AI tennis betting prompts that respect the surface
Tennis has no Unentschieden, no substitutions and one player who can decide everything with a serve. That makes it the cleanest sport for prompt testing — and the fastest way to find out that a prompt reading "recent form" without reading the surface is worthless.
What a tennis prompt has to get right
A tennis price is mostly a serve-quality price. Über a best-of-three Spiel the Favorit usually Siege because they hold more often, not because they are "in form" — so a prompt that asks the model for form in the abstract gets an answer built on the wrong unit. Ask for hold percentage, break-point conversion and the last ten Spiele on the same surface, and the same model suddenly produces numbers you can compare with the Linie.
The second thing a tennis prompt has to handle is the Markt itself. Top-30 moneylines are efficient; the money is in the second tier, in surface transitions (Sand to Rasen), and in players who just spent three hours on court. Both prompts below are anchored to the price you paste in — one deliberately stays close to it, the other is allowed to fight it.
Surface, then everything else
Sand, Hartplatz, Rasen and indoor Hartplatz are effectively four sports. Ask for the last 10 Spiele on the same surface and the H2H filtered to that surface only — a 4-1 career H2H built on Sand tells you almost nothing about a Rasen-court meeting.
Serve and return, not results
Hold %, break-point conversion and first-serve percentage explain the result better than the Sieg/loss column. Two players at 88% and 74% hold is a decided Spiel; two players at 80% and 78% is a coin flip whatever the ranking says.
Fatigue, travel and schedule
Sets played over the last seven days, back-to-back three-setters, a long flight, altitude or extreme heat. This is where the price lags most often, because the Buchmacher priced the name before the quarter-final went to a tiebreak.
Reputation lag in the price
Rankings update slowly and the public bets names. A returning ex-top-10 player is frequently short, a rising qualifier frequently long. Tell the model to name the concrete edge, otherwise it will invent one from the reputation it already has.
Tennis prompts v1 and v2 — and how they differ
The same model, two instruction sets, two different betting personalities. Run both on the same Spiele; that comparison is the only thing that settles the argument.
| Version | Focus | Style | Best for |
|---|---|---|---|
| v1 | Surface-specific form anchored to the price | Disciplined | Favourites, steady hit-rate |
| v2 | Momentum and head-to-head over the Markt | Aggressive value | Underdog value, higher variance |
You are a professional tennis betting analyst. Analyse {home} vs {away} at {tournament} on {surface}, {date}.
Weight, in this order: surface-specific form (last 10 Spiele on {surface}), serve/return numbers (hold %, break Punkte converted, first-serve %), workload and travel (sets played in the last 7 days), then the current Linie {Quoten}.
Anchor your probabilities to the Markt. Deviate only when you can name one concrete edge in a single clause.
Output exactly:
1) Sieger + Sieg Wahrscheinlichkeit % for both players (sum 100%)
2) Confidence 1-10
3) Best Markt (moneyline / games handicap / total games) and the price it becomes value at
4) Predicted set Ergebnis
5) One-Linie reasoning
If the data is too thin or the price is fair, answer "no bet". Be concise, no hedging.
You are an aggressive value-seeking tennis analyst. For {home} vs {away} at {tournament} ({surface}), {date}:
Weight momentum over the Markt: set-by-set dominance in recent Spiele, break-point conversion, head-to-head restricted to {surface}, and record in deciding sets.
Hunt underdogs the Linie overprices because of ranking or reputation. Treat {Quoten} as the number to beat, not the truth.
Output exactly:
1) Sieger + Sieg Wahrscheinlichkeit % for both players (sum 100%)
2) Confidence 1-10
3) Best value bet, naming the edge against {Quoten}
4) Predicted set Ergebnis
5) One-Linie reasoning
If you cannot name the edge in one clause, answer "no bet".
Placeholders in braces are filled automatically when you run a prompt from a Spiel in the AI Lab. Pasting into your own chat window works too — just replace them by hand.
What to feed the model, and what a usable answer looks like
Feed it this
- Exact tournament, round and surface — including indoor or outdoor, and the ball type if you know it.
- Letzte 10 Spiele for each player on that surface, with scorelines rather than just W/L.
- Serve/return numbers: hold %, break Punkte converted, first-serve %, tiebreaks won.
- Workload: sets and minutes played in the last 7 days, plus any recent retirement or medical timeout.
- Head-to-head restricted to the same surface (and note best-of-three vs best-of-five).
- The current Linie: moneyline for both players, games handicap and total games.
Good output has
- Two Sieg probabilities that sum to 100% — not a vague "likely".
- A Sicherheit Ergebnis 1-10 that is allowed to be low.
- One named Markt (moneyline, games handicap or total games) with the price it becomes value at.
- A predicted set Ergebnis, which is the fastest sanity check on the Wahrscheinlichkeit.
- One decisive factor in a single Linie — if the model cannot name it, the Tipp is noise.
- Permission to answer "no bet". A prompt that must produce a Tipp will produce a bad one.
Where tennis prompts usually go wrong
- Career H2H overriding surface form.
- Treating best-of-five like best-of-three (favourites are stronger over five sets).
- Games insgesamt Tipps made without both serve profiles.
- Ranking used as a proxy for current level.
Quoten, model Kontext and Markt drift for each fixture are on the tennis Spiele with Quoten and AI Tipps board, so most of the input list above can be copied straight from the Spiel page.
Measure both versions before you trust either
Store both versions
Save v1 and v2 as separate prompts in the AI Lab so every run is attributed to a version instead of blurring together.
Run them on the same Spiele
Tipp fixtures from the tennis board and lock both forecasts before start. Same slate, same information, no hindsight.
Judge on ROI, not hit-rate
A value prompt taking underdogs will always look worse on hit-rate and can still be the profitable one. Settlement and scoring are automatic once the Spiel finishes.
The AI Lab starts on the $19 tier with one sport and five stored prompts, which is enough for a full v1-versus-v2 comparison in tennis. Open a kostenlos trial to run it on today's card, or read the prompt library Überblick for the shared structure behind every sport.
Tennis prompt questions
Which tennis prompt performs better — v1 or v2?
That depends on your slate, and it is exactly what the AI Lab measures. In general the disciplined v1 produces a higher hit-rate at short prices, while the value-hunting v2 has a lower hit-rate and higher variance because it takes underdogs. Run both on the same Spiele for a few weeks and compare ROI, not hit-rate.
Should the prompt see the Buchmacher Quoten?
Ja. A model that never sees the price cannot tell you where the value is, and it will drift far from reality on players it barely knows. Paste the current Linie and tell the model to anchor to it and only deviate when it can name a concrete reason.
Do these prompts work for Challenger and ITF Spiele?
They work, but supply the data yourself — public statistics are thinner at that level, and a model asked about an unfamiliar player will fill the gap with invention. Give it the surface splits and serve numbers you have, and let it answer "no bet" when the input is too thin.
Prompts for the rest of the board
Find out which tennis prompt actually Siege
Starten the 5-day AI Lab trial without a card. Bring your own AI key, run v1 and v2 on today's tennis card, and let the dashboard settle it on a virtual $10,000 bank.
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