Stat Problem that I solved using simulations; verify answer

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In summary, the question posed is about the probability that team A wins a tournament of seven games against team B, with each team having a 55% chance of winning at their home stadium. The simulation in R gives a 52% win probability for team A, while the analytical solution is 51.57197%. A brute force method and a smarter, more elegant approach are discussed for determining the probability.
  • #1
rollingstein
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Someone posed this Stat / Probablity problem to me:

Two teams A vs B play a tournament of seven games against each other. First team to won 4 games wins the tournament. The games are split over each team's home stadium as A A B B B A A.

Historically the home team wins a game 55% of the time. What's the probability that team A wins the tournament?


Now, I simulated this in R as a series of events drawn from three binomial distributions. I get a 0.52 win probability for team A. i.e. A small advantage remains for the team that starts the tourney since it gets 4 home games vs three for the other team.

Just wanted to double check this answer if it is amenable to an analytic solution.

I tried increasing the number of simulations and the answer of 0.52 is fairly stable but not sure whether I've converged onto the asymptotically right answer or not.
 
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  • #2
The tournament ends the earliest after game 4 (AABB) and the latest after game 7 (AABBBBA). In every case, B has at least as many home games as A. B must have an advantage.

Do you mean A A B B B A A? Then I see that A can have an advantage, although B has a smaller advantage from AABBB.

You can calculate the analytic solution by looking at a few cases.
 
  • #3
Yes. Thanks @mfb. My bad. I do mean A A B B B A A

I've corrected my post.
 
  • #4
Rounded to whole percent, I can confirm 52%, but I think you should give at least one more digit, otherwise the advantage of A is poorly estimated.
 
  • #5
Thanks again @mfb!

I get 51.56612% actually. But I wasn't sure how many digits to use to avoid overstating the accuracy of sims.

Out of curiosity, what's the exact analytical answer just to see how close I got. :)

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  • #6
51.57197%, by going through the cases of (A won x, B won y).
 
  • #7
How did you solve it! I'm so stuck.

I brute force listed all 128 ways the game outcomes may turn out. i.e. a 128 x 7 grid of A / B combinations.

Of course not all tournaments will run 7 games.

But I'm stuck. What next?
 
  • #8
Ok, I did get your answer but by an ugly brute-force method:

My approach:

Out of 128 total permutations I discarded the ones in which A lost by counting only for those rows with 4 or more "A"-wins.

Next, I manually replaced each "A"-win element by either a 0.55 or a 0.45 depending on which of the 7 game columns it was in.

Now multiplied the 7 probablities and summed over all 64 games.

I get 0.5157197.

Is there a smarter, more elegant approach?
 
  • #9
P(B won 3 games, A won 2 games | 5 games played) = 0.55 * P(B won 2 games, A won 2 games | 4 games played) + 0.45 * P(B won 3 games, A won 1 game | 4 games played)

A 5x5 matrix is sufficient. It does not matter how "both won two games out of the first 4" occurred, for example, as long as you know the probability of this intermediate score.
 

Related to Stat Problem that I solved using simulations; verify answer

1. What is a simulation in statistics?

A simulation in statistics is a method of using mathematical models to imitate real-world scenarios in order to obtain insight or make predictions about a system or process. It involves creating a computer-generated model that mimics the behavior of a real-world situation and using it to generate data and analyze outcomes.

2. How is a simulation used to solve statistical problems?

A simulation can be used to solve statistical problems by generating a large number of simulated data points and analyzing their distribution to make predictions or draw conclusions about a particular problem. The simulated data can also be used to test different hypotheses and evaluate the likelihood of different outcomes.

3. Can simulations be used to verify statistical solutions?

Yes, simulations can be used to verify statistical solutions by comparing the results of the simulation to the theoretical or expected outcome. If the simulated results align with the theoretical solution, it provides evidence that the solution is accurate.

4. What are the advantages of using simulations in statistics?

There are several advantages of using simulations in statistics, including the ability to model complex systems, the flexibility to test different scenarios and parameters, and the ability to generate large amounts of data for analysis. Simulations also allow for easier visual representation of data and can provide more accurate results compared to traditional analytical methods.

5. Are there any limitations to using simulations in statistics?

While simulations can be a powerful tool in solving statistical problems, they do have some limitations. One limitation is that the accuracy of the results depends on the quality of the simulation model and the assumptions made. Additionally, simulations can be time-consuming and computationally intensive, making them impractical for certain problems.

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