Expected Value and Losing Streak Risk in a Dice Bet
Summary
The document uses a repeated dice wager to explain expected value. A roll pays a gain when the die shows six and incurs a loss otherwise. Weighting each outcome by its probability gives an expected profit per roll; over a fixed number of independent rolls, linearity of expectation gives the expected cumulative profit. This is an average across possible sequences, not a promise that a particular short run will contain the average proportion of winning outcomes. The law of large numbers concerns averages over large samples, rather than a fixed pattern every few rolls.
A further response connects the example to risk management: even a wager with positive expected value can face long losing streaks, so bankroll survival and stake sizing matter. The post mentions Kelly sizing but does not derive a formula or analyze an optimal stake. Its simple model assumes the stated payoff and fair-die probabilities, and does not address changing odds, transaction costs, or capital constraints. The example teaches the distinction between expected profit and realized path, while leaving detailed bankroll risk analysis open.
Key ideas
- Expected profit per roll is found by weighting each payoff by its probability.
- Expected cumulative profit across a fixed number of rolls is the sum of the individual expectations.
- An expectation describes an average across outcomes, not a guaranteed result in a short sequence.
- Long losing streaks can threaten bankroll survival even when a wager has positive expected value.
- The mention of Kelly sizing highlights stake management but does not provide a full sizing analysis.
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Full text
# Profit estimation with a dice: 10 dollars for 6, -1 dollar for anything else
# Profit estimation with a dice: 10 dollars for 6, -1 dollar for anything else
I recently found the following question: What is your profit estimate throwing a dice in the long run if you get 10 dollars for each time you hit 6 and lose 1 dollar for any other number?
I tried to use probabilistic "common sense" to arrive to the following wrong answer:
Probability tells me that every 6 throws I get one 6 and 5 different numbers. So every 6 throws my profit will be:
10x1 - 1x5 = $5
So if i keep throwing the dice forever i will have a steady flow of 5 bucks and I will eventually become a millionaire.
Where am I wrong in my thinking?
## Answer by Matt Wolf (score 6)
https://quant.stackexchange.com/a/4677
Its a simple expected value question:
Probability of throwing a 6 is 1/6 Probability of not throwing a 6 is 5/6
thus expected pay off per roll: 10 dollars * 1/6 + (-1 dollar) * 5/6 = 5/6 dollars
Edit: And several of your above assumptions are plain wrong:
- "Probability tells me that every 6 throws I get one 6 and 5 different numbers." -> That is NOT what "probability" tells you. The result is an expected value, meaning, something you can expect on average given a large enough sample set. (please google "law of large numbers")
- Your profit will not necessarily be 5 dollars after 6 throws. -> please see above
- "So if i keep throwing the dice forever i will have a steady flow of 5 bucks and I will eventually become a millionaire." -> No, you will end up with infinitely large wealth if you get to roll infinitely many times.
## Answer by Rock (score 1)
https://quant.stackexchange.com/a/4700
The key here lies in risk management. The dice player must survive the long strings of losing throws to make that money. Due to the high volatility of the expected return, even with Kelly's bet sizing, you wouldn't be able to put on huge bets with respect to bankroll.
Kelly's formula http://matdays.blogspot.co.nz/2011/04/kelly-bet-sizing-equity-growth.html
## Answer by SRKX (score 0)
https://quant.stackexchange.com/a/4742
If you want to model this using probability theory, you can define a stochastic variable $X$ as follows:
$$ X = \left\{ \begin{array}{l l} 10 & \quad \text{with probability $p=\frac{1}{6}$}\\ -1 & \quad \text{with probability $1-p=\frac{5}{6}$} \end{array} \right.$$
$X$ models the payoff of a trow of dice.
As Freddy said, this is an expectation problem, so let's compute the expectation of $X$:
$$ \mathbb{E}[X] = 10 \cdot p + (-1) \cdot (1-p) = \frac{10}{6} + \frac{-5}{6} = \frac{5}{6} $$
So, on average, you make on a single throw a profit of $\frac{5}{6}=0.83$ USD.
Now, the term "on the long run" is a bit ambiguous in the question. To stay abstract and general, assume we make $n$ throws and we denote the $i$-th throw as $X_i$. We can model the result of $n$ throws as:
$$S_n= \sum_{i=1}^n X_i$$
To know the expected value of your wealth after $n$ throws, you need to compute the expectation of the variable $S_n$:
$$ \mathbb{E} [S_n] = \mathbb{E} \left[ \sum_{i=1}^n X_i \right] = \sum_{i=1}^n \mathbb{E} \left[ X_i \right] = \sum_{i=1}^n \frac{5}{6} = n \cdot \frac{5}{6}$$
So, over the long run (assumed to be $n$ throws), you can expect to make $ n \cdot \frac{5}{6}$ USD on average.
This means that you could very well throw $n$ dices and get no 6, ending up with a wealth of $-n$.
But this is basic statistics, so I'll ask the question to be moved to Stats.SE.Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.