Passing Well and Setting the Middle: The Texas Tech Internal Data

Today, for my second analysis using Texas Tech internal team data, I look at the relationship between quality of the team's passes on serve receipt, on the one hand, and the location and success of the resulting hit attempts, on the other. Again, my thanks to head coach Don Flora and assistant Jojit Coronel for their willingness to share the data and answer any questions I have. (Here's a link to my first analysis of the Texas Tech data, which focused on side-out rates in different rotations.)

The better the pass a team can make on serve receipt, the easier it will be for the setter to get to the ball and, hence, the better should be the set. A good set should then increase the hitter's likelihood of achieving a kill. A further objective for many teams is to set the ball for the middle hitter, to quicken the offense. Other common plays involve high-arching sets to the outside, which give the other team time to get their blockers in place.

In general, teams collect more specific or "micro-level" data than what are available in published box scores. One form of micro data is an evaluative rating of each pass, made by a coach or other observer who reviews video of a match. On serve-receipt, pass quality can range from 0 (being aced) to 3 (an excellent pass that gives the setter the full range of options for feeding a hitter in an advantageous position).

In the following graph (on which you can click to enlarge), the left-hand column has headings for pass quality 3, 2, and 1. For each level of pass quality, you can read horizontally to see Texas Tech's distribution of hit attempts taken from the outside (left), middle, and right. (The hit attempts were summed for the Red Raiders' first eight matches of the season.) For example, on passes of quality 3, Texas Tech went to the left 11.6% of the time, to the middle 68.8% of the time, and to the right 19.5% of the time.

As Coach Coronel clarified for me, the classification of left, middle, and right refers to the location where the attack took place, not to a player's position as listed on the roster. Thus, if an outside (left) hitter migrated into the middle to attack a ball, the play would be designated as a middle attack. Another example is a slide play, in which a middle hitter runs toward the right antenna to elude the opposing middle blocker, and hits from there.

The graph confirms that Texas Tech was incrementally more likely to set the middle with increasing quality of pass: roughly 10% of the time on a 1-quality pass, 43% of the time on a 2-quality pass, and 69% of the time on a 3-quality pass. For those with some statistical background, a 3 X 3 chi-square test on the raw frequencies indicated that the distributions of spike attempts into left, middle, and right were significantly different for the varying levels of pass quality (X² = 83.6, p < .001).

Also included in the information I received were Texas Tech's hitting percentages from each of the Red Raiders' first eight matches, broken down by pass quality on serve-receipt. (The cryptic column headings, such as "hp_psql1," refer to hitting percentage pass quality 1 or whatever number.) These results are presented in the following table.

When Texas Tech's initial pass of the opponent's serve was of the lowest quality (1), the Red Raiders' average hitting percentage across the eight matches was essentially zero. In four of the matches, the team's hitting percentage was negative, indicating more hitting errors than kills. One thing that prevented the team's average hitting percentage on poor-quality passes from ending up markedly negative was a positive .75 hitting percentage on quality-1 passes vs. Binghamton; however, the .75 value was based on only four hitting attempts.

On medium-quality passes (2), the Red Raiders averaged a .232 hitting percentage, and on their best passes (3), their hitting percentage averaged .326. Analysis of Variance (ANOVA) showed that the linear trend of increasing average hitting percentage with better passing was statistically significant (F = 8.70, p < .05).

Some volleyball analysts offer the analogy between volleyball hitting percentages and baseball batting averages, arguing that .300 signifies high-quality performance in either sport. If one accepts this notion, then Texas Tech seemingly needs to receive serve with a passing proficiency close to 3, in order to have a good chance of hitting .300.

It should be noted that Texas Tech won all eight of the matches that formed the basis for the present analyses. Big 12 conference play has proven to be a more challenging proposition. I hope to be able to analyze statistics from conference play at some time in the future.