Oregon Sweep of USC Highlights Weekend

Two nights after defeating No. 1 UCLA to move to 13-0 on the season, the USC women came crashing back to earth. Oregon came into USC's Galen Center on Friday night and swept the Trojans, 25-19, 25-15, 25-23. The Ducks, now 11-0, must be considered among the nation's elite at this point in the season.

Against the Bruins on Wednesday, the Trojans seemingly sided-out at will, winning points on 71% of UCLA's serves. On Friday, the Ducks took siding-out to a new level against the Trojans, reaching 77% effectiveness. In the first two games especially, Oregon's serve-return performance was spectacular, attaining side-out rates of 84 and 81 percent (box score).

The Ducks outhit the Trojans, .357-.189. Three Oregon players recorded superb hitting nights: outside hitter Liz Brenner, .577 (16 kills, 1 error, 26 attempts); OH Alaina Bergsma, .438 (18-4-32); and middle blocker Ariana Williams, .375 (8-2-16). UO attempted 115 spikes, only 14 of which resulted in errors (6 hit out-of-bounds, 8 blocked by USC for immediate Trojan points). Of Oregon's 101 non-error attacks, USC dug up only 37 of them (37%). In contrast, 'SC dug much more effectively against UCLA, retrieving 46% of the Bruins' non-error attacks [58/(145 - 20)].

***

Across town on Friday, UCLA came back from its USC loss to rout Oregon State, 25-14, 25-18, 25-17. Clearly, the Beavers were not the same team that had beaten then-No. 2 Penn State on September 8.

Against Oregon State, UCLA outside hitters Tabi Love and Rachael Kidder recorded nearly identical hitting lines. Each hit .333 on 27 attempts, with Love amassing 12 kills and 3 errors, and Kidder, 14 kills and 5 errors.

No. 2 USC defeats No. 1 UCLA

No. 2 USC defeated No. 1 UCLA last night, 28-26, 25-20, 24-26, 25-17. The match kicked-off a series of Wednesday-night telecasts on ESPN-U.

In reading the game article on the Trojan athletic website, I was impressed with the writer's attention to putting statistical figures in context. Many volleyball articles mention the players who had the most kills. However, merely stating the number of kills does not tell us how many attempts were required or how many errors the same hitter also had. As shown in the following brief excerpt from the article, the writer always made sure to accompany players' kill totals with their numbers of errors and attempts, and hitting percentage on the night.

USC women's volleyball senior opposite Katie Fuller knocked down 21 kills (5e, 36att) and posted a .444 hitting percentage... Fuller's 21 kills matched a career high, but the Trojans got a major contribution from freshman outside hitter Samantha Bricio who also matched her career mark with 19 kills (6e, 50att, .260) to go with three service aces (23.0 points). Sophomore setter Hayley Crone notched a new career best with 53 assists and added four kills on second contact (0e, 5att, .800) and six digs.

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UCLA's performance was hardly terrible. The Bruins sided-out, for example, at respectable levels of 62 and 63% in the first two games/sets, despite losing them both. The problem was that the Trojans did even better (66 and 80%, respectively). In none of the four games was USC's side-out rate lower than 65% (box score).

The Trojans also outhit the Bruins for the match, .311 to .297. For UCLA, Becca Strehlow's quick-sets to Mariana Aquino were working early. Aquino hit .385 on the night, with an 8-3-13 line for kills, errors, and attempts. As seen in the side-out numbers, the UCLA serve didn't challenge 'SC much. The Bruins' 11 service errors (to only 1 ace) suggest UCLA may have adopted a high-risk serving strategy to combat the Trojans' effective serve-receipt.

Also last night (and televised on Fox Sports' new Pac 12 Network), Stanford swept Cal.

Minnesota and Texas Split Matches in Austin

There's a new excitement surrounding University of Minnesota volleyball, with Hugh McCutcheon, who guided the U.S. men and women to, respectively, gold and silver in the last two Olympiad, taking over for retired coach Mike Hebert. The No. 14 Golden Gophers traveled down to Texas this past week for matches Thursday and Friday nights against the No. 4 Longhorns. The visit reciprocated a Texas trip to the Twin Cities last year, when the Gophers swept both matches.

