Gridiron Edge provides transparent, data-driven college football analytics. Our system processes real-time market data, historical performance, and environmental factors to generate power ratings and identify potential betting edges.
Important: This platform provides analytical insights, not betting recommendations. Users should expect transparent assessment tools, not picks or guarantees.
Core statistical features derived from game-by-game team performance:
Talent assessment based on recruiting data and team composition:
Roster stability and transfer portal activity metrics:
Note: This data is currently labeled as "Labs / V5 Prep" and not yet used in production Hybrid or V3 models. It will be used for future off-season adjustments and mid-season recalibration in V5.
We store individual rows per game/book/line type combination. This allows us to track line movement, compare book offerings, and identify consensus vs. outlier positions.
Moneylines are stored as American odds (e.g., -175, +150). We calculate implied probabilities using standard formulas:
Example: -175 → 175/(175+100) = 63.6% implied probability
Our V1 model generates team power ratings using a balanced four-pillar approach. Each component is normalized to Z-scores and weighted equally (25% each) to create a composite rating that captures multiple dimensions of team strength:
Normalization: Each metric is converted to Z-scores (standard deviations from the mean) across all FBS teams, ensuring equal weight regardless of scale. The composite is then scaled by a factor of 14.0 to convert to "points above average" (where +14 represents approximately one standard deviation above average).
The 25/25/25/25 weight distribution is not arbitrary—it was determined through rigorous backtesting against historical game results. Our calibration engine simulates thousands of past games using different weight combinations to identify the configuration that minimizes prediction error.
We test various weight combinations (e.g., 50% Efficiency/50% Talent, 40% Scoring/30% Efficiency/30% Results, etc.) against actual game outcomes from the 2025 season. For each configuration, we calculate the Mean Absolute Error (MAE) between predicted spreads and actual score margins.
Result: The balanced 25/25/25/25 approach achieved the lowest MAE (approximately 10.8 points) for the 2025 season, outperforming alternative strategies such as "Efficiency-Only" (higher error) and "Talent-Only" (higher error) models.
Continuous Improvement: We re-calibrate these weights periodically (typically at the start of each season or after significant rule changes) to ensure the model adapts to the current season's meta. This ensures our predictions remain accurate as the game evolves.
Spreads are derived directly from the power rating difference between teams, plus a home field advantage adjustment:
Spread (Home Minus Away) = (Home Rating - Away Rating) + HFA
Where HFA (Home Field Advantage) = 2.0 points for home games, 0.0 for neutral sites.
This direct calculation ensures that the spread reflects the model's assessment of team strength without additional overlays or market adjustments. The rating difference is already in "points above average" format, so the spread directly translates to expected margin of victory.
The model identifies betting opportunities by comparing its predicted spread to the market consensus. An "edge" is the difference between the model's spread and the market line:
Edge = |Model Spread - Market Spread|
0.1 Point Threshold: The model recommends a bet when the edge is at least 0.1 points. This minimal threshold ensures that any meaningful disagreement between the model and market is flagged as actionable. There are no caps, overlays, or decay factors—the model trusts its ratings completely.
No Market Capping: Unlike previous versions, the V1 model does not apply "Trust-Market" safety layers. The model's spread is used directly, without capping edges or applying minimum thresholds above 0.1 points. This approach maximizes the model's predictive power while maintaining a low barrier for actionable picks.
Bets are assigned confidence grades based on the magnitude of the edge:
The game's overall confidence grade is determined by the highestgrade among all active bets (Spread, Total, Moneyline) for that matchup.
Moneyline: Win probabilities are derived from the spread using a standard sigmoid conversion (logistic function). The model compares its implied probability to the market's implied probability to identify value. Moneyline bets are only considered for games where the spread is ≤ 24 points (to avoid extreme favorites with unbettable odds).
Totals: Over/Under picks are calculated using a spread-driven totals model that considers both offensive and defensive ratings, adjusted for game pace and scoring efficiency. The same 0.1 point edge threshold applies.
