I’ve made it almost a month since I made the following declaration on investigating prospect Hit grades:

I am going to stay away from more Hit tool predictions until I have collected every one of MLB.com’s prospect grades from 2013 and 2014, not just the top 100. I probably will not be able to compare many to their major league stats but I can with Triple-A.

I broke my position after collecting MLB.com’s 2013 grades. I ventured forward without the 2014 grades. With this larger and more diverse dataset, I compared the hitters’ grades to their batting average, home runs, and stolen bases in both AA and AAA.

Trying to better understand the Hit tool stems from finding it doesn’t contain any predictive power. When looking at players with different grades, major leaguers ended up posting similar batting averages. I concluded two issues were causing the production to level out.

First, hitters needed a talent and/or production baseline to get into the majors. Some hitters with below average grades were under-graded and produced up to the MLB baseline. Additionally, “better” hitters were over-graded but still had just enough talent to make the majors. This talent convergence tends to average out the grades.

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Last week, I analyzed the 2016 home-to-first times for hitters. With the background information out of the way, I’ll examine at the 2017 speed data to find who’s running the faster and slowest, who’s changed the most since 2016, and how home-to-first times compare to Bill James’s speed score.

With all the Statcast batted ball data getting analyzed, I continue examining the home-to-first times. Fantasy owners may believe speed is mainly used to determine stolen base threats. It’s more than that.

It’s an input to many other fantasy related factors which can help explain a player’s age-related decline. Faster players will beat out a few extra ground balls for hits thereby raising their batting average and on-base percentage. Speed allows a player to score more once on base. It can add to a hitter’s power profile. Also, speed can help keep a player maintain their fielding range at a premium defensive position instead of moving to a statue-like position (e.g. first base). Finally, a drop in running speed may point to an injured player.

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One month through the season and those playing ottoneu are likely getting a clearer idea of if they are buying or selling. It’s a tricky pendulum, as no playoff structure exists. In determining to sell, owners are likely targeting players who have performed much better than expected over the first month. While there are several ways to determine who these players are, one of the most common ways is to examine projections to see which players have actually improved. Today, I want to look at a couple of pitchers who have improved their projections the most, while examining a couple underlying skill changes that may or may not exist in each of these. The degree of these changes represents my willingness or unwillingness to buy.

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Our Dark Overlord, David Appelman, finally acquired a minor league pitch-by-pitch database as seen by the new minor league stats available like Contact and Groundball rates. I hoped it would help to better understand the disconnect between a prospect’s Hit tool grades and major league results.  I made some progress but created more questions than answers.

When I examined the database, I was hoping to find some batted ball information as Eli Ben-Porat used at the Hardball Times. No such luck. But there was some x,y data … for every pitch.

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At the time of this writing, there are 37 major league hitters with a wRC+ of 150 or higher. There are the usual suspects: Bryce Harper, Mike Trout, and Nolan Arenado are on the leaderboard to no one’s surprise. There are exciting young prospects, including Mitch Haniger, Aaron Judge, and Joey Gallo. Then there are unexpected names like Eric Thames, Cesar Hernandez, and Robbie Grossman.

Lengthy articles could be, and have been, written about any of the players above. One player who hasn’t received much publicity despite some relatively prolonged success is Grossman.

He checks in with a 158 wRC+ in 67 plate appearances so far this year. Steamer projects a .322 wOBA and 99 wRC+ for the rest of the season (ROS), and that projection puts him just a few ticks behind his highly-touted teammate Max Kepler. Although Kepler is three years younger and may have a higher ceiling, the point is that name recognition can play a pretty big role in how we analyze players.

Also, Grossman used to be bad. From 2013 to 2015, he had just a .281 wOBA and 77 wRC+ in 202 plate appearances against left-handed pitching, despite being a switch hitter. Since 2016, however, Grossman has a .417 wOBA and 166 wRC+ in 170 plate appearances against lefties. Even with his early-career struggles against lefties, Grossman now has a lifetime .344 wOBA and 118 wRC+ against them. Read the rest of this entry »

Just before the season started, the FanGraphs staff (including RotoGraphs contributors) was asked to make its official predictions for the upcoming season. We took our best shot at predicting the playoff teams, MVP and Cy Young Award winners, and Rookies of the Year for 2017. Perhaps in the coming weeks we will check in with the top picks for MVP and Cy Young, but in this article, we’re going to look at the top rookies.

