"What Wins in the Big 12?"

As some long-term readers of this blog may know, I'm a professor at Texas Tech University and I meet occasionally with Red Raider volleyball coach Don Flora to discuss statistical aspects of the sport and find out what kind of analyses he might be interested in at a given time. We last met this past spring and he told me his big question: "What wins in the Big 12?" I took the meaning of the question to be: what combinations of success at hitting, blocking, digging, serving, etc., were associated with winning conference matches in the Big 12? I told Coach Flora I would have something for him, and proceeded to start thinking about how I would conduct analyses.

Now, with the Red Raiders opening their Big 12 portion of the schedule by hosting TCU tonight, I have the fruits of my inquiry. I first created a database of all 72 conference matches played a year ago (despite its name, the Big 12 has only 10 schools and one, Oklahoma State, doesn't field a women's volleyball team; nine teams playing a double round-robin schedule of 16 matches yields 72 total matches). For each team in a given match, I recorded its hitting percentage; blocks, digs, aces, and service errors per game; whether the team won or lost the match; and the number of games it took. Note that with 72 matches and two teams per match, there were 144 records or "stat-lines" possible.

One very basic comparison, among the techniques used by Penn State coach Russ Rose in his 1978 Master's thesis at Nebraska, is to see how often the team that outperformed its opponent on a given statistic won the match. As shown in the following chart, the team with the better hitting percentage in a match won nearly all the time (68 out of 72 matches). Having more blocks, digs, and aces also conferred sizable advantages, but not as powerfully as out-hitting one's opponent.

Hitting does not occur in isolation, however. Some teams might hit well, but not block well; or hit well and not dig well; etc. To probe this issue, I looked at the 144 stat-lines referred to above. To illustrate a stat-line, let's look at Texas Tech's (focal team) when it visited Kansas:

Hitting Percentage = .192; Blocks/Game = .67; Digs/Game = 17.67; Aces/Game = 1.33; Service Errors/Game = 2.67.

I then submitted the 144 stat-lines to a cluster analysis, a technique that attempts to sort cases (or stat-lines) into groups with other similar cases. In other words, the stat-lines within a group should end up relatively similar to each other (i.e., within-group homogeneity), but the different clusters of stat-lines will be dissimilar from each other (i.e., between-group heterogeneity). I obtained 10 clusters, but two of them had only four cases each, which is too small for statistical analysis. Ultimately, our interest will be in seeing the win-loss records of the eight viable clusters, but let's review some basics first. The following graphic illustrates the membership of Cluster 9, as an example (unless you have exceptionally strong eyesight, you'll want to click on the chart to enlarge it).

Seventeen stat-lines ended up in Cluster 9. Each focal team (to whom the stat-line belongs) is highlighted in yellow, with its opponent for the particular match appearing in the second column. Note that many different schools can appear in the same cluster. We're grouping performances, not teams per se. Averages for the complete sample of 144 stat-lines on the various volleyball performance measures are shown in red above each column.

Probably the funnest aspect of conducting cluster analyses is that you get to make up names for the clusters, based on their statistical properties. As seen in the above chart, I named Cluster 9 "Slightly Above-Average Hitting, Below Average Blocking, VERY GOOD DIGGING, HIGH ACES." The digs/game for the 17 cases are shown above in red outline; they range from 17.33 (Baylor, playing at West Virginia) to 20.50 (Texas Tech, hosting Oklahoma). All of these dig statistics exceeded the complete-sample average of 14.92, illustrating why a major part of this cluster's "identity" would consist of "very good digging." Apparently as a result of the digging, the teams in this cluster went 11-6 in the relevant matches, despite hitting only slightly above average in them (the average hitting percentage for the Cluster-9 teams was .223, compared to a complete-sample average of .214).

