UC Irvine Block Party Sweeps BYU in Men's Final

The University of California Irvine repeated as men's NCAA volleyball champions last night, sweeping Brigham Young University in three tightly contested games, 25-23, 25-22, 26-24, in Los Angeles.

UCI was even more dominant on paper, as the Anteaters outblocked and outhit the Cougars by wide margins. At the net, UCI prevailed 17-6 in total team blocks (block assists divided by two, plus solo blocks). Whereas the Anteaters' Colin Mehring was the nation's top blocker on a contending team this season by my calculation, it was his teammate, junior middle-blocker Scott Kevorken, who stepped up in the championship match with one solo block and 11 assists.

Irvine's blocking dominance last night was very different from the teams' relatively even blocking in their two regular-season head-to-head matches, both of which were won by BYU. (Box scores for the three matches are available by clicking on the relevant link: first regular-season match, second regular-season match, NCAA title match). I have graphed BYU and UCI's total team blocks in the teams' three head-to-head matches:

UCI's blocking prowess was the story of the match, all the way down to the final points. The Anteaters trailed 24-21 in Game 3, meaning the Cougars had three set points. However, Irvine ran off five straight points to win 26-24, the last three all coming on blocks!

Irvine outhit BYU for the match, .383-.274. In Game 1, UCI recorded 17 kills with only 4 errors in 29 attempts, for a .448 hitting percentage. In Game 2, the Anteaters reduced their hitting errors even further, again scoring 17 kills, but committing only 2 miscues (on 35 attempts) for a .429 percentage. Kevorken and Mehring each had identical hitting lines for UCI: 7 kills, 1 error, 10 attempts, .600 percentage. Outside hitter Connor Hughes (11-3-22, .364) and opposite (right-side) hitter Zack La Cavera (11-1-21, .476) also contributed offensively for the Anteaters.

BYU OH Taylor Sander, Mountain Pacific Sports Federation Player of the Year and first-team All-America, hit .308 on the night (20-8-39). However, six of his eight hitting errors resulted from being blocked. The Cougars' highly touted frosh opposite Ben Patch had a rougher night (7-5-22, .091), with four of his five hitting errors due to being blocked.

NCAA Men's Final Four Begins Thursday

The Net Set offers some fairly statistically oriented previews of the Loyola (Chicago)-UC Irvine and Penn State-BYU semifinal matches of this year's NCAA men's Final Four.

My Ballot for the 2013 Off the Block Men's Awards (Blocking and Serving)

Once again this year, Vinnie Lopes, operator of the "Off the Block" men's college-volleyball blog, invited me to be a judge for his site's Blocker of the Year award, as well as another award, new this year, for best server.

As seen in this previous posting, I created an elaborate system for determining my vote for 2011 Blocker of the Year. The NCAA compiles the statistic of blocks per set (or game), but I had a few quibbles with that. First, players can commit blocking errors (e.g., touching the net) in addition to successful blocks, so I thought errors should be subtracted from successes (akin to how hitting percentage subtracts hitting errors from kills). Also, a set is not a very fine-grained unit, as a game that goes 25-23, for example, provides greater opportunity to amass blocks (and other statistics) than one that goes 25-13. Therefore, I use total points in a given match as the denominator for my statistic. This year, to save some time, I revised my procedures in two ways relative to 2011:

• Like before, this year I only considered players for Blocker of the Year who were on a top-5 nationally ranked team. However, whereas before I narrowed my candidates to players in the top 50 in the NCAA's blocks-per-set statistic, this year I only looked at players in the top 25 of this category (as of when I conducted my analysis).
• Whereas before I examined box scores for all conference matches played by a candidate, this year I only looked at matches played against the top-5 hitting-percentage teams in the candidate's conference.

