The 2016 Cleveland Indians offense was something of a juggernaut placing fifth in runs scored and sevent in wOBA. This offense added a substantial upgrade in Edwin Encarnacion and was primed to climb the offensive rankings yet again.1
Forty-eight games into the 2017 season,2 however, and the Indians offense has been nothing if not ordinary ranking 20th in runs scored and 14th in wOBA. While the rotation has been a massive frustration in the early going, the offense has under-performed expectations by a significant margin. While starting pitching includes more discrete pieces requiring individualistic consideration, an offense, in many ways, can be evaluated as individual pieces as well as a larger unit.
The question central to the remaining 70 percent of the Indians season is whether the offense can become the juggernaut it very clearly should be. There are a couple of key pieces to evaluate in offensive production the first of which is a popular complaint regarding the Indians offense.
Indians struggles with RISP
The Indians OPS (on-base% +slugging %) overall is .741—good for 14th overall. The Indians OPS with runners in scoring position is .674—good for 28th. There are a couple of ways to approach this problem but the key question is whether these current struggles are in any way predictive or reflective of the Indians talent with runners in scoring position. One of the clear differences? BABIP3. With the bases empty the Indians BABIP is .291; with runners on base it is .238. .238! This is the third worst BABIP in baseball.
In analytical circles BABIP used to be attached to “luck”, but as our understanding of contact quality, contact dispersion, and speed as inputs grows, our belief in player influence grows. These inputs certainly should be considered in the individual context for players as BABIP often reflects these skills with some luck on the edges. However, in the team context, there is a certain amount of luck to it which we will call “variance” or “sequencing”.
First, it is important to emphasize not only the absurd gap between BABIP with the bases empty and BABIP with runners in scoring position but also that BABIP with runners on base is generally higher than when they are empty. This is because when the bases are empty teams can implement more extreme and strategic shifts which reduce BABIP. Whereas, with runners on base, fielders are more confined because they are forced to hold runners and are limited from radicalized shifts. Carlos Santana is an easily understood example of this difference. Santana is shifted every time with the bases empty providing for a BABIP of .255. With men on base and infielders restricted from drastic shifts, Santana has a BABIP of .282.
With the massive BABIP gap above in mind as well as the illustrative Santana example there is a strong argument that the Indians struggles are largely variance driven. In that, the Indians have bad cluster luck in a limited sample of RISP opportunities. Before reaching this conclusion, a batted ball review.
Are the Indians just hitting the ball much more poorly with runners in scoring position? No. the Indians hard hit rate with RISP is 31.4 percent. With the bases empty, it is 32 percent—a negligible difference.
Is anything else changing dramatically? No. The Indians walk rate is changing positively with RISP. Further, there strikeout rate has a negligible at best change from bases empty to RISP of less than 1 percent.
The obvious basis for the Indians struggles with runners in scoring position is a dramatic BABIP gap which is not attributable to any skill based struggle but rather to fluctuation in a limited sample. As the sample improves and the scale of variance can be confined the Indians success with runners in scoring position should improve.