Chad Gordon
Been waiting to dive into this for a while now, so let's get right to it. What you see above is the Service Error % in a given set and the opponent's FBSO efficiency: (opp. won in FBSO - opp. lost in FBSO)/total serves. The teams you see as labels are the serving teams, so... Continue Reading →
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... Continue Reading →
B1G Pass Rating in W/L sets Figured I would build a similar chart for the Pac12 from 2016. It includes all conference matches, not just the big ones. As with the Big 10 numbers, there are certainly some teams where pass rating doesn't necessarily differentiate won/lost sets. By including all sets however, this may... Continue Reading →
I'm not a huge fan of using pass ratings - I don't believe they accurately value different reception qualities. That being said, every single person ever uses pass ratings, so I decided to dive into it a little. In the above chart, pass ratings are valued on a 3 point scale (R# 3, + 2.5,... Continue Reading →
Similar idea - just messing around with the ggjoy package in R. What you see above is the receive efficiency (basically passer ratings valued by Big Ten standards for FBSO eff). I filtered out a bunch of names that failed to consistently post high values - as well as those sets in which the passer... Continue Reading →
Not a sexy topic, but I just figured out how to do these 'joy division' charts in R so I'm kinda pumped to share. What you see is a histogram of each team's point scoring % in every individual set they played (only against the teams you see listed, so Purdue v. OSU but not... Continue Reading →
Was just messing around with some numbers this afternoon and wanted to share.I looked at a few things related to serving, specifically serve error%, point score%, and serve output efficiency. I ran some correlations between these stats and themselves as well as with winning the set overall.As with my last post, I'm only using data... Continue Reading →
Explaining the past and predicting the future are two wildly different concepts. Outcome bias runs rampant in articles and ESPN talking heads - where rationalizing previous behaviors based on results seems to be what these folks are paid to do. After last night's game 3 loss to Boston, the commentators criticized Lebron's play - but had the... Continue Reading →
So what you see above is how well each team in the Big Ten attacks when they use a variety of shots. Originally I was interested in looking at how well teams both used and defended against the tip, so that's why they're ordered in this manner. As you might expect when looking at full... Continue Reading →
