Wednesday, December 17, 2008

Measuring Serving Effectiveness: The Length of Average Serving Stint (LASS)

For the last few weeks, I've been trying to think of new ways to measure serving effectiveness. Box scores and statistical summaries typically report only service aces and errors (example). My concerns are that aces occur infrequently (limiting their statistical usefulness), and that focusing on aces does not take into account how even serves that are picked up by the receiving team can still be advantageous to the serving team (e.g., by preventing the receiving team from setting up its top available hitting threat).

Alternatively, one can obtain detailed statistics by observing and classifying the receiving outcomes of serves into micro-level categories, such as whether a serve disrupted the receiving team's ability to "mak[e] the first tempo attack," as reported in this article. If a team has the staffpower resources to record such statistics, that's great, but not everyone can.

What I've been conceiving of, therefore, is some sort of "middle ground" statistic -- something easily derivable from online play-by-play sheets (as can be accessed, for example, via this NCAA interactive bracket by clicking on particular games), but that goes beyond just service aces and errors. What I've come up with is the Length of Average Serving Stint or LASS.

The longer a serving stint, the more points the serving team is racking up. If a stint lasts for one serve, the serving team has not received a point (i.e., the other team has sided-out). If a serving stint lasts two serves, the serving team has garnered one point. If a stint lasts three serves, the serving team has accumulated two points, etc. Thus, longer serving stints appear to capture -- indirectly, at least -- effective serving.

Before anyone starts sending me e-mails of complaint, I am aware that the identity of a server is systematically connected to (or "confounded" with) the serving team's front-court line-up, due to the rotation. Thus, if Player A tends to have long serving stints, some (or even most) of the credit might be due to the team's having Players B, C, and D in the front court, rather than Player A's vicious serving. I never claimed that my new statistic was perfect!

Also, I suspect that many coaches already chart their teams' success at winnning points and siding-out, by rotation, which is very similar to my scheme. The difference would just be a matter of focus, as I'm interested in who is serving.

What the LASS does have going for it, however, is relative ease of compilation. One can simply look at a play-by-play sheet and see how many plays in a row somebody served. A sample chart of LASS statistics is shown below, for the University of Texas in its recent NCAA Elite Eight match-up against Iowa St. Shown in each box is the length of a given serving stint; going down the first column shows you each player's first stint (in the order they served), the second column shows each player's second stint, etc. You can click on these graphics to enlarge them.

I've gone ahead and calculated LASS statistics for all regular players from the Final Four teams that will be playing Thursday night, based on each team's two games in last weekend's regionals.

If one were going to adopt the LASS, it would be best to use a much larger database than just two matches; my initial calculations were purely for illustrative purposes.

I'm interested in what readers think are the pros and cons of the LASS. I invite you to use the Comments section to provide feedback. Now enjoy the Final Four!


Alexis said...

Interesting. A couple of thoughts:

1 - it might be worthwhile 'eliminating' the first serve from any calculations. The first serve happens no matter what and is not a reflection of serving effectiveness. For example 2 serves in a stint is not 'twice as good as one' because 1 will happen regardless.

2 - The other implication of this statistic is to look at 'clusters' of points. I know there has been work done in this area for 10 years at the very least (though I don't know if anything has ever been published). My recollection is that there are some very clear links between the number of clusters over a certain number (x clusters of more than y points) and winning a set.

ServerLog said...

Which do you think is better, a higher serve/stint ratio or a higher raw number of stints? Looking at your tables and comparing it to my own intuitive sense of players, I noticed that all the players I think of as having great serves are the players with the higest number of stints on their teams, without as strong a correlation to serves/stint.

Anonymous said...

i just found you blog and i love the idea. i am the head volleyball coach at the university of memphis. i am interested in reading the rest of your entries and keeping in touch.

eastivball said...

Love your site!

As I mentioned in volleytalk ( I keep a similar stat to LASS.

In mine, I like to count aces double and subtract errors -- a simple way to further reward players who can create aces and/or always force the other team to side-out.