Analysis of Last Night's Thrilling Penn State-Texas NCAA Women's Final

Last night's NCAA women's championship match between Penn State and Texas was truly, pardon the cliche, one for the ages. There was the historical aspect -- the Nittany Lions winning their 102nd straight match and third straight national title. There was the aspect of the teams' senior leaders trying to will their respective squads to victory -- the Longhorns' Destinee Hooker dominant for most of the match, with the Penn State pair of outside hitter Megan Hodge and setter Alisha Glass getting by with a little help from their friends. And there was the comeback aspect -- Penn State having trailed two games to none -- and the fact of how closely the teams ultimately were matched. There were 10 tie scores in the decisive fifth game, which was won by the Nittany Lions 15-13.

Here at VolleyMetrics, however, our job is statistical analysis. The role of statistics in the sport arguably reached a new milestone during last night's telecast when Penn State coach Russ Rose, in a brief interview heading to the locker room for the "halftime" break between Games 2 and 3, noted the following (as I transcribed from ESPN360.com's archived video of the match):

"...five of [Hooker's] 15 or 16 kills were on tips to the right front on [Penn State's] Blair [Brown]... [Hooker] hits the ball so high, you've got to make some better adjustments."

As we'll explore through the numbers, the one decisive factor for the Nittany Lions can perhaps be distilled to one word: balance. Simply put, Penn State seemed to have a lot more options in the long run than did Texas. Before we begin the major statistical analyses, I must express great thanks to the NCAA for providing a "deluxe" 11-page box score, which provides not only overall match statistics, but also statistics and play-by-play sheets for each game (set). The graphics I've created (shown below) would have been a lot more difficult to produce without the elaborate box score.

In our first graph, we see that Hooker clearly got the best of Hodge in individual hitting statistics.


For readers relatively new to volleyball who aren't familiar with the formula for hitting percentage, ESPN was kind enough to provide an example of a calculation, midway through the match.


Longhorn coach Jerritt Elliott had an interesting, possibly surprising, observation after the match, as reported in the Daily Texan (second linked article above):

“I thought we set [Hooker] a little bit too much,” Elliott said. “We kind of got out of our rhythm a little bit. But for us to win, Destinee has to have a big game. She’s carried us. She performed at a very high level.”

Readers may find it useful to ponder Elliott's statement in light of the following graph.


Yes, Hooker was set a lot, but no more (as a proportion of the respective teams' hit attempts) than was Hodge. With Hooker hitting markedly better than her teammates -- the opposite of what happened with Hodge and Penn State -- it seems hard to fault Longhorn setter Ashley Engle (or Elliott, if he was calling the plays) for calling Hooker's number so often.

The excellent hitting of Hodge's supporting cast manifested itself at the most important time. In Game 5, three Penn State players -- Arielle Wilson, Blair Brown, and Darcy Dorton -- combined for 7 kills (with no errors) on 10 attempts, for an overall .700 hitting percentage.

In my previous posting (below), I speculated that blocking would be important, and boy was I wrong! As shown in the next graph, in Games 2 and 3, the losing team outblocked the winning team. Perhaps at a larger level, however, one could argue that Texas's relative parity with Penn State in blocking -- the Longhorns had only two fewer total team blocks than than the Nittany Lions, 12 to 14 -- helped keep the Burnt Orange so competitive.

Hodge and Hooker Key to Upcoming Penn State-Texas NCAA Women's Final

Saturday night's NCAA Division I women's championship match between Penn State and Texas will pit a couple of star outside hitters against each other. For the Nittany Lions, it's Megan Hodge (shown on top in the following sequences), whereas for the Longhorns, it's Destinee Hooker (who has also won multiple NCAA high-jump titles in track and field). I made these screen captures from ESPN360.com's archived videos of Thursday's two semifinal matches (I added yellow outlines to highlight the ball and Hooker's burnt orange sleeve).



