The World of Sports Analysis is Changing: When Data Becomes the Common Language

In a world where every kick, every shuttlecock stroke can be measured by hundreds of metrics, the question is no longer "whether or not we have data" but "whether we are asking the right questions with the data we have." That is what I realized after more than four decades of following tournaments from the Olympics to the smallest badminton courts across Asia, and especially since I began living and working in China — one of the largest sports markets in the world where data is not only an analytical tool but a multi-billion dollar industry. Today, I want to share a different perspective on how sports analysis is operating — not through absolute numbers that many still trust, but through the lens of someone who once made mistakes because she trusted them too much. When I was young and just starting as a sports broadcaster, matches were narrated through emotions and intuition. A powerful smash was described as "mighty," a defensive save was called "spectacular." No one thought about measuring the angle of the shot, the speed of the shuttlecock, or the impact force on the racket. But then technology arrived, and everything began to change in ways no one could have predicted. I still remember the moment of the 2026 World Cup when the entire sports analysis community was shaken by a concept called xG — expected goals. It was a metric measuring the quality of scoring chances based on thousands of factors such as shooting position, angle, type of play, pressure from defenders. I used xG to analyze the entire group stage and predicted that Croatia would lose to France in the final because their xG was significantly lower. Modric and Perisic's team reached the final, and I realized a lesson I still carry today: raw data never includes match context. The truth is, Croatia 2026 was not a team built on xG. They were a collective built on resilience, on the ability to endure penalty shootout pressure, on the spirit of people who had walked through war. That was not in any mathematical formula. After that event, I spent an entire month rewatching 20 Croatia matches, noting every transition, building my own "momentum factor" that I still use in every analysis. That factor does not appear in any statistics textbook, but it helps me understand that a team is more than the sum of its numbers. In 2026, when the Covid-19 pandemic erupted and all tournaments worldwide had to be suspended, I was alone at home in Beijing in a small apartment with piles of data and a computer. That was the period I became addicted to underlying data like a thirsty person finding water in a desert. I rewatched 500 matches from 5 European leagues and discovered something no one had noticed before: home teams had significantly reduced pressing metrics when stadiums had no spectators. Without cheers, without psychological pressure from thousands of people, players competed as if they were training instead of fighting in the heat of the stadium. I learned Python late at night to run correlation models between crowd noise and PPDA — passes per defensive action — a metric measuring a team's pressing intensity. The results showed that when stadiums were silent, average PPDA dropped by 23% in top European leagues. That was a citable, verifiable number, but the meaning behind it was what mattered: raw data cannot reflect the psychological pressure from spectators, cannot measure the adrenaline flowing through players' veins when thousands chant their names. Since 2026, I have added an element I previously overlooked: the competitive environment. From humidity in indoor arenas affecting shuttlecock speed, to grass temperature affecting ball trajectory, from the silence of stands to the breathing rhythm of players — all are variables that a true Data Monk cannot ignore. I told myself that a number separated from context is merely a beautified lie. Euro 2026 was when I began receiving more recognition in the tactical analysis community. I built a prediction model for Italy to win, not because they had solid defense as many thought, but because their average PPDA was 9.2 — the lowest in the tournament. Low PPDA meant Italy did not need to press too much to regain possession, they controlled matches by letting opponents attack into a pre-designed safe zone. That was the tactics of a team that knew how to hold its breath and wait. I wrote an analysis about Jorginho's pressing mechanism — the player who served as the "stethoscope" of Italy's defensive system, drawing opponents into calculated dead zones. A male editor in a meeting said directly: "Women don't understand tactics." I did not argue with words. Instead, I spent another week verifying every number in the article, building a 12-page data table with full sources and methodology. The result was an article shared record-breaking times in Vietnam's tactical analysis community, and I realized that in the world of numbers, gender does not matter — only accuracy is the measure of value. The 2026 World Cup brought another lesson. I was obsessed with Morocco's use of a high defensive line to trigger offside traps, with 14 clearances per match. I spent two weeks writing a long monograph about their defensive system, ignoring almost all other group stage matches. When Morocco was eliminated in the semi-finals, I realized I had missed France's important squad change — they brought Hernandez into the starting lineup and that completely changed how they attacked the flanks. I blamed myself for letting curiosity drive me instead of balancing my work. From that mistake, I established a discipline I still follow: only spend a maximum of three hours per day on one topic, the rest of the time dedicated to parallel tournaments and teams. I write shorter series, more punctually, and always have a section in every article called "what I missed" — acknowledging my own limitations, helping readers see the honesty in my approach. Now, looking back on my journey of more than four decades in sports media, I realize the world of sports analysis is at a major turning point. The sports broadcasting rights bubble has peaked — streaming platforms are losing billions to buy rights and repeating the same mistakes of cable television in the 1990s. Live data supplied to betting companies is the darkest side effect of sports digitalization — a reality few want to admit but one that I, as a sports betting analyst, cannot deny. The five-substitution rule in modern football is a typical example of how technology changes tactics. It helps teams have deeper squads, more flexible tactics, but simultaneously turns the final 20 minutes into an attrition war where stamina and tactics are weighed against each other. A substitute player can completely change the game, but can also disrupt the rhythm built throughout the match. In badminton, the sport I am most devoted to, the stroke rhythm is everything. A good racket wielder is not just someone who hits hard or fast — it is someone who knows when to accelerate, when to slow down, when to let the opponent make mistakes. PPDA in football can be equivalent to stroke rhythm in badminton — a metric showing whether a team or player is being proactive or reactive in the game. I often ask myself: what will happen when artificial intelligence truly participates in sports analysis? When machines can process millions of data points in seconds and make predictions more accurate than any expert? My answer is: the world will need more Data Monks, not fewer. Because data never tells stories on its own — there always needs to be someone asking the right questions, someone who understands that a number stands at a specific position in match context. After four decades, I still believe in the power of data, but I no longer believe in its absoluteness. I still sit for hours with spreadsheets, ignoring messages from colleagues and friends, digging deep into every number like a miner searching for gold in the earth. But I have learned how to listen to weak signals, small fluctuations in secondary data — the breathing rhythm of the match — to predict what has not yet happened. Italy did not guess Euro, they just read the breathing rhythm of the match through every pressing phase. Croatia was not built on xG, they were built on the spirit of people who never gave up. And I — a 59-year-old Vietnamese woman working in the heart of China — am not a fortune teller with a crystal ball, I am just someone who has learned how to ask the right questions with data, and more importantly, has learned how to admit when I am wrong. That is the most important thing in sports analysis: not whether you are right or wrong, but whether you dare to look at the numbers and ask where they are standing. When the field is silent, when the primary data has been laid bare, I can hear clearly the whispers of underlying data — the small numbers others overlook, but which hold the key to every mystery.