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.

May and Walsh in Women's Olympic Beach Final

Misty May-Treanor and Kerri Walsh Jennings have completed their illustrious career as an Olympic beach volleyball duo, winning their third consecutive gold medal, never losing a match in the three Olympiad, and losing only one set (game) the whole time. Below, I review some statistics from their recent triumph in the London Games (referring to them, for simplicity, as May and Walsh).

Box scores were available online (see links below), but some elements readers are used to seeing in U.S. collegiate box scores were missing. These include side-out rates (winning points on the other team's serve) and hitting percentages (with the hitting errors needed to compute percentages). I thus reviewed play-by-play sheets, as needed, to calculate these stats.

In the final against fellow Americans Jen Kessy and April Ross, May and Walsh won points on 26 of 32 balls served by Kessy/Ross, an 81% side-out rate (compared to 62% for Kessy/Ross). Here are the box score and play-by-play sheet from that match.

Hitting percentages, and their component elements, are shown in the following table:

Player	Kills	Errors*	Total Att.	Hitting%
Walsh	11	3	20	.400
May	16	0	24	.667
Kessy	14	6	27	.296
Ross	8	3	17	.294

HTML Tables

*Hand-counted from play-by-play sheet (kills and attempts from box score).

One thing keeping the Kessy/Ross hitting percentage relatively low was May's 15 digs (more digs than the other three players put together).Point-scoring blocks were relatively even (and rare). Walsh had 3, Kessy had 2, and neither of their partners had any.

One statistic available in the Olympic beach box scores, which does not appear in most conventional box scores, is fastest serve speed. Ross and Kessy got the best of this statistic at 77 and 70 kilometers/hour, respectively (about 48 and 43 miles per hour), whereas Walsh and May's fastest serves were at 58 and 51 km/h, respectively (36 and 31 mph). It must be emphasized that these are the players' fastest serves, not averages of all their serves.

2012 Olympics: Men's Indoor Midway Through Pool Play

The defending gold-medalist U.S. men's squad is off to a fast 3-0 start. The first two wins came over Serbia and Germany. Yesterday, as noted in this Los Angeles Times article, "...the U.S. men stormed back to defeat world No. 1 Brazil in four sets. No longer can the defending Olympic champions fly under the radar here."

Below is a screen-capture of men's hitting statistics from the London 2012 website, to which I added the U.S. players' positions. (You may click on the graphics to enlarge them. For additional explanation of the features of the chart, see yesterday's entry on women's play, immediately below the present entry.)

As can be seen, the U.S. currently ranks second in hitting percentage (or efficiency) at .386 (or 38.55%), behind Poland (.414). The Americans' balance is impressive, with four players hitting between .377 and .458.

On the actual London 2012 webpage, you can click on the tabs (shown at the top of the chart) for Service, Block, Dig, etc., to see statistics in those areas. The U.S. ranks first in ace serves per set (2.00) and second in blocks per set (3.30, behind Bulgaria's 3.60).

2012 Olympics: Women's Indoor Midway Through Pool Play

Starting today with women's indoor volleyball, we'll look at how leading teams are doing. The U.S. women are 3-0 in pool play, with four-game wins over South Korea and Brazil, and a sweep over China (albeit with two of the games won by the minimum two-point margin). Below, I've annotated a screen-capture from the London 2012 website on team hitting percentages (you can click on the graphics to enlarge them). On the website, one can click on a given team's red dot with the plus sign (far right-hand side) to bring out the statistics for that team's individual players.

Fans who watch a lot of U.S. college volleyball will probably be more familiar with the terminology of Kills, Errors, and Total Attempts in computing hitting percentage or efficiency: (K - E) / TA. I have no idea what "shots" refer to; some players have more shots than spikes, whereas others have fewer.

Middle blockers usually are among a team's leaders in hitting percentage (because sets to them are more often planned and less often in desperation, relative to outside hitters). The U.S. is no exception, with Foluke Akinradewo (formerly of Stanford) and Christa Harmotto (formerly of Penn State) each hitting around .400 (or 40%). Fellow Nittany Lion alum Megan Hodge (OH) didn't get in until midway through the China match, but did she ever produce!

Among hitters with at least 60 attempts, the hitting-percentage leaders thus far into the Games are:

1. Simona Gioli (Italy) .433
2. Yeon-Koung Kim (S. Korea) .395
3. Nataliya Goncharova (Russian Fed.) .362

Destinee Hooker and Jordan Larson of the U.S. rank fourth and fifth, respectively.

If one goes to the London 2012 website and clicks on the Blocks tab, one finds that the U.S. (2.64) is tied for third (with Italy) in blocks/game (or set), behind Russia (3.90) and China (3.27). Total block attempts are divided into blocks (sending the ball straight down to the opponent's floor for an immediate defensive point), faults (errors such as touching the net), and rebounds (balls sent back to the hitter's side, but kept in play, I assume).

Serve reception has been a problem for the U.S., which currently ranks seventh in this category. By clicking the Reception tab on the London 2012 website, one finds that total attempts are divided into "excellent receptions," faults (errors), and ordinary receptions. Presumably, excellent receptions are those that give the setter multiple viable options of whom to set. Overall success rate is obtained by dividing excellent receptions by total attempts. I think a potentially better statistic would be taking (excellent receptions - faults) and then dividing by total attempts.

***

During the beach match I'm currently watching, Kessy/Ross (U.S.) vs. Liliana/Baquerizo (Spain), one of the NBC announcers alluded to one of the players "averaging the highest speed per serve of the tournament." I don't know if those data are available online, but I'll try to find them.