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 the bottommost point (USC), means that USC missed around 4% of their serves in that set and held their opponent to -0.115 or so in FBSO. Pretty impressive.
As you’ll see, the blob of data certainly trends positively, indicating that higher service error is associated with, but does not necessarily cause higher opponent FBSO eff. The R-squared of this trend line is only around 0.13, which is pretty mild. This would suggest that you can be successful at limiting your opponent’s ability to FBSO, even at a higher level of error (say 20-25%), as there are teams like UCLA (lowest UCLA dot) who missed around 24% of their serves in the set, but still held their opponent to a negative FBSO eff.
So the next question for me was: if service error doesn’t have a league-wide trend, does it help/hurt individual teams more than others? That’s what the above graphic helps to drill into. Similar to previous charts, blue/teal indicates the team won the set, red means they lost. The curves are of frequency distribution – meaning that for UC Irvine, the highest frequency of won sets occurred primarily in the narrow range of 18-23% service error while fewer won sets occurred outside this range – whereas Stanford’s curve for the bulk of won sets occurred in a wider range from 5-20% with only a few sets won outside this range.
The hypothesis of the casual men’s volleyball observer might be that higher levels of service error would of course manifest more frequently in lost sets, yet what we see is that for most teams, it doesn’t make a difference in terms of winning/losing the set. The fact that these mounds essentially overlap one another for the majority of teams indicates that they miss approximately the same number of serves in won and lost sets.
There are of course a couple outliers. Cal Baptist and Hawai’i both show large effect sizes of service error in won/lost sets. A negative cohen’s d indicates an inverse relationship between service error % and winning the set; as one rises, the other falls. UCSD shows a medium strength relationship between the variables, but you’ll notice that all the other teams, including top teams such as Long Beach, BYU, UCI, and UCLA, all show small to negligible effect sizes for service error %.
So moving forward, unless you’re a fan of Cal Baptist (…unlikely) or Hawai’i (much more likely), don’t let those missed serves ruffle you. In the grand scheme of the set, remember that they’re likely negligible in terms of winning and losing.