A week ago, it was announced that David Ortiz had accepted arbitration and would return to the Red Sox for at least the 2012 season. Though it seemed unlikely he would land anywhere but Boston, the other candidate discussed as in the running to enlist Big Papi’s services was (gasp) the New York Yankees. Former teammate Johnny Damon even suggested as much to Ortiz at his charity golf outing; with the friendlier dimensions of Yankee Stadium, Damon speculated, “[Ortiz’s] 30 home runs turns into 40.”
Obviously, the point is now moot. We’ll never know how Ortiz’s season would have turned out had he migrated to the Bronx. But Damon’s prediction raises an interesting question: just how strong of an effect can a ballpark have on a player’s numbers?
To start, let’s set down a few basic assumptions. The first one –– that, in a Boston uniform, Ortiz would hit 30 homers in 2012 –– is not trivial. As I’ve written before, the 36-year-old Ortiz is now at the age when we’d expect to see some drop-off in his overall offensive output. Of course, we would also have anticipated such a decline in each of his last three seasons, yet his home run and slugging totals have remained remarkably consistent.
Fortunately, his most marketable talent, power, is, as Bill James termed it, an “old player skill” –– it deteriorates at a slower rate than abilities like speed and batting average. Given a comparable level of power in ’12, then, it seems fair to anticipate 30 home runs from Ortiz as a Red Sox, but don’t be surprised if it’s accompanied by a dip in his batting average back into the .270 - .275 range.
If we assume his physical decline will have a negligible effect on his power, the change in his output would come primarily from a new hitting environment: in this case, Yankee Stadium.
One way to account for the effect of a ballpark on a player’s numbers is to adjust them with park factors. A park factor measures how much a given ballpark differs in some statistic (runs, home runs, doubles) from a league-average ballpark for that same statistic. This theoretical league-average ballpark is taken to have a park factor of 100. A park at which 10 percent more runs are scored than at the league-average park would have a score of 110. The same pattern holds for parks that are less hitter-friendly. A park factor of 90 for runs scored means that teams score 10 percent fewer runs than they would at a league-average ballpark.
In their simplest form, park factors (for runs scored, let’s say) are calculated for each ballpark as simply the ratio of total runs scored/home game to total runs scored/away game, multiplied by 100. This explains why the league-average ballpark has a park factor of 100; an equal number of runs are scored in home games as in away games (the ratio above equals one). Yearly park factors can be subject to large fluctuations as a result of random variation in players’ performances –– for instance, Fenway’s run-scoring park factor rose from 108 to 117 between 2010 and 2011 –– so park factors are generally calculated over periods of multiple years.
As a hypothetical member of the 2012 Yankees, all Ortiz’s plate appearances at Fenway would instead come at Yankee Stadium, and vice versa. To translate his production from one ballpark to the other, I’ll apply each one’s home run park factor to Ortiz’s expected production, using only the change in home at-bats.
First, we’ll have to categorize Ortiz’s home runs by direction. Here’s the breakdown of each of his 89 home runs the last three seasons, provided by ESPN Stats and Information.
LF (135 – 118): 5 HR (5.6 percent)
LCF (117 – 100): 10 HR (11.2 percent)
CF (99 – 82): 17 HR (19.1 percent)
RCF (81 – 64): 28 HR (31.5 percent)
RF (63 – 45): 29 HR (32.6 percent)
Below are the home run park factors for both Yankee Stadium and Fenway Park, calculated by the Hardball Times with a more precise methodology than the one I outlined above.
Yankee Stadium: LF – 115, LCF – 100, CF – 72, RCF – 128, RF – 134
Fenway Park: LF – 105, LCF – 106, CF – 57, RCF – 94, RF – 88
Using the previous analysis of his home runs by direction, I can now project the number of home runs Ortiz would hit to each field in 2012, as a member of the Red Sox and, after accounting for park factors, as a hypothetical member of the Yankees (rounded to the nearest home run). The final assumptions: Ortiz has the same number of home plate appearances in each scenario, and the away parks at which he plays have a mean park factor of 100.
Home park: Fenway Park
LF: 2 HR
LCF: 3 HR
CF: 6 HR
RCF: 9 HR
RF: 10 HR
Total: 30 HR
Home park: Yankee Stadium
LF: 2 HR
LCF: 3 HR
CF: 7 HR
RCF: 11 HR
RF: 12 HR
Total: 35 HR
Damon’s offhand projection looks too extreme; while Yankee Stadium would inflate Big Papi’s home run total, it would not do so to the degree he hypothesized. Overall, the short porch would add only three to four home runs to Ortiz’s expected total.
The study might be improved with access to the data for all of Ortiz’s fly balls, which could tell us empirically how many of his warning track shots would leave the yard when adjusted for the dimensions of a different park. Unfortunately, that data is not readily (or cheaply) available.
How important are park factors? Had Ortiz averaged 35 home runs instead of 30 in each of the last three years, his case for a multi-year contract would look a lot more compelling. You can bet the Red Sox studied park factors when pursuing Adrian Gonzalez last winter; they recognized that, playing in the offensive dead zone of Petco Park, his numbers were artificially deflated –– for proof, check out his home and away splits in 2010. In both cases, the discrepancy has nothing to do with ability, but the setting in which that ability is placed. And in a marketplace in which 10 points of batting average or five home runs can mean a difference of millions of dollars in salary, it’s important to have the most accurate information possible.
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He has also authored or made contributions to many books, including the Sports Illustrateds 100 Fenway: A Fascinating First Century.
Now living in Marblehead, hes focusing his attention on the Boston sports scene, specifically delving into the numbers affecting the Red Sox, Patriots, Celtics and Bruins, with the goal of informing and entertaining real fans. You can follow him on Twitter at @SabinoSports.