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Free throws in basketball game about probability


Free throw ProbabilityFree Throw Probability and Expected Number of PointsProbability that out of their next 100 free throws, they will make between $75$ and $80$, inclusive in basketball game.2 out of 3 or 4 out of 6 free-throw problem - how does it mathematically make sense?Conditional probability - Basketball playerBinomial Distribution word problem (basketball)Probability and Basketballdice probability and correct statement to express the unsuccessA probablility puzzle of winning or losing a basketball free throw game.Conditional Probability going wrong













3












$begingroup$


Someone shoots free throws. He/She made the first one and missed the second one. From the third shot, the probability of hitting the ball equals to the free throw percentage he/she made before it. For example, if the made 87 out of 100 tries. Then the probability of making the next one is 87/100.



What is the probability of the person making the n th? Does it matter whether he makes the n-1 th free throws?










share|cite|improve this question











$endgroup$







  • 1




    $begingroup$
    I'm thinking that the question is symmetric, so the probability will be $1/2$.
    $endgroup$
    – Milten
    Mar 28 at 21:57











  • $begingroup$
    Oh, so you are looking for a conditional probability $P(X_n=1 mid X_n-1=1)$? I'll have to think about it...
    $endgroup$
    – Milten
    Mar 28 at 22:44











  • $begingroup$
    @YellowRiver - how did you come up with the formula for $P(H_5 | H_4)$? I did some messy back-of-envelop calculations and came up with $2/3$... In any case, it surely isn't $1/2$ just by the "runaway" nature of the problem.
    $endgroup$
    – antkam
    Mar 29 at 20:21











  • $begingroup$
    I modelled the system with a matrix, and found two things: The number of hits after $n$ throws is always uniformly distributed! (I tested up to $n=9$). This implies that $P(H_n|H_n-1) = 2/3$ for all $n$. (This I tested up to $n=11$). I'm sure there are nice simple arguments for these claims.
    $endgroup$
    – Milten
    Mar 29 at 22:29










  • $begingroup$
    My calculation goes like this for $n=5$: $P(H_5|H_4) = P(H_5 cap H_4) / P(H_4)$. $P(H_4) = 1/2$ (by symmetry) and $P(H_5 cap H_4) = frac12cdot frac23cdot frac34 + frac12cdotfrac13cdotfrac24 = 1/3$. So $P(H_5|H_4) = 2/3$. Do you guys agree?
    $endgroup$
    – Milten
    Mar 29 at 22:34
















3












$begingroup$


Someone shoots free throws. He/She made the first one and missed the second one. From the third shot, the probability of hitting the ball equals to the free throw percentage he/she made before it. For example, if the made 87 out of 100 tries. Then the probability of making the next one is 87/100.



What is the probability of the person making the n th? Does it matter whether he makes the n-1 th free throws?










share|cite|improve this question











$endgroup$







  • 1




    $begingroup$
    I'm thinking that the question is symmetric, so the probability will be $1/2$.
    $endgroup$
    – Milten
    Mar 28 at 21:57











  • $begingroup$
    Oh, so you are looking for a conditional probability $P(X_n=1 mid X_n-1=1)$? I'll have to think about it...
    $endgroup$
    – Milten
    Mar 28 at 22:44











  • $begingroup$
    @YellowRiver - how did you come up with the formula for $P(H_5 | H_4)$? I did some messy back-of-envelop calculations and came up with $2/3$... In any case, it surely isn't $1/2$ just by the "runaway" nature of the problem.
    $endgroup$
    – antkam
    Mar 29 at 20:21











  • $begingroup$
    I modelled the system with a matrix, and found two things: The number of hits after $n$ throws is always uniformly distributed! (I tested up to $n=9$). This implies that $P(H_n|H_n-1) = 2/3$ for all $n$. (This I tested up to $n=11$). I'm sure there are nice simple arguments for these claims.
    $endgroup$
    – Milten
    Mar 29 at 22:29










  • $begingroup$
    My calculation goes like this for $n=5$: $P(H_5|H_4) = P(H_5 cap H_4) / P(H_4)$. $P(H_4) = 1/2$ (by symmetry) and $P(H_5 cap H_4) = frac12cdot frac23cdot frac34 + frac12cdotfrac13cdotfrac24 = 1/3$. So $P(H_5|H_4) = 2/3$. Do you guys agree?
    $endgroup$
    – Milten
    Mar 29 at 22:34














3












3








3


3



$begingroup$


Someone shoots free throws. He/She made the first one and missed the second one. From the third shot, the probability of hitting the ball equals to the free throw percentage he/she made before it. For example, if the made 87 out of 100 tries. Then the probability of making the next one is 87/100.



What is the probability of the person making the n th? Does it matter whether he makes the n-1 th free throws?










share|cite|improve this question











$endgroup$




Someone shoots free throws. He/She made the first one and missed the second one. From the third shot, the probability of hitting the ball equals to the free throw percentage he/she made before it. For example, if the made 87 out of 100 tries. Then the probability of making the next one is 87/100.



What is the probability of the person making the n th? Does it matter whether he makes the n-1 th free throws?







probability






share|cite|improve this question















share|cite|improve this question













share|cite|improve this question




share|cite|improve this question








edited Mar 28 at 21:53







YellowRiver

















asked Mar 28 at 21:27









YellowRiverYellowRiver

227




227







  • 1




    $begingroup$
    I'm thinking that the question is symmetric, so the probability will be $1/2$.
    $endgroup$
    – Milten
    Mar 28 at 21:57











  • $begingroup$
    Oh, so you are looking for a conditional probability $P(X_n=1 mid X_n-1=1)$? I'll have to think about it...
    $endgroup$
    – Milten
    Mar 28 at 22:44











  • $begingroup$
    @YellowRiver - how did you come up with the formula for $P(H_5 | H_4)$? I did some messy back-of-envelop calculations and came up with $2/3$... In any case, it surely isn't $1/2$ just by the "runaway" nature of the problem.
    $endgroup$
    – antkam
    Mar 29 at 20:21











