on a single
pocket, the casino will pay 35 to 1. Thus, the expected return
from a single wager on a fair wheel is ð35 þ1Þ 1
37
þð1Þ2:7%.
5
In the long-run, the house will, naturally,
win. In the eighteenth century, the game was fair and con-
sisted of only 36 pockets. Conversely, an American roulette
wheel is even less fair and consists of 38 pockets. We con-
sider the European, 37 pocket, version as this is of more im-
mediate interest to us.
6
Figure 1illustrates the general
structure, as well as the layout of pockets, on a standard
European roulette wheel.
Despite many proposed “systems,” there are only two
proﬁtable ways to play roulette.
7
One can either exploit
a)
Electronic mail: michael.small@uwa.edu.au.
1054-1500/2012/22(3)/033150/9/$30.00 V C2012 American Institute of Physics22, 033150-1 CHAOS 22, 033150 (2012) an unbalanced wheel, or one can exploit the inherently deterministic nature of the spin of both ball and wheel. Casi- nos will do their utmost to avoid the ﬁrst type of exploit. The second exploit is possible because placing wagers on the out- come is traditionally permitted until some time after the ball and wheel are in motion. That is, one has an opportunity to observe the motion of both the ball and the wheel before placing a wager. The archetypal tale of the ﬁrst type of exploit is that of a man by the name of Jagger (various sources refer to him as either William Jaggers or Joseph Jagger, or some permuta- tion of these). Jagger, an English mechanic and amateur mathematician, observed that slight mechanical imperfection in a roulette wheel could afford sufﬁcient edge to provide for proﬁtable play. According to one incarnation of the tale, in 1873, he embarked for the casino of Monte Carlo with six hired assistants. Once there, he carefully logged the outcome of each spin of each of six roulette tables over a period of 5 weeks. 8 Analysis of the data revealed that for each wheel there was a unique but systematic bias. Exploiting these weaknesses, he gambled proﬁtably for a week before the ca- sino management shufﬂed the wheels between tables. This bought his winning streak to a sudden halt. However, he soon noted various distinguishing features of the individual wheels and was able to follow them between tables, again winning consistently. Eventually, the casino resorted to redistributing the individual partitions between pockets. A popular account, published in 1925, claims he eventually came away with winnings of £65 000. 8 The success of this endeavor is one possible inspiration for the musical hall song “The Man Who Broke the Bank at Monte Carlo” although this is strongly disputed. 8 Similar feats have been repeated elsewhere. The noted statistician Karl Pearson provided a statistical analysis of roulette data, and found it to exhibit substantial systematic bias. However, it appears that his analysis was based on ﬂawed data from unscrupulous scribes 9 (apparently he had hired rather lazy journalists to collect the data). In 1947, irregularities were found, and exploited, by two students, Albert Hibbs and Roy Walford, from Chicago University, 10,11 Following this line of attack, Ethier provides a statistical framework by which one can test for irregular- ities in the observed outcome of a roulette wheel. 12 A similar weakness had also been reported in Time magazine in 1951. In this case, the report described various syndicates of gam- blers exploiting determinism in the roulette wheel in the Argentinean casino Mar del Plata during 1948. 13 The second type of exploit is more physical (that is, deterministic) than purely statistical and has consequently attracted the attention of several mathematicians, physicists and engineers. One of the ﬁrst 14 was Henri Poincar e 3 in his seminal work Science and Method. 15 While ruminating on the nature of chance, and that a small change in initial condi- tion can lead to a large change in effect, Poincar e illustrated his thinking with the example of a roulette wheel (albeit a slightly different design from the modern version). He observed that a tiny change in initial velocity would change the ﬁnal resting place of the wheel (in his model there was no ball) such that the wager on an either black or red (as in a modern wheel, the black and red pockets alternate) would correspondingly win or lose. He concluded by arguing that this determinism was not important in the game of roulette as the variation in initial force was tiny, and for any continu- ous distribution of initial velocities, the result would be the same: effectively random, with equal probability. He was not concerned with the individual pockets, and he further assumed that the variation in initial velocity required to pre- dict the outcome would be immeasurable. It is while describ- ing the game of roulette that Poincar e introduces the concept of sensitivity to initial conditions, which is now a corner- stone of modern chaos theory. 16 A general procedure for predicting the outcome of a rou- lette spin, and an assessment of its utility was described by Edward Thorp in a 1969 publication for the Review of the International Statistical Institute. 9 In that paper, Thorp describes the two basic methods of prediction. He observes (as others have done later) that by minimizing systematic bias in the wheel, the casinos achieve a degree of mechanical perfection that can then be exploited using deterministic pre- diction schemes—efforts to minimize exploitation of statisti- cal anomalies makes deterministic modeling methods easier. Thorp describes two deterministic prediction schemes (or rather two variants on the same scheme). If the roulette wheel is not perfectly level (a tilt of 0:2was apparently suf- ﬁcient—we veriﬁed that this is indeed more than sufﬁcient) then there is effectively a large region of the frame from FIG. 1. The European roulette wheel. In the left panel, one can see a portion of the rotat- ing roulette wheel and surrounding ﬁxed track. The ball has come to rest in the (green) 0 pocket. Although the motion of the wheel and the ball (in the outer track) are simple and linear, one can see the addi- tion of several metal deﬂectors on the stator (that is, the ﬁxed frame on which the rotat- ing wheel sits). The sharp frets between pockets also introduce strong nonlinearity as the ball slows and bounces between pockets. The panel on the right depicts the arrange- ment of the number 0 to 36 and the coloring red (lighter) and black (darker). 033150-2 M. Small and C. K. Tse Chaos 22, 033150 (2012) which the ball will not fall onto the spinning wheel. By studying Las Vegas wheels, he observes this condition is met in approximately one third of wheels. He claims that in such cases it is possible to garner a expected return of þ15%, which increased to þ44% with the aid of a “pocket-sized” computer. Some time later, Thorp revealed that his collabo- rator in this endeavor was Claude Shannon, 17 the founding father of information theory. 18 In his 1967 book, 2 the mathematician Richard A. Epstein describes his earlier (undated) experiments with a private roulette wheel. By measuring the angular velocity of the ball relative to the wheel, he was able to predict correctly the half of the wheel into which the ball would fall. Impor- tantly, he noted that the initial velocity (momentum) of the ball was not critical. Moreover, the problem is simply one of predicting when the ball will leave the outer (ﬁxed) rim as this will always occur at a ﬁxed velocity. However, a lack of sufﬁcient computational resources meant that his experi- ments were not done in real time, and certainly not attempted within a casino. Subsequent to, and inspired by, the work of Thorp and Shannon, another widely described attempt to beat the casi- nos of Las Vegas was made in 1977–1978 by Doyne Farmer, Norman Packard, and colleagues. 