The 2017 July 2 signing period is set to begin on Sunday. Here is the 2017 J2 Sortable Board with tool grades and scouting reports for the players whom I believe to be the best in the class. The changes made to the J2 process when the last Collective Bargaining Agreement was ratified in November are significant and have impacted the way teams approach signing players; altered how a given class’s talent is distributed throughout baseball; changed players’ earning power; and, at least for now, forced prospects to consider how to best time their decision to sign.

Before diving into the details pertaining to this year’s class, allow me to suggest some prerequisite reading should this be your first time navigating FanGraphs’ scouty pages. If the 20-80 scale and scouting terminology is new to you, this piece will be helpful. If you’d like more context on the previous July 2 rules as we discuss the way the new CBA has changed them in this article, I suggest this.

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The draft is right around the corner, and KATOH’s here with some content. Today, I give you projections for the top-250 draft-eligible college players. This list considers all Division 1 players who logged at least 100 plate appearances or batters faced this season. These projections don’t just incorporate this year’s data, but also consider performances from 2016, 2015, and last summer’s Cape Cod League. I consider this to be a vast improvement over the work on amateur prospects I’ve done in the past.

I derived these projections using a methodology similar to the one I use for minor leaguers. I ran a series of probit regression analyses on historical data to determine the likelihood that a player will reach a variety of WAR thresholds (Playing in MLB, >0.5 WAR, >1 WAR, >2 WAR, etc.) through age 28. The resulting probabilities were used to generate a point estimate for each player’s WAR through age 28. The projections take into account performance, conference, age and height. They also account for defensive position for hitters and batters faced per game for pitchers. All of these factors are weighted accordingly based on the major-league careers of historical college players.

There are thousands of Division 1 baseball players, and the data is often unruly and prone to inaccuracies. Furthermore, determining who’s draft-eligible is often tricky, as birthdays and high-school graduation years are sometimes hard to track down. A bunch of front offices didn’t realize T.J. Friedl was eligible for the draft last year, so this isn’t just a me problem. All of this is to say that I can’t be 100% sure nobody was left off erroneously, so feel free to ask if your favorite college prospect isn’t listed.

I will provide further analysis on many of these players once we know where they end up, so check back next week. One quick observation: there’s been much debate over whether Louisville’s Brendan McKay should be selected as a pitcher or a hitter. KATOH sides strongly with Team Pitcher, as it ranks him No. 1 among college players as a pitcher and No. 191 as a first baseman. However, since he’s primarily focused on pitching to date, I suppose one could argue he has more development left than your typical 21-year-old hitter with his numbers.

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What follows is my best guess for the first round of the 2017 amateur draft. I’ll update it the day of the draft itself (June 12), perhaps several times. Players have been assigned to teams based on multiple factors: rumors I’ve heard from various industry sources, the presence of front-office members at certain games (especially lately), each club’s own particular modus operandi, etc. Be sure to check out our draft rankings here.

1. Minnesota – Kyle Wright, RHP, Vanderbilt
It sounds like Louisville LHP/1B Brendan McKay is also under heavy consideration here and that Minnesota would evaluate him both ways in pro ball for a while. Hunter Greene and MacKenzie Gore are dark horses but less likely than the Wright or McKay.

2. Cincinnati – Hunter Greene, RHP, Notre Dame HS (CA)
The Reds had about a half-dozen scouts at the ACC tournament in Louisville and watched Brendan McKay’s middling start, though I think they prefer him as a bat. He’s a possibility, but Greene is more likely and, in my opinion, the better prospect.

3. San Diego – MacKenzie Gore, LHP, Whiteville HS (NC)
I think the Padres would take Greene if he were available here and would be fine with JSerra shortstop Royce Lewis, too, but Padres decision makers have seen some of Gore’s best starts all year.

4. Tampa Bay – Brendan McKay, 1B/LHP, Lousiville
I think this is where McKay stops and that the Rays take him as a bat. If McKay goes at No. 1, I think Wright goes here, though the Rays had multiple high-level executives at MacKenzie Gore’s last start, too.

5. Atlanta – Royce Lewis, SS, JSerra HS (CA)
There have been a lot of crazy rumors about the Braves and they can’t all possibly be true, but of course the Braves haven’t been afraid to do things differently in order to maximize the overall talent they get in a single class before. As such, we have to at least consider the possibility they might get creative here. I think they’d like McKay or Gore and there’s a chance they cut an underslot deal (it would have to be at a huge discount and would still be risky), but Lewis is the best player on the board in this scenario.

Royce Lewis: going to Atlanta? (Photo: Bill Mitchell)

6. Oakland – Austin Beck, OF, North Davidson HS (NC)
Beck had a private workout in Oakland over the weekend and has the kind of tools the A’s can’t buy on the open market.

