There are plenty of matches to get excited about from the English Premier League (EPL) before the final match round on May 25, 2025. Liverpool is currently sitting on top of the ladder, with an eight-point lead over Arsenal.
Other teams in the top four include Nottingham Forest and Manchester City. More teams are also expected to fight harder to find a place in the top four spots, including Manchester United, which is still far off at the 15th on the ladder.
It’s a good thing that more matches are scheduled ahead, and for fans, this also means football punters have more to wager on. These days, however, many of them seem to be interested in using Artificial Intelligence (AI) for accurate match predictions.
Data Analysis and How AI Helps
Data analysis is AI’s primary role in football betting (and even in any sport). Even without AI, everyone has been trying to deduce a match outcome based on historical data and match nuances. This may sound simple for those who haven’t dived into it, but not really.
The key metrics alone that can impact a match result can be overwhelming. Experts would recommend looking into past performance data of teams and players, historical results between two teams of players, and even advanced metrics like the following:
- Expected Goals (xG): Quantifies the quality of scoring opportunities by assigning a probability value to each shot, reflecting how likely it is to result in a goal based on factors such as short distance, angle, and type of assist.
- Expected Assists (xA): Measures the likelihood that a pass will become an assist based on the pass’s quality and the receiver’s position.
- Expected Offensive Value Added (xOVA): Isolates a player’s offensive contribution by evaluating their involvement in creating scoring opportunities.
- Expected Goals on Target (xGoT): Assesses the quality of shots on target, providing a more accurate measure of a player’s shooting ability.
- Passes Allowed Per Defensive Action (PPDA): Measures a team’s pressing efficiency by calculating the number of passes allowed by the opposing team before a defensive action is made.
- Goalkeeper Metrics: Includes metrics like Goals Prevented, which measures a goalkeeper’s ability to stop shots that are expected to result in goals
Those are just a few advanced metrics to look into. Other factors are outside of performance data, like weather and venue conditions and any recent happenings that could impact a player’s or team’s well-being.
All that sounds like a daunting task because it could be especially to those who place bets on football games based on real odds without a background in data analysis. That’s where AI can come in handy.
How Artificial Intelligence Makes It Easier
AI algorithms can find patterns and correlations from the data gathered or “fed” them. Advanced AI tools can also track real-time changes during a match, such as injuries or substitutions, and adjust their analysis accordingly.
So, platforms with AI predictions use machine learning models trained on massive datasets of historical football data. When new data is input, the model identifies relationships and trends that predict potential outcomes.
AI algorithms use the analysed data to forecast match outcomes. Machine learning models, such as neural networks and regression analysis, are used to generate predictions. These models estimate the probability of different results, including win, draw, or loss, and the number of goals scored.
TheOver.ai is an example of a sports prediction site that uses AI. “What sets our approach apart is how we evaluate and rank predictions. We maintain a rolling 7-day leaderboard of our best-performing models, giving users clear insight into the most effective prediction methods.
“Each pick is assigned what we call a Qscore (Quality Score) – an advanced metric we developed that evaluates the performance of predictive models by combining AI analysis with statistical indicators.”
Wrap Up
While AI can make it easier to process data relevant to match prediction outcomes, solely relying on its predictions is risky.
As already established, AI heavily relies on data it was given for valuable insights. However, if you’ve been following football and other sports for a long time, you know that there are still plenty of unpredictable factors that can impact a team’s performance.a
That said, human analysis is still essential. Even if many have shared their luck on using AI predictions, it should only be to enhance your knowledge or to support your analysis.
