Nine N/A Cells and the Data Gap in Vietnamese Volleyball

Third screen from the left returned nine rows, and all nine carried the same mark: N/A. Spike success rate: N/A. Blocks per set: N/A. Ace-to-error ratio: N/A. Perfect-pass rate: N/A. I sat looking at that file for about four minutes, reopened the mail thread to make sure I had not sent the wrong format, then checked the spam folder. There was no technical failure. The report returned exactly what it had, and what it had was a void.

Eleven years ago I would have called this a transmission fault. Now I call it a research result. 2026 taught me how to listen to what the model does not measure.

Results running ahead of measurement infrastructure

Vietnam has a volleyball scene where results move faster than the data system. The women's national team won SEA Games gold at home in 2026, held the regional top spot in the following SEA Games, and in 2026 won the FIVB Challenger Cup for the first time, earning a place at a world-level tournament. At club level, the national championship and international cups hosted at home such as the VTV Cup run on a steady calendar, pulling in crowds, sponsorship contracts and mounting pressure for results each season.

Data infrastructure has not kept pace. A volleyball match generates far denser event data than a football match: every rally is a closed sequence of serve, reception, set, attack, block and cover. A three-set match can log more than 130 separate rallies, each containing at least five codable actions. At major international events this data is captured with dedicated software, split by rotation and published set by set. In domestic competition it mostly stops at a hand-written scoresheet, a handful of aggregate figures supplied by organisers, and the memory of the people sitting courtside.

That is how a report can come back with nine empty cells. There is no shortage of matches to measure. There is a shortage of people measuring, of a single agreed definition of what is being measured, and of the habit of keeping raw data so someone else can check it.

Five metric families, and what each empty cell says

The first family covers attack. People talk loosely about "scoring efficiency", but what determines the value of an outside hitter is the gap between direct points and self-inflicted errors. A spiker with 20 points alongside 11 errors and 9 times blocked has a lower net value than one with 13 points and only 3 errors. A kill percentage above 45% is treated as the threshold for a starting hitter; below 38%, the attack system has to be propped up with second-ball shots, and second-ball shots always cost more physically.

The second family is block and defence. Blocks per set is the most misleading figure on any volleyball sheet. A team averaging 2.5 blocks per set sounds imposing, but if its dig rate is low, that number usually means the block is being stretched and forced to commit centrally, exposing angles the libero cannot cover. I once tracked a team with the highest blocks per set in the group stage and the lowest dig rate in the tournament; they lost the semi-final in straight sets to a side that attacked the wings with balls angled outside the block's reach.

The third family is serve and reception. Ace-to-error ratio is the first figure I check, because it reveals tactical intent rather than superiority. A safe-serving team will post a low ratio, but its opponent's perfect-pass rate rises, and the match is lost in the very first contact. An aggressive-serving team can concede 12 points in one set and then take 8 straight break points when the opponent's reception-set rhythm breaks. One metric, two opposite outcomes.

The fourth family is rotation-level operation. Modern volleyball is decided by sideout rate and break-point rate across each six-player rotation. A team can win 68% of its reception points in the rotation containing its starting setter, then fall to 41% when that rotation pushes the setter into position one. Without rotation-split data, the aggregate sheet hides exactly the hole the opponent is exploiting. And the exploitation is simple: serve at the weakest receiver in that rotation, ten times in a row, until the coach has to substitute.

The fifth family is physical load and competition cycle. Volleyball has a dense scoring rhythm; every point can end with a maximal jump. A hitter playing four matches in seven days will look different in the fourth than in the first, and the difference usually shows not in points scored but in contact height and approach speed. Without tracking data, observers only see more errors, then draw the wrong conclusion about form.

Here I need to be explicit about what those nine N/A cells say. Each empty cell has its own cause. An empty spike-efficiency cell may mean nobody recorded it. An empty perfect-pass cell usually means two people recorded differently: one counting "ball delivered to the setter's hands", the other counting "ball kept above the net". An empty rotation cell is the worst kind, because it means nobody ever asked which rotation this team wins from. A sheet with nine empty cells is not necessarily a bad dataset. It is a dataset that was never started.

The silence of the model

Croatia were not a fairy tale, they were a problem that had to be solved from scratch. I reuse that line for volleyball, because the same logic holds.

In the middle of the pandemic I counted history back and found every cycle wears a familiar face. The teams with the densest schedules during compressed periods all saw their numbers dip by similar margins, no matter how strong they were. That does not mean every number can be read the same way.

Three correlation traps I run into most when analysing domestic volleyball. First, a high dig rate is often a sign of a weak block, because the ball has to get past the block to reach the libero's hands. Second, a high perfect-pass rate may simply reflect easy serving from the opponent rather than a good reception system. Third, an individual's point tally depends on the quality of her teammates' setting, which in turn depends on the quality of first contact. Pulling one player out of that chain to praise or blame her is the most common way to misread data.

The fourth trap is harder to see: opponent effects. A team facing three weak opponents in a row will post a beautiful stat line, and that beautiful stat line is then used to forecast a match against a strong opponent. I have been wrong this way often enough to turn it into a rule: every metric must be adjusted for opponent quality before it is used to say anything at all.

And there is a part the model never touches: dressing-room dynamics, pressure from the coaching staff, a setter losing faith in a hitter after two consecutive blocks. None of that appears in any data cell. All of it shows up in the next set's perfect-pass rate.

The signal for the next round

Based on my experience tracking matches in the group stage of the national championship, one working rule stands out: the first thing to build is not a forecasting model, it is an agreed definition of what is being counted.

Those nine N/A cells will be filled. The question is not whether they can be, but who gets to define what a perfect first contact is, and whether that definition is published alongside raw data so others can verify it. When Vietnam's women walk onto a world stage, their opponents have had an answer to that question for years. Names like Trần Thị Thanh Thúy and Nguyễn Thị Bích Tuyền do not need more praise. They need a system that records accurately what they already did, so nobody has to argue by feel next time.