Stat Week: An Argument for OPS

Throughout the week, Baseballl Tonight will be taking a closer look at the use of "non-traditional" statistics in baseball analysis, with "Stat Week" segments on its program. The Max Info will be providing further analysis on the topics discussed, as well as delve into additional subjects to enhance those discussed on the show.

OPS, defined as on-base percentage plus slugging percentage, has become a widely used term in baseball analysis. However, OPS is often criticized by both the traditionalists of baseball as well as sabermetricians. Traditionalists prefer to measure offensive worth with more established stats, such as batting average.

Sabermetricians feel OPS does not go deep enough. They prefer to use a more in-depth breakdown, such as replacement player analysis. Clearly, there is no right or wrong philosophy. Simplicity is always good, but so is validity. Something easy to understand works, but at what cost to the statistical significance?

Now the question must be addressed, does OPS really do a good job of evaluating offensive production?

It is fairly common practice (and deservedly so) to measure an offense by the number of runs they score. Therefore, a simple way to check which statistics work well in judging the value of an offense is to observe which statistics correlate well with runs scored.

Below is a table showing the correlation between frequently used baseball statistics with runs scored, using team data from 2000 to 2009.

In statistical terms, a correlation coefficient of +1.000 is a perfect positive linear relationship, a coefficient of -1.000 is a perfect negative linear relationship, and a coefficient of 0 means no linear relationship.

In other words, the closer a number is to one, the better the relationship between the two stats.

Correlation with Runs Scored
Team-Level Data ('00-'09)

As shown above, OPS has the strongest relationship with runs scored by a team over the past 10 seasons among the statistics checked. In fact, in each year since 2002 the team that has scored the most runs in Major League Baseball has also had the highest OPS.

I am not suggesting that there is no use for more advanced statistical research in baseball, far from it. The intelligent front offices and sharp fantasy players who utilize cutting edge analysis will certainly have a leg up on their competitors.

The issue of simplicity vs statistical significance is not a problem specific to baseball. The answer most have come up with is known as Occam's razor, which states “entities must not be multiplied beyond necessity.”

In this case, if there is something uncomplicated that does just as good of a job explaining offense as a more complex one does, we should use the simpler stat.

While certainly a clever sabermetrician can (and surely has) found other replacement player or linear weight metrics that show a higher correlation to runs scored than OPS, the simplicity, as well as the utility, of OPS should be appreciated by all.