1 It is supposed that Thorp’s 1969 paper had let the cat out of the bag regarding proﬁtable betting on roulette. However, despite the assertions of Bass, 1 Thorp’s paper 9 is not mathematically detailed (there is in fact no equations given in the description of rou- lette). Thorp is sufﬁciently detailed to leave the reader in no doubt that the scheme could work, but also vague enough so that one could not replicate his effort without considerable knowledge and skill. Farmer, Packard, and colleagues imple- mented the system on a 6502 microprocessor hidden in a shoe, and proceeded to apply their method to the various casinos of the Las Vegas Strip. The exploits of this group are described in detail in Bass. 1 The same group of physicists went on to apply their skills to the study of chaotic dynami- cal systems 19 and also for proﬁtable trading on the ﬁnancial markets. 20 In Farmer and Sidorowich’s landmark paper on predicting chaotic time series 21 the authors attribute the in- spiration for that work to their earlier efforts to beat the game of roulette. Less exalted individuals have also been employing sim- ilar schemes, in some cases fairly recently. In 2004, the BBC carried the report of three gamblers 22 arrested by police after winning £1 300 000 at the Ritz Casino in Lon- don. The trio had apparently been using a laser scanner and their mobile phones to predict the likely resting place of the ball. Happily, for the trio but not the casino, they were judged to have broken no laws and allowed to keep their winnings. 23 The scheme we describe in Sec. II and imple- ment in Sec. III is certainly compatible with the equipment and results reported in this case. In Sec. IV, we conclude with some remarks concerning the practicality of applying these methods in a modern casino, and what steps casinos could take (or perhaps have taken) to circumvent these exploits. A preliminary version of these results was pre- sented at a conference in Macau. 24 An independent and much more detailed model of dynamics of the roulette wheel is discussed in Strzalko et al. 25 Since our preliminary publication, 24 private communication with several individu- als indicates that these methods have now progressed to the point of at least four instances of independent in situ ﬁeld trials. II. A MODEL FOR ROULETTE We now describe our basic model of the motion of the roulette wheel and ball. Let ðr;hÞdenote the position of the ball in polar co-ordinates, and let udenote the angular posi- tion of the wheel (say, the angular position of the centre of the green 0 pocket). We will model the ball as a single point and so let rrim be the farthest radial position of that point (i.e., the radial position of the centre of the ball when the ball is spinning with high velocity in the rim of the wheel). Simi- larly, let rdefl be the radial distance to the location of the metal deﬂectors on the stator. For now, we will assume that drdefl dh¼0 (that is, there are deﬂectors evenly distributed around the stator at constant radius rdefl <r). The extension to the more precise case is obvious, but, as we will see, not necessary. Moreover, it is messy. Finally, we suppose that the incline of the stator to the horizontal is a constant a. This situation, together with a balance of forces is depicted in Figure 2. We will ﬁrst consider the ideal case of a level table, and then in section II B show how this condition is in fact critical. A. Level table For a given initial motion of ball ðr;h;_ h; hÞt¼0and wheel ðu;_ u; uÞt¼0, our aim is to determine the time tdefl at which r¼rdefl. After launch, the motion of the ball will pass through two distinct states which we further divide into four cases: (i) with sufﬁcient momentum it will remain in the rim, constrained by the ﬁxed edge of the stator; (ii) at some point the momentum drops and the ball leaves the rim; (iii) the ball will gradually loose momentum while travelling on the stator as _ hdrops, so will r; and (iv) eventually r¼rdefl at some time tdefl. At time t¼tdefl , we assume that the ball hits a deﬂector on the stator and drops onto the (still spinning) wheel. Of course, the deﬂectors are discrete and located only at speciﬁc points around the edge of the wheel. While it is possible, and fairly straightforward to incorporate the exact position (and more importantly, the orientation) of each de- ﬂector, we have not done this. Instead, we model the deﬂec- tors at a constant radial distance around the entire rim. The exact position of the wheel when the ball reaches the deﬂec- tors will be random but will depend only on uðtdefl Þ—i.e., depending on where the actual deﬂectors are when the ball ﬁrst comes within range, the radial distance until the ball actually deﬂects will be uniformly distributed on the interval ½0;2p=Ndefl, where Ndefl is the number of deﬂectors. 1. Ball rotates in the rim While traveling in the rim ris constant and the ball has angular velocity _ h. Hence, the radial acceleration of the ball is ac¼v2 r¼1 rðr_ hÞ2¼r_ h2, where vis the speed of the ball. During this period of motion, we suppose that ris constant 033150-3 M. Small and C. K. Tse Chaos 22, 033150 (2012) and that hdecays only due to constant rolling friction: hence _ r¼0 and h¼ hð0Þ, a constant. This phase of motion will continue provided the centripetal force of the ball on the rim exceeds the force of gravity maccos a>mgsin a(mis the mass of the ball). Hence, at this stage _ h2>g rtan a:(1) 2. Ball leaves the rim Gradually the speed on the ball decays until eventually _ h2¼g rtana. Given the initial acceleration hð0Þ, velocity _ hð0Þ, and position hð0Þ, it is trivial to compute the time at which the ball leaves the rim, trim to be trim ¼ 1 hð0Þ _ hð0Þ ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ g rtan a r  :(2) To do so, we assume that the angular acceleration is constant and so the angular velocity at any time is given by _ hðtÞ ¼_ hð0Þþ hð0Þtand substitute into Eq. (1). That is, we are assuming that the force acting on the ball is independent of velocity—this is a simplifying assumption for the naive model we describe here, more sophisticated alternatives are possible, but in all cases this will involve the estimation of additional parameters. The position at which the ball leaves the rim is given by hð0Þþðg rtan aÞ_ hð0Þ2 2 hð0Þ 2p where jj 2pdenotes modulo 2p. 3. Ball rotates freely on the stator After leaving the rim, the ball will continue (in practice, for only a short while) to rotate freely on the stator until it eventually reaches the various deﬂectors at r¼rdefl . The angular velocity continues to be governed by _ hðtÞ¼_ hð0Þþ hð0Þt; but now that r_ h2<gtan a the radial position is going to gradually decrease too. The difference between the force of gravity mgsinaand the (lesser) centripetal force mr _ h2cos aprovides inward acceler- ation of the ball r¼r_ h2cos agsin a:(3) Integrating Eq. (3) yields the position of the ball on the stator. 