OPS: A Brief History

Nice article from Alan Schwarz in The New York Times related to OPS and the proper weighting of its elements. Schwarz cites the work of Victor Wang published in the August 2006 issue of SABR's By The Numbers newsletter. His article...

The OBP/SLG Ratio: What Does History Say?

The correct relative contribution of OBP and SLG in "OPS-type" statistics has been the subject of some discussion recently. Here, the author checks historical team run records to see which ratio gives the closest correlation to runs scored.

There has been much debate and research in the past issues of By the Numbers about how much more valuable OBP is to SLG. Values ranging from 1.5 to even 3 have been brought up. No one, however, has actually compared the various values of OBP to SLG to the runs scored of a team. To solve this, I took the OPS and runs scored from every team since 1960. I then adjusted the OPS using the different suggested coefficients for OBP. The adjusted OPS I used were OBP weighted by 1.5, OBP weighted by 1.8, OBP weighted by 1.9, OBP weighted by 2.0, and regular OPS. These OBP weights have all been suggested in one place or another.

The results:

OBP Coefficient Correlation to R

1    0.8386
1.5  0.8394
1.8  0.8408
1.9  0.8407
2    0.8405

We can see that normal OPS has the worst correlation when compared to each adjusted OPS. The correlation keeps improving until the coefficient reaches 1.8, when it starts to decline but still has a higher correlation than with a coefficient of 1. However, the correlations remain very close to each other.

The data shows that the best coefficient to use when weighting OBP is 1.8. This was also confirmed by Tom Tango though I am unaware where his study is located. In fact, The Hardball Times currently uses a stat called "GPA," which adjusts OPS using a 1.8 coefficient for OBP and divides by 4 to make the stat on a similar scale to batting average. If anyone is interested in the complete set of data that contains all teams from 1960 and there adjusted OPS with runs scored, please contact me at the e-mail address below.

This is a subject I've written on in the past and so for those interested...

DePodesta and OPS

A Closer Look at Run Estimation

Run Estimation for the Masses

OPS as a Run Estimator

Contextualizing OPS

The Power of Squares

Nice article by Dave Studeman over at Baseball Analysts on Pythagoras, run estimation and Bill James. I especially liked the following:

"The power of two is everywhere in life. E=MC squared, after all. When you move closer to a light, cutting the distance in half, the light doesn't become twice as bright...So when Bill James discovered that the nature of runs to winning is squared, it seemed as though something essential and fundamental had been discovered."

Another example of this phenomena is the inverse-square law of gravitation which Newton published in his Principia but which was first hinted at by Ismael Bullialdus and known (or guessed at) in some form to the likes of Christopher Wren, Emond Halley, and Robert Hooke as told in James Gleick's wonderful biography of Isaac Newton titled Isaac Newton.

For more thoughts on run estimation see:

Run Estimation for the Masses
A Closer Look at Run Estimation