By clicking this link, you (“Reader”) have opted into an agreement (“Contract”) with FanGraphs, Inc. (“Handsome Author”). Handsome Author agrees to provide Small-Sample Normalization Services (SSNS) to Reader in the following post (“Post”). In return, Reader, presumably interested in Handsome Author’s analysis or merely intrigued by Handsome Author’s curious Post title, shall appreciate said services no matter what.

SSNS seeks to normalize good and bad performances witnessed in the first two weeks of the 2017 Major League Baseball (MLB) season. Handsome Author has noted previously, here and elsewhere, that small-sample booms and busts in March and April would go largely unnoticed in other months in which the sport of professional baseball is played, such as May, June, July, August, or even September.

Accordingly, SSNS looks at a player’s past performance as a benchmark for current performance using FanGraphs’ (and not Handsome Author’s) very nifty player graphs. It answers the question, “Has a player done this before?” Perhaps, Reader. Perhaps. But perhaps not. SSNS then assigns an Excitement-to-Panic Level (EPL) on a 5-point scale from 1 to 5 as well as an Adjusted Excitement-to-Panic Level (AEPL) once Handsome Author has properly assessed the historical significance of the player’s performance — within the context of the player’s self.

In this inaugural edition, Handsome Author will use SSNS to evaluate five hitters primarily in terms of their strikeouts (K%) and walks (BB%) through their first X number of games, with some other statistics incorporated as well. SSNS is not the be-all, end-all of player performance, but knowing we’ve seen a player “do this before,” as they say, is enough to calm one’s turbulent heart and mind.

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I don’t mean to over-hype anyone. Or maybe I do. I don’t know. I seek to provide an adequate amount of hype and keep things in perspective. That said, I’m pretty excited about Josh Bell. You may know him, you may not; he’s slated to be the Pirates’ primary first baseman, maybe with a little bit of backup outfielding thrown in. Josh Shepardson talked about him and his general skill set in November. Bell’s young and a several-time top prospect, although the highest he ever reached on any given list was MLB.com’s, at No. 34 overall prior to 2015. Nothing to sneeze at, but he never carried the same hype as, say, Yoan Moncada currently does. He’s Eric Longenhagen’s No. 50 prospect this year and KATOH’s No. 25. Knowing the gory math that goes into KATOH, I’m very partial to it. Also, all of this suggests I’m not early to any kind of party here. I’m reluctant to claim as much. I just can’t help but produce my own tributes every now and then.

Bell walked more than he struck out last year. As a rookie. That’s what gets to me. Not that it’s never been done before, and it’s not like it was a huge sample size — 153 plate appearances. I found plenty of examples in the last 25 years to compare; Bell’s rookie season is almost a dead ringer for that of Doug Mientkiewicz, a first-ballot Surname Hall of Famer but otherwise mediocre ballplayer with a decent three-year peak.

Where Bell diverges from Mientkiewicz — and everyone else, for that matter — is his hard-hit rate (Hard%). Mientkiewicz decidedly did not hit the ball hard. Among rookies since 2002 (because that’s as far back as our batted ball data from Baseball Info Solution dates) who notched at least a .130 isolated power (ISO), none hit the ball as hard as Bell. Again, small samples, but this is already a decent list to top.

When we expand the sample from rookies to all hitters in that same timeframe, things become even more interesting. Here’s a list of every hitter, in no particular order, who, in any given season, minimum 150 PAs, achieved (1) more walks than strikeouts, (2) an ISO better than .130, and (3) a hard-hit rate better than 33 percent.

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You know the drill! The criteria for each “award,” so to speak, precede each table. Pitchers first, then hitters. Here are last year’s picks, by the way.

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Pitchers

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Edit (3/29/17, 7:55 pm EDT): Brent Hershey of BaseballHQ and Ron Shandler’s Baseball Forecaster (very politely) brought to my attention that this has been done before! By Bill Macey back in 2012. Formerly behind a paywall, it has now been made public for your reading pleasure. I didn’t even know this research existed (so I’m really glad Murphy brought it to my attention); I am always reluctant to ever claim to break ground in this field that progresses so quickly but also has such a rich history of research. Please consider the following research a companion to and external validation of Macey’s work.

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I welcome all constructive criticism. This research is not especially rigorous, but given the nature of the claim — a legitimately significant spring training statistic! — it merits the disclaimer.