  • $begingroup$
    I modelled the system with a matrix, and found two things: The number of hits after $n$ throws is always uniformly distributed! (I tested up to $n=9$). This implies that $P(H_n|H_n-1) = 2/3$ for all $n$. (This I tested up to $n=11$). I'm sure there are nice simple arguments for these claims.
    $endgroup$
    – Milten
    Mar 29 at 22:29










  • $begingroup$
    My calculation goes like this for $n=5$: $P(H_5|H_4) = P(H_5 cap H_4) / P(H_4)$. $P(H_4) = 1/2$ (by symmetry) and $P(H_5 cap H_4) = frac12cdot frac23cdot frac34 + frac12cdotfrac13cdotfrac24 = 1/3$. So $P(H_5|H_4) = 2/3$. Do you guys agree?
    $endgroup$
    – Milten
    Mar 29 at 22:34













  • 1




    $begingroup$
    I'm thinking that the question is symmetric, so the probability will be $1/2$.
    $endgroup$
    – Milten
    Mar 28 at 21:57











  • $begingroup$
    Oh, so you are looking for a conditional probability $P(X_n=1 mid X_n-1=1)$? I'll have to think about it...
    $endgroup$
    – Milten
    Mar 28 at 22:44











  • $begingroup$
    @YellowRiver - how did you come up with the formula for $P(H_5 | H_4)$? I did some messy back-of-envelop calculations and came up with $2/3$... In any case, it surely isn't $1/2$ just by the "runaway" nature of the problem.
    $endgroup$
    – antkam
    Mar 29 at 20:21











  • $begingroup$
    I modelled the system with a matrix, and found two things: The number of hits after $n$ throws is always uniformly distributed! (I tested up to $n=9$). This implies that $P(H_n|H_n-1) = 2/3$ for all $n$. (This I tested up to $n=11$). I'm sure there are nice simple arguments for these claims.
    $endgroup$
    – Milten
    Mar 29 at 22:29










  • $begingroup$
    My calculation goes like this for $n=5$: $P(H_5|H_4) = P(H_5 cap H_4) / P(H_4)$. $P(H_4) = 1/2$ (by symmetry) and $P(H_5 cap H_4) = frac12cdot frac23cdot frac34 + frac12cdotfrac13cdotfrac24 = 1/3$. So $P(H_5|H_4) = 2/3$. Do you guys agree?
    $endgroup$
    – Milten
    Mar 29 at 22:34








1




1




$begingroup$
I'm thinking that the question is symmetric, so the probability will be $1/2$.
$endgroup$
– Milten
Mar 28 at 21:57





$begingroup$
I'm thinking that the question is symmetric, so the probability will be $1/2$.
$endgroup$
– Milten
Mar 28 at 21:57













$begingroup$
Oh, so you are looking for a conditional probability $P(X_n=1 mid X_n-1=1)$? I'll have to think about it...
$endgroup$
– Milten
Mar 28 at 22:44





$begingroup$
Oh, so you are looking for a conditional probability $P(X_n=1 mid X_n-1=1)$? I'll have to think about it...
$endgroup$
– Milten
Mar 28 at 22:44













$begingroup$
@YellowRiver - how did you come up with the formula for $P(H_5 | H_4)$? I did some messy back-of-envelop calculations and came up with $2/3$... In any case, it surely isn't $1/2$ just by the "runaway" nature of the problem.
$endgroup$
– antkam
Mar 29 at 20:21





$begingroup$
@YellowRiver - how did you come up with the formula for $P(H_5 | H_4)$? I did some messy back-of-envelop calculations and came up with $2/3$... In any case, it surely isn't $1/2$ just by the "runaway" nature of the problem.
$endgroup$
– antkam
Mar 29 at 20:21













$begingroup$
I modelled the system with a matrix, and found two things: The number of hits after $n$ throws is always uniformly distributed! (I tested up to $n=9$). This implies that $P(H_n|H_n-1) = 2/3$ for all $n$. (This I tested up to $n=11$). I'm sure there are nice simple arguments for these claims.
$endgroup$
– Milten
Mar 29 at 22:29




$begingroup$
I modelled the system with a matrix, and found two things: The number of hits after $n$ throws is always uniformly distributed! (I tested up to $n=9$). This implies that $P(H_n|H_n-1) = 2/3$ for all $n$. (This I tested up to $n=11$). I'm sure there are nice simple arguments for these claims.
$endgroup$
– Milten
Mar 29 at 22:29












$begingroup$
My calculation goes like this for $n=5$: $P(H_5|H_4) = P(H_5 cap H_4) / P(H_4)$. $P(H_4) = 1/2$ (by symmetry) and $P(H_5 cap H_4) = frac12cdot frac23cdot frac34 + frac12cdotfrac13cdotfrac24 = 1/3$. So $P(H_5|H_4) = 2/3$. Do you guys agree?
$endgroup$
– Milten
Mar 29 at 22:34





$begingroup$
My calculation goes like this for $n=5$: $P(H_5|H_4) = P(H_5 cap H_4) / P(H_4)$. $P(H_4) = 1/2$ (by symmetry) and $P(H_5 cap H_4) = frac12cdot frac23cdot frac34 + frac12cdotfrac13cdotfrac24 = 1/3$. So $P(H_5|H_4) = 2/3$. Do you guys agree?
$endgroup$
– Milten
Mar 29 at 22:34











2 Answers
2






active

oldest

votes


















3












$begingroup$

I will work out the probability of making the $(n+1)$'th throw, given that they made the $n$'th thow. Let $H_n$ be the event that they hit on the $n$'th shot, and let $K_n$ be the total number of hits after $n$ shots.



I will first prove by induction, that after $n$ throws there is an equal probability of having any number of hits. I.e., we have $P(K_n=k) = frac1n-1$ for all $1le k le n-1$.



