Probability of player winning a tennis match

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Discussion Overview

The discussion revolves around calculating the probability of player A winning a 3 set tennis match given the probability of winning a point, denoted as p, with player B's probability being q = 1 - p. Participants explore various methods and models to derive this probability, including binomial trees and simulations.

Discussion Character

  • Exploratory
  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant proposes the initial problem of determining the probability P(p) of player A winning a 3 set match based on point probabilities.
  • Another participant provides a formula but is corrected for misapplying it to a 3 point game instead of a 3 set match.
  • A different participant suggests modeling the game as a binomial tree and discusses the complexities of scoring, especially at deuce and during tiebreaks.
  • One participant mentions the Gambler's ruin problem as a potential framework for analyzing the match probabilities.
  • Another participant highlights the need to account for the potentially infinite duration of games and sets, proposing a recursive approach to calculate winning probabilities.
  • One participant notes that while analytical calculations can be complex, simulations can provide straightforward results for specific values of p.
  • A participant shares a plot illustrating the percentage of games, sets, and matches won based on varying probabilities of winning a point.
  • Another participant details a comprehensive breakdown of the probability of winning a game, considering different scoring scenarios and using combinatorial reasoning.

Areas of Agreement / Disagreement

Participants express various methods and models for calculating probabilities, but there is no consensus on a single approach or solution. Multiple competing views and techniques remain under discussion.

Contextual Notes

Participants acknowledge the complexity of the problem, particularly in accounting for different scoring scenarios and the recursive nature of game outcomes. There are also references to the limitations of analytical methods versus simulations.

Bobmek
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Hey guys, this is just a problem I have been playing around with in my spare time, but it occurs to me that someone might have been working on this before. So I figured I'd ask the wonderful people here for help. The problem is:

Assume that the probability of player A winning a point is p and the probability of player B winning a point is q=1-p. What is the the probability P(p) of Player A winning a 3 set match?
 
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p^3 + 3 p^2 q
 
awkward said:
p^3 + 3 p^2 q
You've given the probability of winning a 3 point game, not a 3 set match.
 
mathman said:
You've given the probability of winning a 3 point game, not a 3 set match.
You're right, I wasn't paying attention to the whole match/game/point thing. The answer I gave would be correct if p were the probability of winning a game.

Finding the probability of winning a game from the probability of winning a point is the more complex question.
 
Hmm couple of thoughts.

For a each game you could describe it as a binomial tree going from (0,0) to (15,0) or (0,15) and so on. Of course you will have to remove the paths that are no longer feasible once the game has been won. Then you will have to take into account that at 40,40 you either move to a node either (adv,40) or (40,adv) from which you either move back to (40,40) or the game ends which i'd solve by conditioning.

Similar idea would then allow you to work from 0,0 games to (1,0) or (0,1). Including the possibility of a tie break and the prob of winning a tie break.

What have you come up with so far?
 
Bobmek said:
Hey guys, this is just a problem I have been playing around with in my spare time, but it occurs to me that someone might have been working on this before. So I figured I'd ask the wonderful people here for help. The problem is:

Assume that the probability of player A winning a point is p and the probability of player B winning a point is q=1-p. What is the the probability P(p) of Player A winning a 3 set match?

Sounds like you could use a variation of the Gambler's ruin problem and apply it in this context. Basically you would find the probability of one player ruining against another and use that as a basis for seeing the probability of person a or b winning the set.
 
You need to deal with the fact that games or sets can last arbitrarily long.

If the game-score is 30-30 or deuce or 5-5 in a tiebreak, there's a probability of p^2 that you win the next 2 games, and a probability of 2pq that the games get shared.
If the probability to win when at deuce is D, then you have

D = p^2 + 2pqD

Once you have D it is easy to calculate the probabilities at 40-30 or advantage,
you win with probability p, or get to a deuce with probabiltiy q.

Finally you can draw a finite tree that must end in a -0 win, -15 win, advantage,
deuce, disadvantage or a loss, and sum the winning probabilities of all those.
You'll get a larger tree for a tie break

And once you have that you can do the same for the set scores.
I doubt that the result would fit on a single page, altough finding
P_game(p), P_tiebreak(p) and then P_set(P_game, P_tiebreak) and
P_match(P_set) is doable.
 
This is an example of a problem that is a hassle to analytically calculate, but trivial to simulate given an explicit value for p.
 
Ok -- here is a plot with

Y
% games won in green
% sets won in orange
% matches won in blue

X
% chance to win the point

Sorry for the bumpiness from low sampling, but I don't have time to remake it with higher stats.
 

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  • #10
If your probability of winning a point is p and your opponent's probability of winning is q=1-p, then the probability of your winning a tennis game is

p^4 + 4 p^4q + 10 p^4q^2 + 20 p^5q^3 + \frac{40p^6q^4}{1-2pq}

In order to win, you must win at least 4 points and be 2 points ahead. Let W(x,y) be the event that you win with a score of x points (you) to y (your opponent). We break your possible wins into 5 cases.

Case 1: A win at (4,0). Clearly
P(W(4,0)) = p^4.

Case 2: A win at (4,1). In order to get to (4,1), the score must reach (3,1) and then you must win a point. To reach (3,1) is a Binomial event with probability C(4,1)p^3q; so
P(W(4,1)) = 4 p^4q.

Case 3: A win at (4,2). In order to get to (4,2), the score must reach (3,2) and then you must win a point. The probability of reaching (3,2) is C(5,2)p^3q^2; so

P(W(4,2)) = 10p^4q^2.

Case 4: A win at (5,3). In order to get to (5,3), the score must reach (3,3) and then you must win 2 points. The probability of reaching (3,3) is C(6,3)p^3q^3; so

P(W(5,3)) = 20 p^5q^3

Case 5: A win after the score reaches (4,4). In order to reach (4,4), the score must reach (3,3) and you and your opponent must each win one point. The probability of reaching (3,3) is C(6,3)p^3q^3; the probability that you will each win one of the next two points is C(2,1)pq; and the probability that you will win once the score hits (4,4) is \frac{p^2}{1-2pq} (an exercise left for the reader). So the probability of a win in Case 5 is
\frac{40 p^6q^4}{1-2pq}.

Adding the probabilities for cases 1-5 results in
P(\text{you win}) =p^4 + 4 p^4q + 10 p^4q^2 + 20 p^5q^3 + \frac{40p^6q^4}{1-2pq}
 

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