Skip to main content

Women's College Liberos: Part I (Serve Receipt)

Over at the VolleyTalk discussion boards, there has been periodic debate as to who the top liberos (backcourt defensive specialists) are in the women's college game. Two measures -- serve-receipt and digs -- seem particularly relevant to judging the merits of different liberos. I will look at serve-receipt in the present posting and then examine digs in Part II. Three players I will focus on, in particular, are USC's Natalie Hagglund, Iowa State's Kristen Hahn, and Michigan State's Kori Moster.

For studying these matters, the ideal would be to have observer ratings of the quality of each pass and dig made by a player, such as many teams compile from their study of game video. Lacking such ratings, we can only look at box scores published online. For serve-receipt, moreover, one really needs to find extended or detailed box scores to get good information (here's an example of both a regular and detailed box score being available).

Regular box scores contain each player's serve-reception errors (RE), but not player-specific information on number of serve-receipt opportunities. For the latter, one needs a detailed box score. A confusing aspect of detailed box scores is that, for some matches, a column in the serve-reception section with "O" at the top presents each player's total number of opportunities, whereas for other matches, the "O" column contains each player's number of successful receipts, which need to be added to reception errors to arrive at reception opportunities. The meaning of "O" for any given match can be determined by looking at the opposing team's number of non-error serves.

What formulas can be devised to combine serve-receipt data into a measure of skill at the task? Certainly, receiving a lot serves without error is an important quality. Also, arguably, receiving a large share of the serves directed to one's team is a marker of receipt skill. Someone who receives, say, 40 percent of balls served to her team probably has a wider range of movement and is willing to take more initiative than someone else who receives, say, 10 percent of serves. If a player can receive a lot of balls and do so with very few errors, that's probably someone you want on your team. (Ideally, that player would exhibit not only non-error passing, but also high-quality passing that lets the team's offensive attack run "in system." However, quality ratings are not available online.*)

My first idea for a formula to measure serve-receipt prowess would multiply, for a given match, a player's success rate at receiving serve (non-error receipts over total receipt opportunities) by the proportion of a team's serve receipts the focal player took (total number of serve-receipts is equal to the opponent's number of non-error serves). So, for example, if a player successfully executes .90 of her serve-receipts and takes .30 of her team's receipts, that would yield a score of .27.

The more I thought about it, however, I felt greater weight should be given to punishing serve-receipt errors. To do so, I simply converted the first part of the formula to receipt-success-rate squared. If a player makes no reception errors, her success rate of 1.000 remains the same when squared. A player with a low error rate (e.g., success rate of .90) is punished mildly, as the success-rate squared becomes .81. A serve-receiver who is mediocre at best (e.g., success rate of .60) suffers more heavily under squaring, as her value plummets to .36. Thus, my current working formula is as follows (^2 stands for squared):

(non-error receipts over total receipt opportunities)^2   X   (proportion of team's serve receipts player took)

Although detailed box scores do not seem to be widely available, Hagglund's school, USC, nearly always provides them. I examined all of SC's Pac-12 conference matches this season, except for the Trojans' loss to Arizona last weekend (for which I couldn't find a detailed box score). Hagglund very rarely botches a serve-receipt, doing so only twice in the seven matches (and 70 reception opportunities) summarized in the following table.

Natalie Hagglund
Serve Receipt
% Success 
% of Team's
Receipts (R)
UCLA11-0-111.000.136 (11/81).136
.909.220 (11/50).182
.136 (8/59)
.237 (23/97)
Oregon St.
.086 (3/35)
.113 (7/62)
Arizona St.
.149 (7/47)
Average of values in final column = .143

However, Hagglund doesn't receive a lot of serves, either, usually between .10-.15 of opponents' launches. Her greatest serve-receipt activity occurred in a five-game win over Cal, in which Hagglund flawlessly passed along 23 of the Golden Bears' 97 non-error serves (.237). Even though the squaring of serve-receipt success rates helps Hagglund (because her success rate is usually 1.000, which is undiminished by squaring), her final scores are not that high, due to her low share of Trojan serve-receipts. (SC outside hitters Sara Shaw and Samantha Bricio regularly field more opposing serves than does Hagglund.) Averaging Hagglund's final scores for the seven matches, she ends up with a value of .143, which is much lower than those for other players we'll review.

Next up is Kristen Hahn. Because of the dearth of this year's Big 12 conference matches for which I could find detailed box scores, I dipped back into the 2012 NCAA tournament (to ensure high quality of opposition). Hahn appears more error-prone than Hagglund, compiling six blown receipts in the five matches examined. However, Hahn is much more active on serve-receipt than Hagglund, a trend that has intensified with Cyclone outside hitter (and frequent serve-receiver) Rachel Hockaday finishing her college career last season.

