Analysis of BYU Men's Two Wins Over USC

Because conference play gets underway very quickly in men's college volleyball (usually just after an early-season tournament or two) and the top teams tend to be concentrated in the Mountain Pacific Sports Federation (MPSF), fans don't have to wait very long for marquee match-ups to take place.

Just this past weekend, No. 1 BYU hosted No. 5 USC for a pair of matches (BYU and Hawai'i always play a given opponent in a two-match home series or road series, presumably to cut down on travel). Also, No. 3 UCLA hosted No. 4 Stanford.

I will focus on the BYU-USC series, as better inferences can be made from two matches than from one. BYU won both matches, but each was highly competitive. The Cougars took Friday night's first match in four games (sets), and Saturday night's rematch in five (15-13 in the fifth, in fact).

As is customary, I stress hitting percentages and the teams' allocation of spike attempts. The following graphic (on which you can click to enlarge) presents this information for the Cougars' and Trojans' main hitters.

As can be seen, the stalwart for BYU was Taylor Sander, a 6-foot-4 sophomore outside (left-side) hitter who hit .419 and .383 in the two matches, taking an average of 45 swings per night. Robb Stowell, a 6-7 senior opposite (right-side) hitter, hit .375 the second night on 40 attempts, after hitting only .200 the first. Russ Lavaja (6-7, junior) contributed .364 and .455 offensive outings, although as is common for a middle blocker he had relatively few spike attempts.

I watched the latter parts of Friday night's match on BYU TV and I can tell you that Sander and Stowell were just pummeling the ball.

'SC was led by two pillars of last year's team. Tony Ciarelli (6-6, senior) and Steven Shandrick (6-7, senior). Ciarelli was very steady, hitting .351 and .364 in the two matches; on Saturday, he took a Herculean 55 spike attempts. Shandrick hit .500 and .375.

The Trojans also feature a number of frosh players, led by setter Micah Christenson. Fellow newcomer MB Robert Feathers led USC with 8 block assists on Friday, but didn't do much (statistically at least) on Saturday. MB Ben Lam, who played only in Saturday's match, recorded an error-free 7 kills on 8 attempts, for an .875 hitting percentage.

By Game 5 of Saturday's match, the two teams' offenses were firing on all cylinders, with the Cougars outhitting the Trojans, .545 (13 kills and only 1 error, on 22 swings) to .474 (10-1-19).

BYU outblocked USC, 15-11 in total team blocks Friday and 13-9.5 Saturday. (There really is no such thing as a half-block in the aggregate; what happens is that on a triple-block, each player is credited with a half-block instead of a one-third block, resulting in the "phantom" half-block in the totals.)

Keeping with the theme of hot-hitting, UCLA registered a .376 percentage in sweeping Stanford. Leading the Bruins (among players with 10 or more swings) were MB Thomas Amberg (.600), OH Gonzalo Quiroga (.538), and MB Weston Dunlap (.333). The Cardinal's Brad Lawson, star of the 2010 NCAA championship match, hit .333, but Stanford as a whole hit only .179. UCLA also enjoyed a large blocking advantage, 8.5-2.

A couple of days ago, the Los Angeles Times had a feature article on UCLA coach Al Scates, who is retiring at the end of this, his 50th, season at the Bruin helm.

Hot Hand in Volleyball?

Science News has just published an article on research by German and Austrian investigators purporting to document a hot hand in volleyball spiking, and the reporter was nice enough to contact me for comment. (I operate another blog, on the statistical study of sports streakiness, and even have a book out on the subject.)

A hot hand in this context would mean that a player who has successfully put away a few kills in a row would have a higher likelihood of a kill on his or her next spike than the player's long-term kill percentage would suggest. A cold hand would represent the opposite, that a player whose last few spike attempts have resulted in errors (e.g., ball hit out of bounds) would have higher than usual odds of an error on the next attempt than his/her long-term percentages would suggest.

Within the constraints of the data set to which the authors had access (partial game-sequence data from top players in a German men's professional league), the analyses were conducted with full rigor and in a manner consistent with previous hot hand research. However, as I elaborate below, I feel there was at least one major limitation in the available data.