Being able to assess the physical parameters of a spike attempt -- how high above and far behind the net it was struck, at what downward angle, and at what speed -- might be the way of the future for volleyball analysts, much like Pitch f/x, which already exists in Major League Baseball. I am not aware of anything similar coming up in the near term for volleyball, so the above photo sequences will have to serve merely as an inspiration of what might become available in the future.

In terms of more conventional volleyball statistics, one that jumped out at me was Hawai'i's total of ZERO team blocks vs. Penn State Thursday night, compared to the Nittany Lions' 15. In the other semifinal, Texas outblocked Minnesota 5-2. (Only when the defense directly earns a point such as by stuffing the ball back onto the hitting team's floor is a block officially credited; see scorekeeping procedures.)

According to official NCAA statistics (through December 13) on blocks per game (or set), Penn State ranked second in the nation at 3.23, Texas was fifth at 3.03, and Hawai'i was way down at 40th with 2.46. If these statistics are any indication, Texas should be more competitive with Penn State than was Hawai'i.

Word Clouds of NCAA Women's Final Four Teams

I thought it would be interesting and fun to analyze the NCAA women's Final Four (beginning Thursday night) via "word clouds." Actually, I stole the idea from someone who used word clouds in connection with the Major League Baseball playoffs a couple of months ago. Anyway, with word clouds, the user can simply copy and paste blocks of text (in my case, game articles from the NCAA tournament) into a field at the website Wordle.net and have it generate "clouds," such as those shown below, that depict the most frequently occurring words in the text.

With Penn State, for example, I went to the school's athletic website and got the four articles reporting the Nittany Lions' wins in each round leading up to the Final Four. I then copied and pasted all four articles, stacked one on top of the other, into the text field at Wordle. I also did the same for Texas, Hawai'i, and Minnesota. You can click on the graphics below to enlarge them (you'll almost certainly need to, in order to see the smallest words).

You'll see for each team the names of its most prominent players standing out in the largest type, such as Megan Hodge, Alisha Glass, and Arielle Wilson for Penn State; Destinee Hooker and Juliann Faucette for Texas; Kanani Danielson, Stephanie Ferrell, and Aneli Cubi-Otineru for Hawai'i; and Lauren Gibbemeyer, Tabitha Love, and Taylor Carico for Minnesota.

Also showing up readily in the clouds are various volleyball terms. The terms "kill" and "kills" (i.e., "put away" spikes that the defense can neither block nor dig) feature prominently in each team's display, but they really seem to stand out (to my eyes, at least) for Hawai'i and Penn State. According to official NCAA statistics, the national rankings of the four teams in kills per game (or set) are Penn State (5), Hawai'i (11), Texas (13), and Minnesota (33). Coincidence? Maybe, maybe not. If you think there are any valuable insights to be gleaned from these clouds, let us know via the Comments feature!

Statistically Minded Penn State Coach Russ Rose Featured in New York Times

Yesterday's New York Times had an article on Penn State women's volleyball coach Russ Rose and his longtime fascination with volleyball statistics. His team currently would have to be considered the nation's most dominant college program in any sport -- with 98 straight matches won and in pursuit of a third straight NCAA national title. The following stretch of paragraphs in the article describes Rose's background in statistics and how he augments the traditional statistics reported by the NCAA:

Rose thought he would be a gym teacher, maybe a basketball coach. But at George Williams College, he began playing volleyball under Jim Coleman, a former Olympic team coach and a future volleyball Hall of Famer. Coleman is credited with creating the modern volleyball statistics system, among other innovations.

Rose then spent two years at Nebraska, where his master’s thesis examined the skills most associated with winning. (“Passing predicts the level of play,” Rose said of his conclusion. “Hitting and blocking are most correlated with winning.”)

Official statistics have always bothered him. Most sports tally what the player did, not what he or she failed to do. He sees that as only half the equation. What about the rebound the basketball player should have had? Or the ground ball the shortstop did not reach? Or the dig that the volleyball player blew?

“On that sheet,” Rose said, pointing to a match’s official N.C.A.A score sheet, “if you don’t hit the ball, you don’t get a statistic. On mine, you do. You didn’t hit the ball.”

