Our Data Methodology

Every insight we deliver is backed by data. This document explains our methodology—how we collect data, detect patterns, and generate narratives that bookmakers can trust.

Data Sources

BidCanvas aggregates data from multiple sources to build a comprehensive view of each match:

Primary Sources

Data Validation

Raw data goes through validation before entering our analysis pipeline:

• Cross-source verification — Match results verified across 2+ sources
• Outlier detection — Statistical anomalies flagged for manual review
• Completeness checks — Matches with missing critical data excluded
• Timestamp normalization — All data converted to UTC

Pattern Detection

We use statistical methods to identify patterns that have predictive value. Not all correlations are patterns—we apply strict criteria.

Minimum Requirements

Pattern Categories

Avoiding False Patterns

Sports data is noisy. Many apparent patterns are random variation. We guard against false positives through:

• Multiple testing correction — Bonferroni adjustment when testing many hypotheses
• Out-of-sample validation — Patterns discovered on training data must hold on test data
• Logical review — Patterns must have plausible causal mechanism
• Regular re-validation — Published patterns re-tested quarterly

AI Narrative Generation

Raw statistics are hard to consume. We use AI to transform data into readable narratives.

The Process

# Simplified narrative generation flow

1. Data Assembly
   - Match context (teams, league, competition stage)
   - Relevant patterns (referee, form, H2H)
   - Current odds and market movements
   - Sharp wallet positions

2. Pattern Ranking
   - Score each pattern by relevance to this match
   - Filter to top 3-5 most relevant insights
   - Ensure diversity (don't repeat similar points)

3. Narrative Generation
   - LLM (Claude) generates human-readable text
   - Structured prompts ensure consistency
   - Facts grounded in source data

4. Quality Control
   - Automated fact-checking against source data
   - Confidence scoring based on pattern strength
   - Human review for high-stakes outputs

What AI Does (and Doesn't) Do

Continuous Improvement

Our methodology evolves based on results:

Feedback Loops

• Pattern accuracy tracking — Monitor hit rate of published patterns over time
• Bet slip performance — Track suggested bets against actual outcomes
• Client feedback — Incorporate operator insights on what bettors find valuable
• New data sources — Continuously evaluate emerging data providers

Deprecation Policy

Patterns that no longer meet our criteria are deprecated:

• If accuracy drops below statistical significance for 2 consecutive quarters
• If sample size becomes too small (e.g., referee retires)
• If underlying conditions change (rule changes, team restructuring)

Transparency

Every insight we deliver includes:

• Sample size — How many instances the pattern is based on
• Time period — When the data was collected
• Confidence level — Statistical significance and effect size
• Source attribution — Where the underlying data came from

We believe transparency builds trust. Bookmakers can evaluate our methodology and decide how to weight our insights alongside their own analysis.

Data Sources Referenced

• Football-data.org - Historical Match Data
• Polymarket - Prediction Market Data
• Kalshi - Event Contract Data