4. Ball reaches the deflectors Finally, we ﬁnd the time t¼tdefl for which r(t), computed as the deﬁnite second integral of Eq. (3),isequaltordefl.We can then compute the instantaneous angular position of the ball hðtdeflÞ¼hð0Þþ _ hð0Þtdefl þ1 2 hð0Þt2 defl and the wheel uðtdeflÞ ¼uð0Þþ _ uð0Þtdefl þ1 2 uð0Þt2 defl to give the salient value c¼jhðtdeflÞuðtdefl Þj2p(4) denoting the angular location on the wheel directly below the point at which the ball strikes a deﬂector. Assuming the constant distribution of deﬂectors around the rim, some (still to be estimated) distribution of resting place of the ball will depend only on that value c. Note that, although we have described ðh;_ h; hÞt¼0and ðu;_ u; uÞt¼0separately, it is possi- ble to adopt the rotating frame of reference of the wheel and treat huas a single variable. The analysis is equivalent, estimating the required parameters may become simpler. FIG. 2. The dynamic model of ball and wheel. On the left, we show a top view of the roulette wheel (shaded region) and the stator (outer circles). The ball is mov- ing on the stator with instantaneous position ðr;hÞ while the wheel is rotating with angular velocity _ u (note that the direction of the arrows here are for illus- tration only, the analysis in the text assume the same convention, clockwise positive, for both ball and wheel). The deﬂectors on the stator are modelled as a circle, concentric with the wheel, of radius rdefl.On the right, we show a cross section and examination of the forces acting on the ball in the incline plane of the stator. The angle ais the incline of the stator, mis the mass of the ball, acis the radial acceleration of the ball, and gis gravity. 033150-4 M. Small and C. K. Tse Chaos 22, 033150 (2012) We note that for a level table, each spin of the ball alters only the time spent in the rim, the ball will leave the rim of the stator with exactly the same velocity _ heach time. The descent from this point to the deﬂectors will therefore be identical. There will, in fact, be some characteristic duration which could be easily computed for a given table. Doing this would circumvent the need to integrate Eq. (3). B. The crooked table Suppose, now that the table is not perfectly level. This is the situation discussed and exploited by Thorp. 9 Without loss of generality (it is only an afﬁne change of co-ordinates for any other orientation) suppose that the table is tilted by an angle dsuch that the origin u¼0 is the lowest point on the rim. Just as with the case of a level table, the time which the ball spends in the rim is variable and the time at which it leaves the rim depends on a stability criterion similar to Eq. (1). But now that the table is not level, that equilibrium becomes r_ h2¼gtanðaþdcos hÞ:(5) If d¼0 then it is clear that the distribution of angular posi- tions for which this condition is ﬁrst met will be uniform. Suppose instead that d>0, then there is now a range of criti- cal angular velocities _ h2 crit g rtanðadÞ;g rtanðaþdÞ. Once _ h2<g rtanðaþdÞ, the position at which the ball leaves the rim will be dictated by the point of intersection in ðh;_ hÞ- space of _ h¼ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ g rtanðaþdcos hÞ r(6) and the ball trajectory as a function of t(modulo 2p) _ hðtÞ¼_ hð0Þþ hð0Þt;(7) hðtÞ¼hð0Þþ_ hð0Þtþ1 2 hð0Þt2:(8) If the angular velocity of the ball is large enough, then the ball will leave the rim at some point on the half circle prior to the low point (u¼0). Moreover, suppose that in one rev- olution (i.e., hðt1Þþ2p¼hðt2Þ), the velocity changes by _ hðt1Þ_ hðt2Þ. Furthermore, suppose that this is the ﬁrst revolution during which _ h2<g rtanðaþdÞ(that is, _ hðt1Þ2 g rtanðaþdÞbut _ hðt2Þ2<g rtanðaþdÞ). Then, the point at which the ball will leave the rim will (in ðh;_ hÞ-space) be the intersection of Eq. (6) and _ h¼_ hðt1Þ 1 2p_ hðt2Þ_ hðt1Þh:(9) The situation is depicted in Figure 3. One can expect for a tilted roulette wheel, the ball will systematically favor leav- ing the rim on one half of the wheel. Moreover, to a good approximation, the point at which the ball will leave the rim follows a uniform distribution over signiﬁcantly less than half the wheel circumference. In this situation, the problem of predicting the ﬁnal resting place is signiﬁcantly simpliﬁed to the problem of predicting the position of the wheel at the time the ball leaves the rim. We will pursue this particular case no further here. The situation (5) may be considered as a generalisation of the ideal d¼0 case. This generalisation makes the task of pre- diction signiﬁcantly easier, but we will continue to work under the assumption that the casino will be doing its utmost to avoid the problems of an improperly levelled wheel. Moreover, this generalisation is messy, but otherwise unin- teresting. In Sec. III, we consider the problem of implement- ing a prediction scheme for a perfectly level wheel. III. EXPERIMENTAL RESULTS In Sec. II, we introduced the basic mathematical model which we utilize for the prediction of the trajectory of the ball within the roulette wheel. We ignore (or rather treat as essentially stochastic) the trajectory of the ball after hitting the deﬂectors—charting the distribution of ﬁnal outcome from deﬂector to individual pocket in the roulette wheel is a tractable probabilistic problem, and one for which we will sketch a solution later. However, the details are perhaps only of interest to the professional gambler and not to most physi- cists. Hence, we are reduced to predicting the location of the wheel and the ball when the ball ﬁrst reaches one of the deﬂectors. The model described in Sec. II is sufﬁcient to achieve this—provided one has adequate measurements of the physical dimensions of the wheel and all initial positions, FIG. 3. The case of the crooked table. The blue curve denotes the stability criterion (6), while the red solid line is the (approximate) trajectory of the ball with hðt1Þþ2p¼hðt2Þindicating two successive times of complete revolu- tions. The point at which the ball leaves the rim will therefore be the ﬁrst intersection of this stability criterion and the trajectory. This will necessarily be in the region to the left of the point at which the ball’s trajectory is tangent to Eq. (6), and this is highlighted in the ﬁgure as a green solid. Typically a crooked table will only be slightly crooked and hence this region will be close to h¼0 but biased toward the approaching ball. The width of that region depends on _ hðt1Þ_ hðt2Þ, which in turn can be determined from Eq. (6). 033150-5 M. Small and C. K. Tse Chaos 22, 033150 (2012) velocities, and accelerations (as a further approximation we assume deceleration of both the ball and wheel to be constant over the interval which we predict). Hence, the problem of prediction is essentially two- fold. First, the various velocities must be estimated accu- rately. Given these estimates, it is a trivial problem to then determine the point at which the ball will intersect with one of the deﬂectors on the stator. Second, one must then have an estimate of the scatter imposed on the ball by both the deﬂectors and possible collision with the individual frets. To apply this method in situ, one has the further complication of estimating the parameters r,rdefl;rrim;a, and possibly d without attracting undue attention. We will ignore this addi- tional complication as it is essentially a problem of data col- lection and statistical estimation. Rather, we will assume that these quantities can be reliably estimated and restrict our attention to the problem of prediction of the motion. To estimate the relevant positions, velocities, and accelera- tions ðh;_ h; h;u;_ u; uÞt¼0(or perhaps just ðhu;_ h_ u; h uÞt¼0), we employ two distinct techniques. In Secs. III AIII C, we describe these methods. In Sec. III A, we introduce a manual measurement scheme, and in Sec. III B, we describe our implementation of a more so- phisticated digital system. The purpose of Sec. III A is to demonstrate that a rather simple “clicker” type of device— along the lines of that utilized by the Doyne Farmer, Norman Packard, and collaborators 1 —can be employed to make suf- ﬁciently accurate measurements. Nonetheless, this system is far from optimal: we conduct only limited experiments with this apparatus: sufﬁcient to demonstrate that, in principle, the method could work. In Sec. III B, we describe a more so- phisticated system. This system relies on a digital camera mounted directly above a