For pretty much the entire four years that I've maintained this blog, I've extolled the importance of hitting percentage as a measure of a team's overall ability. To win games (sets) and matches, teams need points. Hitting percentage is based heavily on teams' productivity in winning points via kills, but penalizes teams for hitting errors, which of necessity give points to the opponent. Dividing (kills - errors) by total attempts further weakens teams' hitting percentages if a lot of their attempts are kept in play by the other team. If a team compiles a high hitting percentage and limits its opponents' hitting percentage, it will probably win a lot of matches.
Last year in previewing the start of the NCAA women's tournament, I showed that there was a strong correlation where the highest seeded teams also had the highest hitting percentages. I used teams' overall regular-season hitting percentages, however, which don't take into account degree of schedule difficulty, both across teams and within different parts of the season for any one given team.
To refine the methodology, I've done similar calculations on the eve (approximately) of this year's tournament, but looking only at seeded teams' hitting percentages against other seeded teams they played during the regular season. (You may click on the table below to enlarge it, noting also that the table appears in separate upper and lower blocks due to space limitations within a single block.)
If we were going purely on teams' average hitting percentages against other teams that ended up being seeded, then Stanford at .290 and Cal at .280 would be undervalued in the seedings (see blue circles in the right-hand column). Cal's average is based on only four matches, however, and in two of them (against Stanford) the Golden Bears hit at sizzling levels around .350. Northern Iowa really looks out of place as the No. 5 seed with its .171 average hitting percentage, which came only against some of the less higher-seeded teams.
Defense (i.e., holding opponents to low hitting percentages) appears to align better with seeding. As can be seen (literally) in the bottom line, the top five seeds each held their seeded opponents to hitting percentages around .200 (this may be Northern Iowa's saving grace).
Between the No. 6-12 seeds, nearly all of the teams held their seeded opponents to roughly .250 hitting. The one exception was No. 10 seed Minnesota, which held its seeded opponents to a paltry .185 average hitting percentage (see red circle). The Golden Gophers did allow Penn State to approach .300 in both Big Ten matches between the teams, but shut down other teams such as Duke (.044) and Dayton (.117).
The No. 13 and 14 teams, LSU and Dayton, respectively, held their seeded opponents to roughly .260 hitting. Lastly, we have No. 15 Hawaii, which played only one match all season against a team that ended up being seeded, and No. 16 Purdue. The Boilermakers were pretty "lights-out" (to borrow a baseball expression) in shutting down opponents' offenses, including in one of two matches against Penn State. The only problem for Purdue is that it hasn't hit that well itself against top competition.
We'll soon see the effectiveness (or lack thereof), in terms of prognostic success, of looking at the teams through this lens.