Penn State Over Cal in Final -- An Unexpected Rout

I don't know that there's a lot to say about last night's three-game sweep that gave Penn State its fourth straight NCAA women's volleyball title. Other than in Game 2, in which Cal had a pair of set points, there was very little drama.

The statistical issue that got the most attention on ESPN's broadcast was how the Golden Bears' Tarah Murrey was getting nearly half of her team's hit attempts (she ended with 46% of them, 56/121), thus letting Penn State devote its attention to stopping her. And stop Murrey the Nittany Lions did, holding her to a very uncharacteristically low .143 hitting percentage (box score). In the semifinals against Texas, in contrast, Murrey hit .413.

For Penn State, middle-blocker Arielle Wilson exhibited her usual blend of steadiness and power, hitting .391, and right-side hitter Blair Brown punched in at .316. Outside-hitter Deja McClendon, though hitting only .250 on the night, got off to a fast start; of her 16 total kills, she recorded six each in Games 1 and 2 (play-by-play sheet).

Cal actually outblocked Penn State, 11 to 7, but the Lion block was there when they needed it, on both of the aformentioned Golden Bear set points in Game 2.

It's Penn State and Cal in Saturday Night's Final

Below, I've circled what I think are some key numbers from last night's two NCAA women's semifinal matches (I did screen captures of the official box scores, then annotated them in PowerPoint). One thing that's clear right off the bat is that the two winning teams, Penn State and Cal, each sided-out extremely well. With those kinds of side-out percentages, teams will very rarely lose games (sets).

In my Penn State-Texas preview, I had concluded that, "Blocking may provide Penn State with a decisive edge in holding down Texas's hitting effectiveness." I don't often make such spot-on predictions, so when I do, I like to toot my own horn a little. As seen in the following boxscore, the Nittany Lions dominated the blocking and slowed down two Longhorn hitters who had been very productive of late, Rachael Adams and Jennifer Doris. Throw in a torrid hitting performance from Penn State's Deja McClendon and a three-game romp is the result. (You may click on the images to enlarge them.)

Similarly, Cal accomplished what I thought it needed to, in order to finally break through and beat USC this season. Adrienne Gehan, who struggled mightily in both Bear-Trojan matches during Pac-10 play, recorded a .393 hitting percentage last night, complementing Bear star Tarah Murrey (.413). Defensively, Cal slowed down nearly the whole SC team, but especially middle blockers Alexis Olgard and Lauren Williams, who had punished the Bears in the the teams' earlier meetings. Alex Jupiter never aligned with Mars (a music lyric reference) nor with Trojan setter Kendall Bateman very much, for that matter.

So, who wins the final? Both teams have been dominant in the postseason, dropping only one game combined (Penn State vs. Duke). Cal has faced tougher competition, including having to play Washington in the Seattle regional. Still, one can never count out Penn State. The old football cliche about teams that "don't rebuild, they reload" seems quite applicable to the Lions. I'll predict a five-game match and leave it at that...

NCAA Women's Final Four Preview II: USC vs. California

Tonight's second semifinal match of the NCAA women's Final Four will be an all-Pac-10 battle, with the University of Southern California (USC) taking on the University of California, Berkeley. What gives this match a little extra intrigue is that these teams have already met twice this season in conference play, with USC winning both times. The Trojans actually had a harder time holding off the Golden Bears -- 17-15 in the fifth game -- October 9 in Los Angeles (boxscore) than up in Berkeley, where USC prevailed in four games (boxscore).

As usual, I've been focusing a lot on hitting percentage during the tournament, and the following table tells us which players have (and have not) done well in this season's USC-Cal head-to-head match-ups.

As discussed in yesterday's preview of tonight's other semifinal between Penn State and Texas, middle blockers will often have higher hitting percentages than outside hitters, because the latter likely receive a greater number of impromptu, desperation sets when a team is out of system. Having said that, though, USC's two giant middle blockers, Alexis Olgard (6-foot-5) and Lauren Williams (6-4, a third-team All-America), have attacked extraordinarily well against Cal this season, with hitting percentages of .389 and .349, respectively.

