When it comes to the graphics and design of the blog I am hardly an expert and have no experience with Photoshop. I rely on the available designs from Blogger. I stumbled upon the "stars and stripes" layout and thought it would work for the site. Baseball, after all, is the national pastime. I really like the new look. I'm still tinkering with the columns on the left and right of the posts but I made the concerted effort to separate the stats so the American League was on one side and the National on the other. I think they're easier to read now.
This past Christmas I was blessed with some spending cash and decided to bone up on some modern baseball writing, specifically on how sabermetrics can be useful and new methods for statistical analysis. I zeroed in on three books: Keith Law's Smart Baseball, Brian Kenny's Ahead of the Curve, and Jay Jaffe's The Cooperstown Casebook. If you follow Keith Law at all online or via Twitter you know what you're going to get. That being said, if you are a sabermetric skeptic (say that three times fast...), it's a surprisingly inviting read to be a better fan and to have an open mind about the new math of baseball. Kenny's book was much more aggressive about abandoning the poorly informed traditions and methods in baseball. I enjoyed both immensely, even if I'm not completely sold on Kenny's campaign for eliminating the traditional starting pitcher role. (I'm still in the middle of Jaffe's book, which is a highly entertaining read...)
Over the past couple of years of playing Statis Pro I was already better incorporating some sabermetric strategies, including lineup construction (best hitter in the #2 spot, best slugger in the #4 spot, etc.) and use of the bullpen (not being afraid to use your best reliever earlier than the ninth inning - to hell with the "save"). Where I really got convicted was on some of the stats I've so diligently recorded and presented to you, the readers. Pitcher wins, saves, and RBIs aren't particularly informative when evaluating a player's ability. Even OPS - maybe the best accepted "modern" stat by baseball traditionalists - is crude and problematic in that it combines two different measures. It's similar to saying "apples plus oranges equals appleoranges."
So, in order to better understand some of the new statistical measures, I've added new statistical categories to my spreadsheets and, in turn, have added them to the blog. Here's a brief rundown of what's new:
ISO - Isolated Power. This stat helps recognize all hits aren't created equal. Simply put, to determine ISO, you subtract your batting average from your slugging percentage. The higher the number, the more power you use when making successful contact with the ball. The current Statis Pro leaders are Kirk Gibson in the American League and Darryl Strawberry in the National League.
wOBA* - Weighted On-Base Average. This measures overall offensive value, particularly related to the weighted advantage of a single compared to a home run, etc. I was quite proud that I could create a formula in my excel sheet to calculate it. I did face one small snag, though. When I first started this endeavor years ago, I would record sacrifice bunts and sacrifice flies in the same column. A true wOBA only uses sacrifice flies. This is really annoying but I can't possibly go through all 699 previous games and parse out how the sacrifices were recorded. So, I've added an asterisk to signal that it's not quite a true wOBA but close enough. The current Statis Pro leaders are Mike Young in the AL and, you guessed it, Darryl Strawberry in the National.
wRAA* - Runs Above Average. This was another fun formula that calculates how many more runs a player is worth on offense compared to an "average" player. I have to add the asterisk here too because wRAA incorporates wOBA. The current leaders are, again, Mike Young (33 runs) and Darryl Strawberry (27). But it's also fun to see who is the most average (theoretically, "zero" runs) player and below average (negative score). In the American League Al Cowens of the Mariners (0.3) is the closest to average and Dick Schofield (-16.9) is the worst regular. In the NL it's Rafael Ramirez with a dead average 0.0 ranking and Dale Berra of the Pirates as the worst (-16).
K/9, BB/9, and HR/9 - Strikeouts per 9 Innings, Walks per 9 Innings, and Home Runs Allowed per 9 Innings. These are a little more self-explanatory, but for pitchers, taking a look at these ratios helps evaluate talent independent of total innings pitched. For instance, Fernando Valenzuela leads the National League in strikeouts partially because he's started more games than some of his competitors. But, he only ranks third in K/9, so some folks have a better ratio than Fernandomania.
RA - Run Average. This is similar to Earned Run Average in that the lower the number is the better. However, with RA, this doesn't give pitchers a pass for runs that score as a result of a fielding error. If you subscribe to the theory that pitchers are responsible for the runs that cross home plate, no matter how they originally got on base, this is the stat for you.
FIP - Fielding Independent Pitching. This stat is useful when trying to eliminate the impact of defense on a pitcher's results as well as the good/bad luck on batted balls in play. Sabermetricians claim this is a "truer" value of pitching success than ERA. This was another squirrely formula to create for an excel sheet but I pulled it off. The current FIP leaders are Bud Black in the AL and Kurt Kepshire (???) of the St. Louis Cardinals in the National League.
Forcing myself to learn these stats and incorporate them in the league leaders columns was a lot more fun than I expected. If this is all new to you I hope this brief primer has been worthwhile. I really want to thank Fangraphs for their wonderful explanations of these advanced measures.
As always, thanks to all of you who continue to read the recaps and visit the blog. It's an obscure hobby for sure, but your patronage makes it all worthwhile. If you have suggestions for other stats you'd like to see just let me know in the comments below!
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