Attack Efficiency vs. Success Rate: The Volleyball Stat Column Everyone Misreads

Two in the morning in Nagoya. I open the technical-scouting export from a VNL preliminary-round match. The perfect-pass column comes back empty. The attack-efficiency column is empty too. Only a few scattered scoring lines still glow at the right edge of the screen. I stare at it for about ten minutes, then close the file and go to sleep. The next morning my editor asks what I can write about that match. I answer: nothing yet. The hardest skill to train in this trade is knowing when to say “not enough.” Newcomers read that as hesitation. People who have been around long enough understand it as a professional decision, and a decision that has to be explained on technical grounds. I learned to say “not enough” in the years when I was still dissecting football. In the summer of 2026, I watched 17 matches just to find the gap Elsinho left behind him. When Elsinho pushed high and Kobayashi received the ball, I could see the ending before it happened. Three years later, with stadiums closed by the pandemic, I doubted a 30 percent increase a colleague had published, so I watched 15 Bayern Munich matches before believing it. Before publishing any model, I try to break it first. Then I moved to volleyball, and the discipline stayed the same while the data language changed completely. Volleyball has its own scouting system, Data Volley, used across most professional leagues and at national-team level. Every rally is coded into dozens of fields: serve position, ball direction, receiver, pass quality, attacker, attack type, and final outcome. The international federation FIVB publishes match-level statistics for the competitions it runs, and the VNL is its flagship annual commercial event and a source of world-ranking points at the same time. That volume is larger than outsiders assume. Which is why I hold that most volleyball analysis errors happen in the reading of a column, not in the supply of data. Two columns, two different stories The first column is success rate: points scored on attacks divided by total attacks. The second is efficiency: points minus attack errors and times blocked, divided by total attacks. The gap between them sounds small. Its weight is not small at all. Take an illustrative case. Outside hitter A takes 100 swings, scores 45, commits 12 errors and is blocked 8 times. A’s success rate is 45 percent. A’s efficiency is 25 percent. Outside hitter B takes 100 swings, scores 38, commits 4 errors and is blocked 3 times. B’s success rate is 38 percent, and B’s efficiency is 31 percent. In the first column, A beats B by seven points. In the second, B beats A by six. The same raw data, two opposite conclusions. If an attacker takes about 40 swings a match, six percentage points is worth more than two points per match, and roughly seventy points across a thirty-match season. The difference between success rate and efficiency is the difference between praising someone for attacking a lot and measuring whether that attacking helps the team. Tournament scoreboards usually print the first column, because it is easy to read and easy to impress with. The second column is the one coaches use when deciding who stands at the opposite position. The reception system decides the third column There is another column viewers rarely notice: the perfect-pass rate. It is the share of first contacts delivered exactly where the setter needs them, enough for him to run the full attacking menu. When that rate is high, the setter can run the quick in the middle, push the ball behind him, or bring the play to the back row. When it drops, the menu narrows fast. Falling from around fifty percent to below forty, the setter is left with essentially one safe option: a high ball to the wing for the opposite. And when the opponent knows the destination in advance, the two-man block is up before the ball leaves the setter’s hands. This is the root of one of the most common attribution errors in volleyball. An opposite who gets blocked repeatedly is read as declining. But if the team’s perfect pass just before that had already broken down, he is attacking in conditions nobody would thrive in. Separating those two causes requires reading the reception column and the attack column together, set by set, not match by match. Stuck rotations and what the scoreboard hides In volleyball, the lineup rotates by rule, and every rotation puts a different configuration of people on the court. Some rotations leave a team unable to win back the serve. The opponent racks up five or six points in a row, and the crowd sees a run. The cause usually sits in one place: a weak passer being served at, or a back-row attacker who cannot solve the situation. A technical scout spots the stuck rotation after three repetitions. The crowd spots it after the match is already decided. The time gap between those two moments is the value of keeping records. A blind spot: data density mistaken for evidence A full Data Volley export looks convincing. Thousands of cells, colour codes, diagrams. That appearance makes it easy to believe the analysis is finished. Data density and evidence are two different things. A thick scouting file can hold exactly one finding. The same file can hold none at all, and that is where the professional trap sits: the writer, pressured to file, fills the gap with plausible-sounding inference. The conclusion is still grammatical, still carries numbers, and still has no basis. In those situations, the correct publishable output is a declared null result with the specific reason attached. That is a finished product, not an abandonment. The mechanism resembles the transfer market, where the disclosed metrics are only the visible part, and the submerged part sits inside representation contracts and clauses nobody is permitted to state aloud. I once lost a month rebuilding my own dead-ball database after a match I had misread. One month, 64 matches, and every dead ball was recorded. Only afterwards did I allow myself to write. At the same time I track another blind spot of the industry: video review time. Volleyball has a challenge system, and each review runs two or three minutes. The rhythm cools after every one of them. A rally that was just loud enough to lift the stands becomes, three minutes later, a single line of decision on the big screen. What to check next match When the season enters its densest stretch, the column worth tracking on the stat sheet is efficiency, placed next to the perfect-pass rate of the same set, with a count of how many times one rotation gets stuck. If the team you follow wins a match while the attack efficiency of its main hitter is still negative, that result is borrowing from somewhere else. And if that hitter is blocked often while the team’s reception rate stays low, the responsible party is not standing where you are looking.