I've placed all the detailed statistics on the clusters below in an Appendix, for anyone who is interested (once again, please click on the graphic to enlarge it). In the remainder of this posting, I provide brief summaries of the clusters:

Cluster 1. Plagued by below average digging (12.92/game) and a high rate of service errors (2.71, compared to the complete-sample average of 1.73), teams whose stat-lines were in this cluster went 5-12 in the relevant matches.

Cluster 2. Cases in this group displayed great hitting on average (.253, compared to the full-sample mean of .214). They also served aces at a higher-than-average rate (1.93/game, compared to 1.14 for the full sample), but also committed more service errors (2.20/game) than the overall average (1.73). The kind of seemingly powerful/aggressive play exhibited in this cluster produced a 12-6 record.

Cluster 3. Characterized by below-average blocking (1.49/game, compared to the overall average of 2.19) and few aces (.74/game), cases in this cluster went 6-13.

Cluster 4. Though this cluster contained only four cases, the signs of poor play were quite vivid (e.g., .031 average hitting percentage, 1.08 blocks/game, a paltry 8.42 digs/game). Although caution is warranted due to the small size of this cluster, the results are just as one would expect: 0-4.

Cluster 5. This cluster excelled in most every way (.256 hitting percentage, 3.12 blocks/set, 1.36 aces/set with only 1.56 service errors), except for digging (12.02/game). The focal teams went 10-5 in the relevant matches.

Cluster 6. This group combined weak hitting (.130), blocking (1.04/game), and digging (11.78/game), with apparent caution from the service line (only .75 aces and 1.27 service errors, per game). This is not a pattern to emulate, as the teams went 1-9.

Cluster 7. Cases in this cluster hit at the overall average (.214), blocked (2.47) and dug (16.68) somewhat above average, but also showed caution when serving (.89 aces and 1.32 errors, per game). I would have expected these cases to have a winning record, but they didn't, going 15-16.

Cluster 8. Cases here hit (.268) and blocked (3.58) extremely well, rarely served aces (.58/game), and were pretty average on the other metrics. Dominating the net paid off big, as these cases went 8-1.

Cluster 9. Discussed above.

Cluster 10. The other cluster with only four cases, the teams here played great defense (22.31 digs, and 2.60 blocks, per game) and went 4-0.

In conclusion, to answer Coach Flora's question, there are multiple ways to win in the Big 12 (see Clusters 2, 5, and 8), but they all seem to revolve around great hitting. One way to increase the sample size and achieve greater precision in a future study would be to look at win-loss records of games rather than matches. Box scores typically include team hitting percentages by game (to correlate with the winning of games), but blocks, digs, and serving statistics are only reported for the match as a whole. One final issue is that the present analysis tells us nothing about whether the findings are in any way unique to the Big 12; the same relationship between performance metrics and winning might emerge for other conferences, as well. We just don't know.

Appendix

Opening of Women's Conference Play (2014)

It's a busy week in women's college volleyball, with conference play opening up around the country. The Pac 12 schedule has each team starting off league play against its respective traditional/geographic rival. A pair of matches will be held tonight, featuring Cal (8-2 in nonconference) at  No. 1 Stanford (10-0), and No. 20 UCLA (9-2) at No. 9 USC (7-3). Other Pac 12 rivalry matches will be held on Wednesday and Thursday. I already wrote about Stanford's fast start this season, so I will discuss UCLA and USC (among other teams) in the present posting.

The Big 10 (or B1G) begins play with matches Wednesday and Friday. The marquee match-up of the week, not just in the conference, but nationally, features a rematch of last December's national championship tilt between No. 3 Penn State (12-1) and No. 5 Wisconsin (9-1), in Madison. The Nittany Lions' only loss so far this season was in a five-gamer to Stanford, whereas the Badgers' only setback was to Washington, likewise in five games.

The following chart (on which you can click to enlarge) displays information on hitting percentages associated with Penn State, Wisconsin, UCLA, and USC, with each team having its own column.

Looking at PSU in the far left column, for example, we see that the Nittany Lions hit an amazing .395 as a team during nonconference play, with four players, led by middle-blocker Nia Grant (.525), exceeding .350. And this is without last year's seniors Deja McClendon, Ariel Scott, and Katie Slay. Talk about reloading rather than rebuilding! Meanwhile, Penn State has held its opponents to an aggregate .125 hitting percentage. The Nittany Lions' schedule has been moderately tough, including games against two NCAA Sweet Sixteen teams from a year ago -- American and Kansas -- one against traditional power UCLA, and the aforementioned match with Stanford.

Penn State's gaudy hitting percentages derive partly, but certainly not entirely, from matches against weaker teams. As a team, the Nittany Lions hit .442 against UCLA, with four PSU players each hitting .444 or higher. In the Kansas match, PSU came out smoking on serve-receipt, siding out on 100% (10-of-10) of the Jayhawks' Game-1 serves. Grant hit .467 in this match, Aiyana Whitney, .571, and the Lions as a team, .319.

Wisconsin, whose most impressive wins include a sweep of No. 7 Colorado State and a four-game victory over USC, is hitting .296 as a team, with three players at or near .400. Washington held the Badgers to a .178 team hitting percentage, however, outblocking them 22.0 to 7.5. Even on such a bleak hitting night for the Badgers, setter-turned-outside-hitter Courtney Thomas hit .406. Thomas was profiled in the September 11, 2014 issue of Wisconsin's Varsity Magazine. In beating 'SC, Wisconsin's hitting was at a more characteristic .320.

Finally, we have UCLA and USC. The Bruins' two losses were sweeps at the hands of Penn State and, very unexpectedly, Loyola Marymount (now ranked No. 21 in the nation), whereas the Blue and Gold's best wins have been over No. 16 Illinois and No. 25 Hawai'i. Senior Karsta Lowe is pacing the Bruin offense, not only hitting a team-leading .368, but also taking 30.7% of UCLA's spike attempts (367/1195). Younger players Claire Felix (So.) and Olga Strantzali (Fr.) are also contributing well offensively.

USC recently experienced a three-match losing streak, falling at home to Texas A&M (3-2) and Florida (3-0) two weekends ago and then to Wisconsin last week. The Trojans' best win so far, at least in terms of rankings, was at No. 14 Kentucky. In sweeping the Wildcats, 'SC hit .313 while holding UK to .090.

During the losing streak, the Trojans faltered both offensively and defensively. Sophomore Ebony Nwanebu has hit above .300 for 'SC since returning from early-season injury problems, but Florida kept her totally in check (5 kills and 5 errors on 21 attempts, for a .000 evening). Also, though not as dramatically, Wisconsin contained 'SC junior Samantha Bricio (11-5-46, .130). Defensively, during their losing streak, the Trojans let all three opponents exceed .300 in hitting percentage (Aggies, .319; Gators, .303; and Badgers, .320).

Bruins and Trojans, Nittany Lions and Badgers. Pretty good matches to begin play in the nation's major conferences!

Stanford Women Off to Fast Start

Heading into the third weekend of the 2014 women's college season, Stanford has been the most impressive team thus far, dominating the most recent AVCA national poll. The Cardinal (4-0) is by no means the only undefeated team; 10 teams in the Top 25 have perfect records. However, it's the difficulty of Stanford's opposition -- Iowa State (in Ames), Nebraska (in Lincoln), Penn State, and Illinois -- that makes the Cardinal's record so noteworthy.

Another challenge Stanford has overcome thus far is the absence of two of last year's seniors, three-time All-American MB Carly Wopat and All-Pac 12 honorable mention OH Rachel Williams. Let's explore how the Cardinal has adapted offensively. I first compared Stanford's offensive statistics for 2013 and 2014 (the latter statistics, based on only four matches, should of course be taken with caution). As the first graph shows, the Cardinal has not hit at quite as high a clip as last year, while its opponents (in the aggregate) have hit a little better this year than last. Still, this year's Stanford squad has outhit the opposition by a sizable margin (.275 to .186).

