Saturday, September 23, 2017

Nebraska Wins with Stunning Ease at Penn State

No. 2 Penn State suffered its first loss of the season last night, as Nebraska came into State College and swept the Nittany Lions, 26-24, 25-19, 25-20. The number that caught my eye was the Huskers' hitting percentage of .347. This was the highest hitting percentage Penn State had allowed an opponent since Minnesota put up a .374 in sweeping the Nittany Lions on October 29, 2016 in Minneapolis.

Nebraska entered the match with three losses on the season, to highly ranked Oregon and Florida in an opening-weekend tournament, and to Northern Iowa on September 16. As noted, Penn State's ledger was perfect before last night and included a pair of wins over defending national champion Stanford.

Tuesday, August 22, 2017

Volleyball Magazine Daily Releases of Women's Conference Previews (2017)

Over at Volleyball Magazine, each day they're releasing a preview of the upcoming women's season in a different conference. There's a lot of good information on each team in a given conference, including returnees, losses, and newcomers. I'll link to each preview as it comes out:

Monday, May 15, 2017

Didn't We See This Movie Before? (2017 NCAA Men's Final)

A little over a week ago, for the second straight year, Ohio State swept BYU for the NCAA men's championship. Whereas last year's result was somewhat of a surprise, this year's was less so, as the Buckeyes were the host team for the Final Four.

As the following chart shows, similarity between the last two years' title matches went beyond the identities of the teams involved and the score. In particular, Ohio State's hitting percentage, as a team, was extremely similar in the 2016 and 2017 finals. BYU's spike attempts totaled 71 last year and 70 this year; each of these substantially trailed OSU's respective number of attempts, suggesting Cougar problems with serve-receipt, passing, and staying in-system.

Statistic
2017 (Box Score)
2016 (Box Score)
Score
OSU (25-19, 25-20, 25-22)
OSU (32-30, 25-23, 25-17)
OSU Hitting Pct.
.358 (42-13-81)
.374 (51-17-91)
Nicolas Szerszen .(OSU) H%
.480 (16-4-25)
.208 (10-5-24)
BYU Hitting Pct.
.243 (33-16-70)
.296 (34-13-71)
Ben Patch (BYU) H%
.333 (6 K-2 E-12 TA)
.091 (10-8-22)

The Buckeyes' Nicolas Szersen took virtually the same number of attack attempts in the two finals (24 last year, 25 this year), but hit much more efficiently this year. Maxime Hervoir, who has played for the French national team and attended two years of college in that country, added to the OSU attack this year, hitting .471 (10-2-17) in the final match. (He did not play in last year's final.)

BYU star Ben Patch, who battled injury most of the season, improved his hitting percentage from a year ago, but only had 12 attack attempts.

Saturday, December 17, 2016

Preview of Women's NCAA Final: Texas vs. Stanford

My last posting, on the Final Four teams' success rate this season in five-game matches, turned out to be useless for the semifinals, as neither match went the distance. Maybe tonight's final will...

Things haven't exactly gone as expected. The vaunted Big 10 conference (abbreviated B1G), which had the three highest national seeds -- Nebraska, Minnesota, and Wisconsin -- has no finalist. Also, this year will be the first in the six years I've compiled my Conference-Adjusted Combined Offensive-Defensive (CACOD) measure that a national title winner will be below a score of 1.94. The two finalists are close though, Stanford at 1.91 and Texas at 1.79.

I found Stanford's four-game semifinal win over Minnesota surprising, but the Cardinal had defeated the Gophers, also in four, way back on August 28.

My reaction to the other semi, which also involved a rematch from the regular season, was: What the hell happened? Defending national champion and this year's No. 1 national seed Nebraska had not merely swept, but routed Texas (25-15, 25-16, 25-21) on August 27. In the national semis, however, the Longhorns returned the favor, sweeping the Huskers!

Certainly, improved defense was a major for the Longhorns' turnaround. UT let Nebraska hit .304 in the teams' first match (with only 5 total blocks for the Horns), whereas Texas held the Huskers to .182 in the rematch (with 10 blocks).

Offensively, Longhorn frosh outside-hitter Micaya White struggled mightily in the first Nebraska match (hitting in negative territory, -.143, with 3 kills and 6 attack errors on 21 attempts). However, she improved to a solid, if unspectacular .269 (7-0-26) the second time around.

Another key player for Texas tonight will be fellow OH Ebony Nwanebu. She hit .378 (15-1-37) in Thursday's semifinal match and .333 (13-3-30) in the early-season match against Nebraska. Although now based in different conferences, Nwanebu and Stanford should be pretty familiar with each other. The reason, as close followers of the women's college game are aware, is that Nwanebu began her career at Stanford's Pac-12 rival USC, before transferring to Texas after her sophomore year. As the following chart shows, Nwanebu had some big matches as a Trojan against Stanford and generally committed few hitting errors.


Stanford combines a very youthful line-up (perhaps a reason for the team's slow start this season) with the experience of Inky Ajanaku, a fifth-year middle-blocker who missed last season with an ACL injury. Ajanaku finished sixth in the nation in blocks per game at 1.52. This article describes Ajanaku as the "grandma" of the team, noting also that she's a "wannabe orthopedic surgeon."

The big question, then, is whether the title will go to the wannabe or the Nwanebu.

UPDATE: Stanford takes it in four.