The case $n=2$ is trivial. Assume $nge3$. We can get $K_n+1=k$ in two ways: Hitting after $k-1$ hits, or missing after $k$ hits. This means:
$$
P(K_n+1=k)
= frac1n-1 cdot frack-1n
+ frac1n-1 cdot fracn-kn
= frac1n
$$



Note that this works even for the edge cases $k=1$ and $k=n$. Now we come back to the original problem. We want to work out:
$$
P(H_n+1|H_n) = fracP(H_n+1 cap H_n)P(H_n)
$$



for $nge 3$. By the symmetry of the setup, we have simply $P(H_n)=1/2$. We can calculate $P(H_n+1 cap H_n)$ by splitting up in the cases for $K_n-1$:
$$ beginsplit
P(H_n+1 cap H_n)
&= sum_k=1^n-2 frac1n-2 cdot frackn-1cdotfrack+1n
= frac1n(n-1)(n-2) left(sum_k=1^n-2 k(k+1)right) \
&= frac1n(n-1)(n-2) cdot fracn(n-1)(n-2)3
= frac13
endsplit
$$



So in the end we get
$$
P(H_n+1|H_n) = frac1/31/2
= frac23
$$



Note that the result is independent of $n$!



Edit:



On antkam's suggestion, I'll prove my observation in the comments. I claim that all sequences of $n$ shots that have the same number of hits are equally likely. (This can actually be proven from the property I proved inductively above, but I'll do it the other way round). For example $P(HHMM) = P(HMMH) = P(MHMH) = ldots$, where $H$ is a hit and $M$ is a miss. Since the first two shots are fixed, the sequences begin at the third shot. This is interesting, because while there are less ways to get a very high or low number of hits, each of those sequences are more likely because of the setup. These tendencies exactly cancel out to give the uniform distribution of $K_n$.



My precise claim is this:
$$
P(S_3S_4cdots S_n+1) = frack!(n-k-1)!n!
$$

for any $n+1 ge 3$, where $S_i$ is the outcome of the $i$'th shot (hit or miss), and $k$ is the total number hits.



Let $p_n = P(S_n = H)$ and $q_n = P(S_n = M)$. Note that we can write
$$
P(S_3S_4cdots S_n+1)
= prod_S_i=Hp_i cdot prod_S_i=Mq_i
$$

We have $p_i=fracK_ii-1$ and $q_i=fraci-1-K_ii-1$. Since the sequence goes from the third to the $(n+1)$'th shot, we get a denominator of $n!$ when we multiply all the probabilities.



Let's consider the numerator. If we hit on the $i$'th shot ($S_i = H$), then
$$
p_i+1 = fracK_i+1i, quad q_i+1=fraci-(K_i+1)i
$$



and if $S_i = M$:
$$
p_i+1 = fracK_ii, quad q_i+1=fraci-K_ii
$$



We see that when we hit, the numerator of $p$ goes up by 1, and when we miss, the numerator of $q$ goes up by 1. Meanwhile the other numerators are unchanged.
Note that $p_3 = q_3 = 1/2$, so they both start with a numerator of 1. Thus, multiplying all the numerators together, we get $k!(n-k-1)!$, which proves the claim.



So how does this get us the uniform distribution of $K_n$? If $K_n+1 = k$, then the sequence of $n-1$ shots will have $k$ hits. There are $binomn-1k$ such sequences, all equally likely, so we rediscover the result that:
$$
P(K_n+1=k) = frack!(n-k-1)!n!cdot binomn-1k
= frack!(n-k-1)!n! cdot frac(n-1)!k!(n-k-1)!
= frac1n
$$






share|cite|improve this answer










New contributor




Milten is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






$endgroup$












  • $begingroup$
    Although it is interesting to see the uniform distribution of $K_n$, I would love to see an answer that doesn't calculate those probabilities. I will add that I came across even more symmetry while thinking about the problem: It seems that any way to get $k$ hits after $n$ throws is equally likely. E.g. we have $P(HMMHH) = P(HHHMM) = P(MHMMH) = ldots$, where $H$ is hit and $M$ is miss. I haven't proven this.
    $endgroup$
    – Milten
    Mar 29 at 23:49











  • $begingroup$
    I don't think they are equally likely. The denominator of the probability is always n! and different consequences of hitting the ball($HHHMM$ or $MHMMH$) clearly lead to different results in the numerator.
    $endgroup$
    – YellowRiver
    Mar 30 at 0:51







  • 2




    $begingroup$
    Oops, that was a typo - there has to be the same number of $H$'s. I believe the probability will always be $k!(n-k)!/(n+1)!$. This cancels out nicely with a binomial coefficient counting the number of ways to get exactly $k$ hits after $n$ shots.
    $endgroup$
    – Milten
    Mar 30 at 1:04







  • 1




    $begingroup$
    I initially got the same answer as you actually, until I turned to the definition of conditional probability. Your method looks like the law of total probability, but the law looks different with conditional probabilities. We actually have $P(H_5|H_4) = P(H_5|H_4cap K_3=1)P(K_3=1|H_4)$ $ + P(H_5|H_4cap K_3=2)P(K_3=2|H_4)$. Now $P(K_3=k|H_4)$ can be worked out with Baye's rule to be $k/3$. So if you replace the two $(1/2)$'s in your expression with $1/3$ and $2/3$, then you get the correct answer of $2/3$.
    $endgroup$
    – Milten
    2 days ago







  • 1




    $begingroup$
    (You can find the formula on the wiki page for Law of Total Probability). I think what is wrong with your way intuitively, is that when we now $X_4 = H$, it changes not only the probabilities after, but also before the fourth throw: If we hit on $n$, it is more likely that we had a high number of hits just before.
    $endgroup$
    – Milten
    2 days ago


















0












$begingroup$

Technically if he made the first one, he could not fail the next one since he did 1 out 1 and this success probability was $100%$.






share|cite|improve this answer








New contributor




Eureka is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






$endgroup$












  • $begingroup$
    The rule starts from the third shot.
    $endgroup$
    – YellowRiver
    Mar 28 at 21:51











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2 Answers
2






active

oldest

votes








2 Answers
2






active

oldest

votes









active

oldest

votes






active

oldest

votes









3












$begingroup$

I will work out the probability of making the $(n+1)$'th throw, given that they made the $n$'th thow. Let $H_n$ be the event that they hit on the $n$'th shot, and let $K_n$ be the total number of hits after $n$ shots.