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Intro
We’re cutting the ribbon on the 2017 MLB Draft Sortable Board. The board will evolve and expand as we approach the draft, and Future Value grades will be added as the cement dries on player evaluations. For info on the 20-80 scale, by which the players are evaluated and, ultimately, the board is governed, bang it here. For info on Future Value, it’s strengths and flaws as a shorthand measurement, read this.

Thoughts on the Class Quality
The 2017 class is about average on overall talent and perhaps a bit below average as far as depth is concerned. The strength atop the class, despite Florida RHP Alex Faedo’s slightly diluted stuff, remains the terrific group of college pitchers who all have a chance to go in the top half of the first round. Faedo, Oregon LHP David Peterson, Louisville LHP Brendan McKay, Vanderbilt RHP Kyle Wright, and North Carolina RHP J.B. Bukauskas are all fairly easy to project as starters and have a chance to make up 33% of the top 15 picks. UCLA righty Griffin Canning also has consensus starter projection but lacks the stuff of those ahead of him and has been used heavily, at times throwing 120-140 pitches in a single start.

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Earlier this morning, Eric Longenhagen rolled out his list of the top-100 prospects in baseball, with Red Sox-turned-White Sox prospect Yoan Moncada at the top of his rankings. Helpfully, Eric’s rankings include the FV grade for each player, so that we can see that he really does see a difference between Moncada and the rest of the pack, as Moncada was the only prospect in the sport to garner a 70 grade.

As Eric notes in his piece, the grade is really the more important number here, as the ordinal ranking can create some false sense of separation, where players might be 20 or 30 spots apart on the list but offer fairly similar expected future value. The FV tiers do a good job of conveying where the real differences lay, highlighting those instances when Eric actually does see a significant difference between players, versus simply having to put a similar group of prospects in some order regardless of the strength of his feelings about those rankings.

But while the FV scale is helpful in binning players, it doesn’t do much to convey the differences between the tiers themselves. How much more valuable is a 60 than a 55? Or is a team better off with one elite 65 or 70 FV prospect or a multitude of 50-55 types? These are interesting questions, and ones that teams themselves have to answer on a regular basis.

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Below is my list of the top-100 prospects in baseball. Each prospect has a brief scouting summary here with links to the full team reports embedded in their names where applicable. Those without links will have them added as I cover the remaining farm systems. Scouting reports are compiled with information provided by industry sources, as well as from my own observations. For more information on the 20-80 scouting scale by which all of my prospect content is governed, you can click here. For further explanation of the merits and drawbacks of Future Value, read this.

Note that prospects below are ranked overall and that they also lie within tiers demarcated by their FV grades. I think there’s plenty of room for argument within the tiers, and part of the reason I like FV is because it can illustrate how an on-paper gap that seems large may not actually be. The gap between prospect No. 3 on this list, Amed Rosario, and prospect No. 33, Delvin Perez, is 30 spots but the difference between their talent/risk/proximity profiles is quite large. The gap between Perez and prospect No. 63, Kyle Tucker, is also 30 numerical places but the gap in talent is relatively small. Below the list is a brief rundown of names of 50 FV prospects who didn’t make the 100. This same comparative principle applies to them.   -Eric Longenhagen

70 FV Prospects

Scouting Summary
The tools are deafening. Moncada is a plus-plus runner with plus-plus arm strength, plus raw power and an advanced idea of the strike zone. He’s going to strike out, and there are some within the industry concerned about how much. That said, I think it’s important to consider that while Moncada was K-ing a lot late last year he was also a 21-year old who had played for just a month and a half above A-ball and, during a large chunk of that time, was learning a new position. The stat-based projection systems, KATOH and otherwise, seem comfortable with it, and so am I. I think he’ll provide rare power and patience while playing a premium position — he’s looked fine at second base in my looks this spring — and, while it might take adjustment at the big-league level, I think he’ll eventually be the best of this crop of minor leaguers.

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It’s that time of year again! This past Friday was the filing deadline for arbitration-eligible player contract offers. Once these numbers are published, I like to create a data visualization showing the difference between the team and player contract filings. (See the 2016 version here.) If you are unfamiliar with the arbitration process here’s the quick explanation from last year:

Teams and players file salary figures for one-year contracts, then an arbitration panel awards the player either with the contract offered by the team or the contract for which the player filed. More details of the arbitration process can be found here. Most players will sign a contract before numbers are exchanged or before the hearing, so only a handful of players actually go through the entire arbitration process each year.

The compiled team and player contract-filings data used in the graph can be found at MLB Trade Rumors.

Three colored dots represent a different type of signing: yellow represents a mutually-agreed contract signed to avoid arbitration, red represents the award of the team’s offer in arbitration, and blue represents the award of the player’s offer. A gray line represents the difference in player and team filings. Only players with whom teams exchanged numbers on January 13, 2017 will have grey lines. These can be filtered by clicking the “Filed” button.

