The second-best part about Tommy Pham is I can basically recycle this post I wrote about Domingo Santana three and a half weeks ago. Like, I could replace Santana’s name with Pham’s throughout it and you wouldn’t blink. Pham, through his first 628 plate appearances, has hit a home run on more than 28% of his fly balls (28% HR/FB); if sustained for another 72 PA, it would be the third-best mark through a player’s first 700 PA in the last 15 years (among more than 600 qualified hitters).

The best part about Tommy Pham, though, is something Santana doesn’t have, and it’s something more than skin deep. Depending on whom you ask, Pham has swung at pitches outside the zone only 19.8% (BIS), 22.2% (Pitch Info) or 22.9% (PITCHf/x) of the time. Those rank, in order, 6th, 11th and 18th among 205 hitters with at least 250 PA — in other words, the 95th percentile (for the former two) or at least the 90th (for the lattermost). In short, he forces pitchers to pitch to him. Few in the game have been more selective, and few in the game have shown this much power this early in a career. (“Early,” by number of games, obviously, because Pham, at 29, is hella old for a guy who barely has a full season’s worth of PA.) The coincidence of his selectivity and his power is nice, to say the least.

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This is a long one. I appreciate your patience in advance.

Mike Podhorzer, I and sporadic others have — but primarily Mike has — carried the torch on developing ‘expected’ metrics, such as xBABIP (expected batting average on balls in play), xHR/FB (expected home run-to-fly ball ratio) and xK% (expected strikeout rate), all and the rest of which can be found here. For the uninitiated, these xMetrics help describe how a hitter or pitcher should have performed based on various measurements of the events that unfolded and typically are more predictive of future performance than the original metric. They’re not perfect, but, like other advanced metrics, they give us a better understanding of player performance and ability.

Each metric — xHR/FB, xK%, etc. — has formulas for both hitters and pitchers, with the hitter metrics typically having stronger correlations than those for pitchers. Unfortunately, pitcher xBABIP has always eluded us. It’s inappropriate to repurpose hitter xBABIP for pitchers, but it’s because the model coefficients (weights) would be different, not because the theory underpinning the model is flawed.

That’s the problem, though: hard hits, line drives, infield fly balls — these all should affect a pitcher’s BABIP allowed. Our intuition begs it to be true. Yet there’s a resounding lack of evidence that suggest otherwise. The correlation between BABIP and hard-hit rate (Hard%), line drive rate (LD%) and infield fly ball rate (IFFB%), among others, borders on nonexistent:

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Bold predictions are inherently jovial, whimsical, outlandish, possibly even blasphemous. You know you probably weren’t bold enough if you got too many correct, and you probably were too bold if you got too many incorrect. Mine typically follow suit. I don’t expect to get every prediction correct by the numbers, but I do hope each prediction is correct in spirit. My “spiritual” success in this regard, I’ve noticed, typically reflects upon how well my teams performed in a given season. Accordingly, I take my predictions seriously, and I sincerely use them as a collective barometer for how I had gauged value plays in the preseason.

I don’t remember any of my predictions, really, and I’m revisiting them in real time with you. My teams are doing well this year — 1st, 2nd, and 3rd in my three home leagues (humblebrag) (no expert or high-stakes leagues this year) — but I have a feeling my predictions have not stood the test of time. Again, this is to be expected. But, again-again, I’m not sure they’ve held up spiritually, either.

Here goes:

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The Cameron Maybin Propaganda Machine (my and Sammy Reid‘s Twitter accounts) is alive and well, spurning hype for the ESPN Player Rater’s 17th-best outfielder. It feels like he hasn’t gotten much fanfare — he’s still only 62% owned in ESPN leagues with scarcely any mentions this year from FanGraphs and RotoGraphs writers. I wrote about Maybin once but more than two years ago.

A lot of Maybin’s merits — well, once his single merit: his speed — remains intact. A preseason scouting report might’ve said if Maybin could stay healthy and earn the favor of his employing team, he could rack up 30 stolen bases while flirting with double-digit power. Prior to the Juiced Ball Era™, his was the kind of power-speed combo over which fantasy owners would typically drool on draft day yet somehow come to underappreciate on Sept. 30. It would have been fairly easy dismiss Maybin’s 2016 because of an inflated batting average on balls in play (BABIP) and what appeared likely to be a timeshare with Ben Revere or… Michael Bourn? or… it’s much less painful not thinking about his left field partners in Los Angeles of Anaheim.

Dismiss no longer, friends, the former consensus top prospect. Maybin’s born-again speed and his position atop the Angels’ batting order are too valuable to ignore. His full-season (650-plate appearance) pace: 14 home runs, 113 runs, 45 runs batted in, 55 stolen bases, a .257/.353/.400 triple-slash line. That’s immensely valuable, as the Player Rater suggests. Prorating can be a reckless exercise, but little in his peripherals suggests we should expect much, if any, second-half drop-off.

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A wise man once told me, “Thirty is the new 20.” He spoke not of my dating prospects but of percentages — specifically, strikeout percentages. The gist of his sentiment was back in the olden days, a player’s fantasy value would have been harmed, perhaps irreparably, if he struck out 20-something percent of the time. Now, we see hitters subsist and more with 30-something strikeout rates — Joey Gallo, Keon Broxton, Miguel Sano, Khris Davis and Aaron Judge, to name a few.