I will first prove by induction, that after $n$ throws there is an equal probability of having any number of hits. I.e., we have $P(K_n=k) = frac1n-1$ for all $1le k le n-1$.



The case $n=2$ is trivial. Assume $nge3$. We can get $K_n+1=k$ in two ways: Hitting after $k-1$ hits, or missing after $k$ hits. This means:
$$
P(K_n+1=k)
= frac1n-1 cdot frack-1n
+ frac1n-1 cdot fracn-kn
= frac1n
$$



Note that this works even for the edge cases $k=1$ and $k=n$. Now we come back to the original problem. We want to work out:
$$
P(H_n+1|H_n) = fracP(H_n+1 cap H_n)P(H_n)
$$



for $nge 3$. By the symmetry of the setup, we have simply $P(H_n)=1/2$. We can calculate $P(H_n+1 cap H_n)$ by splitting up in the cases for $K_n-1$:
$$ beginsplit
P(H_n+1 cap H_n)
&= sum_k=1^n-2 frac1n-2 cdot frackn-1cdotfrack+1n
= frac1n(n-1)(n-2) left(sum_k=1^n-2 k(k+1)right) \
&= frac1n(n-1)(n-2) cdot fracn(n-1)(n-2)3
= frac13
endsplit
$$



So in the end we get
$$
P(H_n+1|H_n) = frac1/31/2
= frac23
$$



Note that the result is independent of $n$!



Edit:



On antkam's suggestion, I'll prove my observation in the comments. I claim that all sequences of $n$ shots that have the same number of hits are equally likely. (This can actually be proven from the property I proved inductively above, but I'll do it the other way round). For example $P(HHMM) = P(HMMH) = P(MHMH) = ldots$, where $H$ is a hit and $M$ is a miss. Since the first two shots are fixed, the sequences begin at the third shot. This is interesting, because while there are less ways to get a very high or low number of hits, each of those sequences are more likely because of the setup. These tendencies exactly cancel out to give the uniform distribution of $K_n$.



My precise claim is this:
$$
P(S_3S_4cdots S_n+1) = frack!(n-k-1)!n!
$$

for any $n+1 ge 3$, where $S_i$ is the outcome of the $i$'th shot (hit or miss), and $k$ is the total number hits.



Let $p_n = P(S_n = H)$ and $q_n = P(S_n = M)$. Note that we can write
$$
P(S_3S_4cdots S_n+1)
= prod_S_i=Hp_i cdot prod_S_i=Mq_i
$$

We have $p_i=fracK_ii-1$ and $q_i=fraci-1-K_ii-1$. Since the sequence goes from the third to the $(n+1)$'th shot, we get a denominator of $n!$ when we multiply all the probabilities.



Let's consider the numerator. If we hit on the $i$'th shot ($S_i = H$), then
$$
p_i+1 = fracK_i+1i, quad q_i+1=fraci-(K_i+1)i
$$



and if $S_i = M$:
$$
p_i+1 = fracK_ii, quad q_i+1=fraci-K_ii
$$



We see that when we hit, the numerator of $p$ goes up by 1, and when we miss, the numerator of $q$ goes up by 1. Meanwhile the other numerators are unchanged.
Note that $p_3 = q_3 = 1/2$, so they both start with a numerator of 1. Thus, multiplying all the numerators together, we get $k!(n-k-1)!$, which proves the claim.



So how does this get us the uniform distribution of $K_n$? If $K_n+1 = k$, then the sequence of $n-1$ shots will have $k$ hits. There are $binomn-1k$ such sequences, all equally likely, so we rediscover the result that:
$$
P(K_n+1=k) = frack!(n-k-1)!n!cdot binomn-1k
= frack!(n-k-1)!n! cdot frac(n-1)!k!(n-k-1)!
= frac1n
$$






share|cite|improve this answer










New contributor




Milten is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






$endgroup$












  • $begingroup$
    Although it is interesting to see the uniform distribution of $K_n$, I would love to see an answer that doesn't calculate those probabilities. I will add that I came across even more symmetry while thinking about the problem: It seems that any way to get $k$ hits after $n$ throws is equally likely. E.g. we have $P(HMMHH) = P(HHHMM) = P(MHMMH) = ldots$, where $H$ is hit and $M$ is miss. I haven't proven this.
    $endgroup$
    – Milten
    Mar 29 at 23:49











  • $begingroup$
    I don't think they are equally likely. The denominator of the probability is always n! and different consequences of hitting the ball($HHHMM$ or $MHMMH$) clearly lead to different results in the numerator.
    $endgroup$
    – YellowRiver
    Mar 30 at 0:51







  • 2




    $begingroup$
    Oops, that was a typo - there has to be the same number of $H$'s. I believe the probability will always be $k!(n-k)!/(n+1)!$. This cancels out nicely with a binomial coefficient counting the number of ways to get exactly $k$ hits after $n$ shots.
    $endgroup$
    – Milten
    Mar 30 at 1:04







  • 1




    $begingroup$
    I initially got the same answer as you actually, until I turned to the definition of conditional probability. Your method looks like the law of total probability, but the law looks different with conditional probabilities. We actually have $P(H_5|H_4) = P(H_5|H_4cap K_3=1)P(K_3=1|H_4)$ $ + P(H_5|H_4cap K_3=2)P(K_3=2|H_4)$. Now $P(K_3=k|H_4)$ can be worked out with Baye's rule to be $k/3$. So if you replace the two $(1/2)$'s in your expression with $1/3$ and $2/3$, then you get the correct answer of $2/3$.
    $endgroup$
    – Milten
    2 days ago







  • 1




    $begingroup$
    (You can find the formula on the wiki page for Law of Total Probability). I think what is wrong with your way intuitively, is that when we now $X_4 = H$, it changes not only the probabilities after, but also before the fourth throw: If we hit on $n$, it is more likely that we had a high number of hits just before.
    $endgroup$
    – Milten
    2 days ago















3












$begingroup$

I will work out the probability of making the $(n+1)$'th throw, given that they made the $n$'th thow. Let $H_n$ be the event that they hit on the $n$'th shot, and let $K_n$ be the total number of hits after $n$ shots.