Most of his scribbles in the notebook reflect missed opportunities, what his players call “error control.” Rose grades each play, too, on a scale — not just whether the serve was in, for example, but how good the serve was.

The degree to which Rose's use of statistics in his coaching plays a causal role in the team's success is probably unknowable. I suspect, though, that when everything else is equal (which it usually is not) between Penn State and its opponent in terms of talent, experience, and so forth, Rose's stat-based strategizing may help the Nittany Lions get a few extra points here and there.

How Conferences Have Fared So Far in the 2009 NCAA Women's Tourney

Thanks to a pair of upsets -- Colorado State over Washington, and Baylor over UCLA -- the Pac 10 was left with only two teams among the Sweet 16 (Stanford and Cal). On the other hand, the Big 12, with five remaining teams, and the Big 10, with four, aquitted themselves well.

These results made me curious as to how Big 10, Big 12, and Pac 10 had done against each other during the regular season. The following chart (which you can click on to enlarge) shows the answers.


Big 10 and Big 12 teams met on 10 occasions, far more than teams from either of these conferences took on Pac 10 opponents. In clashes between the Big 10 and Big 12, the Big 10 won seven times. Arguably, the two most impressive of such wins were Michigan over Nebraska, and Minnesota over Iowa State. In fairness, these match-ups were not necessarily balanced in terms of competitiveness, as a large chunk of the Big 12's losses were by two of its weaker teams, Kansas State and Texas Tech.

Looking at the bracket, the Big 12 is assured of at least one Final Four team, with the Omaha region consisting entirely of teams from the conference (Iowa State, Nebraska, Texas A&M, and Texas). The fifth Big 12 team, Baylor, is in the same region with Penn State, so is extremely unlikely to make the Final Four.

Two-time defending NCAA champion Penn State is one Big 10 team that's a prohibitive favorite to make the Final Four. Illinois and Michigan comprise half the Stanford regional, but the host Cardinal and Hawai'i will be tough opposition. Minnesota, playing its regional at home, would arguably be favored to join the Final Four.

Correlation Between NCAA Women's Teams' Hitting Percentages and Tourney Seeding

As longtime readers of this blog know, I have focused extensively on hitting percentage as a kind of "all-in-one" marker of a team's productivity. Points are what win games and matches, and hitting percentages include a lot of information about points gained (through kills) and lost (through hitting errors), as well as spike attempts that merely keep the ball in play and thus reflect missed opportunities to score points.

If one looks at the seedings of the upcoming NCAA Division I women's tournament and the final regular-season statistics on team hitting percentage, one sees quite a bit of correspondence.

Penn State is the top national seed and led the nation in hitting percentage. Texas is seeded second and finished second nationally in hitting percentage. Florida State is seeded third (a surprise to many, perhaps because the Seminoles did not play many matches against "power-conference" opponents) and was fourth in hitting percentage.

For the 16 nationally seeded teams, I've plotted the relation between seed and hitting percentage, below. (Note that there are also some teams that finished in the top 16 nationally in hitting percentage, but either got into the NCAA field as a non-seed, such as St. Mary's (Cal.), which ranked eighth in hitting percentage, or missed the field entirely, such as Maryland-Eastern Shore, which was sixth).


For readers with some statistical training, the correlation is negative, meaning that smaller seed numbers (where 1 is best) go together with higher hitting percentages. The trend is not quite statistically significant, but with the small sample size of 16, that's not surprising.

I have highlighted two teams -- Washington and Hawai'i -- in underline and italics, as they had hitting percentages above what their seedings would seem to suggest. If the Huskies and Rainbow Wahine do better than what their respective seedings would project, that will further support the importance of hitting percentage (it should be noted, however, that Hawai'i is ranked No. 3 in the nation in a couple of "traditional" coaches' and media polls).

