Wednesday, December 17, 2014

NCAA Women 2014: Wrapping Up the Regionals

My Conference-Adjusted Combined Offensive-Defensive (CACOD) measure successfully predicted the winners of 11 out of 12 matches in the NCAA women's Sweet Sixteen and Elite Eight.

Only in the Sweet Sixteen win by No. 14-seed Nebraska (CACOD = 1.72) over No. 3 Washington (2.23) did a team with a lower CACOD beat one with a higher one. Unseeded BYU (1.99) knocked off No. 6 Florida State (1.65), then followed up by topping Nebraska, sending the Cougars to the Final Four.

All three other regionals went exactly as the CACOD predicted, with No. 1 Stanford, No. 2 Texas, and No. 5 Penn State joining BYU in advancing to the Final Four.

***

In a rematch of last year's national championship match, Penn State once again outlasted Wisconsin in four games (22-25, 25-16, 25-22, 25-19; box score) to win the Louisville regional. One thing I examined from last year's final was how each team won its points (i.e., percent from kills, aces, blocks, opponent service errors, etc.). A natural comparison, therefore, is how Penn State and Wisconsin won their points against each other in last year's final and in Saturday's regional final.

For the Nittany Lions, two differences were apparent. Whereas Penn State amassed 63.5% of its points in last year's final from kills, the figure Saturday against Wisconsin was 57.7% (i.e., the Lions scored 56 of their 97 total points with their own kills). In contrast, Penn State went from getting only 7.3% of its points from Badger service errors in last year's finals (7 of PSU's 96 points coming from Wisconsin serving miscues) to obtaining 14.4% of points last Saturday in this way (14 of the Nittany Lions' 97 points coming from Wisconsin serving errors). The percentages for all other point-scoring categories (e.g., blocks, aces) were pretty similar for PSU between the two matches. Overall, then, Wisconsin made things easier for Penn State in this year's regional final than in last year's national title match by giving the Nittany Lions 7 extra points on Badger serving errors, points PSU did not need to earn via kills.

Compared to last year's final, Wisconsin earned a slightly higher percentage of its points last Saturday from kills (53.7 to 49.5), but smaller share from Penn State service errors (9.8 to 13.2). Percentages in other categories were similar in the two matches.

***

For the second straight year, Washington failed to take advantage of playing in the university's home city of Seattle (last year's national semifinal vs. Penn State and, as noted above, in this year's Sweet Sixteen vs. Nebraska). The Huskies were led in recent years by outgoing senior outside-hitter Krista Vansant. Taking selected matches from this year's Huskies schedule, I looked at how well Vansant (in purple) and her teammates (collectively, in gold) hit in several key U-Dub wins and the team's three losses (shaded in gray). You may click on the graphic to enlarge it.


In the Huskies' losses at Colorado (Vansant .154, teammates .190) and Utah (Vansant .197, teammates .184), everyone struggled with their hitting percentages. Against Nebraska, however, things were a little different. Vansant hit a respectable .273, but her teammates collectively hit .219. In all the other matches graphed, which the Huskies won, the teammates had hit at or around .260, or higher.

Friday, December 12, 2014

2014 NCAA Women's Tourney Second Weekend

The women's NCAA Sweet Sixteen begins tonight. Here are the match-ups, showing teams' scores on my Conference-Adjusted Combined Offensive-Defensive (CACOD) ratings. The team with the higher CACOD in a given match would be favored to win.

Host Site Higher-Seeded Team  CACOD Lower-Seeded Team CACOD
Ames, Iowa Stanford (1) 2.24 Oregon State 1.34
Ames, Iowa Florida (8) 1.98 Illinois (9) 1.78
Louisville Penn State (5) 3.09 UCLA (12) 1.47
Louisville Wisconsin (4) 2.20 Ohio State 1.43
Seattle Washington (3) 2.23 Nebraska (14) 1.72
Seattle Florida State (6) 1.65 BYU 1.99
Minneapolis North Carolina (7) 1.75 Oregon (10) 1.59
Minneapolis Texas (2) 2.29 Colorado State (15) 2.05

BYU would thus be the only lower-seeded team I would pick to win.

Thursday, December 4, 2014

Preview of 2014 NCAA Women's Tourney

With play in the NCAA women's tournament bracket beginning tonight, I wanted to provide a few statistical tidbits in advance.

If there is one team whose players, coaches, and fans have a right to be upset with the seedings, it's Wisconsin. Last year's national runner-up to Penn State, the Badgers closed this season winning 19 straight matches and captured the Big 10 crown. But now, if Wisconsin is to get back to the Final Four, it will have to beat Penn State along the way, in the Elite Eight (barring an upset loss by the Nittany Lions in an earlier round).

As well as the Badgers have played this season (and the latter part of last season), Penn State has been Wisconsin's Kryptonite the past two years. In fact, PSU has won 12 of the 13 games it has played against Wisconsin in 2013 and 2014 (two 3-0 regular-season sweeps and a 3-1 win in the NCAA title match last year, and a 3-0 sweep in the teams' only meeting this year).

