My role here at FanGraphs is to write about minor league players. Nearly all of my articles focus on the output from my KATOH projection system, which produces long-term forecasts for players who are still in the minor league phase of their careers. Today, I’m unveiling some updates to my model that will be reflected in my analysis from this point forward.

I’ve been meaning to work these updates into KATOH for quite some time now, but haven’t had the chance to finish up until now. Some pieces of this took a bit longer than expected, and day job stuff along with this year’s onslaught of prospect debuts pushed things to the backseat a bit. But I’m all caught up now and ready to unveil my new and improved KATOH. Here we go!

Rather than just putting out a straight leaderboard, I thought I’d use this as an opportunity to explain some of the inner workings of KATOH. I wanted to say something more insightful than “These are the best prospects because math.” That’s why this piece runs 2,000+ words without reference to a single baseball player. If you’re just interested in the output rather than the nitty-gritty, check back after Thanksgiving for KATOH’s top 100 list. I just wanted to get all of this background stuff down in one place, rather than cluttering future pieces with extra information.

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Obligatory Technical Details

The general framework of my model is largely the same as it’s always been. As I did in the past, I deployed a series of probit regressions to see what factors are most predictive of major league performance. For each player, I generated probabilities that he would achieve certain benchmarks through his age-28 season: play in the major leagues, earn at least 1 WAR, earn at least 2 WAR, etc. These percentages gave me a probabilistic outlook for each player, and enabled me calculate an “expected value” for his WAR through age 28.

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Theory: the Mariners want to be good soon. I haven’t talked about that with anyone in the industry, so I might be way off, but it’s the assumption I’m going to work with. Another assumption follows: if the Mariners want to be good soon, they probably figure James Paxton could and should be a part of it. The Mariners, probably, want Paxton to become a major contributor as soon as the season ahead. Toward that end, Paxton needs to stay healthy, and the healthy version of Paxton needs to do better.

There’s nothing worth saying about Paxton’s health. Hopefully he doesn’t get hurt. I don’t know why he gets hurt, and I don’t know how he can stop. You cross your fingers. But as far as being better is concerned? Most everything comes down to mechanical repetition. And health, of course, plays some role in that. Out of more consistent mechanics, the Mariners would like Paxton to improve his location. They’d like him to improve his changeup. And there’s another idea, which I already wrote about once some months ago.

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Episode 612
Dave Cameron is both (a) the managing editor of FanGraphs and (b) the guest on this particular edition of FanGraphs Audio, during which edition he discusses the very near future of Aroldis Chapman, Japanese pitcher Kenta Maeda, and the logic behind his assertion that Jason Heyward is the offseason’s best free-agent bargain.

This edition of the program is sponsored by Draft, the first truly mobile fantasy sports app. Compete directly against idiot host Carson Cistulli by clicking here.

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 35 min play time.)

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Before every game, we publish estimated game odds. The odds consider the identities of the starting pitchers, and as the first pitch draws closer, the odds update to factor in the actual starting lineups. I’m not saying it’s an infallible system or anything, but it’s a neat little feature we have, even if it doesn’t get all that much use. And though this is by no means a rigorous test, consider the top 100 most seemingly lopsided games from the season past. Based on the calculated odds, the favorites in those games should’ve won 70 times. The favorites actually won 71 times. So things check out.

The favorite won the game with the single most lopsided odds. Max Scherzer and the Nationals were projected to have 78% odds against Sean O’Sullivan and the Phillies. The favorite also won the next-most lopsided game, and the next-most lopsided game, and the next-most lopsided game, and the next-most lopsided game. The five games with the most imbalanced odds all went to the team expected to win. We find our first upset in sixth. Which would then qualify this as the season’s greatest upset, taking into consideration only pre-game odds. It was an upset when the Royals rallied past the Astros in the playoffs, but that wasn’t a lopsided game at the start. It only became that way later. The biggest upset, considering pre-game outlook? We rewind to June 17, and we go to Los Angeles.

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Episode 611
Jeff Sullivan is a senior editor at FanGraphs. He’s also the periodic guest on this edition of FanGraphs Audio.

This edition of the program is sponsored by Draft, the first truly mobile fantasy sports app. Compete directly against idiot host Carson Cistulli by clicking here.

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 23 min play time.)

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Gerardo Parra’s out there right now as a free agent and soon, a team will sign him to play somewhere in the outfield. Plenty of teams have been linked to Parra, and understandably so. He’s entering just his age-29 season, he’s something like a league average hitter who started to lift the ball to the pull field and hit for some power last year, he’s played all three outfield positions in the past and, at times, he’s played them exceptionally well! He won’t kill you on the bases, and he should come reasonably cheap, especially considering his lack of draft pick compensation. Most everyone can afford Parra, and most everyone would have a spot for him on a roster.

The first team to emerge as a potential landing spot for Parra were the Nationals, who made a push to acquire him at the deadline. Lately the talk has shifted toward the Cubs and Mets being interested in Parra’s services. Between these three teams, a common thread exists in the outfield.

The Nationals’ only outfield opening is in center field. The Cubs’ only outfield opening is in center. The Mets’ only outfield opening is in center. The Cubs have been vocal about prioritizing their outfield defense, and we saw in the World Series that the Mets could benefit from a defensive upgrade at just about any position. So the question becomes: would signing Gerardo Parra to predominately play center field help a defense, or hurt it?

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We are living in a golden age of youthful, historic talent, especially among position players. This was the case even before 2015, when the likes of Kris Bryant, Carlos Correa and Francisco Lindor — all 23 years old or younger — joined the party. Previously, the Cubs had run out a slew of young stud position players on a daily basis, and Manny Machado and Bryce Harper have been around enough to truly be called veterans at this point. All of these greats all reside in the shadow of the best young player of them all, however: some guy named Mike Trout.

With a little luck, or perhaps some better judgment among voters, Trout could very well be celebrating an unprecedented fourth consecutive MVP award right about now. He’s got one of those on the mantle, along with three relatively controversial second place finishes. While I did predict in an ESPN Insider article this past March that Josh Donaldson would win the 2015 AL MVP, there is no doubt that, if I had a ballot, I would have slotted the Blue Jay third sacker on the second line, behind the Angel center fielder.

How great is Trout, and where might all of this be headed? Let’s take a somewhat unorthodox look at his first four seasons relative to some of the game’s all-time inner-circle superstars, and see where he fits in.

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