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See all of today’s starting lineups

# MLB Starting Lineups For Tue 4/16
ARI | ATL | BAL | BOS | CHC | CHW | CIN | CLE | COL | DET | HOU | KC | LAA | LAD | MIA | MIL | MIN | NYM | NYY | OAK | PHI | PIT | SD | SEA | SF | STL | TB | TEX | TOR | WSH

The theme of this edition of my corner infielder rankings is simple: everyone’s amazing. In short, I felt compelled to move a number of players up, not down, which meant a lot of meaningful contributors that I would be happy to roster fell off or down the list. But my overall feeling about the corner infielder positions is a positive one. In other words, they’re so deep that they’re spilling over with talent.

That also made ranking players quite difficult. For instance, I’m super excited about the resurgence of Joey Votto, and I think I buy the changes he has made more than Kyle Seager‘s (more on them below). But weighing my expectations against their year-to-date performance — where Seager has outshined Votto — was difficult. Ultimately, I put Seager six spots ahead of Votto, but I still haven’t fully convinced myself that he’s better. That’s just one example, but it goes to show that you should be happy rostering a lot of guys in the 30s and 40s and, in some cases, they could easily leapfrog those currently ranking above them in my next set of rankings.

Please, blog, may I have some more?

Ranking players is particularly difficult in a 60-game season. Typically, I avoid playing the hot hand, instead choosing to stick with my stars. If I bench Pete Alonso for Jesús Aguilar, and then Alonso ends his rut by hitting two homers that night, I would feel foolish.

I’m not implying that, by any means, I prefer Aguilar to Alonso. But hot and cold streaks are real and, in such a short season, we have to take advantage of them to be successful.

As a result, I decided to shake things up in this edition of my corner infielder rankings.

Please, blog, may I have some more?

With the season underway, there have been a substantial number of movers on my corner infielder rankings. It’s difficult to make too much of a week of play, but with only 60 games total, you’ll have to be aggressive in order to win your league. And that means jumping on breakouts and benching those who might be flaming out.

I made my rankings initially using z-scores based on projections. Now that we have some actual results, it’s a bit of a free for all. Know only that I’m thinking prospectively, i.e., ranking players based on how I expect them to perform relative to one another for the rest of the season. Just because Starlin Castro has been better than Miguel Sanó to date, doesn’t mean that I expect him to be better going forward.

Please, blog, may I have some more?

With that said, I began with ATC projections, which amalgamate the best from other projection systems. I then altered the projections based on my own assumptions about playing time and the five traditional hitting categories. For instance, I accounted for barrel rate by using hitters’ 2019 predicted home runs–which are home run totals I derived based on barrel rate, among other inputs–and projecting out my own 2020 home run totals.

Finally, I performed my own mock 12 team draft to derive player values based on z-scores from my projections. You can read more about that process in my friend Alexander Chase’s excellent article. A z-score shows the relationship to the mean of a group of values, measured in terms of standard deviations—degrees of spread—from the mean. Where a z-score is 0, the value is equivalent to the mean in the sample. Where a z-score is 1.0, the value is one standard deviation greater than the mean.

Z-scores are useful because, in a vacuum, 15 HRs and 15 SBs are meaningless. They are only telling in relation to one another. For instance, if the mean for the sample of players’ SBs is 9 and the mean for their HRs is 25, then those 15 SBs are worth a lot more than those 15 HRs. Z-scores reflect those relative values.

And with my process out of the way, I won’t bury the lede any longer.

Please, blog, may I have some more?