I found a statistically significant spring training statistic.

I’d rather not rehash the history of research and speculation regarding The Spring Training Stat(s) That Matter. Just know that, outside the modest results from this Dan Rosenheck piece in The Economist, it’s generally accepted that Spring Training statistics mean virtually nothing, and you’ll read all manners of baseball writers bashing this notion.

The big caveat is most of this research concerns individual players. Mine: team-level statistics. Alas, it’s an inherently different beast with which I’m dealing. Despite small within-year populations (30 teams rather than hundreds of players), the observation-level sample sizes are much larger (hundreds of plate appearances rather than dozens), making the odds of finding meaningful correlations much better despite fewer data points.

Per usual, I buried the lede: a team’s rate of stolen base attempts (calculated from stolen bases [SB] plus caught stealing [CS]) during spring training is actually meaningful. I’ll get to the implications of this later because there are many. First, let’s dig into the guts of the research. I gathered team-level spring training statistics from 2006 through 2016 and paired it with regular season statistics from the same span plus 2005.

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My first attempt at making bold predictions (2015) was rather aimless. My second attempt (2016) was a little more focused and a little more successful, with my baby boo Jose Ramirez finally making good on his promise (and redeeming my year-too-early prediction for him in 2015). This year, I’ve earnestly attempted to make bold predictions that spawned from research. In other words, they’re not bold for the sake of being bold — not that those kinds of predictions can’t be fueled by research, but, well, you know. Anyway, you don’t care about any of this. Let’s get to the goods.

For those keeping score at home: five predictions apiece for hitters and pitchers, in alternating order.

1) Alex Dickerson is a top-30 outfielder.

Original post from September. The premise is simple: keep an outfield job and sustain his place discipline gains. With prospects Hunter Renfroe and Manuel Margot and speedster Travis Jankowski fighting for playing time, Dickerson seems to have fallen to the wayside. I’m not sure why; he projects to be the Padres’ 4th-best hitter by wOBA and best-hitting outfielder by more than 30 points. Accordingly, the projection systems must believe in his plate discipline gains — and they do. The doubters will doubt, but the gains emerged in 2016 prior to his promotion. In a full season’s work, he looks like a poor man’s outfielding Kyle Seager: 20 home runs, 10 steals, a .270 batting average. As the 70th outfielder off National Fantasy Baseball Championship (NFBC) draft boards, it’ll cost you virtually nothing to find out.

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No need to fret. The unofficial conductor, who is also the author of this post, does not expect the cabin to reach capacity. Not prior to April, at least.

Brett Lawrie’s time with any organization, not just the White Sox, was ticking away; more writing was scribbled on more walls with each passing year since his debut. So when the White Sox released Lawrie, the first thought on most fantasy owners’ minds was not Where will Lawrie end up? but, rather, When will Yoan Moncada get the call? Moncada, a consensus top-5 prospect, changed socks and is now the marquee name of the South Side’s now-promising future.

Yet one could argue Moncada’s not quite ready for the big show. After raking and running absolutely wild in High-A in 2016, Moncada graduated to Double-A and, well, his performance is open to interpretation. On one hand, his 11 home runs, nine stolen bases and .277/.379/.531 triple-slash in 207 plate appearances amounted to a batting line that was more than 50% better than the league. On the other hand, he struck out more than 30% of the time — and that lack of contact carried over into his Major League debut, during which he struck out in 12 of 20 PAs. The tools are immense, but, at 21, he could definitely use some polish, and the White Sox have no incentive to rush him along.

Allow me to (re)introduce you to Tyler Saladino.

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Playing time can make or break a baseball player’s fantasy value. An elite player may not finish above replacement level if he suffers an injury and plays only half the season, and a lackluster player could finish above replacement level simply by playing every single day. This is all intuitive, and the fantasy community generally approaches these kinds of things rationally. In other words, most players are appropriately valued, outside of the market inefficiencies that inevitably warp player values.

One-dimensional speedsters — dudes who steal a bunch of bases and do little else — are much harder to peg. Their value is tied up primarily in one category, as stolen bases (SBs) do not directly correlate with other categories the way home runs would with runs and RBI, for example. The issue becomes all the more confounding when one considers the contemporaneous scarcity of SBs relative to home runs. There’s more to value than just SBs and plate appearances (PAs), but the fact of the matter is the two statistics by themselves correlate very strongly with a player’s end-of-season (EOS) value (which, here, are informed by Razzball’s Player Rater).