The most recent national rankings I could locate listed BYU, UC Irvine, UCLA, Long Beach State, and Pepperdine, in that order, as the top 5 teams. All are from the Mountain Pacific Sports Federation (MPSF). NCAA individual leaders in blocks per set were obtained here. Top blockers (as of last week) who are on a top-5 team include:

2. Taylor Gregory (Long Beach St.) 1.43 blocks/set
6. Colin Mehring (UC Irvine) 1.26
10. Parker Kalmbach (Pepperdine) 1.22
11. Russell Lavaja (BYU) 1.21
23. Nikola Antonijevic (Pepperdine) 1.05

For each of these players, I examined his blocking performances in each match against opponents who ranked in the top 5 of MPSF team hitting-percentage. As shown here, these teams were (as of April 7, 2013): UCLA (.336), BYU (.329), Long Beach St. (.305), UCI (.303), and Pepperdine (.297).

Just to provide a concrete example, I would look at each match Taylor Gregory (Long Beach St.) played this season (excluding preseason tournaments) against UCLA, BYU, UCI, and Pepperdine. For each match, I would take his total number of blocks (solo plus assists), subtract blocking errors, and divide the difference by total points played in the match. I also did the analogous thing for UCI's Mehring (against UCLA, BYU, Long Beach St., and Pepperdine); Pepperdine's Kalmbach and Antonijevic (against UCLA, BYU, Long Beach St., and UCI); and BYU's Lavaja (against UCLA, Long Beach St., UCI, and Pepperdine).

The following graph (on which you may click to enlarge) shows the results. Above each player's name, vertically, are his blocking performances against relevant opponents. These numbers tend to be low, given that a player's blocks minus blocking errors typically will yield a number from 0 to 5, which then gets divided by the total number of points played in a match (often over 100 and sometimes even exceeding 200 for a tight five-gamer!).

Even though Gregory had the best blocks/set among my five finalists, he did not fare that well under my elaborate blocking formula, recording values consistently between .000 and .024 against the best-hitting MPSF teams. The highest individual-match value was .064 for Mehring in UCI's second match against BYU. As seen in the box score from that match, Mehring had an unusually high 14 blocks (all assists) with no errors. There were 219 points in the match (a five-game come-from-behind win by the Cougars), yielding 14/219 = .064.

Even if one considers that match an outlier for Mehring, his median value (half of his matches above it and half below it; shown as a little red bar) exceeds those of his four competitors. Thus, in a close finish, my votes for Blocker of the Year are:

1st Place: Mehring
2nd Place: Lavaja
3rd Place: Antonijevic

Turning our attention to serving, my starting point was the NCAA's aces per set statistic. As with blocking, using set as the denominator is not a sensitive measure of match length. That's the least of the problem with aces per set, in my view. The bigger problem is the statistic does not take into account service errors. A highly aggressive server (probably using a jump-serve) may get a lot of aces, but he or she will probably also amass a lot of service errors. Service errors cost a team an immediate point, so they are no trivial matter.

Indeed, if we look at the top 7 players nationally in aces per set (as of April 7), we see that nearly all of them have far more service errors than aces! (Service-error numbers can be obtained by going to the athletics website of a given player's school.)

NCAA Stats	Total Aces	Aces/Set	Total Errors	Aces/Errors
Smalzer (LUC)	61	.67	77	.79
Ferrer (Coker)	45	.59	82	.55
Christenson (USC)	40 (41*)	.47	44	.93
Quiroga (UCLA)	47 (48*)	.47	82	.59
Dache (Mt. Olive)	38 (39*)	.47	63	.62
Herceg (Ball St.)	35	.47	53	.66
Cabral (Cal Baptist)	53	.46	118	.45

HTML Tables

*Number from player's school website (this is used in computing the player's aces-to-errors ratio). Minor discrepancies regarding aces per set sometimes occurred between NCAA and school-website statistics, possibly because different cut-off dates were used in documents I consulted. Also, on a few school pages, column headings were incorrect (e.g., serving statistics listed under setting statistics).