Iowa State Coach Christy Johnson on Evaluating Setters

The October/November issue of the AVCA's Coaching Volleyball magazine features an in-depth article by Iowa State women's coach Christy Johnson, entitled "Taking Your Setter from Good to Great: Seven Qualities for Which to Strive." Many of the ideas in Johnson's article are amenable to data-recording and quantification, perhaps in ways that some teams are already tabulating for their internal statistical purposes.

Here are some examples, quoting from Johnson, that I feel potentially could inspire objective grading systems for evaluating setters.

"Great footwork allows your setter to contact the ball high in the middle of the forehead every time."

"If a hitter can take a great swing at the ball, then the setter has done her job..."

"To train this concept, I’ll toss balls all around the court. I’ll ask my setter to set a quickset, for example, on every set she can, unless she feels she can’t put up a good ball, in which case she should then set the outside set (or backrow set or backset, whatever she can do at that point). I want her to learn her range, and even though we’ll continue to work on expanding that range, she needs to understand what she is physically capable of."

"If our outside is hitting a high ball, the setter can afford to set her off the net a little bit. She’ll have time to adjust, and there will likely be two blockers waiting for her anyway, so better to keep her off the net. Middles need a ball that is traveling towards the net. They don’t have time to adjust to an off set, and they are often going against only one blocker, so we can keep their sets a little tighter."

Setting does not, of course, occur in a vacuum. Accomplishment of some of the above tasks will depend on the quality of passes a setter receives and the hitting abilities of the players she sets. If there were some way to record reliably the locations of suboptimal passes on serve-receipt, then different setters could be compared on the percent of time they put up a hittable set from a given location. Such attempts to quantify setters' range for "rescuing" errantly passed balls would parallel efforts among baseball analysts to quantify fielders' defensive ranges (see here and here for baseball examples).

Non-Terminal Spike Attempts in Ohio State-Michigan Match (2009)

Your intrepid VolleyMetrics correspondent was in Ann Arbor, Michigan last Saturday night for the match between Ohio State and U of M. Actually, I was in town to attend an academic conference and visit my graduate-school alma mater, and as a bonus the volleyball match fit my schedule.

The night before, the Wolverines had taken two-time defending NCAA champion Penn State to five games. I knew the Penn State match had been sold out, but when I got to the arena after flying all day Saturday, I was amazed to see the Ohio State match was too (notice the crowd-control grate in the lobby in the pictures above). I was on the outside looking in until "halftime" (between Games 2 and 3), when a number of seated spectators left and hangers-on were let in.

The statistical angle I pursued (starting with Game 3) followed up on my immediately prior posting (below), namely what happens on spike attempts where the hitter neither achieves a kill nor commits a hitting error (i.e., a "non-terminal" shot where the ball remains in play). If the non-kill hit attempt still renders the defense unable to launch its own return attack, then the original hit attempt will have achieved some measure of success. On the other hand, as I quoted Tristan Burton in my earlier posting, "An in-swing that the opponent converts for a kill is no different than a hitting error as far as the score is concerned."

I focused only on Michigan and only two Wolverine hitters, Alex Hunt and Juliana Paz, received a large enough number of sets during Games 3 and 4 to compile statistics. While I was there, Hunt had 10 non-terminal hits (three in Game 3 and seven in Game 4); of these 10, the Wolverines and Buckeyes each ultimately won five of the points. Hunt, a left-hander hitting on her far left-hand side of the court, seemed to aim straight (i.e., down the sideline) a great deal of the time, as opposed to cross-court, and the Buckeyes dug her well. Paz had six non-terminal hits (two in Game 3 and four in Game 4) and OSU ultimately won four of these points.

Hunt hit .244 against Ohio State, racking up 15 kills and 5 hitting errors (for a net positive of 10) on 41 spike attempts (box score). Paz was in negative hitting territory for the evening (-.059), based on 8 kills, 10 errors, and 34 attempts. Looking at the Wolverines' seasonal statistics (through games of October 21, at this writing), Hunt's hitting percentage was only .215, based on 203 kills, 80 errors, and 573 attempts; clearly, a great many of her spike attempts remain in play. Paz does better at .281 (265/91/619), and even higher is Veronica Rood at .371 (142/33/294).

