This is a guest blog post by Andy Swingle about a different look on baseball sabermetrics.
Most baseball fans know the story of the 2002 Oakland Athletics. They were subject of the New York Times bestselling book entitled Moneyball. Author Michael Lewis paints a picture of the A’s as a team with no resources competing against the financially abundant (New York Yankees). Lewis, through the muse of Athletics general manager Billy Beane paints a picture of a team that must think in an unconventional way, in a way that goes against what has been considered the norm. With the help of assistant general manager Paul DePodesta (or Peter Brand, if you decided to wait for the movie), this led to the beginning of the sabermetrics revolution.
Having been around for years thanks to pioneers like Bill James, Voros McCracken and others, statistical analysis ushered in a new way to evaluate individual performances. Runs Batted In (RBI) were considered pointless in an individual context due to the reliance on other players to be on base before the batter driving in the runs even came up to bat. Pitcher wins were deemed almost obsolete in the fact that a 15-13 ball game can still give a pitcher a win, even though they may have been responsible for all thirteen runs scored. The cornerstone of DePodesta’s research suggested that the best way to determine the ability of a hitter is not by RBI or batting average, but by how often hitters get on-base (OBP) or avoid getting out. With the A’s success, many other teams began looking to statistical analysis to adjust their team philosophy. The problem for Oakland was that their secret was out. They found a market inefficiency and exploited it for as long as they could until almost every other team stole their ideology and used it to their advantage, most with the resources that the Athletics organization did not have.
With this sort of baseball statistical enlightenment that is occurring, their come a paradox. If most of the businesses in the same industry see data the same way, and are coming up with the same conclusions and reactions from the data, then how does an organization go against the prevailing thought? What must a company do to remain innovative in a marketplace that is so very much a copy-cat industry? The answer is in the structure, or should I say the structure of the building they work at.
During this sabermetrics renaissance, one aspect that people began looking at was how many runs a team scores at home as well as how many runs the opponent scored at the home team’s stadium. Doing the math is actually pretty easy.
- PF: ((homeRS + homeRA)/(homeG)) / ((roadRS + roadRA)/(roadG))
- homeRS: Runs scored at home
- homeRA: Runs allowed at home
- homeG: Home games
- roadRS: Runs scored on the road
- roadRA: Runs allowed on the road
- roadG: Road games
A ballpark with a total of 1.00 or more signifies that the park favors the hitters, while a score of below 1.00 means the advantage goes to the pitchers.
In 2011, the most hitter-friendly ballpark was Rangers Ballpark in Arlington with a score of 1.409, second place was Coors Field in Denver, home of the Colorado Rockies at 1.347. The intriguing part of this study comes in the teams that are the pitcher-friendly ballparks. The top five pitcher-friendly ballparks in order of friendliest to not quite as friendly are: AT&T Park (San Francisco Giants) 0.737, Tropicana Field (Tampa Bay Rays) 0.817, Petco Park (San Diego Padres) 0.819, Angels Stadium of Anaheim (LA Angels) 0.836, and Safeco Field (Seattle Mariners) 0.855. At first glance one would say, of course these stadiums are the worst for a hitter because they are so big. Granted, they are large parks and it does take a little more effort to get the ball out of the park in San Diego than it does Texas or Colorado, however, these stadiums can create an opportunity for these five teams.
Jason Vargas was the Webster’s defintion of a AAAA pitcher. A fastball in the mid-to-high 80′s with serviceable off-speed stuff. He came to Seattle after brief stints with the Marlins and Mets, both of which did not bring close to the results he was looking for. Now he is going to be the number two starter for the Mariners behind King Felix. His WAR (Wins Above Replacement) of 2.4 in 2011 was the same as NL All-Star Ryan Vogelsong, and more than Jeremy Guthrie (2.1), Wandy Rodriguez (1.5), and A.J. Burnett (1.5). Just a side note, A.J. Burnett makes $16.5 million dollars, Rodriguez makes $10 million dollars in 2012, and Jason Vargas will make $4.85 million dollars in the 2012 season.
This brings me to my point. Tim Lincecum had a 2011 WAR of 4.4, Brandon McCarthy of the Oakland Athletics had a WAR of 4.7, while making thirteen times as much money as McCarthy ($13 million to $1.00 million in 2011). Wouldn’t the money that the San Francisco Giants pay Tim Lincecum be better spent on solving their anemic offense, instead of overpaying for a pitcher that is according to WAR, the third most valuable pitcher in their rotation? It is with this logic that I encourage the San Diego and Seattle to continue what they have done in their prospective trades this winter, trading young pitchers with lots of hype and getting the hitters that they need to help them win, knowing they can rely on their ballpark to mask the inferior pitching talent they bring to the mound. This will be the new inefficiency of the 2010′s, the next evolution of Moneyball.
Andy Swingle is a personal friend of mine who has just started putting his thoughts online. Check out his new blog about sports and life with his pregnant wife.