AI football betting prompts built around xG and the fair price
ฟุตบอล is the hardest sport to prompt well: three ผล, low scoring, and the most efficient ตลาด in betting. A prompt that ignores the เสมอ 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 ส่วนต่างเจ้ามือ out of the 1X2 prices so the โมเดล starts from a fair baseline instead of an inflated one. Everything after that is an adjustment — form measured in xG rather than แต้ม, 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 เสมอ honest. Roughly a quarter of แมตช์ 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 ความน่าจะเป็น set that sums to 100%, so an under-weighted เสมอ becomes visible immediately instead of hiding inside a confident "home ชนะ".
The third job is scale. ฟุตบอล 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 แมตช์ page, and to say so when that is not enough.
De-vig before you predict
Convert 1X2 ราคาต่อรอง to implied probabilities, remove the overround, and treat the result as the baseline. A prompt that starts from raw ราคาต่อรอง is starting from a number that already sums to more than 100%.
xG over results
Five-แมตช์ samples of ประตู are almost noise. xG for and against, shot volume and shot quality tell you whether a run of ชนะ is real. Value lives where the table lags the underlying performance.
เจ้าบ้าน-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 โมเดล 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 โมเดล cannot guess — you have to supply them.
ฟุตบอล prompts v1 and v2 — and how they differ
The same โมเดล, two instruction sets, two different betting personalities. Run both on the same แมตช์; that comparison is the only thing that settles the argument.
| Version | Focus | Style | Best for |
|---|---|---|---|
| v1 | อากาศดี 1X2 baseline, then small adjustments | Disciplined | 1X2, consistency |
| v2 | Shot quality and ประตู ตลาด | ประตูs-hunting | Totals and BTTS value |
You are a disciplined football betting analyst. แมตช์: {home} vs {away}, {league}, {date}. ตลาด 1X2: {ราคาต่อรอง}.
Step 1: convert the 1X2 ราคาต่อรอง to implied probabilities and remove the ส่วนต่างเจ้ามือ 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 เสมอ honest: it is roughly 25% in most top leagues.
Output exactly:
1) เจ้าบ้าน / เสมอ / away probabilities summing to 100%
2) Main ทายผล + ความมั่นใจ 1-10
3) Best value ตลาด (1X2 / สูง-ต่ำ 2.5 / BTTS) and the reason
4) Most likely correct สกอร์
5) One-เส้นราคา reasoning
If your fair price แมตช์ 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 เส้นราคา plays — not on results. Lean into ประตู ตลาด when both attacks create real chances, and away from them when either side suppresses shot quality.
Compare every conclusion with the posted เส้นราคา {ราคาต่อรอง} and flag where the ตลาด disagrees with the underlying numbers.
Output exactly:
1) Predicted สกอร์
2) สูง/ต่ำ ทายผล with the เส้นราคา you are using
3) BTTS ใช่/no
4) 1X2 ทายผล + ความมั่นใจ 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 แมตช์ in the AI Lab. Pasting into your own chat window works too — just replace them by hand.
What to feed the โมเดล, and what a usable answer looks like
Feed it this
- ลีก, matchweek and kick-off date — plus cup บริบท if the แมตช์ is a dead rubber.
- Last five แมตช์ per side with xG for and against, not just scorelines.
- เจ้าบ้าน 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 แมตช์ and travel distance.
- The เส้นราคา: 1X2, สูง-ต่ำ 2.5 and BTTS. สภาพอากาศ too, if it is extreme.
Good output has
- Three probabilities for home / เสมอ / away summing to exactly 100%.
- A main ทายผล plus ความมั่นใจ 1-10, with a low สกอร์ allowed.
- The best value ตลาด of the three (1X2, สูง-ต่ำ, BTTS) and why.
- A most-likely correct สกอร์, which exposes an incoherent ความน่าจะเป็น set fast.
- One decisive factor in one เส้นราคา — no paragraph of hedging.
- A clear "no bet" when the fair price and the offered price เห็นตรงกัน.
Where football prompts usually go wrong
- ต่ำ-weighting the เสมอ (it is around 25% in most top leagues).
- Reading one 4-0 as a step change instead of variance.
- Motivation narratives ("they need the ชนะ") replacing data.
- Totals ทายผล that ignore how each side actually creates shots.
ราคาต่อรอง, โมเดล บริบท and ตลาด drift for each fixture are on the football แมตช์ with ราคาต่อรอง and AI ทายผล board, so most of the input list above can be copied straight from the แมตช์ page.
Prompting the three ตลาด football actually offers
A prompt that only answers "who ชนะ" throws away most of a football card. The three liquid ตลาด 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 สกอร์ that contradict each other tell you the โมเดล is guessing.
Three-way, เสมอ included
Demand three probabilities that sum to 100% and compare each with the de-vigged price. The เสมอ is the honesty test — a โมเดล that prices it under 20% in a tight league แมตช์ is not reasoning, it is picking a ตัวเต็ง.
Totals need shot creation, not results
สูง-ต่ำ is a question about how each side generates and concedes chances: shot volume, shot quality, set-piece threat, defensive เส้นราคา height. Two ทีม that both create little produce unders regardless of how attacking their reputations are.
ทั้งสองทีมทำประตู
BTTS is close to two independent scoring questions, so ask for each side's chance of scoring separately before the ใช่/no. It is also where a weak keeper or a missing centre-back moves the honest number most.
Handicaps and correct สกอร์
Ask for a most likely correct สกอร์ even when you are not betting it: it exposes an incoherent ความน่าจะเป็น set instantly. If the โมเดล says 55% home ชนะ 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 แมตช์
ทายผล 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 แมตช์ 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 ฟรี trial to run it on today's card, or read the prompt library ภาพรวม for the shared structure behind every sport.
ฟุตบอล prompt questions
Why should a football prompt de-vig the ราคาต่อรอง first?
Because เจ้ามือ prices include a margin, so implied probabilities sum to more than 100%. If the โมเดล treats them as fair it will systematically overestimate every ผล 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 แหล่งข้อมูล you already trust works, as long as you use the same แหล่งข้อมูล consistently — mixing providers introduces differences bigger than the effects you are trying to measure. On PropickAI แมตช์ pages the โมเดล and ตลาด บริบท are แสดง alongside the ราคาต่อรอง, which is usually enough for the disciplined v1 prompt.
Do these prompts work for lower leagues?
The v1 ตลาด-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 เสมอ?
As a real ผล with a real ความน่าจะเป็น, not as a rounding error. Force three numbers that sum to 100% and compare the เสมอ against the de-vigged ตลาด price. In tight, low-scoring leagues the เสมอ is frequently the fairest price on the coupon, and a โมเดล that never ทายผล it is telling you about its bias rather than about the แมตช์.
Should I run one prompt ทั่ว every league, or write one per competition?
เริ่ม with one prompt and one league so the comparison is clean, then widen. ลีก บริบท (typical ประตู, home advantage, refereeing) shifts the reference แต้ม enough that a prompt tuned on the Premier ลีก 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 ชนะ
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