***

As longtime readers of this blog are aware, the way I like to evaluate teams heading into the NCAA tournament is by dividing a team's season-long hitting percentage by the cumulative hitting percentage it allowed its opponents during the season. I also multiply the result by a "fudge factor" (usually a little above or below 1.00) to reflect my impression of conferences' national competitiveness. Predicting the winner of a match by which team has the higher score on my simple measure has done about as well as more complicated algorithms in forecasting NCAA tournament results (here and here). The formula is shown here.

For the top eight seeded teams in the 2014 NCAA women's tournament, here are their scores:

1. Stanford... (.316/.176) (1.25) = 2.24
2. Texas........(.288/.151) (1.20) = 2.29
3. Washington...(.312/.175) (1.25) = 2.23
4. Wisconsin.....(.287/.163) (1.25) = 2.20
5. Penn State.....(.351/.142) (1.25) = 3.09
6. Florida State....(.271/.181) (1.10) = 1.65
7. North Carolina.....(.261/.164) (1.10) = 1.75
8. Florida...(.320/.162) (1.00) = 1.98

Last year's NCAA champion (and No. 2 seed) Penn State entered the 2013 tournament with a score of 2.91, to 1.69 for runner-up No. 12-seed Wisconsin (last year's Badgers hit.233 and allowed their opponents a .172 percentage). No. 7-seed Stanford, which gave PSU a very tight match in the Elite Eight, compiled a 2.30 in the 2013 regular season. None of the other top eight seeded teams last year exceeded 2.13.

***

Here's another note about Penn State: Following up on a midseason posting, the Nittany Lions successfully finished the season as the only major team in the nation to score at least 20 points in every game this season (excluding fifth games). PSU lost three matches this season, as well as one or two games in some matches it won. But even in losing games, the Nittany Lions never dropped below 20 points. Since I last posted on the topic, Penn State lost only two more games (at Purdue), but scored 23 and 22 points in them. To me, this shows that PSU sticks around in every game and never makes things easy for an opponent.

***

Recent history suggests no need to raise the SEC's strength factor from its current 1.00. Last year, for example, two highly seeded teams from the conference, No. 4 Missouri and No. 5 Florida, each exited in the second round of the NCAA tournament.

Wednesday, October 29, 2014

Hitting Value Relative to Average

In baseball "sabermetrics," various statistics have been developed to quantify a player's contribution to winning games, such as Bill James's Win Shares. Other, related statistics incorporate a comparison to possible replacement players (e.g., Wins Above Replacement and Value Over Replacement Player).

Some of these statistics have been carried over into other sports (e.g., Wins Shares for basketball). However, to my knowledge, these kinds of statistics have not been developed for volleyball. Winning at volleyball encompasses many skills: serving, passing, setting, hitting, and blocking. There has not been enough volleyball statistical work, in my judgment, to attempt an all-encompassing metric analogous to Win Shares, WAR, or VORP at this time.

With the aim of eventually taking us toward Volleyball VORP (or some such term), I have decided to focus on one skill for the moment, the important ability to hit for kills without committing attack errors (hitting the ball out of bounds or getting blocked for an immediate point by the opponents). These aspects of attacking are, of course, operationalized in the formula for hitting percentage: (Kills - Errors)/Total Hitting Attempts. Hitting percentage is strongly correlated with winning, so this seems like a good place to start.

My new statistic involves taking a given player's hitting percentage and subtracting some reference value of hitting percentage (akin to that of an average replacement player), The resulting difference is then multiplied by 100. As an example, if a player ended up with a value of 5.0, it would mean that, per 100 swings for each player, the focal player's kill-minus-error number is five higher than the hypothetical reference player's. Stated differently, the focal player would generate five more kills per 100 hitting attempts than would a reference player (having subtracted hitting errors from each player's number of kills).

Because middle blockers tend to have higher hitting percentages than outside hitters, I conducted all analyses separately for middles and outsides (opposite/right-side hitters, who were not always distinguished on teams' roster pages, were combined with traditional outsides who hit from the left-hand side). Thus, the new statistic will evaluate middles in comparison to a reference middle blocker, and outsides in comparison to a reference outside hitter.

I chose the Big 12 conference (which only has nine volleyball-playing schools) for this first effort, as I am based at one of the schools (Texas Tech). Also, each team has played eight conference matches so far, marking the halfway point of league competition,*

To get at the "above replacement" aspect, I wanted to take the average hitting percentage of all players at a given position who did not start for their respective teams as the reference value. However, not all schools included matches-started totals in their statistics and, even if they had, there were not that many usable reserves out there. Many non-starting OH and MB players had very small numbers of total hit attempts, so I didn't want to use their hitting percentages. Imposing minimum criteria for inclusion, for which I settled on at least 20 Total Attempts in conference play or at least 40 TA for the entire season, the number of "replacement players" appeared to be small.

Therefore, instead of averaging all reserve OH and MB in the conference, I averaged all the OH in the conference (starters and reserves) with the necessary minimum of hitting attempts (yielding an average hitting percentage of .197), and all the MB in the conference with the necessary minimum (yielding an average of .274). The "reference player" I alluded to above is now the same as the "average player" at the given position (OH or MB).

