Episode 724
Lead prospect analyst Eric Longenhagen is the guest on this edition of the pod. In this episode, he discusses certain themes that emerge from the top-100 prospect list he published earlier this week. He also elaborates on the possible value of a college game pitched by two starters who fail to break 90 mph.

Don’t hesitate to direct pod-related correspondence to @cistulli on Twitter.

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Audio after the jump. (Approximately 1 hr 24 min play time.)

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Earlier today, my first post about catch probability was published. In that post, I mostly looked at the two years of information, together. In here, I’d like to keep the two years separate. I mean, this is a whole new data source, with a number of potential applications. Why would I ever write just the one article and stop there? I have a weekly quota to hit, and this is better than whatever else there is to analyze.

Catch probability is a new Statcast metric, which you can read about here. As complicated as it might seem, it’s actually quite simple to understand, and it can give us better answers to questions people have been asking for decades. This is where you can find all the data, so you can poke around on your own. This is all brand new, and it’s kind of a first draft. The data will improve as adjustments are made for batted-ball direction and for outfield dimensions. Already, though, we can learn from what’s been provided.

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Have you seen what Yonder Alonso is doing this spring? You might not recognize it. If he qualified, his .421/.560/.789 line would the third-best in baseball. While it’s easy to dismiss a spring fling from an established player, this player spent the offseason thinking differently. Now he’s moving differently at the plate, too.

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The Reds’ pitching staff was absolutely dreadful last year. Their rotation and bullpen were both the worst in baseball. With a ghastly -0.5 WAR, their staff was the worst since the 1800s, which basically means it was the worst of all time.

Not much has changed since last year. Here’s the current depth chart for the Cincinnati rotation:

Name	IP	K/9	BB/9	HR/9	BABIP	LOB%	ERA	FIP	WAR
Brandon Finnegan	171.0	8.2	3.9	1.4	.297	73.0 %	4.42	4.66	1.4
Anthony DeSclafani	136.0	7.7	2.4	1.2	.305	72.8 %	4.06	4.14	1.9
Scott Feldman	133.0	6.1	2.6	1.3	.305	69.6 %	4.65	4.65	1.1
Robert Stephenson	119.0	8.9	5.5	1.5	.301	71.8 %	5.07	5.25	0.5
Bronson Arroyo	117.0	5.0	2.2	1.8	.299	68.8 %	5.34	5.44	0.1
Homer Bailey	93.0	7.6	2.8	1.2	.309	70.7 %	4.37	4.27	1.1
Cody Reed	81.0	8.3	3.1	1.3	.306	72.8 %	4.24	4.38	0.9
Tim Adleman	37.0	6.6	3.0	1.5	.300	70.3 %	4.90	5.03	0.1
Austin Brice	19.0	8.0	4.2	1.3	.306	70.8 %	4.79	4.88	0.1
Amir Garrett	9.0	7.9	4.7	1.3	.303	71.6 %	4.77	4.94	0.1
Keury Mella	9.0	6.4	4.2	1.4	.306	69.1 %	5.27	5.32	0.0
Nick Travieso	9.0	6.4	4.0	1.4	.301	69.4 %	5.12	5.23	0.0
Total	933.0	7.4	3.3	1.4	.303	71.3 %	4.62	4.72	7.4

Brandon Finnegan is at the top, which is fine, I guess. After that, however, things go downhill in a hurry — especially with Homer Bailey and Anthony DeSclafani slated to open the year on the DL. For example: both Bronson Arroyo and Scott Feldman are apparently not only still pitching, but are penciled in for 250 innings in Cincinnati’s rotation. The rest of the list is made up of unproven youngsters. Each of them has some promise, but none have had much success in the major leagues, either. Hence their middling projections.

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This is Kate Preusser’s second piece as part of her month-long residency.

It’s no secret that there’s considerable overlap between bookish types and baseball fans, all tucked cozily in the center of that particular Venn diagram. Distance readers have an inborn patience for the nine-inning drama of a baseball game. These are a people who understand the three-act structure, exposition leading to rising action leading to the climactic moment; this past World Series, in fact, felt a little like the baseball gods had taken a Khan Academy class on Aristotle’s Poetics.

But there’s a narrative that most serious baseball fans have come to reject, and that’s the one of spring training. Every year there seem to be pieces poking fun at the cliches of spring training — Best shape of his life! Swing mechanics change! Learning a new pitch/grip/arcane religious philosophy! LASIK! — and those pieces exist because these cliches persist, so much so that poking fun at spring-training cliches is now itself a form of cliche.

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I’m going to be up front with you: that headline is seriously misleading. Based on what we can tell, Miguel Sano is probably not a good defensive player. Last year, though, to try and get the club’s offense going, the Twins decided to try Miguel Sano in right field. Regret kicked in pretty quickly.

He played right field most days for the first couple of months of the season, until a strained hamstring put him on the disabled list for the entire month of June. During the month that the team didn’t have to watch Sano chase balls around the outfield, they decided that they didn’t really want to see that ever again. When he returned to the team at the beginning of July, he was promptly moved back to third base. He split his time between there and DH over the rest of the year.

As a pretty large human being, Sano certainly doesn’t look like an outfielder, and while we only saw him out there for a little over 300 innings, the early returns weren’t particularly positive.

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Here’s the thing about catch probability: It’s existed for close to two decades. It’s at the heart of what we refer to as the advanced defensive metrics. Defensive Runs Saved, Ultimate Zone Rating — they couldn’t exist and do anything without catch probabilities in some form. It’s just that, for the longest time, those probabilities were generalized, educated guesses. You might’ve heard that baseball has entered the information era.

Here’s a weekend tweet from Daren Willman:

Here’s the new @statcast catch probability leaderboard we introduced @sabr https://t.co/I1NDOCRVFO

— Daren Willman (@darenw) March 12, 2017

If you missed the link in there somehow, here it is again: the Statcast Catch Probability Leaderboard. We have most of two years of Statcast information, and now we’re getting to see it applied to player defense. Specifically, in this case, outfielder defense. If you don’t entirely understand what catch probability is, here’s the MLB.com glossary entry. Take a given fly ball or line drive to the outfield. What are the odds a given batted ball is caught? Statcast can tell us, by considering hang time and necessary distance to cover. This is the start of something beautiful.

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Episode 723
The prolific Travis Sawchik is a former beat reporter for the Pittsburgh Tribune-Review and author of the book Big Data Baseball. He’s also the guest on this edition of the program, during which he discusses players like Jose Bautista, J.D. Martinez, and others who’ve been compelled to follow unconventional paths to success.

Don’t hesitate to direct pod-related correspondence to @cistulli on Twitter.

You can subscribe to the podcast via iTunes or other feeder things.

Audio after the jump. (Approximately 1 hr 6 min play time.)

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