How to Handle Different Categories: OBP

This is a subject I tackled last year, but the basic idea for this article is that people playing in leagues that use non-traditional roto categories may not be equipped with proper draft materials. Almost any set of rankings you can find online are compiled with the standard 5×5 roto categories in mind. Some sites may have rankings published specifically for leagues that replace batting average with OBP, but the vast majority assume standard categories. If you’re using a set of rankings from a site or magazine, you’re doing yourself a disservice unless those rankings were designed for OBP leagues.

I’ve compiled my own projections for 242 hitters to generate my own rankings, and I’ve generated rankings specifically for standard 5×5 leagues and leagues that have replaced batting average with OBP. I want to highlight the players who gain the most in OBP leagues and the players who are hurt the most when OBP replaces average. I base my rankings off a number Zach Sanders calls Fantasy Value Above Replacement (FVARz). Essentially I assign each player a value for each category and add them all up while adjusting for positional scarcity.

Below I’ve included two lists; one shows the players whose FVARz increases the most when OBP replaces average, and the other shows the players whose FVARz decreases the most. I’ve also included their ranking among hitters and auction values for both batting average and OBP leagues. Over on a site of which I’m the managing editor,, I’ve posted how the rankings and auction values change for all 242 hitters I have projections for when the switch is made to OBP. You can find that here. All values are based on 10-team leagues with 25-man rosters, 13 starting hitter slots and three bench slots. If you have questions about the methodology, the charts or anything else, hit me up in the comments.

OBP Plus

As you’ll notice, studs like Trout, Cabrera and Goldschmidt aren’t on this list because they’re top five players in either league type. Their FVARz goes up because they have great OBPs, but in this article we’re simply looking for players who see big value changes in OBP leagues. The goal is to help people using rankings based on standard categories in identifying guys they should target and avoid in OBP leagues.

We’ve got two types of players here. We have power/high strikeout guys who return somewhere between almost no value and negative value in batting average leagues, and we have some solid players who range from comfortably above average to near elite.

Among the power/strikeout guys, Dan Uggla and Adam Dunn are beyond saving. But Ike Davis and Kelly Johnson become worth a late round flier. My guess is that someone in your league is going to go an extra buck or two on Davis, but I’d be surprised if anyone bid you up after throwing out Johnson for a dollar. He’s not good enough for your middle infield spot in batting average leagues, but he definitely makes that cut in OBP leagues. And then you have Chris Carter who goes from being nothing more than someone you’re forced to take late if you’re desperate for power to someone who falls somewhere near the middle of the pack.

The more interesting names are Joey Votto, Shin-Soo Choo, Giancarlo Stanton and Carlos Santana. Votto goes from borderline first round talent to definite first round talent, and Choo jumps up into the first round as well. People seem to be well aware of Votto’s OBP prowess, so I imagine most consider him a first round talent in that format. But even though Choo posted a monster OBP last year, I’d be surprised if many had the stones or wherewithal to take him in the top ten. To be clear, I’ve got Choo’s OBP regressing about 20 points but remaining above .400. If you think it regresses more than that, maybe he’s an early second rounder for you.

Stanton goes from a late third/early fourth to a solid second round pick in OBP leagues. Many would probably disagree with having him as a borderline third rounder in batting average leagues. But I have his home run total at 32, which is lower than any of the projection systems listed on his player page, and I have his R+RBI total at around 160, which is lower than all but ZiPS. If you like his counting stats more, he’s probably a borderline first rounder for you in OBP leagues. Finally you have Carlos Santana who I think is wildly overvalued in batting average leagues but who might last long enough in an OBP league to be worth his price. But he’s still only a borderline top 50 hitter and thus still not someone I’d take until the end of the fifth or early sixth in an OBP league.

Now on to the guys who are hurt the most when OBP replaces average.


You were already ignoring David Lough and Marcell Ozuna in 12-team mixed leagues, but you can add Ben Revere to that list. It’s OK to take him for a buck in batting average leagues if you’re desperate for steals, but look elsewhere in OBP leagues. Look at someone like Jonathan Villar instead. Salvador Perez, Torii Hunter and Avisail Garcia go from mid-range players to nothing more than dollar or three players. This makes sense considering all three had a walk rate of four percent or lower last year.

Among the more interesting names, Carlos Beltran and Wilin Rosario go from top 50 hitters to top 75 hitters. Alex Rios goes from a late second round pick to a late fourth. The biggest name is Adam Jones. He’s the only player in this exercise whose FVARz dropped by two standard deviations or more when switching to OBP. Because he’s such an all category contributor, he’s still probably worth an early third round pick in OBP leagues. But if you take him, you’ve got to be very conscious of the OBP hit you’re taking and make up for it elsewhere.

I’m hoping to have some similar posts in the coming days for slugging, OPS, quality starts and K/9.

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The Stranger

Maybe not the best place to ask this, but what’s the best player pool to determine z-scores from? Do you use all fantasy starters, all above-replacement players, or all regulars regardless of fantasy relevance? My concern is that including a large number of players who aren’t fantasy-relevant skews the data. I want to compare the pool of players I might actually draft to each other, so it feels wrong to include a large sample of players who will probably never be rostered.

I’ve been running the numbers with a large pool of players (everybody projected for 400 PA), sorting by FWARz, and then running the numbers using only the top 200 or so hitters to try to approximate the pool of fantasy-relevant players before doing my positional adjustments. That feels arbitrary, though, and sometimes doesn’t capture enough players at thin positions like C and SS. But if I include more players I feel like I’m skewing my numbers by including players who aren’t fantasy-relevant.

It seems like this choice has far-reaching implications in how my rankings turn out, and I’m not mathematically adept enough to fully identify where the biases are. I’ve seen various people suggest different approaches (usually either all fantasy-relevant players or all players with a minimum PA or IP), but I can’t recall seeing an in-depth discussion of what goes into that choice.


You should feel comfortable with the play pool you’re using. As a rule I exclude players get <250 AB. My dollar values correlate very well to actual draft $ amounts, so I feel secure in my approach. How does your league value hitting/pitching? Mine is 69% hitting/31% pitching