Fantasy Value Above Replacement: Part Three

This is the third part in a published four part series. If you’re interested in reading the Update, click through!

If you haven’t done so, please make sure to read the first two pieces on Fantasy Value Above Replacement from earlier today.

Before we get into the auction conversion, there are a couple notes that need to be addressed.

Weighing Rate Stats – We can all agree that a batter that hits .300 in 600 at-bats is more valuable than one who does it in 400 at-bats, right? We have to take this into account and adjust for AB’s and IP in rate stats. Simply multiple a players normal z-score in the batting average category by their AB total. We’ll call this wBA. Once that is done for every player at that position, you take the z-score of those wBA numbers and you have your final batting average value. You do the same with ERA and WHIP as well, using IP instead of AB. It can actually end up helping some players with poor ERA numbers, because having a 5.00 ERA isn’t as bad if it’s only 150 innings. On the flip side, power hitters who play everyday and have a bad batting average will be penalized even more for it.

Pitcher Adjustment – Because pitchers can only contribute to four stat categories (SP don’t get saves, and it’s always best to disregard RP wins), we need to curb their value. I do this simply by multiplying their FVAAz number by 0.8 to reflect the ⅘ ratio.

Inputs – Please note, and this is a biggie, that this system is only as good as its inputs. If your projections are way off, the rankings might be, too.

Now that we’ve gotten that out of the way, we can take a look at how to convert FVARz into auction values.

Auction Conversion
The auction conversion formula takes into account the number of players on each team, as well as the simple idea that every player taken in an auction has to cost at least a dollar. The conversion formula is below.

[(Team Budget – (1*no. of players per team)) / number of players per team] * (z-score above replacement / average z-score for above-replacement players) +1 = Dollar Value

Note: “Draftable” player are considered to be players who are projected to produce at a level above replacement level.

In our case, a 12-team standard league, it would look something like this:

[(260-(1*23))/23]*(FVARz / average FVARz for above-replacement players) + 1

If you want to make things easier, you can substitute 3.0 for “average FVARz for above-replacement players.” The number varies a bit year-to-year, but it is usually around 3.0. So our final formula is

10.3*(FVARz/3)+1

This formula works because it makes two things clear: The average player should be payed the average amount of money a team can spend on a player, and a replacement level player is worth $1.

A Small Sample Using Marcel
In case you’re having a hard time visualizing this whole process, I have done a small sample of FVARz using the Marcel projections found right here on FanGraphs. The sample is done using a 400 AB minimum, and the positions are done simply based on what Marcel says.

If you want to view the sample, click here and your wish will be granted.

Thanks
While I was the only one doing any direct work on this project, many others helped me out by letting me bounce ideas off them, and other such things. So, let me thank Michael Jong and Joel Goodbody, as well as FanGraphs’ own David Appelman and Eno Sarris.

We hoped you liked reading Fantasy Value Above Replacement: Part Three by Zach Sanders!

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Zach is the creator and co-author of RotoGraphs' Roto Riteup series, and RotoGraphs' second-longest tenured writer. You can follow him on twitter.

newest oldest most voted
Sky Kalkman
Member

First of all, love to see articles like these written. For me, strategy articles are much more interesting than the “player-analysis-disguised-as-fantasy-advice” stuff that you can find everywhere (not that some isn’t better than others.)

Two thoughts on this article:

1. I first weight rate stats by playing time before taking standard deviations. The reason is that otherwise unweighted rate stats are influencing the distribution. I don’t have a reason your way is wrong, it just seems wrong.

2. Why cut SP value like that? They already get a 0 in the saves category, so when you calculate z-scores, they’ll look really bad. Lack of saves already seems to be accounted for, no?

Kris
Guest

Thank you.

If you want to discount RP wins, just go ahead and discount RP wins in Z-Scores.

With that said, if you want to say that pitchers get hurt more often or are more volatile and thus you want to discount their overall value, go ahead and do that. I’m not against weighing pitchers at 80%, that actually works aaaaight for meee.

My biggest problem with systems like this is the failure to actually disclose where the mean lies. This is about getting MOAR total stats, not MOAR compared to your position.

I’m glad Russell Martin has a great Z-Score, but if the mean for the catchers is well below that for OF, taking an OFer with a lower Z-Score is advantageous. Sure, you’ll get a catcher that’s better relative to his competition, but he’ll put up 10 more RBI while my OF puts up 13 more RBI.