Note: Due to an unfortunate data error, the numbers in this story did not include park factors upon publication. We have updated the data to include the park factors, and the data you see below is now correct. We apologize for the mistake.
For an explanation of this series, please read the introductory post. The data is a hybrid projection of the ZIPS and Steamer systems with playing time determined through depth charts created by our team of authors. The rankings are based on aggregate projected WAR for each team at a given position.
Update: Boy! What a difference park factors make! In the original iteration of this article — the one where we thought the park factors were park factoring, but they weren’t — the distribution of DH talent appeared skewed left. Now, not only have the teams shifted closer together, but teams from hitter-friendly parks — such as the Yankees and White Sox — have slunk to the rear while those in pitcher havens — the Mariners and Rays — have edged to more prominent slots.
Because I attempted to weave together these rankings into a grander sort of narrative, much of my original text requires revision. I am happy to report, however, the majority of my in-post complaining about the rankings became validated by the fixed park factors. However, in lieu of covering this article with strike-throughs, I am going to just update the test (as minimally as possible) to reflect the updated rankings.
Originalish post: These rankings are fun. They do not affect the results on the field or the players ranked in them or the GMs glowering over the players. But we are inexorably drawn to these sorts of rankings. With egos invested into our teams, rankings give us pre-season bragging rights or grinding axes.
In all this fun, however, it is important to remember the function of our list. As we are wont to do at FanGraphs, we have attempted to make our lists in the most clinical, mathematical and unbiased ways as possible. Whereas many MLB power rankings are based on gut judgements or broad, basic analyses, we have computed a scientific power ranking system that requires human input only when it is an improvement over an algorithm.
This means, however, the space between each team is discrete. The distance between No. 1 and No. 2 is much greater than, as you will see, between No. 13 and No. 14:
Two are clustered near the top, others are rounding errors apart, and two teams appear clustered near the bottom. But an ordinal ranking does not represent that accurately.
And even despite our best utilization of projection systems and playing time predictions, the season is unpredictable. Not just hard to predict, but unpredictable. If it weren’t, who would watch it? But as of now, as of our best playing time estimations, as of the best projection systems, this is how the DH world settles. This is how the big and sluggerish stand.
Without further ado, I present the Slow and Sluggering Show:
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