Just got access to the men’s side of the data so I’m still playing around with a few things.
What you see above is a data-driven visualization of what coaches might term “the key guy to stop.” In recent years with a team like BYU, the common phrase was “Taylor Sander is going to get his kills, let’s focus on the other guys.” So what I’ve built is essentially a histogram of what players hit in any set they appear in – and then color code lost sets in red and won sets in…turquoise? *only sets in which players have 3 or more in-system hitting attempts count – and players must appear in a minimum of 30 sets during the mpsf conference season to be counted.
What you’ll notice from the visuals is that yes, TJ DeFalco certainly has an impact between won/lost sets, it’s actually Amir Lugo Rodriquez who’s hitting efficiency carries the most weight. The likely reason is that if Amir gets going, LBSU can get pin hitters 1 on 1’s much easier as opponents move to front the quicks. Another possibility is that this doesn’t actually prove causation – and that Amir hits better when LBSU passes better. This is also fair, but again, we include the data if Amir has at least 3 attempts in the set.
I like this visual because it makes sense to look at – coaches can see that shift between won and lost sets, but to also include the actual cohen’s d and magnitude levels supplies additional statistical weight to the problem. I’d like to use this approach more frequently moving forward – in both the men’s data from the spring and the women’s data currently coming in from the Big Ten and Pac 12 this fall once conference kicks off.
Rethinking Volley 