Preview of 2010 Big Four Tournament

In anticipation of this weekend's Big Four tournament -- bringing No. 1 Penn State, No. 2 Stanford, No. 4 Florida, and No. 5 Texas to Gator country -- I've created hitting proficiency/attempt (P/A) graphs for each of the teams. I first introduced these graphs in my August 30 posting, with statistics specific to the Florida-Nebraska match; in contrast, the ones presented today are based on each team's cumulative season-to-date statistics.

For each of a given team's hitters (excluding those with small numbers of attempts), the player's hitting percentage is depicted as the height of a vertical bar, with the bar's width representing the number of spike attempts. Whereas I used the actual number of hit attempts as the horizontal-axis units for the Florida-vs.-Nebraska graphs, I'm now using percentage of the team's hit attempts. As you'll see, for each team I've arranged the players left-to-right from highest to lowest hitting percentages.

The ideal is for the tallest (leftmost) bars to also be the widest. That means the players with the highest hitting percentages (i.e., the most proficient) are also getting the most attempts. Here's how the four teams (listed in order of national ranking) stack up, heading into the Big Four event (you may click on the graphics to enlarge them)...

Penn State is unique in getting such a large proportion of its swings from just four players. Beyond that, however, the Nittany Lions successfully limit the number of hit attempts by their weakest attacker (in terms of hitting percentage). Penn State's article archive and stats page are excellent resources on the team.

Stanford does a good job of getting 6-foot-4 senior outside hitter Alix Klineman a lot of attempts, but might benefit from getting 6-2 frosh middle blocker Carly Wopat more sets.

Florida was, of course, led in its opening-weekend victory over Nebraska by hitting/setting double-threat Kelly Murphy, a 6-2 junior. Murphy continues to hit well and see a large share of sets in her direction. Lauren Bledsoe, who last weekend hit .533 (10 kills and 2 errors in 15 attempts) against 15th-ranked Colorado State and .625 (5-0-8) against Bowling Green State, now leads the Gators in hitting percentage. Tangerine Wiggs, who hit .833 (5-0-6) in the BGSU match and .500 (5-1-8) against Albany, has also improved since the Nebraska match. Gator stats are available here.

Finally, we have Texas, last year's national runner-up to Penn State and the only team in the Big Four to have lost a match this season (to Illinois, who is now ranked No. 3). The Longhorns' graph above appears to show the undesirable pattern of the most hit attempts going to players who do not have the highest hitting percentages (wide bars on the right). One of the players in that category, however, is senior outside hitter Juliann Faucette, a highly decorated All-America and All-Big 12 player. Faucette doesn't appear to be off to as hot a start as some of the other players, but her current hitting percentage of .292 is perfectly respectable.

Penn State, with its active 108-match winning streak, opens with Florida in the Big Four. This contest is a rematch of when the Lions and Gators met in last year's NCAA Sweet Sixteen round, a three-game sweep for Penn State (article, box score). Of note is that Penn State completely neutralized Florida's Murphy in this match, holding her to a .000 hitting percentage (2 kills, cancelled out by 2 hitting errors, in 12 attempts).

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Other Notes:   Dave Reed's ESPN.com women's volleyball column is back again this season. That's where I found one of the preview articles cited above and other miscellaneous pieces of information, including the following... You've probably heard of Sunday Night Baseball, Monday Night Football, and Friday Night Lights (high school football). Now make room for Wednesday Night Volleyball on ESPN-U, the schedule for which is available here. Most of the Wednesdays will feature doubleheaders! These telecasts will offer great opportunities for volleyball stat-heads (leatherheads?) to keep our own statistics and conduct analyses.

Summary of Florida-Nebraska Match

This past weekend saw the opening of women's college play in the U.S., with the nationally televised (on CBS College Sports cable channel) Runza/AVCA Showcase from Omaha, Nebraska taking center stage. Each match featured a Big 12 school (either Nebraska or Iowa State) taking on an SEC school (Florida or Kentucky). As it turned out, tournament organizers saved the best for last, as yesterday's closing match between Florida and Nebraska came down to an exciting finish, with the Gators prevailing 15-12 in the fifth (boxscore).

For this match, I created the two figures below (one for each team), which convey two aspects of offensive attack: players' hitting percentages (on the vertical axis) and number of hitting attempts (horizontal axis). Players are arranged left-to-right in descending order of hitting percentage. You may click on the figures to enlarge them.

The ideal would be to have rectangles that were both tall and wide, indicating that a player maintained a high hitting percentage over a large number of attempts. The Gators' Kelly Murphy epitomized this combination. Undesirable shapes are tall-and-thin (a player who hits well, but gets few attemps) and short-and-wide (a player who hits for a low percentage, but gets a lot of attempts). I've depicted these suboptimal situations in the figures with paler shades of red and blue.

