Welcome to the Pac 12.
So here’s the same FBSO vs. SE% graphic I built for the Big Ten. Looks pretty similar, with a loose, positive correlation between the two metrics. If you don’t remember from the original post, these are the individual match performances of each team – meaning that UCLA in the bottom left there only missed 1.37% of their serves in a match where they also held their opponent (sorry Cal) to an FBSO Efficiency of -0.027. FBSO Eff again referring to the receiving team winning or losing the point on that first receive to attack possession, divided by the number of times they’re served to (which accounts for SE’s).
An interesting observation when comparing leagues is the correlation coefficient between the metrics in question . For the Big Ten, r = 0.45. For the Pac 12, r = 0.28. Translated, service error percentage has a stronger relationship with FBSO Eff in the Big Ten than in the Pac 12. What this suggests is that if you play in the Big Ten, missing your serves hurts you more than it does in the Pac 12.
But the next thought I had, which I think I’ve alluded to in most posts, is that the context matters – who you’re playing matters. Teams have different strengths, different weaknesses and maybe you just need to get them to 10 feet so they can’t set the quick – maybe you need to get them to 20 feet to really pressure them – or maybe you just need to serve in to pressure them…So we need to break this down by opponent.
This is still Opponent FBSO Eff vs. Team SE% on a per-match basis, but the opponents are listed on the left. The linear regression lines you see going through each set of dots is a visual representation of how related the two metrics are. If you look at Cal or Washington State, you’ll see pretty strong, positive relationships. This means if you miss fewer serves, you’re likely to lower your opponent’s FBSO Eff.
But the surprising thing here is that for the majority of teams there is little to no relationship between the two. If you take UCLA for example, the almost horizontal regression line means that increasing your SE% does not result in a corresponding increase or decrease in FBSO Eff, it’s effectively a wash. The way I interpret this is that while you might be serving “tougher” the additional errors are offsetting the overall outcome. So you might be reducing FBSO Eff when your serve is in (since you’re serving tougher), but you’re also missing more often, essentially counteracting any benefit.
Colorado is the only opponent where an increase in SE% actually has the relationship with FBSO Eff that most coaches seem to think – and it’s a crazy weak relationship (r = -0.13).
So for some teams, the takeaway here is that missing serves doesn’t really matter – they’re going to have the same FBSO Eff either way. But for some, like Stanford (r = 0.49) or Cal (r = 0.68), you might want to tone down the aggressive serving as the more errors you make, the more likely your opponent is going to raise their FBSO Eff.
Just for fun – and because I needed a break from my thesis – I ran this same type of thing using the Big Ten data again.
Playing against Ohio State has the strongest relationship between SE% and FBSO Eff at r = 0.68. Again, this means that you’re very likely to raise OSU’s FBSO Eff if you raise your SE% – so keep your freaking serves in play.
The Illinois chart is kind of funny. We had a running joke in the office that we shouldn’t pass perfectly if we could help it because our overall efficiency was better off a medium pass than a perfect one. This is likely because Jocelynn Birks was usually the best in-system choice but if the pass the perfect, our setter could make the mistake of involving someone else in the offense…Granted, Birks graduated last year and this data is from this season, but I’m glad to see the team sticking with tradition.
As you can see from the breakdown by opponent, the overall Big Ten relationship between FBSO Eff and SE% being stronger makes sense – as the majority of opponents show a weak to moderate relationship between the variables.
A complaint of this viewpoint might be that better teams serve better and that teams who miss a bunch of serves are less likely to stop an opponent in general. I think this is fair, Minnesota is a low-error team while Michigan State is often the opposite. One of these teams is also objectively better than the other, so should we compare them the same way? That’s where Efficiency Change comes into play. My next post will likely be Service Error % against Eff Change, broken down by team. This will help clarify the real value of serving against a specific opponent and take into account the overall context of the situation much better than FBSO Eff in general can.
Anddddd back to my thesis work…
Rethinking Volley 