Saturday, September 26, 2009

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.

Sunday, September 20, 2009

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).

Sunday, September 6, 2009

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.

Hawai'i Sweeps Long Beach State to Claim Second Straight NCAA Men's Championship

Hawai'i swept Long Beach State last night in Los Angeles to win its second straight NCAA men's championship. Scores were 25-22, 25-...