roulette wheel and is therefore unlikely to be employed in practice (although alternative, more subtle, devices could be imagined). Nonetheless, our aim here is to demonstrate how well this system could work in an optimal environment. Of course, the degree to which the model in Sec. II is able to provide a useful prediction will depend critically on how well the parameters are estimated. Sensitivity analysis shows that the predicted outcome (Eq. (4)) depends only lin- early or quadratically (in the case of physical parameters of the wheel) on our initial estimates. More important however, and more difﬁcult to estimate, is to what extent each of these parameters can be reliably estimated. For this reason, we ﬁrst take a strictly experimental approach and show that even with the various imperfections inherent in experimental mea- surement, and in our model, sufﬁciently accurate predictions are realizable. Later, in Sec. III C, we provide a brief compu- tational analysis of how model prediction will be affected by uncertainty in each of the parameters. A. A manual implementation Our ﬁrst approach is to simply record the time at which ball and wheel pass a ﬁxed point. This is a simple approach (probably that used in the early attempts to beat the wheels of Las Vegas) and is trivial to implement on a laptop computer, personal digital assistant, embedded system, or even a mobile phone. 2628 Our results, depicted in Fig. 4illustrate that the measurements, although noisy, are feasible. The noise intro- duced in this measurement is probably largely due to the lack of physical hand-eye co-ordination of the ﬁrst author. Figure 4serves only to demonstrate that, from measurements of suc- cessive revolutions T(i) and T(iþ1) the relationship between T(i) and T(iþ1) can be predicted with a fairly high degree of certainty (over several trials and with several different initial conditions). As expected, the dependence of T(iþ1) on T(i) is sub-linear. Hence, derivation of velocity and acceleration from these measurements should be relatively straightfor- ward. Simple experiments with this conﬁguration indicate that it is possible to accurately predict the correct half of the wheel in which the ball will come to rest. Using these (admittedly noisy) measurements, we were able to successfully predict the half of the wheel in which the ball would stop in 13 of 22 trials (random with p<0:15), yielding an expected return of 36=18 13=22 1¼þ18%. This trial run included predicting the precise location in which the ball landed on three occasions (random with p<0:02). Quoted p-values are computed against the null hy- pothesis of a binomial distribution of ntrials with probability of success determined by the fraction of the total circumfer- ence corresponding to the target range—i.e., the probability p of landing by chance in one of the target pockets p¼number of target pockets 37 : FIG. 4. Hand-measurement of ball and wheel velocity for prediction. From two spins of the wheel, and 20 successive spins of the ball we logged the time (in seconds) T(i) for successive passes past a given point (T(i) against T(iþ1)). The measurements T(i) and T(iþ1) are the timings of successive revolutions—direct measurements of the angular velocity observed over one complete rotation. To provide the simplest and most direct indication that handheld measurements of this quantity are accurate, we indicate in this ﬁg- ure a deterministic relationship between these quantities. From this relation- ship, one can determine the angular deceleration. The red (slightly higher) points depict these times for the wheel, the blue (lower) points are for the ball. A single trial of both ball and wheel is randomly highlighted with crosses (superimposed). The inset is an enlargement of the detail in the lower left corner. Both the noise and the determinism of this method are evident. In particular, the wheel velocity is relatively easy to calculate and decays slowly, in contrast the ball decays faster and is more difﬁcult to measure. 033150-6 M. Small and C. K. Tse Chaos 22, 033150 (2012) B. Automated digital image capture Alternatively, we employ a digital camera mounted directly above the wheel to accurately and instantaneously measure the various physical parameters. This second approach is obviously a little more difﬁcult to implement in- cognito. Here, we are more interested in determining how much of an edge can be achieved under ideal conditions, rather than the various implementation issues associated with realizing this scheme for personal gain. In all our trials, we use a regulation casino-grade roulette wheel (a 32” “President Revolution” roulette wheel manufactured by Mat- sui Gaming Machine Co. Ltd., Tokyo). The wheel has 37 numbered slots (1 to 36 and 0) in the conﬁguration shown in Figure 1and has a radius of 820 mm (spindle to rim). For the purposes of data collection, we employ a Prosilica EC650C IEEE-1394 digital camera (1/3” CCD, 659 493 pixels at 90 frames per second). Data collection software was written and coded in Cþþ using the OpenCV library. The camera provides approximately (slightly less due to issues with data transfer) 90 images per second of the posi- tion of the roulette wheel and the ball. Artifacts in the image due to lighting had to be managed and ﬁltered. From the re- sultant image, the position of the wheel was easily deter- mined by locating the only green pocket (“0”) in the wheel, and the position of the ball was located by differencing suc- cessive frames (searching for the ball shape or color was not sufﬁcient due to the reﬂective surface of the wheel and ambi- ent lighting conditions). From these time series of Cartesian coordinates for the position of both the wheel (green “0” pocket) and ball, we computed the centre of rotation and hence derived angular position time series. Polynomial ﬁts to these angular position data (modulo 2p) provided estimates of angular velocity and acceleration (deceleration). From this data, we found that, for out apparatus, the acceleration terms where very close to being constant over the observation time period—and hence modeling the forces acting on the ball as constant provided a reasonable approximation. With these parameters, we directly applied the model of Sec. II to predict the point at which the ball came into contact with the deﬂectors. Figure 5illustrates the results from 700 trials of the pre- diction algorithm on independent rolls of a fair and level rou- lette wheel. The scatter plot of Fig. 5provides only a crude estimation of variance over the entire region of the wheel for a given prediction. A determined gambler could certainly extend this analysis with a more substantial data set relating to her particular wheel of interest. We only aim to show that certain non-random characteristics in the distribution of rest- ing place will emerge and that these can then be used to fur- ther reﬁne prediction. Nonetheless, several things are clear from Fig. 5. First, for most of the wheel, the probability of the ball landing in a particular pocket—relative to the predicted destination— does not differ signiﬁcantly from chance: observed popula- tions in 30 of 37 pockets is within the 90% conﬁdence inter- val for a random process. Two particular pockets—the target pocket itself and a pocket approximately one-quarter of the wheel prior to the target pocket—occur with frequencies higher than and less than (respectively) that expected by chance: outside the 99% conﬁdence interval. Hence, the pre- dicted target pocket is a good indicator of eventual outcome and those pockets immediate prior to the target pocket (which the ball would need to bounce backwards to reach) are less likely. Finally, and rather speculatively, there is a relatively higher chance (although marginally signiﬁcant) of the ball landing in one of the subsequent pockets—hence, suggesting that the best strategy may be to bet on the section of the wheel following the actual predicted destination. C. Parameter uncertainity and measurement noise The performance of the model described above will depend on the accuracy of estimates of each of the model pa- rameters: a(the inclination of the wheel rim), rrim (the radius of the wheel rim), and rdefl (the location of the deﬂectors). Using the data collected in Sec. III B, we systematically vary each of the parameters by a factor ð0:9<<1:1Þso that, for example, we estimate the ball position with an inclination of a. Figure 6(a) depicts the results, and, as expected, de- pendence on each of these parameters is linear (and negative in the case of rrim). Moreover, the location of the deﬂectors rdefl is not critical, whereas the correct estimation of the other two parameters is. Nonetheless, this would not pose a FIG. 5. Predicting roulette. The plot depicts the results of 700 trials of our automated image recognition software used to predict the outcome of inde- pendent spins of a roulette wheel. What we plot here is a histogram in polar coordinates of the difference between the predicted and the actual outcome (the “Target” location, at the 12 o’clock position in this diagram, indicating that the prediction was correct). The length of each of the 37 black bars denote the frequency with which predicted and actual outcome differed by exactly the corresponding angle. Dotted, dotted-dashed, and solid (red) lines depict the corresponding 99.9%, 99%, and 90% conﬁdence intervals using the corresponding two-tailed binomial distribution. Motion forward (i.e., ball continues to move in the same direction) is clockwise, motion back- wards is anti-clockwise. From the 37 possible results, there are 2 instances outside the 99% conﬁdence interval. There are 7 instances outside the 90% conﬁdence interval. 033150-7 M. Small and C. K. Tse Chaos 22, 033150 (2012) signiﬁcant problem for prediction as in all case the variation in these parameters introduces a systematic bias which could easily be corrected for, or even used to estimate the true value. What is more striking is the effect of measurement noise depicted in Fig. 6(b). We add Gaussian noise to each timing measurement (each frame, recording at 90 frames per sec- ond) over the duration of the observation period (25 frames) used to estimate initial velocity and deceleration of the ball and velocity of the wheel. The added noise has an effect of increasing the variation in the predicted resting place of the ball (since the noise is unbiased) and the strength of this effect is linear with the level of noise. As independent noise realizations are added to 50 measurements (25 each for the ball and wheel), this is a substantial amount of error—even at a fairly low amplitude. Nonetheless, the ﬁnal results are still within 2–3 pockets of the original prediction for noise of up to 2% on every scalar observation. IV. EXPLOITS AND COUNTER-MEASURES The essence of the method presented here is to predict the location of the ball and wheel at the point when the ball will ﬁrst come into contact with the deﬂectors. Hence, we only require knowledge of initial conditions of each aspect of the system (or more concisely, their relative positions, velocities and accelerations). In addition to this, certain pa- rameters derived from the physical dimensions of the wheel are required—these could either be estimated directly, or inferred from observational trajectory data. Finally, we note that while anecdotal evidence suggests that (the height of the) frets plays an important role in the ﬁnal resting place of the ball, this does not enter into our model of the more deter- ministic phase of the system dynamics. It will affect the dis- tribution of ﬁnal resting places—and hence this is going to depend rather sensitively on a particular wheel. We would like to draw two simple conclusions from this work. First, deterministic predictions of the outcome of a game of roulette can be made, and can probably be done in situ. Hence, the tales of various exploits in this arena are likely to be based on fact. Second, the margin for proﬁt is quite slim. Minor manipulation with the frictional resistance or level of the wheel and/or the manner in which the croupier actually plays the ball (the force with which the ball is rolled and the effect, for example, of axial spin of the ball) have not been explored here and would likely affect the results signiﬁcantly. Hence, for the casino the news is mostly good—minor adjustments will ameliorate the advantage of the physicist-gambler. For the gambler, one can rest assured that the game is on some level predictable and therefore inherently honest. Of course, the model we have used here is extremely simple. In Strzalko et al., 25 much more sophisticated model- ing methodologies have been independently developed and presented. Certainly, since the entire system is a physical dy- namical system, computational modeling of the entire system may provide an even greater advantage. 25 Nonetheless, the methods presented in this paper would certainly be within the capabilities of a 1970s “shoe-computer.” ACKNOWLEDGMENTS The ﬁrst author would like to thank Marius Gerber for introducing him to the dynamical systems aspects of the game of roulette. Funding for this project, including the rou- lette wheel, was provided by the Hong Kong Polytechnic University. The labors of ﬁnal year project students, Yung Chun Ting and Chung Kin Shing, in performing many of the mechanical simulations describe herein are gratefully acknowledged. M.S. is supported by an Australian Research Council Future Fellowship (FT110100896). FIG. 6. Parameter uncertainty. We explore the effect of error in the model parameters on the out- come by varying the three physical parameters of the wheel (a) and introducing uncertainty in the measurement of timing events used to obtain estimates of velocity and deceleration (b). In (a), we depict the effect of perturbing the estimated values of a(green—afﬁne, increasing steeply) rrim (red—afﬁne, decreasing) and rdefl (blue—afﬁne and increasing slowly) from 90% to 110% of the true value. In (b), we add Gaussian noise of magnitude between 0.5% and 10% the variance of the true measurements to initial estimates of all positions and velocities. Horizontal dotted lines in both plots depict error corresponding to one whole pocket in the wheel. The vertical axis is in radians and covers 6p 2—half the wheel. In the upper panel, least varia- tion in outcome is observed with errors in estima- tion of rdefl. 033150-8 M. Small and C. K. Tse Chaos 22, 033150 (2012) 1 T. A. Bass, The Newtonian Casino (Penguin, London, 1990). 2 R. A. Epstein, The Theory of Gambling and Statistical Logic (Academic, New York, 1967). 3 E. T. Bell, Men of Mathematics (Simon and Schuster, New York, 1937). 4 The Italian mathematician, confusingly, was named Don Pasquale 2 , a sur- name phonetically similar to Pascal. Moreover, as Don Pasquale is also the name of a 19th century opera buff, this attribution is possibly fanciful. 5 F. Downton and R. L. Holder, “Banker’s games and the gambling act 1968,” J. R. Stat. Soc. Ser. A 135, 336–364 (1972). 6 B. Okuley and F. King-Poole, Gamblers Guide to Macao (South China Morning Post, Hong Kong, 1979). 7 Three, if one has sufﬁcient ﬁnances to assume the role of the house. 8 C. Kingston, The Romance of Monte Carlo (John Lane The Bodley Head Ltd., London, 1925). 9 E. O. Thorp, “Optimal gambling systems for favorable games,” Rev. Int. Stat. Inst. 37, 273–293 (1969). 10 Life Magazine Publication, “How to Win$6,500,” Life, 46, December 8,
1947.