Because USC setter Micah Christenson is the only player above who has close to a 1-to-1 ratio of service aces to errors, I give him my first-place vote for Server of the Year. I vote Joseph Smalzer of Loyola University Chicago 2nd, and Greg Herceg of Ball St. 3rd.

More refined analyses of serve proficiency are, of course, possible. One could look at a team's typical rate of scoring points on its own serve. Let's say a team scores on 40% of its serves (i.e., its opponents side-out 60% of the time). In other words, each time a team serves, on average, it scores .40 point. An ace, as an automatic point, increases the serving team's "yield" per serve by .60 (1 - its usual .40), whereas a service error (worth 0 points) decreases it by .40 (0 - .40). Such analyses were beyond my available time, however.

New Article on Attack Speed by BYU's Fellingham and Colleagues

The latest issue of the Journal of Quantitative Analysis in Sports includes an article entitled "Importance of attack speed in volleyball" by Gilbert Fellingham, Lee Hinkle, and Iain Hunter of Brigham Young University.

The abstract (brief summary) of the article can be found here. According to this summary, the authors used high-speed photography to measure the time a set was in the air (to .01 of a second) in a number of men's collegiate matches. Set speed was then examined for correlation with kill probability. Quoting from the abstract:

...sets that traveled a further distance had significant increases in the probability of success with a faster set. No trends were seen with sets that were delivered to hitters that were closer to the setter.

A video on Dr. Fellingham's work with BYU's volleyball programs, this time with the women's team, is available here.

UCLA Men's String of Five-Game Matches

During his weekly appearance on Internet-radio's "The Net Live," UCLA athletics administrator and volleyball analyst Mike Sondheimer pointed out an unusual stretch of matches played by the Bruin men. As shown on the team's schedule/results log, the Bruins played seven straight five-game matches from January 4-12, winning five of them and losing two.

Last night (after Sondheimer's appearance on the Monday show), UCLA lost 3-1 to Long Beach State, ending the streak.

Mike challenged all listeners and volleyball observers to come up with any other streak of seven (or presumably more) straight five-game matches played by a team at any level of volleyball. If you know of any, please add it in the Comments section, preferably with a link to corroborating documentation.

Broadcasts of "The Net Live" are archived, for later listening. See the Volleyball Magazine link in the right-hand column to access these archives.

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 Attempts). You may click on the graphics to enlarge them.

Against USC, the Longhorns achieve a kill on half the swings they took (.505, 50/99), whereas the other three Final Four teams all had kill percentages in the high .30s in their Elite Eight matches. Then, in the final, a 25-11, 26-24, 25-19 Longhorn win over the Ducks (box score), UT nearly reached .500 in kill percentage again, clocking in at .483 (43/89).

If a team commits no hitting errors, then kill percentage and hitting percentage are equal to each other. That the black and orange bars for Texas against Oregon are nearly equally tall tells us that the Longhorns made very few attack errors in that match -- 4 to be exact, compared to 43 kills on 89 swings. As a further sign of how unstoppable Texas was offensively, Oregon came up with only 1 team block in the final, compared to the Longhorns' 15.

In the semifinal round, Michigan extended Texas to five games, with the Longhorns prevailing 25-11, 21-25, 23-25, 25-12, 15-11. For the match as a whole,Texas's hitting percentage of .316 was literally double Michigan's .158. However, when game-specific hitting percentages are examined, we can see that in the two sets the Wolverines won, they somehow managed to slow down the Horns' offense, while raising their own hitting percentages into the mid-high .200s.

