Big Data · Probability Theory · Game Theory

EDGE THE
LINE.

Professional-grade NBA & NHL analytics powered by statistical modelling, Bayesian inference, and game-theoretic frameworks — built for analysts who think in probabilities.

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Score Distribution Model
-σ +σ -2σ +2σ μ
P(A|B) = P(B|A)·P(A) / P(B)
E[X] = Σ xᵢ · P(xᵢ)
σ² = E[(X−μ)²]
Nash: ∀i, uᵢ(s*) ≥ uᵢ(sᵢ, s*₋ᵢ)
Kelly: f* = (bp − q) / b
Live Team Stats NBA · 2024–25
Boston Celtics 118.4 +8.2
Oklahoma City Thunder 116.1 +7.4
Cleveland Cavaliers 112.9 +6.1
Denver Nuggets 110.2 −1.8
New York Knicks 109.7 −2.3
BOS vs NYK · W 124–109 · Avg Total 233.4 EDM vs VAN · W 4–2 · H2H 12W–6L MIL vs IND · L 108–115 · Margin −3.1 P(cover|home) = 0.612 · σ = 8.3 · n = 847 DEN vs LAL · W 118–112 · EV +4.2% Kelly f* = 0.031 · Nash eq. detected · Δσ = 2.1 TOR vs FLA · W 3–1 · Handicap Cover 68% BOS vs NYK · W 124–109 · Avg Total 233.4 EDM vs VAN · W 4–2 · H2H 12W–6L MIL vs IND · L 108–115 · Margin −3.1 P(cover|home) = 0.612 · σ = 8.3 · n = 847 DEN vs LAL · W 118–112 · EV +4.2% Kelly f* = 0.031 · Nash eq. detected · Δσ = 2.1 TOR vs FLA · W 3–1 · Handicap Cover 68%
🏀NBA
🏒NHL
🎾Tennis
MORE COMING SOON
Methodology

THE MATH
BEHIND THE EDGE

Not vibes. Not gut. Rigorous statistical inference applied to every game in the database.

0
Games Processed
0
Seasons Covered
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Stat Dimensions
0
% Data Accuracy
P(W|μ,σ) = Φ((x−μ)/σ)
Bayesian Win Probability

Each matchup's win probability is derived from the Gaussian CDF fitted to historical score differentials. Prior updated game-by-game.

f* = (bp − q) / b
Kelly Criterion Sizing

Optimal stake fraction computed from edge (p) and odds (b). Prevents ruin while maximising long-run geometric bankroll growth.

EV = Σ pᵢ · xᵢ − cost
Expected Value Engine

Every line evaluated against our probability model. Positive EV opportunities surfaced with confidence intervals and sample sizes.

∀i: uᵢ(s*) ≥ uᵢ(sᵢ, s*₋ᵢ)
Nash Equilibrium Analysis

Game theory applied to bookmaker line-setting. We model the market as a strategic game to find lines deviating from equilibrium pricing.

r = Σ(xᵢ−x̄)(yᵢ−ȳ)/nσxσy
Correlation Modelling

Pearson correlation between offensive/defensive ratings, pace, and cover rate — exposing hidden dependencies across team profiles.

Δσ = |σ_model − σ_market|
Volatility Arbitrage

When modelled variance diverges from market implied volatility, total line edges emerge. We quantify and track this spread systematically.

Win Probability Engine

DATA-DRIVEN
CONFIDENCE

Every team, every matchup — modelled probability scores from the full historical sample. See the edge before the line moves.

BOS vs NYK · Win Probability73%
OKC vs DEN · Handicap Cover61%
EDM vs VAN · Over 5.558%
MIL vs CLE · Under 225.544%
TOR vs FLA · Puck Line52%
// Offensive Rating vs Cover Rate · NBA 2024–25
What you get

BUILT FOR SERIOUS
ANALYSTS

Every metric that matters. No noise, no fluff — just data that moves the needle.

📊
Team Performance

Games played, avg totals, scored & allowed, margin — aggregated across custom date ranges and seasons.

Per Period / Year
🏆
W/L/D Breakdown

Full win-loss-draw records with handicap cover metrics to evaluate line performance over time.

Handicap Metrics
⚔️
Head-to-Head

Deep H2H dossiers vs any opponent: games, margins, avg totals, trend charts over multiple seasons.

Year-over-Year
📋
Match Transparency

See every source match behind the stats. Full game log table for auditing and verification.

Full Source Log
✅
Data Verification

Built-in test mode cross-checks stats against external sources, flagging mismatches in real time.

Mismatch Detection
🎴
Tennis Duel Cards

Compare two tennis players side by side with surface strengths, weaknesses, comments, photos, and H2H context.

Player Intel
Head-to-Head Analytics

MATCHUP
INTEL

Full historical H2H record — wins, losses, point margins, and year-by-year trend charts to spot momentum shifts.

  • 01Select league, home team, and opponent
  • 02Get H2H record, avg totals, and point margins
  • 03Visual trend chart — win/loss dynamics by year
  • 04Drill into source match table for full transparency
Boston
Celtics
14 W
VS
New York
Knicks
8 W
Total H2H Games22
Avg Total Points224.6
Avg BOS Scored116.4
Avg NYK Scored108.2
Win ProbabilityP = 0.734
Win/Loss Trend · 12 games
Access

SIMPLE PRICING

One plan. Full access. No hidden fees.

Full Access
$10
/ month · billed via Solana
  • ✓ NBA & NHL team statistics
  • ✓ Head-to-head matchup analytics
  • ✓ Probability model outputs
  • ✓ Handicap coverage metrics
  • ✓ Year-over-year trend charts
  • ✓ Full match source tables
  • ✓ Data verification module
  • ✓ Tennis duel player cards
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