Below are the results. You'll notice that Haley Eckerman of Texas leads outside hitters with a +11.3 value. This tells us that, given 100 swings, Eckerman would generate 11.3 more kills (deducting hitting errors) than the average OH/OPP in the Big 12. Most of the results are not so dramatic. Many players will only get you an extra kill or two (or even just a fraction of one kill) per 100 swings, or lose you an equivalent amount.

Outside Hitters
Increased or Decreased Kills (Minus Errors) Per 100 Attempts (Relative to Average OH)

Eckerman (UT) +11.3
Attea (WVU) +8.5
Ward (OU) +7.7
Albers (KU) +7.1
Holland (TCU) +5.9

Anderson (WVU) +5.4
Holst (OU) +5.0
Sassin (KSt) +4.7
Victoria (UT) +3.5
Bigbee (ISU) +3.2

Ybanez (TTU) +2.7
Neal (UT) +2.5
Malloy (Bay) +1.8
Cerame (UT) +1.7
Hurtt (ISU) +1.6

Pickens (TCU) +1.6
Zumach (KSt) +0.9
Smith (TCU) +0.6
Gardiner (OU) +0.4
Stacy (TTU) +0.2

Keating (KSt) -0.6
Staiger (Bay) -0.6
Baker (UT) -0.7
Fragniere (TTU) -0.9
Dockery (KU) -1.0

Mikels (TCU) -1.2
David (TTU) -2.0
Sackett (WVU) -2.6
Capezio (ISU) -3.1
Kurht (ISU) -3.1

Allen (TTU) -3.5
McClinton (KU) -4.3
Rigdon (KU) -6.1
Montgomery (WVU) -7.2
Bardali (Bay) -10.7

Jones (Bay) -12.5
Munch-Soe. (Bay) -15.2

Among the middles, the leader was Mia Swanegan of TCU. The Horned Frogs' volleyball statistics page does not list Total Attempts, but I inferred that she likely has had at least 20 swings in conference play, because she has 16 kills (and a .433 hitting percentage).

Middles
Increased or Decreased Kills (Minus Errors) Per 100 Attempts (Relative to Average MB)

Swanegan (TCU) +15.9
Payne (KU) +11.1
Ogbogu (UT) +10.1
Bell (UT) +9.2
Jones (KSt) +9.1

McCage (UT) +5.9
Douglass (TTU) +5.5
Soucie (KU) +5.3
Conaway (ISU) +3.3
Hazelwood (OU) +2.7

McGuire (TCU) +0.4
Reininger (KSt) -0.6
Hill (Bay) -1.3
Mills (TTU) -1.4
Burleson (TCU) -1.9

Richburg (Bay) -2.8
McCoy (WVU) -2.8
West (ISU) -3.6
Spann (OU) -4.6
Wells (WVU) -4.8

Vondrak (ISU) -6.1
Shreve (WVU) -6.1
Itiola (Bay) -6.2
Knuth (ISU) -12.4
Grant (TTU) -23.0

As usual, some cautions apply to these results. The quality of passing and setting is obviously not uniform across teams, so any player's value of kills added/lost would likely be different if she played for another school. Also, as noted earlier, a player who does not appear to add value through hitting may nevertheless do so through blocking, serving, or other skills. I will try to develop new measures of these skills (relative to average) in the future. At that point, an across-the-board Volleyball VORP may be within reach.

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*The plan was to use only conference matches in the analysis, but only full-season statistics were available for Iowa State, Texas Tech, and West Virginia.

Thursday, October 16, 2014

Rare Hitting Feat Powers UCLA Over Oregon

UCLA had two players hit at least .400 on at least 20 spike attempts each, en route to a four-game win over Oregon. As shown in the box score, Karsta Lowe hit .473 for the Bruins, based on 33 kills and 7 attack errors on 55 swings, whereas teammate Reily Buechler registered a .429 hitting percentage with 13 kills and only 1 miscue in 28 attempts. I don't comprehensively track all matches nationally, but a team having two of its players reach the .400/20 mark in the same match appears to be pretty rare.

So much of a slugfest was this match that Oregon nearly matched UCLA's accomplishment. The Ducks' Martenne Bettendorf met the criteria (.500, 12-1-22), with teammate Naya Crittenden falling three hitting attempts short (.412 , 9-2-17).

All four of the above players hit from the outside, either as traditional outside-hitters who attack from the  left side of the front row, or as "opposite" hitters, who attack from the right side (the name deriving from being opposite the setter in the rotation). Middles tend to have higher hitting percentages than outsides, but middles get fewer hitting attempts, making it hard for them to join the .400/20 club.

Monday, October 13, 2014

B1G Upsets Highlight Weekend's Women's Play

.310 hitting percentage, 13 total team blocks (compared to 8 for the opponent), and a nearly 1-to-1 ratio of service aces to errors (6/8). 