11
Alternatively, and apparently erroneously, reported to be from Californian
Institute of Technology in Ref. 2.
12
S. N. Ethier, “Testing for favorable numbers on a roulette wheel,” J. Am.
Stat. Assoc. 77, 660–665 (1982).
13
Time Magazine Publication, “Argentina—Bank breakers,” Time 135, 34,
February 12, 1951.
14
The ﬁrst, to the best of our knowledge.
15
H. Poincar
e, Science and Method (Nelson, London, 1914). English transla-
tion by Francis Maitland, preface by Bertrand Russell. Facsimile reprint in
1996 by Routledge/Thoemmes, London.
16
J. P. Crutchﬁeld, J. Doyne Farmer, N. H. Packard, and R. S. Shaw,
“Chaos,” Sci. Am. 255, 46–57 (1986).
17
E. O. Thorp, The Mathematics of Gambling (Gambling Times, 1985).
18
C. E. Shannon, “A mathematical theory of communication,” Bell Syst.
Techn. J. 27, 379–423, 623–656 (1948).
19
N. H. Packard, J. P. Crutchﬁeld, J. D. Farmer, and R. S. Shaw, “Geometry
from a time series,” Phys. Rev. Lett. 45, 712–716 (1980).
20
T. A. Bass, The Predictors, edited by A. Lane (Penguin, London, 1999).
21
J. D. Farmer and J. J. Sidorowich, “Predicting chaotic time series,” Phys.
Rev. Lett. 59, 845–848 (1987).
22
BBC Online, “Arrests follow £1m roulette win,” BBC News March 22,
2004.
23
BBC Online, “Laser scam” gamblers to keep £1m,” BBC News December
5, 2004.
24
M. Small and C. K. Tse, “Feasible implementation of a prediction algo-
rithm for the game of roulette,” in Asia-Paciﬁc Conference on Circuits
and Systems (IEEE, 2008).
25
J. Strzalko, J. Grabski, P. Perlikowksi, A. Stefanski, and T. Kapitaniak,
Dynamics of gambling, Vol. 792 of Lecture Notes in Physics (Springer,
2009).
26
Implementation on a “shoe-computer” should be relatively straightforward
too.
27
C. T. Yung, “Predicting roulette,” Final Year Project Report, Hong Kong
Polytechnic University, Department of Electronic and Information Engi-
neering, April 2011.
28
K. S. Chung, “Predicting roulette II: Implementation,” Final Year Project
Report, Hong Kong Polytechnic University, Department of Electronic and
Information Engineering, April 2010.
033150-9 M. Small and C. K. Tse Chaos 22, 033150 (2012)
... Furthermore, periodicity and chaotic behaviors have been investigated. One also reports numerous applications of the bouncing ball model in various fields such as granular media [5], nanotechnology [6], neuro-sciences [7], gambling [8] or in the bouncing droplets dynamics [9]. ...
... Indeed, the set of Eqs. (8) can be rewritten as follows. Between two impacts, one has ...
... The minimal value of Γ required to takeoff, Γ th , is provided by the set of Eqs. (8). Substituting α 1 and α 2 by the expression above, one obtains, with the first equation of (8) ...
... Since the invention of this game, several approaches and methods were introduced to try to beat roulette, such as martingale systems [1], chaos theory [2], physics 1 Data is avaible upon request to: giancarlo.salirrosas@usil.pe [3], and statistical analysis [4], [5], [6]. While the core of martingale systems indicates the gambler to double his bet in a consecutive manner after every loss until a profit occurs which the following form: 2 0 · x, 2 1 · x, 2 2 · x, ..., 2 n−1 · x thus, the cumulative loss is n i=1 (2 i−1 )(x), where x is the amount of the initial bet and n the number of bet the gambler has done. ...
... This strategy could not be implemented in a real casino due to constraints on the gambler's initial capital -which is finite -and the casino's maximum size per bet limit. Moreover, one of the most recent studies in the field of physics done by Small and Tse (2012), where they provided a model for the motion of a roulette wheel and ball, showed that knowledge of initial position, velocity and acceleration was sufficient to predict the outcome of roulette and obtain an average expected return of 18%. In the statistical analysis' approach, large samples of data sets are gathered from roulette spins in order to analyse the outcomes and spot patterns to gain an edge over the house. ...
Article
The purpose of this research paper it is to present a new approach in the framework of a biased roulette wheel. It is used the approach of a quantitative trading strategy, commonly used in quantitative finance, in order to assess the profitability of the strategy in the short term. The tools of backtesting and walk-forward optimization were used to achieve such task. The data has been generated from a real European roulette wheel from an on-line casino based in Riga, Latvia. It has been recorded 10,980 spins and sent to the computer through a voice-to-text software for further numerical analysis in R. It has been observed that the probabilities of occurrence of the numbers at the roulette wheel follows an Ornstein-Uhlenbeck process. Moreover, it is shown that a flat betting system against Kelly Criterion was more profitable in the short term.
... Similar phenomena and effects can arise in the mathematical modelling of many processes, for example, in the dynamics of gambling [21,22] . We expect that the detected scattering and selection phenomena are typical for systems with an infinite number of attractors with different properties. ...
Nonlinear phenomena caused by a one-parameter family of steady-states are under investigation. We consider a mechanical system with constraint defined by a surface (like a Mexican hat) in the three–dimensional space. The corresponding system of the ordinary differential equations has an ellipse of stable equilibria and demonstrates strong multistability. We study the realization of equilibria under linear damping through numerical simulation. Our results indicate the complicated behaviour of trajectories when initial energy is high, and/or damping is low. In this case, the realization of equilibria strongly depends on the initial state of the system and demonstrates chaotic scattering. We explain these phenomena as a memory effect about conservative chaos at zero friction.