This year, it was a split, with each match going four games (or sets). The Gophers won the opener by scores of 25-22, 25-13, 27-29, 29-27, whereas the Longhorns bounced back the following evening to win 25-20, 20-25, 25-22,  25-21. Here are the box scores for Thursday and Friday nights. I like these two-match series, as the added data provide a more reliable picture of how the teams are doing than the usual single match.

In Thursday night's Gopher win, offensive balance was the key. The following table shows the hitting percentages achieved by five leading Minnesota spikers, with their numbers of swings in parentheses. Two newcomers complement the established Gopher trio of Katherine Harms (senior outside hitter), Tori Dixon (junior middle blocker), and Ashley Wittman (junior OH). Daly Santana is a frosh OH from Puerto Rico, whereas Dana Knudsen (MB) is a senior transfer from Santa Clara. As the table shows, all five hit .276 or better in the series opener at Texas; Knudsen had 14 attempts, but all the others had 25 or more.

	Thursday	Friday
Tori Dixon	.286 (28)	.320 (25)
Katherine Harms	.319 (47)	.357 (28)
Dana Knudsen	.286 (14)	.000 (12)
Daly Santana	.276 (29)	.030 (33)
Ashley Wittman	.400 (25)	.000 (34)

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Friday night was a very different story, however. Dixon and Harms continued to hit well for the Gophers, but the Longhorns shut down Knudsen, Santana, and Wittman. One apparent reason for this is that Texas dominated the blocking Friday, by a 15.5-7 margin (the teams were essentially even on Thursday night, Texas 12, Minnesota 11.5).

For Texas offensively, it was the Haley and Bailey show, as outside hitters Haley Eckerman and Bailey Webster took the largest shares of the team's swings. The problem was that only one of the two could get untracked each night, Webster hitting .368 on Thursday and Eckerman, .289, on Friday.  The following table shows the same statistics as in the earlier one for Minnesota.

	Thursday	Friday
Haley Eckerman	.080 (50)	.289 (45)
Bailey Webster	.368 (38)	.100 (50)

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On Thursday, Eckerman and Webster took 60% of the Horns' hitting attempts (88/147), with MB Khat Bell hitting .292 on 24 swings. Bell was held out on Friday; she is recovering from a torn ACL last season and Coach Jerritt Elliott evidently thought it prudent to rest Bell rather than have her play on back-to-back nights. Eckerman and Webster took on more of the hitting load Friday night, their combined 95 swings constituting 71% of the Longhorns' 134 total attempts. 

Another apparent factor in Minnesota's Thursday win was the Gophers' digging advantage. Looking at the proportion of possible opponent attacks dug up (i.e., digs divided by opponents' non-error attacks), we see the following:

Thursday
Minnesota 67 digs / (Texas 147 spike attempts -  22 hitting errors) = .536
Texas 46 digs / (Minnesota 147 spike attempts - 23 hitting errors) = .371

Friday
Minnesota 51 digs / (Texas 134 spike attempts - 20 hitting errors) = .447
Texas 54 digs / (Minnesota 141 spike attempts - 29 hitting errors) = .482

On Thursday, the Gophers dug up more than half of the Longhorn hitting attempts that were not hit out-of-bounds or blocked for immediate Minnesota points. The following night, Minnesota was just under 50% in this department. Texas performed well in this area Friday night (48%), but not Thursday.

Texas coach Elliott did some shifting of players from their usual positions on Thursday, and then of course went without Bell on Friday. Perhaps the Longhorns will show greater match-to-match consistency as they settle into a regular lineup later in the season (if they do).

Texas Tech Coaches Share Team's Internal Statistics

I am pleased to announce a major new development here at VolleyMetrics, involving a faculty-athletics collaboration at Texas Tech University. Head Coach Don Flora and Assistant Coach Jojit Coronel have agreed to share with me the team's internal statistical data, which are recorded with Data Volley software.