As the next chart shows, Williams (807 total spike attempts) and Wopat (606) together took roughly 35% of the Cardinal's 4,005 total swings last year. These two non-returning players from 2013 are shown in different shades of grey below, and the percentages on the second line below each player's name signify the share of the team's total swings they have taken (you can click on the graphics to enlarge them).

That's a lot of offense to replace. Three key Stanford returnees are Brittany Howard (shown in dark cardinal red in the chart), Jordan Burgess (pink), and Inky Ajanaku (bright red). The fact that Burgess's and Ajanaku's line-segments are wider this year than last signifies that they are each taking on a larger share of the Cardinal hitting. Whereas Ajanaku took 12.8% of Stanford's spike attempts last year, she is taking 16.7% of them this year. Burgess's share has gone from 20.3% to 28.1%. (It's pretty common for outside hitters such as Burgess to take more attempts than middle blockers such as Ajanaku.)

Morgan Boukather, who attempted only 48 spikes (around 1% of the team's total) in 2013, is way more active this season, having taken 17.7% of the Cardinal's attempts. Note that Boukather hits from the right side (opposite the setter in the rotation), whereas Williams and Wopat hit, respectively, from the left and middle positions on the front line.

Beyond how frequently an attacker is called upon to hit, there is the question of how effectively she is doing it. Ajanaku has upped her hitting percentage from the already high .438 in 2013 to .474 this season. Burgess, though getting more swings, is not hitting as efficiently this year (a hitting percentage of .194, compared to .294 last year). We'll see if she continues to get so many attempts. Boukather has been a bit up-and-down so far this season, hitting .167 vs. Iowa State, .417 vs. Nebraska, .353 vs. Penn State, and .097 vs. Illinois.

Loyola-Chicago's "Bic" Clicks, as Ramblers Win Men's Title in Offense-Dominated Year

A little over a week ago, Loyola University Chicago won its first NCAA men's volleyball title, as the host Ramblers stopped Stanford in four games. According to Off the Block's preview of the final, "Loyola en route to being ranked No. 1 throughout the majority of the season... had a nation-best .363 attack percentage. In addition, Stanford... was second in the nation with a .336 attack percentage." Further, according to the ESPN-U broadcast, this year's final was the first ever to feature the nation's top two hitting teams.

For this reason, and others, one might dub 2014 the "Year of the Offense" in men's collegiate volleyball. One type of attack in particular, known as the "bic," was instrumental to Loyola's final-match victory. According to many accounts, bic is a contraction of "back-row quick" attack. Another story, recounted in this video, is that the UCLA team's hand-signal for the play many years ago (featuring Jeff Nygaard and Stein Metzger) was a flick of the thumb, as though activating a BIC lighter.

A player in the back row can attack above the net only if he or she leaves the ground behind the 10-foot line. On a bic, the quick-set near the net gives the appearance of being intended for the front-row middle-hitter (who jumps up to fake an attack), which lures the opposing blockers into the air. When the front-row attacker and blocker(s) return to the ground, the back-row attacker can spike the ball unopposed. The term "pipe" is sometimes used interchangeably with "bic," whereas some volleyball experts distinguish the two. The bic is a devastating attack when executed properly, as shown in this video compilation (which uses the pipe terminology). An extended discussion of the bic/pipe is available here from VolleyTalk.

Loyola's Cody Caldwell was the tournament's Most Outstanding Player, recording 20 kills in the final match with only 2 hitting errors on 32 swings, for a .562 hitting percentage (box score). Caldwell produced nine of those kills in Game 1, three via bic plays and six from his normal perch as an outside hitter on the left side of the front row.

The Ramblers, as a team, hit .452 (59-12-104) in the final. Each of their players who took at least one swing hit .333 or better. They did not commit their first hitting error until Game 2, having hit .696 (16-0-23) in Game 1. The Cardinal hit .266 (47-18-109), its lowest percentage since an April 11 match at UC Irvine (.256).

