A Quick Look at Four Hitting Rates

When Voros McCracken invented DIPS (which stands for Defense-Independent Pitching) several years ago, he broke down pitching and batting performance into four simple ratios. DIPS has become fairly well-known and quite controversial since then, but those ratios have been largely forgotten by the general baseball community—I’m not aware of any website that lists them on a regular basis.

So, as long as we’re in the middle of that pregnant pause known as spring training, I thought I’d remind you what they are, and how powerful they can be. Here is each one, with a definition:

  • Walk rate (walks divided by plate appearances; you can include HBP, too)
  • Strikeout rate (Strikeouts divided by at-bats. In other words, not including walks and HBP. You can add back in sacrifice flies if you want)
  • Home run rate (Home runs divided by batted balls)
  • BABIP (our old standard—the proportion of batted balls that fall in for a hit, not including home runs)

Four simple ratios that tell you a lot. Of course, there are many baseball stats out there that track similar things, but the beauty of these ratios is that they build on each other. The impact of each event is removed from the denominator of the next event. A batter can’t hit a home run unless he actually hits a ball, so walks and strikeouts aren’t considered in the home run rate.

For pitchers, you often see strikeout and walk rates, but as a proportion of total plate appearances. Here, they are treated sequentially. The strikeout ratio implies that a pitcher can’t strike out a batter if he walks him first, so the formula takes walks out of consideration when calculating strikeout rates.

Jim Albert has researched these deceptively simple ratios. In SABR’s By the Numbers (February 2006), he found that the four rates did a better job of predicting a team’s runs scored than OPS or Runs Created. He also wrote another article for the Journal of Quantitative Analysis in Sports (try finding that at your local bookstore) that focused on pitcher strikeout rates and concluded that Dazzy Vance‘s 1924 was the greatest strikeout performance of all time. Our own Steve Treder could have told him that.

Anyway, there’s something important mathematically here. Each one of these rates is a binomial probability, like flipping a coin. Either the event (walk, strikeout, home run or hit) happens or it doesn’t. That allows you to do some interesting things with the ratios, such as estimating the approximate randomness of each one. As you might expect, Albert found that three of the four ratios were relatively predictable. The fourth, BABIP, was much more random.

What’s more, BABIP doesn’t correlate highly with the other rates. There is a natural positive relationship between walks, strikeouts and home runs, but not batting average on balls in play. BABIP is the orphan ratio.

To invite a little more consideration of the four rates, I calculated the 2006 leaders and laggards for each one. To compute them, I first took intentional walks and sacrifice bunts out of total plate appearances. I included HBP in the walk rate calculation. My “sample” consisted of all batters with at least 400 plate appearances.

First, the walk rates:

Best                       Worst
Player          BB%        Player            BB%
Giambi J.      .201        Olivo M.         .027
Ensberg M.     .201        Cedeno R.        .029
Bonds B.       .191        Estrada J.       .030
Abreu B.       .178        Uribe J.         .031
Johnson N.     .177        Betancourt       .031
Burrell P.     .171        Berroa A.        .032
Thome J.       .169        Cano R.          .034
Hafner T.      .166        Francoeur J      .038
Dunn A.        .158        Rodriguez I      .040
Ramirez M.     .157        Payton J.        .043

I admit that I had no idea Morgan Ensberg would rank so highly. I should have expected it; after all, he batted .235 last year but had an OBP of .396. The “worst” list consists of a bunch of middle infielders and catchers. And Jeff Francouer.

Walk rates are fairly predictable from year to year. Last year’s leader was also Jason Giambi.

Next, the strikeout rates (or, putting the positive spin on it: contact rates):

Best                       Worst
Player          K%         Player            K%
Pierre J.      .054        Dunn A.          .344
Polanco P.     .058        Howard R.        .308
Garciaparra    .063        Hall B.          .299
Lo Duca P.     .074        Thome J.         .296
Eckstein D.    .082        Gomes J.         .294
Catalanotto    .084        Granderson       .289
Walker T.      .085        Shelton C.       .286
Vizquel O.     .087        Edmonds J.       .285
Sanchez F.     .088        Burrell P.       .281
Hatteberg S    .089        Bautista J.      .272

The usual suspects, but I have to sheepishly admit (again) that I didn’t realize Bill Hall struck out quite that often last year. Turns out that he struck out 162 times! Of course, he also had a career year in most every other batting category. Most of the worst strikeout rates belong to some of the majors’ best sluggers. In fact, Albert found that strikeouts have the smallest impact on run scoring of any of the four ratios.