One type of analysis done by the authors used the runs test. This statistical technique requires the researcher first to list the sequence of events, in this case, a given player's order of kills (K) and errors (E). A "run" is an uninterrupted sequence of the same outcome, either all K's or all E's. The following hypothetical sequence, with few runs, would indicate streaky performance (i.e., clustering of K's and of E's):

KKKKEEEKKKKK (3 runs)

Another hypothetical sequence (with the same number of total attempts), this time with many runs, would indicate less (or absent) streakiness:

KKEKEEKKKKEK (7 runs)

According to the Science News piece:

An analysis of playoff data from the 1999/2000 season for 26 top scorers in Germany’s first-division volleyball league identified 12 players as having had scoring runs that could not be chalked up to chance. Hot-handed players’ shots contained fewer sequences of consecutive scores than expected by chance, the result of a small number of especially long scoring runs.

As we know, however, there is a third category of outcome for spike attempts, namely the ball is dug up (or otherwise kept in play) by the defense, and the rally continues. As I told the reporter, I definitely think those hit attempts should have been included in the analyses, but they apparently were unavailable in the data set the authors received. Hitting errors were very rare in the data, so balls kept in play may have been a better measure than errors of unsuccessful spike attempts.

(Cross-posted with Hot Hand in Sports.)

Comparing Forecasting Models for 2011 Women's NCAA Tourney

Happy New Year! I wanted to close out discussion of the 2011 NCAA women's volleyball tournament by examining the effectiveness of my newly developed Conference-Adjusted Combined Offensive/Defensive (CACOD) ranking system at predicting the outcome of tournament matches.

Details of the formula and the full set of CACOD rankings are available here. In short, however, for each team in the NCAA field, the CACOD took the "ratio of its own overall [regular] season hitting percentage (offense) divided by the overall hitting percentage it has allowed the opposition (defense)." This ratio was then multiplied by an adjustment factor based on a team's conference (the stronger the conference, the more the adjustment factor raised the team's ranking).

For each of the 63 matches in the tournament, I simply looked at whether the team with the higher CACOD rating won or lost. The CACOD's record is shown below, along with those from other leading rating systems (shown in a screen capture from a VolleyTalk discussion thread). You may click on the graphics below to enlarge them.

The CACOD successfully predicted the winner of 45 tournament matches, which means it generally did as well as the more established ranking systems did. (The reason some of the above records include only 62 matches is that the captured image was from before the final match.* I suspect that, in cases where other systems' records don't add up to 62, it's because some matches featured teams that were tied in the rankings.) What's unique about the CACOD is that teams' win-loss records during the regular season play no role in formulating the rankings, just offensive and defensive hitting-percentage statistics.

During the Sweet Sixteen round and beyond, the CACOD seemed to outperform the other systems, as the CACOD didn't do so well regarding the 48 matches of the first two rounds (32 in the first round, 16 in the second). The results of the two initial rounds are shown in the next graphic.

Indeed, the CACOD trailed the top performing system (Pablo) by five matches after the first two rounds. However, the CACOD went 10-5 the rest of the way to catch up. The results of the last 15 matches are listed below, with teams' CACOD rankings at the close of the regular season shown in parentheses. Successful predictions appear in black, unsuccessful ones in red.

Sweet Sixteen

Texas (7) d. Kentucky (39)
UCLA (11) d. Penn State (12)
Florida St. (28) d. Purdue (2)
Iowa St. (9) d. Minnesota (31)
Illinois (14) d. Ohio State (25)
Florida (10) d. Michigan (33)
USC (5) d. Hawai'i (6)
Pepperdine (29) d. Kansas St. (40)

Elite Eight

UCLA (11) d. Texas (7)
Florida St. (28) d. Iowa St. (9)
Illinois (14) d. Florida (10)
USC (5) d. Pepperdine (29)

Final Four

UCLA (11) d. Florida St. (28)
Illinois (14) d. USC (5)

Championship

UCLA (11) d. Illinois (14)

For next year, I may tweak the formula a little to, for example, place greater weight on hitting-percentage statistics from later in the season than earlier. Seeing how the CACOD did in the end, however, any revisions will likely be more minor than I had expected would be the case after the first two rounds!

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*I overlooked this point in my original posting, but have now added it.

NCAA Women's Final Four Wrap-Up

Illinois entered this past weekend's NCAA Women's Final Four with an interesting proposition. To win the national championship, the Fighting Illini would most likely have to win the championship of Los Angeles. USC stood as the opponent in Thursday's semi-finals, with UCLA up next in Saturday's finals provided the Bruins could handle Florida State (which UCLA did, via sweep).

The Illini passed its first test, besting USC in a five-game classic. On the final point of the Illinois-USC match, shown here on YouTube, the ball crossed the net 20 times, before the Trojans hit it out to give the Illini a 15-10 win in the closing set.

Unfortunately for Illinois, its difficulty in putting the ball away on the final point vs. USC foreshadowed troubles it would have doing so two nights later against UCLA. The Bruins had the upper hand for most of the championship match and won in four, 25-23, 23-25, 26-24, 25-16.

Thanks to a pair of Illini spurts -- outscoring UCLA 12-3 to take Game 2 after trailing 20-13, and 5-1 to get two set-points in Game 3 -- the match could have gone in a different direction (see box score/play-by-play sheet). The Illini couldn't close out Game 3, however, as their spike attempts while leading 24-22 and 24-23 were dug by UCLA. The Bruins won both rallies to tie the score at 24-24 and then won the next two points as well (if you check the match video at the ESPN3.com archive, the final stages of Game 3 appear at the 1 hour, 50 minute point).

The two teams' final hitting percentages were very similar, .218 for UCLA and .215 for Illinois. Rather, the Bruin win seems mainly attributable to defense, with UCLA outblocking (15-11.5) and outdigging (87-76) Illinois. The serving game was also important, with the Illini committing 11 errors to none for the Bruins; despite Illinois's seemingly more aggressive serving approach, it racked up only two more aces than did UCLA (4-2).

The Illini's powerful offensive game was limited to a .215 hitting percentage by UCLA, in contrast to the .279 Illinois was able to generate vs. USC. The following graphic (on which you may click to enlarge) compares what happened on all of Illinois's total spike attempts (TA) vs. USC (left column) with what happened on all of the Illini's swings vs. UCLA (right column).

Whereas Illinois got kills on 39.1% of its hitting attempts vs. USC, the Illini succeeded only 32.6% of the time vs. UCLA. A team is officially credited with a block when the ball goes rocketing back to the floor on the hitting team's side of the net for a defensive point. UCLA scored via block on 8.3% of Illinois's swings,  whereas USC did so on only 4.6% of them (USC more frequently induced the Illini to commit the other type of hitting error, hitting the ball out of bounds, than did UCLA, 6.6% of the time compared to 2.8%). UCLA also dug 48.1% of Illinois's spike attempts, compared to the 44.2% of Illini hits that USC dug.

UCLA dropped only three sets in its six NCAA tourney matches, one each to San Diego, Texas, and Illinois. The Bruins are fortunate the championship match didn't go to five games, as the Illini's record this season in matches going the distance was 7-0...

NCAA Women's Regional Round-Up & Final Four Preview

This year's tournament has to be right up there for the volume of upsets, near-upsets, and all-around strangeness. UCLA appeared to be floundering towards the end of the regular season, losing three of its last five matches. Florida State, playing in the relatively low-profile Atlantic Coast Conference, certainly didn't seem like Final Four caliber during the season. Yet, the Bruins and Seminoles will be playing each other this upcoming Thursday night in one national semi-final.

The other national semi will feature two teams that each looked dominant for most of the season, but also had some unexpected losses, USC and Illinois. The mighty Big 10 conference, which had six of its member teams advance to the Sweet 16 round, ended Friday night's regional semi-finals with the minimum number of surviving teams it could have, one (because Illinois played Ohio State and someone had to win!).

In the remainder of this entry, I summarize developments in the four regions and discuss what to look out for in the Final Four.

LEXINGTON, KY REGION

A night after sweeping four-time defending national champion Penn State out of the tournament, UCLA ousted the national No. 1 seed Texas, 19-25, 25-22, 25-22, 25-21 (box score). Texas's decline after Game 1 is evident in many ways. As shown in the following graph, the Longhorns' hitting percentage fell from a torrid .517 in Game 1 to roughly .200 in each of the remaining games.

