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Positions to Prioritize: Statistical Scarcity Breakdown

Rafael Devers - Fantasy Baseball Rankings, MLB DFS Picks, Betting Picks

Jon Anderson's statistical analysis on positional scarcity for 2022 fantasy baseball and advice on which positions to prioritize in drafts.

Position scarcity is a big topic of debate every fantasy baseball season. Not all positions on the baseball diamond are created equal, and they often give us quite different arrays of offensive production for fantasy purposes. In this post, I will take a close look at what's going on at each position by the projections and see if there are any adjustments we should make to our draft strategies because of it.

The first thing to mention is that we have to be careful not to weigh this stuff too heavily. You should not be selecting a wildly inferior player just because he fills a weaker position. The most important thing is still to draft the best statistics. It is completely inevitable that the projection systems will be seriously wrong on lots and lots of players, so at the end of the season, things will not have played out how we were thinking, even in the arena of these positional breakdowns.

Projections are the best things we have to work with right now for this kind of analysis, and this kind of stuff is still useful - you just have to take it with proper weighting. Let's talk about it.

Be sure to check all of our fantasy baseball draft tools and resources:

 

Which Stats are Where?

Here's an interactive plot you can use to see which positions are heavier on what statistics. I took the top-300 players by current ADP (197 of them are hitters) along with their primary position (one position per player, so, for example, Trea Turner is a second baseman here, not a shortstop), and then I averaged out the projected stats across the different positions.

Important note, the "UT" spot is utility and it encompasses just three players, Shohei Ohtani, Franmil Reyes, and Nelson Cruz. You can click around on the five different stats here and see the numbers update.

What we find:

  1. Steals are mostly in the middle infield. The top two projected steals sources (Adalberto Mondesi and Starling Marte) play third base and outfield respectively, but after that, we have seven of the next 12 being middle-infielders.
  2. Homers are pretty evenly spread out, except at second base and catcher. That gives you a chance to catch up on the field in homers by taking one of the few second base or catcher eligible players that can hit the long ball.
  3. The catcher is a wasteland for the batting average, with the average catcher projected to hit just .246. Shortstop is the only position above .270, really showing how far the average has slid in that category over the years.

 

Points Leagues Positional Depth

Next, I took the ATC projections and converted the projections into DraftKings points, using their scoring system. Then I found the average total DK projection for each position, here are the results:

The utility spot comes out on top just because it has just three hitters in it, all three of which are pretty good hitters. The true top position here is shortstop, with an average projection of 1,152 DraftKings points for the top-17 shortstops, and that is even with Trea Turner being in the 2B category.

The third base stands out as pretty weak as well, with the top-22 third basemen averaging just 1,063 projected DraftKings points. They are narrowly edged out by outfielders despite there being 70 players in that bin. And catchers remain pathetic, being just three-fourths of second base, the second-worst projection.

 

Players Compared to Positional Average

Now for your feature film. For this, I calculated Z-Scores for the standard 5x5 league type. For more on Z-Scores, you can read this. In short, a z-score is just a number that compares a player's projection in a single category with the overall field in that category. It's a good way to compare the categories against each other. I found each player's z-score in all five categories and averaged them out to get one score for the player. After that, I found the average overall z-score for each position and then compared each player's individual z-score with that.

The full results are in the table below, here's a quick description of each column.

Player Z: The individual player's average Z-Score across the five different categories
Position Z: The average "Player Z" for every player at the given position
Delta: Player Z minus Position Z, the higher the number, the better the player is compared to the rest of his position.

Full results here, you can search a player or position in the search box and you can sort by any column you like.

Some takeaways:

  • We see the lack of depth at the catcher position really showing here as Salvador Perez comes in at #8 overall. By this criterion, that makes him a first-round pick. There are all kinds of reasons not to pick Perez in the first round that outweigh this one reason to do it, but it does make the point that you can really get ahead of your league-mates by having the catcher that has a huge offensive year on your team.
  • While Fernando Tatis Jr. has the highest individual Z-Score by quite a large margin, he comes in behind Trea Turner in the "Delta" column just because of the difference in positional depth between SS and 2B.
  • There is a serious drop-off after the top-eight overall hitters. After Bo Bichette and his 1.37 Z-score, you go the whole way down to 1.26 to Shohei Ohtani. I would not consider taking a pitcher inside the top-eight picks because of this.
  • We also see the stock of Rafael Devers and Manny Machado come up a bit, as they are both in the top-15 players here. After those top three names at 3B, there's a steep drop-off, as you can see if you search the table for "3B".
  • On the flip side of that, you have guys like Xander Bogaerts and Francisco Lindor, who I would downgrade because of the depth of their position. It's pretty easy to find a similar projection to them as the position, so there's no real reason to reach too high for them (although I do believe we see this at least slightly reflected in the ADP).

I'll leave it to you to sort through the rest of the table and see what you can find. Reach out to me on Twitter @JonPGH as always with questions or data requests!



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