Blog: understanding AI tennis predictions
In-depth, hand-written articles about what is really inside a tennis prediction model. No picks of the day here: the predictions have their own pages.
Method7 October 2026Indoor tennis: are favourites more reliable under a roof?
Indoors, the favourite wins 64.9% of matches, against 65.0% on outdoor hard courts. A good indoor record mostly reflects a good player.
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Method5 October 2026Tennis fatigue and rest: do tired players really lose more?
Stringing matches together is barely measurable. Returning after a month without playing is: nearly ten points of win probability less than Elo predicts.
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Method1 October 2026Head-to-head in tennis: does the H2H really predict the winner?
Across 38,123 matches, the head-to-head leader wins less often than the Elo favourite. But a record against the favourite really does count.
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Method29 September 2026Clay, hard or grass: what the surface really changes in tennis
Across 36,210 matches, a player’s overall level predicts better than their record on the day’s surface, and grass is where favourites fall most often.
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Betting26 September 2026How to bet on tennis: bankroll, value betting and risk
How much to stake, what value really means, and why our own model lost 9% betting on its disagreements with the market.
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Method24 September 2026Tennis odds and implied probability: what they really say
How to read bookmaker odds, strip out the margin, and why the market stays the benchmark most models can’t durably beat.
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Method18 September 2026Elo in tennis: understanding the rating that predicts matches
Why a rating designed for chess predicts a tennis match better than the ATP ranking, and which adjustments it needs to really work.
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Tennis prediction algorithm: how an AI model actually works
What a tennis prediction model really receives, how it learns without cheating with time, and why its accuracy plateaus around the market’s.
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