As perhaps a microcosm of the match, Texas's Haley Eckerman amassed 4 kills in Game 1, and 5 in Game 2, but only 1 more the rest of the contest (fairly late in Game 4 with UT down 15-12). Eckerman sat for the middle stages of the match, leading the ESPN-U announcers and other observers to wonder what might have been going on.

Texas outblocked UCLA 10-7 for the match, but the way the Longhorns' blocks were distributed among the four games shows why UT didn't gain a bigger advantage from its blocking.

The Longhorns recorded 5 blocks in Game 1 (including 3 straight to advance their lead from 17-15 to 20-15). However, UT had no blocks in the second set, 2 in the third, and none for most of the fourth. Only after the Longhorns had fallen behind 20-13, did they somehow put together 3 more blocks, but it was too little, too late. (These game-specific statistics come from looking at the play-by-play sheet on the archived CBS Sports gametracker.)

For UCLA, Rachael Kidder provided the offensive lift against Texas, hitting .417 from 26 kills and 6 errors on 48 swings. As seen in the graph above, the Bruins experienced what seems to be their customary Game-3 dip in hitting percentage, but still won the game.

MINNEAPOLIS REGION

Twelfth-seeded Florida State stunned No. 5 Purdue and No. 4 Iowa State, the latter in five sets, to advance to the Seminoles' first volleyball Final Four. Blocking appeared to play a large role in FSU's win over Iowa State. Florida State outblocked Iowa State 16-8, as well as outhitting the Cyclones .245-.204 (box score).

It was blocking that arguably turned around Game 5, as the Seminoles stuffed the Cyclones at four key points (to tie the game 8-8, and boost their leads to 10-8, 12-10, and 14-11). If, instead of being blocked, Iowa State could have made good on most of these spike attempts, the match could well have had a different outcome.

FSU junior middle-blocker Sareea Freeman personally played a large role in thwarting the Cyclones' attack, contributing 2 solo blocks and 8 block assists for the match, while hitting .500 (12-2-20). Jekaterina Stepanova also hit big for the Seminoles in a workhorse role (.378, 20-3-45).

GAINESVILLE REGION

In the regional final, Illinois withstood a Florida team that was hot and playing on its home court, 25-22, 23-25, 25-14, 25-20 (box score). Colleen Ward, who once played for Florida before transferring, led the Fighting Illini with a .500 hitting percentage against the Gators (23-2-42). The error-free hitting of middle blockers Anna Dorn, .600 (6-0-10), and Erin Johnson, .562 (9-0-16), also paced the Illini.

Illinois won the first game, in which both teams hit very poorly (UI .068, UF .024). The Illini then perked up to hit .455, .394, and .514 in the final three games, for a .338 night overall as a team. The Gators hit .405 in Game 2, below Illinois's average, but enough to win the set.

The Gators' overall hitting percentage against Illinois was .225, with team leader Kelly Murphy registering only a .205. In sweeping Michigan the night before, Florida hit a whopping .439. Betsy Smith (.700), Chloe Mann (.533), Murphy (.517), and Kristy Jaeckel (.435), led the Gators (all four of these hitters took at least 10 swings).

HONOLULU REGION

Just as Illinois had to contend with playing an opponent on its home floor, so did USC. In this regional, the Trojans faced host Hawai'i in the semi-final, winning in five, 19-25, 29-27, 19-25, 25-23, 15-12 (box score). The teams were very balanced in their overall hitting (UH .220, USC .211) and blocks recorded (UH 15, USC 14).

The Trojans managed to turn up their offensive intensive in the fifth set, when they needed to, hitting .400 (12-2-25) and siding out (winning points on the Rainbow Wahine's serve) 69% of the time. Seven of those kills were by Alex Jupiter. The fact that 'SC scored 12 of its 15 Game-5 points on kills (with the others on an ace and two blocks) is noteworthy in that Hawai'i wasn't exactly giving things away with errors.

