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I’m the new owner of the Angels.
Disney kept the team from leaving Anaheim, but their tax break was mostly expended, and running the team took energy the company wanted to spend persecuting peer-to-peer file sharing. The franchise didn’t come cheap, mind you, but I think it will be worth the money. Now, I’m Bud Selig’s worst nightmare, because I’m going to derive millions of dollars through his proposed revenue-sharing plan and field a team that’s going to thrash his precious Brewers for the foreseeable future.
Nevertheless, in the wake of the most bizarre deal we’ve seen in a very long time, I couldn’t help myself; I peeked around. Now, I have a lot of respect for Rob Neyer, and for Rob’s work. As a fellow product of the analysis revolution of the ’80s, I suspect we share a basic philosophy of trying to inject some element of quantitative analysis to provide better qualitative commentary. That said, I think any attempt to quantitatively assess the trade of Jeremy Giambi–regardless of your opinion of Win Shares and their utility–ignores two basic problems.
When Jose Canseco finally announced his retirement last week, I thought little of his case for Cooperstown, citing him as a one-dimensional player, with too much of his value wrapped up in a five-year span. However, after reading Joe Sheehan’s Tuesday edition of the Daily Prospectus, I slowly began to rethink my position.
I know I’m supposed to write about the Jeremy Giambi trade. It’s one of the strangest moves we’ve seen in a while, it involves a GM whose praises we’ve been singing for years and a player whose abilities we’ve promoted. Acquiring John Mabry makes no sense from any standpoint for the A’s, a fact I’m sure Chris Kahrl will address in the next Transaction Analysis.
Drafted out of a Baltimore high school in June 1993, Ken Cloude was the Mariners’ top pitching prospect by 1996 and participated in the Mariners’ mound chaos of the late 1990s. Now 27 years old, Cloude is trying to resurrect his career following Tommy John surgery in 2000 and after missing the entire 2001 season with a torn Achilles tendon.
Before claiming any success for any measure in predicting injury, we must fundamentally recognize that any PAP-style metric will be positively correlated with raw pitch counts. Pitchers with high pitch count totals will tend to have high PAP totals. If a PAP function provides no additional insight into which pitchers will be injured that pitch count totals alone, there is no reason to add the added complexity of a PAP system to our sabermetric arsenal.
In my book, you can’t be an All-Star based on six weeks of good play. I absolutely hate that standard for picking All-Stars, and yet every year, we hear that some guy coming off the hottest month of his life should be an All-Star over an established star playing a bit below his level.
There are two related effects we are interested in studying. The original intent of PAP was to ascertain whether a pitcher is at risk of injury or permanent reduction in effectiveness due to repeated overwork. And in particular, does PAP (or any similar formula) provide more insight into that risk that simple pitch counts alone?
As you read this, remember my standards: I’m looking for the best player, not the guy having the best season, and will generally take the established star unless said star has clearly been passed by someone else.