Kristen Hahn (Iowa State)
Serve Receipt
% Success
% of Team's
Receipts (R)
IPFW*13-3-16.812.222 (16/72).146
N Carolina*27-0-271.000.284 (27/95).284
Stanford*26-2-28.929.394 (28/71).340
.364 (16/44)
Kansas47-1-48.979.490 (48/98).470
Average of values in final column = .321
*2012 NCAA Tournament

Hahn's most recent performance, in Iowa State's five-game win Wednesday night at Kansas, was quite remarkable. She received nearly half of the Jayhawks' serves (48 of 98) and made only one error in the 48 attempted receipts. Hahn compiled a .470 score in the Kansas match and averaged a .321 for the five matches of hers that were studied.

Hahn recently told the Iowa State Daily that, "I think serve receive is a very mental game. I think just making sure that mentally, I’m ready to go... I like to observe their servers during warm-ups and know who the starting servers are and what their go-to zone is and what they practice."

Our third contender is Michigan State's Kori Moster, who Spartan coach Cathy George discussed on the October 21 edition of the Internet radio volleyball show, The Net Live.

Kori Moster (Michigan State)
Serve Receipt
% Success
% of Team's
Receipts (R)
Ohio St.
 .484 (30/62)
.273 (12/44)
.308 (33/107)
.176 (15/85)
Average of values in final column = .288

Again focusing on this year's conference matches, I found only four of Moster's Big 10 matches that had detailed box scores. In three of these, Moster received serve impeccably. However, Moster made five serve-reception errors October 17 vs. Minnesota, an unusually high number for a potentially elite libero. In that match, the Gophers' Daly Santana served seven aces, at least some of which were at Moster's expense. For the quartet of matches studied for Moster, she averaged a score of .288, slightly below Hahn's.

One other libero I wanted to mention is Rachel Brummitt of Texas Tech (where I'm on the faculty). During 2012, Brummitt twice won Big 12 Defensive Player of the Week (to Hahn's eight times). I was only able to find a detailed box score for one of the Red Raiders' matches this season, against Kansas. In that match, Brummitt successfully received all 11 serves she faced, but these 11 made up a small proportion of non-error Jayhawk serves (11/73 = .151).

If anyone has additional liberos to suggest, please let us know in the Comments section to this blog. Remember, the current entry was only on serve-receipt. Liberos' digging statistics will be examined in a future posting.


*As I later learned from VolleyTalk, the FIVB's serve-receipt statistics for international play include a count of how many receipts were "excellent."


Harold said…
Rather than taking the mean of the values in the S2R column, it would be better to sum the columns and then calculate an overall S2R. The differences typically aren't large, but for the MSU libero, but for Moster, the mean of the values in S2R is .289, but the S2R you get for the sum of the columns is .269 (85 successes, 5 errors, 90 receptions out of 298 serves.) The order of operations matter when the sample sizes are different. For Moster, the Minnesota match dominates her overall performance. Imagine the limiting case where a team plays 9 matches against very, very bad teams (sweeps at 25-1). If the libero successfully receives (or doesn't receive) the 1 serve attempt a set, she'll have a 1 for all of those matches. Then, in the 10th match, there are 73 serves against the team and libero receives 13 of them, making errors every time. Her overall numbers are 27-13-40, 40/100, S=.675, R=.4, S2R=0.182. The mean of the 10 matches' S2R gives a value of 0.9.

Popular posts from this blog

My Simple Prediction Equation for the NCAA Women's Tourney

Two years ago, I created a very simple prediction equation for the NCAA women's tournament. Each team gets its own value on the predictive measure. To calculate it, you take a team's overall hitting percentage at the end of the regular season and divide it by the hitting percentage the team allowed its opponents (in the aggregate). The result is then multiplied by an adjustment factor for conference strength, as shown here. For any match in the NCAA tourney, the team with the higher value on my measure would be expected to win.

In both 2012 and 2011, my formula did about as well as other, more complicated ranking formulas. I'm not going to do a full-scale analysis for this year's bracket, but I wanted to mention the formula and provide some sample calculations, in case anyone wanted to compute a score this week for his or her favorite team. The necessary information should be available from the volleyball page of a given school's athletics website. Here are 2013 va…

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 ser…

Statistical Notes Heading into Women's Final Four (2013)

With this year's NCAA women's Final Four getting underway Thursday night in Seattle, today's posting offers some statistical observations. The two semifinal match-ups feature defending champion Texas vs. upstart Wisconsin, and Penn State vs. hometown favorite Washington.

Wisconsin, a one-time power that had missed the NCAA tourney from 2008 through 2012, is now back in an ascendant mode under new coach Kelly Sheffield. Seeded 12th nationally, the Badgers benefited in their part of the bracket from the fact that SEC teams Missouri (No. 4 seed) and Florida (No. 5 seed) were Paper Tigers and Gators, respectively. Having said that, Wisconsin may be the kind of team that can give Texas a tough match (like Michigan in last year's semifinal).

A year ago, I developed a statistic that attempts to measure teams' "grind-it-out" tendencies. To me a grind-it-out team is one that lacks spikers with pulverizing power, but digs opponents' attacks well and avoids hitt…