Some may have expected a Trojan cakewalk over Pepperdine the next night (I did), but instead, USC was again extended to five games, 25-16, 26-28, 19-25, 25-19, 15-10 (box score). The Trojans outhit the Waves, although neither team hit that well in an absolute sense (.223-.175). USC's blocking advantage was much more one-sided, 14-6.5.

SUMMARY

The four teams that will be competing for the NCAA title all have shown great resiliency, overcoming deficits, hometown crowds for their opponents, and the pressures of fifth games. Interestingly, Florida State, the team I would give the least chance to win it all, may have the best balance between hitting and blocking. Illinois probably has the most consistently strong hitting, whereas USC can dominate with the block (which is not to say the Trojans don't have some fine hitters, when they're on). UCLA's problem this season has been closing out matches (squandering 2-set-to-1 leads at Pepperdine and Arizona, and a 2-0 lead at Oregon, to lose all three of these matches). Lately, however, the Bruins have shown no trouble closing the deal.

Round-Up of NCAA Women's Tourney Opening Weekend

The headlines from the first weekend of this year's NCAA Division I women's volleyball tournament would have to be upsets, in general, and the poor showing of the Pac 12 conference, in particular. In the following chart, I summarize the performances of teams from the three major conferences (Big 10, Big 12, and Pac 12), other seeded teams, and any other teams that advanced to next weekend's Sweet 16. You may click on the graphic to enlarge it.

The highest-seeded upset victim was No. 2 Nebraska, which fell at home in Lincoln to former conference rival Kansas State in five games, 25-22, 22-25, 31-29, 22-25, 15-11, in the second round. The Cornhuskers' downfall appeared to be on defense. Offensively, Nebraska hit exactly at its regular-season percentage (.262) against the Wildcats. However, whereas the Huskers held their regular-season opponents to a collective .143 hitting percentage, K-State hit nearly .100 better, registering a .241 evening (box score). The Huskers still slightly outhit the Wildcats (.262-.241), so one must look further for possible explanations of Nebraska's loss. Leading candidates are blocking (where K-State held the edge, 13-10) and serving (where the Wildcats had 4 aces and 7 errors to the Huskers' 2 and 11, respectively).

The second-highest seeded team to lose was No. 6 Northern Iowa, to Florida, also in the second round. Despite the 33-1 record UNI brought into the match, its loss to the Gators would probably not be considered such a stunner by most observers, as the Panthers' schedule is not the nation's most challenging in the Missouri Valley Conference. Florida mainstay Kelly Murphy led the way with a spectacular .452 hitting night, on 15 kills and 1 error on 31 spike attempts. Fellow senior Kristy Jaeckel did Murphy one better on error-avoidance, hitting .333 (13-0-39).

Florida will next take on Michigan, which ousted 11-seed Stanford on the Cardinal's home court (the Wolverines did the same in 2009). The resurgence of Michigan's senior OH Alex Hunt, which I coined "The Hunt for Blue December," continues onward, as she hit .341 (17-3-41) vs. Stanford. Claire McElheny also took a lot of swings for the Wolverines and came up big (.410, 18-2-39). In addition, UM kept the Cardinal's recently hot Carly Wopat in check (.250, 10-5-20).

In another previous posting, I wrote about Michigan's "unfortunate penchant for failing to capitalize on game and match points during conference play..." What may have been the turning point for the Wolverines in NCAA tournament play came in Game 3 of their opener against Baylor. Having split the first two sets, Michigan and Baylor went "overtime" in the next game, which the Wolverines pulled out 29-27. UM then closed things out easily in Game 4, 25-17.

There were two other matches in which Big 10 teams defeated Pac 12 teams head-to-head. No. 13-seed Minnesota came back from two games down to oust Washington in a second-round match. The Huskies neutralized the Gophers' big hitter Tori Dixon (.048), but couldn't do the same to Ariana Filho (.357) or Katherine Harms (.356). For U-Dub, Krista Vansant (.327) and Bianca Rowland (.323) hit well in defeat. Also, in a first-round match, Michigan State knocked off Arizona, before getting swept by top-seeded Texas.