Above, I characterized Michigan as having a "grind-it-out" style, for which being able to dig opposing spikes to keep setting up the offense is essential. As one possible measure of a team's grind-it-out propensity, I've simply taken that team's total number of attack attempts (TA) in a given match and divided it by the total number of points in the same match (to control for match length). Looking at all matches in the Sweet 16 and beyond (a total of 15 matches and thus 30 team-specific values), Michigan took first and second in grind-it-out propensity:

Mich (vs. Tex): 193 total points, Mich 196 TA (1.02)
Mich (vs. Stan): 180 total points, Mich 173 TA (.961)
IowaSt (vs Stan): 127 total points, ISU 121 TA (.953)
Stan (vs. IowaSt): 127 total points, Stan 118 TA (.929)
Ore (vs. PSU): 195 total points, Ore 176 TA (.903)
Min (vs. Pur): 192 total points, Minn 173 TA (.901)
Pur (vs. Min): 192 total points, Pur 173 TA (.901)
Neb (vs. Ore): 172 total points, Neb 155 (.901)
Ore (vs. BYU): 178 total points, Ore 159 TA (.893)
Ore (vs. Neb): 172 total points, Ore 151 TA (.878)
Ore (vs. Tex): 130 total points, Ore 114 TA (.877)

Oregon had four of the top 11 grind-it-out values. Grind-it-out teams will not always out-dig their opponents, as a lot of their own spikes are being dug. Indeed, Texas out-dug Michigan 76-71. However, Oregon did out-dig Penn State 80-67 in their national semifinal match (box score).

With the exception of the Michigan match, Texas didn't take that many swings in its final three matches, yielding relatively low grind-it-out ratios for the Longhorns: (vs. USC, 99 swings/130 points =.762; vs. Michigan 158/193 = .819; and vs. Oregon, 89/130 = .685).

***

My Conference-Adjusted Combined Offensive-Defensive (CACOD) ranking, which is based almost entirely on teams' regular-season hitting percentages and the hitting percentages they allowed their opponents, correctly predicted 47 of the 63 tournament matches this year. Thus, the CACOD placed right up there with other, more established ranking systems (screen capture from here):

For each of the 63 tournament matches (shown below), the team with the higher CACOD ranking (shown in parentheses) would be predicted to win. Matches in which the team with the higher CACOD lost are shown in red.

1st Round (26-6)

PSU (1) d Bing (63) 3-0
BGSU (51) d Yale (47) 3-2
Ohio St (28) d ND (34) 3-0
Ky (33) d ETS (59) 3-0
FSU (6) d Hof (61) 3-0
Pur (23) d ColSt (20) 3-0
Crei (14) d Marq (27) 3-0
Minn (13) d Lib (60) 3-0

Ore (9) d NCol (55) 3-0
Day (17) d Pepp (48) 3-2
Ok (37) d ASU (39) 3-2
BYU (4) d NMSU (38) 3-0
Wash (8) d CArk (49) 3-0
Haw (18) d SClara (54) 3-0
UNI (31) d KSt (21) 3-0
Neb (5) d MdES (53) 3-0

Tex (2) d Colg (64) 3-0
TAMU (29) d NCSU (41) 3-1
CofC (57) d Mia (22) 3-2
Fla (7) d Tulsa (43) 3-0
KU (10) d CleveSt (42) 3-1
WichSt (46) d Ark (36) 3-2
StM (52) d SDSU (35) 3-2
USC (12) d Fair (58) 3-0

UCLA (11) d LIU (45) 3-0
MichSt (15) d USD (40) 3-2
Mich (26) d Tenn (32) 3-2
Lou (16) d Bel (62) 3-1
IaSt (24) d IPFW (56) 3-2
NCar (25) d Cal (30) 3-1
WKy (19) d LMU (44) 3-0
Stan (3) d JxSt (50) 3-0

2nd Round (11-5)

PSU (1) d BGSU (51) 3-0
Ky (33) d OhioSt (28) 3-1
Pur (23) d FSU (6) 3-2
Minn (13) d Crei (14) 3-1

Ore (9) d Day (17) 3-0
BYU (4) d Ok (37) 3-0
Wash (8) d Haw (18) 3-2
Neb (5) d UNI (31) 3-0

Tex (2) d TAMU (29) 3-1
Fla (7) d CofC (57) 3-0
WichSt (46) d KU (10) 3-1
USC (12) d StM (52) 3-0

