Showing posts with label System Predictions. Show all posts
Showing posts with label System Predictions. Show all posts

Martingale (betting system)

Originally, martingale referred to a class of betting strategies popular in 18th century France. The simplest of these strategies was designed for a game in which the gambler wins his stake if a coin comes up heads and loses it if the coin comes up tails. The strategy had the gambler double his bet after every loss, so that the first win would recover all previous losses plus win a profit equal to the original stake. Since a gambler with infinite wealth will, almost surely, eventually flip heads, the Martingale betting strategy was seen as a sure thing by those who advocated it. Of course, none of the gamblers in fact possessed infinite wealth, and the exponential growth of the bets would eventually bankrupt those who chose to use the Martingale. It is therefore a good example of a Taleb distribution - the gambler usually wins a small net reward, thus appearing to have a sound strategy. However, the gambler's expected value does indeed remain zero because the small probability that he will suffer a catastrophic loss exactly balances with his expected gain. It is widely believed that casinos instituted betting limits specifically to stop Martingale players, but in reality the assumptions behind the strategy are unsound. Players using the Martingale system do not have any long-term mathematical advantage over any other betting system or even randomly placed bets.

Effect of variance

As with any betting system, it sometimes happens that one achieves a better result than the expected negative return, by temporarily avoiding a losing streak. Furthermore, a straight string of losses is the only sequence of outcomes that results in a loss of money, so even when a player has lost the majority of his bets, he can still be ahead overall, since he always wins 1 unit when a bet wins, regardless of how many previous losses.

Intuitive analysis

When the expected value of the stopping time is finite (which is true in practice), the following argument explains why the betting system fails: Since expectation is linear, the expected value of a series of bets is just the sum of the expected value of each bet. Since in such games of chance the bets are independent, the expectation of each bet does not depend on whether you previously won or lost. In most casino games, the expected value of any individual bet is negative, so the sum of lots of negative numbers is also always going to be negative.

The martingale strategy fails even with unbounded stopping time, as long as there is a limit on earnings or on the bets (which are also true in practice). It is only with unbounded wealth, bets and time that the martingale strategy can succeed.

Mathematical analysis

One round of the idealized martingale without time or credit constraints can be formulated mathematically as follows. Let the coin tosses be represented by a sequence X0, X1, … of independent random variables, each of which is equal to H with probability p, and T with probability q = 1 – p. Let N be time of appearance of the first H; in other words, X0, X1, …, XN–1 = T, and XN = H. If the coin never shows H, we write N = ∞. N is itself a random variable because it depends on the random outcomes of the coin tosses.

In the first N – 1 coin tosses, the player following the martingale strategy loses 1, 2, …, 2N–1 units, accumulating a total loss of 2N − 1. On the Nth toss, there is a win of 2N units, resulting in a net gain of 1 unit over the first N tosses. For example, suppose the first four coin tosses are T, T, T, H making N = 3. The bettor loses 1, 2, and 4 units on the first three tosses, for a total loss of 7 units, then wins 8 units on the fourth toss , for a net gain of 1 unit. As long as the coin eventually shows heads, the betting player realizes a gain.

What is the probability that N = ∞, i.e., that the coin never shows heads? Clearly it can be no greater than the probability that the first k tosses are all T; this probability is qk. Unless q = 1, the only nonnegative number less than or equal to qk for all values of k is zero. It follows that N is finite with probability 1; therefore with probability 1, the coin will eventually show heads and the bettor will realize a net gain of 1 unit.

This property of the idealized version of the martingale accounts for the attraction of the idea. In practice, the idealized version can only be approximated, for two reasons. Unlimited credit to finance possibly astronomical losses during long runs of tails is not available, and there is a limit to the number of coin tosses that can be performed in any finite period of time, precluding the possibility of playing long enough to observe very long runs of tails.

As an example, consider a bettor with an available fortune, or credit, of 243 (approximately 9 trillion) units, roughly the size of the current US national debt in dollars. With this very large fortune, the player can afford to lose on the first 42 tosses, but a loss on the 43rd cannot be covered. The probability of losing on the first 42 tosses is q42, which will be a very small number unless tails are nearly certain on each toss. In the fair case where q = 1 / 2, we could expect to wait something on the order of 242 tosses before seeing 42 consecutive tails; tossing coins at the rate of one toss per second, this would require approximately 279,000 years.