This does not look like the statistics of a losing team in a match, especially when that team is Penn State, playing at home in State College. Yet, if you consult the box score of Saturday's match between No. 15 Illinois* and the No. 5 Nittany Lions, you'll find that Penn State most certainly did lose with the above stats. The Illini hit just a bit better than the Lions, .315 to .310, in pulling the four-game upset (26-24, 16-25, 25-23, 25-22).

The gaudy hitting numbers produced by Penn State in the early weeks of the season made only a cameo appearance vs. Illinois, with PSU putting together a .471 percentage in Game 2; the team's hitting percentage ranged between .250-.289 in the other three games. Frosh middle Haleigh Washington led Penn State with a .538 hitting percentage on 14 kills and 0 errors, on 26 swings.

Illinois's attack sparkled in Games 1 (.500) and 3 (.375). In the clinching Game 4, Illinois somehow managed to prevail with a .214 hitting percentage, compared to .250 for Penn State. According to the play-by-play sheet, the Illini's 25 points in Game 4 came from the following combination: 16 kills, 4 PSU attack errors, 1 PSU setting error, 2 Illinois service aces, and 2 PSU service errors. It took the Illini 42 swings to get those 16 kills in Game 4 (with 7 attack errors mixed in); these numbers suggest Illinois really had to grind out its final-game victory through a lot of long rallies. Junior OH Jocelynn Birks led the visitors, recording 19 kills and only 2 errors on 36 attacks, for a .472 percentage.

Though Penn State has now lost three matches this season, it remains the only major team not to score fewer than 20 points in any single game (excluding fifth games, which are played to 15). As shown above, the Nittany Lions scored 24, 25, 23, and 22 vs. Illinois. In losing at Stanford, PSU scored 25, 23, 22, and 25, before losing the decisive fifth game, 15-10. Finally, in the loss at Nebraska, the Nittany Lions scored 25, 22, 20, and 23 points.

I have checked and verified that each of the following Top 10 teams (some of whom have not lost a match) has at least one sub-20 game. In the interest of time, I list only one example for each team: No. 1 Stanford (25-18 Game-1 loss to Penn State), No. 2 Texas (25-19 Game-2 loss to West Virginia), No. 3 Washington (25-18 Game-1 loss to Cal), No. 4 Florida State (25-18 Game-1 loss to Nebraska), No. 6 Wisconsin (25-16 Game-4 loss to Washington), No. 7 Colorado State (25-18 Game-3 loss to Arizona State), No. 8 Nebraska (25-19 Game-3 loss to Texas), No. 9 Florida (25-16 Game-1 loss to Texas), and No. 10 BYU (25-18 Game-1 loss to Washington).

For No. 8 Nebraska, a problem of late has been closing out potential weekend sweeps. On Friday, October 3, the Cornhuskers opened a two-match homestand with a 3-1 victory over Penn State. The following night, however, Nebraska lost in five to Ohio State, the Huskers blowing a 14-11 lead in the final game.

Fast-forward a week to Friday, October 10. On this night, Nebraska again started the weekend off on a positive note, knocking off Michigan State, 3-1, in East Lansing. However, on the back leg of the trip in Ann Arbor Sunday afternoon, the Huskers could not even take a game against unranked Michigan. To be sure, the Wolverines (8-8 overall, 3-3 in conference) are improving with the return of senior setter Lexi Dannemiller, who missed the first month of the season. Still, a Michigan sweep of Nebraska would have to be considered a big upset, in my book.

The B1G is exhibiting a lot of parity this year. Along with the above examples, Ohio State followed up its win over Nebraska by executing a nearly identical fifth-game comeback against Illinois. I can see 11 of the conference's 14 teams -- Purdue, Wisconsin, Penn State, Ohio State, Illinois, Nebraska, Minnesota, Northwestern, Indiana, Michigan State, and Michigan -- either being shoo-ins or contenders for the NCAA tournament. Whether Northwestern and Indiana can keep up the pace remains to be seen.

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*National rankings cited in this posting are from the October 6 AVCA poll.

Wednesday, October 8, 2014

As Goes Lowe...

In one of the marquee matches of this past weekend, the Washington Huskies upped their overall record to 15-0 with a comeback from two games down against UCLA. Scores were 18-25, 24-26, 25-17, 25-20, 15-10. With the hot hitting of 6-foot-4 senior OH Karsta Lowe pacing the Bruins this season, I was curious how well she attacked in each of the five games vs. the Huskies.

Lowe hit an uncharacteristically low .143 for the entire match, with 28 kills and 19 attack errors on 63 swings. Particular players' game-specific hitting percentages are not available from standard box scores, but play-by-play sheets, such as the one for UCLA-UW, can at least give us a player's kills and hitting errors by game (only final dispositions of plays are listed, so spike attempts kept in play don't appear). As shown in the following chart, I used the play-by-play to tabulate Lowe's kills and attack errors in each of the five games (you can click on the graphic to enlarge it).


Not surprisingly, Lowe's kills well outnumbered her errors in the first two games, won by the Bruins. Counter to expectation, however, the same pattern continued into Game 3, which the Huskies won. In the final two games, won by Washington, Lowe's errors exceeded her kills.

