AI football betting prompts built around xG and the fair price
Football is the hardest sport to prompt well: three sonuçlar, low scoring, and the most efficient market in betting. A prompt that ignores the beraberlik or trusts a 4-0 result over the xG behind it will lose slowly and confidently.
What a football prompt has to get right
The first job of a football prompt is arithmetic, not opinion: strip the bahis şirketi marjı out of the 1X2 prices so the model starts from a fair baseline instead of an inflated one. Everything after that is an adjustment — form measured in xG rather than puan, the home-away split of each side, who is missing, and how many days of rest each team had.
The second job is to keep the beraberlik honest. Roughly a quarter of maçlar in the big European leagues end level, and models left to their own instincts systematically under-price that. Both prompts below force a three-number olasılık set that sums to 100%, so an under-weighted beraberlik becomes visible immediately instead of hiding inside a confident "home galibiyet".
The third job is scale. Football is by far the largest slate on the board — well over a thousand fixtures land in a fortnight — so a prompt that only works when you have read the team news is a prompt you will use twice. Both versions below are written to run on whatever you can paste in from a maç page, and to say so when that is not enough.
De-vig before you predict
Convert 1X2 oranlar to implied probabilities, remove the overround, and treat the result as the baseline. A prompt that starts from raw oranlar is starting from a number that already sums to more than 100%.
xG over results
Five-maç samples of gol are almost noise. xG for and against, shot volume and shot quality tell you whether a run of galibiyetler is real. Value lives where the table lags the underlying performance.
Ev sahibi-away split, not season averages
Many sides are a different team away from home — deeper block, fewer shots, more draws. Feed the split explicitly, otherwise the model averages the two into something that describes neither.
Rotation, injuries and congestion
A midweek European tie three days earlier, a suspended centre-back, a keeper change. These move the price more than most narratives, and they are the factors a model cannot guess — you have to supply them.
Football prompts v1 and v2 — and how they differ
The same model, two instruction sets, two different betting personalities. Run both on the same maçlar; that comparison is the only thing that settles the argument.
| Version | Focus | Style | Best for |
|---|---|---|---|
| v1 | Fair 1X2 baseline, then small adjustments | Disciplined | 1X2, consistency |
| v2 | Shot quality and gol marketler | Gols-hunting | Totals and BTTS value |
You are a disciplined football betting analyst. Maç: {home} vs {away}, {league}, {date}. Market 1X2: {oranlar}.
Step 1: convert the 1X2 oranlar to implied probabilities and remove the bahis şirketi marjı to get a fair baseline.
Step 2: adjust that baseline only for verifiable factors — form measured in xG for/against (last 5), confirmed injuries and suspensions, home form for the home side and away form for the away side, days of rest and travel. Do not adjust for narratives or motivation.
Keep the beraberlik honest: it is roughly 25% in most top leagues.
Output exactly:
1) Ev sahibi / beraberlik / away probabilities summing to 100%
2) Main tahmin + güven 1-10
3) Best value market (1X2 / Üst-Alt 2.5 / BTTS) and the reason
4) Most likely correct skor
5) One-oran reasoning
If your fair price maçlar the offered price, answer "no bet".
You are an attacking-metrics football analyst. For {home} vs {away} ({league}, {date}):
Base your read on xG for and against, shot volume and shot quality, set-piece threat and how high each defensive oran plays — not on results. Lean into gol marketler when both attacks create real chances, and away from them when either side suppresses shot quality.
Compare every conclusion with the posted oran {oranlar} and flag where the market disagrees with the underlying numbers.
Output exactly:
1) Predicted skor
2) Üst/Alt tahmin with the oran you are using
3) BTTS evet/no
4) 1X2 tahmin + güven 1-10
5) The single decisive factor
If the xG samples are too small to separate the sides, say so and answer "no bet".
Placeholders in braces are filled automatically when you run a prompt from a maç 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
- League, matchweek and kick-off date — plus cup bağlam if the maç is a dead rubber.
- Last five maçlar per side with xG for and against, not just scorelines.