The rate with the largest run impact is the home run rate. Here are the leaders and laggards:

Best                       Worst
Player          HR%        Player            HR%
Howard R.      .143        Kendall J.       .002
Hafner T.      .121        Taveras W.       .002
Ortiz D.       .121        Punto N.         .003
Thome J.       .120        Gathright J      .003
Dunn A.        .108        Eckstein D.      .004
Giambi J.      .107        Roberts D.       .005
Dye J.         .103        Pierre J.        .005
Berkman L.     .103        Miles A.         .005
Pujols A.      .100        Clayton R.       .005
Thomas F.      .100        Ausmus B.        .005

Of course, there is a correlation between strikeouts and home run rates, as Phil Birnbaum pointed out in this article. The more you strike out, the more likely you are to hit a home run. Isn’t it sort of odd to see Jason Kendall‘s name right there next to Willy Taveras and Joey Gathright?

The last ratio, BABIP, has the second-largest impact on run scoring. But, as I noted earlier, it is the orphan of the group: random and unrelated to the other rates.

Best                       Worst
Player           H%        Player             H%
Jeter D.       .391        Molina Y.        .226
Cabrera M.     .379        Uribe J.         .240
Abreu B.       .366        Barmes C.        .241
Johnson R.     .366        Gomes J.         .244
Paulino R.     .365        Giambi J.        .245
Mauer J.       .364        Thomas F.        .247
Sanchez F.     .364        Griffey Jr.      .248
Cano R.        .359        Bonds B.         .251
Howard R.      .356        Ensberg M.       .251
Ethier A.      .354        Biggio C.        .254

Morgan Ensberg again. Good bet for 2007 is that Ensberg’s batting average will jump back up. On the other hand, check out Ryan Howard on the “Best” list. A home run hitter with a high BABIP is an awesome recipe for success. Unfortunately, I would guess that he won’t maintain that BABIP this year.

Derek Jeter‘s .391 is tremendous, of course. He’s always among the league leaders in BABIP, but don’t bet on him hitting .391 again.

Finally, Albert outlined a formula to turn these rates into runs per game approximations. I thought it would be fun to apply his formula to our rates to see which players contributed the most and least runs per game. As you can see, the two lists are pretty predictable, closely following the leaders and laggards of Runs Created Per Game.

Best                       Worst
Player          R/G        Player            R/G
Howard R.       9.2        Barmes C.         2.2
Hafner T.       8.9        Berroa A.         2.5
Ramirez M.      8.5        Cedeno R.         2.5
Pujols A.       8.4        Molina Y.         2.7
Thome J.        8.2        Anderson B.       2.8
Berkman L.      8.0        Ausmus B.         2.9
Ortiz D.        7.8        Everett A.        3.0
Dye J.          7.7        Gathright J       3.1
Jones C.        7.7        Uribe J.          3.2
Cabrera M.      7.4        Clayton R.        3.2

To the extent that BABIP drove each player’s relative standing, you can expect his performance to change next year. For instance, expect improvement from Clint Barmes, Juan Uribe and Jonny Gomes, and some decline from Howard, Jeter and Cabrera.

References & Resources
Albert’s formula for runs per game is -3.2 + 13.2*BB% – 12.3*K% + 40.9*HR% + 24.5*BABIP. Please note that these BABIP figures differ slightly from the BABIP figures posted in our Statistics section, because I included sacrifice flies in the denominator.

Print Friendly
 Share on Facebook0Tweet about this on Twitter0Share on Google+0Share on Reddit0Email this to someone
« Previous: THT Mailbag: Double Stuff Edition
Next: THT Links: Ho-Hum »

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <strike> <strong>

Current ye@r *