This version of the game is likely to be unattractive to both players. The player with the fortune can expect to see a head and gain one unit on average every two tosses, or two seconds, corresponding to an annual income of about 31.6 million units until disaster (42 tails) occurs. This is only a 0.0036 percent return on the fortune at risk. The other player can look forward to steady losses of 31.6 million units per year until hitting an incredibly large jackpot, probably in something like 279,000 years, a period far longer than any currency has yet existed. If q > 1 / 2, this version of the game is also unfavorable to the first player in the sense that it would have negative expected winnings.

The impossibility of winning over the long run, given a limit of the size of bets or a limit in the size of one's bankroll or line of credit, is proven by the optional stopping theorem.

Mathematical analysis of a single round

Let one round be defined as a sequence of consecutive losses followed by either a win, or bankruptcy of the gambler. After a win, the gambler "resets" and is considered to have started a new round. A continuous sequence of martingale bets can thus be partitioned into a sequence of independent rounds. We will analyze the expected value of one round.

Let q be the probability of losing (e.g. for roulette in America, with two zeroes, it is 20/38 for a bet on black or red). Let B be the amount of the commencing bet. Let n be the finite number of bets you can afford to lose.

The probability that you lose all n bets is qn. When you lose all your bets, the amount of money you lose is

\sum_{i=1}^n B \cdot 2^{i-1} = B (2^n - 1)

The probability that you do not lose all n bets is 1 − qn. If you do not lose all n bets, you win B amount of money (the initial bet amount). So the expected profit per round is

(1-q^n) \cdot B - q^n \cdot B (2^n - 1) = B (1 - (2q)^n)

Whenever q > 1/2, the expression 1 − (2q)n < 0 for all n > 0. That means for any game where you are more likely to lose than to win each bet (e.g. all chance gambling games) you are expected to lose money on average per round. Furthermore, the more times you are able to afford to bet, the more you will lose.

As an example, suppose you have 6,300 available to bet. You bet 100 on the first spin. If you lose, you bet 200 on the second spin, then 400 on the third, 800 on the fourth, 1,600 on the fifth, and 3,200 on the sixth.

If you win 100 on the first spin, you make 100, and the martingale starts over.

If you lose 100 on the first spin and win 200 on the second spin, you make a net profit of 100 at which point the martingale would start over.

If you lose on the first five spins, you lose a total of 3,100 (3,100 = 100 + 200 + 400 + 800 + 1,600). On the sixth spin you bet 3,200. If you win, you again make a profit of 100.

If you lose on the first six spins, you have lost a total of 6,300 and with only 6,300 available, you do not have enough money to double your previous bet. At this point the martingale cannot be continued.

In this example the probability of losing 6,300 and being unable to continue the martingale is equal to the probability of losing 6 times or (20/38)^6 = 2.1256%. The probability of winning 100 is equal to 1 minus the probability of losing 6 times or 1 - (20/38)^6 = 97.8744%.

The expected amount won is (100 x .978744) = 97.8744 . The expected amount lost is (6,300 x .021256)= 133.9118 . So overall you can expect to lose (133.9118 - 97.8744) = 36.0374 .
[edit] An alternative mathematical analysis

The previous analysis calculates expected value, but we can ask another question: what is the chance that one can play a casino game using the Martingale strategy, and avoid the losing streak long enough to double one's bankroll.

As before, this depends on the likelihood of losing 6 roulette spins in a row assuming we are betting red/black or even/odd. Many gamblers believe that the chances of losing 6 in a row are remote, and that with a patient adherence to the strategy they will slowly increase their bankroll.

In reality, the odds of a streak of 6 losses in a row are much higher than the many people intuitively believe. Psychological studies have shown that since people know that the odds of losing 6 times in a row out of 6 plays are low, they incorrectly assume that in a longer string of plays the odds are also very low. When people are asked to invent data representing 200 coin tosses, they often do not add streaks of more than 5 because they believe that these streaks are very unlikely. This intuitive belief is sometimes referred to as the representativeness heuristic.

