My last few articles have been deep dives on hitters who I felt may need their projection adjusted. Today, I’m going through the last few. Remember, this analysis is focused on the player’s talent projection changing and not on playing time.

Kyle Tucker (220nd in NFBC ADP)

I knew Tucker would make this list before the list ever existed. The 22-year-old flew through the minors and just destroyed AAA with a .332/.400/.590 triple slash line last season. Once he got the majors, the results stunk with him hitting .141/.236 /.203 in just 72 plate appearances.

And now the xStats come to his rescue since he was extremely unlucky with his batted balls. Here are his various actual and expected values.

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A few days ago, I did a deep dive on several hitters I felt that I needed to personally adjust their projection. Today, I’m going through a few more. This analysis is focused on the player’s talent profile changing and not on playing time.

Paul DeJong (NFBC ADP of 191st)

The reason Dejong popped up on my radar was his broken hand from mid-May to early July. It seems like he may have returned a little too early and his results suffered.

Month: OPS
Apr: .866
May: .728
June: DNP
July: .589
Aug: .726
Sep: .777

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Since the end of the regular season, I’ve been helping various publications write player previews for the upcoming season. During the process, I kept a list of hitters who I felt there may be reasons to adjust their talent projections. I’ve found that even when I normally deep dive into hitters, I come up close to the standard projection and wasted my time. I found I’m better off focusing my effort on pitchers. I can’t afford to ignore hitters, though. I’m now going to analyze these few in detail over a series of articles to see if their projection holds up or do I need to adjust it.

For some of the hitters, the reason behind the talent change (e.g. injury) is obvious. For others, I don’t remember the exact reason. Oops. I’m going to start working my way through them starting with the ones with the highest ADP.

Note: I’m only dealing with the player’s talent. Projecting playing time is an entire separate fiasco.

Justin Turner (109th in NFBC ADP)

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There wasn’t a ton of moves with fantasy implications over the weekend but there was enough to lump together some. I’m still at the point that no major team breakdowns can really start with the two big bats still available. Anyways, here is a breakdown of the moves:

Phillies sign David Robertson

The 33-year-old righty immediately joins the Phillies closer discussion for the simple reason he’s projected to be the best bullpen arm. Steamer has him for a 3.22 ERA with Seranthony Dominguez next at 3.39 and previous closer Hector Neris at 3.62.

The Phillies front office would prefer Robertson to close instead of the other two to save money. The team is already on the hook for his $23 million dollar contract, so his price is set. The more Saves the pre-arbitration arms get, the higher their arbitration contracts will cost.

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When Yusei Kikuchi was posted in early December, he had only a month to sign with a team. For this reason, his signing decision was going to fall over the holidays and Seattle broke the news right before the end of the year. Because of the unique timing, he didn’t get a ton of press, but owners should be aware that he’s a decent starter and not to be ignored on draft day.

First off, the other Jeff beat me to writing about the 27-year-old lefty. Twice to be exact. Once before the signing and once after.
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This study came about from a comment made in passing by Jeff Erikson and Scott Jenstad in a RotoWire podcast. They were discussing how they were worried more about hitters who were striking out on fastballs instead of breaking balls. They figured it was worse to strikeout on fastballs and it showed the hitter was in decline. I don’t remember the exact show but I agreed and now have time verify. And as usual, the pair was right with three-hitter groups differentiating themselves from the pack.

The first key was that I wasn’t interested in the batter ability to tell balls from strikes. Instead, I wanted to focus on pitches right down the heart of the plate while keeping the strike zone as big as possible to increase the sample size. In the end, the taken pitches in the zone used were called strikes 97% of the time.

Next, I found the swinging strike rate for pitches in the strike zone. I grouped the pitches into several groups.

• All fastballs
• Fastballs > 94 mph
• Fastball < 94 mph
• All non-fastball
• All changeups including splitters
• All breaking balls

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Merrill Kelly

The Diamondbacks signed Merrill Kelly after Kelly spent four years in the Korea Baseball Organization. I never heard of Kelly until the signing some it’s time to put together a profile for him.

First, here’s a tweet from Homin on Kelly’s profile:

KBO season ended. Merrill Kelly, former #rays prospect will play MLB next year. 92-94 mph FB, cutter, two-seam, CH, CV. Various pitches. No plus pitch, but reliable pitcher. Could be good long RP at mlb level. #rays are scouting him heavily.

— Homin Lee (@Homein22) November 13, 2018

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Introduction:

Today, I will introduce a game theory approach for comparing baseball projection systems. The day’s venture will not be a typical statistical analysis. I won’t be using any Chi-squared tests, nor will I calculate Type I or Type II errors. I won’t be evaluating MSEs or the like.

Instead, I will look to determine the profitability potential of each projection system by simulating what would have happened in a fantasy auction draft. Instead, I’ll play a game.

What do I mean by this?

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I heard in passing from a credible source:

“The effectiveness of a pitcher’s slider relies on it having the same spin as his fastball.”

I figured it would be an easy test and could help to immediately identify top-rated sliders. After looking at the data every conceivable way and came up with the following conclusion: publicly available spin information has near ZERO correlation to a slider’s effectiveness. But while rooting around, I did find two factors which do matter, fastball velocity and the difference in slider and fastball velocity.

The theory behind the quote is that a hitter has a tougher time differentiating a fastball and slider if they are spinning at the same rate. So, the closer the difference, a higher chance for a swing-and-miss.

I compared 2018 pitchers with at least 200 sliders and 200 four-seamers thrown. Then, I compared just the difference, the absolute value of the difference, square of difference. Nothing tangible. Nothing matched.

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One of the keys to fantasy success involves finding when projections can be systematically off. The hard part for fantasy owners is that most of these findings, like pitch velocity, get quickly incorporated into projections. Since it’s difficult to find these discrepancies, I was intrigued when I saw this quote by Mitchel Lichtman (MGL) in an article he wrote:

So, the substantial under-projections seem to occur when a player gains speed but his wOBA remains about the same.

And by substantial, it was a 22 point difference is wOBA. This is a major difference and could point owners to some nice upside plays. I decided to go ahead and dive in.

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