In no way is this posting intended to blame Lowe for the UCLA loss. Hitting is only the final product of a sequence that flows from passing and setting. Also, Washington likely made defensive adjustments after the second game that curtailed not just Lowe's, but other UCLA players' hitting effectiveness, as shown in the next graph.


Finally, the Huskies' hitting effectiveness rose dramatically after Game 2. This suggests that the Bruins' defense was as much, or more, of a problem, relative to their offense, as the match started slipping away.

Wednesday, September 24, 2014

"What Wins in the Big 12?"

As some long-term readers of this blog may know, I'm a professor at Texas Tech University and I meet occasionally with Red Raider volleyball coach Don Flora to discuss statistical aspects of the sport and find out what kind of analyses he might be interested in at a given time. We last met this past spring and he told me his big question: "What wins in the Big 12?" I took the meaning of the question to be: what combinations of success at hitting, blocking, digging, serving, etc., were associated with winning conference matches in the Big 12? I told Coach Flora I would have something for him, and proceeded to start thinking about how I would conduct analyses.

Now, with the Red Raiders opening their Big 12 portion of the schedule by hosting TCU tonight, I have the fruits of my inquiry. I first created a database of all 72 conference matches played a year ago (despite its name, the Big 12 has only 10 schools and one, Oklahoma State, doesn't field a women's volleyball team; nine teams playing a double round-robin schedule of 16 matches yields 72 total matches). For each team in a given match, I recorded its hitting percentage; blocks, digs, aces, and service errors per game; whether the team won or lost the match; and the number of games it took. Note that with 72 matches and two teams per match, there were 144 records or "stat-lines" possible.

One very basic comparison, among the techniques used by Penn State coach Russ Rose in his 1978 Master's thesis at Nebraska, is to see how often the team that outperformed its opponent on a given statistic won the match. As shown in the following chart, the team with the better hitting percentage in a match won nearly all the time (68 out of 72 matches). Having more blocks, digs, and aces also conferred sizable advantages, but not as powerfully as out-hitting one's opponent.


Hitting does not occur in isolation, however. Some teams might hit well, but not block well; or hit well and not dig well; etc. To probe this issue, I looked at the 144 stat-lines referred to above. To illustrate a stat-line, let's look at Texas Tech's (focal team) when it visited Kansas:

Hitting Percentage = .192; Blocks/Game = .67; Digs/Game = 17.67; Aces/Game = 1.33; Service Errors/Game = 2.67.

I then submitted the 144 stat-lines to a cluster analysis, a technique that attempts to sort cases (or stat-lines) into groups with other similar cases. In other words, the stat-lines within a group should end up relatively similar to each other (i.e., within-group homogeneity), but the different clusters of stat-lines will be dissimilar from each other (i.e., between-group heterogeneity). I obtained 10 clusters, but two of them had only four cases each, which is too small for statistical analysis. Ultimately, our interest will be in seeing the win-loss records of the eight viable clusters, but let's review some basics first. The following graphic illustrates the membership of Cluster 9, as an example (unless you have exceptionally strong eyesight, you'll want to click on the chart to enlarge it).


Seventeen stat-lines ended up in Cluster 9. Each focal team (to whom the stat-line belongs) is highlighted in yellow, with its opponent for the particular match appearing in the second column. Note that many different schools can appear in the same cluster. We're grouping performances, not teams per se. Averages for the complete sample of 144 stat-lines on the various volleyball performance measures are shown in red above each column.

Probably the funnest aspect of conducting cluster analyses is that you get to make up names for the clusters, based on their statistical properties. As seen in the above chart, I named Cluster 9 "Slightly Above-Average Hitting, Below Average Blocking, VERY GOOD DIGGING, HIGH ACES." The digs/game for the 17 cases are shown above in red outline; they range from 17.33 (Baylor, playing at West Virginia) to 20.50 (Texas Tech, hosting Oklahoma). All of these dig statistics exceeded the complete-sample average of 14.92, illustrating why a major part of this cluster's "identity" would consist of "very good digging." Apparently as a result of the digging, the teams in this cluster went 11-6 in the relevant matches, despite hitting only slightly above average in them (the average hitting percentage for the Cluster-9 teams was .223, compared to a complete-sample average of .214).

I've placed all the detailed statistics on the clusters below in an Appendix, for anyone who is interested (once again, please click on the graphic to enlarge it). In the remainder of this posting, I provide brief summaries of the clusters:

Cluster 1. Plagued by below average digging (12.92/game) and a high rate of service errors (2.71, compared to the complete-sample average of 1.73), teams whose stat-lines were in this cluster went 5-12 in the relevant matches.

Cluster 2. Cases in this group displayed great hitting on average (.253, compared to the full-sample mean of .214). They also served aces at a higher-than-average rate (1.93/game, compared to 1.14 for the full sample), but also committed more service errors (2.20/game) than the overall average (1.73). The kind of seemingly powerful/aggressive play exhibited in this cluster produced a 12-6 record.