Michigan has made the round of 16 in each of the last two years' NCAA tournaments. The Wolverines seem to have the potential to advance further this year, but to do so, they'll probably have to become more proficient at putting balls away.

Experts Weigh In on Non-Terminal Spike Attempts

Over at the VolleyTalk discussion site, frequent contributor "P-Dub" raises an interesting question about hitting percentage, defined as: (kills-errors)/total attacks.

Player A: 6/3/15
Player B: 3/0/15

Both players have hit .200, but the first has done it with more kills and more errors. Which of these contributions is better?

To answer the question -- in theory, if not in practice -- P-Dub suggests looking at what the defensive team does with the balls the offensive team has neither put away (kills) nor failed to place in-bounds on the other side of the net (errors); in other words, what happens to the balls that remain in play?

For example, if a team is really good at converting opponents' non-kills into its own kills, then the aforementioned Player B's 3/0/15 line isn't good, because it gives the other team 12 opportunities to produce its own kills. This seems like a productive line of thinking, but it would be good to add some actual data to the debate.

The full discussion thread, which has now reached three pages, can be accessed here.

UPDATE (9/28): Tristan Burton, whose work has been cited before on this blog, sent me the following comment on evaluating hitting performances (with his permission to reproduce it).

I just saw your post about hitting efficiency. My paper defines "hitting effectiveness", which includes the outcome of any non-terminal swings by a hitter. So if I attack and the opponent digs me and then immediately gets a kill of their own then it counts against my hitting effectiveness. In English (instead of mathspeak), hitting effectiveness is hitting efficiency minus (the fraction of my swings on which the opponent gets their own attack) times (their hitting efficiency on those attacks). Usually, hitting effectiveness is lower than hitting efficiency and for those hitters who make a living just putting the ball in play it might be substantially lower (depending on whether or not the opponent is good at converting). I've looked at data for Pac-10 women where two OH's had virtually the same hitting efficiency but drastically different hitting effectiveness numbers because one of the players was aggressive and had a higher kill% and higher error% but the opponent could not easily convert her "in-swings" while the other player was putting more balls in play and the opponent was converting easily. There seems to be a focus in the volleyball community on minimizing errors but that's not what you are really trying to do, you're actually trying to increase the score difference between yourself and your opponent as much as possible with every swing (this is what hitting effectiveness actually tells you) . An in-swing that the opponent converts for a kill is no different than a hitting error as far as the score is concerned. As with so many things in life, there's a risk vs. reward relationship that needs to be considered.

Serve-Receipt Success in Different Rotations

Yesterday was the home opener at my university, Texas Tech, as the Red Raiders hosted Texas A&M in Big 12 play. It was also the home debut for new Tech coach Trish Knight, who faces an enormous rebuilding job. Prior to Knight's arrival, Tech had lost 39 straight conference matches. After yesterday's 25-15, 25-11, 25-17 shellacking by the Aggies, the streak is now at 41 (the Raiders lost a Big 12 road match before returning home to play Texas A&M).

With pencil, paper, and camera in hand, I decided to focus my statistical analysis yesterday on the serve-receipt success of Texas Tech's six rotations. I took the following picture (which you can click on to enlarge) during Game 3. We see that for the Red Raiders (near court), No. 11 (Amanda Dowdy) is front left, No. 4 (setter Caroline Witte) is front center, No. 13 (Barbara Conceicao) is front right, No. 1 (Hayley Ball) is back right, No. 10 (Aleah Hayes) is back center (her number doesn't show in the picture, but I got it from my notes), and No. 9 (libero Jenn [Harrell] Goehry) is back left. Once the ball is served, players can shift laterally; as shown in the photo, the setter Witte (No.4) is getting ready to move to the right, to leave Conceicao (No. 13) in her natural position of middle-blocker.