Today, I will present my first statistical analysis of these data. At this point, the Red Raiders have completed roughly half of their non-conference schedule, compiling a perfect 8-0 record thus far. On Tuesday, the Raider coaches sent me a batch of printouts. Each printout covers one match. I see one of my roles, therefore, as testing for trends across multiple matches. Although each printout contains information on Texas Tech and the team's opponent, I am only using data on the Red Raiders today.

For starters, I will examine the side-out rates and points won on serve for each of Texas Tech's six rotations on the court, to see if there are any noteworthy statistical patterns after eight matches. In Rotations 1, 2, and 3, the setter is in the back row (right, center, and left), giving the team three hitters up front. When the setter is in the front row (Rotations 4, 5, and 6), the team only has two hitters. Having three available hitters presumably is more advantageous than having two, in terms of offensive options and greater potential to keep the blockers guessing. Coach Coronel suggested an alternative line of argument to me, however. Three hitters can lead to a "cluttered" front row, with each having less room to maneuver.

Back in 2009, I attempted to compare the effectiveness of Texas Tech's six rotations in the team's home match vs. Texas A&M, compiling statistics myself from the stands. That analysis obviously included a lot less data than what I have available this season, but some readers may be interested in the diagrams I created back then of the players in each rotation (click here for my 2009 analysis).

With these ideas in mind, I used the 2012 data to look first at the average side-out rates of the six rotations. These mean levels are shown in the following graph as red circles.

As can be seen, Rotation 5 achieved the highest side-out rate of the rotations (77%), whereas most of the other rotations' side-out rates were around 60%, give or take a few percentage points. However, as shown via the little grey shapes above and below each red circle, each rotation's success at siding-out showed a great deal of scatter across the team's matches. Due to this scatter and the small number of matches, the differences between the six means were not statistically significant. However, as data from more matches pour in, we'll see if Rotation 5 continues to side-out the best.

The other statistic I examined is the counterpart to siding-out, namely how proficiently the team wins points on its own serve in the different rotations. Of course, if whoever serves in a given rotation is a great server and the team garners a sizable share of points through aces or serves that take the opponent out of its offense, the location of the other players in the rotation doesn't really matter. On all other plays, however, the rotation could well matter (e.g., who is in the front row to try to block the opponent's attack).

The mean percentages of points won on Texas Tech's own serve in Rotations 1 through 6, respectively, across the eight matches, are as follows: 44%, 38%, 45%, 53%, 38%, and 50%. Again, there are no statistically significant differences between these values. On a purely descriptive basis, however, Rotation 5 was the team's least effective when serving, in contrast to its performance on serve-receipt.

Over the next few weeks, I'll be presenting additional new analyses from Texas Tech's first eight matches. Then, as I receive new sets of printouts, I'll analyze those. I hope you'll enjoy this exciting new feature on VolleyMetrics.

Women's College Round-Up Aug. 31-Sept. 1, 2012

No. 4 Penn State hosted two other top-10 teams in the AVCA poll, No. 2 Texas and No. 9 Stanford, along with No. 18 Florida, this weekend in the Big Four Classic.

The Nittany Lions came away with the championship, outlasting Stanford in a five-set match Friday and then sweeping Texas on Saturday.

In the Penn State-Stanford match, four of the five games were decided by the minimum two points (the Lions' score is listed first for each game; 25-13, 25-27, 25-23, 23-25, 15-13). It was not a big-hitting match, on the whole, as the Nittany Lions bested the Cardinal .231 to .173 (box score). There were some notable individual hitting percentages, however.

For Penn State, sophomore outside-hitter Nia Grant (9-0-13, .692) and junior middle-blocker Katie Slay (10-0-21, .476) each turned in error-free hitting performances against the Cardinal. Teammate Ariel Scott (junior right-side hitter) took a whopping 85 swings (39% of the team’s 216 attack attempts), registering a .165 percentage. Soph OH Morgan Boukather paced Stanford (11-2-21, .429).

In the Penn State-Texas match, the Longhorns' side-out percentages in the three games -- 63%, 54%, and 75% -- really don't look like those of a team that was swept. The only problem is that the Lions' side-out rates were 72%, 63%, and 86% (box score). Frosh OH Megan Courtney aided Penn State with an (8-0-15, .533) hitting line.