I will first prove by induction, that after $n$ throws there is an equal probability of having any number of hits. I.e., we have $P(K_n=k) = frac1n-1$ for all $1le k le n-1$.



The case $n=2$ is trivial. Assume $nge3$. We can get $K_n+1=k$ in two ways: Hitting after $k-1$ hits, or missing after $k$ hits. This means:
$$
P(K_n+1=k)
= frac1n-1 cdot frack-1n
+ frac1n-1 cdot fracn-kn
= frac1n
$$



Note that this works even for the edge cases $k=1$ and $k=n$. Now we come back to the original problem. We want to work out:
$$
P(H_n+1|H_n) = fracP(H_n+1 cap H_n)P(H_n)
$$



for $nge 3$. By the symmetry of the setup, we have simply $P(H_n)=1/2$. We can calculate $P(H_n+1 cap H_n)$ by splitting up in the cases for $K_n-1$:
$$ beginsplit
P(H_n+1 cap H_n)
&= sum_k=1^n-2 frac1n-2 cdot frackn-1cdotfrack+1n
= frac1n(n-1)(n-2) left(sum_k=1^n-2 k(k+1)right) \
&= frac1n(n-1)(n-2) cdot fracn(n-1)(n-2)3
= frac13
endsplit
$$



So in the end we get
$$
P(H_n+1|H_n) = frac1/31/2
= frac23
$$



Note that the result is independent of $n$!



Edit:



On antkam's suggestion, I'll prove my observation in the comments. I claim that all sequences of $n$ shots that have the same number of hits are equally likely. (This can actually be proven from the property I proved inductively above, but I'll do it the other way round). For example $P(HHMM) = P(HMMH) = P(MHMH) = ldots$, where $H$ is a hit and $M$ is a miss. Since the first two shots are fixed, the sequences begin at the third shot. This is interesting, because while there are less ways to get a very high or low number of hits, each of those sequences are more likely because of the setup. These tendencies exactly cancel out to give the uniform distribution of $K_n$.



My precise claim is this:
$$
P(S_3S_4cdots S_n+1) = frack!(n-k-1)!n!
$$

for any $n+1 ge 3$, where $S_i$ is the outcome of the $i$'th shot (hit or miss), and $k$ is the total number hits.



Let $p_n = P(S_n = H)$ and $q_n = P(S_n = M)$. Note that we can write
$$
P(S_3S_4cdots S_n+1)
= prod_S_i=Hp_i cdot prod_S_i=Mq_i
$$

We have $p_i=fracK_ii-1$ and $q_i=fraci-1-K_ii-1$. Since the sequence goes from the third to the $(n+1)$'th shot, we get a denominator of $n!$ when we multiply all the probabilities.



Let's consider the numerator. If we hit on the $i$'th shot ($S_i = H$), then
$$
p_i+1 = fracK_i+1i, quad q_i+1=fraci-(K_i+1)i
$$



and if $S_i = M$:
$$
p_i+1 = fracK_ii, quad q_i+1=fraci-K_ii
$$



We see that when we hit, the numerator of $p$ goes up by 1, and when we miss, the numerator of $q$ goes up by 1. Meanwhile the other numerators are unchanged.
Note that $p_3 = q_3 = 1/2$, so they both start with a numerator of 1. Thus, multiplying all the numerators together, we get $k!(n-k-1)!$, which proves the claim.



So how does this get us the uniform distribution of $K_n$? If $K_n+1 = k$, then the sequence of $n-1$ shots will have $k$ hits. There are $binomn-1k$ such sequences, all equally likely, so we rediscover the result that:
$$
P(K_n+1=k) = frack!(n-k-1)!n!cdot binomn-1k
= frack!(n-k-1)!n! cdot frac(n-1)!k!(n-k-1)!
= frac1n
$$






share|cite|improve this answer










New contributor




Milten is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






$endgroup$












  • $begingroup$
    Although it is interesting to see the uniform distribution of $K_n$, I would love to see an answer that doesn't calculate those probabilities. I will add that I came across even more symmetry while thinking about the problem: It seems that any way to get $k$ hits after $n$ throws is equally likely. E.g. we have $P(HMMHH) = P(HHHMM) = P(MHMMH) = ldots$, where $H$ is hit and $M$ is miss. I haven't proven this.
    $endgroup$
    – Milten
    Mar 29 at 23:49











  • $begingroup$
    I don't think they are equally likely. The denominator of the probability is always n! and different consequences of hitting the ball($HHHMM$ or $MHMMH$) clearly lead to different results in the numerator.
    $endgroup$
    – YellowRiver
    Mar 30 at 0:51







  • 2




    $begingroup$
    Oops, that was a typo - there has to be the same number of $H$'s. I believe the probability will always be $k!(n-k)!/(n+1)!$. This cancels out nicely with a binomial coefficient counting the number of ways to get exactly $k$ hits after $n$ shots.
    $endgroup$
    – Milten
    Mar 30 at 1:04







  • 1




    $begingroup$
    I initially got the same answer as you actually, until I turned to the definition of conditional probability. Your method looks like the law of total probability, but the law looks different with conditional probabilities. We actually have $P(H_5|H_4) = P(H_5|H_4cap K_3=1)P(K_3=1|H_4)$ $ + P(H_5|H_4cap K_3=2)P(K_3=2|H_4)$. Now $P(K_3=k|H_4)$ can be worked out with Baye's rule to be $k/3$. So if you replace the two $(1/2)$'s in your expression with $1/3$ and $2/3$, then you get the correct answer of $2/3$.
    $endgroup$
    – Milten
    2 days ago







  • 1




    $begingroup$
    (You can find the formula on the wiki page for Law of Total Probability). I think what is wrong with your way intuitively, is that when we now $X_4 = H$, it changes not only the probabilities after, but also before the fourth throw: If we hit on $n$, it is more likely that we had a high number of hits just before.
    $endgroup$
    – Milten
    2 days ago













3












3








3





$begingroup$

I will work out the probability of making the $(n+1)$'th throw, given that they made the $n$'th thow. Let $H_n$ be the event that they hit on the $n$'th shot, and let $K_n$ be the total number of hits after $n$ shots.



I will first prove by induction, that after $n$ throws there is an equal probability of having any number of hits. I.e., we have $P(K_n=k) = frac1n-1$ for all $1le k le n-1$.



The case $n=2$ is trivial. Assume $nge3$. We can get $K_n+1=k$ in two ways: Hitting after $k-1$ hits, or missing after $k$ hits. This means:
$$
P(K_n+1=k)
= frac1n-1 cdot frack-1n
+ frac1n-1 cdot fracn-kn
= frac1n
$$



Note that this works even for the edge cases $k=1$ and $k=n$. Now we come back to the original problem. We want to work out:
$$
P(H_n+1|H_n) = fracP(H_n+1 cap H_n)P(H_n)
$$



for $nge 3$. By the symmetry of the setup, we have simply $P(H_n)=1/2$. We can calculate $P(H_n+1 cap H_n)$ by splitting up in the cases for $K_n-1$:
$$ beginsplit
P(H_n+1 cap H_n)
&= sum_k=1^n-2 frac1n-2 cdot frackn-1cdotfrack+1n
= frac1n(n-1)(n-2) left(sum_k=1^n-2 k(k+1)right) \
&= frac1n(n-1)(n-2) cdot fracn(n-1)(n-2)3
= frac13
endsplit
$$



So in the end we get
$$
P(H_n+1|H_n) = frac1/31/2
= frac23
$$



Note that the result is independent of $n$!



Edit:



On antkam's suggestion, I'll prove my observation in the comments. I claim that all sequences of $n$ shots that have the same number of hits are equally likely. (This can actually be proven from the property I proved inductively above, but I'll do it the other way round). For example $P(HHMM) = P(HMMH) = P(MHMH) = ldots$, where $H$ is a hit and $M$ is a miss. Since the first two shots are fixed, the sequences begin at the third shot. This is interesting, because while there are less ways to get a very high or low number of hits, each of those sequences are more likely because of the setup. These tendencies exactly cancel out to give the uniform distribution of $K_n$.



My precise claim is this:
$$
P(S_3S_4cdots S_n+1) = frack!(n-k-1)!n!
$$

for any $n+1 ge 3$, where $S_i$ is the outcome of the $i$'th shot (hit or miss), and $k$ is the total number hits.



Let $p_n = P(S_n = H)$ and $q_n = P(S_n = M)$. Note that we can write
$$
P(S_3S_4cdots S_n+1)
= prod_S_i=Hp_i cdot prod_S_i=Mq_i
$$

We have $p_i=fracK_ii-1$ and $q_i=fraci-1-K_ii-1$. Since the sequence goes from the third to the $(n+1)$'th shot, we get a denominator of $n!$ when we multiply all the probabilities.



Let's consider the numerator. If we hit on the $i$'th shot ($S_i = H$), then
$$
p_i+1 = fracK_i+1i, quad q_i+1=fraci-(K_i+1)i
$$



and if $S_i = M$:
$$
p_i+1 = fracK_ii, quad q_i+1=fraci-K_ii
$$



We see that when we hit, the numerator of $p$ goes up by 1, and when we miss, the numerator of $q$ goes up by 1. Meanwhile the other numerators are unchanged.
Note that $p_3 = q_3 = 1/2$, so they both start with a numerator of 1. Thus, multiplying all the numerators together, we get $k!(n-k-1)!$, which proves the claim.



So how does this get us the uniform distribution of $K_n$? If $K_n+1 = k$, then the sequence of $n-1$ shots will have $k$ hits. There are $binomn-1k$ such sequences, all equally likely, so we rediscover the result that:
$$
P(K_n+1=k) = frack!(n-k-1)!n!cdot binomn-1k
= frack!(n-k-1)!n! cdot frac(n-1)!k!(n-k-1)!
= frac1n
$$






share|cite|improve this answer










New contributor




Milten is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






$endgroup$



I will work out the probability of making the $(n+1)$'th throw, given that they made the $n$'th thow. Let $H_n$ be the event that they hit on the $n$'th shot, and let $K_n$ be the total number of hits after $n$ shots.



I will first prove by induction, that after $n$ throws there is an equal probability of having any number of hits. I.e., we have $P(K_n=k) = frac1n-1$ for all $1le k le n-1$.



The case $n=2$ is trivial. Assume $nge3$. We can get $K_n+1=k$ in two ways: Hitting after $k-1$ hits, or missing after $k$ hits. This means:
$$
P(K_n+1=k)
= frac1n-1 cdot frack-1n
+ frac1n-1 cdot fracn-kn
= frac1n
$$



Note that this works even for the edge cases $k=1$ and $k=n$. Now we come back to the original problem. We want to work out:
$$
P(H_n+1|H_n) = fracP(H_n+1 cap H_n)P(H_n)
$$



for $nge 3$. By the symmetry of the setup, we have simply $P(H_n)=1/2$. We can calculate $P(H_n+1 cap H_n)$ by splitting up in the cases for $K_n-1$:
$$ beginsplit
P(H_n+1 cap H_n)
&= sum_k=1^n-2 frac1n-2 cdot frackn-1cdotfrack+1n
= frac1n(n-1)(n-2) left(sum_k=1^n-2 k(k+1)right) \
&= frac1n(n-1)(n-2) cdot fracn(n-1)(n-2)3
= frac13
endsplit
$$



So in the end we get
$$
P(H_n+1|H_n) = frac1/31/2
= frac23
$$



Note that the result is independent of $n$!



Edit:



On antkam's suggestion, I'll prove my observation in the comments. I claim that all sequences of $n$ shots that have the same number of hits are equally likely. (This can actually be proven from the property I proved inductively above, but I'll do it the other way round). For example $P(HHMM) = P(HMMH) = P(MHMH) = ldots$, where $H$ is a hit and $M$ is a miss. Since the first two shots are fixed, the sequences begin at the third shot. This is interesting, because while there are less ways to get a very high or low number of hits, each of those sequences are more likely because of the setup. These tendencies exactly cancel out to give the uniform distribution of $K_n$.



