Shortcomings of OPS as an advanced metric

From SABR member “Patriot” at Sports Data Research on July 13, 2013:

To many in the general population of baseball fans, the singular metric associated with sabermetrics might be On Base Plus Slugging (OPS).  OPS has gained a measure of acceptance in the mainstream as an offensive metric in both its raw form and the park- and league-adjusted variant OPS+, and given the long history of their use in sabermetrics (both having been developed by pioneering sabermetrician Pete Palmer), it is no surprise that the metrics are associated with the field and are still in common use.  However, OPS has shortcomings that can be problematic for serious application:

  • OPS is not expressed in meaningful units: Ideally, a metric should be expressed in units that are fundamental to the game itself (such as runs or wins) or that can be easily explained.  On their own, the components of OPS do just fine by this standard.  On Base Average can easily be understood as the proportion of plate appearances in which the batter reaches safely, and Slugging Average is the average number of total bases per at bat (although the use of the name “Slugging Percentage” is misleading at best).  But when they are combined into OPS, it becomes impossible to articulate what the unit of measurement is or what the result is meant to represent.  While a batter with a .400 OBA reaches safely 40% of the time and a batter with a .500 SLG averages one total base every two at bats, the meaning of his corresponding .900 OPS cannot be similarly stated.  The best one can do is to state what its user intends it to represent–a measure of overall hitting productivity.
  • OPS is not as accurate as competing overall measures of offensive productivity: Generally, OPS does a decent job of predicting runs scored on the team level, but it  tends to be slightly less accurate than more refined metrics.  OPS still performs credibly, but metrics based on linear weights or Base Runs perform better.

OPS could be improved by weighting OBA more heavily; studies have suggested a multiplier in the neighborhood of 1.8 to maximize correlation with team runs scored (i.e., OBA*1.8 + SLG).  Doing so, though, would take away one of the strongest selling points of the metric, which is ease of calculation.

Read the full article here:

Originally published: July 15, 2013. Last Updated: July 15, 2013.