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Showing posts from 2010

Penn State Over Cal in Final -- An Unexpected Rout

I don't know that there's a lot to say about last night's three-game sweep that gave Penn State its fourth straight NCAA women's volleyball title. Other than in Game 2, in which Cal had a pair of set points , there was very little drama. The statistical issue that got the most attention on ESPN's broadcast was how the Golden Bears' Tarah Murrey was getting nearly half of her team's hit attempts (she ended with 46% of them, 56/121), thus letting Penn State devote its attention to stopping her. And stop Murrey the Nittany Lions did, holding her to a very uncharacteristically low .143 hitting percentage ( box score ). In the semifinals against Texas, in contrast, Murrey hit .413. For Penn State, middle-blocker Arielle Wilson exhibited her usual blend of steadiness and power, hitting .391, and right-side hitter Blair Brown punched in at .316. Outside-hitter Deja McClendon, though hitting only .250 on the night, got off to a fast start; of her 16 total kills,

It's Penn State and Cal in Saturday Night's Final

Below, I've circled what I think are some key numbers from last night's two NCAA women's semifinal matches (I did screen captures of the official box scores, then annotated them in PowerPoint). One thing that's clear right off the bat is that the two winning teams, Penn State and Cal, each sided-out extremely well. With those kinds of side-out percentages, teams will very rarely lose games (sets). In my Penn State-Texas preview , I had concluded that, "Blocking may provide Penn State with a decisive edge in holding down Texas's hitting effectiveness." I don't often make such spot-on predictions, so when I do, I like to toot my own horn a little. As seen in the following boxscore, the Nittany Lions dominated the blocking and slowed down two Longhorn hitters who had been very productive of late, Rachael Adams and Jennifer Doris. Throw in a torrid hitting performance from Penn State's Deja McClendon and a three-game romp is the result. (You may cli

NCAA Women's Final Four Preview II: USC vs. California

Tonight's second semifinal match of the NCAA women's Final Four will be an all-Pac-10 battle, with the University of Southern California (USC) taking on the University of California, Berkeley. What gives this match a little extra intrigue is that these teams have already met twice this season in conference play, with USC winning both times. The Trojans actually had a harder time holding off the Golden Bears -- 17-15 in the fifth game -- October 9 in Los Angeles ( boxscore ) than up in Berkeley, where USC prevailed in four games ( boxscore ). As usual, I've been focusing a lot on hitting percentage during the tournament, and the following table tells us which players have (and have not) done well in this season's USC-Cal head-to-head match-ups. As discussed in yesterday's preview of tonight's other semifinal between Penn State and Texas, middle blockers will often have higher hitting percentages than outside hitters, because the latter likely receive a great

NCAA Women's Final Four Preview I: Penn State vs. Texas

The first of Thursday night's two NCAA women's national semifinal matches presents a rematch of last year's championship contest, Penn State vs. Texas. The nightcap will feature two Pac-10 foes, USC and Cal. The present write-up will focus on Penn State and Texas, with another one tomorrow for USC and Cal. My starting point in analyzing the Nittany Lions and Longhorns is to examine to what degree, if any, the teams have changed over the past three months in their allocation of sets to different hitters and these players' hitting percentages. Back in early September, on the eve of the Big Four tournament -- with Florida hosting Penn State, Texas, and Stanford -- I presented graphs of each team's leading hitters. The key elements of these graphs are as follows. Each team gets its own graph. The graph consists of several bars, one for each hitter. A bar's height represents that player's hitting percentage (based on some reference timeframe) and its width

Graphing the Trajectories or Arcs of Sets to Hitters in a Match (UCLA-Texas 2010 NCAA Second Round)

Today's entry falls under the rubric of, "It seemed like a good idea at the time." While watching last Saturday night's webcast of the UCLA-Texas women's NCAA second-round match, I decided to create what, to my knowledge, would be a novel type of play-by-play sheet that visually depicted the trajectories of each team's initial sets in mounting an attack from serve receipt. Being able to check, at a glance, whether a team was varying its attacks between high and outside (a "4 set"), quick middle hits (a "1 set"), and other varieties of plays , and its success in siding-out with the various types of attacks, would seem to be valuable information. Further, because the webcast was shown entirely from an "end zone" camera, it was relatively easy to observe the arcs of the sets. What I didn't bargain for was that, even graphing merely a single game (Game 2, which ended up being the only one taken by the Bruins), the process of

2010 NCAA Women's Preview: Seeded Teams' Hitting Percentages Against Other Seeded Teams

For pretty much the entire four years that I've maintained this blog, I've extolled the importance of hitting percentage as a measure of a team's overall ability. To win games (sets) and matches, teams need points. Hitting percentage is based heavily on teams' productivity in winning points via kills, but penalizes teams for hitting errors, which of necessity give points to the opponent. Dividing (kills - errors) by total attempts further weakens teams' hitting percentages if a lot of their attempts are kept in play by the other team. If a team compiles a high hitting percentage and limits its opponents' hitting percentage, it will probably win a lot of matches. Last year in previewing the start of the NCAA women's tournament, I showed that there was a strong correlation where the highest seeded teams also had the highest hitting percentages. I used teams' overall regular-season hitting percentages, however, which don't take into account degree o

JQAS Article Examines Relationship Between Opponent's Blocking Strategy and Allocation of Sets to Different Hitters

The latest issue of the online Journal of Quantitative Analysis in Sports  includes the article "Relationship between the Opponent Block and the Hitter in Elite Male Volleyball," by Rui Manuel Araújo, José Castro, Rui Marcelino, and Isabel R. Mesquita. A brief summary (abstract) is available here . Full-text access is by subscription, but the journal has guest viewing privileges for individual articles. This study is based on observations from the 2007 World Cup of men's volleyball. The authors studied setters' allocation decisions in relation to two features of the opposing block:  the spacing of the blockers along the front row at the start of the point, and the type of block being faced (none, single, double, triple; the latter two also categorized as "compact" or with open spaces between the blockers). Analyses (via chi-square) were entirely two-way (allocation vs. spacing; and allocation vs. type of block), with no three-way analyses. Because the ar

Side-Out Success Based on Whether Teams Stay "In System" on Serve-Receipt

Increasingly, it seems, one hears of volleyball teams getting "out of system" or having to recover from same. According to Bonnie Kenny and Cindy Gregory's book Volleyball: Steps to Success , "Out-of-system play occurs during a rally when something happens to take the team away from the preferred pass, set, hit or dig, set, hit sequence" (p. 141). I decided several weeks ago that, while watching several upcoming matches on television, I would keep some statistics on women's college teams' ability to stay in-system on their serve receipt, and how this would relate to their likelihood of ultimately winning the rally (i.e., siding-out). I coded one game (set) each from the following matches: Illinois at Minnesota ( box score , ESPN 3 video ); South Carolina at Florida ( box score ); Nebraska at Texas ( box score ); Oklahoma at Texas A&M ( box score ); and Penn State at Michigan ( box score ). As teams attempted to run their offense in immediate resp

Texas Tech Ends 64-Match Conference Losing Streak

Texas Tech University's women's volleyball team tonight ended its 64-match losing streak in Big 12 conference play, with a five-game win over Kansas . I'm on the faculty at Texas Tech, so I've eagerly been awaiting this day! The Big 12 schedule is 20 games (Oklahoma State doesn't field a volleyball squad, so each team has 10 opponents, each played home and away). The Red Raiders won their conference opener in 2007, then dropped their remaining 19. Seasons of 0-20 followed in 2008 and 2009 , and then the team started off 0-5 in the Big 12 this year. Tonight's win seemed less a matter of Texas Tech raising its overall team hitting percentage compared to the previous five losses (black bars in the graph immediately below), than dramatically curtailing the opponent's hitting percentage (blue bars in the second graph).  The Red Raiders recorded 17.5 blocks against Kansas to contain the Jayhawks' offense (Texas Tech's block total is, of

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 i

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

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

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 sc

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 ATTEMP

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 whe

Karch Kiraly's Hypothesis on "Better" Kind of Hitting Error

There are two main types of hitting error. According to the NCAA volleyball statistical manual , one type of error involves hitting the ball somewhere other than in-bounds on the opponent's side of the court (i.e., "Hits the ball out of bounds" or "Hits the ball into the net resulting in a four-hit violation"). The other major type of hitting error is when the attacker is stuff-blocked (where the ball is "blocked down by the opposition to the same side as the attacker, and cannot be kept in play as a direct result of the block"). There are additional types of attack error such as the hitter contacting the net, back-row attack violations, and “thrown”/double-hit balls; the present analysis is not all that concerned with this last set of errors, however. Of the two main types of error -- failing to hit the ball in bounds, and getting blocked -- ESPN commentator and former UCLA and Olympic great Karch Kiraly feels that one of these types of error is