The “Signed” button filters out players who have signed a contract for 2017; this will change as arbitration hearings occur. Finally, “All” includes every player represented in the graph. This year Jake Arrieta and Bryce Harper had the two largest contracts ($15.367M and $13.625M, respectively), but they both signed contracts before the filing deadline. This causes changes on the x-axis scale on the “Signed” and “All” tabs compared to the “Filed” tab, which is scaled to contracts under $10M.

The chart is sorted either by contract value or by the midpoint of the arbitration filings. The midpoint is the average of the two contracts and determines which contract the arbitrator awards based on his assessment of the relevant player’s value. The final contract value takes precedent over the midpoint since this represents the resolved value. Contract extension details will be written out over the data points. For our purposes, an extension is a multiyear deal that can’t be shown on the graph, since we are looking only single-year contracts for 2017.

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Every year, we run lots of pieces here in the FanGraphs family of blogs. We take a look at the “best” of them each week, but that’s fairly subjective, and there’s also an effort to spread the love, since a true “best of” post would just be 10-15 articles by Jeff Sullivan every week, and that wouldn’t be an entertaining exercise. Not even for Jeff.

This article is something we try to do every year, though sometimes I forget. This is all about the numbers — which posts were the most popular? We’ll do an overall top 15, since we do 15 posts for the “best of” post, with some honorable mentions as well. We’ll start at No. 15, because if nothing else, I want to make you scroll you down the page a little. Just in case it’s not clear, this top-15 list is going to be limited to pieces which were posted in 2016.

No. 15 (Mar. 29)
Let’s Find Rusney Castillo a New Home, by Dave Cameron
Sadly, Rusney Castillo did not find a new home, though he did hit better in the second half in Triple-A. I’m still not ready to give up on him, but Boston’s outfield is beyond crowded, so this story might not have a happy ending.

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It’s been over a year now since Sean Dolinar and I published our article(s) on reliability and uncertainty in baseball stats. When we wrote that, we had the intention of running reliability numbers for even more statistics, including pitching statistics, of which we had included none.

That didn’t happen. So a little while ago, when I was practicing honing my Python skills by rewriting our code in, well, Python (it was originally in R), I figured, “Hey, why not go back and do this for a bunch more stats?” That did happen. Sean was/is swamped making the site infinitely better, though, so I was on my own rewriting the code.

In case you need a refresher, never read our original article, and/or don’t want to now, here’s a quick description of reliability and uncertainty: reliability is a coefficient between 0 and 1 that gives a sense of the consistency of a statistic. A higher reliability means that there’s less uncertainty in the measurement. Reliability will go up with a larger sample size, so the reliability for strikeout rate after 100 plate appearances is going to be much lower than the reliability for strikeout rate at 600. Reliability also changes depending on which stat is being measured. Since strikeout rate is obviously a more talent-based stat than hit-by-pitch rate (well, maybe not for everybody), the reliability is going to be higher for strikeouts given two identical samples. You can think of it like strikeouts “stabilize” quicker than hit-by-pitches.

Reliability can be used to regress a player’s stats to the mean and then to create error bars around that, giving a confidence interval of the player’s true talent. To continue with the strikeout example, I’ll add another point — namely that, the more plate appearances a player has recorded, the closer the estimate of his true talent will be to the strikeout rate he’s running at the time. In fact, strikeout rate is so reliable that, after a full season’s worth of plate appearances, a player’s strikeout rate will probably be almost exactly reflective of his true talent. The same cannot be said for many other stats, like line drive rate, which is mostly random; the reliability for LD% never gets very high, even after a full season’s worth of batted balls.

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Back in January, I made some tweaks to my KATOH projection system, and have been using that updated model for the past several months. That model was unquestionably better than the previous versions, but it left me unsatisfied. While it addressed many of the flaws from previous iterations, there was still a lot of information it wasn’t taking into account.

I’ve been plugging away behind the scenes, and finally have a new version KATOH to share with the world. In what follows, you’ll find some detail on the new model, including its notable updates. I’ll be using this model in all of my prospect analysis from this point forward. Below, you’ll find a quick run-through of the notable tweaks, followed by an updated top-100 list.

*****

Added Features

Choosing projection window based on level, rather than age

In my previous model, I projected out based on a player’s age. If a player were 22, I projected him through age 28; If he were 24, I projected through age 30. This resulted in KATOH undervaluing players who were old for their level. The goal of KATOH is to predict the value a player will generate during his six-plus years of team control. By projecting a 22-year-old through age 28, KATOH failed to capture some of that value in cases where the 22-year-old was still in A-ball.

This time around, I chose my windows based on level, rather than age. I projected the next six seasons for players in Triple-A. I did the next seven for players in Double-A, eight for A-ballers, and nine for Rookie ballers.

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