Another noteworthy element of Loyola's title-match performance was its high side-out rate. Overall, the Ramblers won points 75% of the time that Stanford served (88%, 78%, and 81% in Games 1, 3, and 4, respectively, which Loyola won; and 60% in Game 2, which Stanford won). Judging by the Cardinal's serving statistics vs. the Ramblers (5 aces and 14 errors), it seems Stanford tried to serve aggressively to derail Loyola's serve-receipt game. This (apparent) strategy was to little avail, as Loyola sided-out well and all of Stanford's serving errors only raised Loyola's side-out rate further. Below, I have created a frequency distribution of side-out rates Stanford allowed to all of its opponents during the 2014 season, which is available from the Cardinal's match-by-match log (you may click on the graphic to enlarge it).

Loyola's 75% side-out rate actually wasn't the highest Stanford had allowed all season, but it was close (Pepperdine sided-out 76% of the time on March 7, yet the Cardinal still prevailed, 3-1). Both of these side-out rates are considerably higher than what Stanford typically allowed this season (mean = 61%, median and mode = 62%).

***

Besides Loyola and Stanford recording the nation's two highest hitting percentages during the season and both reaching the NCAA title match, one other team led me to think of 2014 as the Year of the Offense, as noted above. In the quarterfinals of the Mountain Pacific Sports Federation tournament, BYU swept USC on the strength of some spectacular hitting by the Cougars. BYU hit .519 in this match, amassing 44 kills, with only 4 errors, on 77 attempts. Four hitting errors is an amazingly low total; two of them resulted from the Trojans stuff-blocking spike attempts back onto the Cougar side of the floor for USC points, whereas BYU hit two balls out of bounds.

BYU had won all three match-ups with Stanford prior to the NCAA tourney (twice in MPSF league play and once in the conference tourney), but the Cardinal turned things around against the Cougars in the NCAA semifinal.

My Vote for Off the Block's Men's Collegiate Server of the Year

I was invited once again this year to vote for the Off the Block men's collegiate volleyball awards. The number of awards has increased and I've been very busy this semester, so I may not have time to conduct statistical analyses for all of the categories. However, I have conducted an analysis to determine my votes for National Server of the Year.

The NCAA men's volleyball statistics site (see links column to the right) provides an aces-per-set statistic. Aces are only one part of judging serving ability, in my view. Someone might be able to amass a large ace total by attempting extremely hard jump serves at every opportunity, but such aggressive serving likely would also lead to a high rate of service errors. Another aspect to consider would be serves that, while not aces, still took the opposing team out of its offensive system. Only aces and service errors are listed in publicly available box scores, however.

What I did, therefore, was find out the top 10 players in serve-per-set (through matches of March 30) via the NCAA site. For these players, I looked at their ace totals (from the NCAA site and the players' respective school athletic websites, using the latter in the event of slight discrepancies) and their service-error totals (available only from the school athletic websites). I then plotted the 10 players' ace and error totals (using this plotting website). As shown in the following graph (which you may click to enlarge), aces and service errors are positively correlated (r = .34), which means that the more aces a player serves, the more errors he serves. The upwardly sloping red line in the graph illustrates the trend.

The best serving is thus depicted in the lower-right corner, highlighted in yellow. Players in this area of the graph served large numbers of aces, but had far fewer errors than would have been expected based on the trend line. The blue arrows below the trend line indicate the top servers, the further down the line drops, the better. Using this approach, my top three servers are:

1. Gonzalo Quiroga (UCLA), who served 47 aces with only 60 service errors. By comparison, Lindenwood's (Missouri) Colin Hackworth also had 47 aces, but committed a whopping 114 errors. Relative to the trend line, Quiroga had roughly 20 fewer service errors than would have been expected.

