MatchUp Tennis methodology: how our model analyzes ATP matches

Our philosophy

Comprendre avant de parier

Most tipping sites hand you a “tip” with no explanation. We do the opposite: we show you pourquoi a player holds an edge in a given match, and we let you decide.

Playing style: the hidden variable

What we believe: playing style is the most underrated factor in tennis. Two players with the same ATP ranking can produce radically different results depending on the opponent’s style, court speed and match conditions.

That is what MatchUp Tennis captures. We don’t replace your judgment — we enrich it with data you won’t find anywhere else.

Playing styles

13 profiles to classify every ATP player

This is the heart of MatchUp Tennis. Every ATP player is classified into one of our 13 styles de jeu, built on dozens of parameters: serve patterns, return aggressiveness, rally management, performance under pressure, and more.

Matchups: some styles beat others

The idea: certains styles dominent certains autres. A Big Server dominates weak returners. A Counter Puncher neutralizes servers. An All-Court Elite adapts to everything. These matchup dynamics are measurable and predictive.

Our matrice 13×13 shows the historical win rate of each playing style against every other. It is our competitive edge — no other site does this analysis.

What we analyze for every match

For every match of the day, our proprietary model combines several layers of analysis :

The 6 dimensions of the v4.0 hybrid model

These factors are combined by our proprietary algorithm to produce a win probability. When that probability differs significantly from what market odds imply, we detect value.

The Speed Index

Objectively measure each court’s speed

Not all courts are equal. A match on Wimbledon grass has nothing in common with Roland Garros clay. And even within one surface, pace varies enormously.

Our Speed Index (0-100) objectively measures each tournament’s pace from aggregated serve statistics. It is a unique tool for comparing playing conditions across tournaments.

Every player has performance stats by speed band. A Big Server who thrives on fast courts (Speed 60+) can have a far lower WR on slow ones (Speed 20-). Our model factors this in.

Value signals

We don’t give a “tip”. We detect the gaps between our estimate and the market’s. When our model sees a probability significantly different from what the odds imply, we raise a signal.

Trois niveaux de confiance

Every signal comes with confirmation criteria visible on the match card: Elo, Playing style, H2H and Fatigue. The more criteria align, the stronger the signal.

Per-player history

For every match, you can review the Each player’s last 10 matches — overall and specifically against today’s opponent’s playing style.

It’s a powerful feature: if Sinner faces a Big Server, you immediately see his last 10 matches against Big Servers, with scores and results. That gives you concrete context, not just a percentage.

The scale of our data

Data is collected in real time and cross-referenced with our historical base. Every match is automatically enriched with serve stats, recent results and player workload.

Frequently asked questions