Shown next is a chart of Tech's six rotations in Games 1 and 3 (the rotation with the court depicted in yellow is the one in the photograph). In a few cases, I was unsure about a uniform number and/or positioning, but I've re-created the rotations to be as logically coherent as possible (e.g., if a given player were in the front left position in one rotation, she should be in the front center position in the next rotation). Between the libero role and just ordinary substitution, charting a team's rotations was not nearly as easy as I thought it might be.


As it turns out, you don't really need advanced statistical methods to see which rotations did better or worse on serve-receipt in Game 1 (I did not keep statistics when Tech served, but by locating server names in the play-by-play sheet and consulting the Red Raider roster, the success of the different rotations on serve should be able to be determined).

The ideal for a serve-receipt opportunity, would of course be to have a successful First-Ball Attack (FBA). In other words, the served ball would be dug, set, and spiked for an immediate kill. Texas Tech's starting rotation (the top-most in the left-hand column) achieved this ideal both times it had the chance. Starting setter Karlyn Meyers (No. 3) was in the back row, meaning that she had three front-row attackers at her disposal.

The Raiders' weakest rotation was clearly the third one down (one of three rotations in which the setter is in the front row, thus leaving only two eligible front-row attackers). In this rotation, Tech exhibited just about every problem in the book. Mostly, the Red Raiders mounted an FBA where the hit was Not Put Away (NPA), leading to a rally that the Aggies eventually won. Tech also failed to get its FBA onto the Aggie side of the court inbounds (twice), sent over a free ball, and made an overpass.

I assume that all teams keep their own statistics of this type. Further, there appear to be computer software packages available to assist with such data-collection efforts (just do a Google search with keywords such as: computer software volleyball rotation).

Tristan Burton Offers "Comprehensive Statistics System for Volleyball..."

I recently discovered that the American Volleyball Coaches Association (AVCA) makes its bimonthly magazine, Coaching Volleyball, free online. Naturally, I reviewed the last several issues in search of any statistically oriented articles and I hit paydirt.

UC San Diego assistant men's coach Tristan Burton, who earned a Ph.D. in mechanical engineering from Stanford with a 2003 dissertation entitled "Fully Resolved Simulations of Particle-Turbulence Interaction," contributed an article to the latest (August/September 2009) issue of Coaching Volleyball.

The title of Burton's AVCA article says it all: "A Comprehensive Statistics System for Volleyball Match Analysis." Whether using the game (set) or match as the unit of analysis, the system decomposes the final total point difference between the teams into seven categories.

As a concrete example, Burton uses the 2008 Olympic men's semifinal between the U.S. and Russia. With the U.S. winning 25-22, 25-21, 25-27, 22-25, 15-13, the Americans garnered 112 total points to the Russians' 108. The Americans' +4 overall differential could then be broken down into the following components (where PD = Point Difference):

Service (SPD)
1st Ball Attack (1PD)
Transition Attack (TPD)
Opponent Terminal Serve (OTSPD)
Opponent Giveaway Transition Attack (OGTPD)
Opponent Block and Cover Transition Attack (OBACTPD)
Miscellaneous (MPD)

As Burton notes, "Given that the service line is not an advantageous location from which to attack, [one's own service performance] is usually a negative number, i.e. on average a team loses points when they serve" (p. 17). In the example Olympic match, the U.S. had an SPD of -51, whereas for Russia it was -58.

The Americans' seven component scores, listed in the same order in which the terms appear above, were -51, 40, 9, 15, -6, -3, and 0, which sums to +4 (corresponding to the U.S. team's winning four more total points in the match than the Russians, as detailed above). Russia's sum would naturally come out to -4 (-58, 35, 17, 11, -6, -3, 0). I have not explained how each of these component scores is obtained; these procedures are fairly complicated, so interested readers will need to look at Burton's original article to see how everything works.

As Burton advises, "In addition to looking at these statistics for the entire team, it is also possible to look at them for individual players or individual rotations in order to identify more specific areas for improvement" (p. 18).

