Let’s shift back to the bottom of the Statcast leaderboard and discuss seven names you might be surprised to find down there. The surprise won’t be driven by a bunch of guys who just posted 20% HR/FB rates yet don’t have the barrels to explain such results, but rather one of “whoa, his power really did dry up in 2017, what happened?!”.

In 2017, league average Brls/BBE was 6.4%, while the median mark was 5.3%. The median is much lower because it includes the many hitters who recorded few plate appearances and batted ball events, a group which likely posted lower Brls/BBE marks. After all, more power equals more playing time, all else being equal. The average is weighted and so the 437 batted ball events from Giancarlo Stanton, who posted a 17.4% Brls/BBE is going to pull up that mark, but ends up counting the same as the 16 batted ball events from Brett Eibner, who posted a slightly better 18.8% Brls/BBE.

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Let’s continue diving into the Statcast leaderboard, sticking with my favorite metric, Brls/BBE (barrels per batted ball event). While it’s no surprise to find Aaron Judge atop the leaderboard, followed by Joey Gallo, and J.D. Martinez, there are other names that do surprise and provide actionable information. So let’s peruse the top 50 hitters (there are 540 players on the list, including pitchers, as the board simply includes everyone with at least 30 batted ball events) sorted by Brls/BBE and discuss six legit sleepers for home runs in 2018.

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Yesterday, I shared 19 hitters whose Brls/BBE marks spiked from 2016 to 2017. A surge in this metric usually coincides with a jump in HR/FB rate, and sure enough, 18 of the 19 batters did enjoy an increase. Now let’s check in on the hitters who suffered a decline in their Brls/BBE marks. We shall assume that each of their HR/FB rates also declined. Was this actually the case?

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Towards the end of 2016, one of the most exciting new metrics was introduced using Statcast data — Barrels, which are “well-struck ball[s] where the combination of exit velocity and launch angle generally leads to a minimum .500 batting average and 1.500 slugging percentage”. But counting stats aren’t exactly the best when evaluating performance, as playing time factors in, and we don’t want that. Luckily, Baseball Savant also provides us with a “batted ball event (BBE)” number, and then generously does the math for us, calculating a Barrels per Batted Ball Event (Brls/BBE), which I then use for my analysis. Given that Brls/BBE is a pure measure of power, it stands to reason that we want to see our favorite power surgers enjoy major growth in the metric from one year to the next, in order to validate that increased output. So let’s check in on the Brls/BBE surgers from 2016 to 2017.

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“… pros were more likely to ride a wave of irrational exuberance than to fight it. One reason is that it is risky to be a contrarian. ‘Worldly wisdom teaches that it is better for reputation to fail conventionally than to succeed unconventionally’” – Richard Thaler in Misbehaving

At the root level, fantasy baseball is about acquiring more undervalued assists than your opponents. Everyone wants a first-round talent for a last round price (e.g. Aaron Judge). With teams clamoring to acquire every advantage, they are insistent on wasting away an early draft advantage. In early 2018 drafts I’ve participated in, an early emphasis on middle infielders is inflating their value way beyond their projected production. Is the observation wrong? If so why? If not, how can an owner take advantage of this mispricing?

Note: For this article, I will lump second basemen and shortstops together into one middle infield position. Neither position has more talent than the other and the bottom players will be used to fill a middle infield position.

For those who have recently created mixed-league valuations, positional scarcity doesn’t exist besides with catcher. I use the method outlined in Larry Schechter’s book, Winning Fantasy Baseball to determine my values. I’m not going to go into the process’s exact details but it’s the standard procedure used by fantasy experts to prep for auctions. Even a couple years ago a small amount of positional scarcity existed but a huge influx of good middle infielders has raised the group’s overall talent level up to the other positions.

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Last week, the Athletics traded for Stephen Piscotty, alleviating a bit of the great depth in the Cardinals outfield. Piscotty is coming off a forgettable offensive performance, in which he dealt with injuries, a minor league demotion, and the terrible news that his mother was diagnosed with ALS. Typically, the knee-jerk reaction is a move to Oakland will likely hamper a hitter’s offensive results. But surprisingly, this appears to be one of those rare instances in which the park switch may actually provide a boost. Let’s dive in.

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So the Angels are apparently going for it all, eh? First, they signed Shohei Otani, then traded for Ian Kinsler, and have now signed Zack Cozart, who figures to play third base with defensive stud Andrelton Simmons entrenched at shortstop. Moving out of the perceived hitter friendly Great American Ballpark in Cincinnati and into an environment perceived to be far more favorable for pitchers, let’s find out how the relevant park factors may impact Cozart’s performance.

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In leagues with Runs and RBIs as categories, predicting how a player’s mix will change with a new team can be guessing. Some clown at Fantrax yesterday wrote the following:

“As for Headley, his value drops by going to a worse offensive team in a pitcher-friendly park. Part of the decline could be offset by a move up in the lineup since he mainly batted seventh for the Yankees last season.”

It could go up, it could go down, who really knows? While writing the statement, I needed a better answer so I created a couple quick and simple tool. If an owner can estimate a few stats, they can predict changes in plate appearances, Runs, RBI when a hitter moves from one team to another.

The key was to be simple and quick. For simplicity, only the following stats are needed.

• New likely lineup location
• Estimate of projected home runs
• Estimated games played such 150 out of 162 games as a percentage.
• Estimated Runs scored by a team. Used over on-base percentage because team level runs scored is easier to find and remember.

The estimated runs scored is the toughest value to come up with. I’d just go to FanGraphs team projection page to get a decent idea. Just take that year’s RS/G and multiply it by 162. Another method is to take the previous season value and plug it into the following regression equation:

`RS in Y2` = .575 * `RS in Y1` + 311

The goal is just to get a basic idea of possible changes.

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I think Ryan Schimpf is my favorite player. He takes the word extreme to an entirely new level, ranking at or near both the top and bottom of various statistical categories, for the better and for the worse. That’s what makes him such a fascinating hitter. He debuted with the Padres in 2016 to excellent results over about a half a season’s worth of plate appearances. He was a new breed of hitter – a five true outcomes type, as his plate appearances generally ended with either a walk, strikeout, fly out, pop-up, or home run. The approach worked that season, but failed miserably in 2017. His performance earned him a demotion to the minors, and ultimately a ticket out of San Diego.

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The best inspiration for an article is one in which you find a meaningful leaderboard, sort away, and identify who doesn’t belong. The surprise of the group, if you will. That player is most certainly going to be fun to discuss! That brings me to Eddie Rosario. If you perform a Statcast search and select only “Barrel” for “Quality of Contact” from August through the end of the season, you will be presented with a leaderboard of top sluggers ranked by number of barreled balls they hit during that time period. The top 10 is littered with your standard who’s who of the game’s best power hitters. Then you get down to #14 and who do you find, none other than Eddie Rosario.

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