On the other hand, the Trojans' outside-hitting corps, led by first team AVCA All-America Alex Jupiter, has been held in check against Cal. As can be seen above, Jupiter, Falyn Fonoimoana, and Katie Fuller have all hit in the range of .222-.235 in the two matches against the Golden Bears. One important task for Trojan setter Kendall Bateman, a second-team All-America, will therefore be to get her outside hitters going.

Cal OH Tarah Murrey, powered by her fellow Cal first-team All-America, setter Carli Lloyd, has overcome any difficulties outside hitters have in recording high hitting percentages, going .364 in the Bears' two matches against the Trojans.

Middle blocker Kat Brown has held her own against SC, hitting .312, while opposite hitter Correy Johnson, an honorable mention for All-America honors, has hit .250. Adrienne Gehan (OH/opposite) and Shannon Hawari (middle) have been almost totally neutralized by USC, compiling virtually as many hitting errors as successful kills.

So there you have what I think are the key questions for tonight. Can the Golden Bears limit the damage from the Trojans' middle blockers, while still keeping the SC outside hitters under wraps? And can Tarah Murrey get a little help from her friends, as Cal tries to match SC offensively?

NCAA Women's Final Four Preview I: Penn State vs. Texas

The first of Thursday night's two NCAA women's national semifinal matches presents a rematch of last year's championship contest, Penn State vs. Texas. The nightcap will feature two Pac-10 foes, USC and Cal. The present write-up will focus on Penn State and Texas, with another one tomorrow for USC and Cal.

My starting point in analyzing the Nittany Lions and Longhorns is to examine to what degree, if any, the teams have changed over the past three months in their allocation of sets to different hitters and these players' hitting percentages. Back in early September, on the eve of the Big Four tournament -- with Florida hosting Penn State, Texas, and Stanford -- I presented graphs of each team's leading hitters.

The key elements of these graphs are as follows. Each team gets its own graph. The graph consists of several bars, one for each hitter. A bar's height represents that player's hitting percentage (based on some reference timeframe) and its width represents the player's share of the team's hitting attempts. For convenience, a given team's bars are arranged, left to right, from the player with the highest hitting percentage to the one with the lowest. One wants to see rectangles that are both tall and wide, signifying that players with high hitting percentages are taking the most attempts.

What we're going to see is a comparison of "old" and "new." The old graphs are the ones from September, based on matches prior to the Florida tournament. The new ones are based on teams' statistics from the first four rounds of this year's NCAA tourney, the four matches a team won to reach the Final Four.

To create the new graphs, we of course have to look at each team's tournament matches, one-by-one, before aggregating the statistics. In the first table immediately below, we see the statistical hitting lines (kills, errors, and total attempts) for each of Texas's and Penn State's main hitters, match-by-match. Together, the five listed Longhorn players accounted for 95.4% of UT's spike attempts in the first four rounds of the NCAA tournament, whereas the listed Lion players accounted for 86.5% of Penn State's spike attempts. As always, you can click on the graphics to enlarge them.

One Texas player immediately jumps out at me, namely middle-blocker Jennifer Doris, who has only one hitting error in the entire postseason (on 58 hitting attempts). In the regionals, Doris had 8 kills and no errors in 14 swings against Illinois, and 10 kills with no errors in 18 attempts against Purdue. I'll discuss Doris and other Longhorn hitters further when I get to the team's graph (below).

Among the Nittany Lions in their table, middle-blocker Arielle Wilson has performed well in most of the team's tournament matches, outside-hitter Blair Brown (who hits on the right-hand side, in contrast to most prominent OH's) excelled vs. Oklahoma, and OH Deja McClendon exhibited her most sustained productivity against Duke.

Let's now look at the graphs. The larger (light-orange) graph represents Texas in the current postseason, with the September graph (smaller, burnt-orange) shown for comparison.

Middle-blocker Rachael Adams, who was just named first-team All-America by the American Volleyball Coaches Association (AVCA), comes out as the Longhorns' highest-percentage hitter in both graphs. In what seems like a wise move (as seen by Adams's light-orange bar being wider than her burnt-orange one), UT has increased Adams's share of the team's spike attempts, from 11.6 percent in September to 18.8 percent presently.

Sha'Dare McNeal has seen her proportion of Longhorn hit attempts shrink from 17.0 to 11.3 percent, perhaps as a way of increasing Doris's swings (from 8.6 to 12.4 percent). Of course, I have no way to know if such changes are intentional or just coincidental.

