Advanced Statistics Made Easier (Sort of).
July 30, 2010 6 Comments
It’s funny that Joe Morgan is on the cover of Baseball for Dummies. Joe Morgan is an idiot, but Joe Morgan is a Hall of Famer. Joe Morgan doesn’t believe in advanced statistics, citing the fact that he played the game as to why he is right. This sort of faulty logic is common amongst many retired players that were probably too scared to use cell phones and the internet. Go take a nap, Joe, your warm milk will be ready shortly.
Editor’s Note: I was going to make this one giant post, but droning on and on for close to 2,000 words is becoming less and less attractive. Instead, this will be part 1 of a 4 part series. It will also make me look like I blog a lot more than I actually do. So, there’s that.
I’ve been kicking around the idea of writing about advanced baseball statistics for a while now. The other day, Hilson mentioned something about it, so I figured now would be as good a time as any. As a disclaimer, note that in no way do I consider myself versed on all things related to sabermetrics. I am not a sabermetrician, and I don’t pretend to be one. What I’m about to cover isn’t some super complex, difficult to understand math equation; rather, I’m attempting to put these formulas and statistics into terms the casual fan can understand. If you’re knowledgeable in the area of sabermetrics, this isn’t for you. My fear is that instead of finding this helpful, my dear readers will look at this as verbose and uninteresting. That is not my intent, and I apologize beforehand if it turns out that way.
Having said all of that, sabermetrics aren’t as scary as they seem. I’ll admit, when I first took the plunge into the world of advanced statistics, I was terrified. First, I’m about as good at math as Ben Roethlisberger is at not raping chicks. Sabermetrics involves a lot of math and funky equations. For the purpose of this blog post, however, don’t worry about said equations. All I’m attempting to do is make it so my girlfriend better understands me when I ramble on about why Franklin Gutierrez is more than just a sexy, sexy man (although, he is pretty easy on the eyes). Also, Geoff, reading this will make you far less stupid.

Pure Sex.
There are a myriad of statistics to look at, but I’ll focus on the four that I believe are the most important. None of these stats are perfect, and none should be looked at as an end-all when evaluating players. With that in mind, the first stat we’ll examine is WAR.
WAR stands for Wins Above Replacement, and is by far the easiest stat to understand. People may not understand slugging percentage, but even my 9-year-old knows that winning is important. A replacement level player is not a league average player. This is an important distinction to make. A league average player is worth ~2 wins per season, while a replacement level player is worth zero. Every team has replacement players. Think of them as the guys who do well enough in AAA to get called up, and then basically ride the bench. The baseball blogosphere refers to these guys as AAAA players. If you’re a Seattle fan and follow the Mariners at all, think Mike Carp. Is he going to kill you if he fills in for a week or two? Probably not. Is he the long term answer at his position though? Not likely. Braves fans, think of 2009 Garrett Anderson. He was actually worse than replacement level (his WAR was negative, at -0.9). It’s nice being able to quantify with numbers what your eyes see. Watching Anderson was probably excruciatingly painful as a fan, but instead of saying things like “God he’s awful”, we can say, “God he’s worse than replacement level” and “I legitimately believe I could help this club more”. For (Devil) Rays fans, think Delmon Young circa 2007. He posted a WAR of 0.1 that year, and somehow turned into Matt Garza (and Jason Bartlett).
As an aside, I just covered the favorite teams of both SC authors, Bryan Holt and Robbie Hilson. The bromance gets stronger by the day.
If you’re interested in reading about WAR in greater detail, there are far better places in which to do so. Beyond the Boxscore, a part of the fantastic SB Nation, has a ridiculously well written piece (if you don’t care about length). Also, Tom Tango, who might actually have a higher IQ than Walter Bishop (Fringe references!), breaks it down over at his blog. Both are extremely interesting and thought-provoking reads. I would also suggest bookmarking FanGraphs, as it is basically the mecca of all baseball stat sites.
I’ve always enjoyed looking at numbers and lists more than reading entire articles. With that in mind, here are the top 10 players from 2009 (since WAR is cumulative, not projective) according to their WAR (final WAR in parenthesis):
- Zack Greinke, KC (9.4)
- Albert Pujols, StL (8.7)
- Ben Zobrist, TB (8.3)
- Tim Lincecum, SF (8.2)
- Justin Verlander, DET (8.2)
- Joe Mauer, MIN (8.0)
- Chase Utley, PHI (7.6)
- Derek Jeter, NYY (7.4)
- Roy Halladay, TOR (7.3)
- Hanley Ramirez, FLA (7.2)
Basically what this means is, over the course of an entire season, Albert Pujols is almost 9 wins better than someone like Mike Carp. So if he got hurt and St. Louis used a replacement level player in his absence, they’d win 9 less games over the course of an entire year. That’s a lot of games attributed to a single player. The great thing about WAR is it also factors in things like defense and base running. Offensive statistics are nice, but WAR looks at the entire package and lets you know just how valuable a player really is.
What’s cool about this list is that 4 of the top 6 players on it have the post-season hardware to back it up. Greinke was #1 in WAR last year and took home the AL Cy Young. Pujols was #2, and took home the NL MVP. Lincecum was #4, and took home the NL Cy Young. Mauer was #6, and took home the AL MVP. With the exception of Zobrist, you knew all of these players were elite. Instead of judging them by All-Star appearances or Home Runs, we can look at their WAR and say how valuable they all were.
Now, again, WAR is by no means a flawless stat. Although, for judging a players true value, it’s the best measure currently at our disposal. I hope I’ve shed some light on a seemingly complicated matter. I’m not expecting all of you to start citing career WAR for players like WSU alum John Olerud (62.4, Go Cougs!). However, when debating how good Player A is compared to Player B, you can now use what is — more or less — the most complete statistical measure available. WAR is your friend; use it wisely.
























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