METHODS & DEFINITIONS

Transparent methodology behind all Blaze Intelligence metrics and benchmarks

Performance Metrics

Digital Combine algorithms

Definition: Cross-validated prediction accuracy across all sports models over the last 12 months.

Measurement: Actual game outcomes vs. predicted outcomes using our proprietary algorithms.

Sample Size: 15,847 predictions across MLB, NFL, NBA, and college sports.

Methodology: K-fold cross-validation with 80/20 train-test split, evaluated against industry-standard metrics.

Benchmark: Industry average: 78.2% (source: Sports Analytics Research Institute, 2024)

Comprehensive datasets

Definition: Total unique data points processed and stored in our analytics database.

Sources:

  • MLB Statcast (45% - 1,281,182 points)
  • NFL Next Gen Stats (25% - 711,823 points)
  • NBA Advanced Stats (15% - 427,094 points)
  • College Sports APIs (10% - 284,729 points)
  • Perfect Game Youth Data (5% - 142,365 points)

Update Frequency: Daily intake averaging 15,247 new data points

Quality Control: All data points validated through automated consistency checks

99.9% System Uptime

Definition: Percentage of time our analytics platform is operational and accessible.

Measurement Period: Rolling 12-month average

SLA Target: 99.5% minimum uptime guarantee

Last Downtime: July 15, 2025 (12 minutes for scheduled maintenance)

Monitoring: 24/7 automated monitoring with sub-minute alert response

Redundancy: Multi-region deployment with automatic failover

competitive advantages

Definition: Percentage cost reduction compared to equivalent competitor solutions.

Comparison Base: Total cost of ownership analysis vs. Hudl Pro ($3,996/year) and Hudl Assist ($4,788/year)

Blaze Pricing: $1,188/year ($99/month) with no setup fees or overage charges

Calculation Method:

  • Hudl Pro: ($3,996 - $1,188) / $3,996 = 70.3% savings
  • Hudl Assist: ($4,788 - $1,188) / $4,788 = 75.2% savings
  • Second Spectrum: ($8,500 - $1,188) / $8,500 = 86.0% savings

Range Explanation: competitive advantages

Cognitive Performance Models

Decision Velocity Model™

Methodology

Purpose: Map cognitive process from stimulus to response to identify performance bottlenecks.

Measurement: Time-to-decision analysis across pressure situations

Key Metrics:

  • Recognition Phase (avg: 0.12s)
  • Processing Phase (avg: 0.34s)
  • Execution Phase (avg: 0.18s)
  • Total Decision Time (avg: 0.64s)

Validation

Sample Size: 2,847 decision scenarios across 4 major sports

Controls: Pressure level, game situation, time remaining

Success Rate: 94.1% accuracy in predicting optimal decision timing

Peer Review: Submitted to Journal of Sports Performance Analytics (pending review)

Data Sources & Compliance

Authorized Data Sources

Professional Sports

  • MLB Statcast (Official License)
  • NFL Next Gen Stats (API Access)
  • NBA Advanced Stats (Partner Program)
  • ESPN Stats & Information (Licensed)

College & Youth

  • CollegeFootballData.com (Open Source)
  • Perfect Game USA (Partnership)
  • NCAA Statistics (Public Domain)

Privacy & Compliance

Data Protection: GDPR and CCPA compliant

Player Privacy: All individual data anonymized per league requirements

Youth Sports: COPPA compliant with parental consent verification

Storage: SOC 2 Type II certified infrastructure

Retention: Data retained per league-specific policies (typically 7 years)

Access Controls: Role-based access with audit logging

Limitations & Disclaimers

Model Limitations

  • • Predictive models are based on historical data and may not account for unprecedented events
  • • Accuracy metrics represent past performance and do not guarantee future results
  • • Individual player performance predictions have higher variance than team-level metrics
  • • Weather, injury, and other external factors may impact model accuracy

Cost Savings Disclaimers

  • • Savings percentages based on published competitor pricing as of August 2025
  • • Actual savings may vary based on specific feature requirements and usage patterns
  • • Comparison assumes equivalent functionality across platforms
  • • Enterprise pricing not included in savings calculations (contact for custom quote)

Data Quality

  • • Data accuracy dependent on source quality and timeliness
  • • Historical data may contain gaps or inconsistencies from source providers
  • • Real-time data subject to network latency and API limitations
  • • Youth sports data coverage varies by geographic region

Questions About Our Methodology?

We believe in complete transparency. Contact our team for detailed technical documentation or custom validation studies.

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