Tagged: strand rate

A look at how strand rates affect ERA

Scott Spratt of ESPN wrote a nice, albeit brief piece about pitchers who have their defenses to thank for their deflated ERAs, which for some of them have contributed to their breakout seasons.

Many advanced metrics in baseball don’t have inherent benchmarks that differentiate good from bad or lucky from unlucky.  The metrics, however, self-define their benchmarks given time and larger samples. BAbip, the metric Spratt uses, can not be measured not only for one pitcher during several seasons but also for several pitchers during one season. The MLB average BAbip is .299. The number itself doesn’t mean anything when it stands alone, but when it is paired with numbers such as .246 (for Patrick Corbin) or .350 (for Barry Zito), you can gauge who is getting lucky or unlucky.

Let’s take a look at left-on-base percentage (aka LOB% or strand rate), another advanced metric fantasy owners can access on websites such as Baseball Reference and FanGraphs. Fortunately, the casual observer needs not fully understand how LOB% works to identify its trends. (It is good to know, however, that the number of runners a pitcher leaves on base is not entirely out of his hands, so one should not attribute a skewed LOB% entirely to good or bad luck.)

For now, just know that the median LOB% for qualified starters in 2013 is 73.9 percent — that is, about three of every four baserunners are stranded on the basepaths. Now, let’s take a look at three poor seasons from otherwise very good pitchers.

Yovani Gallardo, MIL
2013 LOB%: 67.2
2012 LOB%: 78.4
Career LOB%: 74.2
Difference: -11.2 from last year, -7.0 from career

Gallardo had defined pitching consistently the past two years, registering the second-most quality starts between 2011 and 2012 (behind only Clayton Kershaw — who would’ve guessed?), but he has been mostly unreliable for all of 2013. His ERA may be inflated from his poor strand rate — his xFIP is 3.80, well below his actual ERA of 4.58 — but a deeper dig into his peripherals reveals declining velocity and a major drop-off in strikeout rate. Gallardo is a case where you can’t take the low LOB% and chalk it all up to bad luck.

Cole Hamels, PHI
2013 LOB%: 69.9
2012 LOB%: 78.1
Career LOB%: 76.3
Difference: -8.2 from last year, -6.4 from career

Hamels strikeout (K/9) and walk (BB/9) rates have trended in the wrong direction since last year. However, those rates are not the worst we’ve seen from him during his career — his 8.26 K/9 and 2.42 BB/9 surely beat out his worst rates of 7.76 and 3.26 — or even the worst we’ve seen from him in the past three years. Hamels’ K/9 has fluctuated pretty wildly throughout his career with no discernible pattern to it — 9.10 in 2009, 8.08 in 2010, 9.03 in 2011, 8.26 in 2012 — so that would be the least of my concerns as a Hamels owner. And with an inflated BAbip relative to his career mark, the LOB% looks more and more like an anomaly. The real question, however, is: can he shore up the walks? Because if not, the 1.25 WHIP may become reality, and a sub-3.50 ERA a thing of the past.

Matt Cain, SF
2013 LOB%: 64.1
2012 LOB%: 79.0
Career LOB%: 74.3
Difference: -14.9 from last year, -10.2 from career

Cain suffers from the biggest differences in LOB% of the trio. His 1.20 WHIP and .258 BAbip are very similar to his career marks of 1.17 and 2.64, yet his ERA is more than a run and a half higher. He’s even striking out pitchers at a rate unseen from him since 2006, his first full year in the majors. Then two numbers leap off the page: his LOB%, and a HR/FB% that’s more than five percent higher than usual. The ratio of home runs to fly balls has been proven to largely be out of the hands of the pitcher, and if Cain is giving up more home runs especially while more men are on base, it helps explain his 5.00 ERA (although it doesn’t explain why he has been so unlucky). Cain’s WHIP is a far better indicator of the type of pitcher he has been this year. Look forward to him returning to form next year.

Know that the metrics I presented are not the be all, end all of a pitcher’s performance. But understanding advanced metrics and learning to recognize trends (and abrupt distortions) are beneficial skills for any fantasy owner.