Cluster 3. Characterized by below-average blocking (1.49/game, compared to the overall average of 2.19) and few aces (.74/game), cases in this cluster went 6-13.

Cluster 4. Though this cluster contained only four cases, the signs of poor play were quite vivid (e.g., .031 average hitting percentage, 1.08 blocks/game, a paltry 8.42 digs/game). Although caution is warranted due to the small size of this cluster, the results are just as one would expect: 0-4.

Cluster 5. This cluster excelled in most every way (.256 hitting percentage, 3.12 blocks/set, 1.36 aces/set with only 1.56 service errors), except for digging (12.02/game). The focal teams went 10-5 in the relevant matches.

Cluster 6. This group combined weak hitting (.130), blocking (1.04/game), and digging (11.78/game), with apparent caution from the service line (only .75 aces and 1.27 service errors, per game). This is not a pattern to emulate, as the teams went 1-9.

Cluster 7. Cases in this cluster hit at the overall average (.214), blocked (2.47) and dug (16.68) somewhat above average, but also showed caution when serving (.89 aces and 1.32 errors, per game). I would have expected these cases to have a winning record, but they didn't, going 15-16.

Cluster 8. Cases here hit (.268) and blocked (3.58) extremely well, rarely served aces (.58/game), and were pretty average on the other metrics. Dominating the net paid off big, as these cases went 8-1.

Cluster 9. Discussed above.

Cluster 10. The other cluster with only four cases, the teams here played great defense (22.31 digs, and 2.60 blocks, per game) and went 4-0.

In conclusion, to answer Coach Flora's question, there are multiple ways to win in the Big 12 (see Clusters 2, 5, and 8), but they all seem to revolve around great hitting. One way to increase the sample size and achieve greater precision in a future study would be to look at win-loss records of games rather than matches. Box scores typically include team hitting percentages by game (to correlate with the winning of games), but blocks, digs, and serving statistics are only reported for the match as a whole. One final issue is that the present analysis tells us nothing about whether the findings are in any way unique to the Big 12; the same relationship between performance metrics and winning might emerge for other conferences, as well. We just don't know.

Appendix


Tuesday, September 23, 2014

Opening of Women's Conference Play (2014)

It's a busy week in women's college volleyball, with conference play opening up around the country. The Pac 12 schedule has each team starting off league play against its respective traditional/geographic rival. A pair of matches will be held tonight, featuring Cal (8-2 in nonconference) at  No. 1 Stanford (10-0), and No. 20 UCLA (9-2) at No. 9 USC (7-3). Other Pac 12 rivalry matches will be held on Wednesday and Thursday. I already wrote about Stanford's fast start this season, so I will discuss UCLA and USC (among other teams) in the present posting.

The Big 10 (or B1G) begins play with matches Wednesday and Friday. The marquee match-up of the week, not just in the conference, but nationally, features a rematch of last December's national championship tilt between No. 3 Penn State (12-1) and No. 5 Wisconsin (9-1), in Madison. The Nittany Lions' only loss so far this season was in a five-gamer to Stanford, whereas the Badgers' only setback was to Washington, likewise in five games.

The following chart (on which you can click to enlarge) displays information on hitting percentages associated with Penn State, Wisconsin, UCLA, and USC, with each team having its own column.


Looking at PSU in the far left column, for example, we see that the Nittany Lions hit an amazing .395 as a team during nonconference play, with four players, led by middle-blocker Nia Grant (.525), exceeding .350. And this is without last year's seniors Deja McClendon, Ariel Scott, and Katie Slay. Talk about reloading rather than rebuilding! Meanwhile, Penn State has held its opponents to an aggregate .125 hitting percentage. The Nittany Lions' schedule has been moderately tough, including games against two NCAA Sweet Sixteen teams from a year ago -- American and Kansas -- one against traditional power UCLA, and the aforementioned match with Stanford.

Penn State's gaudy hitting percentages derive partly, but certainly not entirely, from matches against weaker teams. As a team, the Nittany Lions hit .442 against UCLA, with four PSU players each hitting .444 or higher. In the Kansas match, PSU came out smoking on serve-receipt, siding out on 100% (10-of-10) of the Jayhawks' Game-1 serves. Grant hit .467 in this match, Aiyana Whitney, .571, and the Lions as a team, .319.

Wisconsin, whose most impressive wins include a sweep of No. 7 Colorado State and a four-game victory over USC, is hitting .296 as a team, with three players at or near .400. Washington held the Badgers to a .178 team hitting percentage, however, outblocking them 22.0 to 7.5. Even on such a bleak hitting night for the Badgers, setter-turned-outside-hitter Courtney Thomas hit .406. Thomas was profiled in the September 11, 2014 issue of Wisconsin's Varsity Magazine. In beating 'SC, Wisconsin's hitting was at a more characteristic .320.

Finally, we have UCLA and USC. The Bruins' two losses were sweeps at the hands of Penn State and, very unexpectedly, Loyola Marymount (now ranked No. 21 in the nation), whereas the Blue and Gold's best wins have been over No. 16 Illinois and No. 25 Hawai'i. Senior Karsta Lowe is pacing the Bruin offense, not only hitting a team-leading .368, but also taking 30.7% of UCLA's spike attempts (367/1195). Younger players Claire Felix (So.) and Olga Strantzali (Fr.) are also contributing well offensively.

