Weighting Category Values For Fantasy Baseball Drafts Using ADP

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:

The equation for converting my category dollar values to these weights is: 1.43 + $R * 1.01 + $HR * 1.83 + $SB * 1.07 + $AVG * 0.96. The ‘default’ equation is: 1 + $R + $HR + $RBI + $SB + $AVG.

While $HR and $RBI are certainly correlated (meaning players with higher $HR tend of have higher $RBI and vice versa), I was still surprised to see that $RBI was useless in explaining a player’s NFBC ADP. It could be that $R are still effective because they reflect expected playing time and batting order spot without as high a correlation to $HR.

Here are the five players that were most helped/hurt by the adjustments. Note that the value of $1 changes throughout the draft. Miggy was only knocked down 2 spots, Bogaerts went down 26 spots, while Granderson went up 50 spots.

The common denominator here is that the five players with the highest increase are power guys (average $HR of $3.7) with low average (average $AVG of -$2.2). The players who decreased the most are solid AVG/RBI guys (average $7 per category) with so-so power ($HR of $3.7).

Below is a side-by-side comparison I ran against the FanGraphs Auction Calculator using the same roster parameters (15 team mixed, NFBC roster (2 catchers), 64/36 split) with Steamer Projections and including all hitters that the FanGraphs calculator viewed as $1 or more. I am just using their category $ values which include no positional weights as they bundle positional weighting under a variable called ‘aPos’ that ranges, in this run, from $6.7 to $9.1 for non-Catcher positions and $21.9 for catchers. (This is why their ‘intercept’ is so much higher than mine as i’m just adding $1 to the sum of the category values since that’s the minimum bid).

I see the same result. Power is going at a premium in NFBC drafts. This probably holds true for just about every mixed draft (at least based on my category values). It is not that HRs are more valuable or SBs are less valuable. It’s just cheaper to acquire the other categories.

My kids love bananas. Bananas are stupid cheap at the supermarket. We’d pay three times the market rate for them. But why would I? That’d be you know what. I can spend that surplus on milk. Blech. Those dairy farmers owe big cereal some commissions.

Mock drafting with my adjusted dollar values convinced me that this weighting makes for more effective drafting. Instead of chasing power late in the draft (when it becomes scarce) while wondering why my team skewed too heavy on average, I front-loaded power (Bryant/Upton/Fielder) and targeted a few hitters like Duda and Ozuna to maintain. Everything equal – I chose the power guy. I liked the value on a few power guys in the last half of my LABR draft (Schoop and Rasmus come to mind) but it is not a good idea to be in a position where you are dependent on a couple players to go at the right price for your draft strategy to work.

So why is this so important? Because you typically only have 60-90 seconds to decide on the right player. Any differences in category weight from ADP affect the top of your queue. In my case, it would be high AVG / low HR guys. Proactively adjusting for this before the draft allows you to avoid wasting time shuffling through your queue to find players to counteract the market’s different weighting of the various categories.

One added bonus to all this is that if your category values are fakakta, this could help fix them (or at least minimize the damage).

It’ll be interesting to see if the same holds true in ESPN and Yahoo drafts. I’ll write another post in early March once their ADPs stabilize. I imagine it will hold true.