Comparing the Categories

In fantasy, each category is a little different. For example, players who get a lot of blocks are rare, rebounds aren’t so rare, and a single free throw albatross (such as Shaq for the past 10 years) can sink your team. Unfortunately, however, most of this type of knowledge comes from intuition, observation, editorial opinion, or other informal sources. Let’s look at some hard data that will hopefully help you evaluate the differences across the categories, and make decisions to help your teams.

The following is the distribution of scores, in each category, for the top 180 players in the RotoPoll rankings.

Category Maximum Median Minimum
Blocks 3.22 -0.33 -0.82
Assists 3.08 -0.62 -1.58
Points 2.60 -0.93 -2.32
Rebounds 2.28 -0.44 -1.42
Steals 2.11 -0.40 -1.69
Threes 2.02 -0.36 -1.14
FT 2.01 -0.00 -2.57
FG 1.78 -0.10 -1.40

And here’s a distribution of each category (click to see a larger version):

category distributions

So what can we learn from this data? Here are some initial thoughts.

  • The category with the highest average is free throws, and if you look at the chart, you can see that it drops off precipitously at the end (in fact, it has a noticeably different shape than all other categories, which tail off gradually), meaning that there are just a few guys who are really dangerous. It seems you want to avoid those guys (Ben Wallace, Tyson Chandler, etc.), and if you need to bump up your FT% a little, there are a lot of guys out there who can help you.
  • By contrast, the category with the lowest line on the graph is points. This means that it’s harder to find someone who can help you in the points category than any other category. This might seem counterintuitive, since almost everyone scores some points, and it’s not that hard to find a waiver pickup who can give you 10-12 per game, but it’s harder to find someone who can actually help you than in other categories.
  • Blocks is the most top-heavy category on the graph. It has the highest minimum and maximum, which suggests that production is concentrated in fewer players than in other categories. Conversely, a player who gets no blocks doesn’t hurt you as much as a player with a zero in another category (or poor percentages).

And of course, these observations apply to each category in greater or lesser degrees.

There’s certainly a lot more to take away from this data, but that’s a start.

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6 Responses to Comparing the Categories

  1. Patrick says:

    This is something I’ve been thinking about – but how do we relate this info to overall player ratings? Isn’t this really a step back? For example, Tyson Chandler can’t shoot free throws, but he can get a lot rebounds. So how does looking at A and B separately provide more useful information then A + B?

  2. rotopoll says:

    I think one way it can help is if you’re thinking about how you might need to improve, in certain categories, in the future. For example, say your team is doing poorly in the points category. According to the numbers, you’re less likely to be able to find someone who can help you in points down the line (all other things being equal), so if you have a chance to grab a guy now, via trade or waivers, maybe you should do it.

  3. rotopoll says:

    Also, it might help if you’re considering giving up in a category. Looking at the distributions, I think the “best” category to tank is actually points, since it has the most concentration of value in a small group of players (or free throws if you have the guys at the tail end).

  4. Patrick says:

    I’m probably only speaking for myself here, but I’d be interested to look at the correlations (or perhaps another measure of relatedness) between categories to see how they relate to each other. Like looking at individual categories, I think that information would also help for strategy purposes. I’m going to speculate that free throw percentage and blocks are negatively correlated with each other, so trying to build a high percentage free throw and high block team would be a disaster, even though both have small, distinct set of players to target (or avoid).

  5. rotopoll says:

    Patrick, that’s something I’ve thought about too, for exactly the reason that you mention. I don’t actually think it would be that hard either–it’s just a simple correlation.

  6. […] RotoPoll didn’t start my thought process with this post, but it’s a good one about how v…. Then, Philthy over at Fantapedia (Your fantasy basketball encyclopedia) has an excellent essay slash dissertation on the value of Turnovers in Roto Leagues. Both of these posts play with something I’ve been thinking about since I was invited to be part of the DroppingDimes Expert’s Leauge. Basically, that thought I’ve been mulling is: Roto is actually pretty great. I’ve written about Roto vs. Head-to-Head before, and if I wanted to look harder, I’m sure I could find something in the archives to show how mean I was to Roto in the past. I’ve always like H2H better before, and I still think it’s probably more fun and better for casual fantasy players (one of the reasons a writer can actually make money writing about Fantasy Football). […]

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