Whether volleyball coaches and analysts find these graphs useful remains to be seen. One immediate application of the graphs could be in goal-setting. Players with large numbers of attempts but low hitting percentages could be shown the graph, with the coach setting the goal of some specific, higher hitting percentage for the player to work towards.

Alexis Lebedew on Evaluating Setters

I recently received an e-mail from Alexis Lebedew of the Australian Institute of Sport, bringing to my attention some of his writings. Lebedew's focus is the evaluation of setting, a skill that has gone relatively unanalyzed over the years. The statistic of a setting "assist" exists, but because it represents the number of balls leading to kills, it overlaps considerably with hitting statistics.

In a piece entitled, "A Reconceptualisation of Traditional Volleyball Statistics to Provide a Coaching Tool for Setting" (link), Lebedew proposes a way to rate the quality of sets by taking into account not just the spike attempt following the set, but also the pass preceding the set. In short, setters are most rewarded for making "lemonade" from a "lemon" pass. As Lebedew states more technically, "...the combination of a [high-quality] spike and a [poor] pass has the top Rating... within the ‘Excellent’ outcome."

In fact, sets can be graded on a scale of 0-12, based on combinations of quality ratings for pass and spike. Lebedew notes that coaches who are used to grading passing and hitting performances on a metric different from his own (e.g., rating hit attempts on a 3- rather than 4-point scale) will still be able to construct a meaningful scale for setting, although the top value may differ from 12.

Lebedew also attempted to validate his setting metric in two ways. He first showed that computer software designed to link passes and hit attempts within the same sequences to derive set attempts only rarely missed a set attempt when compared to video footage. Second, he charted teams' percentages of sets (games) won for different averages of setting proficiency. For example, teams won roughly 95% of time when their set quality averaged 9 or higher, roughly 90% of the time when it averaged 8.5 or higher, etc., down through roughly 55% when averaging 6 or higher on setting. Lebedew encourages coaches and setters to strive for setting-proficiency averages of around 7.5-8.

All of the data were from international beach volleyball, which qualifies the generalizability of the findings in some important ways. With two-person teams, of course, there's no way to assess the setter's savviness in choosing which hitting-eligible teammate to set (as noted by Lebedew). Also, at levels of play beneath international caliber, more realistic setting-proficiency aspirations than the aforementioned 7.5-8 may need to be established.

JQAS Article on Quality of Skill Performance and Winning Points

A recent issue of the Journal of Quantitative Analysis in Sports(Volume 6, Issue 2) contained an article by Michelle Miskin, Gilbert Fellingham, and Lindsay Florence entitled "Skill Importance in Women’s Volleyball." Access to articles is by subscription, but the journal has guest-visitor privileges for single articles.

Miskin and colleagues analyzed data for a particular women's Division I team (not identified by name) during the 2006 season. When the team played at home, play on its side of the net was videotaped and later coded. Serves, passes, and digs were rated by judges on quantitative scales (e.g., 0-to-5), sets were evaluated in terms of their distance from the net, and spike attempts were coded by area of the court from where they were hit.

Essentially, the authors appear to be looking at correlations (or associations) between characteristics and quality of skill performance, and likelihood of winning the point. As they state on page 2:

The importance score incorporates not only the impact of a specific skill..., but also the uncertainty associated with the performance... Thus, a skill whose association with scoring a point is less certain will be penalized when using this metric when compared to a skill where performance at a given level is more closely associated with a positive outcome.

The article throws a barrage of statistical terms at the reader (e.g., Bayesian analysis, Markov Chains, Dirichlet prior, Gibbs sampling, gamma distributions), some of which I was familiar with, but many of them not. Fortunately, the authors translated the complex statistical results into plain English recommendations for the team that was investigated:

1. Keep sets and passes away from the net.

2. Force the attack to the middle and right side if at all possible.

3. Devote a considerable proportion of practice time to transition offense.

4. Get to blocking positions more quickly following a serve.

Presumably, if a team wanted to apply the analytic tools described in the article in their full glory, it would need to hire a pretty high-powered statistical consultant (in addition to acquiring the videotaping and coding resources). Perhaps similar analyses could be done via more basic correlational and regression techniques, but I suspect that the resulting conclusions may be somewhat imprecise, compared to those from the fully sophisticated analyses.

Lawson Powers Stanford to NCAA Men's Title

Stanford's Brad Lawson had an incredible offensive night as the Cardinal blew out Penn State for the NCAA title, 30-25, 30-20, 30-18. Lawson, a 6-foot-7 sophomore outside hitter who was one of four players from the state of Hawaii to take the court for Stanford tonight, compiled the following line: 24 kills with only 1 hitting error, in 28 attempts, for a remarkable .821 percentage (box score). For those who don't follow volleyball closely, a hitting percentage in the .300's would be considered very good and in the .400's, outstanding. For the season (including the championship match), Lawson hit .387 (522 kills and 143 errors on 980 attempts).