The odds of losing a single spin at roulette are q = 20/38 = 52.6316%. If you play a total of 6 spins, the odds of losing 6 times are q6 = 2.1256%, as stated above. However if you play more and more spins, the odds of losing 6 times in a row begin to increase rapidly.

* In 73 spins, there is a 50.3% chance that you will at some point have lost at least 6 spins in a row. (The chance of still being solvent after the first six spins is 0.978744, and the chance of becoming bankrupt at each subsequent spin is (1-0.526316)x0.021256 = 0.010069, where the first term is the chance that you won the (n-6)th spin - if you had lost the (n-6)th spin, you would have become bankrupt on the (n-1)th spin. Thus over 73 spins the probability of remaining solvent is 0.978744 x (1-0.010069)^67 = 0.49683, and thus the chance of becoming bankrupt is 1-0.49683 = 50.3%.)
* Similarly, in 150 spins, there is a 77.2% chance that you will lose at least 6 spins in a row at some point.
* And in 250 spins, there is a 91.1% chance that you will lose at least 6 spins in a row at some point.

To double the initial bankroll of 6,300 with initial bets of 100 would require a minimum of 63 spins (in the unlikely event you win every time), and a maximum of 378 spins (in the even more unlikely event that you win every single round on the sixth spin). Each round will last an average of approximately 2 spins, so, 63 rounds can be expected to take about 126 spins on average. Computer simulations show that the required number will almost never exceed 150 spins. Thus many gamblers believe that they can play the Martingale strategy with very little chance of failure long enough to double their bankroll. However, the odds of losing 6 in a row are 77.2% over 150 spins, as above.

We can replace the roulette game in the analysis with either the pass line at craps, where the odds of losing are lower q=(251:244, or 251/495)=50.7071%, or a coin toss game where the odds of losing are 50.0%. We should note that games like coin toss with no house edge are not played in a commercial casino and thus represent a limiting case.

* In 150 turns, there is a 73.5% chance that you will lose 6 times in a row on the pass line.
* In 150 turns, there is a 70.7% chance that you will lose 6 times in a row at coin tossing.

In larger casinos, the maximum table limit is higher, so you can double 7, 8, or 9 times without exceeding the limit. However, in order to end up with twice your initial bankroll, you must play even longer. The calculations produce the same results. The probabilities are overwhelming that you will reach the bust streak before you can even double your bankroll.

The conclusion is that players using Martingale strategy pose no threat to a casino. The odds are high that the player will go bust before he is able even to double his money.

Table limits are not specifically designed to prevent players from using Martingale strategy. The table limits exist so that the casino is not gambling more money than they can afford to lose. (E.g., a casino that takes in an average of $1000 a day on a roulette table might not accept a $7000 bet on black at that table; that bet would have a 18/38 chance of negating an entire week's profits.)

Anti-martingale

In a classic martingale betting style, gamblers will increase their bets after each loss in hopes that an eventual win will recover all previous losses. The anti-martingale approach instead increases bets after wins, while reducing them after a loss. The perception is that in this manner the gambler will benefit from a winning streak or a "hot hand", while reducing losses while "cold" or otherwise having a losing streak. As the single bets are independent from each other (and from the gambler's expectations), the same conclusions as above apply.

Betting Strategy vs. Card Counting

In a true random memoryless game any betting strategy (like Martingale) cannot improve the expected winnings. The odds of winning are entirely governed by the expected calculation of house edge. Classic casino games such as European Roulette has an expected loss of 19/37 for each play, American roulette is 10/19, Pass line bet in craps is 251/495, and the Don't pass Line is 1031/1980. Slot machines also have an expectation but it is legally permitted to be kept secret from the player in some countries. The acronym for the table that shows the probability for different outcomes for a given machine is its PARS, which is an acronym for Paytable and Reel Strips.