Thursday, December 15, 2016

Final Four Teams' Records in Five-Game Matches

There seem to have been a lot of five-game matches this year, both in the regular season and in the NCAA tournament. Some of the Final Four teams went five in roughly a quarter of their matches this season. Here's how the remaining teams fared in five-game matches:

Texas: 4-3 overall; 2-1 home; 2-2 road

Nebraska: 4-1 overall; 2-0 home; 2-1 road

Stanford: 4-4 overall; 1-3 home; 2-1 road; 1-0 neutral
  • Included in Stanford's record is a win over visiting Cal Poly. How did the Cardinal even let Cal Poly take the match to a fifth game?
Minnesota: 5-3 overall; 4-0 home; 1-3 away

Thursday, December 1, 2016

2016 NCAA Women's Tourney Preview

With this year's NCAA women's tournament getting underway today (brackets), I'm back with my Conference-Adjusted Combined Offensive-Defensive (CACOD) measure to forecast teams' success. The CACOD simply divides a team's own hitting percentage during the regular season by the overall hitting percentage it allowed its opponents, and then multiplies the resulting ratio by an adjustment factor to reward being in stronger conferences (details here). Teams that hit well and don't allow their opponents to do so will get the highest CACOD scores.

I have been calculating the CACOD for the past five years. The following table (which you can click to enlarge) shows scores for all teams making the Elite Eight during that time frame. Again, CACOD scores are based entirely on regular-season play (i.e., NCAA-tourney games are not factored in), so they can be judged for their prognostic efficacy.


The table tells us three main things, in my view:

  • No team below a CACOD of 1.94 has won the national championship during the five years I've computed the statistic. Thus, if your favorite team is well below a CACOD of 2.00, it is unlikely to ascend the victory podium. 
  • Although we're talking small sample-sizes, the CACOD appears to distinguish eventual national-championship teams (mean = 2.43) from national runners-up (1.90), losers in the national semifinals (2.03), and losers in the Elite Eight (1.96). However, it does not differentiate the latter three groups from each other.
  • Only twice in the past five years has the team with the very highest CACOD won the NCAA title (Penn State in 2013 and 2014). One leader lost in the national semis (Penn State, 2012), one lost in the Elite Eight (Washington, 2015), and one even lost in the round of 32 (Nebraska, 2011). 

So which teams have the highest CACOD scores this year? The next table tells us...


No. 1 seed Nebraska leads the way by a sizable margin, with a CACOD of 2.56. Penn State, though seeded all the way down at 16, has the second-highest CACOD (2.10). The Nittany Lions frequently do well on the CACOD, which I take as a sign of its "face validity." Unfortunately for Penn State, it would have to play Nebraska in the Sweet Sixteen. Several discrepancies between teams' seeds and their CACODs are evident. Eleventh-seeded Florida has the third-highest CACOD, whereas fourth-seeded Texas has only the tenth-highest CACOD.

We'll see how well the CACOD performs as the games get underway!

Saturday, September 17, 2016

Hitting Percentages vs. Different Numbers of Blockers Up

How much of an advantage is it for a hitter to go against one blocker instead of two? Or against two blockers who have not closed ranks (i.e., a  split block) instead of two blockers who are side-by-side with no gap between them? The following analysis, a collaboration of Volleymetrics (the blog where you are now) and Volleymetrics (the statistical-analysis consulting firm owned by Giuseppe Vinci), addresses questions such as these.

After several conversations, Giuseppe agreed to share some of his data with me for analysis. Given my location at a Big 12 school, we decided to explore data from women's play in this league (2015 within-conference matches only). Far and away, most spiking attempts occurred against two blockers side-by-side (10,615). The next most swings took place facing one blocker (2,712) or a split-block (1,182). Attempts against zero (409) or three (273) blockers were relatively rare.  Here are the average hitting percentages against each type of blocking scenario:


The primary comparison, in my view, is between hitting against one blocker (with a .315 hitting percentage) instead of two (.203).* The difference of .112 corresponds to 11 additional kills (minus errors) for every 100 attempts hitting against one rather than two blockers.

Ideally, for a truly "apples-to-apples" comparison, all characteristics of a hitting attempt (height and arc of the set, position of the hitter, quality of the hitter, etc.) would be the same, on average, for swings against one blocker and swings against two. That is almost certainly not the case, as sets to the outside tend to be higher than those to the middle and, thus, more likely to allow two blockers to get into position. Caution is warranted, therefore, in interpreting these results.

I told Giuseppe via e-mail that, "I would have thought the hitting percentage vs. 0 blockers would have been higher [than it was]." He replied that, "the reason [hitting percentage against] no blockers might be low can be due to attacks far from the net not having any blockers. If they are attacking just to get it over the net, it won't have a very high efficiency. When we limit attacks against no blockers to being within the ten-foot line, the efficiency against no blockers increases to .3202."

Not surprisingly, spike attempts against three blockers have a very low efficiency (.114). Swings against split-blocks, however, tend to be extremely successful (.423). Apparently, when hitters see a gap between blockers, the former are quite good at exploiting the situation.

Thanks again to Giuseppe; I greatly appreciate this gesture.

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*The calculation of mean hitting percentages in the bar graphs was done using statistics at the match level. In other words, let's say a team in a given match hit .300 against two blockers on 20 attempts; another team in another match hit .200 against two blockers on 10 attempts; and a third team in yet another match hit .500 against two blockers on 25 attempts. In this hypothetical example, the unweighted average would be .333 ([.300 + .200 + .500] / 3), not giving the matches differential weight by attempts. The weighted average would give greater weight to matches with more attempts: [(.300 X 20) + (.200 X 10) + (.500 X 25)] / 55 = 20.5 / 55 = .373. The bar-graph shows unweighted averages. The weighted averages (0 blockers: .276; 1 blocker: .300; 2 blockers: .196; 3 blockers: .143; and split-block: .433) were similar to the respective unweighted ones.