Would that have given similar results?

What I did was simply go to the main projection page for the team, and took the number of number runs/number at bats as the %, then 650 as the number of trials, so, for Alex Gordon, CHONE projects 15 HR in 548 ABs, so the equastion (excel) is:

=(1-(BINOMDIST))

Where B3 is the number of home runs. So for the chart, I did 1, 5, 10, 15, 25… you get the picture.

Make sense?

by the way, using the “FALSE” Cumulative leads to some cool results when running this with win%

Just imagine two bell curves side by side with the right tail overlapping the left tail of the other one. The more they overlap, the better chance the worse team has of outperforming the better team (or worse HR hitter has of out-performing the better HR hitter).

Think of it this way.

We’re going to do 100 coin flips. Coins are typical 50/50 propositions. Let’s say we want to know the odds of flipping 45 heads or more. Assuming that 50/50 split, we’d guess that it would happen 86% of the time.

But what if someone tells us that the coin is weighted and will never come up heads? In that case, we would never flip 45 heads or more out of 100.

Then again, we don’t know this guy too well and think he might be lying. We’re evenly split between whether he’s lying or telling the truth. So, depending on what’s true, we have a good chance of getting 0 coin flips. But if we just took the average of the two probabilities of the coin, 0.5 and 0.0, our coin would simply “project” to being a 1-in-4 heads coin.

If we have a coin that comes up heads 1 time in 4, the odds of flipping tails 100 times in a row is 3.2 trillion-to-1. But in this scenario, our chances of flipping tails 100 times in a row is actually over 50%!

Does this make more sense?