Tennis Total Games Predictor – Free Betting Tool

1. What the tool does

• Projects the expected total number of games in a tennis match.
• Estimates the probability that the total goes Over or Under your target line.
• Converts those probabilities into fair American odds.
• Lets you stress-test results with a sensitivity view.

When to use it:

• Pricing totals markets (Over/Under).
• Sanity-checking book lines vs your model.
• Exploring how surfaces, conditions, and playing styles affect totals.

2. Quick start (fastest workflow)

• Choose a Preset: Pick ATP/WTA and surface to load realistic baseline stats.
• Confirm Surface and Format: Best of 3 (most tour matches) or Best of 5 (Slams, some men’s finals).
• Set the Target Total: Enter the sportsbook line (e.g., 22.5).
• Click Calculate: Review Projected Total, P(Over), and Fair Odds.
• Toggle Sensitivity: See how probabilities shift for line ±1.5 and ±3.0 games.

Tip: After applying a preset, tweak player inputs if you have matchup-specific info.

3. Inputs explained

• Player A/B Hold Rate (%): Probability a player wins their service game. Bigger holds usually increase total games.
• Player A/B Return Points Won (%): Their returning strength; higher RPW increases break chances and can reduce total games in mismatches (but may increase if both return well).
• Aces + Double Faults per service game: A volatility proxy. Higher values imply more swinginess; the tool uses this to widen the distribution slightly.
• Surface: Hard, Clay, Grass, Indoor Hard. Surface changes hold dynamics (grass fastest, clay slowest).
• Format: Best of 3 or Best of 5. Bo5 generally increases totals.
• Altitude/Indoor boost: Faster conditions increase holds and totals.
• Wind: Stronger wind lowers holds; can reduce totals and tiebreak chances.
• Injury/fitness nudge: Small edge to one player’s hold ability; use sparingly.
• Volatility slider: Controls set-to-set randomness. Higher volatility widens the outcomes.
• Correlation slider: How similarly both players’ performances vary set-to-set (shared conditions). Higher correlation makes outcomes more clustered.
• Monte Carlo samples: Number of simulated matches. More samples = smoother, slower.

4. Using Presets effectively

• Start with a preset that matches the event (e.g., ATP — Grass for Wimbledon).
• The preset sets equal baselines for both players. Then:
  • Adjust holds if the server quality differs (e.g., big server vs average returner).
  • Adjust RPW if someone is an elite returner.
  • Adjust volatility if the matchup is serve-dominant (up) or grindy (down).
• Recalculate and compare to the book’s line.

5. Interpreting outputs

• Projected Total Games: Average games across all simulations. Not a line, but a central tendency.
• P(Over line): Probability total ≥ your target line. If P(Over) = 56%, the fair odds for Over are about -127.
• Fair Odds (Over/Under): The model price with 0% vig. Compare to the sportsbook:
  • If your fair Over is -120 and the book offers -105, the Under might be the value side (since -105 is better than your fair price for Over).
  • If your fair Over is -120 and the book offers +100, Over could be positive EV.
• Distribution Snapshot: A quick bar showing P(Over).
• Sensitivity: How robust your edge is if the line moves ±1.5 or ±3. If your edge disappears with small shifts, be cautious.

6. Practical workflow for handicapping

• Step 1: Preset → Apply (ATP/WTA + surface).
• Step 2: Enter the book’s line as Target Total.
• Step 3: Tweak player-specific inputs (holds, RPW) based on:
  • Recent serve/return form.
  • H2H serve pressure (subjective).
  • Surface-specific splits.
• Step 4: Consider Context:
  • Indoor/altitude boosts for faster venues.
  • Wind for outdoor sessions.
  • Fitness/injury rumors: apply a small nudge only.
• Step 5: Run Calculate. Note P(Over) and fair odds.
• Step 6: Toggle Sensitivity to check edge stability.
• Step 7: Compare with market prices. Look for mismatches between fair odds and book odds.

7. Common patterns and what to adjust

• Two big servers on fast courts:
  • Increase holds slightly; increase volatility a touch.
  • Expect higher totals and more tiebreaks.
• Elite returner vs mediocre server:
  • Decrease the weaker player’s hold.
  • Totals may drop in Bo3; in Bo5, one-sided sets can reduce total despite longer format.
• Windy outdoor session:
  • Apply wind reduction; totals often drop and variance can increase in break-heavy sets.

8. Tips to avoid overfitting

• Use modest adjustments. A 1–2% change in hold can meaningfully impact totals.
• Cross-check against historical match totals for similar matchups and conditions.
• If you lack data, prefer the preset and small tweaks over big swings.

9. Troubleshooting and performance

• If the Calculate button feels slow, reduce Monte Carlo samples (e.g., 10,000).

