The other day, Patrick suggested something (in response to the post comparing category distributions) I’ve been thinking about doing for a while too: Generating correlations between all the roto stat categories. This way, you could see which categories “go well” together, for the purposes of fine-tuning your team.
Without any further ado, here it is (based, of course, on the RotoPoll data):
For those not familiar with the concept, a correlation is a statistical measure of how well two sets of data match up. In the case of basketball stats, for example, two categories will have a high correlation if players who do well in one of them also tend to do well in the other. A negative correlation suggests that players doing well in one category tend to do poorly in the other. A correlation around zero suggests there’s no relationship at all.
Ok, so what does this mean? To help demonstrate, I’ve highlighted the best (FG and rebounds) and worst (FT and steals) correlations. If your team needs to improve in both FG% and rebounds, the good news is you’ll be able to find a lot of players who can help you in both. If you need FT% and steals, however, you’re going to have a much harder time finding someone.
This data may also be useful if you’re considering tanking (giving up in) a category. For example, if you intend to tank FG%, it’ll be hard to do it without taking a hit in rebounds as well.
Another interesting thing to observe is the correlation between cateogories and overall value. Two cateogries, FT and assists, stand out as being the most correlated with overall value. I’m not sure, but this seems to suggest that those categories are more valuable in some way. For one thing, it means that it’s harder to find low-value players who can help you in those categories, as opposed to, say, steals or points, where the correlation with overall value is actually negative. So hold on to Steve Nash.
And of course, if anyone knows stats well enough to suggest or criticize something here, by all means, go ahead.