This NCAA men's volleyball records page (current only through 2006) presents two championship records, for a single match and for both games of a tournament combined:

HITTING PERCENTAGE, MATCH (MIN. 15 ATTEMPTS)
.867--Jeff Nygaard, UCLA (3) vs. Ohio St. (0), 5-7-93.

HITTING PERCENTAGE, TOURNAMENT (MIN. 20 ATTEMPTS)
.788--Rick Tune, Pepperdine, 1998 (.833 vs. Princeton, 10-0/12; .762 vs. UCLA, 17-1/21).

Nygaard's record, based on a 13-0-15 line, was achieved in a semifinal match, arguably making it slightly less impressive than a comparable hitting percentage in a championship match. Also, Nygaard was a middle blocker, as was Tune.

A couple of other notes:

*Interestingly, this year Stanford also saw an .800 hitting performance on the opposite side of the net. On February 19, Pepperdine's Cory Riecks recorded a 17-1-20 night against the Cardinal.

*As a follow-up to yesterday's posting (immediately below), Penn State managed only 4.5 total team blocks against Stanford in the title match.

Preview of Penn State-Stanford NCAA Men's Final

In anticipation of tomorrow night's (7:00 Eastern) NCAA men's championship match between Penn State and Stanford, the Nittany Lion athletic department has put out a press release that includes some interesting statistical facts.

According to the release, Penn State is 21-6 when two or more players record double-digit kills, 7-3 when two or more players record double-digit digs, and 13-2 when achieving 10 or more blocks (among other things). Such statistics can potentially provide useful insights in assessing a team's chances of winning a particular match. However, caution should be exercised for a few reasons. Before I go any further in my comments, though, I want to state that I am thrilled any time I see statistically oriented writing in the coverage of volleyball and that I intend my remarks in a constructive spirit.

First, the presented statistics do not make use of all the known information. Using the last statistic given above, the Nittany Lions are 13-2 when getting 10 or more blocks. What is their record when getting fewer than 10 blocks? As shown below, we can fill out the picture by knowing that the team's overall record is 24-7.


With all of the cells filled in, we can thus see that Penn State has a pretty good record, too, when getting fewer than 10 blocks.

Second, there is considerable variation in the quality of Penn State's opposition during the season. Playing other eastern (or midwestern) schools presumably is not as difficult as going against the traditional Mountain Pacific Sports Federation powers the Nittany Lions faced during the regular season (USC, Hawai'i, UC Irvine, BYU, Cal State Long Beach, UC Santa Barbara, and Cal State Northridge). As accessed from Penn State's game-by-game log, here are the Nittany Lions' total blocks (in red) and match outcomes against MPSF opponents:

USC 4 L(0-3)
Hawai'i 16 W(3-2)
UCI 9.5 W(3-2)
BYU 6 L(1-3)
CSULB 5 W(3-0)
UCSB 6 L(3-0)
CSUN 8.5 L(3-0)

As shown, only once in these seven matches did Penn State achieve 10 or more blocks, and it happened in a five-game match, which provides more opportunity to accumulate blocks (and other statistical markers). Blocks per game might be more appropriate to cite.

Of course, though, in a stunning turnaround from Penn State's 0-3 loss at Cal State Northridge on April 10, the Nittany Lions turned things around on Northridge in last night's NCAA semifinals, winning 3-0 on the strength of 11 blocks. Eleven blocks in three games yields a robust 3.67 average. Penn State's opponent in the championship game, Stanford, piled up 12 blocks in a three-game sweep over Ohio State.

Lastly, as is drilled into the heads of all students taking social-science research methodology courses, correlation (i.e., that two things co-occur) does not by itself prove that one thing has actually caused the other. In basketball, for example, one might find that when a given team makes under 10% of its three-point attempts, it loses the game a high percentage of the time. One might intuitively interpret such a statistic to mean that poor shooting caused the team to lose. However, the team could have been trailing in some of its games for reasons having little to do with three-point shooting and then taken a lot of desperation threes (that were missed) in an attempt to get back into those games. In other words, it may have been the losing that caused the missed shots from behind the arc. Similar examples for football are discussed here.

I could envision a volleyball scenario where a team would have a poor won-loss record in matches in which it committed a large number of service errors. Maybe the service errors cost the team a lot of points early and paved the way to eventual defeat. But, it could be that the team fell behind for reasons unrelated to serving and then decided to serve aggressively in an attempt to catch up, only to have the high-risk/high-yield serves mostly fail. Something to think about.

Chuck Rey's Analysis of Penn State-Texas NCAA Women's Final

Prompted by a re-airing of last December's NCAA women's final between Penn State and Texas, fellow volleyball blogger Chuck Rey has produced a statistical analysis of the match. Interested readers can compare and contrast Coach Rey's analysis to the one that I did.