When Input Data is Empty: Lessons on Sports Analysis Standards in the AI Era

In the sports commentary industry, there is a principle I have adhered to for four decades: never fabricate what does not exist. But recently, I witnessed a concerning phenomenon — AI analysis systems designed to process sports information are outputting reports full of N/A, meaning "insufficient information", without anyone acknowledging this as a serious problem.

Specifically, a Stage-2 analysis framework for martial arts returned completely empty results. No match names, no athlete profiles, no organizational information, no event data, no core viewpoints. All eight analysis dimensions — from technical-tactical analysis, athlete condition, organizational landscape, business model, rules-compliance, health-career risk, public narrative, to industry transmission — recorded only one status: N/A.

This is not merely a technical error. This is a test of professional standards in the sports media industry, threatened by the very tools created to serve it.

Martial arts does not forgive information gaps

I have been following and commenting on martial arts matches since my early career in Australia, through my work in Vietnam, to my current role as a multi-sport commentator in Beijing for the Chinese market. In those forty years, I learned one thing: in martial arts, every decision must be based on real information — opponent position, reaction speed, injury history, competition cycle. A coach who makes tactical decisions based on "feelings" instead of actual data will endanger their fighter.

Applying this to sports commentary, the same principle holds. A martial arts analysis without athlete names, weight classes, strike statistics, event organization information — is not an analysis at all, it is a blank paper stamped with "expertise".

The Stage-2 analysis framework I am referencing was designed with eight comprehensive evaluation dimensions. Dimension one focuses on competition and technical-tactical analysis, requiring clear identification of comparison subjects, competition disciplines and rule sets, style matchup evaluation, finishing ability, record quality, and key metrics such as strikes landed per minute (SLpM), strikes absorbed per minute (SApM), accuracy rates, and takedown numbers. Dimension two assesses athlete condition and athletic longevity, including age curves, weight-cut risks, injury wear, and camp quality. Dimension three analyzes event and organizational landscape, examining barriers such as exclusive contracts, title fragmentation, and cross-promotion superfights. Dimension four evaluates business models and markets, including pay-per-view (PPV) revenue, gate revenue, fighter pay, and sponsorship. Dimension five checks rules and compliance, from judging-scoring, drug testing, weight management, to disciplinary actions. Dimension six analyzes health and career risks, including brain health, weight-cut incidents, injuries, retirement security, and psychological safety. Dimension seven assesses public narrative and market expectations, including narrative sustainability, expectation-gap analysis, beef authenticity, and crossover-fight evaluation. Dimension eight analyzes industry transmission, tracking impacts on segments from gyms, broadcasting, betting, equipment, entertainment crossover, to regional policy.

All eight dimensions require specific input data. When input data is empty, the entire analysis framework becomes meaningless.

Philosophy: "Truth lies in the dark space between numbers"

I once wrote that "when data starts to resist, tactics finally speak up." This statement came from my real experience — in 2026, I wrote a 4,000-word analysis of the Real Madrid vs Barcelona El Clasico with a 2-3 score, using data from 30 matches, drawing player position charts, and comparing the concept of "zone control" with DOTA 2. The article only had 1,200 views, but an editor at CCTV Sports noticed and invited me to try out for the 2026 World Cup. That was the first time I realized that data is not just a support tool — data is the foundation of all valuable analysis.

That match was not a linear chain of events from cause to effect. It was a complex system of interacting variables, and only when I presented branching scenarios — "if this happens, what would the result be" — did I truly understand that match. This method, which I call "branching scenarios," has become a core characteristic of my writing.

But the branching method only works when there is real data to branch from. When there is nothing — no match, no fighter, no statistics — then there are no branches to take. The Stage-2 analysis framework returning all N/A results is not because it was poorly designed, but because it correctly follows the fundamental principle of evidence-based analysis: no evidence, no conclusions.

The danger of "filling in the blanks"

In the framework's comprehensive assessment report, there is a high-level risk warning: "Empty input may lead an automated or rushed downstream process to hallucinate fight narratives, athlete assessments, or market claims." This is a more serious problem than many people realize.

I have witnessed this phenomenon in football. AI analysis platforms sometimes generate "analysis" articles based on generic templates, filling gaps with generic phrases like "recent form," "coach's tactics," "importance of this match" — without any specific data. The result is articles that sound professional but have absolutely no informational value. They are grammar puppets — grammatically correct, factually wrong.

In martial arts, the consequences are even more serious. If an AI system "analyzes" a UFC fight without real data, it could give completely wrong betting recommendations, or entirely misjudge a fighter's winning chances. Martial arts does not forgive such mistakes — both in the arena and in the analysis room.

Lessons in professional standards

The tactical scandal at the 2026 World Cup in Russia taught me an important lesson about handling insufficient information. In the Germany vs South Korea 0-2 match in Kazan, when Son Heung-min scored at 90+3 minutes, I commented live that this was a classic counter-attack — and I was asked by a former player sitting next to me: "Are you sure you're talking about football?" That clip spread with 2.5 million views. But I did not stay silent. Instead of making excuses, I wrote a 5,000-word analysis of my own mistakes, analyzing my beliefs based on which data, and publicly critiquing myself. The article was shared 12,000 times; I lost 4% of my followers but gained respect from senior colleagues.

That is how a professional sports analyst should handle information limitations: acknowledge them, analyze them, and turn them into lessons for the community. No fabrication, no cover-up, no creation of illusions about accuracy.

Transfer market and information noise

One of my core viewpoints is that player agents are the biggest hidden cost in the transfer market, and the noise they create distorts the market. This is directly related to the problem of empty input data.

In the transfer market, countless rumors are created by interested parties — agents want to push prices up, clubs want to lower prices, media want clicks. If an AI analysis system cannot distinguish between rumors and verified information, it becomes a noise-spreading machine. And when noise drowns out signal, the market distorts.

Similarly, in martial arts analysis, without real data — competition records, strike statistics, injury information — then every "analysis" is just noise. And noise in martial arts not only distorts the betting market or media; it can endanger the fighters themselves.

Future direction: signals to track

The comprehensive assessment report proposes three signals to track when facing empty input status. First, re-submission of complete Stage-1 results — checking whether the "Information" field contains non-empty entries. Second, identification of article source and date — verifying source quality and time sensitivity. Third, populated entity list — confirming that fighters, organizations, events, coaches appear.

These are basic but essential verification steps. Before any analysis system — whether AI or human — draws conclusions, it must confirm that there is enough information to analyze. If not, it must clearly state: "Insufficient information to analyze" — instead of creating a false report about that emptiness.

Conclusion: sports as a common language

I believe sports is humanity's common language. But language needs grammar, vocabulary, and context. When these basic elements are missing, the story becomes meaningless. The Stage-2 analysis framework returning all N/A is not a failure — it is correct adherence to the principle of evidence-based analysis. The real failure would be if someone tried to fill those gaps with fabrication.

In a world where AI is increasingly involved in sports content production, standards for information integrity must be raised, not lowered. A good sports article is not one with flowery language, but one with enough facts for readers to draw their own conclusions. And when there are no facts, a responsible commentator should say: "I don't know" — instead of fabricating an answer.

That is the lesson I have learned from four decades in the industry, and it is also the message I send to every AI analysis system trying to replace real sports commentators: data does not know how to lie, but the person holding the pen can. And when the system fills in blanks without basis, it is turning itself into an automatic liar.

Let data speak. And when there is no data, let silence be the answer.