AI Predicts Premier League Champion: Was It Accurate?

A look at what’s predictions got right and wrong during the 2023/24 Premier League season

Ben Wodecki, Jr. Editor

May 30, 2024

10 Min Read
Michael Regan/Getty Images

The English Premier League concluded recently with Manchester City winning the league. Last August, AI predicted the Sky Blues would win the 2023/24 Premier League season — but how accurate were those predictions?

AI Business previously partnered with to predict the 2023-24 season, using its machine learning model to predict the probability of each team’s likelihood of winning the title.

AI gave the Manchester City team the highest probability of winning the league. Manchester City did go on to win and became the first English side to win four Premier League titles in a row.

Liverpool, the team with the second highest probability of winning the league finished third, with Arsenal the team that ended up chasing Manchester City for the title until the final day.

Surprise package Aston Villa came in fourth, finishing above top-six regulars like Tottenham and Manchester United, despite the AI giving them only a 13% chance of securing a top-four finish.

Here’s a look back at the 2023/24 Premier League season, looking at what the AI got right and wrong.


Final Score? “Not Bad”’s predictions largely got the 2023/24 Premier League season right: Manchester City winning and three correct teams in the four.

“The top of the table was quite in line with the model's expectations,” said’s Victor Kristof, who is also the founder and CEO of DemoSquare.

Related:AI Picks Manchester City to Win the Premier League

The results look a bit different at the bottom. The AI model predicted Burnley would have a strong season. In reality, Vincent Kompany’s Clarets finished second from bottom, his side having struggled to adapt following their promotion to the Premier League.

For relegation,’s model had marked Bournemouth and Nottingham Forrest, but the clubs ended up finishing 12th and 17th, respectively.

“In terms of looking at the outcomes, the lower part of the table was a little bit more surprising,” said’s Lucas Maystre, who is also a research scientist at Spotify.

Villians Are Anomalies


There were several surprises this season. Premier League fans witnessed Unai Emery turn Aston Villa into a force to be reckoned with since he took over in October 2022.

Aston Villa exceeded the AI model’s expectations, but outliers will always appear.

Leicester City, for example, famously won the Premier League in 2015/16, a huge shock considering the bookies had them at odds of 5000/1 to lift the trophy.

“It's kind of expected that one or two such things are going to happen every season,” Maystre said.

Another of the AI’s predictions was that Wolverhampton Wanderers were going to struggle. Their manager Julien Lopetegui departed on the eve of the Premier League season over a lack of transfer funds with’s model suggesting they would struggle. 

Related:Google DeepMind, Liverpool Develop AI System for Football Tactics

However, under the steady stewardship of Gary O’Neil, Wolverhampton finished a respectable 14th despite having a low net spend.

In cases like Wolves changing manager, for example, how does an AI prediction model account for such changes? runs live predictions every week on their website, factoring in changes like recent momentum and managerial changes.

“We have this dynamic rating for a team that takes into account changes that have been happening,” Maystre said. “That’s the difficult thing when you're predicting one year ahead. The AI hedges its bets, that's why we saw a broad range of teams listed as plausible contenders for a top-four finish, for example.”

How Does it Work? created a statistical machine learning model that uses data from previous season performances, the team modeled the outcome of matches to figure out where teams would end up.

It employs a method dubbed “Kickscore” – a rough measure of a team's performance over time. Kickscore encodes how a model "sees" a team based on the data it has. The website explains: “As it is dynamic, it is possible to interpret how a team's strength evolved over the past decades.”

Related:Researchers: Deep Learning can automate, enhance soccer analysis

Predicting the Inevitable


Premier League fans expect Manchester City will win the league. They’re the richest club on Earth and have one of the greatest soccer coaches ever at the helm of a roster of superstars.

How impressive is it if an AI can predict what every fan knows will happen every season?

“Any sensible predictor would pick the favorite to win,” Maystre said. “What's challenging is being able to understand what are the odds of an outlier. When we discussed these predictions last August, I thought the 61% to Man City was bold. I would prefer our model to have given a slightly lower probability, not just because I’m an Arsenal fan, but because that was a lot of eggs in one basket.”

“We're not making a prediction in the sense that we're saying Manchester City will win, we’re just estimating the probability of this outcome to happen,” Kristof said. “A model would never say Leicester City would win the league, that’s impossible, but it is interesting to look at the odds of that outcome actually happening.”

Manchester City winning the league was no surprise, but the pair suggested that in the 2023/24 season, no real surprises occurred.

