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Two athletic teams play a series of games; the first team to win $4$ games is declared the overall winner. Each team has a probability of $.5$ of winning each game. What is the expected number of games played?

My question is what is the best way to solve this problem?

I guess I could approach it like:

$P(X = 4) = 2(.5)^{4}$ (two shots of $1$ team winning $4$ straight)

and enumerating each possibility from $X=4$ to $X=7$, then solving for $E[X]$, but what about large values of this problem like first to $50$ wins or something?

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My bad, edited. – Matt May 1 '12 at 2:46
If you start enumerating the possibilities you will see patterns, which will enable you to write down the sum that evaluates to $E[X]$; the sum may be of a type well-known to combinatorialists and amenable to summation. – Gerry Myerson May 1 '12 at 2:54
the probabilty that team A wins in 5 is negative binomial, the probability that some team team wins in 5 is just twice that. – mike May 1 '12 at 11:13
up vote 1 down vote accepted

A general strategy is to compute the expected number of games $t(xy)$ played until a team is declared the winner, starting from every possible partial score of $x$ games won by a player vs $y$ games won by the other one. Then the expected total number of games is $t(00)$.

Some simple remarks: (i) $t(xy)=t(yx)$ by symmetry; (ii) $t(x4)=0$ for every $0\leqslant x\leqslant3$; (iii) looking at the result of the first game played yields a relation between $t(xy)$ and $t((x+1)y)$ and $t(x(y+1))$, for each $xy$.

Starting from the highest possible partial scores and going backwards, one gets successively, using remarks (i), (ii) and (iii), $$t(33)=1,\quad t(23)=1+\tfrac12t(33)=\tfrac32,\quad t(22)=1+t(23)=\tfrac52, $$ $$t(13)=1+\tfrac12t(23)=\tfrac74,\quad t(03)=1+\tfrac12t(13)=\tfrac{15}8, $$ $$ t(12)=1+\tfrac12t(13)+\tfrac12t(22)=\tfrac{25}8,\quad t(02)=1+\tfrac12t(03)+\tfrac12t(12)=\tfrac72, $$ $$ t(11)=1+t(12)=\tfrac{33}8,\quad t(01)=1+\tfrac12t(11)+\tfrac12t(02)=\tfrac{77}{16}, $$ and, finally, $t(00)=1+t(01)=\frac{93}{16}$.

Edit: To check this result, note that $$ t(00)=\frac{2{3\choose 0}2^3\cdot4+2{4\choose 1}2^2\cdot5+2{5\choose 2}2^1\cdot6+{6\choose 3}2^0\cdot7}{2{3\choose 0}2^3+2{4\choose 1}2^2+2{5\choose 2}2^1+{6\choose 3}2^0}. $$

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why $t(..)=1+0.5t(..)$, and how did you checked your result, and why isn't the answer $\sum_{k=4}^{\infty}k\binom k4 (0.5)^k=18$? – ADG Nov 27 '14 at 4:53
@eaxdpiotnyeantial "why t(..)=1+0.5t(..)" Well, what can I say? This is the point of the whole answer to explain why. "and how did you checked your result" I did not, I provided a formula for the OP to check their own computations against it. "and why isn't the answer... 18" Why should it? Note that every series lasts at least 4 games and at most 7 games hence a mean number of games per series of 18 would be surprising. – Did Nov 27 '14 at 7:43
so would you help me please :( here(link)? – ADG Nov 28 '14 at 16:57
@eaxdpiotnyeantial Which part of [looking at the result of the first game played yields a relation between $t(xy)$ and $t((x+1)y)$ and $t(x(y+1))$, for each $xy$] do you fail to understand? One is at the partial scores $xy$, one plays one game, then the partial scores become $(x+1)y$ or $x(y+1)$, each with probability $\frac12$, thus $t(x,y)$ is related to the value of $t$ at these two other pairs. What is unclear in this? – Did Nov 28 '14 at 17:23
OK ${}{}{}{}{}$ – ADG Nov 28 '14 at 17:31

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