Burton's system is not for the faint-of-heart. It requires extensive manual record-keeping during a match and the use of computer software to calculate the various parameters. The article has so many variables and abbreviations that it will almost certainly leave any reader's head spinning (it did mine, and as a professor who teaches statistics, I'm usually quite comfortable with numbers and formulas).

Another potential use of Burton's article would be to select a few relatively straightforward tabulations to use for one's team, instead of immersing oneself in the full system. One statistic in the article that caught my eye is the following: "Russia was able to respond to slightly more (73.9% vs. 73.4%) serves with a 1st ball attack" (p. 17). I would have thought such elite teams would have more of a tendency to mount an attack directly off of serve receipt, but by the same token, I guess, elite teams would also be delivering a lot of tough serves!

ADDENDUM/CLARIFICATION: Dr. Burton and I have exchanged e-mails, in an attempt to clarify the statistic in the paragraph immediately above regarding teams' mounting a 1st ball attack only around 73-74% of the time. These figures include opponents' serving errors as non-1st ball attacks. Dr. Burton was kind enough to run some new numbers for readers of the blog. Limiting the situation to when a receiving team faced an in-play serve, how often did the receiving team successfully set up a spike attempt as a first response, as opposed to being aced or sending a feeble (i.e., freeball) response back to the serving team? The answer is generally around 90%, both from some Olympic men's and Pac-10 women's matches Dr. Burton analyzed.

JQAS Article Examines Match-Length Implications of Rally- vs. Server-Only Scoring

In the latest issue of the online Journal of Quantitative Analysis in Sports, Balazs Kovacs presents an article entitled "The Effect of the Scoring System Changes in Volleyball: A Model and an Empirical Test" (the journal requires subscriptions, but free guest privileges are available). The article focuses on the change, implemented about a decade ago in many different levels of volleyball competition, from server-only scoring (with side-outs) to rally scoring.

Back when only the serving team could score, matches could drag on indefinitely if the receiving team kept winning rallies (i.e., siding-out); several plays would go by and the score would remain unchanged. Rally scoring was not necessarily adopted to make matches end more quickly, as the number of points needed to win a set (also known as a game) was increased from 15 to either 25 or 30 (depending on league) coinciding with the introduction of rally scoring (except for fifth games of a match). Rather, the change was intended to narrow the range of how long matches took to play (by eliminating the kinds of long scoreless periods alluded to), which could be helpful for television programming.

Kovacs provided a number of computer simulations of matches, but also presented analyses from actual games (using women's play in the NCAA Division II Northern Sun Intercollegiate Conference), before and after the switch to rally scoring. The key results, comparing server-only to rally scoring, were as follows: "the average match length increased from 92.5 minutes to 99.8 minutes. The variance of the match length has decreased from 27.82 in 2000 to 22.56 in 2001" (p. 8). For readers more familiar with the standard deviation as a measure of spread, the variance is simply the squared SD.

Thus, from this one conference at least, rally scoring appeared to accomplish its aim of providing more regularity to the length of matches. Going beyond the scope of Kovacs's article, a concern I've always had about rally scoring is that it may impair teams' ability to stage comebacks. Hypothetically, take a team that's serving while trailing 14-10 in a game to 15. Under the old system, the trailing team could not lose, as long as it was serving. The leading team would first have to win a side-out (which receiving teams are well positioned to do, as they have first crack at running their offense) and then win a point on serve (which is harder, for the same reason). Under rally scoring, however, the leading team could win the game merely with a side-out.

Yahoo Group: VolleyStats

A while back, I joined a Yahoo discussion group called VolleyStats. As a result, I've been receiving e-mails from the group that fall into two categories: junk messages and serious reports of statistical analysis from someone named Leo van Hal. Because van Hal's reports are written in Dutch, I really didn't learn anything more about volleyball from them than I did from the junk e-mails.