Card counting has a superficial resemblance to betting strategies because the initial wager is increased and decreased. However a deck of cards is not a random memoryless game. The strategy is based on the reality that a deck with many Aces and 10 value cards remaining is very likely to produce a blackjack (or natural). The blackjack is just as likely to go to the dealer as the player. However, the player gets paid a bonus for getting a blackjack (traditionally 50%) while the dealer only wins the original stake. If the player uses a strategy to estimate that the remaining cards in the deck are rich in 10s and Aces he increases his bet to take advantage of the increased possibility of a blackjack. This is one of the advantages of additional 10s and Aces. The traditional game previous to 1960 used a single deck of cards and was dealt nearly to the bottom of the deck. It was possible to invent strategies that could easily be taught to make good estimates of the number of Aces and 10s remaining in the deck. Card counting is not cheating because the player does not use subterfuge to know the outcome, he merely knows that the probabilities have changed to be more in his favor. So an effective card counter must have a large bankroll to cover the times when his initial bet is large, but the dealer still wins. Casinos have implemented multiple rule changes that make this estimation much more difficult. The easiest one is that the pit boss does not permit a wide range of initial wagers and invites the player to change to a different game if the pit boss realizes that the player has an advantage.

Sports betting systems

Sports betting systems refers to a set of events that when combined for a particular game for a particular sport represents a profitable betting scenario. Since sports betting involves humans, there is no deterministic edge to the house or the gambler. Systems supposedly allow the gambler to have an edge.
Sportsbooks use systems in their analysis to set more accurate odds. Therefore the novice gambler may believe that using a system will always work, it is the general consensus that at some point, the oddsmakers will have adjusted for the system to make it no longer profitable. Very short-lived systems are called trends. Any single event that estimates a selection to have a higher likelihood of winning is called an angle as they are meant to be used in conjunction with other angles and trends to produce systems.
Betting on systems
Systems can be deceiving. Any sample space can be constrained enough with meaningless criteria to create the illusion of a profitable betting system. For example, a coin can be flipped with heads being home teams and tails being road teams. Heads and tails each have a 50% probability of landing but if the amount of flips is limited to a small number, it is conceivable to create the illusion of predicting heads will come up 75% of the time.
That, in conjunction with the fact that sportsbooks adjust their odds according to the systems makes it difficult to follow systems indefinitely. The sportsbooks are slower to adjust the odds in some sports versus other sports depending on the number of games played and the amount of money they take in from bettors.
Determining systems
Determining systems is a matter of using computer analysis tools and extracting all the possible games that meet a bettor's criteria. Then the bettor analyzes the results of those games to make a determination if one team is favored over the other.
Two Types of Betting Systems
Multiple regressions
The stronger the historical info you have the more accurate your results will be. You will never be able to call a game within 100% of accuracy because there will always be undetermined agents, but if you have the right info you are surely able to anticipate the game like the pros.
Historical data you need to know to apply multiple regressions is: Wins, Losses, Win to Lose ratio, home game record, away game record, past ten game outcomes, win/loss streaks, injuries, team changes (new coach, loss of important player, etc.)
Statistical anomalies
These are deviations from the common rule and give you a competitive advantage. You may be surprised to know that most scores in a football game happen only in the 3 and 7 point markers? Of course there are always missed extra points, safeties and conversions, but when compared to all points scored, they only account for a minor percentage. This point statistical distribution factor opens up the possibility of statistical anomalies.
To find anomalies you need to cautiously review players and team statistics. You should also know significant factors such as: injuries, does the team tend to win more in indoor or outdoor sports stadiums, weather (for outdoor games), what atmospheric conditions is the team used to playing in, etc.. You can also look for anomalies based on public opinion and team psyche.
Factors that are used into determining betting systems are a mix of psychological, motivational, biological, situational factors that, based on past performances, support one team over another. It is generally believed that more than one factor pointing towards a team is needed to have a successful betting system.

Statistical association football predictions

Statistical Football prediction is a method used in sports betting, to predict the outcome of Association football matches by means of statistical tools. The goal of statistical match prediction is to outperform the predictions of bookmakers, who use them to set odds on the outcome of football matches.
The most widely-used statistical approach to prediction is ranking. Football ranking systems assign a rank to each team based on their past game results, so that the highest rank is assigned to the strongest team. The outcome of the match can be predicted by comparing the opponents’ ranks. Today, eight different football ranking systems exist: FIFA World Rankings, World Football Elo Ratings, AQB Sports Ratings, The Roon Ba, InternationalMark, Rsoccer, Mondfoot and Chance de Gol.
There are three main drawbacks to football match predictions that are based on ranking systems:
1. Ranks assigned to the teams do not differentiate between their attacking and defensive strengths.
2. Ranks are accumulated averages which do not account for skill changes in football teams.
3. The main goal of a ranking system is not to predict the results of football games, but to sort the teams according to their average strength.
Another approach to football prediction is known as rating systems. While ranking refers only to team order, rating systems assign to each team a continuously scaled strength indicator. Moreover, rating can be assigned not only to a team but to its attacking and defensive strengths, home field advantage or even to the skills of each team player (according to Stern [1]).
History
Publications about statistical models for football predictions started appearing from the 90s, but the first model was proposed much earlier by Moroney,[2] who published his first statistical analysis of soccer match results in 1956. According to his analysis, both Poisson distribution and negative binomial distribution provided an adequate fit to results of football games. The series of ball passing between players during football matches was successfully analyzed using negative binomial distribution by Reep and Benjamin [3] in 1968. They improved this method in 1971, and in 1974 Hill [4] indicated that soccer game results are to some degree predictable and not simply a matter of chance.
The first model predicting outcomes of football matches between teams with different skills was proposed by Michael Maher [5] in 1982. According to his model, the goals, which the opponents score during the game, are drawn from the Poisson distribution. The model parameters are defined by the difference between attacking and defensive skills, adjusted by the home field advantage factor. The methods for modeling the home field advantage factor were summarized in an article by Caurneya and Carron [6] in 1992. Time-dependency of team strengths was analyzed by Knorr-Held [7] in 1999. He used recursive Bayesian estimation to rate football teams: this method was more realistic in comparison to soccer prediction based on common average statistics.
Football Prediction Methods
All the prediction methods can be categorized according to tournament type, time-dependence and regression algorithm. Football prediction methods vary between Round-robin tournament and Knockout competition. The methods for Knockout competition are summarized in an article by Diego Kuonen.[8]
The table below summarizes the methods related to Round-robin tournament.