2. Aaron Russell (Penn State), who recorded 44 aces with 61 errors, who edges out...

3. Loyola's (Chicago) Joseph Smalzer, who aced the opposition 51 times, while committing 71 service errors.

NCAA Women's Final: Penn State Dominates Early, Hangs on Rest of the Way vs. Wisconsin for Title

Penn State won its sixth NCAA women's volleyball title in school history last Saturday night, stopping an amazingly tenacious Wisconsin Badger squad in four games, 25-19, 26-24, 20-25, 25-23.  The Badgers had lost 3-0 to the Nittany Lions both times in Big 10 play this season (although there were some tight "deuce games" in there). Other Wisconsin losses during conference play (e.g., 0-3 to Michigan and 1-3 to Illinois, both in Madison) made it seem even less likely the Badgers would make it to -- and seriously contest -- the national championship match. But, two nights after shocking defending champion Texas, Wisconsin most certainly did make a serious run for the national title!

Penn State dominated for a stretch, spanning roughly the middle of Game 1 to the middle of Game 2 (discussed below). Once Wisconsin woke up in Game 2, however, the Badgers outplayed the Nittany Lions for most of the rest of the evening. PSU seemed to struggle to play on even terms with Wisconsin from the middle of Game 2 onward. Still, the Nittany Lions came through with brief flourishes at the end of Games 2 and 4, which, coupled with Badger miscues at the same time, proved enough for a Penn State championship.

Based on the play-by-play sheet from the championship match, I created the following graphic of the scoring sequences in the four games (Wisconsin in red, Penn State in blue). Unless you have some amazing eyesight, you'll want to click on the graphic to enlarge it.

The Nittany Lions' dominant stretch, to which I alluded above, began right after Wisconsin had cut PSU's Game-1 lead to 16-15. The Nittany Lions proceeded to close out the opening game 25-19 and then dart off to an 11-5 lead in Game 2. During this run, Penn State hit a sizzling .538 according to my unofficial tally (14 kills with no attack errors, on 26 swings).

At this point, it looked as though the Nittany Lions might waltz off with the national title as easily as they had dispatched the hometown Washington Huskies in Thursday night's semifinal romp (25-14, 25-13, 25-16). Seemingly out of nowhere, however, Wisconsin rebounded from its 11-5 Game-2 deficit to outscore Penn State 14-6 and take a 19-17 lead. The Badgers maintained the upper hand to hold a game-point at 24-23, but a missed serve (coming on the heels of a missed Wisconsin serve while holding a 23-22 lead), kept the Lions alive at 24-all. Back-to-back kills by PSU's Ariel Scott then put Game 2 in the Lions' column, 26-24. Scott, a senior right-side hitter, was Penn State's most effective attacker from the "pins" (outside, by the antennae that define the width of the court), with a .294 evening (21 kills and 6 errors, on 51 attempts).

One likely factor in Wisconsin's turnaround -- and near-win -- in Game 2 was the disappearance of the Penn State block. The Nittany Lions took a 2-1 lead in Game 2 on a stuff by senior middle-blocker Katie Slay and then did not score again via the block until well into Game 3, when Nia Grant and Megan Courtney teamed up to give the Lions a 9-8 lead. (Wisconsin outblocked Penn State on the evening, 14 to 9.) The Badgers pulled off a 6-0 run in Game 3 to take a 17-12 lead and closed things out 25-20.

Jumping ahead to Game 4, Wisconsin used a pair of 4-0 runs to help take a 23-20 lead, and a decisive fifth game seemed all but certain. However, reprising their late comeback against Stanford in the regional finals, the Nittany Lions scored the final five points against the Badgers to take Game 4 (25-23), the match, and the championship. PSU setter Micha Hancock put her signature on the win with two service aces down the stretch.

