Skip to main content

Probability and Cumulative Dice Sums

Gambling to Optimize Expected Median Bankroll

Gambling to optimize your expected bankroll mean is extremely risky, as you wager your entire bankroll for any favorable gamble, making ruin almost inevitable. But what if, instead, we gambled not to maximize the expected bankroll mean, but the expected bankroll median?

Let the probability of winning a favorable bet be \(p\), and the net odds be \(b\). That is, if we wager \(1\) unit and win, we get back \(b\) units (in addition to our wager). Assume our betting strategy is to wager some fraction \(f\) of our bankroll, hence \(0 \leq f \leq 1\). By our assumption, our betting strategy is invariant with respect to the actual size of our bankroll, and so if we were to repeat this gamble \(n\) times with the same \(p\) and \(b\), the strategy wouldn't change. It follows we may assume an initial bankroll of size \(1\).

Let \( q = 1-p \). Now, after \(n\)  such gambles our bankroll would have a binomial distribution with probability mass function \[ \Pr(k,n,p) = \binom{n}{k} p^k q^{n-k}, \] where \(k\) is the number of wins, \(n-k\) the number of losses. Note the median occurs at \( k=n p \), corresponding to a bankroll of \[ \left(1+f\cdot b\right)^{n p} \left(1-f\right)^{n q} .\] Now, maximizing this value is equivalent to maximizing its \(\log\), which is \[ n p \log\left(1+f\cdot b\right) + n q\log\left(1-f\right) .\] But this is maximized when \[ p \log\left(1+f\cdot b\right) + q\log\left(1-f\right)\] is maximized, and this is precisely the condition for a Kelly optimal bet! It follows that if we gamble to optimize our expected median, this is equivalent to Kelly optimal betting, and hence maximizing expected log wealth.

With a little more work, we can show that the same conclusion holds if we gamble to optimize any expected quantile \(x\), with \( 0 < x < 1\). Maximizing the expected quantile \( 0 \) corresponds to "riskless" gambling, i.e. only gambling when there's no chance of a loss. Maximizing the expected quantile \( 1 \) corresponds to maximizing the expected bankroll mean, which we can refer to as the "reckless" strategy. Thus, under our assumptions, there are only three quantile maximization strategies - riskless, Kelly and reckless.

Comments

Popular posts from this blog

A Bayes' Solution to Monty Hall

For any problem involving conditional probabilities one of your greatest allies is Bayes' Theorem . Bayes' Theorem says that for two events A and B, the probability of A given B is related to the probability of B given A in a specific way. Standard notation: probability of A given B is written \( \Pr(A \mid B) \) probability of B is written \( \Pr(B) \) Bayes' Theorem: Using the notation above, Bayes' Theorem can be written:  \[ \Pr(A \mid B) = \frac{\Pr(B \mid A)\times \Pr(A)}{\Pr(B)} \] Let's apply Bayes' Theorem to the Monty Hall problem . If you recall, we're told that behind three doors there are two goats and one car, all randomly placed. We initially choose a door, and then Monty, who knows what's behind the doors, always shows us a goat behind one of the remaining doors. He can always do this as there are two goats; if we chose the car initially, Monty picks one of the two doors with a goat behind it at random. Assume we pick Door 1 an...

Baseball, Chess, Psychology and Pychometrics: Everyone Uses the Same Damn Rating System

Here's a short summary of the relationship between common models used in baseball, chess, psychology and education. The starting point for examining the connections between various extended models in these areas. The next steps include multiple attempts, guessing, ordinal and multinomial outcomes, uncertainty and volatility, multiple categories and interactions. There are also connections to standard optimization algorithms (neural  nets, simulated annealing). Baseball Common in baseball and other sports, the log5 method provides an estimate for the probability \( p \) of participant 1 beating participant 2 given respective success probabilities \( p_1, p_2 \). Also let \( q_* = 1 -p_* \) in the following. The log5 estimate of the outcome is then: \begin{align} p &= \frac{p_1 q_2}{p_1 q_2+q_1 p_2}\\   &= \frac{p_1/q_1}{p_1/q_1+p_2/q_2}\\ \frac{p}{q} &= \frac{p_1}{q_1} \cdot \frac{q_2}{p_2} \end{align} The final form uses the odds ratio , \( \frac{p}{q}...

Simplified Multinomial Kelly

Here's a simplified version for optimal Kelly bets when you have multiple outcomes (e.g. horse races). The Smoczynski & Tomkins algorithm, which is explained here (or in the original paper): https://en.wikipedia.org/wiki/Kelly_criterion#Multiple_horses Let's say there's a wager that, for every $1 you bet, will return a profit of $b if you win. Let the probability of winning be \(p\), and losing be \(q=1-p\). The original Kelly criterion says to wager only if \(b\cdot p-q > 0\) (the expected value is positive), and in this case to wager a fraction \( \frac{b\cdot p-q}{b} \) of your bankroll. But in a horse race, how do you decide which set of outcomes are favorable to bet on? It's tricky, because these wagers are mutually exclusive i.e. you can win at most one. It turns out there's a simple and intuitive method to find which bets are favorable: 1) Look at \( b\cdot p-q\) for every horse. 2) Pick any horse for which \( b\cdot p-q > 0\) and mar...