USC recently experienced a three-match losing streak, falling at home to Texas A&M (3-2) and Florida (3-0) two weekends ago and then to Wisconsin last week. The Trojans' best win so far, at least in terms of rankings, was at No. 14 Kentucky. In sweeping the Wildcats, 'SC hit .313 while holding UK to .090.

During the losing streak, the Trojans faltered both offensively and defensively. Sophomore Ebony Nwanebu has hit above .300 for 'SC since returning from early-season injury problems, but Florida kept her totally in check (5 kills and 5 errors on 21 attempts, for a .000 evening). Also, though not as dramatically, Wisconsin contained 'SC junior Samantha Bricio (11-5-46, .130). Defensively, during their losing streak, the Trojans let all three opponents exceed .300 in hitting percentage (Aggies, .319; Gators, .303; and Badgers, .320).

Bruins and Trojans, Nittany Lions and Badgers. Pretty good matches to begin play in the nation's major conferences!

Thursday, September 11, 2014

Stanford Women Off to Fast Start

Heading into the third weekend of the 2014 women's college season, Stanford has been the most impressive team thus far, dominating the most recent AVCA national poll. The Cardinal (4-0) is by no means the only undefeated team; 10 teams in the Top 25 have perfect records. However, it's the difficulty of Stanford's opposition -- Iowa State (in Ames), Nebraska (in Lincoln), Penn State, and Illinois -- that makes the Cardinal's record so noteworthy.

Another challenge Stanford has overcome thus far is the absence of two of last year's seniors, three-time All-American MB Carly Wopat and All-Pac 12 honorable mention OH Rachel Williams. Let's explore how the Cardinal has adapted offensively. I first compared Stanford's offensive statistics for 2013 and 2014 (the latter statistics, based on only four matches, should of course be taken with caution). As the first graph shows, the Cardinal has not hit at quite as high a clip as last year, while its opponents (in the aggregate) have hit a little better this year than last. Still, this year's Stanford squad has outhit the opposition by a sizable margin (.275 to .186).


As the next chart shows, Williams (807 total spike attempts) and Wopat (606) together took roughly 35% of the Cardinal's 4,005 total swings last year. These two non-returning players from 2013 are shown in different shades of grey below, and the percentages on the second line below each player's name signify the share of the team's total swings they have taken (you can click on the graphics to enlarge them).


That's a lot of offense to replace. Three key Stanford returnees are Brittany Howard (shown in dark cardinal red in the chart), Jordan Burgess (pink), and Inky Ajanaku (bright red). The fact that Burgess's and Ajanaku's line-segments are wider this year than last signifies that they are each taking on a larger share of the Cardinal hitting. Whereas Ajanaku took 12.8% of Stanford's spike attempts last year, she is taking 16.7% of them this year. Burgess's share has gone from 20.3% to 28.1%. (It's pretty common for outside hitters such as Burgess to take more attempts than middle blockers such as Ajanaku.)

Morgan Boukather, who attempted only 48 spikes (around 1% of the team's total) in 2013, is way more active this season, having taken 17.7% of the Cardinal's attempts. Note that Boukather hits from the right side (opposite the setter in the rotation), whereas Williams and Wopat hit, respectively, from the left and middle positions on the front line.

Beyond how frequently an attacker is called upon to hit, there is the question of how effectively she is doing it. Ajanaku has upped her hitting percentage from the already high .438 in 2013 to .474 this season. Burgess, though getting more swings, is not hitting as efficiently this year (a hitting percentage of .194, compared to .294 last year). We'll see if she continues to get so many attempts. Boukather has been a bit up-and-down so far this season, hitting .167 vs. Iowa State, .417 vs. Nebraska, .353 vs. Penn State, and .097 vs. Illinois.

Sunday, May 11, 2014

Loyola-Chicago's "Bic" Clicks, as Ramblers Win Men's Title in Offense-Dominated Year

A little over a week ago, Loyola University Chicago won its first NCAA men's volleyball title, as the host Ramblers stopped Stanford in four games. According to Off the Block's preview of the final, "Loyola en route to being ranked No. 1 throughout the majority of the season... had a nation-best .363 attack percentage. In addition, Stanford... was second in the nation with a .336 attack percentage." Further, according to the ESPN-U broadcast, this year's final was the first ever to feature the nation's top two hitting teams.

For this reason, and others, one might dub 2014 the "Year of the Offense" in men's collegiate volleyball. One type of attack in particular, known as the "bic," was instrumental to Loyola's final-match victory. According to many accounts, bic is a contraction of "back-row quick" attack. Another story, recounted in this video, is that the UCLA team's hand-signal for the play many years ago (featuring Jeff Nygaard and Stein Metzger) was a flick of the thumb, as though activating a BIC lighter.

