PROJECT · DATA · RESULTS
DeepTennisAI: how I use AI, data and real odds to analyze tennis
I'm not trying to guess scorelines with a nice sentence. I'm building a process that records the prediction, matches it against the market price, and keeps the result, even when it goes wrong.
Deep Tennis AI is a project I run alone. It was born from a very specific discomfort: tennis is full of predictions, but it's hard to find a track record that lets you verify what was published, at what odds, and what happened afterward.
The problem isn't getting names right
Saying a favorite will win can be correct and still have no value. Odds already contain an implied probability. If the market requires getting it right eight times out of ten and the analysis only justifies seven, the prediction can sound convincing while being a bad decision.
Besides, “who wins” and “which bet is worth it” are different questions. A player can win 2-1, cover the winner market, and lose a −1.5 sets handicap. That difference is central to understanding the results.
A useful prediction needs three pieces: a probability, a price, and a rule that decides when not to bet.
How the system works
The process starts with the matches on the ATP and Challenger calendar. For each matchup I gather the format, surface, round, recent player data and odds available across several markets. Not every match reaches the analysis stage: if reliable information or sufficient pricing is missing, it gets discarded.
The AI receives that context and returns a structured read of the match. Then, a rules layer checks consistency, quality, and the relationship between confidence and odds. That layer determines whether a pick exists, which market best represents the edge, and what stake applies. The AI proposes; the control algorithm decides what can be published.
BO3 and BO5 aren't the same problem
Best-of-three and best-of-five matches follow different paths. The frequency of comebacks, fatigue, totals and handicaps all change. Treating a men's Grand Slam like an ATP 250 would introduce mathematical errors even though the market names look the same.
Deep Signals: public rules, not opinions
Alongside the Premium Picks there are automatic signals on the match winner: Winner Elite for strong favorites, Tapado IA for less obvious selections, and Combi Elite for a daily combo. Each one has a defined rule and a visible track record in Deep Signals.
What the data says as of July 21, 2026
The platform had processed 2,100 matches and 2,100 analyses across 83 tournaments. In the Premium track record since May 1 there were 134 picks recorded: 126 decided, with 78 wins and 48 losses, and 8 pending.
Reading note: these figures are a closed snapshot from July 21, 2026. “Flat” uses one unit on the main entry; “with stake” respects the recorded stake. They are not a promise of future performance.
The week that changed handicap protection
Between July 14 and 20, 27 main Premium entries closed: 12 won and 15 lost, for −4.80 flat units. The interesting detail appeared when reviewing the type of error: in 12 of those 15 losses, the selected player did win the match, but did so 2-1.
In other words, the winner read was correct in 24 of 27 matches, while several −1.5 sets handicaps failed by a single set. The system was correctly capturing the player's superiority, but concentrating all the exposure on the most demanding outcome: winning 2-0.
Requires a 2-0 win in BO3.
Also pays out if the player wins 2-1.
Based on that evidence I added a protection algorithm for certain BO3 picks: it keeps the main entry when it still offers value and accompanies the selection with limited exposure to the moneyline. The distribution uses confidence, stake and risk limits; it isn't an independent additional bet.
The change applies only going forward. I haven't recalculated July or turned historical losses into protected results. That separation between backtest and official result is necessary for the track record to remain auditable.
What the AI doesn't know
A model has no certain knowledge of an undisclosed niggle, a personal problem, or how a player will respond to a pressure moment. Recent data can have small samples and odds can change. Even a real edge goes through negative streaks from pure variance.
That's why the system can publish nothing, limits the stake, keeps the failures, and separates future changes from the historical record. The goal isn't to look infallible; it's to make every decision reviewable.
CHECK IT YOURSELF
The track record is open.
You can review results, markets and periods without relying on cherry-picked screenshots.
Informational content for adults over 18. Betting involves risk of loss. Never bet money you cannot afford to lose.