Rarely is OPS (on-base percentage plus slugging percentage) a fantasy stat. It’s off many people’s radar but it’s widely available and closely mimics a position player’s overall hitting talent. While other stats (e.g. wOBA and wRC+) also give a hitter an overall value, these stats aren’t available at every website. Most sites have their own unique blend but OPS is commonly available. Because of this availability, I’ve been using it as a baseline in recent articles on adjusting projections based on prospect pedigree and when hitters get platooned ($$). Now, it’s time to use OPS to help predict the individual categories.

The process I used for this study was to simply see how much various stats changed when OPS changed a certain amount. For rate stats (e.g. batting average) the conversion is straightforward. For counting categories, I put the stats on a per 600 plate appearance scale. Additionally, I only compared data from 2015 to 2017 during the current “juiced” ball era. I know the process is not close to being 100% precise and that is fine. I’m just trying to create general adjustments and can look to hone the process later. I’m putting in 20% effort to get 80% of the answer.

For the study, I matched hitters who had 100 PA in each season. I grouped OPS changes into 50 point categories to find the adjustments and here is are the resulting graphs.

The rate stats almost line up perfectly while each of the counting stats has an r-spared over .90. From the best fit lines, I collected the average change for 1-point OPS (.001) change. The counting stats are adjusted to 600 PA.

Stat Change Based 1 OPS Point Change Stat Change AVG 0.00030 OBP 0.00033 SLG 0.00067 Runs 0.09150 HR 0.04530 RBI 0.10450

It’s nice to idiot-check the change with on-base (.00033) and slugging (.00067) adding up to the OPS change. Taking the data one step future, here are the changes in each stat at different OPS intervals. Again, the counting stats are adjusted to 600 PA.

Stat Change Based OPS Change OPS change AVG OBP SLG Runs HR RBI .150 .045 .050 .100 13.7 6.8 15.7 .125 .038 .042 .083 11.4 5.7 13.1 .100 .030 .033 .067 9.2 4.5 10.5 .075 .023 .025 .050 6.9 3.4 7.8 .050 .015 .017 .033 4.6 2.3 5.2 .025 .008 .008 .017 2.3 1.1 2.6 .000 .000 .000 .000 0.0 0.0 0.0 -.025 -.007 -.008 -.017 -2.3 -1.1 -2.6 -.050 -.015 -.017 -.033 -4.6 -2.3 -5.2 -.075 -.023 -.025 -.050 -6.9 -3.4 -7.8 -.100 -.030 -.033 -.067 -9.2 -4.5 -10.5 -.125 -.038 -.042 -.083 -11.4 -5.7 -13.1 -.150 -.045 -.050 -.100 -13.7 -6.8 -15.7

Here’s an example of how to use these values with top prospect, Ronald Acuna.

From my prospect work, I found top-ranked prospects exceed their OPS projections by 50 points. For a reference, here is Acuna’s pre-season Steamer projection.

Ronald Acuna’s 2018 Steamer Projection PA AVG OBP SLG R HR RBI 432.7 .280 .329 .450 48.7 13.8 53.7

The changes to his rate stats are easy to calculate. With the counting stats, I needed to adjust them to the 600 PA rate, add in the extra stats, and then adjust back to the original PA total. From these values, here are his new projections.

Ronald Acuna’s 2018 Adjusted Steamer Projection PA AVG OBP SLG R HR RBI 432.7 .295 .346 .483 52.0 17.1 57.1

The improvement isn’t jaw dropping but going off historical projections, it’s more likely to be close to his 2018 results.

For future study, I don’t mind one bit finding the adjustment for other common fantasy scoring methods (e.g. CBS points). If you want me to run the adjustment on a common scoring platform, let me know in the comments. I may answer them in the comments or likely write a second article with the answers.