🏀 HoopCall
Live In-Game Monitor

How the Model Works

Hoop Call finds edge by comparing a fair value it computes from multiple data sources against the current Kalshi ask price. When fair value exceeds the ask by more than 4¢, the market is flagged UNDERVALUED. When the ask exceeds fair value by more than 4¢, it's OVERVALUED. Everything within 4¢ is FAIR.

Step 1 — Base Fair Value

The starting point before any adjustments. Sources are used in priority order — the best available source wins.

1st
Consensus Odds — blended average of Kalshi price and DraftKings moneyline when both are available. Most reliable starting point as it reflects two independent liquid markets.
2nd
DraftKings Moneyline — vig-removed implied probability from DK. When a spread line also exists, it's blended 80% ML + 20% spread-implied to add signal from point spreads.
3rd
Statistical Model — equal blend of three sub-models when no external odds exist:
  • Net Rating Model — logistic curve on net rating differential (≈0.3% per point)
  • Pythagorean Matchup — Bill James log5 applied to each team's points scored vs. allowed
  • Pace-Adjusted Projection — harmonic mean pace applied to offensive/defensive averages, +2.5 pt home court bonus built in
Step 2 — Adjustments (14 Factors)

Each factor nudges the base fair value up or down. Adjustments are computed independently and summed, then clamped to keep the result in a realistic range.

Factor What It Measures Max Impact
🏥 Injury Absence PPG-weighted penalty for each OUT/Doubtful player (≈0.15¢ per PPG, capped 4.5¢ per player). Questionable players penalized at 40%. ±15¢
🏠 Home Court Team's actual home/away win% vs. 0.500 league baseline. Falls back to flat ±3.5¢ if split data unavailable. ±12¢
📊 Net Rating Gap Difference in net rating between teams scaled at 0.3¢ per point of net rating differential. ±5¢
🏆 Motivation Playoff situation: PLAY-IN/TIGHT RACE +2¢, BUBBLE +1¢, CLINCHED -2¢, ELIMINATED -3.5¢. ±3.5¢
😴 Rest / Back-to-Back B2B (1 day rest) -3¢ penalty. 3+ days rest +1¢ bonus. Applied net between teams. ±3¢
📈 Form Win% Blended 60% last-10 win% + 40% season win%, scaled around 0.500. A 60% L10 team gets +1.25¢. ±2.5¢
💰 Price Momentum Direction of Kalshi price movement (current ask vs. last traded price). Sharp buying pressure lifts fair value. ±2¢
📉 Form Margin Avg point differential blended 60% last-5 / 40% last-10. A +10 avg margin adds ~1.5¢. ±2¢
🔥 Form Trend PEAKING (last-5 better than season avg) +1.5¢, DECLINING -1.5¢, STABLE 0. ±1.5¢
📊 Volume Skew When ≥80% of Kalshi money is on one side: +1.5¢ to that side (contrarian market signal). ±1.5¢
⚡ Volume Velocity 24h volume vs. total volume ratio. Accelerating money flow on one side suggests sharp activity. ±1.5¢
🔄 Head-to-Head Average scoring margin over last 5 H2H meetings (minimum 3 games required). 5-pt avg margin → +1¢. ±1.5¢
💪 Strength of Schedule Average opponent win% in recent games. A team going 9-1 vs. weak opponents (avg opp .400) gets discounted. ±1.5¢
✈️ Road Trip Fatigue Consecutive away games: 3 in a row -0.5¢, 4 in a row -1¢, 5+ -1.5¢. Home teams unaffected. ±1.5¢
Step 3 — Kelly Criterion Sizing

Once fair value is established, Kelly criterion determines optimal bet size. Kalshi binary markets have asymmetric payoffs — a YES contract at 37¢ pays 63¢ profit if correct and loses 37¢ if wrong.

Edge = fair_value − kalshi_ask
Full Kelly = edge / (1 − kalshi_ask), capped at 25%
Half Kelly = Full Kelly × 0.5 ← recommended
Confidence scaling: HIGH 1.0×, MEDIUM 0.75×, LOW 0.5×

Confidence is set by data quality: HIGH when consensus odds are available, MEDIUM when DraftKings lines exist, LOW when falling back to statistical model only or when the bid-ask spread exceeds 10¢.

Verdict Definitions
UNDERVALUED Fair value exceeds Kalshi ask by >4¢. The market is underpricing this team — potential YES bet.
OVERVALUED Kalshi ask exceeds fair value by >4¢. The market is overpricing this team — fading signal or NO bet.
FAIR Within ±4¢ of fair value. No exploitable edge — pass.
UNCERTAIN Insufficient data to compute a reliable fair value for this team.
Known Limitations

⚠️ Injury data reflects market knowledge. By the time a report is generated, Kalshi has often already priced in known injuries. The model may double-count injury impact that the market has already absorbed.

⚠️ Form streaks are not opponent-adjusted. A team going 9-1 looks strong but SOS can only discount this by ±1.5¢ — if that run was against weak opponents the model may still overrate the team significantly.

⚠️ Pure pricing-gap signals are more reliable. When edge comes from DK vs. Kalshi spread discrepancy rather than model adjustments, hit rates tend to be higher. Injury-driven underdog calls carry more uncertainty.

⚠️ Small sample size. This model has limited historical validation. Kelly sizing recommendations should be treated as guidance, not gospel. Never risk more than you can afford to lose.

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