- Ev sahibi form for the home team, away form for the away team — separately.
- Confirmed absences: injuries, suspensions, and any keeper or centre-back change.
- Days of rest since the last maç and travel distance.
- The oran: 1X2, Üst-Alt 2.5 and BTTS. Weather too, if it is extreme.
Good output has
- Three probabilities for home / beraberlik / away summing to exactly 100%.
- A main tahmin plus güven 1-10, with a low skor allowed.
- The best value market of the three (1X2, Üst-Alt, BTTS) and why.
- A most-likely correct skor, which exposes an incoherent olasılık set fast.
- One decisive factor in one oran — no paragraph of hedging.
- A clear "no bet" when the fair price and the offered price hemfikir.
Where football prompts usually go wrong
- Alt-weighting the beraberlik (it is around 25% in most top leagues).
- Reading one 4-0 as a step change instead of variance.
- Motivation narratives ("they need the galibiyet") replacing data.
- Totals tahminler that ignore how each side actually creates shots.
Oranlar, model bağlam and market drift for each fixture are on the football maçlar with oranlar and AI tahminler board, so most of the input list above can be copied straight from the maç page.
Prompting the three marketler football actually offers
A prompt that only answers "who galibiyetler" throws away most of a football card. The three liquid marketler reward different reasoning, and asking for all three in one answer is also the cheapest coherence check you have: a 1X2 read, a totals read and a correct skor that contradict each other tell you the model is guessing.
Three-way, beraberlik included
Demand three probabilities that sum to 100% and compare each with the de-vigged price. The beraberlik is the honesty test — a model that prices it under 20% in a tight league maç is not reasoning, it is picking a favori.
Totals need shot creation, not results
Üst-Alt is a question about how each side generates and concedes chances: shot volume, shot quality, set-piece threat, defensive oran height. Two takımlar that both create little produce unders regardless of how attacking their reputations are.
Karşılıklı gol
BTTS is close to two independent scoring questions, so ask for each side's chance of scoring separately before the evet/no. It is also where a weak keeper or a missing centre-back moves the honest number most.
Handicaps and correct skor
Ask for a most likely correct skor even when you are not betting it: it exposes an incoherent olasılık set instantly. If the model says 55% home galibiyet and predicts 1-1, one of those two numbers is wrong.
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 maçlar
Tahmin fixtures from the football 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 maç 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 football. Open a ücretsiz trial to run it on today's card, or read the prompt library genel bakış for the shared structure behind every sport.
Football prompt questions
Why should a football prompt de-vig the oranlar first?
Because bahis şirketi prices include a margin, so implied probabilities sum to more than 100%. If the model treats them as fair it will systematically overestimate every sonuç and see "value" where there is none. Removing the overround gives a baseline that is honest enough to argue with.
Where do I get xG numbers to paste in?
Any public source you already trust works, as long as you use the same source consistently — mixing providers introduces differences bigger than the effects you are trying to measure. On PropickAI maç pages the model and market bağlam are gösterilen alongside the oranlar, which is usually enough for the disciplined v1 prompt.
Do these prompts work for lower leagues?
The v1 market-anchored prompt travels well, because the price carries most of the information. The xG-driven v2 needs data that often does not exist below the top divisions — in that case either supply what you have or stick to v1.
How should the prompt treat the beraberlik?
As a real sonuç with a real olasılık, not as a rounding error. Force three numbers that sum to 100% and compare the beraberlik against the de-vigged market price. In tight, low-scoring leagues the beraberlik is frequently the fairest price on the coupon, and a model that never tahminler it is telling you about its bias rather than about the maç.
Should I run one prompt genelinde every league, or write one per competition?
Başla with one prompt and one league so the comparison is clean, then widen. League bağlam (typical gol, home advantage, refereeing) shifts the reference puan enough that a prompt tuned on the Premier League will misprice a low-scoring second division — which is exactly the kind of drift the AI Lab dashboard makes visible.
Prompts for the rest of the board
Find out which football prompt actually galibiyetler
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