With that said, I began with ATC projections, which amalgamate the best from other projection systems. I then altered the projections based on my own assumptions about playing time and the five traditional hitting categories. For instance, I accounted for barrel rate by using hitters’ 2019 predicted home runs–which are home run totals I derived based on barrel rate, among other inputs–and projecting out my own 2020 home run totals.
Finally, I performed my own mock 12 team draft to derive player values based on z-scores from my projections. You can read more about that process in my friend Alexander Chase’s excellent article. A z-score shows the relationship to the mean of a group of values, measured in terms of standard deviations—degrees of spread—from the mean. Where a z-score is 0, the value is equivalent to the mean in the sample. Where a z-score is 1.0, the value is one standard deviation greater than the mean.
Z-scores are useful because, in a vacuum, 15 HRs and 15 SBs are meaningless. They are only telling in relation to one another. For instance, if the mean for the sample of players’ SBs is 9 and the mean for their HRs is 25, then those 15 SBs are worth a lot more than those 15 HRs. Z-scores reflect those relative values.
And with my process out of the way, I won’t bury the lede any longer.Please, blog, may I have some more?