Florida, which lost both of its matches (to Texas and Stanford), has lost a lot of top seniors from last year, including setter-hitter Kelly Murphy. However, one player who may lead the Gators back into elite company is frosh OH Ziva Recek from Slovenia. She hit .475 against the Horns and .278 against the Cardinal.

***

Yesterday, I attended Texas Tech's match against Northwestern State (Louisiana), as the Red Raiders held off a surprisingly tough Demon squad in four games to go 7-0 on the season.

Making the headlines for Texas Tech at this early point in the season is libero Rachel Brummitt, a sophomore transfer from Radford. She is averaging 4.92 digs per game, second in the Big 12 to Kansas's Brianne Riley, whose average is 5.57.

A dig is defined as "when a player receives an attacked ball and keeps the ball in play" (see  the AVCA's "Making Volleyball Statistics Simple" in the links section to the right). After a ball is dug, a few different things can happen, such as the digging team running its offense "in system" culminating in a spike attempt or the digging team having to bump the ball back over to the other team, which gets a "free ball" attempt.

During the recent Olympics, I was impressed by how the Italian men were able to dig a lot of spike attempts by the U.S. and transition quickly into running their offense. Knowing ahead of time about Brummitt's digging prowess, I decided to keep an eye out for digs leading to an in-system attack, a new statistic I'm tentatively calling DLISA. The way I'm conceiving DLISA in my mind, the opponent's attack must be hard-hit and/or well-placed in order for the digger to be eligible for a DLISA.

I know there already are grading systems in place, in which raters can study videotapes and assign a quality score on each dig, for example, from 0 (passing error) to 5 (pass enabling multiple attack options). However, I'm simply looking for a way to augment the "dig" statistic reported in box scores with an extra designation for really good digs, hence DLISA.

Brummitt was credited in the box score with 21 digs vs. Northwestern State. I (unofficially) credited her with 4 DLISA. I'll have to watch a lot more matches to get an idea of what a high number of DLISA digs is for one player, but yesterday's match is a start!

A scientific ideal is that two judges, watching the same play, will have a high level of agreement in awarding credit for a particular kind of play (such as a DLISA). Scoring decisions are sometimes made unilaterally, however. In baseball, for example, there is one "official scorer" who decides between hit and error on a play.

Opening Weekend: Nebraska-UCLA Match Analysis

No. 4 Nebraska, playing at home, edged defending champion and preseason No. 1 UCLA in five games, as the women's college season opened this weekend. Both teams return most of their top players from a year ago, but each has some niches to fill.

I have proposed hitting allocation (the percentage of a team's spike attempts taken by each player) as one way to characterize a team's offense. Whether one player takes an enormous share of a team's attempts, or a team has two or three hitters who lead the team with roughly the same share of attempts, tells us something. So does the way a team changes from year to year in hitting allocation.

The Cornhuskers no longer have middle-blocker Brooke Delano, who was a senior in 2011. She led the team in hitting percentage a year ago, with a .331 mark. Among UCLA's seniors from last year were setter Lauren van Orden and MB Sara Sage, who hit .386 last season, albeit on only 184 attempts.

Let's look first at Nebraska's hitting last Saturday night vs. UCLA, which is shown in the bottom row of the following table. (You can click on the graphics to enlarge them.) For each player, the top number (with a % sign) shows the share of the team's spike attempts taken by that player, with the player's hitting percentage shown beneath in parentheses. Under the heading "TOTAL," we see that the Huskers, as a team, took 184 swings and hit .207 against the Bruins. For comparison purposes, I use Nebraska's match last year vs. Illinois (Oct. 22, 2011), which may have been the Huskers' finest performance of the season.

As can be seen in the table, the four Nebraska hitters who played in both last year's match against Illinois and this year's against UCLA (excluding those with very few spike attempts) were fairly consistent across both matches in their share of the Cornhuskers' swings. Gina Mancuso took nearly identical percentages of Nebraska's swings in the two matches (27.1% vs. Illinois and 28.8% vs. UCLA). No player diverged by more than 5.5 percentage points. Meghan Haggerty, who had a late (June 2012) change of heart in switching from Wisconsin to Nebraska, nicely filled Delano's niche of taking roughly 15% of Nebraska's spike attempts.

