How We Predict Tennis Matches with Machine Learning

The Problem with Tennis Betting

Most tennis bettors rely on gut feel, recent form, or surface preferences. Bookmakers, on the other hand, use sophisticated models and massive datasets to set their lines. This information asymmetry is why most bettors lose money long-term.

At Net Worth Picks, we built a machine learning model that levels the playing field.

Our Approach

Data Collection

We track thousands of professional tennis matches, collecting:

• Head-to-head records between players
• Surface-specific win rates (clay, hard, grass)
• Recent form and momentum
• Current rankings and ranking trajectories
• Tournament context and match timing

The Model

Our model is trained on historical match data using walk-forward validation — meaning we never train on future data. Each month, the model is retrained on all data up to that point and tested on the following month. This gives us realistic, out-of-sample performance estimates.

Finding Edges

Having a prediction isn't enough — you need an edge. We compare our model's win probability for each player against the implied probability from bookmaker odds. When our model significantly disagrees with the market, that's a potential bet.

For example, if our model gives Player A a 70% chance of winning, but the odds imply only 55%, that's a 15% edge.

Bet Sizing with Kelly Criterion

Not all edges are equal. We use the Kelly criterion to size bets proportionally to our edge — betting more when we're more confident and less when the edge is slim. We use a fractional Kelly approach (betting a fraction of the full Kelly amount) to reduce variance while maintaining positive expected value.

Predictions vs Bets

We make a prediction for every upcoming match — our model's estimate of who will win and with what probability. But a prediction alone doesn't mean you should bet on it.

A bet recommendation only happens when our model finds a meaningful edge over the bookmaker odds. Most predictions don't result in a bet. On a typical day we might predict 10-15 matches but only recommend 1-3 bets where the edge justifies risking money.

When we do recommend a bet, we tell you who to bet on, the size of the edge, and how much to wager based on Kelly criterion sizing. The stats on our homepage reflect our model's actual betting results — wins, losses, and profit from recommended bets only.

Understanding Our Stats

On our homepage you'll see two sets of numbers:

• Prediction accuracy — how often our model correctly predicts the match winner, across all predictions. This is currently around 65%.
• Bet win rate, ROI, and profit — how our recommended bets perform. Because we only bet when there's a meaningful edge, the bet win rate is different from overall prediction accuracy.

Our model has been live since March 2026. We publish all results transparently on our homepage.

Walk-forward backtesting across 12 months of historical data shows consistent positive ROI across different market conditions.

Why Transparency Matters

We publish every prediction and every result. No cherry-picking winners, no hiding losses. You can see our full track record on the homepage — both live results and backtested performance.

Important Disclaimer

Sports betting carries inherent risk and past performance does not guarantee future results. Even with a positive edge, variance means you can and will experience losing streaks. Our model is a tool to help inform your decisions — it is not financial advice. Never bet more than you can afford to lose, and be aware that no model can predict outcomes with certainty. Bet responsibly.

Beat the Model

Think you know tennis better than our AI? We run a public experiment where anyone can predict match winners and compare their accuracy against the model.

Before each match starts, pick who you think will win — either on the dashboard or by voting in our Telegram channel polls. After the match finishes, we track whether you or the model got it right.

You can see the running Human vs AI scoreboard on our experiment page. It tracks the crowd consensus (majority vote) against the model, plus your personal record if you're signed in.

It's free to participate and a fun way to test your tennis knowledge against a machine learning model trained on thousands of matches.

Get Started

We offer a free daily prediction on our Telegram channel and through our email newsletter. Subscribe for full access to all predictions with edge analysis and Kelly bet sizing.