# Deep Engine 1

For the ABL draft & season I’ll develop some ratings based on the card ranges. The batter’s card is pretty straightforward. The ranges can be used to directly compute things like OBP & Slugging that are independent of pitcher-card rolls. The one vital correction, however, is the power, which will determine how many HR result from Deep! rolls. So, I need to get a handle on how power affects those probabilities. Later, I can make calculations based on Deep! ranges, either averages or against particular pitchers.

I wrote a “Deep Engine” that captures the location and distance data and allows me to run some Monte Carlo simulations. For now I’m going to ignore robbed HRs, which is a pretty small effect. For the first calculation I’ll assume a random distribution of pull types (Lsp, Lp, Rsp, Rp) and use all the 2006 parks. (Later I should whittle it down to the 2007 parks in the ABL.) I ran ten million trials for each power rating and came up with the following probabilities.

```     power:     5        4        3        2        1

home run:   48.61%   32.40%   19.17%    9.27%    3.11%
caught:   47.51%   63.71%   76.93%   86.82%   92.98%
foul:    3.89%    3.89%    3.89%    3.91%    3.91%
```

So a power-5 guy has about a 50% chance of hitting it out on a Deep! The average power-1 batter is more likely to send it foul than over the fence fair.

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