... Casinos routinely post some 20 outcomes of previous rounds (spin #s), enticing gamers to detect patterns in the sequential numbers (slot #s). Indeed, there is a vast literature on beating the roulette wheel by finding patterns, using mathematical probabilities, ballistic physics and even applying chaos theory [1][2][3][4]. In the spirit of expert systems analysis, I sat down with the enthusiast and had him slowly describe and diagram his methodology for discovering patterns in sequences of actual roulette data. ...
Chapter
Full-text available
Splayed Recurrence Analysis (SRA) is a new method for identifying and quantifying recurrent events in iterated systems. The technique is fully applicable to difference equations, Poincaré sections of continuous time series, and independent random events. Inspiration for SRA comes from American roulette wheel gaming. It has been postulated that non-random wheel determinism is introduced by unbalanced wheels (mechanical) and non-random repeated motions of house spinners (human). Primary data were taken from actual roulette outcomes in which ball landing slots were reported sequentially according to spin orders. These data were stored in a matrix [slot #, spin #] and lines were passed through all possible pairs of points in the matrix and extrapolated to the border. Centers of points falling exactly on these extended lines, including the initial pair, were scored as recurrent points. Necessarily, there were gaps between points which led to point-to-point intervals being splayed-out. Six variables were extracted from the recurrent points comprising lines: (1) number of recurrent points per line; (2) intervals between recurrent points; (3) lengths of lines; (4) slopes of lines; (5) entropy of line lengths; (6) density of recurrent points. Besides the American roulette data, these SRA strategies were also applied to natural random numbers, chaotic models, and natural phenomenon. No differences could be detected for roulette data and naturally occurring random processes. But SRA was able to detect non-random structures in mathematically chaotic systems as well as in eruption times of the Old Faithful geyser. Because the methodology does not depend upon embeddings and delays as required for nonlinear analyses, SRA is classified as fully linear.
... The ball eventually loses momentum and falls onto the wheel and into one of 37 colored and numbered pockets on the wheel [1]. It has been shown a long time ago that a casino has always better chances than a gambler for the simple reason that the probability of realisation of any number is 1/37, while in the case of win the gambler collects his/her bet times 36 (for a recent review see [2]). In the American roulette, the chances of a gambler are even lower as the wheel has 38 sectors (numbers from 0 to 36 and the double zero sector), while betting on a number a player only collects 36 times the bet in the case of win. ...
Article
Full-text available
Chances of a gambler are always lower than chances of a casino in the case of an ideal, mathematically perfect roulette, if the capital of the gambler is limited and the minimum and maximum allowed bets are limited by the casino. However, a realistic roulette is not ideal: the probabilities of realisation of different numbers slightly deviate. Describing this deviation by a statistical distribution with a width {\delta} we find a critical {\delta} that equalizes chances of gambler and casino in the case of a simple strategy of the game: the gambler always puts equal bets to the last N numbers. For up-critical {\delta} the expected return of the roulette becomes positive. We show that the dramatic increase of gambler's chances is a manifestation of bunching of numbers in a non-ideal roulette. We also estimate the critical starting capital needed to ensure the low risk game for an indefinite time.
... It is also worth mentioning that aspects of the unpredictability associated with the movement of a pinball is not unrelated to the roulette wheel for example [9,10]. ...
Article
This paper describes some typical behavior encountered in the response of a harmonically-excited mechanical system in which a severe nonlinearity occurs due to an impact. Although such systems have received considerable recent attention (most of it from a theoretical viewpoint), the system scrutinized in this paper also involves a discrete input of energy at the impact condition. That is, it is kicked when contact is made. One of the motivations for this work is related to a classic pinball machine in which a ball striking a bumper experiences a sudden impulse, introducing additional unpredictability to the motion of the ball. A one-dimensional analog of a pinball machine was the subject of a detailed mathematical study in Pring and Budd (2011), and the current paper details behavior obtained from a mechanical experiment and describes dynamics not observed in a conventional (passive) impact oscillator.
... One can then build effective prediction models in this embedded space. One of the first uses of this approach was to predict the future path of a ball on a roulette wheel, as chronicled in [3] and revisited recently in [25]. Nonlinear modeling and forecasting methods that rely on delay-coordinate embedding have since been used to predict signals ranging from currency exchange rates to Bach fugues; see [6,27] for good reviews. ...
Conference Paper
Full-text available
Computers are nonlinear dynamical systems that exhibit complex and sometimes even chaotic behavior. The models used in the computer systems community, however, are linear. This paper is an exploration of that disconnect: when linear models are adequate for predicting computer performance and when they are not. Specifically, we build linear and nonlinear models of the processor load of an Intel i7-based computer as it executes a range of different programs. We then use those models to predict the processor loads forward in time and compare those forecasts to the true continuations of the time series
Chapter
Numerous collaboration websites struggle to achieve self-sustainability—a level of user activity preventing a transition to a non-active state. We know only a little about the factors which separate sustainable and successful collaboration websites from those that are inactive or have a declining activity. We argue that modeling and understanding various aspects of the evolution of user activity in such systems is of crucial importance for our ability to predict and support success of collaboration websites. Modeling user activity is not a trivial task to accomplish due to the inherent complexity of user dynamics in such systems. In this chapter, we present several approaches that we applied to deepen our understanding of user dynamics in collaborative websites. Inevitably, our approaches are quite heterogeneous and range from simple time-series analysis, towards the application of dynamical systems, and generative probabilistic methods. Following some of our initial results, we argue that the selection of methods to study user dynamics strongly depends on the type of collaboration systems under investigation as well as on the research questions that we ask about those systems. More specifically, in this chapter we show our results of (1) the analysis of nonlinearity of user activity time-series, (2) the application of classical dynamical systems to model user motivation and peer influence, (3) a range of scenarios modeling unwanted user behavior and how that behavior influences the evolution of the dynamical systems, (4) a model of growing activity networks with explicit models of activity potential and peer influence. Summarizing, our results indicate that intrinsic user motivation to participate in a collaborative system and peer influence are of primary importance and should be included in the models of the user activity dynamics.
Article
Full-text available
In 1980 and 1981, two pioneering papers laid the foundation for what became known as nonlinear time-series analysis : the analysis of observed data—typically univariate—via dynamical systems theory. Based on the concept of state-space reconstruction, this set of methods allows us to compute characteristic quantities such as Lyapunov exponents and fractal dimensions, to predict the future course of the time series, and even to reconstruct the equations of motion in some cases. In practice, however, there are a number of issues that restrict the power of this approach: whether the signal accurately and thoroughly samples the dynamics, for instance, and whether it contains noise. Moreover, the numerical algorithms that we use to instantiate these ideas are not perfect; they involve approximations, scale parameters, and finite-precision arithmetic, among other things. Even so, nonlinear time-series analysis has been used to great advantage on thousands of real and synthetic data sets from a wide variety of systems ranging from roulette wheels to lasers to the human heart. Even in cases where the data do not meet the mathematical or algorithmic requirements to assure full topological conjugacy, the results of nonlinear time-series analysis can be helpful in understanding, characterizing, and predicting dynamical systems.
