Have you ever been in the middle of a draft and said to yourself, “How could this player still be on the board? I would TOTALLY draft him if I hadn’t already drafted a guy with a similar skill set!”?

If so, that is crazy I was able to nail every word of your thought. But even if the above is just a paraphrasing of your in-draft frustrations, this post may be helpful.

It is common practice to compare one’s fantasy baseball rankings with the best ADP proxy for your draft to ensure that you aren’t reaching for your draft picks. Through this type of analysis, you quickly get a feel for positions that you are valuing more or less than the market so you can determine whom your values rate as the best bargains at each position.

While prepping for my first draft 0f 2016 (see my LABR draft recap), I leveraged ADP in a more powerful way for hitters. I did an analysis to identify how NFBC ADP values correlate with each of the category dollar values in my Preseason Player Raters. This allowed me to see how the market is weighting each category and adjust my rankings accordingly so I wasn’t overpaying for one category (say AVG) vs another (like HRs).

To do this, I ran a regression test using 136 hitters where my playing time estimates seemed about in line with consensus. The reason category dollars work so well for this analysis is because it puts each of the five categories on the same scale and, thus, you can quickly identify that any category with a weight above 1 is valued higher and under 1 valued lower. The same principle is in place if you have category SGP, Z-scores, the FanGraphs calculator, etc. You might be able to do the same with just projected stats but the weights will look crazier since the scales are so different.

When I tested with all five hitting categories, my $RBI came in with a weight of -0.004 which means that it is not a relevant variable for predicting the market’s valuation of a player. Recalculating with the remaining four categories resulted in the following: