Models applied to football draw on four families of data: expected goals (xG), recent form, performance indicators (possession, shots, duels) and market odds. Their limit is structural: football is a low-scoring sport, and research shows that the fewer decisive events a sport produces, the more randomness weighs on its outcome.
What models look at in football
Four families of data come up again and again in work applied to football. None of them is specific to AI: what models bring is the ability to combine them at scale.
| Family | What it captures | Its limit |
|---|---|---|
| Expected goals (xG) | The quality of the chances created and conceded | Says nothing about the actual finishing of a given squad |
| Recent form | Momentum over the last few match weeks | Sensitive to the level of the opponents faced |
| Performance indicators | Possession, shots, duels, pressing | Correlation with the result varies |
| Market odds | The information aggregated by operators | Makes the model dependent on the consensus it is trying to assess |
That last row deserves particular attention: a great deal of work uses odds as an input variable, because they condense information that is hard to reconstruct any other way. The price then becomes both a point of comparison and a variable of the model, which limits how much a gap between the two can tell you.
This page stays with football: xG, low scoring, the weight of the draw. The general framework of AI applied to predictions, across all sports, is covered by AI and sports predictions.
xG, and what it does not tell you
Expected goals estimate the probability that a chance becomes a goal, based on its position, its configuration and the context of the passage of play. The total across a match gives a measure of attacking and defensive performance that is independent of the final score.
Its main value is to correct how a result is read. A team winning 1-0 with 0.4 xG against 2.1 has not produced the same performance as a team winning by the same scoreline with 2.3 against 0.5.
Its limits are just as clear: xG is an average built on a large history, it does not know the specific finishing quality of a given player, and on a single match the gap between expected goals and goals scored remains considerable.
Why football resists modelling
Football produces few decisive events, which gives randomness a structural weight.
The review by Bunker and Susnjak, which synthesises more than two decades of work on predicting results in team sport, is explicit on this point. Two figures, checked against the text of the review:
- football is the most studied sport in the whole corpus, and yet the best accuracy recorded there reaches 78%, which places it only fifth among the sports compared;
- sports ranked higher on accuracy, such as rugby union, owe part of that gap to less balanced competitions — the review insists that the depth of the competition weighs at least as much as the sport itself.
The review also cites work estimating that pure chance can be the determining factor in the outcome of up to 18% of the matches in a season.
These figures concern the prediction of results in a research setting. They are not a success rate that transfers to a bet: a bet is judged against an odd and its margin, not against a percentage of correct answers.
Responsible gambling — a model does not remove the risk. Sports betting carries a risk of financial loss and presents, according to the Autorité nationale des jeux (the French gambling regulator), the highest individual risk of problem gambling among regulated activities. A figure produced by an AI makes no bet risk-free. Set a budget, do not chase your losses and use the limit-setting or self-exclusion tools available. Learn more about responsible gambling.
The special case of the draw
Football is one of the few major sports with a third outcome, and it is the hardest one to model.
A draw has no statistical signature of its own: it corresponds neither to dominance nor to an imbalance, but to a balance that can result from very different configurations. Several pieces of work surveyed in the literature get around the difficulty by working on two classes rather than three — a simplification that mechanically improves the accuracy on display without improving the understanding of the match.
This is a practical point of vigilance when reading a figure announced by a tool: accuracy on two outcomes and accuracy on three outcomes are not comparable.
What AI brings to football — and what it does not
Three levels never to confuse.
- What it lets you do — process entire seasons of data, compare indicators from one match to the next in a homogeneous way, and follow continuously what the odds are doing.
- What it lets you estimate — a probability per outcome, with a margin of error that remains substantial in a low-scoring sport.
- What it never lets you conclude — the result of a match, still less its exact score.
This page covers the data and models angle. The human method for analysing a football match — the 1X2 market, the weight of the draw, the fixture list — is covered on football prediction, and the markets the platform actually tracks on football odds.
Back to the AI and sports predictions guide →
Back to the complete sports predictions guide →
Dig into the market
Odds movements are only part of the story. Here are the next topics to read.
AI and sports predictions
What AI can do to read data, and what it never turns into a certainty.
Understand the role of AIFootball prediction: how to analyse a match
The 1X2 market, the weight of the draw, form and the fixture list.
See the football guideFootball odds: the markets tracked by OddScore
1X2, over/under goals and the competitions covered.
See the marketsSports predictions: analyse before you bet
The complete cluster guide: data, odds, reliability, bias and the role of AI.
Read the guideFrequently asked questions
Can an AI model predict an exact score?
That is not a realistic objective. At best a model estimates a distribution of possible scores, within which the most likely score still remains very much a minority.
What is xG and what is it for?
Expected goals measure the quality of the chances created rather than the goals scored. The indicator helps separate a solid performance from a result obtained off very few chances.
Why is football hard to model?
Because it produces few decisive events. The review by Bunker and Susnjak (2022) notes that low-scoring sports carry a higher share of randomness in their results, which mechanically limits how accurate models can be.
Are odds used as an input for models?
Often, yes. Several pieces of work surveyed in the literature use odds as a variable, precisely because they aggregate a large amount of information.
Does OddScore provide tips?
No. OddScore compares the odds of several bookmakers, removes the margin and tracks how they evolve. The platform publishes no bet selections and provides no staking advice.
Sources & methodology
This page draws on an academic review devoted to predicting team-sport results with machine learning, on a recent systematic review of the use of machine learning in sports betting, and on the French regulatory framework published by the Autorité nationale des jeux.
- Report the accuracy figures exactly as the review publishes them, sport by sport.
- Never present the accuracy of an academic model as a success rate that transfers to a bet.
- Distinguish estimating a probability from naming an outcome.
- Present work that has not yet been peer-reviewed as preprints.
- Bunker, R. & Susnjak, T. (2022). The Application of Machine Learning Techniques for Predicting Match Results in Team Sport: A Review. Journal of Artificial Intelligence Research, 73, 1285-1322.
- Galekwa, R. M., Tshimula, J. M., Tajeuna, E. G. & Kyandoghere, K. (2024). A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions. Preprint arXiv 2410.21484 (not peer-reviewed).