Article
Full-text available
Au cours de la dernière décade on a constaté que le joueur pouvait avoir l'avantage dans certains jeux de hasard. On verra que le "blackjack", la mise latérale au Baccara - tel qu'il est joué dans le Névada - la roulette et la "roue de la fortune", peuvent tous offrir au joueur une espérance de gain positive. La Bourse a beaucoup de traits communs avec ces jeux de hasard [5]. Elle offre des situations particulières avec des gains attendus allant au-delà d'un taux annuel de 25% [23]. Dès que la théorie particulière d'un jeu a été utilisée pour identifier des situations favorables, se pose le problème de savoir comment répartir au mieux nos ressources. Parallèlement à la découverte de situations favorables dans certains jeux, les grandes lignes d'une théorie mathématique générale pour exploiter ces opportunités s'est développée [2. 3. 10. 13.]. On décrira d'abord les jeux favorables mentionnés ci-dessus: ce sont ceux que l'auteur connaît le mieux. On discutera ensuite la théorie mathématique générale, telle qu'elle s'est développée jusqu'à maintenant, et son application à ces jeux. Une connaissance détaillée d'un jeu particulier n'est pas nécessaire pour suivre l'explication. Chaque discussion portant un jeu favorable dans la partie I est suivie d'un résumé donnant les probabilités correspondantes. Ces résumés sont suffisants pour la discussion de la partie II de sorte qu'un lecteur qui n'a aucun intérêt dans un jeu particulier peut passer directement au résumé. Des références sont données pour ceux qui désirent étudier certains jeux en détail. Pour l'instant, "jeu favorable" veut dire, jeu dans lequel la stratégie est telle que $P({\rm lim}\,S_{n}=\infty)>0$ où Sn est le capital du joueur après n essais.
Conference Paper
Full-text available
We present a mathematical model of the game of roulette and describe the implementation of an image processing and data analysis system to successfully predict the outcome of the game. Both the case of a fair (level) and biased (tilted) wheel will be described but the focus in this presentation is the (harder) case of a perfectly fair wheel.We show that when implemented on a casino-grade roulette wheel our technique obtains an expected return of over 40%, in contrast, the expected return for an uninformed gambler is -2.7%.
Article
The Gaming Clubs (Banker's Games) Regulations, 1970, which implemented that part of the Gaming Act, 1968, concerned with bankers' games came into effect on July 1st, 1970. These regulations allowed four games--roulette, dice, baccarat and blackjack--to be played in licensed clubs and laid down the rules of play. This paper examines the properties of these four permitted games. Roulette and dice, being games of pure chance, are relatively simple to analyse, but have been considered in some detail to illustrate our approach. Work on baccarat, particularly chemin-de-fer, has been reviewed, and the optimum strategies for the banker and for the opposing player have been given for this game. It is shown that, provided the banker uses his optimum strategy, the choice of strategy by the opposing player makes no difference to the banker's expected gain. In these circumstances chemin-de-fer is, in effect, a game of pure chance unless sophisticated card-counting techniques are used. Baccarat banque and blackjack may therefore be regarded as the only bankers' games permitted under the regulations which involve skill. Some results for these two games are presented, including a near-optimum basic strategy for the player in the permitted version of blackjack.
Article
We are forced to accept as [an] alternative that the random spinning of a roulette manufactured and daily readjusted with extraordinary care is not obedient to the laws of chance, but is chaotic in its manifestations!(Karl Pearson 1894)Given integers k ≥ k0 ≥ 2, a test of fixed sample size and a sequential test are constructed for the purpose of testing the null hypothesis that max1≤i≤kpi ≤ 1/k0, against the alternative that max1≤i≤kpi > 1/k0, where p1, …, pk are the parameters of a multinomial distribution with k cells. Results are applied to the problem of testing for favorable numbers on a roulette wheel, in which case k = 38 and k0 = 36, thereby providing a partial solution to a problem posed by Wilson (1965).
Article
Man invented a concept that has since been variously viewed as a vice, a crime, a business, a pleasure, a type of magic, a disease, a folly, a weakness, a form of sexual substitution, an expression of the human instinct. He invented gambling. Recently, there has been a surge of interest in the statistical and mathematical theory behind gambling. Columbia pictures released a new movie 21 in July 2008 staring Jim Sturgess, Kevin Spacey, Kate Bosworth, Laurence Fishburne, Aaron Yoo, and Liza Lapira. Inspired by the true story of MIT students who mastered the art of card counting and took Vegas casinos for millions in winnings. Richard Epstein's classic book on gambling and its mathematical analysis covers the full range of games from penny matching, to blackjack and other casino games, to the stock market (including Black-Scholes analysis). He even considers what light statistical inference can shed on the study of paranormal phenomena. Epstein is witty and insightful, a pleasure to dip into and read and rewarding to study. The book is written at a fairly sophisticated mathematical level, this is not- Gambling for Dummies- or 'how to beat the odds' book, and a background in upper level undergraduate mathematics is essential to reading and understanding this book. o Comprehensive and exciting analysis of all major casino games and variants o Covers a wide range of interesting topics not covered in other books on the subject o Depth and breadth of its material is unique compared to other books of this nature.
Article
Advances made in understanding terrestrial dynamics through the use of VLBI are reviewed. The way in which radio signals emitted by distant quasars are used in this application of VLBI is described. Progress achieved in understanding the mechanism sustaining the polar motion, measuring fluctuations in the earth's rotation, analyzing the effects of the atmosphere on the rotation, and studying the structure of the earth's interior are addressed.
Article
The extent to which physical systems are predictable is considered with emphasis on techniques for predicting the chaotic behavior of systems with random components. Analytical techniques for describing dynamical systems are reviewed, noting the necessity of finding closed-form solutions to follow the evolution of simple systems over time. Attention is given to attractors and repeated returns to attractors after cycles through a sequence of states, e.g., a pendulum. Chaotic attractors feature small random elements that diverge rapidly, but whose motions stretch and fold to remain finite, thereby generating fractals. The state of the chaotic system is a small region of state space that can be determined to an accuracy allowed by instrumental noise. Experimentation w hich has supported the concept of chaotic systems is described.
Article
It is shown how the existence of low-dimensional chaotic dynamical systems describing turbulent fluid flow might be determined experimentally. Techniques are outlined for reconstructing phase-space pictures from the observation of a single coordinate of any dissipative dynamical system, and for determining the dimensionality of the system's attractor. These techniques are applied to a well-known simple three-dimensional chaotic dynamical system.