Blue Jays 2018: Shapiro, Statcast and KenMo

BOSTON, MA - SEPTEMBER 26: Kendrys Morales
BOSTON, MA - SEPTEMBER 26: Kendrys Morales /

Some recent decisions by Team Shapkins are easier to understand when examined in the context of Statcast data.  What are the implications for2018?

Mark Shapiro is a stats geek from way back.  He was the primary force behind the Cleveland DiamondView analytics database in the early 2000s (arguably the first hard-core baseball sabermetrics database ever invented).  He has spoken many times on the value of good information, properly interpreted.  So when he and his team make a decision that appears counter-intuitive, it can be useful to ask “what is it that he is seeing, that (perhaps) others are not?”

The Justin Smoak extension in 2016 is a prime example.  Many fans and writers were surprised at this contract, as Justin’s performance (by conventional metrics) did not seem to warrant it.  But Statcast had recently come out with a series of new hitting metrics which measured things like average exit velocity on line drives (Smoak’s 97.6 mph in 2015 was third in baseball among players with 200 PAs), percentage of balls hit at 95 mph+ (Justin was 15th) and percentage of balls hit on the barrel per plate appearance (He was 12th).  So the bottom line was that Smoak was hitting the ball a ton, just not showing the results.  On that basis, the team made a gamble that (I think most would agree!) worked out pretty well.

Fast forward to the 2016-17 offseason.  After Edwin turned them down (don’t get me started), the Jays moved quickly to grab Kendrys Morales.  Again, many writers were surprised, both at the speed and at the three-year term, even though many analyses put Morales clearly at the front of the free agent hitter pack.  One possible explanation for the Jays’ enthusiasm is the same Statcast analysis.  In 2016, the designated hitter was 6th in the majors in average exit velocity (200 PAs) – ahead of Ortiz, Donaldson, etc.  His 49.9% of balls hit that were >95 mph was 4th best in baseball, and his 7.8% barrels-per-PA was good for 24th.  In short, much like Smoak in 2015, he was a clobberin’ machine, and the Jays saw the upside.

So what happened in 2017?  Did all of this power disappear?  Because a negative 0.7 WAR is clearly not what the Jays bargained for.

Toronto Blue Jays
Toronto Blue Jays /

Toronto Blue Jays

Statcast and KenMo

For a partial answer, let’s turn again to Statcast.  Have a look at this table, which compares Morales’ 2017 to a handful of other middlin’-good players.  Kendrys was 9th in the majors in average exit velocity (>200 PAs), 14th in average line drive velocity, 13th in percentage of balls hit >95 mph, and 42nd in barrels-per-PA.  These are good numbers.

In particular, Kendrys beat the “Mystery Player” in average exit velocity, average FB/LD velocity, maximum and average HR distance, % of balls hit >95 mph, and barrels per PA.  Who is the mystery player (if you have not already guessed)?  Yes, it is Edwin Encarnacion.

So how can KenMo have such a poor 2017 with batted ball statistics like these?  Well, there are several factors.  For one, his 2017 BABIP was ~20 points lower than his career figure.  That is usually a function of bad luck.  For another, his walks were down and his strikeouts up.  Could he have been trying to do too much, both to impress a new fanbase and to help a team struggling with its offense?  And finally, he was having trouble with breaking balls, surprising after a 2016 breakout in that area.

What does this mean for 2018?

KenMo’s career average is a .270/.328/.462 batting line for a 112 wRC+.  His wRC+ was 111 in 2016 and 131 in 2015.  Yes, at 34 years old he is not getting any younger, and some age-related decline might be expected.  But a drop of the magnitude he experienced in 2017 is unexpected, particularly given his Statcast batted ball stats.  So, unless he really has forgotten how to hit a curveball, all signs point to a positive regression in 2018.

The wild card is Justin Smoak.  The Jays likely did not expect a Smoak breakout of the magnitude he experienced in 2017.  I suspect that the game plan was for Steve Pearce to get significant time at first, with Morales holding down the DH gig.  Once it became clear that Smoak was not letting go of first, the Jays were left with Pearce and Kendrys fighting for DH time.  Many writers believe that the Jays will have to trade one of the two of them this offseason, regardless of their 2018 upside, just to clear this logjam.  Morales might well be the odd man out, unless they’re comfortable with Pearce as the full time left fielder.

Statcast and the 2018 Jays

Suppose, for the sake of the argument, we assume that the Jays are incorporating Statcast hitting metrics into their strategic decisions.  What might the implications be for 2018 and beyond?

Teoscar HernandezTeo had the 8th highest barrels-per-PA in the majors in 2017.  Insert all the usual small sample size and Gillick September caveats here, but this might impact on the Jays’ optimism about the youngster, and accordingly about the intensity of their search for outfield help in the offseason

Trade targets – If the Jays are looking to catch lightning in a bottle this offseason, Smoak style, they might be looking for players that fit a similar profile.  Ones who are not perceived as stars, but who have exceptionally good Statcast metrics.  Like Randal Grichuk of the Cardinals (6th in mlb in 2017 in barrels-per-PA) or Matt Davidson of the White Sox (15th)?  Or Efren Navarro of the Tigers (tied with some Judge for highest percentage of balls hit >95mph, albeit in a small sample).

Free agents – On the free agent market, does Alex Avila‘s 51.3% balls hit >95mph (5th best in the majors) and 7.7 barrels-per-PA (40th best in the majors) make the Jays more inclined to pay him the big free agent bucks he will likely receive? And could Lorenzo Cain‘s well-below-average >95mph and barrels-per-PA impact on the Jays’ perception of his value?

Next: Possible speedy options for the 2018 roster

The bottom line

There is more to hitting that exit velocities and barrels.  The Statcast metric, like any other baseball metric, is therefore only a piece of a larger puzzle.  But with any new statistic or sabermetric tool, the first teams to recognize the tool’s value and incorporate it into their decision making, will be the ones who reap the greatest benefit.  Could the Jays be ahead of the pack where Statcast is concerned?