My precise claim is this:
$$
P(S_3S_4cdots S_n+1) = frack!(n-k-1)!n!
$$

for any $n+1 ge 3$, where $S_i$ is the outcome of the $i$'th shot (hit or miss), and $k$ is the total number hits.



Let $p_n = P(S_n = H)$ and $q_n = P(S_n = M)$. Note that we can write
$$
P(S_3S_4cdots S_n+1)
= prod_S_i=Hp_i cdot prod_S_i=Mq_i
$$

We have $p_i=fracK_ii-1$ and $q_i=fraci-1-K_ii-1$. Since the sequence goes from the third to the $(n+1)$'th shot, we get a denominator of $n!$ when we multiply all the probabilities.



Let's consider the numerator. If we hit on the $i$'th shot ($S_i = H$), then
$$
p_i+1 = fracK_i+1i, quad q_i+1=fraci-(K_i+1)i
$$



and if $S_i = M$:
$$
p_i+1 = fracK_ii, quad q_i+1=fraci-K_ii
$$



We see that when we hit, the numerator of $p$ goes up by 1, and when we miss, the numerator of $q$ goes up by 1. Meanwhile the other numerators are unchanged.
Note that $p_3 = q_3 = 1/2$, so they both start with a numerator of 1. Thus, multiplying all the numerators together, we get $k!(n-k-1)!$, which proves the claim.



So how does this get us the uniform distribution of $K_n$? If $K_n+1 = k$, then the sequence of $n-1$ shots will have $k$ hits. There are $binomn-1k$ such sequences, all equally likely, so we rediscover the result that:
$$
P(K_n+1=k) = frack!(n-k-1)!n!cdot binomn-1k
= frack!(n-k-1)!n! cdot frac(n-1)!k!(n-k-1)!
= frac1n
$$







share|cite|improve this answer










New contributor




Milten is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.









share|cite|improve this answer



share|cite|improve this answer








edited 2 days ago





















New contributor




Milten is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.









answered Mar 29 at 23:42









MiltenMilten

3746




3746




New contributor




Milten is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.





New contributor





Milten is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






Milten is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.











  • $begingroup$
    Although it is interesting to see the uniform distribution of $K_n$, I would love to see an answer that doesn't calculate those probabilities. I will add that I came across even more symmetry while thinking about the problem: It seems that any way to get $k$ hits after $n$ throws is equally likely. E.g. we have $P(HMMHH) = P(HHHMM) = P(MHMMH) = ldots$, where $H$ is hit and $M$ is miss. I haven't proven this.
    $endgroup$
    – Milten
    Mar 29 at 23:49











  • $begingroup$
    I don't think they are equally likely. The denominator of the probability is always n! and different consequences of hitting the ball($HHHMM$ or $MHMMH$) clearly lead to different results in the numerator.
    $endgroup$
    – YellowRiver
    Mar 30 at 0:51







  • 2




    $begingroup$
    Oops, that was a typo - there has to be the same number of $H$'s. I believe the probability will always be $k!(n-k)!/(n+1)!$. This cancels out nicely with a binomial coefficient counting the number of ways to get exactly $k$ hits after $n$ shots.
    $endgroup$
    – Milten
    Mar 30 at 1:04







  • 1




    $begingroup$
    I initially got the same answer as you actually, until I turned to the definition of conditional probability. Your method looks like the law of total probability, but the law looks different with conditional probabilities. We actually have $P(H_5|H_4) = P(H_5|H_4cap K_3=1)P(K_3=1|H_4)$ $ + P(H_5|H_4cap K_3=2)P(K_3=2|H_4)$. Now $P(K_3=k|H_4)$ can be worked out with Baye's rule to be $k/3$. So if you replace the two $(1/2)$'s in your expression with $1/3$ and $2/3$, then you get the correct answer of $2/3$.
    $endgroup$
    – Milten
    2 days ago







  • 1




    $begingroup$
    (You can find the formula on the wiki page for Law of Total Probability). I think what is wrong with your way intuitively, is that when we now $X_4 = H$, it changes not only the probabilities after, but also before the fourth throw: If we hit on $n$, it is more likely that we had a high number of hits just before.
    $endgroup$
    – Milten
    2 days ago
















  • $begingroup$
    Although it is interesting to see the uniform distribution of $K_n$, I would love to see an answer that doesn't calculate those probabilities. I will add that I came across even more symmetry while thinking about the problem: It seems that any way to get $k$ hits after $n$ throws is equally likely. E.g. we have $P(HMMHH) = P(HHHMM) = P(MHMMH) = ldots$, where $H$ is hit and $M$ is miss. I haven't proven this.
    $endgroup$
    – Milten
    Mar 29 at 23:49











  • $begingroup$
    I don't think they are equally likely. The denominator of the probability is always n! and different consequences of hitting the ball($HHHMM$ or $MHMMH$) clearly lead to different results in the numerator.
    $endgroup$
    – YellowRiver
    Mar 30 at 0:51







  • 2




    $begingroup$
    Oops, that was a typo - there has to be the same number of $H$'s. I believe the probability will always be $k!(n-k)!/(n+1)!$. This cancels out nicely with a binomial coefficient counting the number of ways to get exactly $k$ hits after $n$ shots.
    $endgroup$
    – Milten
    Mar 30 at 1:04







  • 1




    $begingroup$
    I initially got the same answer as you actually, until I turned to the definition of conditional probability. Your method looks like the law of total probability, but the law looks different with conditional probabilities. We actually have $P(H_5|H_4) = P(H_5|H_4cap K_3=1)P(K_3=1|H_4)$ $ + P(H_5|H_4cap K_3=2)P(K_3=2|H_4)$. Now $P(K_3=k|H_4)$ can be worked out with Baye's rule to be $k/3$. So if you replace the two $(1/2)$'s in your expression with $1/3$ and $2/3$, then you get the correct answer of $2/3$.
    $endgroup$
    – Milten
    2 days ago







