Winning and losing teams probability distribution

In summary, the problem can be solved using a Markov chain with states representing the difference in games won by Team A and Team B. The total number of games played, Y, is the first passage time from state 0 to the 'stop' state, where one team has won two more games than the other.
  • #1
libragirl79
31
0
Suppose two teams play a series of games, each producing a winner and a loser, until one team has won two more games than the other. Let Y be the total number of games played. Assume each team has a chance of 0.5 to win each game, independent of results of the previous games. Find the probability distribution of Y.



The Attempt at a Solution



At first I thought I'd say that given that team A has had X games and team B has Z games, then Y=X+Z, and since it takes two more games for the winner to get, then Z=X+2, so Y=2x+2. Probability function would be then P(Y<=y)=P(Y<=2x+2) but I am not sure where to go from here...Also, was thinking that it may be a binomial, with (Y choose 2x+2)(0.5)^(2x+2)(0.5)^y where x=1,2,3...y.

Any input is appreciated!
 
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  • #2
libragirl79 said:
Suppose two teams play a series of games, each producing a winner and a loser, until one team has won two more games than the other. Let Y be the total number of games played. Assume each team has a chance of 0.5 to win each game, independent of results of the previous games. Find the probability distribution of Y.



The Attempt at a Solution



At first I thought I'd say that given that team A has had X games and team B has Z games, then Y=X+Z, and since it takes two more games for the winner to get, then Z=X+2, so Y=2x+2. Probability function would be then P(Y<=y)=P(Y<=2x+2) but I am not sure where to go from here...Also, was thinking that it may be a binomial, with (Y choose 2x+2)(0.5)^(2x+2)(0.5)^y where x=1,2,3...y.

Any input is appreciated!

Have you studied Markov chains yet? If so, it is a simple exercise to get the generating function of Y, hence to find the distribution P{Y = n}, n = 2,3,4,... .

Here is a hint: let X = #won by A - #won by B. We start at X = 0. X increases by 1 if A wins and decreases by 1 if B wins. The game stops when X reaches ± 2. So, we have a simple 4-state Markov chain with states X = {0,1,-1,'stop'}, and Y = first passage time from state 0 to state 'stop'.

RGV
 
  • #3
No, we haven't done Markov Chains yet...
What do you mean by "first passage time"?
 
  • #4
libragirl79 said:
No, we haven't done Markov Chains yet...
What do you mean by "first passage time"?

The first passage time from any state i to state 'stop' is the first time (t = 1,2,3,...) at which state 'stop' is reached. It is exactly the Y that you seek. (Calling it a first-passage time allows you to do a meaningful Google search of that term to find out more about it.)

RGV
 

What is a probability distribution?

A probability distribution is a mathematical function that maps the likelihood of each possible outcome of an event. It assigns a probability to each possible outcome, with the total sum of all probabilities equal to 1.

How is a probability distribution used in analyzing winning and losing teams?

A probability distribution can be used to analyze the likelihood of different outcomes in a game or competition between two teams. It can help determine the probability of a certain team winning or losing, based on factors such as past performance, team composition, and other variables.

What factors can affect the probability distribution of winning and losing teams?

The probability distribution of winning and losing teams can be affected by a variety of factors, including individual player skills, team dynamics, coaching strategies, home field advantage, and even luck or chance occurrences during a game.

How can probability distribution help in making predictions about winning and losing teams?

By using a probability distribution, analysts can make predictions about the likelihood of a certain team winning or losing in a game or competition. This can help coaches and team managers make strategic decisions and adjustments to improve their team's chances of winning.

Can a probability distribution accurately predict the outcome of a game or competition?

No, a probability distribution cannot accurately predict the exact outcome of a game or competition. It can only provide a likelihood or probability of different outcomes. Other factors, such as unexpected events or individual player performance, can also impact the final outcome.

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