# Admittance to a Tennis Club

Below I apply the notion of mathematical expectation to the example of a novice player seeking admittance to a tennis club.

To be admitted, the fellow had to beat in two successive games members G (**g***ood*) and T (**t***op*) of the club. With probabilities g and t

We shall be looking for the expected number of wins. Using L for a loss and W for a win for the aspiring novice, we shall consider two sample spaces. Following Havil, the space consists of 8 possible outcomes of a sequence of three games:

LLL, LLW, LWL, LWW, WLL, WLW, WWL, WWW

However note that in the sequences LLL, LLW, WLL, WLW the third game is superfluous as the result of the first two make it impossible for the fellow to win two successive games, whereas the third game is unnecessary in the last two sequences WWL, WWW because the two first wins already gain the fellow admittance to the club. This makes possible and reasonable to consider a smaller sample space:

LL, LWL, LWW, WL, WW

For the sequence TGT we have the following probabilities:

Win/Loss sequence | Probability | |
---|---|---|

LLL | (1 - t)(1 - g)(1 - t) | |

LLW | (1 - t)(1 - g)t | |

LWL | (1 - t)g(1 - t) | |

LWW | (1 - t)gt | |

WLL | t(1 - g)(1 - t) | |

WLW | t(1 - g)t | |

WWL | tg(1 - t) | |

WWW | tgt |

for the first sample space and

Win/Loss sequence | Probability | |
---|---|---|

LL | (1 - t)(1 - g) | |

LWL | (1 - t)g(1 - t) | |

LWW | (1 - t)gt | |

WL | t(1 - g) | |

WW | tg |

for the second. In both cases, the probabilities add up to 1, as required. Choosing the easier way out, we verify this only for the latter:

(1 - t)(1 - g) + (1 - t)g(1 - t) + (1 - t)gt + t(1 - g) + tg | |

= (1 - t)(1 - g) + [(1 - t)g(1 - t) + (1 - t)gt] + t(1 - g) + tg | |

= (1 - t)(1 - g) + (1 - t)g + t(1 - g) + tg | |

= [(1 - t)(1 - g) + t(1 - g)] + [(1 - t)g + tg] | |

= (1 - g) + g | |

= 1. |

Now we introduce the random variable **N** that denotes the number of wins for the candidate. In the first case, **N** may be 0, 1, 2, or 3; in the second case, the are only three possible values: 0, 1, 2. The expectations E_{1} and E_{2} are

E_{1}(N, TGT) | = 0·(1 - t)(1 - g)(1 - t) | |

+ 1·(1 - t)(1 - g)t | ||

+ 1·(1 - t)g(1 - t) | ||

+ 2·(1 - t)gt | ||

+ 1·t(1 - g)(1 - t) | ||

+ 2·t(1 - g)t | ||

+ 2·tg(1 - t) | ||

+ 3·tgt | ||

= 2t + g |

and, correspondingly,

E_{2}(N, TGT) | = 0·(1 - t)(1 - g) | |

+ 1·(1 - t)g(1 - t) | ||

+ 2·(1 - t)gt | ||

+ 1·t(1 - g) | ||

+ 2·tg | ||

= t + g + tg - t^{2}g. |

Similarly,

E_{1}(**N**, GTG) = t + 2g and

E_{2}(**N**, GTG) = t + g + tg - tg^{2}.

Since t < g, we see that

E_{1}(**N**, TGT) < E_{1}(**N**, GTG),

as expected (pun intended). We also have

E_{2}(**N**, TGT) < E_{2}(**N**, GTG),

which ameliorates the paradoxical situation that arose from the pure count of probabilities. Although, the probability of gaining the membership playing the top guy first is larger than when playing first just a good member, the expected number of the wins is greater when postponing the confrontation with the top player.

### References

- J. Havil,
*Nonplussed!*, Princeton University Press, 2007

- What Is Probability?
- Intuitive Probability
- Probability Problems
- Sample Spaces and Random Variables
- Probabilities
- Conditional Probability
- Dependent and Independent Events
- Algebra of Random Variables
- Expectation
- Admittance to a Tennis Club
- Average Number of Runs
- Number of Trials to First Success

- Probability Generating Functions
- Probability of Two Integers Being Coprime
- Random Walks
- Probabilistic Method
- Probability Paradoxes
- Symmetry Principle in Probability
- Non-transitive Dice

|Contact| |Front page| |Contents| |Probability|

Copyright © 1996-2018 Alexander Bogomolny

63576223 |