  • 1




    $begingroup$
    (You can find the formula on the wiki page for Law of Total Probability). I think what is wrong with your way intuitively, is that when we now $X_4 = H$, it changes not only the probabilities after, but also before the fourth throw: If we hit on $n$, it is more likely that we had a high number of hits just before.
    $endgroup$
    – Milten
    2 days ago















$begingroup$
Although it is interesting to see the uniform distribution of $K_n$, I would love to see an answer that doesn't calculate those probabilities. I will add that I came across even more symmetry while thinking about the problem: It seems that any way to get $k$ hits after $n$ throws is equally likely. E.g. we have $P(HMMHH) = P(HHHMM) = P(MHMMH) = ldots$, where $H$ is hit and $M$ is miss. I haven't proven this.
$endgroup$
– Milten
Mar 29 at 23:49





$begingroup$
Although it is interesting to see the uniform distribution of $K_n$, I would love to see an answer that doesn't calculate those probabilities. I will add that I came across even more symmetry while thinking about the problem: It seems that any way to get $k$ hits after $n$ throws is equally likely. E.g. we have $P(HMMHH) = P(HHHMM) = P(MHMMH) = ldots$, where $H$ is hit and $M$ is miss. I haven't proven this.
$endgroup$
– Milten
Mar 29 at 23:49













$begingroup$
I don't think they are equally likely. The denominator of the probability is always n! and different consequences of hitting the ball($HHHMM$ or $MHMMH$) clearly lead to different results in the numerator.
$endgroup$
– YellowRiver
Mar 30 at 0:51





$begingroup$
I don't think they are equally likely. The denominator of the probability is always n! and different consequences of hitting the ball($HHHMM$ or $MHMMH$) clearly lead to different results in the numerator.
$endgroup$
– YellowRiver
Mar 30 at 0:51





2




2




$begingroup$
Oops, that was a typo - there has to be the same number of $H$'s. I believe the probability will always be $k!(n-k)!/(n+1)!$. This cancels out nicely with a binomial coefficient counting the number of ways to get exactly $k$ hits after $n$ shots.
$endgroup$
– Milten
Mar 30 at 1:04





$begingroup$
Oops, that was a typo - there has to be the same number of $H$'s. I believe the probability will always be $k!(n-k)!/(n+1)!$. This cancels out nicely with a binomial coefficient counting the number of ways to get exactly $k$ hits after $n$ shots.
$endgroup$
– Milten
Mar 30 at 1:04





1




1




$begingroup$
I initially got the same answer as you actually, until I turned to the definition of conditional probability. Your method looks like the law of total probability, but the law looks different with conditional probabilities. We actually have $P(H_5|H_4) = P(H_5|H_4cap K_3=1)P(K_3=1|H_4)$ $ + P(H_5|H_4cap K_3=2)P(K_3=2|H_4)$. Now $P(K_3=k|H_4)$ can be worked out with Baye's rule to be $k/3$. So if you replace the two $(1/2)$'s in your expression with $1/3$ and $2/3$, then you get the correct answer of $2/3$.
$endgroup$
– Milten
2 days ago





$begingroup$
I initially got the same answer as you actually, until I turned to the definition of conditional probability. Your method looks like the law of total probability, but the law looks different with conditional probabilities. We actually have $P(H_5|H_4) = P(H_5|H_4cap K_3=1)P(K_3=1|H_4)$ $ + P(H_5|H_4cap K_3=2)P(K_3=2|H_4)$. Now $P(K_3=k|H_4)$ can be worked out with Baye's rule to be $k/3$. So if you replace the two $(1/2)$'s in your expression with $1/3$ and $2/3$, then you get the correct answer of $2/3$.
$endgroup$
– Milten
2 days ago





1




1




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(You can find the formula on the wiki page for Law of Total Probability). I think what is wrong with your way intuitively, is that when we now $X_4 = H$, it changes not only the probabilities after, but also before the fourth throw: If we hit on $n$, it is more likely that we had a high number of hits just before.
$endgroup$
– Milten
2 days ago




$begingroup$
(You can find the formula on the wiki page for Law of Total Probability). I think what is wrong with your way intuitively, is that when we now $X_4 = H$, it changes not only the probabilities after, but also before the fourth throw: If we hit on $n$, it is more likely that we had a high number of hits just before.
$endgroup$
– Milten
2 days ago











0












$begingroup$

Technically if he made the first one, he could not fail the next one since he did 1 out 1 and this success probability was $100%$.






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  • $begingroup$
    The rule starts from the third shot.
    $endgroup$
    – YellowRiver
    Mar 28 at 21:51















0












$begingroup$

Technically if he made the first one, he could not fail the next one since he did 1 out 1 and this success probability was $100%$.






share|cite|improve this answer








New contributor




Eureka is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






$endgroup$












  • $begingroup$
    The rule starts from the third shot.
    $endgroup$
    – YellowRiver
    Mar 28 at 21:51













0












0








0





$begingroup$

Technically if he made the first one, he could not fail the next one since he did 1 out 1 and this success probability was $100%$.






share|cite|improve this answer








New contributor




Eureka is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






$endgroup$



Technically if he made the first one, he could not fail the next one since he did 1 out 1 and this success probability was $100%$.







share|cite|improve this answer








New contributor




Eureka is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.









share|cite|improve this answer



share|cite|improve this answer






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answered Mar 28 at 21:47









EurekaEureka

679113




679113




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New contributor





Eureka is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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  • $begingroup$
    The rule starts from the third shot.
    $endgroup$
    – YellowRiver
    Mar 28 at 21:51
















  • $begingroup$
    The rule starts from the third shot.
    $endgroup$
    – YellowRiver
    Mar 28 at 21:51















$begingroup$
The rule starts from the third shot.
$endgroup$
– YellowRiver
Mar 28 at 21:51




$begingroup$
The rule starts from the third shot.
$endgroup$
– YellowRiver
Mar 28 at 21:51

















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