How Our Eyes Lie To Us

Actually, as any statistician will tell you, numbers do lie.   Frequently.   Not because they aren't factual, but because people use them to predict things that they are not capable of predicting.   For example, the goal of a baseball team is to win as many games as it can  (ignoring teams that intentionally lose for other reasons.)   So statistics that have a high correlation coefficiency with winning the most games are the most useful.  The problem is we don't have many (any?) of those.   So we are forced to take statistics that correlate to something that is useful to winning games, and try to combine them somehow, hoping we don't violate statistical basics such as independence of variables in doing so.   And then we use judgment to weight the various statistics--and by using judgment we are now out of the purely statistical realm.

Another example of statistical failure is in looking at results of (perhaps now defunct) loogys.   We've all seen cases of loogys having much better stats than other middle relievers, and often better than closers.   Does that mean that loogys are better pitchers?   Of course not.   They are simply given a much more limited role where they have a higher likelihood of success.   But the statistics don't really measure that--at least not the statistics I know.   More broadly, if you consider all relief pitchers, it can be very difficult to do a statistical evaluation because they haven't all had the same opportunities/played the same roles.   To do an accurate statistical comparison of closers vs non-closers you'd have to have a quantification of how much harder hitters bear down in the last at-bat and the effect of pinch-hitting or the threat of pinch hitting.   I don't think that exists.   Without that, any precise statistical comparison of effectiveness is impossible.  T his is not to say statistical measures aren't useful.   Of course they are.   But their limitations need to be understood, and the way you try to compensate for those limitation is through eye tests.

In the end, though, I'm not sure that's relevant to your article.   Perhaps I didn't read it well, but it seemed to me that you didn't offer support for your claim that the eye test didn't work, you merely compared a common statistical measure--ERA, with what have been called more advanced measures.   I'm not convinced though, that those advanced measures are more likely to predict wins than the basic measures.   In fairness, I'm not convinced the other way either.   However neither of those, at least in my use of the term, would constitute an eye test.   An eye test is what a scout would say after watching the two pitchers.    And those thoughts would be an interesting contrast to the numerical evaluations we call statistics.   I don't know, but I wouldn't be surprised to hear that the scouts' eye tests supported a contention that Rogers has been the better pitcher.

I don't quite get this. All the statistical analysis to determine that Rogers has "bad luck." What's the difference between being bad at baseball and being unlucky? To my eyes, they both look like losing. I understand that the advanced statistics can show potential, but my eyes watched Rogers last year and recognized the potential. I just don't get taking solace in the fact that we are losing games because of "luck" rather than being bad, they both look the same in December. 

I'm not against advanced stats, at all. It's how I discuss the game with friends. I just find it odd that we point out all these numbers as a way to disprove what we think we are seeing. Then chalk it all up to a nebulous thing called luck. 

The stat I want, though I won't phrase it precisely enough, is percentage of the time the pitcher hits the target.

 

I just looked through some of the bad games Taylor had in 2020, and while "luck" may be a culprit, a lot of the hits were occurring when the ball went somewhere different than the catcher set up for. Now, that form of analysis is the classic statistical mistake, looking only at a biased sample. And I will also acknowledge, after looking at some 2019 footage, that some of Taylor's strikeouts occur where the catcher is fooled too.

 

So I want a complete picture. The technology has to be there - they draw that little box on the screen, and presumably software could locate the catcher's mitt as well, and automate the whole process.

 

How do you define "hitting the target" so it's foolproof and can't mislead you? No idea. And what kinds of "missing the target" are OK? Surely a center-cut fastball when the catcher sets up... well, probably literally anywhere else... is different than just a wild toss that gets away from you high and outside. Details, schmetails.

 

Anyway, I can ask.

So let's say you have a pitcher who hits the target 100% of the time--when the ball makes it past the batter.  Unfortunately, Batters are hitting .400 against him.  And a second pitcher who hits the target 50% of the time, but batters are hitting .100 against him--what would be called "effectively wild".  The second pitcher is probably more effective.   Obviously it's a bit of a silly example; my only point is that command, which is measured by hitting the target, isn't the same as effectiveness.  However I do believe, as I suspect you do too, that the two are pretty well correlated, despite my silly example.

A truer comparison of worth to the Twins would be game-by-game outcomes. How often was each each successful at their intended jobs?

If "outcomes"  means win or loss, even that isn't always a good measure.  The absolute "right" measure is your second sentence.  For example, consider the guy who comes in for the bottom of the ninth with his team up by 8 runs and trying to get to the airport.  His job is to get his team off the field as fast as possible (without coming close to losing the lead.)  Nibbling at the corners, giving up a hit, and walking 2 guys but striking out 3 may look good in stats, but even though that's a win, that's an inferior performance to the guy that attacked hitters, gave up two solo home runs, and got his team off the field in 12 pitches with 3 ground outs.

The problem is it's hard to statistically quantify intended performance.  It takes eye tests to do that.