Production Levels and Mike Trout

USA TODAY Sports

A closer look at potential production levels for Mike Trout and what some regression might actually look like

Last week I took a foggy, sleep deprived look at Carl Crawford prime years and what his numbers would look like with a completely arbitrary 20% regression built in. From there I looked at what 90%-80%-70%-60% of that slash line would look like. So what was the point of all of that and why am I about to do it again (without the arbitrary 20% original regression)? Well, there may not really be a point. But if I can try to rationalize it anyway...

Some people are visual learners. So while they may understand what it means when one says "Mike Trout is due for some regression", it might not be readily apparent what the regression would look like. The obvious flaw in this is that... Mike Trout's (or anyone else's) regression won't look like what my table is going to show you either. I'm looking at across the board regressions in this case, and that's just not how things work. That being said, I do think it can be useful to see what each statistic would look like at different percentages of last year's production, and adjusting from there.

To address another flaw in this whole exercise...yes, it's likely that other people do this already. I know I don't though. Or haven't before last week. So there's probably some percentage of people who might benefit from such a thing. If you do already do something like this, well... good on you for beating us to it. Also, if I'm doing it wrong, tell me. Seriously though - if what I'm presenting can be improved in it's utility - let me know. I'm no genius and I'm wrong a lot. I don't mind hearing it from someone who might know better. We can all learn from each other ! Hugs!

PA AB BA OBP SLG R H HR RBI SB
Baseline: 2012 639 559 .326 .399 .564 129 182 30 83 49
90% 639 559 .293 .364 .507 116 164 27 75 44
80% 639 559 .260 .329 .451 103 146 24 58 39
70% 639 559 .228 .292 .394 90 127 21 58 34
60% 639 559 .196 .255 .338 77 109 18 50 29

Obviously Mike Trout was amazing in 2012, so even regressing those numbers is going to produce big results. But I thought it worthwhile to see exactly what those numbers were. If you had said "I see a 10% decline from Trout from his 2012 numbers" before the season, you'd still be predicting Trout to go 27/44 with 116 runs scored. That's a dominant fantasy player.

Dropping down to the 70% stat line...to me, that's Justin Maxwell's upside. And Maxwell is someone people in moderately deep leagues are getting excited about (or did the first week of the season). That's staggering to me. If you made Mike Trout 30% less efficient as a player across the board, he'd still be a 20/30 player. So yeah. Mike Trout will regress. He will also still be a darn good fantasy player in even a worst-case circumstance.

As I said before, across the board regressions almost never happen. Trout might not live up to his 2012 numbers because 2012 was a magical season. But that doesn't mean he can't steal more bases, or hit more home runs or walk more, or...any number of things. Fact is, he's 21 and he's still learning; that's going to offset some regression. He added a bunch of muscle this offseason; that could offset some power regression. Barring injury, he's going to play a full season and those additional at-bats will counteract some counting stats regression. This is by no means a perfect process, but I hope it has a bit of utility for some of you. I'll be forthright in saying I didn't realize how good 90% of Mike Trout's 2012 would still look. I knew it would be good, but didn't realize how good.

I used Mike Trout for this exercise at Ray's request. Is there anyone else anyone wants to look at for this next week? Or should we just call it a failed experiment? Let me know in the comments.

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