That situation recently changed, however. I found a new Google application website (new to me, at least) called Google Translate, which is quite easy to use. You simply type (or copy and paste) text from the originating language into a box, select the "from" and "to" languages, and click! van Hal's reports often contain graphs, so rather than copy and paste his entire paper into Google Translate, I do it a few paragraphs at a time, avoiding the graphs. The translations aren't always perfect, sometimes leaving me with odd English constructions. With a little inference ("reading between the lines"), however, I'm confident that I'm picking up the key points.

One message I received was titled "[volleystats] Digest Number 447 [2 Attachments]," dated May 17, 2009. The two attached analyses were titled "Punten verdeling" (Distribution of points) and "Winst Kansen" (Profit Opportunities). Both reports have to do with testing different probabilities of teams' winning points on their serves, and how this affects the probability of different point totals in the games and teams' probabilities of winning the games. (In the English translations, the Dutch word "opslag" appears in English as "storage," but apparently "service" is another synonym; the passages will read much easier in English if you substitute "service" for "storage.")

If you would like English-translated copies of these two van Hal reports, please e-mail me via my faculty webpage in the upper-right portion of this page.

Improved Blocking Helps UCI Take Men's Final Over USC

As most people looking at this blog would already be aware, the University of California, Irvine (UCI) defeated the University of Southern California (USC) in an exciting five-game NCAA men's championship match last night. Adding to the drama and excitement was the story of the Trojans' late-season turnaround, from a fifth-place finish in the Mountain Pacific Sports Federation, to a juggernaut that swept through the MPSF postseason tournament to capture an automatic bid in the four-team NCAA tournament and overpowered Penn State in the national semifinals.

The reason for USC's recent success was no secret -- it was killer hitting, especially by 6-foot-8 sophomore Murphy Troy. Whether Troy was in the front row or back row (where a player can still hit, as long as he takes his jump from behind the 10-foot line), USC would frequently set the ball to him, and Troy would deliver. Troy's hitting statistics in SC's last five matches, in terms of number of kills and hitting percentage were as follows: 28/.380 vs. Stanford, MPSF; 19/.600 vs. UCI, MPSF; 23/.220 vs. Pepperdine, MPSF; 24/.595 vs. Penn State; NCAA; and 26/.367 vs. UCI, NCAA.

Among the tactics an opposing team could try against a hot-hitting team, two would be tough serving (to disrupt the passing to the setter, and perhaps the sets to the hitters) and finding a way to block better than you ever have, by getting two (or even three) players up against the opponent's top hitters.

As shown in the following chart of key statistics from USC's postseason matches (which you can click to enlarge), the high rate of service errors by Trojan opponents suggests that they may have been opting for the aggressive serving tactic. Penn State, with 21 such errors in only four games, stands out in this regard (although the elevated altitude in Provo, Utah, which would cause the ball to carry further, also could have been a factor).


In the national championship match, UCI had only 11 service errors (very low, considering the match went five games). More importantly, though, the Anteaters greatly increased their blocking productivity.

I've read several online articles this afternoon about last night's match, but I haven't been able to find any quotes from UCI Coach John Speraw regarding strategies he employed so that his team's block could be so effective against USC. The best I could find was: "We did a much better job of taking away their tendencies" (from a list of postgame quotes on the UCI athletics site). Comments from any "X's and O's" people would be welcome!

Chuck Rey on "In Play Efficiency"

Chuck Rey, volunteer assistant coach at the University of Minnesota, e-mailed me this morning with a statistical variation on hitting percentage.

Rather than putting the emphasis on kills, which the usual measure of hitting percentage does, the Gophers focus on putting the ball in play (i.e., getting a kill or at least not spiking the ball out of bounds or into the net). They even have a name for it, the "In Play Efficiency" or IPE. As Chuck stated in his e-mail to me, the "IPE stat is just a 1 - the error %."

In addition to the Hitting IPE, there's also "Total IPE," where the error rate is based on "all team errors commited (blocks errors, hitting errors, service errors, ball handling errors, passing errors)."

Chuck was kind enough to present me with season-long IPE statistics for the Final Four NCAA women's teams from last December, which I am posting with his permission.


Chuck also has his own website at http://www.coachrey.com .