We — or, if I dare not speak for you, you intellectual, you, then just I — have been forced to reassess how we (I) “scout” the intersection of contact and power for fantasy purposes. This monologue is peripherally relevant to the eventual subject of this post, Domingo Santana, because he, too, once ran a 30-something strikeout rate. He no longer does that, though, which is good. That’s part of the reason why I’m here. But it’s more of the icing on this cake, so allow me to bake the cake first.

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#3: May 3
#2: April 24
#1: April 13

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It’s episode No. 4 of my Small-Sample Normalization Services, which, in Star Wars terms, means this will be, like… the 3rd-best post of the series? Is that how that works? I know to nothing about what’s believed to be the consensus on the merits of each film. I already regret making this stupid comparison.

Allow me, then, to touch upon (and revisit) some players whose performances through six weeks are worth critiquing. Six weeks is still a considerably small sample when it takes hundreds of plate appearances (or, for ball-in-play metrics, batted balls) for certain standard and advanced metrics to become reliable (or, in common but sometimes misused parlance, “to stabilize”). Check previous posts for the rules, but know that a rating of 1 means Hype City and a rating of 5 means, uh, Alarm City. A 3, therefore, would be neutral.

All graphs pulled prior to yesterday’s games.

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Name: Masahiro Tanaka, NYY SP
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If you’ve paid any attention to the San Francisco Giants, you’ll know that they stink something awful right now. The parts generally are no greater than the whole. Jeff Samardzija, he of the 5.44 ERA, is not blameless here.

In an alternate universe, though, he could be. Some in(s)ane factoids about Samardzija: Only Chris Sale has as many starts as Samardzija in which he struck out more hitters than he completed innings (6). (In Sale’s first start of the season, he went seven and struck out seven. So close.) Samardzija is also one of only six starters with four-plus starts of eight-plus strikeouts. And among pitchers who have thrown at least 75 innings since August 8, 2016*, Samardzija’s 3.12 xFIP ranks 7th-best, behind only Carlos Carrasco, Noah Syndergaard, Clayton Kershaw, Sale, Michael Pineda and James Paxton. That is elite company.

*Why August 8? I was trying to see who has been better than Ivan Nova since he was traded to the Pirates. Nova shows up 8th on that list above. Seeing Samardzija’s name directly before his floored me.

Samardzija is striking out the world yet has little to show for it. His advanced stats (28.7% K, 5.2% BB) suggest excellence, and his peripherals (11.8% SwStr) affirm them. In short, his 3.43 FIP and 2.87(!!!) xFIP depict a much more effective starting pitcher. It’s his strand rate (LOB%) — a catastrophically bad 58.1% — that has done him in. Normalize it, and he’s sitting pretty with a mid-3.00s ERA.

All that said, I’m here to investigate what changed. Once upon a time, Samardzija was a touted prospect, cracking multiple top-100 lists in 2009. The strikeouts lived up to the hype, yet the results lagged. Then the K’s eroded, and the results eroded further. They K’s are back, and they’re back with a vengeance.

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#2: April 24
#1: April 13

If you’ve tuned in before, you know what this is about. If not: the Small-Sample Normalization Service (SSNS) seeks to, ah, normalize a player’s performance in the context of his own previous achievements (or lack thereof). Most of us are human, and our humanity leaves us vulnerable to the biases that cloud rational thought and critical analysis. Such vulnerability is eagerly exploited by the small sample size, never more so than in April. While midseason small samples cower under the cover of hundreds more plate appearances, April performances have no such luxury.

A month’s worth of playing time is certainly more worthwhile to assess than one week’s worth, but 30 innings or 100 plate appearances can still be pretty volatile. Here are a few still-small samples that recently caught my eye.

All graphs pulled prior to yesterday’s games.

Name: Byron Buxton, MIN OF
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If you’re a nostalgic fantasy baseballer, you’ll remember that Patrick Corbin generated 3.5 wins above replacement (WAR) in his first full season of baseball before suffering the dreaded curse of Tommy John. (If you’re even more nostalgic, or more likely an Angels fan, you’ll remember he was traded alongside Tyler Skaggs for Dan Haren.) Corbin returned to baseball in 2015, and he shoved, seemingly indicating he suffered no ill effects of his surgery.

Yet 2016 was an unmitigated disaster, culminating in a midseason move to the bullpen and a full-season 5.15 ERA. A low strand rate (LOB%) is the blame — virtually no one suffers a 64.8% strand rate for a full season without some bad luck — but poor control and a home run problem complicated things. It appears to me Corbin ran afoul in two distinct ways in 2016.

It also appears to me he may have recalibrated himself. In his last three starts, he has struck out 23 and walked only four across 19.1 innings, good for a 1.86 ERA / 2.53 xFIP / 2.59 FIP.

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Last week, I inaugurated RotoGraphs’ Small-Sample Normalization Services, or SSNS. Said services attempt to contextualize good and bad starts within a particular player’s history of achievements (or lack thereof). Assessing player performance based on small samples seems distinctly difficult in April, when, for whatever reason, we perceive players with tattered histories as blank slates. Occasionally, there’s merit to these perceptions. More often, we find out a player’s April is no different than his May or June or July, for example, when a small-sample performance might go less noticed than it would when starting from zeroes.

Here are a handful of players that have caught my eye lately.

Name: Jason Vargas, KCR SP
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