Tennis Glicko

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Glicko Tennis Betting Analytics — VOPO Score vs Pinnacle Odds for ATP & WTA

Predict.Choose.Repeat.

Explore match probabilities generated from a Glicko-based rating model. Compare model estimates with market odds across ATP, WTA, ITF and Challenger events.

Tennis Analytics Dashboard showing VOPO score and match predictions

Model accuracy: 72.8%. Brier Score: 0.178. Data coverage: ATP, WTA, ITF, Challenger. Matches in Calibration: ~440k.

Why does Tennis Glicko work?

We have identified differences between the model probabilities and the market probabilities. Use these differences as part of your own analysis and decision-making process.

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ATP, WTA, ITF, and Challenger matches across all surfaces. Surface-specific ratings because clay form does not transfer to others.

Years of historical match data. Ratings update after every result. The model re-prices each upcoming match as new data arrives.

Glicko-2 tracks classification uncertainty; players with few records receive wider confidence intervals, not false accuracy.

Find value. Not just favorites.

Match Analysis 1
Match Analysis 2

How the model works

01

Dynamic Rating

Continuously measures a player's true competitive strength. Ratings update after every match, weighting opponent quality and match context to update.

Ex: Alcaraz defeating Djoko moves the rating more than beating a Top-200 player

02

The Volatility

Measures performance stability match to match. Identifies players capable of beating top names one day and losing unexpectedly the next. Profiles often mispriced by the market.

Ex: Bublik beating a Top-10, then losing to a qualifier player = high volatility detected

03

Rating Deviation

Quantifies how trustworthy the rating really is. Players returning from injury or long breaks carry higher uncertainty, forcing the model to reduce confidence automatically and instantly.

Ex: Nadal returning after 3 months because of a injury = cautious probability

04

Fair Probability

Converts rating, uncertainty and volatility into a true win probability. Directly compares model output with market odds to highlight long-term positive expected value.

Ex: Model gives Medvedev 64% vs market implied 56% = value opportunity

Rankings, Matches, Dashboard and much more features...

Rankings

Men and women from around the world, showcasing their skills on every surface.

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Why are we selling such a powerful tool?

In the gold rush, the first to get rich was the shovel seller. We don't have access to all markets. But we validate the tool and offer it so that everyone can find their gold mines. There are opportunities all over the world, in all tennis leagues, and we will seek opportunities everywhere.