Mich St (15) d UCLA (11) 3-1
Mich (26) d Lou (16) 3-1
IaSt (24) d NCar (25) 3-2
Stan (3) d WKy (19) 3-0

3rd Round (6-2)

PSU (1) d Ky (33) 3-0
Minn (13) d Pur (23) 3-1

Ore (9) d BYU (4) 3-1
Neb (5) d Wash (8) 3-0

Tex (2) d Fla (7) 3-0
USC (12) d Wich St (46) 3-0

Mich (26) d MSU (15) 3-0
Stan (3) d Iowa St (24) 3-0

4th Round (2-2)

PSU (1) d Minn (13) 3-1
Ore (9) d Neb (5) 3-1
Tex (2) d USC (12) 3-0
Mich (26) d Stan (3) 3-1

Final Four (1-1)

Tex (2) d Mich (26) 3-2
Ore (9) d PSU (1) 3-1

Championship (1-0)

Tex (2) d Ore (9) 3-0

The CACOD also did well in predicting the 2011 tournament.

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 Penn State hit the ball out-of-bound or never cleared the net. These bad outcomes for Penn State are depicted in red and purple, respectively. Minnesota dug up 61 (39.1%) of Penn State's spike attempts, and the remaining 8 (5%) are designated as "Other" (e.g., Minnesota blocking the ball back, but Penn State keeping it in play).

Looking at the four horizontal bars, it is clear that Texas (who played USC in its regional final) did the best of any Final Four team at putting the ball away for kills (50.5% of the time, with no other team reaching 40%). There is no way to know for sure whether the four losing teams in the Elite Eight (Minnesota, Nebraska, USC, and Stanford) were equally good defensively, so comparing the hitting statistics of Penn State, Oregon, Texas, and Michigan must be done with caution. Still, it must be noted that USC features Natalie Hagglund, one of the top diggers in the the country (here and here), so it's not like the Longhorns faced a weak opponent in the backcourt.

Oregon had nearly half (46.4%) of its attack attempts dug up by Nebraska, the highest such percent among the Final Four teams. Penn State, Oregon's Thursday opponent, is probably a little better digging team than Nebraska (see Big 10 rankings in statistical categories), so the Ducks may struggle somewhat to put balls away. Penn State (9%) and Michigan (8.7%) exhibited the greatest proneness to getting their spikes blocked.

That Michigan took 173 swings against Stanford shows what a grind-it-out team the Wolverines are. To control for match length, let's note that there were 180 total points in the match (20-25, 25-20, 25-20, 25-20). The Wolverines thus took nearly one hitting attempt for each point played (173/180 = .96). Because many rallies don't have any spike attempts (e.g., aces, free balls), Michigan would thus have taken multiple swings on many plays. The swings/total points ratios for the other teams are as follows:

• Oregon, 151 swings/172 total points (15-25, 25-22, 25-18, 25-17), ratio = .88
• Penn State, 156 swings/181 points (25-19, 19-25, 26-24, 25-18), ratio = .86.
• Texas, 99 swings/130 points (25-19, 25-22, 25-14), ratio = .76.

As seen, none of the other Final Four teams played as swing-intensive a match in the Elite Eight as Michigan. By comparison, Texas hardly tired out its arms at all.

Next, let's look at how the Final Four teams did defensively, focusing on how all of the opponents' spike attempts turned out.

Penn State was best at minimizing the percent of opponent spike attempts that yielded kills, as only 49 (31%) of Minnesota's swings bore immediate fruit (remember, red is bad, so the bigger the red bands, the worse the defense). Texas allowed 38.3% of USC's swings to go for kills, in part because the Longhorns dug a relatively small percent (42.1) of Trojan attempts.

Whether tomorrow night's matches will follow the scripts suggested by the above analyses -- Michigan trying to keep the ball alive on Texas's big swings off the high outside sets and the Wolverines scrapping for points on offense, and Penn State likewise digging up Oregon's attacks -- remains to be seen.  If nothing else, I hope these analyses provide viewers with something to think about during the matches.