There are lots and lots of ways to score runs, but there are probably even more formulas for calculating how runs are created. This very mathematical article includes an analysis of 39 different run estimation formulas, and I don’t think the author covered them all. Bill James, for instance, has even created at least fourteen different versions of his Runs Created formula. We use the most recent, and complicated, version on our site. And it seems that every baseball analyst has their own take on the best run estimator.

I find the whole debate interesting, but I’m the kind of person who better understands things through pictures rather than numbers. If you’re that kind of person too, I’ve created several graphs that you might find interesting. If you’d rather read tables of numbers, then go nuts on the links in the first paragraph.

The first graph is our standard “runs created” graph here at THT, because it’s a simple graph of On Base Percentage (OBP) and Slugging Percentage (SLG). These are the two fundamental components of Runs Created, which is why people pay so much attention to OPS, and which is why it makes sense to plot them on two axes of a graph.

Actually, I like to use Isolated Power (ISO, which is SLG minus Batting Average) instead of SLG on the graph because it better illustrates the differences between teams and players. Remember, I’m not trying to create formulas with the best results; I’m trying to create pictures that best illuminate what’s happening.

Here’s the American League graph. Teams to the upper right of the graph have the best offenses, vice versa for the lower left. Teams above the line are relatively better at slugging, while those below the line are relatively better at getting on base.

According to the graph, the Red Sox have the best offense in the league, and, lo and behold, they have in fact scored the most runs in the league. Now, it’s important to remember that these stats aren’t adjusted for ballpark factors, and the Red Sox play in a decidedly hitter-friendly park. But they are still the best offensive team in the league.

Other things of interest are the Rangers’ and White Sox’s relative reliance on slugging average (both factors influenced by ballparks) and the reliance on OBP by the Indians, Angels and Orioles. It’s also interesting that the Angels and Orioles pretty much occupy the same space on the graph, but the Angels have scored about thirty more runs. So this graph is good, but it’s not perfect. Anyway, here is a link to the National League version of this graph.

That’s the Runs Created approach, but there are other ways to picture an offense. One approach I like is to graph three simple components, which I estimate account for over 90% of all runs scored. I’m not saying this is the best mathematical way to estimate runs or anything. I’m just saying this provides an interesting, useful take on the subject:

__First__, hit a home run. Whenever you want, as often as you can.

__Second__, if you don’t hit a home run, get yourself into scoring position (second and third base).

__Third__, hit with runners in scoring position. Drive them home.

To show you what I mean, let’s take a look at each of these components. First, here’s a list of the number of home runs hit by all AL and NL teams:

American League National League =============== ================ Teams HR's Teams HR's NYY 191 CHC 187 CHW 188 STL 169 TEX 188 COL 167 BOS 179 PHI 165 OAK 158 LAD 160 DET 153 CIN 158 MIN 147 SFG 148 CLE 143 NYM 146 BAL 127 ATL 139 ANA 125 HOU 135 KC 117 MON 117 TBD 112 FLO 116 SEA 107 ARI 115 TOR 106 PIT 115 MIL 104 SDP 100

Ballparks are a significant factor in these ratings, too. As noted earlier, Chicago is the place to be if you want to hit home runs. Nevertheless, the Cubs are clearly the class of the NL in hitting home runs, while four teams (Yankees, White Sox, Rangers and Red Sox) are at the head of the AL class. Notice, too, that the Angels and Orioles have hit about the same number of home runs.

By the way, sorry about the table. Let’s move onto the second two components of scoring runs, and let’s also get back to pictures. Here’s a graph of the number of times major league teams have batted with runners in scoring position (RISP), along with their batting average with RISP.

Ah, I love this graph. Again, the leading offenses tend to be on the upper right part of the graph (Red Sox, Indians) and vice versa (Expos, Diamondbacks). Also, there’s a natural upward slope to the data (as drawn by the line) because teams that are good at getting into scoring position are teams that bat well.

This graph also separates the Angels from the Orioles. The Angels have scored more runs than the O’s because they’ve gotten more runners into scoring position AND they’ve hit better with runners in scoring position. The White Sox are the truly odd team on this chart — they lead the league in batting with RISP, but they have the third-least total number of runners in scoring position.

BA with RISP tends to be equal to overall BA. In the AL, teams are batting .271 overall, and .271 with RISP. But the Sox are batting .291 with RISP vs. .268 overall. In other words they’ve been lucky by hitting well in clutch situations, despite what you may have heard about injuries to their top hitters.

In general, runners get into scoring position one of three ways:

– 20% get there by reaching first (via a single or walk) and moving on by stealing a base or by a teammate’s “productive out”.

– 30% get there directly by hitting a double or triple.

– 50% get there by reaching first, and subsequently moving on via a positive contribution (hit, walk, etc.) from a teammate.

When first hired, White Sox manager Ozzie Guillen talked a lot about getting his runners in motion, but the Sox don’t reach first base enough in the first place; they are third from last in singles, walks and HBP combined. They’ve batted well with RISP and hit a lot of home runs, but reaching scoring position has been their problem.

Speaking of home runs, let’s make this chart complete by adding home run information. I’ll add a circle to each data point that is proportional to the number of home runs each team has hit. Here’s the American League:

I think this paints a fairly complete picture of each team. For instance, you can see how the Yankees’ relatively average position on the graph is bolstered by their home runs (circle size), which makes them the third-best offense in the league. Instead of adding additional comments, I’ll let you reflect on your favorite teams. Here’s the National League.

In case you were wondering how San Diego was scoring, now you know. And consider Milwaukee, the unclutchiest team in the NL. Pictures are indeed worth a thousand words. I’ll mention that to my editor.