A player in the back row can attack above the net only if he or she leaves the ground behind the 10-foot line. On a bic, the quick-set near the net gives the appearance of being intended for the front-row middle-hitter (who jumps up to fake an attack), which lures the opposing blockers into the air. When the front-row attacker and blocker(s) return to the ground, the back-row attacker can spike the ball unopposed. The term "pipe" is sometimes used interchangeably with "bic," whereas some volleyball experts distinguish the two. The bic is a devastating attack when executed properly, as shown in this video compilation (which uses the pipe terminology). An extended discussion of the bic/pipe is available here from VolleyTalk.

Loyola's Cody Caldwell was the tournament's Most Outstanding Player, recording 20 kills in the final match with only 2 hitting errors on 32 swings, for a .562 hitting percentage (box score). Caldwell produced nine of those kills in Game 1, three via bic plays and six from his normal perch as an outside hitter on the left side of the front row.

The Ramblers, as a team, hit .452 (59-12-104) in the final. Each of their players who took at least one swing hit .333 or better. They did not commit their first hitting error until Game 2, having hit .696 (16-0-23) in Game 1. The Cardinal hit .266 (47-18-109), its lowest percentage since an April 11 match at UC Irvine (.256).

Another noteworthy element of Loyola's title-match performance was its high side-out rate. Overall, the Ramblers won points 75% of the time that Stanford served (88%, 78%, and 81% in Games 1, 3, and 4, respectively, which Loyola won; and 60% in Game 2, which Stanford won). Judging by the Cardinal's serving statistics vs. the Ramblers (5 aces and 14 errors), it seems Stanford tried to serve aggressively to derail Loyola's serve-receipt game. This (apparent) strategy was to little avail, as Loyola sided-out well and all of Stanford's serving errors only raised Loyola's side-out rate further. Below, I have created a frequency distribution of side-out rates Stanford allowed to all of its opponents during the 2014 season, which is available from the Cardinal's match-by-match log (you may click on the graphic to enlarge it).


Loyola's 75% side-out rate actually wasn't the highest Stanford had allowed all season, but it was close (Pepperdine sided-out 76% of the time on March 7, yet the Cardinal still prevailed, 3-1). Both of these side-out rates are considerably higher than what Stanford typically allowed this season (mean = 61%, median and mode = 62%).

***
Besides Loyola and Stanford recording the nation's two highest hitting percentages during the season and both reaching the NCAA title match, one other team led me to think of 2014 as the Year of the Offense, as noted above. In the quarterfinals of the Mountain Pacific Sports Federation tournament, BYU swept USC on the strength of some spectacular hitting by the Cougars. BYU hit .519 in this match, amassing 44 kills, with only 4 errors, on 77 attempts. Four hitting errors is an amazingly low total; two of them resulted from the Trojans stuff-blocking spike attempts back onto the Cougar side of the floor for USC points, whereas BYU hit two balls out of bounds.

BYU had won all three match-ups with Stanford prior to the NCAA tourney (twice in MPSF league play and once in the conference tourney), but the Cardinal turned things around against the Cougars in the NCAA semifinal.

Wednesday, April 9, 2014

My Vote for Off the Block's Men's Collegiate Server of the Year

I was invited once again this year to vote for the Off the Block men's collegiate volleyball awards. The number of awards has increased and I've been very busy this semester, so I may not have time to conduct statistical analyses for all of the categories. However, I have conducted an analysis to determine my votes for National Server of the Year.

The NCAA men's volleyball statistics site (see links column to the right) provides an aces-per-set statistic. Aces are only one part of judging serving ability, in my view. Someone might be able to amass a large ace total by attempting extremely hard jump serves at every opportunity, but such aggressive serving likely would also lead to a high rate of service errors. Another aspect to consider would be serves that, while not aces, still took the opposing team out of its offensive system. Only aces and service errors are listed in publicly available box scores, however.

What I did, therefore, was find out the top 10 players in serve-per-set (through matches of March 30) via the NCAA site. For these players, I looked at their ace totals (from the NCAA site and the players' respective school athletic websites, using the latter in the event of slight discrepancies) and their service-error totals (available only from the school athletic websites). I then plotted the 10 players' ace and error totals (using this plotting website). As shown in the following graph (which you may click to enlarge), aces and service errors are positively correlated (r = .34), which means that the more aces a player serves, the more errors he serves. The upwardly sloping red line in the graph illustrates the trend.


The best serving is thus depicted in the lower-right corner, highlighted in yellow. Players in this area of the graph served large numbers of aces, but had far fewer errors than would have been expected based on the trend line. The blue arrows below the trend line indicate the top servers, the further down the line drops, the better. Using this approach, my top three servers are:

1. Gonzalo Quiroga (UCLA), who served 47 aces with only 60 service errors. By comparison, Lindenwood's (Missouri) Colin Hackworth also had 47 aces, but committed a whopping 114 errors. Relative to the trend line, Quiroga had roughly 20 fewer service errors than would have been expected.

2. Aaron Russell (Penn State), who recorded 44 aces with 61 errors, who edges out...

3. Loyola's (Chicago) Joseph Smalzer, who aced the opposition 51 times, while committing 71 service errors.