In the last five years, baseball has seen 75 player-seasons of 30-plus SBs — 15 steals a year on average, a trend that didn’t fundamentally change in 2016 (although that doesn’t mean SBs aren’t scarce). A simple linear regression of SBs and PAs, the latter of which serves as a proxy for other counting stats such as runs and RBI, against EOS value produces a remarkable 0.71 adjusted R²:

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The fantasy community is down on Justin Upton. I get it, but it’s a little strange to me given our collective penchant for recency bias. Upton had a monster second half and finished the season an almost-perfect replica of his usual self. (The operative qualifier being “almost.” We’ll get to that in a second.) Sure, it was a rocky year, but hey, Joey Votto had one, too. Dude was batting .213 with a 27 percent strikeout rate (K%) through May…

Right, so Upton was an almost-perfect replica of himself. In a vacuum, his production looks nearly identical to his typical annual accomplishment, down to nearly every statistic except for his batting average on balls in play (BABIP). In my investigation of his woes, I noticed his uncharacteristically low infield hit rate (IFH%). Here’s a list of hitters with higher infield hit rates than Justin Upton in 2016:

• Salvador Perez
• Kendrys Morales
• Albert Pujols
• One hundred and thirty five other players not surnamed Ortiz, Mauer, Seager, Granderson, Cabrera, Votto or Kemp

Yes, Upton ranked among the bottom 6 percent of hitters in terms of infield hits. If there’s a single bone to pick about Upton’s season — well, aside from the insane volatility — it’s that his BABIP failed to get back on track, continuing to linger at a league-average mark. It seems a trend has emerged; accordingly, it’s easy to accept said trend as a new normal, as a resignation of Upton’s fifth tool.

I’m here to make the classic* Infield Hit Rate Defense, or IHRD, as it’s known in the infield hit community.**

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Robbie Ray is already shaping up to be one of 2017’s most contentious starting pitchers headed into draft day. (This isn’t even my first time writing about him in the last half-year.) His 28-percent strikeout rate (K%) and 3.45 xFIP scream of an elite starter, but his 4.90 ERA and 1.47 WHIP, sustained during more than 170 innings pitched, seem to say otherwise.

Analysts and laymen who have expressed optimism about Ray have done so in regard to his alleged hittability. That 1.47 WHIP didn’t come from nowhere: his .352 batting average on balls in play (BABIP) got him there. You’ll hear a variety of arguments: he struggles on his third time through the zone; he lacks a quality third, or maybe even second, pitch; and so on. I’m not here to argue the validity of those sentiments.

I want to talk exclusively about Ray’s BABIP. Well, his sinker, too. And maybe even his strand rate (LOB%)… But mostly his BABIP. Please, have a seat. I don’t want to fluster you.

Ray’s .352 BABIP in 2016 was the second-worst of the last 15 years. That’s out of 1,281 individual player-seasons posted by qualified starting pitchers. His BABIP was historically bad — strange, you’d think, for a pitcher who has quickly demonstrated a lot of promise. So, I want to approach this whole BABIP thing in a vacuum. Let’s just look at the facts — not even alternative facts, but real facts!

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Around this time last year (edit: actually, it was more like sometime in 2014), Eno Sarris introduced the Arsenal Score. It was, and still is, a novel concept: for every pitcher, evaluate each of his pitches based strictly on their strikeout- and ground ball-inducing tendencies. Each pitch would be evaluated relative to its contemporaries — in other words, Corey Kluber’s slider would be compared to all other sliders in the league.

I’ll speak for Eno when I say the original Arsenal Scores weren’t meant to be especially rigorous. They received some flak for being mathematically inaccurate — to which I say, it doesn’t really matter. Originally, Eno calculated separate Z-scores for the ground ball rate (GB%) and swinging strike rate (SwStr%) — called “Z-BIP” and “Z-Whiff,” respectively, in the results to follow — of each pitch for every pitcher. The aggregate Z-scores — two Z-scores times X number of pitches — comprise the full Arsenal Score.

This time around, I propose a few tweaks:

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