Another surprise advancer from the Big 10 is Ohio State, which scored a five-game victory over No. 14-seed Tennessee in Knoxville. In the 1990's, then-ESPN studio host Dan Patrick used to say of various athletes, "You can't stop [name of player], you can only hope to contain him!" (audio clip). Well, the Buckeyes' didn't exactly stop the Volunteers' high-powered offense -- Shealyn Kolosky (.391, 11-2-23) and DeeDee Harrison (.308, 12-4-26) put up some nice numbers for UT -- but OSU contained the rest of the team, leaving the Vols with a .171 hitting percentage for the match. OSU bested this with a .207.

Click here to see the official NCAA bracket and match-ups for the round of 16.

NCAA 2011 Women's Preview: Introducing the Conference-Adjusted Combined Offensive-Defensive Rating

With the NCAA Division I women's tournament starting Thursday and rampant displeasure at the tournament committee's seedings (believed to be based heavily on RPI ratings), the fans at VolleyTalk are awash in different alternative rating schemes for evaluating the teams.

The major known ranking systems -- the AVCA Coaches' Poll, RPI, Pablo Rankings, and Rich Kern Rankings -- all appear to take teams' win/loss records and strength of schedule into account. As anyone who has read my blog over the past five years knows, my focus has always been on hitting percentage. I think it's a great singular statistic for incorporating many aspects of the game.

If you hit well (not just keep the ball in play, but get kills), your (individual or team) hitting percentage goes up. An attack kept in play by the other team hurts, as does a hitting error (spiking the ball out of bounds or getting stuff-blocked for an opponent's point). In order to hit well, a team must pass and set well. If you block or dig your opponent's spike attempts, that drives down the opponent's hitting percentage.

What I've done, therefore, is create a national ranking metric based heavily on each team's ratio of its own overall season hitting percentage (offense) divided by the overall hitting percentage it has allowed the opposition (defense). A ratio is maximized when a large numerator is divided by a small denominator. For example, hitting .300 for the season and allowing one's opponents (in the aggregate) to hit .100 yields a ratio of 3. Hitting .250 and allowing one's opponents to hit .200 yields a ratio of 1.25.

But that's not all. Teams play in differentially tough conferences, so I wanted to adjust for that. I came up with a very simple adjustment system out of thin air. We'll see how well my rankings predict this year's tournament matches and I can modify my conference adjustments as needed. Here's my current adjustment system:

• If a team plays in the Big 10, Big 12, or Pac 12, I multiplied its hitting percentage-to-opponent hitting percentage ratio by 1.25. This way, teams that faced what I (and others) consider the top opposition are rewarded for doing so.
• Teams from the ACC, SEC, Big East, Atlantic 10, Mid America, Missouri Valley, West Coast, Big West, Mountain West, Western Athletic (WAC), or Conference USA had their ratios multiplied by 1.00 (i.e., leaving their ratios alone).
• Teams from all remaining conferences, whose schools tend to have relatively low athletic budgets and little or no track record of national success in women's volleyball, had their ratios multiplied by 0.75. Teams dominating these smaller conferences could hit really well and keep their opponents' hitting low, so to account for this, I adjusted their ratios downward.

What follows now are my 1-through-64 rankings of the NCAA tournament teams, based on my Conference-Adjusted Combined Offensive/Defensive (CACOD) rankings. You may click on the chart (which is divided into three panels) for an enlarged view.

Nebraska coming out top-ranked seems to give my system a little "face validity." Further, I have USC ranked higher than does the NCAA tournament committee! And if Dayton or Colorado State makes a big run in the tourney, you heard it here first. Like all other ranking systems, mine will stand or fall on how well it predicts tournament games. For any given match, we would predict the higher-ranked team to beat the lower-ranked one. We'll see how it works.

A note on sources:  I obtained all teams' (offensive) hitting percentages from the NCAA statistics page (see link in right-hand column). To glean teams' opponent (defensive) hitting percentages, I looked at a variety of conference and team-specific pages. When looking at conference and team pages, I checked whether the listed offensive hitting percentages matched those on the NCAA site, to verify that the statistics were from the same time-frame. As it happened, a few tiny discrepancies appeared between the NCAA and conference/team pages regarding teams' offensive hitting percentages (e.g., the NCAA page said Yale had hit .253, whereas the Ivy League page said the figure was .254).