The next chart shows a similar comparison for UCLA. As some readers may have anticipated, one reason for choosing Illinois as Nebraska's 2011 comparison match is that the Bruins also played Illinois last year -- in the national championship match!

Though it's a little risky to generalize too much from only two matches, it looks like UCLA changed up its attack somewhat from last year's Illinois match to this weekend's showdown with Nebraska. I have highlighted in a darker shade of blue outside hitters Rachael Kidder and Tabi Love, who led last year's squad in hitting attempts (Kidder with 1,420 and Love with 940).

UCLA's game plan, as executed by frosh setter Becca Strehlow, apparently called for Kidder to relinquish some of her hitting attempts, down from 39.7% of Bruin swings vs. Illinois to 26.1% vs. Nebraska. Meanwhile, Love took on a heavier load, up from 19.0% of the team's attempts against the Illini to 29.0% against the Huskers. The extra work didn't hurt Love, either, as she upped her hitting percentage from slightly below .300 to slightly above it. OH Karsta Lowe also looked to be taking on a larger role in the Bruins' offense this year.

Not only do the Huskers appear to be steady in their hitting allocations. They were extremely consistent in their side-out percentages (winning points on the opponent's serve) in the five games vs. UCLA. As shown in the following graph, Nebraska's side-out rate never deviated more than 6 percentage points above or below 60% (no more than +/- 2 percentage points in the final three games). The Bruins, on the other hand, were all over the place in their side-out percentages.

Again, we shouldn't make too much out of one match, played during the first weekend of the season. It will be interesting, though, to see if UCLA and Nebraska continue to display the patterns discussed above further into the season.

Year-to-Year Consistency of Hitting Percentage in Top Women's College Spikers

It's time to put the Olympics behind us and start thinking about the new season of women's indoor college volleyball, which begins this weekend. In this posting, I attempt to answer, for women's college volleyball, a question raised in the book Stumbling on Wins by economists David Berri and Martin Schmidt.

Berri and Schmidt, drawing upon the earlier writings of J.C. Bradbury, argue that there are two dimensions on which to evaluate the importance of skills in sports:

• Do players tend to exhibit them with consistency? In baseball, for example, are pitchers who lead their league one year in the proportion of opposing batters they strike out likely also to lead the league in this category the next year? In football, are running backs who amass a high yards-per-carry average one year likely to do the same the next year? As Berri and Schmidt characterize Bradbury's original point, "a measure that's consistent over time is probably measuring a skill. In contrast, inconsistent metrics are probably capturing luck or the impact of teammates" (p. 34).
  
• Do skills tend to correlate with winning? Do basketball teams with high three-point shooting percentages win more than teams low on this metric? Do tennis players who record aces on high percentages of their serve attempts win matches more often than do players with low ace rates?

(As an aside, readers with some background in social-science research may recognize the parallel between the above two criteria and the terms reliability and validity, respectively.)

The present entry will concentrate only on the first issue, that of consistency, as applied to hitting percentage in volleyball. I have looked at the second question, that of connection to winning, previously (here and here). In a nutshell, I've selected a group of hitters (middle and outside/opposite), observed their hitting percentages in 2010 and 2011, and conducted analyses of correlation between the two. In other words, did the players with the highest hitting percentages in 2010 also rank highly on this measure in 2011, and did those with low hitting percentages in 2010 also exhibit relatively low proficiency in 2011?

The players in the analyses are not a random sampling of all hitters in women's collegiate volleyball. Rather, they are leading hitters (in terms of hitting percentages and share of their team's spike attempts taken) I featured a year ago in my 2011 previews of the Big 10, Big 12, Pac 12, and other conferences.

The data set gleaned from these previews originally consisted of 87 players. Five apparently played very little or not at all in 2011, due to injury, coach's decision, or player's decision. Many players were listed on their respective team's roster as playing both middle-blocker and outside/opposite hitter. I examined game articles involving these players to see if they were identified with one position more than another. For seven players, their predominant position could not be determined, so they were omitted from analyses comparing middle and outside/opposite hitters. Players listed as setters were classified as outside/opposite hitters.

