By Sabin Hart '24
You are probably familiar with volleyball’s most iconic action, the spike. You may even understand the ideal possession: bump, set, spike! But even a single spike, when an offensive player smashes the ball to the opponent’s floor, is much more complex than you may have realized. Not every spike, nor every spiker is the same, and in this article, I will explain some of the phenomena that I observed while performing some formal analysis of published performance data across the men’s highest college level.
Before digging into the available data, however, let us review what the positions in volleyball are and how performances are commonly measured. First, we have the Middle Blocker (also sometimes called Middle Hitters, MBs, or middles). They are traditionally the tallest players, pinning down blocks and hits in the front row. Next are the Outside Hitters (OH or outsides), who are generally shorter than the middles. In addition to taking much of the offensive load, outsides play both front and back row, and do a lot of the digging (returning spikes) and receiving (returning serves). Setters are one of two shorter positions and focus only on the second touch, which sets the ball before it is spiked. Because a lot of spiking involves extremely precise timing and aiming to as many as 5 players at once, specialists at setter are necessary to succeed. Opposite Hitters (Right Sides or OPP) are similar to outsides, but there is usually only one per team, and he hits opposite the setter and is also usually a lefty. Finally, there is the Libero (L), which is a special position. Focused only on defense, they are the shortest position, often the strongest returners and diggers, and indicated with a uniform of a different color than the rest of the team. Attached is an article discussing these positions in more depth if you would like to read further. Because this analysis is focused on the last hit in the volleyball sequence, I will be primarily discussing outsides, right sides, and middles.
We will begin by looking at the NCAA’s 2021 top 100 players as ranked by “Hitting Percentage” to identify patterns for analysis. When a player spikes the ball, there are three outcomes: a kill (where the one or fewer players on the opposing team touch the ball before losing the point), an error (the spiking team immediately loses the ball out of bounds or is blocked), or a continued point (where the opposing team digs the ball). Hitting percentage is defined as kills minus errors divided by total attempts. In other words, it is net points gained per swing attempt. Although titled Hitting Percentage, the result is presented as a 3-digit decimal between 1 (100% kills) and -1 (100% errors). A link to the published data, compiled by the NCAA, is attached here. But because their table does not include the players’ positions, I have appended them in my own table of the top 50 that is attached at the bottom. To provide examples, I have attached links to the timestamp of the 2021 NCAA D1 championship between the University of Hawaii Rainbow Warriors and the Brigham Young University Cougars. I do not claim ownership of the broadcast and claim Fair Use for educational purposes.
The most apparent statistical pattern is that many of the highest-ranked players are Middle Blockers. In fact, 17 of the top 20 players list Middle Blocker as their primary position. This has a lot to do with shot selection, however, and does not necessarily indicate that middles are the best players. Though setters have a large number of options they can give to a middle, at the highest level the most popular by far is a “One set.” Named for the height between the setter’s hands and the hit (one ball length), it encompasses all extremely fast spikes delivered by the middle. Since the middle is usually the tallest player, the ball travels a very short distance into his hand, and it comes from the middle of the court, this spike is by far the most difficult shot to block and the easiest to keep in bounds.
However, to set a successful “One,” the setter must be relatively close to the front center of their side of the court and the middle must have time to run up before the set leaves the setter’s hands to time their jump to the peak of the ball’s height. With serves regularly topping 60 to 70 mph, receiving them accurately becomes extremely difficult. If the receiver is off in their positioning or timing by almost any amount, that set becomes impossible or loses its advantages. In fact, since all three blockers on the opposing team can reach a slow spike in the middle of the court, where usually only two can get the spikes of the opposite or outsides, the defense against any middle set that is not a “One” is significantly stronger. That is to say, in ideal situations, the middle blocker is the strongest offensive option, but as soon as the situation is any less than ideal, he becomes the weakest player to set. Knowing this, a good setter will avoid setting middles in bad situations, meaning, on average, middle blockers have higher Hitting Percentages, but relatively few total attacks.
On the other hand, the story of opposites and outsides is flexibility. Instead of primarily relying on a single set, there are dozens of sets regularly used at the highest level by outsides and opposites. Outsides have the slow, high “Four set,” and several faster sets at different positions and heights (like a “Hut”). Opposites have these same options, just mirrored, with the most common being the “Five set.” Additionally, where middles are subbed out by the libero when they would play in the back row, outsides and opposites play their entire rotation, so they have attacks from behind the 10-foot line in the middle of the court, like the tall and powerful “Pipe.” The sets can vary in speed heavily but are all slower than the “One,” meaning they give more time for the defense to set up good blocks. As well, because they are hitting from the edges of the court, errors missing out of bounds are more common. When a play is “out of system,” meaning the pass or dig was poor and the play becomes unplanned, the outsides and opposites are the ones given the subprime sets. These conditions explain the phenomena of having significantly lower percentages but twice as many attacks.
So how does your position affect your Hitting Percentage? Quite significantly. So much so that Hitting Percentage is completely ineffective for analysis across positions. The best middle blockers in D1 Men’s Volleyball are all north of .333 while outside hitters can barely crack that, ending up with numbers in the .200s. In terms of relative strength, an outside who hits .300 is far more valuable than a middle of the same statistical caliber. As a side note, the NCAA also only publishes stats for the top 100 players, and not aggregate scores like league averages. They also fail to mention the criteria to make the list – during my research I found players whose Hitting Percentage would have placed in the top 20 but were absent for a reason I did not understand. This makes advanced statistical analysis of the effect of position on Hitting Percentage very difficult and even a simple mean, median, and standard deviation of this data set would be more misleading than helpful. I strongly believe that improving the available statistics is not very difficult and necessary to allow analysis of volleyball to grow.
And how does Hitting Percentage stack up as an overall measure of offensive efficiency? It has some value but remains largely unhelpful.
What does it do well? Hitting Percentage accounts for kills, errors, and neutral outcomes, which is more sophisticated than simply counting kills per set or net points gained (kills minus errors). Within a position, this measure more accurately measures both conservative and aggressive players. Additionally, Hitting Percentage is a relatively simple statistic for the layperson to understand. Because volleyball is a less popular sport, using statistics that are easily understandable and well-named is important to gaining and retaining viewership, and leading conversations into greater statistical work. Lastly, Hitting Percentage is a concrete and immediately understandable description of a series of events - it is not unlike well-understood measures like Batting Average, Yards per Carry, or Field Goal Percentage. However, I would argue that these benefits are outweighed by the same issues that plague simple measures like the ones mentioned above.
For example, as discussed above, Hitting Percentage is not applicable across positions even though it is often used in this manner. As you can see from the NCAA’s only published data, they choose not to segregate, let alone even list, the player positions. This is misleading and lends less credibility to using the statistics at all. OSU’s Sotiris Siapanis hitting .356 as an outside in 2021 is extremely noteworthy, but a layperson will question what the fourteen people ranked ahead of him had done better. In truth, they just had the good fortune to be born taller, play middle, and get to hit sets that lead more often to better outcomes. This contrasts with baseball, where offense is played identically regardless of defensive position. A player’s batting performance is isolated equally, irrespective of the position he plays. Therefore, it makes sense that offensive statistics are some of the most commonly cited in measuring a baseball player’s success. But volleyball is not like that, and that difference is not clear. If Hitting Percentage is not normalized to account for position, then it should at least be split up to demonstrate this fact.
In addition, Hitting Percentage does little to account for defensive pressure. A fantastic hitter will cause the blockers to cheat towards his position to have a better chance to defend the hit. This will decrease the defensive pressure on every other hitter on the team. Just as in many other sports, a great volleyball player might have benefits that would not show on their Hitting Percentages and instead on the performance of the rest of the team. For example, Steph Curry is well-regarded as the best modern shooter in basketball, who draws considerable attention from opposing defense - opening his teammates up for easier shots. Without measuring the positive effect Curry has on his teammates, his overall offensive impact is not fully captured. Similar principles apply to volleyball. Patrick Gasman and Tyler Mitchem are the two best on the Hitting Percentage chart, but if Mitchem regularly draws in three blockers while Gasman draws only two, it would be quite clear that Mitchem is a stronger offensive weapon. Accounting for this difference is not easy, and not necessarily a “failure” of Hitting Percentage, but it does show an aspect that this statistic misses.
Finally, Hitting Percentage gives no credit or blame to the skill of the setter. Given the strength of blocking at the D1 Men’s level, a pass that is too fast, too tight to the net, or too short by more than a little will result in mis-hits and errors. It could even be argued that more than half of the credit for a perfect spike should go to the set rather than the hit. Because Hitting Percentage measures total offensive success but its results are used only to analyze the hitter, this stat rewards hitters with great setters and punishes those with bad ones. Comparing this to football, Hitting Percentage is similar to Completion Percentage for quarterbacks. Patrick Mahomes could throw an identical pass three times and get a completion, a drop, and an interception, based entirely on the actions of his receiver. Granted, a better throw will more often get a better outcome, but without normalizing for the conditions of the catch, completion percentage is not a great sole measure of the offensive efficacy of a quarterback. For a statistic to correctly capture the impact of a player, it must normalize the situation to account for only that player’s actions. Perhaps Hitting Percentage should be listed for the setter and the hitter together, indicating the role both positions play.
Overall, I hoped to delve into a little analysis of a performance measure I have not seen previously discussed. Drawing on data published by the NCAA for D1 Men’s Volleyball and tape of the games themselves I hoped to show how Hitting Percentage may change across positions and how effective it is at analyzing a hitter’s offensive impact. Though Hitting Percentage has benefits, its downsides remain large. I discussed how it does not account for position, relative defensive pressure, and setter strength. There are also factors I did not discuss which have great effects, like coaching style and relative opponent strength. There are many external factors on a player’s hitting success and Hitting Percentage does not account for any of them. To improve this statistic, I want Hitting Percentage to be akin to baseball’s OPS+, which normalizes for external factors and is designed to be even easier to interpret. In addition, I recommend the NCAA publish its data for all players, segregated by position, with transparency for its criteria. Having done scoring and libero tracking for many D1 games, I know the wealth of data that the NCAA has. I hope that they will use that data and these recommendations to create statistics that allow greater analysis and development of the game.