This is a post for the fantasy baseball drafters who use Excel, Google Docs, or some other war room software that automatically totals a drafted team’s stats while in the middle of a draft. Or perhaps for those of you who do mock drafts or simulated drafts.

The below grid represents my projected 75% mark in each stat category across 10/12/14/15/16 team ESPN and Yahoo default roster format leagues.

These numbers should only be used directionally. Please note that each projection source projects to a different league average so your team may look great if using a ‘bullish’ source and look poor if using a ‘bearish’ source.

While I stand behind these numbers as they are part of the foundation behind my Player Rater $ estimates, I do not use these as part of my draft. I prefer to add up the dollar values per category. Same difference I suppose but it is easier to see counting totals for ratios and it lets me fixate less on the numbers (e.g., I see $7, I know they are good…I don’t fixate on 20 SBs vs 25 SBs).

I was thinking about ideas for a draft strategy post to complement Grey’s recent draft strategy post when I concluded that the vast majority of my strategic thinking goes into my draft preparation. The draft itself is more just execution. So I figured it might help some of you if I laid out my draft preparation and then some notes on how my strategy might change based on league format.

My larger goal with any draft is to be in control. With the right preparation, I can take advantage of whatever advantages the draft room is giving me. I drafted the 2nd and 3rd pitchers off the board in this year’s Tout Wars and was the last player to take a pitcher in LABR.  Yet my hit/pitch split for the two teams was within one percentage point (63.7/36.3 vs 64.6/35.4).

I also strive for zero ‘instant regret’ picks. Those are the picks where you are ‘under the gun’ and pick someone that you regret while the draft is still going on. I think most drafters get ‘lost’ during snake drafts more than they’d care to admit. I can say with full honesty that I only made one of those picks combined between LABR and Tout (LABR – Yasmany Tomas in 16th when none of my planned bats were still on board and Storen went the pick before).

You do not need to follow each and every step (you could just from my $/projections and edit from there) but I do think all this research makes for a smarter, more efficient draft.

For those of you in leagues where SP and RP are rostered positions, the below list includes all SP/RP eligible pitchers (defined as 5+ Games Started and 5+ Relief Appearances in 2015). You can arrange the list via seven categories; 2015 games started, 2015 games pitched in relief, projections for both 2016 games started and games pitched in relief, and by ERA, WHIP, and K9. Any questions, feel free to post them below!

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: