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Showing posts from 2012

NCAA Women's Final Four Wrap-Up: Powerful Texas Overcomes Grind-it-Out Michigan and Oregon

Looking back on this past weekend's NCAA women's Final Four, what stands out to me is the contrast in teams' strong suits. The champion Texas Longhorns set the ball high, hit over the block, and delivered surgical strikes to win points quickly. Michigan and Oregon, UT's opponents in the semifinals and finals, respectively, got as far as they did with more of a grind-it-out approach. Defensively, Michigan and Oregon persistently dug opponents' spike attempts, and when the Wolverines and Ducks went on offense, they sometimes would need multiple hitting attempts on a single rally to finally put the ball away. The following graph conveys the Longhorns' devastating offensive prowess in their final three matches of the tournament (vs. USC in the Elite Eight, and the Michigan and Oregon matches). In addition to the conventional hitting-percentage statistic ([Kills - Errors]/Total Attempts), I have presented a slight variation, namely kill percentage (Kills/Total Atte

Preview of the NCAA Women's Final Four

The NCAA women's Final Four begins Thursday night, with Michigan facing Texas, followed by Penn State taking on Oregon. To preview the Final Four, I've decided to look back on each team's victory in the Elite Eight. To begin, for each of the victorious teams, let's look at the result of every spike attempted in its respective Elite Eight match (you may click on the following graphics to enlarge them). Box scores for the matches are available at the following links: Penn State-Minnesota , Oregon-Nebraska , Texas-USC , and Michigan-Stanford . Let's walk through the first case, to illustrate the format. Penn State attempted 156 spikes vs. Minnesota. Sixty-two of these attempts (39.7%) were successful, resulting in kills. Outcomes depicted in black (or dark blue, later) are good. Twenty-five Nittany Lion attacks resulted in hitting errors, 14 (9%) because they were blocked right back onto the Penn State side of the floor for immediate Gopher points and 11 (7%) because

Favorites March On in Friday's NCAA Women's Sweet 16

A tournament in which favored teams overwhelmingly prevail is known, via an expression from horse racing , as following the "chalk." This year's NCAA women's competition is definitely looking like a chalk tournament, as all but one of the eight remaining teams after last night's Sweet 16 round is a top-eight seed. Today's match-ups thus include the following (see bracket for starting times): No. 1 Penn State vs. No. 8 Minnesota This match does not look to be all that competitive, at least on paper, as the Nittany Lions took both matches from the Gophers during Big 10 conference play. The first match, in Minneapolis, was a 25-23, 25-8, 25-20 Penn State rout , as the Lions outhit the Gophers .404-.098. The second match, in State College, was closer, but still decisive for Penn State, 25-21, 25-19, 23-25, 25-21. If one delves further into the statistics of the second match, however, there are some bright spots for the Gophers. Minnesota actually outhit (.

NCAA Women's Tourney -- the First Weekend

The Big 10 (which actually has 12 schools) and Pac 12 conferences each had seven teams in this year's NCAA women's tournament. At this stage, with two rounds complete after the first weekend, the Big 10 is looking a bit stronger than the Pac 12. The former has lost only one team ( Ohio State ), whereas the latter has lost three (No. 7-seed and defending national champion UCLA , Cal , and Arizona State ). Many of the top teams from the two conferences  -- No. 1-seed Penn State and No. 4 Nebraska of the Big 10; and No. 2 Stanford, No. 5 Oregon, and No. 6 USC of the Pac 12 -- made it through to next weekend's Sweet 16 without having lost as much as a single game (set). Other teams faced tougher competition and thus struggled to varying degrees to advance. From the Big 10: No. 8 Minnesota dropped the first set of its second-round match against Creighton, before taking the next three ( 20-25 , 25-17, 25-23, 25-17). The Gophers hit .314 as a team against the Blue Jays, with

Ranking the 64 NCAA Women's Teams on the Conference-Adjusted Combined Offensive-Defensive (CACOD) Metric

With the NCAA Division I women's volleyball tournament scheduled to begin play on Thursday, I am unveiling my second annual ranking of the tournament teams on the Conference-Adjusted Combined Offensive-Defensive (CACOD) metric. Though the CACOD is extremely simple to calculate (see below), it held its own with the more established volleyball ranking systems (e.g., Pablo, RPI, Rich Kern) in predicting match outcomes of last year's women's NCAA tournament. In fact, only three quantities go into the CACOD formula: a team's hitting percentage for the entire season, the hitting percentage the team allowed its opposition to achive (cumulatively) on the season, and a conference-difficulty factor that I determine. I wrote last year about hitting percentage being "a great singular statistic for incorporating many aspects of the game." As I elaborated: If you hit well (not just keep the ball in play, but get kills), your (individual or team) hitting percentage go

Bruin OH's Kidder and Love Play Regular-Season Finale as Trojans Visit UCLA

Defending NCAA champion UCLA hosts USC in the Bruins' regular-season finale tonight, marking the final pre-tournament home appearance for UCLA seniors Rachael Kidder, Tabi Love, and Bojana Todorovic. Outside hitters Kidder and Love have been the mainstays of the UCLA offense. Accordingly, I decided to examine the Bruins' win-loss records this season when Kidder alone, Love alone, both of them, or neither of them had strong hitting nights. I chose a .300 hitting percentage to define "strong" hitting. Volleyball announcers often make an analogy to baseball hitting, saying that a .300 average in either sport is a sign of success. Also, using .300 as the dividing line breaks UCLA's 28 matches into mostly equal-sized groups. My analysis is summarized in the following graph, on which you can click to enlarge. The blue bar shows that when Kidder and Love both hit .300 or better in the same match, the Bruins are a perfect 8-0 (1.000 winning percentage). The only

Wild Weekend in the Pac 12

An amazing finish in the Oregon at Washington match and a resurgence by USC against the northern California schools are the big stories in this weekend's Pac-12 play. The Huskies fought off no fewer than 14 match points to defeat the Ducks, 26-24, 16-25, 21-25 , 32-30, 25-23. Keeping in mind that fifth games are to 15 points, we see how deeply into overtime the decisive set went. Two of the match points were in Game 4 and 12 were in Game 5. As the linked article notes, "Making the run all the more impressive was that it came without UW's offensive leader, sophomore Krista Vansant, who landed awkwardly and suffered what appears to be a sprained ankle early in the fourth set, and did not return to the match." Several players were able to maintain hitting percentages of .300 or better over a large number of attempts ( box score ). For Oregon, they included Liz Brenner (.410; 22 kills and 6 errors on 39 attempts) and Alaina Bergsma (.302; 31-12-63). For Washington

Michigan Hitting Attack Comes Alive

For most of this season, I haven't had much to write about my graduate-school alma mater, the University of Michigan. Over their last five matches, however, the Wolverines have been playing their best volleyball of the season. They are 5-0 during this stretch, including wins over then-No. 4 Nebraska and then-No. 10 Minnesota ( game-by-game log ). Michigan has greatly elevated its hitting performance during these matches, as shown in the following graph (on which you can click to enlarge). The graph depicts the hitting percentages for four leading Wolverine hitters (different shades and styles of blue), and the team as a whole (yellow), in all of the team's conference matches to date. The first thing to notice is that, until the recent hot streak (indicated by the red arrow), Michigan as a team had hit at or below .200 in most of its matches, a fairly anemic level. In its five most recent matches, in contrast, Michigan's hitting percentages have ranged from .298 to .4

Nebraska Slumping of Late

Perennial power Nebraska heads into this weekend's homestand vs. Indiana (tonight) and Purdue (tomorrow) with three losses in its last four matches. After dropping their Big 10 opener at Penn State -- certainly no crime -- the Cornhuskers won nine straight. The next time out, seemingly out of nowhere, Nebraska fell at home to Ohio State in four games, before rebounding to beat then-No. 1 Penn State in five. A winless trip to Michigan and Michigan State followed, with the loss to the Wolverines particularly jarring because the Huskers had led two games to none. The Huskers' season to date is nicely encapsulated in this weekend's match notes from the Nebraska athletic department. Given the importance of hitting percentage in teams' success, I decided to plot Nebraska's hitting percentages for all of its conference matches so far this season, for the team as a whole and for the five players who take the most swings (you may click on the following graphic to enlarge

Upset Friday!

Last night saw four major upsets, three of which were in the Pac 12. All four of the victorious teams were unranked in the latest AVCA national poll . No. 2 Oregon lost at home in five to Cal, No. 4 Nebraska squandered a two-games-to-none lead and fell at Michigan, No. 5 UCLA was swept at Arizona, and No. 6 USC was swept at Arizona State. UCLA really seems to have a hard time with Arizona, for whatever reason. Even though the Bruins ultimately won last year's NCAA title, they lost both 2011 regular-season matches to the Wildcats. This year, the teams split, with UCLA winning at home on October 7, before last night's Arizona win. In order to get an idea of what might be going on, I've created the following table of key statistics for the last four matches between the Bruins and Wildcats (box-score links are at the top of each column; H% = hitting percentage). My usual warnings about concluding too much from small samples apply, however.  2011@Ariz 2011@UC

Hitting Charts for Washington vs. USC/UCLA

This past weekend, the University of Washington women went down to Los Angeles where they lost matches to USC (in five games ) and UCLA (in four ). Both matches were televised on the Pac 12 Network, so I was able to compile hitting charts for selected games from both matches. You may click on the following graphics to enlarge them. My notation and terminology are evolving as I create these diagrams. One recent development is that, if you see "IP" only, it means a hit attempt was kept in play due to being dug, whereas IP accompanied by "b reboot" means that the hit attempt was blocked back to the attacking team, which had to start over with a new attack. As I have noted previously, I'm doing my best to identify the player who took each spike attempt, but sometimes I'm only able to identify the team of the attacker. First, we have Game 4 of the USC-Washington match... Next, we have two diagrams for Games 3 and 4, respectively, of the UCLA-Washington co

Hitting Allocation Graph from Last Friday's Stanford-Washington Match

Below is a hitting allocation chart I made for Game 2 of last Friday night's Stanford-Washington match . The Cardinal took both this particular game/set and the match as a whole, 10-25 , 28-26, 10-25 , 26-24, 15-7. The chart shows which players took hitting attempts off of serve-receipt, from where on the court, at what angle, and with what result. An  introduction to the notation is available from this earlier posting in which I introduced the chart. One new piece of terminology today is that an offensive "reboot" is when a spike attempt is blocked back to the attacking team, which then starts over. For this chart, I tried harder to catch the names of the specific players taking each swing, but I was not always successful. You may click on the graphic to enlarge it. I don't think there are really any big surprises here. Stanford went heavily to middle-blocker Carly Wopat, both in the middle and on the slide play to the right, and she produced several kills. Was

Passing Well and Setting the Middle: The Texas Tech Internal Data

Today, for my second analysis using Texas Tech internal team data, I look at the relationship between quality of the team's passes on serve receipt, on the one hand, and the location and success of the resulting hit attempts, on the other. Again, my thanks to head coach Don Flora and assistant Jojit Coronel for their willingness to share the data and answer any questions I have. (Here's a link to my first analysis of the Texas Tech data, which focused on side-out rates in different rotations.) The better the pass a team can make on serve receipt, the easier it will be for the setter to get to the ball and, hence, the better should be the set. A good set should then increase the hitter's likelihood of achieving a kill. A further objective for many teams is to set the ball for the middle hitter, to quicken the offense. Other common plays involve high-arching sets to the outside, which give the other team time to get their blockers in place. In general, teams collect more

New Kind of Hitting Allocation Charts

This past weekend featured as much televised women's collegiate volleyball as I can remember seeing over a similar period. A big reason is the new Pac 12 Network and its extensive coverage of volleyball, but ESPN 2 and ESPN-U also played a part. With so many matches available, I decided to test a new type of chart to track teams' hitting allocations off of serve receipt, including the locations and angles of the spike attempts. I did something similar for a UCLA-Texas match in the 2010 NCAA women's tournament, based on video coverage from the Longhorns' website using an "end-zone" camera. Here's a link to that previous analysis. Traditional television coverage uses a sideline camera (for the most part), however, so I developed a new graphing approach using this perspective. One benefit of mapping the locations and angles of spike attempts is that doing so provides richer information than a typical box score. For example, a box score would list how many

Controversy Over NCAA Rule Change on No. of Subs

On this week's episode of the Internet-radio show The Net Live , a spirited discussion broke out on the merits of this year's new NCAA women's rule expanding the number of substitutions from 12 to 15 per game/set (see link to the archived broadcasts in the right-hand column). Substitution policy clearly has analytic implications, thus making it a worthy topic for VolleyMetrics; I provide no statistics in this write-up. This July 31 article focusing on the Nebraska program provides a lot of background perspective. A key impetus for the rule change appears to be the opportunity to get more players into matches. In terms of volleyball training, the substitution issue raises questions of player specialization vs. well-roundedness.  As the article notes: With 15 substitutions, coaches will likely not have to worry about reaching their limit and can take out their top hitters when it is time to rotate to the back row, replacing them with passing and serving specialists...

Success of Texas Blocking Line-Ups at Texas Tech

I attended last Saturday afternoon's University of Texas at Texas Tech match. The Longhorns won 3-0, but the Red Raiders were very competitive in two of the games/sets, as reflected in the 25-16, 25-23, 25-22 score. My focus was on the Longhorns' blocking -- how they lined up in their rotations and with which combinations were they able to stuff the Red Raiders for points. Texas's front lines in their six rotations (A through F), depicted schematically with the players' faces to the net, are shown in the graphic below. You may click on the graphic to enlarge it. The figure pertains only to Game 2, in which the Longhorns achieved 6 of their total 14 team blocks in the match (Texas Tech had only 3 team blocks total in the match). The Texas uniform numbers in the figure correspond to actual players, as follows: 1 Kat Bell (MB, 6-1, soph) 5 Molly McCage (MB, 6-3, frosh) 10 Haley Eckerman (OH, 6-3, soph) 12 Hannah Allison (S, 5-11, junior) 14 Sha'Dare McNeal (RS

Breaking Down Wednesday's UCLA-Wash. Thriller

UCLA has shown a flair in this young season for the dramatic, beginning with an opening-weekend loss to Nebraska (15-13 in the fifth). Wednesday night, the Bruins played another epic match, losing in five at Washington as the Huskies won all three of their games by the minimum two-point margin ( 22-25 , 30-28, 19-25 , 28-26, 16-14.) Despite entering the UCLA match undefeated, Washington was largely untested (a match against Purdue being the exception). Now, however, the Huskies have shown that they belong among the nation's elite. The UCLA-Washington showdown featured a match-within-a-match aspect, namely a battle between the teams' slugging outside hitters. For the Huskies, it was sophomore Krista Vansant going against Bruin seniors Tabi Love and Rachael Kidder. Based on the play-by-play sheet (which I accessed by going to the Huskies' schedule page and then clicking on the archived Gametracker for the UCLA match), I created the following chart of what I thought

Oregon Sweep of USC Highlights Weekend

Two nights after defeating No. 1 UCLA to move to 13-0 on the season, the USC women came crashing back to earth. Oregon came into USC's Galen Center on Friday night and swept the Trojans , 25-19, 25-15, 25-23. The Ducks, now 11-0, must be considered among the nation's elite at this point in the season. Against the Bruins on Wednesday, the Trojans seemingly sided-out at will, winning points on 71% of UCLA's serves. On Friday, the Ducks took siding-out to a new level against the Trojans, reaching 77% effectiveness. In the first two games especially, Oregon's serve-return performance was spectacular, attaining side-out rates of 84 and 81 percent ( box score ). The Ducks outhit the Trojans, .357-.189. Three Oregon players recorded superb hitting nights: outside hitter Liz Brenner, .577 (16 kills, 1 error, 26 attempts); OH Alaina Bergsma, .438 (18-4-32); and middle blocker Ariana Williams, .375 (8-2-16). UO attempted 115 spikes, only 14 of which resulted in errors (6 h

No. 2 USC defeats No. 1 UCLA

No. 2 USC defeated No. 1 UCLA last night, 28-26, 25-20, 24-26 , 25-17. The match kicked-off a series of Wednesday-night telecasts on ESPN-U. In reading the game article on the Trojan athletic website, I was impressed with the writer's attention to putting statistical figures in context. Many volleyball articles mention the players who had the most kills. However, merely stating the number of kills does not tell us how many attempts were required or how many errors the same hitter also had. As shown in the following brief excerpt from the article, the writer always made sure to accompany players' kill totals with their numbers of errors and attempts, and hitting percentage on the night. USC women's volleyball senior opposite Katie Fuller knocked down 21 kills (5e, 36att) and posted a .444 hitting percentage... Fuller's 21 kills matched a career high, but the Trojans got a major contribution from freshman outside hitter Samantha Bricio who also matched her c

Minnesota and Texas Split Matches in Austin

There's a new excitement surrounding University of Minnesota volleyball, with Hugh McCutcheon, who guided the U.S. men and women to, respectively, gold and silver in the last two Olympiad, taking over for retired coach Mike Hebert. The No. 14 Golden Gophers traveled down to Texas this past week for matches Thursday and Friday nights against the No. 4 Longhorns. The visit reciprocated a Texas trip to the Twin Cities last year, when the Gophers swept both matches . This year, it was a split, with each match going four games (or sets). The Gophers won the opener by scores of 25-22, 25-13, 27-29 , 29-27, whereas the Longhorns bounced back the following evening to win 25-20, 20-25 , 25-22,  25-21. Here are the box scores for Thursday and Friday nights. I like these two-match series, as the added data provide a more reliable picture of how the teams are doing than the usual single match. In Thursday night's Gopher win, offensive balance was the key. The following table shows