Texas Tech professor Alan Reifman uses statistics and graphic arts to illuminate developments in U.S. collegiate and Olympic volleyball. [For archives of this blog and extensive links to other volleyball sites, please click the three-line icon in upper-right corner.]
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
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
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'
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 o
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
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 n
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 t
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 &q
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.
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 pictu
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-
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 chang
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 l
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
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.coachr