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.
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 |
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.
3 comments:
I agree with your original premise, but why did you use total number of points in the match as denominator when you could even more easily use the number of opponent's attacks? Surely that would be even more accurate.
Excellent suggestion. I'll use it next year. It might even be better to divide opposing hit attempts by 2, as a given blocker is only in the front row half the time.
I am too a big hater of per set averages because a player may have a high per set average because they had more opportunity to do so, e.g. as you mentioned the 25-23 score compared to a 25-12. You can read my blog post on it. Total points scored is a good denominator, as is total opposition attack attempts, However, it still doesn't deal with the number of opportunities a player gets to block. For example, the middle player will be an opportunity to block every single opposition attack (when in the front row) whereas the outside hitter and opposite can only block their side. I imagine most of the blockers mentioned are middle players and maybe an opposite because most attacks go to the left side.
http://thevolleyballanalyst.blogspot.co.uk/2013/08/on-why-i-dont-like-per-set-averages.html
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