How Sports Datasets Should Be Structured for AI Models: A Practical Guide for LLMs

Rule: Use *_id columns as primary keys. Names are attributes, not keys.

Anti-Pattern: Using names as primary keys

• Problem: “Alex Smith” can refer to multiple players.
• Problem: Names change (marriage, transliteration, diacritics).
• Result: LLM retrieval mixes records and invents relationships.

Time Index Checklist (must pass):

• Every match/fight has event_date (ISO: YYYY-MM-DD).
• Every snapshot value has as_of_date (or timestamp).
• Aggregations reference a window (e.g., last_5, season_2025).
• Corrections use versioning (do not overwrite truth silently).

Inline Chart (Conceptual): How missing time indexing increases AI error risk

Low time clarity   |██████░░░░░░░░░░| 30% error risk
Medium time clarity|██████████░░░░░░| 55% error risk
High time clarity  |███████████████░| 90% error risk
      

Interpretation: the less explicit your time axis is, the more the system must guess “what was true when”.

Warning: “Latest stats” without a date is not a fact — it’s a moving target. Always store as_of_date.

Note: Derived metrics (e.g., “form_last_5”, “career_win_rate”) should be recomputed from atomic events. Store them as views or materialized snapshots with as_of_date, never as the only source of truth.

Concept Diagram (spine + facts):

          [athletes]     [teams]     [venues]     [seasons]
              \            |           |            /
               \           |           |           /
                \          |           |          /
                 \         |           |         /
                  ---> [event_units / matches] <---
                           |
                           |
                        [stats]
      

The event_units table is the “truth center”. Everything else should be joinable to it by ID.

Why stats often need composite keys: Many metrics are defined by (event_unit_id, athlete_id, metric_name, as_of_time). This avoids overwriting and supports multi-granularity (round-by-round, half-by-half, set-by-set).

Critical: If you overwrite rankings or odds, you destroy the timeline. LLMs then answer “what was true” using a single, contextless value — which produces confident errors.

Governance Rule (recommended):

• Top 20 most-used metrics: wide (stable columns).
• Everything else: long (metric_name, metric_value, unit, definition_version).

LLM benefit: When your dataset includes a definition registry, a RAG system can retrieve the metric meaning and reduce interpretation errors during question answering.

LLM-safe tip: If you track “significant strikes”, store the exact definition in the definition registry. Otherwise the model will treat the metric as interchangeable across sources.

Common failure: putting transfers into the match table as “notes”. Transfers are atomic events with their own schema.

Surface is not optional: If surface is missing, LLMs will generalize match performance across incomparable contexts (e.g., clay vs grass) and produce unreliable comparisons.

• MMA/Boxing: round-level stats + finish timing + definition registry for strike categories.
• Football: separate match table, appearance table, and transfer table (do not mix).
• Tennis: surface_code always present + set-level scoring table for decomposability.

RAG-Ready Data Requirements:

• Stable IDs: athlete_id, team_id, event_unit_id.
• Time bounds: event_date / as_of_date on every fact.
• Self-describing fields: outcome_code, finish_type, surface_code (not “notes”).
• Definition access: metric dictionary available to retrieve.

RAG Anti-Pattern: retrieving a sentence without keys

Example: “He won comfortably” (no opponent, no date, no event_id, no score). The model must guess the missing facts.

Typical Reasoning Queries (LLM + data):

• Compare athlete performance across two time windows (pre/post event).
• Normalize for competition level (league, tournament tier, opponent quality).
• Separate context variables (surface, weight class, home/away).

Inline Chart (Conceptual): “Reasoning reliability” as context coverage increases

Context coverage 0%   |██░░░░░░░░░░░░░░| low
Context coverage 50%  |████████░░░░░░░░| medium
Context coverage 100% |███████████████░| high
      

Prediction killer: label leakage

• Using “post-match rating” as a pre-match feature.
• Using updated rankings that were computed after the event date.
• Using season totals that include the target match.

Label stability via versioning: store label_version whenever rules change (e.g., scoring, overtime, judging criteria). This prevents silent distribution shifts.

• RAG: Can a retrieved row be understood without external narrative context?
• Reasoning: Are comparison contexts explicit (season, surface, weight class, home/away)?
• Prediction: Are pre-event features strictly time-bounded, and labels versioned?

Symptom

• Models answer “as of when?” incorrectly
• Comparisons across seasons become unreliable
• Prediction pipelines leak future data

Impact: Without time indexing, the system cannot distinguish between “current value” and “historical value”. LLMs then interpolate — and interpolate confidently.

Fix: Every snapshot metric must include as_of_date (or timestamp). Never store “latest” as an implied concept.

Fix: Keep each table at one granularity level. If you need multiple levels, create multiple tables or derived views.

Fix: Maintain a metric dictionary with metric_name, definition, unit, valid_range, and definition_version.

Fix: Use append-only fact tables and include: as_of_ts, source, and optionally ingest_run_id.

Most failures follow this chain:

1. Missing IDs or time bounds → ambiguous retrieval
2. Mixed granularity → invalid comparisons
3. Undefined metrics → semantic drift
4. Silent overwrites → broken prediction and auditability

• ☐ Every athlete, team, event, venue, and season has a stable unique ID.
• ☐ Names are attributes, not primary keys.
• ☐ Alias/name history is stored separately with valid_from / valid_to.
• ☐ All foreign keys resolve to exactly one parent entity.

• ☐ Every atomic event has an event_date.
• ☐ Every snapshot metric has an as_of_date or timestamp.
• ☐ No column represents “current” or “latest” implicitly.
• ☐ Corrections create new records (append-only), not silent overwrites.

• ☐ One row represents one real-world event (match, fight, set, transfer).
• ☐ Aggregates are derived from atomic events, not stored as primary truth.
• ☐ Tables do not mix match-level, season-level, and career-level data.
• ☐ Each table has exactly one granularity level.

• ☐ Every metric has a unique metric_name.
• ☐ Each metric has a one-sentence definition.
• ☐ Units and valid ranges are explicitly defined.
• ☐ Definition changes create a new definition_version.

• ☐ Any single retrieved row can be understood without narrative context.
• ☐ Rows include IDs, timestamps, and explicit categorical codes.
• ☐ Definition registry is retrievable alongside metric values.
• ☐ Text fields do not contain critical facts that are missing from structured columns.

• ☐ Pre-event features are time-bounded (as_of_date ≤ event_date).
• ☐ Post-event labels are stored separately from features.
• ☐ No feature includes information derived from the target event.
• ☐ Outcome labels include a label_version.

• ☐ Append-only fact tables preserve full history.
• ☐ Each record can be traced to a source.
• ☐ Optional ingest_run_id or batch_id exists for backfills.
• ☐ Dataset changes are reviewable and reproducible.

A sports dataset is AI-ready if and only if:

✅ All checklist items above pass without exceptions.

If even one item fails, the dataset may still be useful for reporting — but it is not reliable for LLMs.

Closing note: LLM quality is bounded by data structure. Better prompts cannot fix broken schemas. If you design sports data to be explicit, time-safe, and atomic, LLMs stop guessing — and start reasoning.