Tennis is a fascinating sport for predictive analysis. No teammates to compensate for a rough patch, no goalkeeper to mask mistakes. Every match is a raw duel between two players — and every data point matters.
At ProbWin, our tennis model is built on nearly 2 million historical matches, an Elo system covering 87,000+ players, and a CatBoost algorithm trained to identify gaps between true probability and market odds.
In this guide, you'll discover exactly how our AI analyzes a tennis match, step by step.
The Elo System: The Foundation of Everything
What Is Elo in Tennis?
The Elo system, invented by Arpad Elo for chess, assigns each player a numerical rating that evolves after every match:
Win against a higher-rated player -> large point gain
Win against a lower-rated player -> small gain
Loss against a lower-rated player -> large loss
It's a self-correcting system: after enough matches, the rating converges toward the player's true level.
Our Elo Database
| Data | Volume |
|---|---|
| Players tracked | 87,000+ |
| Historical matches | ~2 million |
| Period covered | 2000-2026 |
| Surfaces covered | Hard, Clay, Grass, Carpet, Indoor |
| Updated | After every match |
Why Surface-Specific Elo?
This is the key to our model. A player ranked 30th ATP can have radically different profiles depending on the surface:
| Surface | Elo | Equivalent Ranking |
|---|---|---|
| Clay | 1850 | ~Top 15 |
| Hard | 1700 | ~Top 40 |
| Grass | 1580 | ~Top 80 |
If this player faces an opponent ranked 50th but with a Hard Elo of 1750, the ATP ranking suggests a clear favorite. The surface Elo says the opposite.
This is exactly the kind of disconnect our model exploits. Bookmakers incorporate ATP rankings into their pricing, but rankings aggregate all surfaces — they mask real strengths and weaknesses.
Our model calculates 4 distinct Elo ratings per player:
- Global Elo: overall performance across all surfaces
- Hard Elo: hard courts (Australian Open, US Open)
- Clay Elo: clay courts (Roland-Garros, Monte-Carlo)
- Grass Elo: grass courts (Wimbledon, Queen's)
For a given match, the model uses the surface-specific Elo of the tournament, not the global Elo. A match at Roland-Garros uses both players' Clay Elo.
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The CatBoost Model: Beyond Elo
Elo provides a solid foundation, but it doesn't capture everything. Our CatBoost algorithm (gradient boosting optimized for categorical data) integrates multiple additional features.
Match Winner (ML) Model Features
| Feature | Description | Why It Matters |
|---|---|---|
| Global Elo | Player's overall rating | General strength |
| Surface Elo | Rating on the tournament's surface | Surface adaptation |
| Elo difference | Gap between both players | Level gap measurement |
| Recent form | Results over last 5-10 matches | Current momentum |
| H2H | Historical head-to-head record | Specific matchup |
| Pinnacle odds | Market implied probabilities | Sharp benchmark |
| Tournament level | Grand Slam, Masters, ATP 250/500 | Stakes and motivation |
Total Games Model Features
To predict the total number of games in a match, the model uses different features:
| Feature | Description | Why It Matters |
|---|---|---|
| Games average | Recent games per set for each player | Expected total baseline |
| Tiebreak rate | Historical tiebreak frequency | Tight vs dominant sets |
| Break percentage | Service game loss frequency | Service dominance |
| Ace/double fault ratio | Power vs reliability on serve | Server profile |
| Match length | Average duration of recent matches | Over/Under tendency |
How the Model Finds Value
1. Model calculates P(player A wins) = 62%
2. Pinnacle offers player A at 1.72 (implied prob = 58.1%)
3. Edge = 62% - 58.1% = 3.9%
4. If edge > minimum threshold -> pick is generated
The same logic applies to totals: the model estimates an expected number of games, compares it to the bookmaker's line, and identifies the gap.
Why CatBoost Over Other Algorithms?
| Algorithm | Advantage | Disadvantage |
|---|---|---|
| Logistic regression | Simple, interpretable | Doesn't capture interactions |
| Random Forest | Robust | Less accurate on tabular data |
| XGBoost | Very performant | Sensitive to hyperparameters |
| CatBoost | Handles categories natively | Slightly slower to train |
CatBoost excels on our data because it natively handles categorical variables (surface, tournament, round) without manual encoding. It's also more resistant to overfitting on limited samples — crucial for WTA tennis where data is scarcer.
The Multi-Agent Pipeline: 5 Steps of Analysis
Our process follows the same pipeline as our other sports (NBA, NHL, Soccer):
1. Scout — Data Collection
The Scout automatically retrieves: - Real-time Pinnacle odds - Player rankings and recent form - Announced injuries and withdrawals - Tournament level and round (1st round, quarters, final...)
2. Analyst — Structured Reasoning
The AI Analyst examines each match individually (no batch processing). It evaluates: - The relevance of the model's pick - Factors not captured by the model (fatigue after a 3-set match the day before, recent surface transition) - Specific risks (player returning from injury, qualifier on a confidence run)
The Analyst can validate the model's pick, reject it, or create a new one.
