Probability of outcome of combined events

AI Thread Summary
The discussion centers on calculating the probability of winning multiple games based on individual game probabilities. It highlights that while one might consider averaging the probabilities of winning each game, this approach is misleading, especially with small sample sizes. Instead, the expected number of wins can be calculated by summing the individual probabilities for each game, which provides a more accurate estimate of total wins. The conversation also emphasizes that probabilities must remain between 0 and 1, and winning a non-integer number of games indicates a range of possible outcomes rather than a definitive count. Overall, understanding the distinction between independent events and the proper method for calculating expected outcomes is crucial.
mayhawman
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I'm a bit lost on use of mutually exclusive and independent rule on this. I know probability of winning a game is a loose term, but...
If I had a team's schedule of 12 games with probabilities listed of winning each game, would I add those probabilities, then divide sum by 12 for an average, and multiply by 12 for probability of season wins?
I was thinking it would have to be much more difficult than this.
 
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Suppose each game showed an exactly identical probability (say, 50%). What would you conclude about season wins?
 
I'd conclude nothing froma sample this small, especially sporting events. Regardless of %, I thought it would still be the equation
Pr(A+B+C)= Pr(A) X Pr(B) X Pr(C).
It's just when I calculate all 12 "50%, I come up with the worls worst probability of winning even one game, much less 12. To accomplish a season so bad, they'd have to fumble every snap of every game and maybe forfeit some games from last year to make up for it.
Are these not independent events? I have no evidence in a dispute with the individual who's saying just add the % and divide.
Please show me where I err.
 
You're correct, I think.

If you have a certain percent chance to win a game, then you can (for seasons with lots of games) take that win percentage and add it to your season win total.

For example:
Game 1 - 40% chance to win
Game 2 - 30%
Game 3 - 70%
Game 5 - 35%
Game 6 - 25%

You can take these percentages as parts of games won to find the expected wins for the season.

For my example season:
.4 + .3 + .7 + .35 + .25 = 2 games
So you'll probably win two games. Just remember that the smaller the sample size, the less accurate this prediction is.
 
.4 + .3 + .7 + .35 + .25
I remember the probability must lie between 0 and 1, so it couldn't be 2 for total probability(1 would be an almost certain 5-0 season). Isn't that right?
Since each is 1/5 of the total, maybe .4X.2+ .7X.2, etc
=.08 + =.14 + etc?
 
For example, if you're 100% sure you will win 5 out of 5 games, then you can calculate the expected number of games you will win using the method I described above (although it's obvious):

1 * 100% = 1
+ 1 * 100% = 1
+ 1 * 100% = 1
+ 1 * 100% = 1
+ 1 * 100% = 1
= 5 games

If you play five games which you have a 50% chance to win:

1 * 50% = .5
+ 1 * 50% = .5
+ 1 * 50% = .5
+ 1 * 50% = .5
+ 1 * 50% = .5
=2.5 games

Now since you obviously cannot win 2.5 games (unless you can tie?), you can infer that you have an equal chance to win either 2 or 3 games but you also have some chance to win 0, 1, 4, or 5 games.
 
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