Before we look at the results, let's review the correlation coefficient statistic, which measures how well two variables (in this case, hitting percentage in 2010 and in 2011) follow the same trend. A positive correlation refers to higher scores on one variable going along with higher scores on the other, and lower scores on one going along with lower scores on the other (i.e., like with like). The maximum value for a positive correlation is +1.00. A correlation of .00 reflects absolutely no relationship between the two variables; if someone has a high score on the first variable, it tells us nothing about whether that person scores high or low on the second variable.

When each spiker's hitting percentages for 2010 and 2011 are plotted against each other (with each player represented by a dot), a positive correlation will be revealed by an upwardly trending "best fit line" (the line that comes as close to as many data points as possible). 

(There is also such a thing as a negative correlation, where high scores on the first variable are associated  systematically with low scores on the second, and vice-versa.There may be a small number of volleyball spikers with extremely high hitting percentages one year and low percentages the other year, but it is unlikely such a trend would broadly characterize the entire sample of players.)

First, let's look at separate graphs for hitters whose teams had different vs. the same setters in 2010 and 2011 (if a team used a two-setter offense and only one setter returned, such a team was classified as having the same setter). I expected the correlation (i.e., year-to-year continuity of hitting percentages) to be lower when hitters played with different setters in the two years than when they played with the same setter. The former situation would require an adjustment period for hitters to get used to how the new setter delivered the ball, whereas the latter would not.

As shown in the graphs below (which you can click to enlarge), hitters who faced a change in setters from 2010 to 2011 (left graph) exhibited a slightly smaller (flatter) correlation than hitters who had the benefit of the same setter in both years (right graph). Numerically, the correlation between hitting percentage in 2010 and in 2011 was .58 with different setters and .65 when each hitter had the same setter in both years.

The data were also split by the hitters' position. Before looking at year-to-year continuity, it should be noted that middle-blockers tend to have higher hitting percentages than outside and opposite hitters (who are positioned on the left- and right-hand sides of the front row on the court, respectively). The conventional wisdom is that outside hitters get a lot of desperation sets, whereas middle-blockers are set more as part of structured plays. In the present sample, middles hit better on average than outsides in both 2010 (.325 vs. .265) and 2011 (.308 vs .254). Because of these mean differences, we see in the graphs below that the data points for middles (left graph) are further along the horizontal and vertical axes than is the case for the outside hitters (right graph)

Still, the upward slopes of the best-fitting lines are very similar in the two graphs. The correlations between 2010 and 2011 hitting percentages were .50 for middle-blockers and .57 for outside/opposite hitters. Among the outside/opposite hitters, the data points for virtually all players were close to the best-fit line, with the exception of Sha'Dare McNeal (Texas), who followed up her .300 hitting percentage in 2010 with a .425 in 2011. Such an improvement thus exceeded what would be typical for outside/opposite hitters.

Putting aside the statistical calculations for the moment, practical implications can be gleaned simply from the graphs. For the outside/opposite hitters in the analysis, we can say that knowing a player's hitting percentage one year (2010) tells us within a fairly narrow range the hitting percentage the player is likely to achieve the next year (2011). For example, outside/opposite hitters who recorded a hitting percentage of approximately .200 in 2010 all hit between roughly .150-.250 the next year. Those who hit around .300 in 2010 nearly all hit between .200-.325 the next year.

Middle-blockers, for whatever reason, had wider ranges in estimating their 2011 hitting percentages from what they hit in 2010. For example, a middle-blocker who hit around .300 in 2010 would have been expected from the graph to hit somewhere between .200-.400 in 2011.

The sample sizes for these analyses were small, of course, so additional research with larger samples is necessary to corroborate these findings. The present analyses at least provide estimates of the range of possible hitting percentages a player is likely to attain in an upcoming season, based on what she hit the previous season.

***

Here is a link to the 2012 AVCA preseason coaches' poll. Defending NCAA champion UCLA received the overwhelming share of the first-place votes. Following in positions 2 through 5 are "usual suspects" Texas, Penn State, Nebraska, and USC.

The marquee match of this opening weekend will take place Saturday night, with the Cornhuskers hosting the Bruins.