The Real Explanation for Cole Hamels’ Mets-Related Woes
At CSN Philly, Corey Seidman examines the struggles Cole Hamels has had against the New York Mets throughout the years. It was highlighted by Tuesday’s rain-soaked, walks-riddled start in which Hamels issued five free passes along with eight hits. He did not escape the fifth inning.
But the main issue seems to be the Mets’ approach to Hamels. They wait him out. They make him throw pitches. GM Sandy Alderson and manager Terry Collins have preached plate discipline since Day 1 and it especially shows against Hamels.
On Tuesday, Eric Young Jr. began the game with a six-pitch at-bat. In the fourth inning, when Hamels walked four batters, he threw six pitches to four different Mets. In this particular game, it was a case of Hamels simply not locating. It was a cold, wet night and his pitches were all over the place.
But more often than not, the Mets make him work and let him beat himself.
Seidman adds that of the seven instances in which Hamels has walked five or more in a game, three of them have come against the Mets.
But, shockingly enough, Hamels has a higher career walk rate — than his 6.3 percent unintentional walk rate in 164 2/3 innings against the Mets — against seven other opponents. Of course, he’s racked up fewer than 15 innings against five of them, but the other two are the Cincinnati Reds (7.4% unintentional walk rate in 68 2/3 innings) and the Atlanta Braves (7.1% in 190 2/3 innings).
So why does Hamels have a 4.65 ERA against the Mets, but 1.70 against the Reds and 3.54 against the Braves? The Mets have by far the highest BABIP against Hamels than any other team he’s faced more than three times.
|New York Mets||27||726||.367|
|San Francisco Giants||12||353||.310|
|Los Angeles Dodgers||8||218||.284|
|St. Louis Cardinals||10||241||.274|
|Boston Red Sox||5||123||.218|
|San Diego Padres||14||363||.218|
Hamels’ career average BABIP is .288, so hits are falling in about eight percent more often than they normally do. Since the start of 2010, Hamels has averaged 19 balls put in play per start, so an eight percent difference accounts for about 1.5 hits, or between one and two hits per game, which is not all that meaningful.
What we have here is using a narrative to explain, post hoc, a statistical outlier. That an item is a statistical outlier is not interesting, so we have to come up with other explanations. We would find outliers looking at any pitcher’s stats against various opponents. Cliff Lee, for example, has a career .300 BABIP. Two opponents, the Colorado Rockies and San Diego Padres, are way above at .369 and .360, respectively. The Los Angeles Dodgers and San Francisco Giants are way below, at .238 and .227, respectively. It’s less interesting to highlight symmetric good and bad fortune against NL West opponents, however, because it doesn’t lend itself to a reasonable narrative.
Even if we look at walk rate, we find similar outliers in Lee’s history. Lee’s career average walk rate is five percent. Among teams he’s faced at least ten times, the Twins and Athletics are way above at 7.5 percent and 8.6 percent, respectively. The Braves are way below at 1.8 percent.
The explanation’s for any player’s abnormal performance against a single opponent could be simple randomness. Or it could be any of a number of variables adding up — poor game conditions, certain players getting more playing time and affecting the defense, jet lag, an organizational directive (e.g. the Athletics’ appreciation for drawing walks), etc. Or a mix of both randomness and other influences.
If we were to create a narrative for every outlier, the narratives would lose our interest just as quickly. Think of it as “the boy who cried wolf” of sportswriting. So we pick and choose which outliers make for the best stories and run with it. We should expect outliers for almost every set of baseball data. It would be way more shocking if there were no outliers at all — that would be worth writing about.