The Empty Cell: The Limits of Modern Tennis Analytics

That night, the screen in a small flat in Liverpool lit up with a single string of characters beside the name Emma Raducanu: N/A. I had reopened my scouting file for the 2026 US Open, and the most important cell — tour-level return points won — was blank. I was not being lazy, and nothing was broken. The eighteen-year-old had not played the minimum number of matches for any model to dare assign her a percentage. The pipeline returned exactly what it was supposed to return: unknown.

Three weeks later, Raducanu won the US Open without dropping a set. Ten matches, twenty sets, up through qualifying, ranked No. 150 at the time of the draw. The most complete data system in professional tennis missed the champion before the tournament began, and it missed her honestly.

Last Tuesday morning, a nine-section deep analysis report landed in my inbox. Every cell carried the same line: “N/A — insufficient information.” The team behind it refused to invent a player, refused to stage a fictional match, and chose to say plainly that they had nothing to say. Thirty-eight years in this trade have taught me that the hardest discipline for an analyst is not finding a new metric. It is accepting an empty cell.

Russia taught me that silence is the deepest layer of data there is.

How an empty cell is born

A modern tennis data room runs much like a weather station. Every player has columns: first-serve percentage, first-serve points won, return points won, break-point conversion, average rally length, net approaches. Every column has a minimum sample, usually between sixty and seventy per cent of a tournament’s matches. Below that threshold, the system does not compute a percentile. It returns N/A.

There are two kinds of empty cell, and they differ in kind. The first is missing data: the event happened, but nobody recorded it well enough to conclude anything. The second is an event that has never occurred: a player has never faced a break point in a fifth set, so there is nothing to measure. Both appear on screen as the same three characters, but they demand opposite responses. Confusing them is the most common error in my line of work.

In September 2026, Raducanu’s column was empty in the first sense. She had not played enough WTA main-draw matches on hard courts for the model to speak. The spreadsheet was honest. My head was not.

Ten matches, twenty sets, and an enormous confidence interval

What makes this story worth retelling is not the trophy. It is that the spreadsheet was right and I was almost wrong.

Across ten matches, the confidence interval on any metric is so wide it becomes meaningless. A player who wins seventy per cent of return points over ten matches might in truth be winning fifty-two, and vice versa. No model can build a career from that sample; it can only build an anecdote. The 2026 US Open title was a sharp peak, not a trend.

Three years on, that column had still not filled the way people assumed it would. Raducanu won no further WTA titles across the next two seasons, struggled with injuries and repeated coaching changes, and fell out of the top 100. Only at the end of 2026 did she close a season inside the top 60. The data team that wrote N/A had been right; the mistake would have been to hand her a projection.

On the other side sits Iga Świątek, whose column is full. A thirty-seven-match winning streak in 2026, four Roland Garros titles across 2026, 2026, 2026 and 2026, plus the 2026 US Open — a sample thick enough that every percentile carries meaning.

Then, on 28 November 2026, the International Tennis Integrity Agency announced a one-month suspension after Świątek’s sample tested positive for trimetazidine. The spreadsheet was full, and the decisive cell — intent — stayed empty. Both sides offered an explanation. Neither filled it.

Jannik Sinner travelled a different route. An independent tribunal cleared him in August 2026 in the clostebol case; the World Anti-Doping Agency appealed; on 15 February 2026 a settlement closed the matter with a three-month suspension running from 9 February to 4 May 2026. How much of the substance entered his body, and why — N/A again.

I retell those three cases not to judge anyone. I retell them to make one point: the empty cells in this sport do not sit at the edges. They sit in the middle.

Money has empty cells too

The spreadsheet I care about most this season belongs to money, not to ball-striking.

In October 2026, in Riyadh, the Six Kings Slam exhibition paid its champion six million US dollars for three matches. The 2026 Wimbledon champion collected three million pounds after seven matches and a fortnight. Divided per match, a single evening in Riyadh pays several times what a Centre Court match pays.

Both trophies were lifted by the same hands. That is a measurable signal about where the sport’s financial centre of gravity is drifting. An event with no ranking points, no draw and no qualifying pays more than the oldest tournament in the world. Anyone who says data never tells a story about power has not read the right column.

Meanwhile, the old generation closes its pages one by one. Roger Federer retired in September 2026 at the Laver Cup. Rafael Nadal retired in November 2026 at the Davis Cup in Málaga. Novak Djokovic is still standing, with twenty-four Grand Slam titles and the Olympic gold he won in Paris on 4 August 2026. The columns of the next generation are full now, but the cells we left blank in 2026 — how a young body digests a major title — remain blank.

The contrarian angle

Every analyst keeps one mantra: correlation is not causation. True, and not enough.

Raducanu’s title was explained by the fearlessness of youth. Świątek’s dominance was explained by her forehand. Sinner was explained by his physical base. Each explanation fills an N/A cell with a story. Stories carry no confidence interval, no sample, no way of being tested. They are more comfortable than a spreadsheet, and for that reason they travel faster.

In the summer of 2026, in Moscow, I wrote an analysis of Russia covering 148 kilometres in their quarter-final, twelve kilometres above their own group-stage average, and I predicted they would collapse in extra time. That piece drew twenty-three reads. A colleague’s emotional column about fighting spirit was shared thousands of times. I sat alone in a hotel and understood that correct data can still be lonely data.

There are things data never touches — the way a stadium breathes.

In tennis we can time a serve to a hundredth of a second and track movement to the metre. We cannot measure the fear before a second serve at 4-5 in the fifth set on Centre Court. In the summer of 2026, when European football played to empty stands, I analysed five hundred matches and found home teams lost only 0.18 expected goals per match without supporters — but trailing teams switched to long balls seven minutes earlier than usual. Signals do not vanish when the crowd does. They move somewhere else. When the stands stood empty, the numbers began to learn how to sing.

What I could be wrong about

An empty cell can also be a hiding place. An analyst who says N/A too often is never wrong, and never useful. The line between intellectual honesty and professional cowardice is thin, and I have stood on the wrong side of it more than once. If I refuse to make a call until every cell is full, I will never publish anything before a major begins.

I may also be wrong that the Riyadh money distorts this sport. It may simply be an honest price for the value of the game’s leading stars in a new market.

Signals for the next round

Over the coming months, I will watch three things.

First, which column is still empty before each major — because the next champion may well be sitting inside an N/A nobody has noticed.

Second, whether the governing bodies publish the cells they already hold. A transparent sport is not one without empty cells; it is one willing to say which cells are empty and why.

Third, whether the Riyadh money becomes ranking points. If it does, the empty cells will vanish for an entirely different reason — not because we understand more, but because we agreed to change the ruler.

I am too old to believe in miracles, and young enough to know which miracles can be measured.