ScoreGPT Methodology: How Our AI Football Predictions Work

ScoreGPT predicts each football match with a multi-model pipeline: five frontier large language models analyse the fixture independently, then a consensus is computed and every pick is graded publicly. As of September 2026 the process is:

1. Gather the fixture. Match data, live scores and standings come from API-Football; everything is stored in our database (Supabase).
2. Run five models independently. Five frontier large language models analyse the same match separately, with no cross-contamination — none of them sees another model's answer.
3. Collect a structured prediction from each. Every model returns the same fields: predicted result (home/draw/away), a scoreline, a confidence level, the key factors behind its call, and a read on the betting market.
4. Compute the ScoreGPT consensus. Each model also gives a probability for a home win, a draw and an away win. ScoreGPT averages the five sets: the outcome with the highest average is the consensus call, and that average is its confidence. A language-model writing step then explains the call and names the most likely scoreline consistent with it. The consensus is the five models combined, not a sixth model.
5. Grade it later. After the match finishes, the prediction is compared to the real result and stored — kept whether it won or lost.

This is an AI-native design: the predictions come from reasoning models, not a single statistical or hand-built model.

ScoreGPT runs five named frontier large language models on every match, each from a different provider. As of October 2026 they are:

Model	Provider
GPT-6.1 Sol	OpenAI
Claude Opus 5.5	Anthropic
Grok 4.7	xAI
DeepSeek-V4-Pro	DeepSeek
Kimi K3	Moonshot

Each model analyses the match on its own and produces its own structured prediction. We name every model so you can see exactly which engines are behind a pick — full transparency about our method is a core part of how ScoreGPT works.

The ScoreGPT consensus is the average of five independent AI models' probabilities — agreement across models, not the betting-industry meaning of "consensus."

In betting, "consensus" usually means the public-betting percentage: how the crowd's wagers are split. ScoreGPT's consensus is different. Since 10 September 2026 it is computed, not generated:

• Call and confidence: each of the five models gives a probability for a home win, a draw and an away win. ScoreGPT averages the five sets; the outcome with the highest average is the consensus call, and that average is its confidence.
• Agreement: a line such as "4 of 5 models back the home win" counts how many of the five individually picked the consensus result.
• Summary and scoreline: a language-model writing step explains the call and names the single most likely scoreline consistent with it. It cannot change the call or its confidence.

The consensus appears in the app, and on the accuracy leaderboard, as the "ScoreGPT" row — but it is not a sixth model: it is the five models combined. In the app, the Matchday board sorts each day's matches into High, Medium and Low by how firmly the panel backs its call.

What the website's match pages show: the same consensus call as the app, and that call is what the page grades. A line such as "2 of 5 back Lyon" counts the models that picked that result themselves; the scoreline beside it is the average of their own predicted scores, shown only when at least two of them give a scoreline that agrees with the call. One exception: when the five models' own picks split evenly, the page says the panel is split, names no winner, and grades the tied result that comes first in the order home win, draw, away win. A pick written after the final whistle stays on the page, marked, and is not graded.

Because each model reasons separately first, the consensus reflects genuine multi-model agreement rather than one engine's output.

Every ScoreGPT prediction is graded against the real match result and stored — including the ones that lose.

After a match finishes, each prediction is checked against the actual outcome and recorded. The full graded record (wins and losses) is shown in the app; the website shows a running graded tally where the data exists.

We do not hide losing picks. ScoreGPT keeps the track record complete, so you can see every call rather than a curated highlight reel.

Important: ScoreGPT does not claim a win rate, accuracy percentage, ROI or profit, and does not claim to be more accurate than any other tool. Football is unpredictable and our sample is still small. What we offer is a transparent, fully graded record — not a performance guarantee.

More on what a graded record does and does not prove: how accurate are AI football predictions? and how do AI models predict soccer scores?

The same five models and the same public grading also cover American football: the NFL and ranked college football. As of September 2026:

• Which games: every NFL game outside the preseason, and college football (FBS) games in which at least one team is in the current AP Top 25 poll.
• What each model returns: a winner — home or away, with no draw option — with a probability for each side, a predicted final score in points, and the key factors behind the call.
• What the website shows: before kickoff, each game page shows the panel's call — the side most of the five models back — with an averaged final score, two models' own predicted scores and the other models' leans. If the models split evenly, the page makes no call. Confidence and the full written analysis stay in the app.
• How it is graded: the same way as football. The call is locked before kickoff, checked against the final score (overtime included) and kept whether it was right or wrong. A tied NFL game is rare; no winner pick can be right or wrong on it, so it is not graded.

The accuracy leaderboard covers football only for now. The NFL and college football hubs carry their own graded record, hits and misses alike.

Match predictions are generated on a rolling, kickoff-relative schedule: one pass per match, about 36 hours before kickoff.

• Generation: match predictions are produced about 36 hours before kickoff, as each fixture enters the analysis window.
• No re-run: a published match prediction is not re-run as kickoff approaches. Predictions lock before kickoff. Every graded match page in our football leagues shows its lock time. 13 matches from 26 February–3 June 2026 were written after kickoff; they are marked and stay in the record.
• World Cup bracket: the tournament bracket is re-run after each stage completes. Kimi K3 is excluded from the heavy bracket run because it times out on that very large prompt, so bracket predictions there use the remaining models.

After each match, the corresponding prediction moves into the graded record.

We grade only matches played to a result: match outcomes on the final score (extra time counts, penalty shoot-outs don't), picks on the 90-minute score. A pick written after the final whistle is never graded; a pick written during a match is marked and stays in the record. The whistle is estimated from our data feed's second-half start and reported stoppage time, or set at 150 minutes after kickoff when those are missing. Abandoned, cancelled, postponed and awarded matches are not graded.

ScoreGPT combines three things — multi-LLM consensus across five named frontier models, an AI-native design, and full public grading of every pick including losses — that, as of July 2026, we have not seen together in any other football-prediction tool. This is a method-and-transparency distinction, not a performance claim.

Many widely used tools take a different, equally valid approach. We describe these as different approaches, not better or worse, and we make no claim about whose predictions are more accurate. The table below compares approaches on method and transparency only — it contains no accuracy, win-rate or ROI figures:

Approach	How it predicts	Models named publicly?	Losing picks shown publicly?
ScoreGPT (multi-LLM consensus)	Five named reasoning LLMs predict independently, then their probabilities are averaged into one consensus call	Yes — all five named	Yes — every pick graded, wins and losses
Statistical / mathematical model	Math on historical data and odds; no language model	n/a (no LLMs)	Varies by tool
Human / editorial tipster desk	Analysts apply statistics and judgment by hand	n/a (human-led)	Varies by tool
Live-scores app + AI layer	A scores product layers algorithmic predictions on top	Varies	Varies by tool
Other multi-LLM tools	Several LLMs analyse each match, combined into one view	Varies	Varies by tool

We describe categories generally and make no claim that ScoreGPT is more accurate than any of them.

No. ScoreGPT predictions are for information and entertainment only. A prediction is an opinion about an uncertain event, never a guarantee of the result.