*the net is at the top. the endline is closest to this text
In the land of cones and zones…and subzones, it’s easy to forget that these are merely representations of locations on the court. Equipped with that logic – and sparked by our friends at VolleyMetrics – I converted the zones to xy coordinates.
Yes, it’s annoying that the x-axis is above the heatmap, but I wanted to throw this up here anyway. It’s the hitting efficiency of attackers in the Big Ten in 2016 based on where the set came from – and it’s what you’d expect. Perfect passes lead to good things, moving your setter forward isn’t terrible, but moving your setter backwards is horrible. Below the hitting efficiencies in each grid are the frequencies.
One thing I think that gets overlooked is being content with medium passes. It goes back to the FBSO posts and why getting aced hurts. Yeah you’re hitting .100 instead of .300, but at least you aren’t losing the point 100% of the time like when you get aced or fail to dig a ball. That’s why keeping digs on your side is huge. As long as they don’t result in a set from the endline, you’ll be hitting positively – rather than defending against a perfect pass after you overpass.
Here’s the same thing for the Pac 12 from 2016. I’ve set the cutoff for both graphs between green and red at 0.150. But again, the frequencies are below the efficiencies so I would definitely not devise an offensive system that runs through zone 1D. Just thought I’d add it for those who were curious.
Anyway, looking forward to messing around with heatmap-type stuff to answer a variety of questions. The inherently great thing about heatmaps is that they convey ideas incredibly quickly. You glance at it and understand that you’d want to pass into the green rather than the red. Stuff like this for serving targets, setter tendencies, attacker tendencies, etc can all be addressed in this fashion. Heatmaps aren’t a novel concept for scouting by any means – both VM and Oppia have employed them (I believe) – but I got curious if I could build something similar and it looks like it’s prety easy to do with either R + ggplot2 or Tableau. If you have suggestions for ways to employ this, I’m all ears.