Juliann Faucette (OH), who has taken about one-third of UT's swings in the postseason, joined Adams on the AVCA All-America first team. Faucette's hitting percentage is lower than those of her aforementioned teammates; however, as an outside hitter, she may be called upon to take a greater number of improvised swings to bail the team out of danger, in contrast to middle-blockers whose quick attempts tend to be by design. (See my earlier posting on out-of-system play and the related commentary on VolleyTalk.)

For Penn State, the larger (lighter-blue) graph shows the current postseason, whereas the smaller (dark-blue) graph is from September.

The Nittany Lions have featured a "big-three" attack this season, with the aforementioned Wilson, Brown, and McClendon. All three also received AVCA All-America honors, with Wilson and Brown making the first team and McClendon, the second (also being voted Freshman of the Year). Other than somewhat of a shift from September from when McClendon and Brown took about the same share of Penn State's hit attempts to the postseason, where Brown has been getting more attempts than McClendon, the Nittany Lion offense looks structurally pretty similar at the two stages of the season.

On the whole, in fact, I'm struck by both teams' consistency between their respective September and postseason graphs, even though the games comprising the September and postseason graphs are mutually exclusive.

Blocking may provide Penn State with a decisive edge in holding down Texas's hitting effectiveness. I've gone on for a long time, but let me just conclude with the observation that Penn State ranks fifth in the nation in blocks per set, whereas Texas is 35th.

Graphing the Trajectories or Arcs of Sets to Hitters in a Match (UCLA-Texas 2010 NCAA Second Round)

Today's entry falls under the rubric of, "It seemed like a good idea at the time." While watching last Saturday night's webcast of the UCLA-Texas women's NCAA second-round match, I decided to create what, to my knowledge, would be a novel type of play-by-play sheet that visually depicted the trajectories of each team's initial sets in mounting an attack from serve receipt.

Being able to check, at a glance, whether a team was varying its attacks between high and outside (a "4 set"), quick middle hits (a "1 set"), and other varieties of plays, and its success in siding-out with the various types of attacks, would seem to be valuable information. Further, because the webcast was shown entirely from an "end zone" camera, it was relatively easy to observe the arcs of the sets.

What I didn't bargain for was that, even graphing merely a single game (Game 2, which ended up being the only one taken by the Bruins), the process of manually creating the following PowerPoint slides from my original written notes and verifying them as best as I could against the conventional play-by-play sheet consumed several hours over the next few nights. If there is to be a future for this type of diagram -- and I'm eager to see if people think there should be -- we have to hope that someone creates special software for this purpose.

Before discussing the diagram (which actually consists of four stacked images, due to space limitations for any one image), some limitations must be acknowledged: (1) only the teams' plays directly off of serve receipt are shown, not any sets from continuing rallies; (2) the arcs and ultimate results of each play are based on my handwritten notes taken live, as there did not appear to be a way to save a file of the completed video to watch it multiple times; and (3) as a result of the second limitation, I just plain missed some serves (for example, I may have still been writing from the previous play).

OK, here are the diagrams. I would urge readers to click on each frame to enlarge it. The rectangles at the top for each team are meant to symbolize a net, relative to which the arcs of the sets can be seen. The camera was behind UCLA's end, so the charts for the Bruins are a direct reproduction of my hand drawings. For the Longhorns, I initially drew the arcs as they appeared to me and later flipped them horizontally to reflect how they would look from the UT side of the net.

As can be seen, UCLA frequently went to outside (left) hitter Dicey McGraw and she often produced kills for quick side-outs (I did not record the identity of the hitter while watching live, getting the names from the play-by-play sheet for kills and hitting errors). In contrast, Texas seemed to sport a more diversified attack, setting several different hitters at various locations along the net.

Unfortunately, conclusions and inferences are limited by having a record of just one game. Manual production of such diagrams for three or more games per match, even aided by the copy-and-paste functions, would be prohibitively time-consuming. Here's hoping that someone comes up with a way for an observer to enter minimal raw data into a software package that can generate visual diagrams similar to (or even better than) the ones above.

UPDATE:  In response to a note I put on VolleyTalk about this posting, one of the discussants suggested DataVolley as a software program for recording and plotting information from volleyball matches. There apparently is a free version one can download, as well as more elaborate versions for purchase.