It’s going to be a while yet before I go to the ballpark and see a full, informative stat line on the scoreboard…even AVG/OBP/SLG may not happen for a while. At Safeco Field, for instance, all last season, the first time a batter came up we got:

  • AVG
  • AB
  • H
  • 2B
  • 3B
  • HR
  • BB
  • RBI
  • SB

Maybe strikeouts, too. But no caught stealing, for instance. Then every appearance after the first you got AVG/HR/RBI–you might as well stare at the Hebrew National Kosher hot dog sign and ponder how you make a kosher hot dog, exactly.

And no one wants to print out a batch of Prospectus statistical reports and take them to the game. So like MacGyver, we take some mental stats and make some ugly improvised devices. My goal is to make every step something I can do while drinking a beer–a quick bit of easy mental division and a comparison, for instance. And as a friend of mine was once advised by a fortune cookie: “If you want to find an easier way to do something, ask a lazy man.”

We’ll start with hitting. What’s the difference between AVG and OBP besides the walks, anyway?

Here’s the semi-hard way to do this: (H+BB)/(AB+BB). But… that’s not enough for a lazy guy like me. Instead, look at it as BB/AB. If that’s about 10%, which is easy enough to eyeball, that’s a contribution of about 70 points to a player’s OBP ahead of average. Roughly:

100 AB, 25 H = .250 Average
0 BB = .250 OBP
10 BB = .318 OBP
20 BB = .375 OBP

And so on. So here’s the real, real lazy way to do this: take ABs. Drop the last digit, rounding up or down (or not, this isn’t rocket science). Is that higher or lower than the number of walks?

Super-patient hitters like Jim Thome and Carlos Delgado run up on 20%.
Average players see about their BB/AB ratio at about 10%
Really hacktastic players can see walk rates as low as zero, but that takes some doing.

And on the flip side, if you’re given strikeouts, you can do the same ballpark estimation. The most strikeout-prone guys can run up to 40% if they’re suffering from Russ Branyan disease. These are the Thome types, who work deep into counts frequently and draw a lot of walks. You’ll also see Jose Hernandez, who struck out 177 times last year while only walking 46 times (35% of ABs), and Preston Wilson who struck out 139 times while walking 54 times (23% of ABs).

An average hitter runs a hair under 20%, so if you divide ABs by five, you can gauge their whifftasticness pretty easily: higher is whiffier, lower means harder to strike out. Not that this, in itself, tells you anything about the player’s worth. Take Felix Fermin (please!), a player of limited offensive talents who enjoyed a strangely good reputation in part because he was able to put the ball in play (and into a fielder’s glove) consistently rather than strike out.

To estimate power, there’s no way I’m going to ask you to figure out slugging percentage. And the raw totals don’t seem to tell you much about ability. So add up the 2B, 3B, and HR totals and compare it to the number of hits. Even a rough number will tell you a lot about a player’s power.

Albert Pujols had 95 extra-base hits in 212 hits last year, a whopping 44%. An average hitter’s ratio runs at about a third of all hits for extra bases (in the AL in 2002, 35% of all hits went for extra bases). A really bad hitter will approach zero: Ramon Santiago, formerly of the Tigers and now a Mariner, had 444 AB in 2003 and a rate of 20%. Of his 100 hits, only 21 were for extra bases.

Anyone reading this column can compare a small number to a larger one and figure out if it’s about half the size (woo-hoo!), a third the size, or if it’s much less than that. And with that, you’ll have a good idea of how to estimate whether a player hits for contact (AVG, supplied by the ballpark), if they take walks (BB/AB), and if they hit for power (XBH/H).

That’s a pretty decent picture of a hitter…if that hitters plays in a neutral park. It’s important to remember that you’re not comparing like players when you do this. You have to cut players some slack if they play in a pitcher’s park, and know to be harder on them if they’re playing in a hitter’s park. For instance, given two players who (by these metrics) are patient hitters who hit for good power, the one that hits for San Francisco is way superior to the one hitting in Coors Field half the time. Unfortunately for purposes of this article, doing park adjustments on the fly is pretty hard. I propose that after you’ve arrived at your initial conclusion (Scale: Tony Womack, terrible, bad, average, good, great, Barry Bonds), you shift that evaluation one position to the left if that player’s home park is dramatically unbalanced for hitters, and one position to the right if that player’s home park is a terrific park for pitchers.

Next time: pitchers.