Beyond the main Power Rating (V1), Gridiron Edge analyzes specific unit matchups to identify tactical advantages. The V2 system breaks down team performance into granular unit grades (Run Offense, Pass Defense, Explosiveness) and compares them head-to-head to find hidden edges.
These unit grades are displayed on the Game Detail page in the "Unit Matchup" card, showing how each team's offensive and defensive units stack up against their opponent.
Each unit grade is calculated by:
A team's Run Offense Grade combines:
Unit grades are displayed on the Game Detail page using a letter grade system (A+ to F) converted from Z-scores:
The V2 system calculates net advantages for each matchup:
These net advantages are then weighted (40% Run, 40% Pass, 20% Explosiveness) and converted to a spread prediction using an optimized scale factor.
The V2 unit matchup analysis is combined with the V1 Power Rating in a "Hybrid" model that blends both approaches:
Hybrid Spread = (V1 Spread × 70%) + (V2 Spread × 30%)
This blend leverages the stability of V1 (results-aware) with the matchup specificity of V2 (stats-only). The weights were optimized through backtesting against 2025 season results.
Production Status: Hybrid V2 is the only production spread model used for "My Picks" and live betting recommendations. All other models (V1, V2 standalone, V4) are experimental/Labs-only.
The V2 model applies situational penalties to unit grades based on weather conditions. These adjustments are visible on the Game Detail page when the "Weather Adjustment" toggle is enabled.
Threshold: Wind speed > 15 mph
Effect: Passing offense grades are penalized by 0.05 Z-score per mph above 15 mph
Example: 20 mph wind = (20 - 15) × 0.05 = 0.25 Z-score penalty to Pass Offense Grade
High wind conditions make passing more difficult, reducing the effectiveness of teams that rely heavily on the aerial attack. The penalty is capped at -3.0 Z-score to prevent extreme adjustments.
Threshold: Precipitation probability > 50%
Effect: Offensive explosiveness grades are penalized by 0.2 Z-score
Heavy rain or snow conditions make ball handling more difficult and reduce the likelihood of explosive plays. This penalty affects both teams equally and is applied to the Explosiveness component of the V2 matchup calculation.
Note: Weather adjustments only affect the V2 component of the Hybrid model. The V1 Power Rating remains unchanged, as it reflects season-long performance that already accounts for typical weather conditions.
V4 is an experimental spread rating model inspired by SP+ and FEI methodologies. It uses drive-based metrics (Finishing Drives, Available Yards %) and advanced efficiency stats to generate spread predictions. V4 is not used in production and is available only in Labs for research and backtesting purposes.
V4, as a standalone model, is currently unprofitable and is not used in production. However, fading its picks ("Fade V4 (Labs)") has shown positive ROI in backtests for both 2024 and 2025. This overlay remains experimental and subject to regression.
V4 Labs (standalone):
Fade V4 (Labs):
V4 Labs (standalone):
Fade V4 (Labs):
All results are backtests, not guarantees. Fade V4 is Labs-only and is not part of the main "My Picks" flow. Hybrid V2 remains the only official spread model.
We ingest a curated subset of SportsGameOdds team season stats and aggregate them into structured metrics stored in team_season_stats.raw_json.sgo_stats. These include:
This data is currently Labs-only and not yet used in the production Hybrid V2 model. It is being collected for future V5 model development and analysis.
We compute conflict types for games with Hybrid V2 spread bets to understand where performance comes from relative to V4 (Labs):
Note: Conflict types are Labs-only diagnostics used for performance analysis in Week Review and Season Review. They do not change which bets are shown or how bets are selected—they are purely analytical tools for understanding where profit comes from.
We provide SQL queries and data export tools to verify our calculations and ensure transparency. See our runbook documentation for detailed post-run expectations and verification steps.
/api/weeks/csv for spreadsheet analysisSwitched to single-week polling to prevent workflow timeouts
Implemented chunked upserts and deduplication for better performance
Added moneyline support across API and UI
Integrated Odds API as primary source with SGO fallback
Implemented FBS-only filtering to reduce data volume
Added CI safety guards and performance optimizations