Two other trends defined the match in my mind. First was the "Battle of the Middles," featuring Penn State's Slay and Wisconsin junior Dominique Thompson. When in the front row, Slay typically remains in the center to hit quick sets. Thompson, in contrast, frequently runs the slide play, in which a middle hitter darts out to the right-hand side in an attempt to hit against one blocker instead of two. Thompson, who ended up hitting .314 on the evening (16-5-35), compiled four kills in rapid succession on slide plays in Game 3, as the Badgers rallied from a 5-3 deficit to even the score at 7-7. Here's a graph of Slay and Thompson's kills per game.

Slay's hitting line for the match was an excellent .481 (14-1-27), helped by an almost complete absence of hitting errors. As the graph shows, however, 10 of her 14 kills occurred in the first two games (6 in Game 1 and 4 in Game 2). Thompson, in contrast, appeared to peak in Games 2 and 3, with only a single kill in Game 4.

The second trend that struck me was how, relative to the Nittany Lions, the Badgers didn't even obtain that many of their points from kills. Instead, using what I call a "grind-it-out" style, Wisconsin kept a lot of balls alive and capitalized on Penn State errors of various kinds (e.g., service errors; ball-handling errors; and hitting errors, which include balls spiked out of bounds and those blocked back to the floor on the hitter's side of the court for an immediate defensive point). In pie-chart form, the following graphs (again, which you can click to enlarge) show how each team got its points (the Lions scored a total of 96 points on the evening, the Badgers, 91).

The Badgers amassed 45 kills*, accounting for just under half of their 91 total points. The Nittany Lions, in contrast, pounded out 61 kills, accounting for nearly two-thirds of their 96 points. However, Wisconsin received larger shares of its respective points than did Penn State from blocking, opponent spikes hit out of bounds (calculated from the box score as opponent hitting errors minus a team's own blocks), opponent serving errors, and opponent ball-handling errors.

Most observers probably would not have expected this relatively high rate of errors from a senior- and junior-laden Penn State squad. Nevertheless, the Nittany Lions' moxie and offensive firepower were enough to earn them yet another crown.

---

*The box score shows 46 kills for Wisconsin and 62 for Penn State. However, it also shows 1 blocking error for each team, which typically involves a blocker touching the net. According to AVCA scorekeeping guidelines, “if there is a block error, there MUST be a kill for the other team.” In other words, once a net-violation or other blocking error is called, the play must also have a kill recorded, as a technical requirement. To prevent the disposition of any play from being counted twice, therefore, I counted block errors without their accompanying kills. The AVCA guidelines ("Making Volleyball Statistics Simple") are linked in the right-hand column of this page.

Statistical Notes Heading into Women's Final Four (2013)

With this year's NCAA women's Final Four getting underway Thursday night in Seattle, today's posting offers some statistical observations. The two semifinal match-ups feature defending champion Texas vs. upstart Wisconsin, and Penn State vs. hometown favorite Washington.

Wisconsin, a one-time power that had missed the NCAA tourney from 2008 through 2012, is now back in an ascendant mode under new coach Kelly Sheffield. Seeded 12th nationally, the Badgers benefited in their part of the bracket from the fact that SEC teams Missouri (No. 4 seed) and Florida (No. 5 seed) were Paper Tigers and Gators, respectively. Having said that, Wisconsin may be the kind of team that can give Texas a tough match (like Michigan in last year's semifinal).

A year ago, I developed a statistic that attempts to measure teams' "grind-it-out" tendencies. To me a grind-it-out team is one that lacks spikers with pulverizing power, but digs opponents' attacks well and avoids hitting errors of its own. A grind-it-out team may need two or more spike attempts within the same rally to finally win a point. The calculation of a team's grind-it-out statistic is quite simple: the number of spike attempts it takes in a match, divided by total points in the match (to control for match length). A power team, which puts away a lot of spikes immediately, will (usually) have few total swings in a match and thus a low grind-it-out score. Last year, Michigan scored highly on the grind-it-out measure.

