Tennis Games Handicap (Spread) free betting model calculator

What this tool does

• Prices game spreads like -2.5, -3.5, +2.5, etc.
• Uses Service Games Won% (SGW) and Return Games Won% (RGW) as core drivers, with court/conditions and advanced nudges.
• Runs a Monte Carlo simulation of full matches to produce:
  • P(A covers spread)
  • P(B covers spread)
  • Fair American odds for both sides
  • Optional value flags if you enter sportsbook prices

Key concepts

• Spread line: Enter the handicap you’re pricing. If line = -2.5, the model computes P(A wins by 3+ games).
• SGW (Service Games Won%): How often a player holds serve. Higher SGW increases spread for that player.
• RGW (Return Games Won%): How often a player breaks. Higher RGW reduces opponent holds and increases spread.
• Context: Surface, altitude/indoor, and wind shift hold probabilities. Volatility and correlation affect randomness and shared set-to-set swings.
• Advanced nudges: Optional small adjustments for second‑serve matchups, pressure conversion, and fatigue/travel.

Recommended workflow (2–4 minutes)

1. Load a preset that matches tour and surface (ATP/WTA, Hard/Clay/Grass/Indoor).
2. Input the book’s spread line (e.g., -2.5).
3. Enter SGW% and RGW% for both players, ideally surface-specific (last 12–24 months). You can google this.
4. Adjust context:
   • Altitude/Indoor for faster conditions.
   • Wind for outdoor sessions (reduces holds).
   • Injury/fitness nudge if you have reliable info.
5. Optional advanced nudges:
   • Second-serve: Enter A’s 2nd-serve points won on serve and B’s 2nd-serve return vs 2nd (and vice versa).
   • Conversion nudge: Slightly favor returners or servers in pressure points.
   • Fatigue: Negative means A is fresher; positive means B is fresher.
6. Click Calculate. Review P(A covers)/P(B covers) and fair odds.
7. If you have book prices, paste them to see value badges (difference between fair probability and implied from book odds).

How to source inputs quickly

• SGW% and RGW%:
  • ATP/WTA official stats (Service Games Won %, Return Games Won %), Tennis Abstract, Ultimate Tennis Statistics.
  • Prefer surface splits. If you only have overall, use preset as baseline and nudge ±1–2%.
• Second‑serve stats (optional):
  • Infosys ATP/WTA match centers or Tennis Abstract if available.
• Context:
  • Surface and indoor/outdoor from tournament site.
  • Altitude from event page/Wikipedia (e.g., Madrid ~650m).
  • Wind from a weather app for the match time.

Parameter tips

• Volatility: Higher for big-serve matchups or variable conditions; lower for grindy clay matchups.
• Correlation: Higher when conditions affect both players similarly (windy day, slow balls).
• Injury/Fatigue: Keep nudges modest unless confirmed. Small changes can move spread probabilities a lot.

Interpreting outputs

• P(A covers): Probability that A’s total games − B’s total games ≥ line.
• Fair odds: No-vig American odds derived from those probabilities.
• Value badges: Show probability edge vs your entered book prices. Positive “pts” suggests potential value, but still sanity-check with market and matchup.

Common scenarios and adjustments

• Big server vs average returner on fast court:
  • Slightly increase A’s SGW; small boost from Altitude/Indoor; maybe lower wind.
  • Volatility a tick higher; expect larger spreads.
• Elite returner vs shaky second serve:
  • Use second-serve nudges to favor returner; consider a “returner-favoring” conversion nudge.
  • Spreads can swing toward the returner even if raw SGW looks close.
• Windy outdoor session:
  • Apply -3% to -6% wind. Lowers holds, narrows spreads, increases variance in breaks.

Working sample for context and for you to follow along.

Scenario

• Tour/surface: ATP — Hard (outdoor), calm wind, no altitude.
• Line: A -2.5 games.
• Player A: Strong server, average returner.
• Player B: Solid all-rounder.

Inputs

• Preset: ATP — Hard
• SGW% / RGW% (surface-weighted last 12–24 months):
  • Player A: SGW 84, RGW 20
  • Player B: SGW 80, RGW 22
• Volatility proxies (aces+DF per service game):
  • A 0.70, B 0.55
• Context:
  • Altitude/Indoor: Neutral
  • Wind: Calm
  • Injury: Neutral
• Advanced nudges:
  • A 2nd-serve PW on serve: 53
  • B 2nd-serve return vs 2nd: 51
  • B 2nd-serve PW on serve: 50
  • A 2nd-serve return vs 2nd: 52
  • Conversion nudge: Neutral
  • Fatigue: 0.00
• Volatility slider: 0.32
• Correlation: 0.30
• Sims: 20,000

Interpretation (example output you might see; your run will vary slightly)

• P(A covers -2.5): ~56–58% → fair odds around -130 to -138
• P(B covers +2.5): ~42–44% → fair odds around +130 to +138
• Notes:
  • A’s stronger SGW and slight second‑serve edge nudge A upward.
  • If wind becomes Moderate (-3%), A’s cover probability typically dips a couple of points.
  • If you enter a sportsbook price of -110 on A -2.5 (implied ~52.4%), the value badge should flag a positive edge vs a ~57% fair.

Quick troubleshooting

• Results feel too favorite-heavy:
  • Lower Altitude/Indoor or add Wind.
  • Reduce SGW if your inputs used overall rather than surface-specific numbers.
• Too returner-heavy:
  • Check that RGW isn’t overstated; use a 12–24 month, surface-filtered split.
  • Reduce Conversion nudge if set to favor returners.
• Performance:
  • If it feels slow, reduce Monte Carlo samples to 10,000 during iteration; raise to 30–50k to finalize.

FAQ

• Do I need second-serve stats? No. They’re optional. Use them when high-confidence and surface-relevant.
• Can I model alt lines? Yes—run multiple lines or ask me to add an alt-lines table.
• Best-of-5 note: Spreads widen for favorites. Expect higher P(cover) with the same SGW/RGW gap.