# Code Prediction Method Regression Algorithm Time Dependence Performance
1. TILS Time Independent Least Squares Rating Linear Least Squares Regression No Poor
2. TIPR Time Independent Poisson Regression Maximum Likelihood No Medium
3. TISR Time Independent Skellam Regression Maximum Likelihood No Medium
4. TDPR Time-Dependent Poisson Regression Maximum Likelihood Time dumping factor High
5. TDMC Time-Dependent Markov Chain Monte-Carlo Markov Chain model High
Time Independent Least Squares Rating
This method intends to assign to each team in the tournament a continuously scaled rating value, so that the strongest team will have the highest rating. The method is based on the assumption that the rating assigned to the rival teams is proportional to the outcome of each match.
Assume that the teams A, B, C and D are playing in a tournament and the match outcomes are as follows:
Match # Home Team Score Away Team Y
1 A 3 - 1 B y1 = 3 − 1
2 C 2 - 1 D y2 = 2 − 1
3 D 1 - 4 B y3 = 1 − 4
4 A 3 - 1 D y4 = 3 − 1
5 B 2 - 0 C y5 = 2 − 0
Though the ratings rA, rB , rC and rD of teams A, B, C and D respectively are unknown, it may be assumed that the outcome of match #1 is proportional to the difference between the ranks of teams A and B: y_{1}=r_{A}-r_{B}+\varepsilon _1. In this way, y1 corresponds to the score difference and \varepsilon _1 is the noise observation. The same assumption can be made for all the matches in the tournament:
\begin{matrix} y_{1}=r_{A}-r_{B}+\varepsilon _{1} \\ y_{2}=r_{C}-r_{D}+\varepsilon _{2} \\ ... \\ y_{5}=r_{B}-r_{C}+\varepsilon _{5} \\ \end{matrix}
By introducing a selection matrix X, the equations above can be rewritten in a compact form:
\mathbf{y}=\mathbf{Xr}+\mathbf{e}
Entries of the selection matrix can be either 1, 0 or -1, with 1 corresponding to home teams and -1 to away teams:
\begin{matrix} \mathbf{y}=\left[ \begin{matrix} 2 \\ 1 \\ -3 \\ 2 \\ 2 \\ \end{matrix} \right], & \mathbf{X}=\left[ \begin{matrix} 1 & -1 & 0 & 0 \\ 0 & 0 & 1 & -1 \\ 0 & -1 & 0 & 1 \\ 1 & 0 & 0 & -1 \\ 0 & 1 & -1 & 0 \\ \end{matrix} \right], & \mathbf{r}=\left[ \begin{matrix} r_{A} \\ r_{B} \\ r_{C} \\ r_{D} \\ \end{matrix} \right], & \mathbf{e}=\left[ \begin{matrix} \varepsilon _{1} \\ \varepsilon _{2} \\ \varepsilon _{3} \\ \varepsilon _{4} \\ \varepsilon _{5} \\ \end{matrix} \right] \\ \end{matrix}
If the matrix \mathbf{X}^{T}\mathbf{X} has full rank, the algebraic solution of the system may be found via the Least squares method:
\mathbf{r}=\left( \mathbf{X}^{T}\mathbf{X} \right)^{-1}\mathbf{X}^{T}\mathbf{y}
The final rating parameters are \mathbf{r}=[1.625,\ 0.75,\ -0.875,\ -1.5]^{T}. In this case, the strongest team has the highest rating. The advantage of this rating method compared to the standard ranking systems is that the numbers are continuously scaled, defining the precise difference between the teams’ strengths.
Time-Independent Poisson Regression
According to this model (Maher [5]), if Xi,j and Yi,j are the goals scored in the match where team i plays against team j, then:
\begin{align} & X_{i,j} \sim Poisson(\lambda ) \\ & Y_{i,j} \sim Poisson(\mu ) \\ \end{align}
Xi,j and Yi,j are independent random variables with means λ and μ. Thus, the joint probability of the home team scoring x goals and the away team scoring y goals is a product of the two independent probabilities:
P\left( X_{i,j}=x,Y_{i,j}=y \right)=\frac{\lambda ^{x}\exp (-\lambda )}{x!}\frac{\mu ^{y}\exp (-\mu )}{y!}
while the generalized log-linear model for λ and μ according to Kuonen [8] and Lee [9] is defined as: \log \left( \lambda \right)=c^{\lambda }+a_{i}-d_{j}+h and \log \left( \mu \right)=c^{\mu }+a_{j}-d_{i}, where ai,di,h > 0 refers to attacking and defensive strengths and to home field advantage respectively. cλ and cμ are correction factors which represent the means of goals scored during the season by home and away teams.
Assuming that C signifies the number of teams participating in a season and N stands for the number of matches played until now, the team strengths can be estimated by minimizing the negative log-likelihood function with respect to λ and μ:
\begin{align} & L(a_{i},d_{i},h;\ i=1,..C)=-\log \prod\limits_{n=1}^{N}{\frac{\lambda _{n}^{x_{n}}\exp (-\lambda _{n})}{x_{n}!}\frac{\mu _{n}^{y_{n}}\exp (-\mu _{n})}{y_{n}!}}=-\sum\limits_{n=1}^{N}{\log \left( \frac{\lambda _{n}^{x_{n}}\exp (-\lambda _{n})}{x_{n}!}\frac{\mu _{n}^{y_{n}}\exp (-\mu _{n})}{y_{n}!} \right)} \\ & =\sum\limits_{n=1}^{N}{\lambda _{n}}+\sum\limits_{n=1}^{N}{\mu _{n}}-\left( \sum\limits_{n=1}^{N}{x_{n}\log \left( \lambda _{n} \right)} \right)-\left( \sum\limits_{n=1}^{N}{y_{n}\log \left( \mu _{n} \right)} \right)+\sum\limits_{n=1}^{N}{\log \left( x_{n}! \right)}+\sum\limits_{n=1}^{N}{\log \left( y_{n}! \right)} \\ \end{align}
Given that xn and yn are known, the team attacking and defensive strengths \left( a_{i},d_{i} \right) and home ground advantage \left( h \right) that minimize the negative log-likelihood can be estimated by Expectation Maximization:
\underset{a_{i},d_{i},h}{\mathop{\min }}\,L(a_{i},d_{i},h,i=1,..C)
Improvements for this model were suggested by Mark Dixon (statistician) and Stuart Coles.[10] They invented a correlation factor for low scores 0-0, 1-0, 0-1 and 1-1, where the independent Poisson model doesn't hold. Dimitris Karlis and Ioannis Ntzoufras [11] built a Time-Independent Skellam distribution model. Unlike the Poisson model that fits the distribution of scores, the Skellam model fits the difference between home and away scores.
Time-Dependent Markov Chain Monte Carlo
On the one hand, statistical models require a large number of observations to make an accurate estimation of its parameters. And when there are not enough observations available during a season (as is usually the situation), working with average statistics makes sense. On the other hand, it is well-known that team skills change during the season, making model parameters time-dependent. Mark Dixon (statistician) and Coles [10] tried to solve this trade-off by assigning a larger weight to the latest match results. Rue and Salvesen [12] introduced a novel time-dependent rating method using the Markov Chain model.
They suggested modifying the generalized linear model above for λ and μ:
\begin{align} & \log \left( \lambda \right)=c^{\lambda }+a_{i}-d_{j}-\gamma \cdot \Delta _{i,j} \\ & \log \left( \mu \right)=c^{\mu }+a_{j}-d_{i}+\gamma \cdot \Delta _{i,j} \\ \end{align}
given that \Delta _{i,j}=\frac{\left( a_{i}-d_{j} \right)+\left( d_{i}-a_{j} \right)}{2} corresponds to the strength difference between teams i and j. The parameter γ > 0 then represents the psychological effects caused by underestimation of the opposing teams’ strength.
According to the model, the attacking strength \left( a \right) of team A can be described by the standard equations of Brownian motion, B_{a,A}\left( t \right), for time t1 > t0:
a_{A}^{t_{1}}=a_{A}^{t_{0}}+\left( B_{a,A}\left( t_{1}/\tau \right)-B_{a,A}\left( t_{0}/\tau \right) \right)\cdot \frac{\sigma _{a,A}}{\sqrt{1-\gamma \left( 1-{\gamma }/{2}\; \right)}}
where τ and \sigma _{a,A}^{2} refer to the loss of memory rate and to the prior attack variance respectively.
This model is based on the assumption that:
{a_{A}^{t_{1}}}/{a_{A}^{t_{0}}}\; \sim N\left( a_{A}^{t_{0}},\ \frac{t_{1}-t_{0}}{\tau }\sigma _{a,A}^{2} \right)
Assuming that three teams A, B and C are playing in the tournament and the matches are played in the following order: t0: A-B; t0: A-C; t1: B-C, the joint probability density can be expressed as:
\begin{align} & P(a_{i},d_{i},\gamma ,\,\tau ;\ A,B,C)=P\left( \lambda _{A},t_{0} \right)\cdot P\left( \lambda _{B},t_{0} \right)\cdot P\left( \lambda _{C},t_{0} \right) \\ & \times P\left( X_{A,B}=x,Y_{A,B}=y|\lambda _{A},\mu _{B},t_{0} \right)\cdot P\left( X_{A,C}=x,Y_{A,C}=y|\lambda _{A},\mu _{C},t_{0} \right) \\ & \times P\left( \lambda _{A},t_{1}|\lambda _{A},t_{0} \right)\cdot P\left( \mu _{C},t_{1}|\mu _{C},t_{0} \right) \\ \end{align}
Since analytical estimation of the parameters is difficult in this case, the Monte Carlo method is applied to estimate the parameters of the model.