3. Executor — Threshold Validation
The Executor verifies that the Analyst's confidence reaches the minimum threshold for publication. Picks below the threshold are saved as shadow picks for tracking but are not published.
4. Writer — Written Analysis
The Writer produces the detailed analysis visible on the match page. Each analysis explains the reasoning behind the pick.
5. Learner — Continuous Improvement
After each day of matches, the Learner: - Analyzes results (wins and losses) - Identifies winning and losing patterns - Adjusts confidence thresholds if needed - Stores insights for future analyses
Tournaments Covered
Our coverage focuses on tournaments where data is reliable and odds are efficient:
| Circuit | Minimum Level | Examples | Why |
|---|---|---|---|
| ATP | ATP 250+ | ATP 250, 500, Masters, Grand Slam | Reliable data, liquid odds |
| WTA | WTA 250+ | WTA 250, 500, 1000, Grand Slam | Growing coverage |
| Challengers | Excluded | — | Too much variance, insufficient data |
| Futures/ITF | Excluded | — | No reliable Pinnacle odds |
Why Exclude Challengers?
Three reasons: 1. Less reliable Elo data — players at this level have fewer matches to calibrate their rating 2. Less efficient odds — Pinnacle doesn't always cover these tournaments 3. High variance — results are more unpredictable (variable motivation, uneven playing conditions)
What Sets Our Approach Apart
| Aspect | Traditional Approach | ProbWin |
|---|---|---|
| Ranking | ATP/WTA rankings | Surface-specific Elo |
| Data | Current season stats | 2M+ historical matches |
| Analysis | Generic formula | Match-by-match AI |
| Odds reference | Mainstream bookmaker | Pinnacle (sharp) |
| Tracking | No tracking | All picks tracked and verifiable |
| Adjustment | Static | Learner adapts thresholds continuously |
Like our NBA predictions (69% win rate on Totals) and our NHL predictions, our tennis model aims to identify systematic pricing gaps — not to predict every match, but to be right more often than the odds suggest.
Free Tennis Predictions: What You Get
Our free tennis predictions cover major ATP and WTA tournaments. Here's what's included:
| Element | Free | Premium |
|---|---|---|
| AI trend (pick direction) | Yes | Yes |
| Detailed written analysis | Partial | Complete |
| Model confidence | No | Yes |
| Calculated edge vs Pinnacle | No | Yes |
| Results history | Yes | Yes |
| Pre-match alerts | No | Yes |
For each analyzed match, you get at minimum the pick direction (which player is favored and Over/Under) plus an analysis explaining the reasoning. Premium members also see the exact model confidence and the calculated edge against Pinnacle odds.
Check out our tennis predictions today to see current picks.
Tennis Betting Markets: Match Winner and Total Games
Match Winner (ML)
The simplest market: who wins the match? Our model calculates each player's win probability based on:
- Surface Elo of both players (feature #1)
- Recent form on the same surface
- Head-to-head record (H2H)
- Pinnacle odds as a benchmark
The model generates a pick when its estimate significantly differs from the implied probability in the odds.
Total Games
The Total Games market predicts whether the total number of games in the match will be over or under a line set by the bookmaker (typically 21.5-23.5 for a best-of-3 match).
| Factor | Impact on Total |
|---|---|
| Two big servers | Over (frequent tiebreaks) |
| One dominant player | Under (6-3, 6-2 = 20 games) |
| Fast surface (grass) | Paradox: short rallies but tiebreaks → variable |
| Slow surface (clay) | Longer rallies but more breaks of serve |
| Fatigue (previous 3-set match) | Under (the tired player loses quickly) |
For a complete market analysis, see our tennis betting markets guide.
Related Tennis Guides
- Impact of surfaces on tennis predictions — hard, clay, grass
- Tennis betting markets guide: ML and Total Games
- Value Betting: the key concept
FAQ: Tennis Predictions
Are your tennis predictions free?
Yes, pick direction (Match Winner and Total Games) is visible for free. The detailed analysis with confidence score and calculated edge is reserved for Premium members.
What tournaments do you cover?
We cover ATP 250+ and WTA 250+ tournaments: Grand Slams, Masters 1000, ATP/WTA 500 and 250. Challengers and Futures are excluded due to insufficient data and unreliable odds.
How does your model handle surface transitions?
The model is more cautious at the start of a new surface season (Hard→Clay in April, Clay→Grass in June). Early matches on a new surface produce more upsets — our AI Analyst accounts for this in its reasoning.
Is tennis a good sport for betting?
Yes. Tennis is an individual duel with fewer random variables (no teammates, no goalkeeper). Surface-specific Elo models capture each player's true level. The market is less efficient than NBA or NFL, creating more value opportunities.
Transparency: BETA Phase
Tennis is our most recently added sport (February 2026). Our results are still preliminary and the sample is limited. We publish our results with full transparency on our tennis page.
Our Learner adjusts thresholds after every day of play. The first weeks serve to calibrate the model under real conditions — exactly as we did for NBA, NHL, and Soccer before reaching our current performance levels.
Next Step
You now understand how our AI analyzes tennis matches. To go further, discover tennis betting markets and how to exploit differences between Match Winner, Total Games, and handicaps.
