Unlucky Number 55
Last year, Russell Martin’s praise came from his impressive work behind the plate, somewhat of a rare sight for Yankee fans. After a hot April start, where he OPS’d .963, his bat seemingly slowed down and waned into an afterthought. Russell Martin’s .237/.324/.408 2011 triple-slash was still above average, and most importantly he showed signs of power that were absent in his 2009 and 2010 seasons. The current ZIP projections for his 2012 season are .249/.346/.382 and a 92 OPS+, which isn’t much different from his 2011 season. There is a slight increase in batting average, which subsequently increases OBP, and a narrow drop in slugging. There’s reason to be more optimistic about Martin in 2012; his 2011 BABIP indicates he may have been unlucky.
BABIP, batting average on balls in play, calculates how many pitches put into play become base hits. The resulting number should fall in the .300 area; for example the average BABIP for all hitters in 2011 was .295. To apply to a specific hitter, this numbers should be taken into context, the best BABIP to compare to a player is their own career average, as it takes into account a large sample size. For a hitter, a change in BABIP within a year’s worth of data either indicates a change in talent or issues with small sample size, otherwise known as poor luck. (Be aware there is a third possibility for pitchers, who may see lower or higher BABIPs based on the level of defense that accompanies them.) For Russell Martin, there was indeed a significant change in BABIP, a difference of .043 between his career (.295) and 2011 (.252) numbers. So the question posed is now, was the drop off in BABIP a matter of Martin’s declining ability to hit for average, or was it a product of small sample size?
One way, and perhaps the most obvious, is to look at the types of batted balls a player hits. A significant decline in line drive rate or increase in fly ball rates would impact a BABIP because they fall into play at different rates. From a large sample size taken, Hardball Times found that the average flyball becomes an out 79% of the time (.210 BABIP), the groundball 72% of the time (.280 BABIP), and the line drive 26% of the time. (.740 BABIP) So if Russell Martin’s low 2011 BABIP was created by lost speed on his bat, a new inability to identify pitches, forgetting how to hold a bat, or any other long-term issue to put hitting for contact at risk, we should see a significant drop off in line drive rate or an increase in flyball rate. His 2011 19.2 LD% (career 19.4%), 47.3 GB% (career 49.5%), and 33.4 FB% (career 31.1%) are all within a respectable range compared to his career rates. So that’s the end of it, right? Wrong. We need to go deeper.
This is the point where you go to iTunes and start playing the Inception theme music.
| Month | BABIP | GB/FB | LD% | AVG |
| Mar/Apr | .281 | 1.13 | 19.0 | .293 |
| May | .214 | 0.85 | 18.6 | .200 |
| June | .200 | 1.38 | 17.4 | .185 |
| July | .258 | 2.24 | 15.4 | .213 |
| Aug | .286 | 1.59 | 30.2 | .286 |
| Sept/Oct | .293 | 1.85 | 11.9 | .232 |
You can see the breakdown of BABIP, GB/FB, LD%, and average for Martin’s 2011 season by month above. Despite looking at an even smaller sample size, there is still no correlation between LD% and BABIP. You can see that his most impressive month, April (.963 OPS), sports a 19.0 LD% and .281 BABIP, which is remarkably close to his career 19.4 LD% and .295 BABIP. In May, he saw a rather significant decrease in his GB/FB, which was at 0.85 despite a career ratio of 1.59. The decrease in groundballs and increase in flyballs would create a decline in BABIP, but the difference between May’s BABIP and his career (.081) was too big to be a product of a 7% increase in FB% and 7% decrease in GB%. June saw a rebound to a career average GB/FB ratio and slight dip in LD%, but he somehow had a .200 BABIP. He again increase his GB/FB and slightly decreased his LD% in July, increasing his BABIP to .258, but only saw a slight increase in that month’s batting average. The last two baseball months of August and September, had both a very high and very low LD%, but with a consistent BABIP. As commonly happens in small sample size, a correlation is hard to spot, but if there was an issue with declining ability, a prolonged low LD% or high FB% would be evident.
The graph above illustrates Martin’s BABIP year to year based on the type of balls hit put in play. In 2011 he BABIP’d .188 on groundballs (career .234), .105 on flyballs (career .117), and .631 on line drives. (career .710) Looking at this trend, his career numbers, and the average numbers we from the league, the groundball and line drive BABIPs are significantly lower than their expected ranges. Almost one out of every ten of his line drives were outs when they should have been hits. This leads me to believe that Martin’s low BABIP was a product of an unlucky rate of outs on his line drives and groundballs.
With a return to normal BABIP numbers and perhaps a little optimistic hope that he continues his slugging rate, you can somewhat project Martin’s 2012 season. If I had to, I would predict a triple slash of .275/.360/.420. With the sort of defense he provides catching, there’s a chance he competes with Matt Wieters for best catcher in the 2012 AL East.
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Yankees had tons of depth last year at catcher with Cervelli, Montero, Martin and Posada. Going to be interesting to see how the team adjusts this season to it’s top young prospet and veteran leader not being there.
BABIP for hitters isn’t like pitchers. There isn’t a regression factor per se where all hitters should fall within the .285-.315 zone. There certainly is a luck factor that comes into play, but it doesn’t seem to be quite as elastic as it is for hitters.
Great job breaking things down by batted ball types. Very interesting piece.