10. Advanced use ideas

• Create your own “player presets” by noting typical hold/RPW for frequent players you bet on.
• For Slams (Bo5), consider increasing samples and slightly lowering volatility if matchups are predictable.
• Track your projections vs results to calibrate how much to adjust holds/RPW for specific surfaces.

Here are reliable places to pull player serve/return and context data you can feed into the tool. I grouped them by what they’re best for.

Serve/return performance (hold %, return points won, aces/DF, splits)

• Tennis Abstract (Jeff Sackmann)
  • Best all-around for serve/return splits by surface, year, opponent strength.
  • Men: https://www.tennisabstract.com/
  • Women: https://www.tennisabstract.com/cgi-bin/wta-rankings.cgi
  • Tips: Use the Player Pages → “Match Charting/Stats,” filter by surface; the “Stat Leaders” pages show tour averages.
• Ultimate Tennis Statistics (UTS)
  • Strong profiles with surface breakdowns, service/return performance, Elo ratings.
  • https://www.ultimatetennisstatistics.com/
  • Tips: Player → Profile → Surfaces; Player → Service/Return tabs.
• ATP Tour and WTA Tour official stats
  • Season and career leaders for service games won, return games won, aces, DFs.
  • ATP: https://www.atptour.com/en/stats
  • WTA: https://www.wtatennis.com/stats
  • Tips: Use “Under Pressure,” “Service Games Won,” and filter by season/surface when available.
• Infosys ATP Stats (ATP events)
  • Deeper analytics for select tournaments (serve speeds, patterns, rally metrics).
  • https://www.atptour.com/en/stats/infosys
  • Good for recent form and court-speed context during ATP events.
• Tennis Live Stats
  • Match-by-match serve/return summaries, H2H breakdowns.
  • https://www.tennislive.net/
  • Handy for quick checks; verify against official sources.

Historical results, H2H, and context

• Flashscore / SofaScore
  • Fast schedules, H2H, recent form, basic serve/return match stats.
  • https://www.flashscore.com/ | https://www.sofascore.com/tennis
  • Tips: Use H2H → Surface filter to see how matches played out on similar courts.
• ITF World Tennis (qualifying, lower tours)
  • Useful if you handicap Challengers/ITFs where main sites are thin.
  • https://www.itftennis.com/
• OddsPortal / BetExplorer
  • Market closing totals and odds history to benchmark your projections.
  • https://www.oddsportal.com/tennis/ | https://www.betexplorer.com/tennis/
  • Tips: Compare your fair odds to close; track where your edge is consistent.

Court speed, conditions, event info

• Wikipedia tournament pages
  • Each event lists surface, indoor/outdoor, altitude for some venues.
  • Great for quick altitude checks (e.g., Gstaad, Madrid).
• Court Pace Index references
  • Look for “ITF Court Pace Ratings” or community lists for approximate fast/slow tags.
• Tournament/venue sites and social feeds
  • Day-of wind forecasts, indoor decisions, ball types.

Live/in-match metrics (to inform volatility or short-term adjustments)

• TennisTV (ATP) / WTA TV match centers
  • Live stats including first-serve %, points won on serve/return.
• LiveScore/tennis APIs (if you use them) to track real-time serve performance.

How to translate site data into inputs

• Hold Rate (%)
  • From ATP/WTA “Service Games Won %” or Tennis Abstract “Hold %.”
  • If you only have “Service Points Won %,” expect hold% roughly maps via models, but as a shortcut: 63–65% SPW often implies ~80–84% hold for ATP, lower for WTA. Prefer direct hold% when available.
• Return Points Won (%)
  • Directly from “Return Points Won %” or Tennis Abstract “RPW%.”
  • Use surface-specific splits if possible. If not, start with overall and nudge ±1–2% for surface.
• Volatility (aces + double faults per service game)
  • From aces and DFs per match divided by service games (Tennis Abstract often lists per-serve rates; otherwise compute: (aces+DFs)/service games).
  • Big servers on fast courts trend 0.7–0.9; grinders on clay trend 0.4–0.6.
• Context
  • Surface: from the tournament site.
  • Altitude/indoor: check venue; examples: Madrid (~650m), Gstaad (~1050m), indoor hard = slight speed boost.
  • Wind: use a weather app for the session time; strong wind → choose a negative wind setting.

Workflow to build inputs quickly

1. Open Tennis Abstract or ATP/WTA stats for both players.
2. Note their surface-specific Hold% and RPW%.
3. Set both into the tool; adjust 1–3 percentage points to reflect matchup (e.g., elite returner vs weak server).
4. Set volatility using aces+DF per service game from recent surface matches.
5. Set surface/altitude/wind per venue; pick Bo3/Bo5.
6. Enter the book’s total as Target and Calculate.