This year's Badgers, who ranked sixth in the Big 10 in hitting percentage, but first in digs, seem to me to be a grind-it-out team. Below are my calculations of every team's match-specific grind-it-out statistics within the Sweet Sixteen and Elite Eight. Indeed, Wisconsin is the only team to exceed .90 in both of its matches (vs. Florida State; and Purdue). It must be noted that even a power team, such as Penn State, which led the Big 10 in hitting percentage, can find itself in grind-it-out matches, as the Nittany Lions did in their very tight regional final against Stanford. Similarly, Washington (vs. Kansas) and USC (vs. BYU) didn't need many swings in their respective regional semifinals, but sure did in their epic regional final. Wisconsin is the only team shown below to record high grind-it-out numbers while winning its matches relatively easily (each 3-1).

"GRIND-IT-OUT" PROPENSITIES

Neb (vs. Tex): 139 total points, Neb 142 TA (1.02)
PennSt (vs. Stan): 217 total points, PSU 219 TA (1.01)
Wash (vs. USC): 224 total points, Wash 217 TA (.97)
Min (vs. Stan): 149 total points, Min 142 TA (.95)
Wisc (vs. FlaSt): 175 total points, Wisc 165 TA (.94)
Wisc (vs. Pur): 184 total points, Wisc 172 TA (.93)
MichSt (vs. PennSt): 179 total points, MSU 167 TA (.93)
USC (vs. Wash): 224 total points, USC 209 TA (.93)
Amer (vs. Tex): 175 total points, Amer 159 TA (.91)

USD (vs. Neb):  140 total points, USD 125 TA (.89)
Tex (vs. Neb): 139 total points, Tex 124 TA (.89)
Stan (vs. Min): 149 total points, Stan 132 TA (.89)
BYU (vs. USC): 187 total points, BYU 166 TA (.89)
Pur (vs. Wis): 184 total points, Pur 162 TA (.88)
Tex (vs. Amer): 175 total points, Tex 148 TA (.85)
FlaSt (vs. Wisc): 175 total points, FSU 149 TA (.85)
PennSt (vs. MichSt): 179 total points, PSU 152 TA (.85)
Kan (vs. Wash): 131 total points, Kan 110 TA (.84)
USC (vs. BYU): 187 total points,  USC 155 TA (.83)
Stan (vs. PennSt): 217 total points, Stan 181 TA (.83)
Ill (vs. Pur): 143 total points, Ill 117 TA (.82)
Pur (vs. Ill): 143 total points, Pur 116 TA (.81)
Neb (vs. USD):  140 total points, Neb 114 TA (.81)

Wash (vs. Kan): 131 total points, Wash 96 TA (.73)

***

Serving may also play a large role in determining the next national champion. On Monday's broadcast of "The Net Live," a volleyball talk show on Internet radio, the coaches of the Final Four teams were each interviewed (archived broadcasts can be accessed via the link to Volleyball Magazine in the right-hand column of this page). Penn State's Russ Rose, a noted statistical guru, pointed out that Washington, his team's Thursday night opponent, achieved an unusually strong ratio this year of aces to service errors. Serving aggressively will usually drive up both a team's ace and error numbers, but the Huskies managed to keep their miscues relatively in check. Here are the ratios, as I calculated them for each Final Four team.

Team................Aces..........Service Errors...............Ratio

Washington.......201........................221....................... .91

Penn State........152........................243....................... .63

Texas...............117........................226....................... .52

Wisconsin.........162........................355....................... .46

Volleyblog Seattle has a more elaborate look at the Final Four teams' serving prospects.

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Finally, I wanted to acknowledge the amazing performance of USC frosh right-side hitter Ebony Nwanebu against Washington. Nwanebu recorded 30 kills and no errors on 53 spike attempts, for a .566 hitting percentage (box score). She took 53 swings and never once hit the ball out of bounds or into the net, or had the Huskies block the ball back onto the Trojan side of the floor for a point. Each time Nwanebu was set, she either got a kill or the ball was kept in play by U-Dub. Such a high hitting percentage on that many swings is quite unusual.