Our staff picks on the American League side had Andrew Benintendi (40 votes) as the overwhelming favorite to be named the league’s top rookie, with Jharel Cotton (4) and Mitch Haniger (4) rounding out the top three.

Over in the National League, Dansby Swanson (27 votes) was the favorite by a wide margin, followed by Robert Gsellman (12), Manuel Margot (5), and Hunter Renfroe (4).

While it’s extremely early and still much too soon to make any concrete statements about who will win this year’s awards, let’s take a look at the wide-ranging early season performances of the players we expect to be the game’s top newcomers: Read the rest of this entry »

A couple of weeks ago, I examined the prospect Hit tool grade and how it provides useless information as it is currently being distributed. It’s time to dive back in. First, I am going to answer a couple questions which have come up on the topic and then get into my recommended changes.

Are there any systematic differences between Baseball America’s grades and those from MLB.com?

This study was easy. I grouped all players who had grades from both sources in the same season and I found the average differences.  The following table contain the averaged difference of the Baseball America grade minus the MLB.com grade for the 154 matched pairs.

The final differences are small with Baseball American being higher on power while MLB.com is higher on Speed and Defense.

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1. A.J. Pollock receives first-place MVP votes

Pollock put together a season for the ages in 2015. In 157 games and 673 plate appearances, he slashed .315/.367/.498 with a .371 wOBA and 131 wRC+. He hit 20 home runs, scored 111 runs, and stole 39 bases in 46 attempts. That offense, combined with his elite center field defense and base-running, netted him 6.5 WAR ­­­— fifth-best total in the National League. Pollock’s 2015 production wasn’t a fluke: in 75 games and 287 plate appearances the previous season, he hit a similar .302/.353/.498 with a .372 wOBA and 134 wRC+. 2016, however, was a lost season for Pollock, who missed most of the year due to a broken elbow. Entering 2017, he’s only 29 years old and he appears to be healthy. Assuming good health, he’s certainly capable of putting up MVP numbers.

2. Aledmys Diaz has a better offensive season than Trea Turner

Much was made of Turner’s spectacular big league debut in 2016, and rightfully so. The rookie slashed .342/.370/.567 with 13 home runs and 33 steals in just 73 games and 324 plate appearances. Turner’s performance, however, was buoyed by an unsustainable .388 BABIP. While his skill set lends itself to higher-than-average BABIP’s, it’s expected to land somewhere closer to .350 in 2017, bringing his likely batting average down below .300. What’s more, his minor league track record suggests that he may not crack 15 home runs all year, despite nearly reaching that total in half a season like he did in 2016. Turner, 23, is one of the most exciting fantasy players around, especially since he’s eligible at shortstop, second base, and outfield. However, because substantial regression is expected, there’s another young shortstop in St. Louis who could easily be the superior offensive weapon in 2017 and beyond. Read the rest of this entry »

Earlier in the offseason, I examined out how reported Hit tool grades compared to actual MLB batting averages. I called the process a “mess” but figured it had some value. When I implemented the formula on MLB.com’s 2017 grades, commenters had the following to say about the projected batting average values:

“… not enough differentiation there in my opinion”
“… adjust your outputs to create more difference..”
“… hoping the table would be more conclusive…”
“…way too tightly grouped to the mean…”
“…it’s better to have no projection than to project everyone to be average…”
“… regressing too much to the mean…”
“… hit tool grades should be ignored…”
“…hit tool is undervalued in prospect analysis…”

I have no issue with the hit values being regressed to the mean. What I do have a problem with is if the hit tool is not measuring the correct factors. I needed to find out if reported hit grades provide any value. The following is a detailed look at how the hit tool is graded and how it fails to predict one simple factor, a hitter’s ability to get hits.

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Alas, it’s finally time to wrap up the Pod vs Steamer Projections series, which pitted my Pod Projections against the Steamer projections in several fantasy categories, discussing which players I’m significantly more bullish and bearish on. Last week, I identified 13 pitchers I was far more bullish on than Steamer for ERA. In doing this exercise, I realized I was actually forecasting lower ERAs for the majority of the pitchers we both projected. So now turning to the pitchers I forecasted a higher ERA for, there was literally only 21 to choose from, most of which were within 0.10 runs of each other, which is, like, nothing. But here are seven fantasy relevant pitchers I’m a bit more bearish on than Steamer.

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