When Data Is Empty: The Sports Analysis Challenge in the Information Age 2026

At my analysis desk, there are days when the paper is blank and the ink is also white. Not because there are no matches to watch, but because the input source — what analysts call "Stage-1 extraction" — returns a report with no content. This happens more often than we think, especially in chess and esports, where news speed always races with accuracy. I have been following sports for over 49 years. In my youth, I used to sit in the back rows of Dynamo stadium, recording every play with a fountain pen and school notebooks. Back then, lack of information was commonplace — newspapers came out a day after the match, radio signals were unstable, and long-distance calls took hours to confirm scores. But even in those circumstances, a genuine sports journalist always had something to write: their emotions before the match, the breath of the pitch, and the eyes of players as they stepped off the field. Now, at 65, living in Saint Petersburg, I witness a different paradox: information overwhelms but analysis is empty. Artificial intelligence systems designed to "deconstruct" articles into information points, core viewpoints, and related entities. The result of this process, called "Stage-1," is the foundation for building deeper analysis at the next level. But when the data table returns all cells as "N/A — insufficient information," what does that mean? In chess, where I began my career as a player and tournament organizer before moving to media, data has always been vital. A move is recorded in algebraic notation, a tournament is measured by ELO rating, and a career is evaluated through thousands of games played. When analysis systems cannot extract any information points from a chess article, this can happen for three main reasons. First, the original article may not exist or be unreadable — a purely technical error. Second, the article exists but lacks elements the system is designed to recognize: player names, scores, specific events, or interpretable viewpoints. Third, and this is the most concerning case, the article belongs to a topic the system cannot process — such as philosophical pieces about the nature of the game, or subjective memoirs about a deceased player. From my experience, I recall matches at chess tournaments in the Soviet Union in the 1970s and 1980s. Back then, there was no Internet, no analysis engines, no online databases. What we had was memory, a chessboard, and notebooks passed between players. Each game was a story, and that story could not be reduced to dry information points. An article about Karpov vs Korchnoi was not just a list of moves — it was also the political atmosphere of the era, the psychological pressure of the Cold War, and the sweat on the players' foreheads. This is where artificial intelligence hits its limit. Modern analysis systems are built on the assumption that any article can be broken down into quantifiable elements. But sports, especially chess, does not always work that way. There are articles whose value lies in ambiguity, in questions without answers, in emotions that cannot be digitized. Returning to the "Stage-1 empty output" issue — this is not just a technical problem but also a philosophical one. When we build sports analysis systems based on data, are we losing something? The answer, I believe, is yes. We are losing the layer of human emotion, the stories behind the numbers, and the historical context that only a journalist living in the same era can feel. However, this does not mean we should abandon data analysis. On the contrary, we need to understand that data is a tool, not the ultimate goal. In the seven analytical areas I mentioned — from game technique, player data, tournament systems, competitive landscape, rules and governance, risk analysis, public expectations, to sports media transmission — each requires data to draw valuable conclusions. But when data does not exist, the only conclusion that can be drawn is "cannot assess" — and that is still a valuable conclusion. In the information age of 2026, when everything is digitized and analyzed automatically, there is one thing I deeply believe: a good sports journalist is not only someone who can read data, but also someone who can see beyond data. That is why, despite being 65 and living far from my Vietnamese homeland, I still sit at my computer every night, writing commentary articles about chess for the Russian market. Because in every match, there is a story waiting to be told — and that story does not depend on whether the Stage-1 system returns data or not. Tonight, Luzhniki stadium has no ball, but in my heart, the ball is still rolling. That is how I view sports, and that is also how I view sports analysis: as an endless journey between data and emotion, between machine and human, between bare numbers and endless stories.