On-base Plus Slugging (OPS) is exactly what it sounds like: the sum of a player’s on-base percentage and their slugging percentage. Many sabermetricians don’t like OPS because it treats OBP as equal in value with SLG, while OBP is roughly twice as important as SLG in terms of its effect on run scoring (x1.8 to be exact). However, OPS has value as a metric because it is accepted and used more widely than other, more accurate statistics while also being a relatively accurate representations of offense. You can find OPS on baseball cards and in broadcasts, and it’s a simple statistic that has made its way into the main stream,
On-base Plus Slugging Plus (OPS+) has not gained as much widespread acceptance, but is a more informative metric than OPS. This statistic normalizes a player’s OPS — it adjusts for small variables that might affect OPS scores (e.g. park effects) and puts the statistic on an easy-to-understand scale. A 100 OPS+ is league average, and each point up or down is one percentage point above or below league average. In other words, if a player had a 90 OPS+ last season, that means their OPS was 10% below league average. Since OPS+ adjusts for league and park effects, it’s possible to use OPS+ to compare players from different years and on different teams.
OPS’ name tells you how to calculate it. On-base plus slugging:
OPS = OBP + SLG
If you’re looking for a more technical understanding, here is how you calculate OBP and SLG individually:
OBP = (H + BB + HBP) / (AB + BB + HBP + SF)
SLG = (1B + 2*2B + 3*3B + 4*HR) / AB
We don’t house OPS+ on the site, but the calculation can be found at Baseball-Reference.
In general, OPS is better than something like batting average or RBI because it captures a player’s ability to get on base and their ability to hit for extra bases. For the most part, those two factors capture more of what hitters are trying to do. Generally speaking, if you sort hitters by OPS, you are sorting them based on their production to date with some minor exceptions.
The problem with OPS is that one point of OBP and one point of SLG are not equal. OBP is about twice as valuable as SLG, meaning that OPS overrates power hitters and underrates high-OBP guys. It’s a rather mild issue overall, but we have a better statistic, wOBA, which does not do that. OPS has the benefit of being very easy to calculate in a pinch, and it is more widely understood, but there is really no reason to choose OPS over wOBA if you have the choice.
How to Use OPS:
OPS is used to determine how well a hitter has performed over a given period of time. It is not as precise as wOBA or wRC+, but it is a reasonably good estimator if it’s the only thing available. The key to using OPS is understanding the current offensive climate. A .900 OPS is much more impressive in 2015 than it was in 2000. These days, league average is about .710 or so, with the best hitters around 1.000.
In general, OPS needs a decent sample size to be reflective of true talent, as it is very easy for a good hitter to have a rough couple of weeks and produce a .500 OPS over 50 PA, even though they are one of the best hitters on their team. When using OPS, make sure you understand the context and sample size involved.
Please note that the following chart is meant as an estimate, and that league-average OPS varies on a year-by-year basis. To see the league-average OPS for every year from 1901 to the present, check the FanGraphs leaderboards.
League-average OPS+ is always 100.
Things to Remember:
● If you’re looking to evaluate a player’s offense, OPS is a better metric to use than batting average, but should always be used in conjunction with other statistics as well. It’s a good gateway statistic to get people thinking beyond the traditional statistics.
● If you have the choice, use Weighted On-Base Average (wOBA) instead of OPS. OPS weighs both OBP and SLG the same, while wOBA accounts for the fact that OBP is actually more valuable.
● Since it provides context and adjusts for park and league effects, OPS+ is better to use than straight OPS, especially if you’re comparing statistics between seasons.
Links for Further Reading: