The Empty Spreadsheet in Ligue 1: When Football Loses a Page of Its Diary

In March 2026 I sat in front of a spreadsheet in Marseille and counted the empty cells. The sheet held 1,204 rows — exactly the number of shots I had once recorded by hand for the first half of the 2026-18 season, when Opta first published xG data for Ligue 1. This time, 96 of those rows contained nothing at all. No xG. No pass counts. No distance covered. Three French league matches had been postponed, and our data provider simply never sent logs for them. A young colleague asked whether I wanted to reconstruct the numbers from video. I said no. I wanted to keep those empty cells in the file, because they are data too. An empty cell is not a zero. An empty cell is a signal. People assume football data analysis is a job of reading the numbers you already have. In practice, most of my working time goes to identifying what is missing. When a match's xG table is blank, that does not tell me the match produced no chances. It tells me someone in the processing chain dropped a page. Telling those two things apart is the entire difference between an analyst and a man hired to read out a report. In the summer of 2026 I learned to trust something nobody had named yet: xG. I was 57 then, working as a transfer-market administrator in Marseille. Opta published xG tables for Ligue 1 and I did not believe them quickly. I recorded 1,204 shots from 20 clubs across the first half of 2026-18 by hand and matched each one against the goals that actually followed. The correlation coefficient came out at 0.84. That was enough for me to build my own striker-valuation dataset. Colleagues said I reacted slowly. I needed verification before use. Because I counted by hand, I know something that people who only read summary tables do not: every data row has a process behind it, and any process can break. A camera fails. A wide-angle lens is blocked by a corner flag. A player's GPS unit dies in the 63rd minute. A match is halted by flare smoke. None of that appears in the final report. It appears as an empty cell. My job in Marseille is valuing players for the transfer market. In that trade, an empty scouting file is more dangerous than a bad one. A bad file tells me the player runs little and passes poorly. An empty file tells me only that the sender did not do the work. Yet plenty of clubs still pay for that emptiness, because they read it as "no problems found". Take the biggest example of my career. In 2026 European football restarted after the pandemic, and because I had used data at the 2026 World Cup, my editor assigned me to the Bundesliga. Sitting in Marseille, I analysed 81 matches played in empty stadiums during the 2026-20 season. Home teams won only 26% of them, against 43% before the pandemic. I wrote a report titled "Empty stands kill home advantage". What gave that report its value was not the 26% figure. It was my willingness to say that a familiar variable — home advantage — had dropped out of the equation, at least across those 81 matches. An empty stadium is the finest laboratory a data obsessive could ask for. When crowd noise is removed from the equation, other variables surface: pitch quality, travel schedules, and above all the psychology of young players who only perform well when there is someone to cheer them. A Ligue 2 club, Le Havre, used that report to negotiate down the purchase of a young striker. He had scored 9 goals in 11 home matches before the pandemic. Afterwards, in empty stadiums, he scored 1 in 10. Le Havre's board did not say he was poor. They said: we do not have the data to justify a high price. That was an honest conclusion, and it saved them roughly 1.8 million euros. Then came Qatar, 2026. I was 62, sent to the World Cup by Canal+. The entire pundit class praised Achraf Hakimi for 142 sprints and an average of 2.3 chances created per match. Those numbers were correct. But when I pulled positional data, the corridor behind him was vacant for 34% of his minutes. Morocco stayed safe because their two centre-backs ran above 31 km/h and were always there to cover. I filed a note warning that the attacking-full-back model only holds when the defence has enough speed to compensate. In the match against France, the opposition kept pouring the ball into Morocco's right flank. In all three cases, what determined the quality of the analysis was the variable nobody wrote down in the official report. A 26% home win rate says nothing if you do not know the stands were empty. 142 sprints say nothing if you do not know how fast the centre-backs run. And 9 home goals say nothing if you do not separate home and away splits. Here is a smaller example, closer to me. This January I received a report on a 19-year-old midfielder in the Portuguese first division. The report carried 14 metrics, complete to the point of suspicion. But the column for "minutes played while trailing" was empty. The column for "possessions lost in his own half" was empty. The column for "physical output after the 75th minute" was empty. The sender had collected exactly what was easy and skipped exactly what was hard. I did not buy the player. Not because he was poor, but because I did not know what he becomes in the situations that define a central midfielder's value. That is why, in every statistical table I have built since 2026, I leave one empty column labelled "unverified". It is never filled with guesswork. It is filled only when I have a second independent source. Some weeks that column takes up 12% of the sheet, and I accept it. A dataset with marked holes is still more honest than a dataset packed full where every cell is an unverified belief. I am 66, old enough to know a number never tells a story unless you ask it a question. An empty column asks me something very specific: do you have another source? If the answer is no, I leave the column empty and write "insufficient data to conclude" into the report. That sentence once earned me a scolding from a young editor for indecisiveness. I kept it anyway, and three years later that same editor called me to ask how to rebuild a dataset whose source had been lost. This is the part the analytics trade rarely says out loud. Emptiness is not always a defect to be filled. There are three hypotheses behind an empty cell, and I write all three down before concluding anything. First, the data never existed — a cancelled match, a player who never came on, a competition that has not started. Here the empty cell is a fact, not a fault. Second, the data existed but was dropped during processing. A camera broke, a file was never uploaded, a provider changed its interface. Here the empty cell is an incident, and the correct response is to trace it back to its source rather than improvise. Third, the data exists but nobody bothered to collect it, because it does not sit inside the fashionable metric set. This is the most dangerous case, and the most common. It is how an entire generation of analysis ignored dressing-room chemistry simply because no number measures it. Confusing those three cases is the elementary error of the profession. And that error is usually camouflaged by confidence. An analyst who looks at an empty cell and says "I have no view" is treated as weak. An analyst who looks at an empty cell and says "in my experience" is treated as seasoned. In reality the second man has just filled a hole with memory, and memory has no sample size. Correlation is not causation. But the silence of data is not evidence that nothing happened either. A cancelled match is not lost points; it is a lost page of the diary. Players are variables, the market is a function, but most of my life has been a constant. Next matchday I will open the file again and count the empty cells before I read the numbers. If someone hands me a dataset with no empty cells at all, I will ask them exactly one question: what did you leave out?