“The AI is trained to reason, to learn and make predictions and minimize surprises. If it puts a very high probability on one thing and that happens, then the surprise is minimal. But if it puts a high probability on one thing and then something else happens, that's a big surprise, Maystre said. “If Man City had been relegated, something that our model would have given probably a one in a thousand-year probability, that would have been maybe a big red flag for our model.”

Manchester City being relegated might seem like an extreme example but there’s some logic there. The club faces 115 charges over allegedly financial irregularities, something the club vehemently denies. If found guilty, they could face a hefty points deduction.

Financial implications had a major impact on the Premier League table this season, with Everton and Nottingham Forrest both losing points over breaching the league’s Profit and Sustainability Rules (PSR).’s model did not account for these off-field issues, but with Everton facing further financial uncertainty and newly promoted Leicester City facing the prospect of starting the season with a points deduction for PSR breaches, these could become a major consideration ahead of making future league predictions.

Euro 2024: Analyzing Leagues vs. Knockout Tournaments


The Premier League is over, but the soccer continues this summer as the European Championships head to Germany.

The team plans to use its AI predictions model throughout the upcoming tournament.

Unlike the Premier League, the Euros are a knockout tournament. Just one loss can decide a team’s fate and in the knockouts, anything can happen — like England’s 2016 exit to lowly Iceland.

But is it easier to predict a league table compared to a knockout tournament?

“For a league, we have more matches to train the model on,” Kristof explained. “For an international competition, it’s much more scarce. We can train only using the qualifying rounds or friendly games, which are not always very representative of what's happening during a competition."

The project began around the time of the 2016 Euros and was used to accurately predict the probability of games at the World Cup in Russia two years later.

“One thing that's different with a league is that because every team plays so many games, the position and the table tend to be a little bit more stable as a function of slight variations in team form over the year compared to a knockout tournament where just a single loss can knock out a team,” Maystre said. “You’d never see our Euros model give a 61% chance of winning to a single team. The highest chance you would give to any one team would be around 20 to 30%.”

What’s Next? Generative AI, XG

redictions themselves have become a mainstream part of the soccer viewing experience through xG or expected goals — a metric that assesses the quality of scoring chances based on factors including the position from which a shot at goal is taken.

XG has gone from an obscure piece of information reserved for soccer nerds and data analysts to a concept that fans interact with and understand. XG stats are routinely displayed during live games.

AI has the potential to take xG predictions one step further.

“XG takes away some of the randomness of predictions,” Maystre explained. “Extending this idea to every event is exciting. Every single decision a player makes on the field moves the needle one way or another.

“If we started having a really good understanding of the importance of a defensive interception or a run that creates space for another play in a data-driven way that's powered by AI insights, they would be great for spectators because it will bring a lot of insights into the game.”

Since made its Premier League predictions last August, not much has changed in the way of improving prediction modeling.

But amid the generative AI wave, could this have an impact on the future of AI’s ability to predict the probability of sporting events?

The team suggests generative AI could help compile previously overlooked data into their prediction models

“Generative AI will just be much better at mining sentiments on Twitter, like people's belief about a game or rumors about injuries, things we struggle to currently feed into our model because it’s unstructured and scattered all around the internet,” Maystre said. “I feel that eventually with progress in large language models, for example, these things will find their way into sports prediction models like ours.”

Away from generative AI, the team spoke about the potential for computer vision to analyze games in real-time to track players, the movement of the ball and a team’s shape and strategy to provide a more accurate prediction of a potential result.

Real-time probability predictions are already displayed during Premier League broadcasts — but they aren’t always accurate.

During the 2023/24 season, for example, Bournemouth found themselves 3-0 down against Luton Town in March. Oracle provides the Premier League’s live prediction probability and after the third goal, Bournemouth was given a 97.6% chance of losing the game.

Bournemouth went on to win 4-3 in a match that saw their opponent have the highest win-probability percentage ever recorded for a team that didn't end up winning in a Premier League game.

Not even AI in its current form could have predicted Bournemouth would win that game. But with innovations in computer vision and generative AI, future sports prediction models could be able to identify dramatic swings in games. And it’s something that is looking at.

“We explored with regards to some ideas to make predictions in real-time during games, Kristof said. “That would be the next stage for us, to track what's happening during the game into adaptive probabilities of the final score. It’s definitely something we’d like to deploy at some point.”

Manchester City is already the bookie's favorite to win the 2024/25 season in what would be Pep Guardialo’s final season and a fifth consecutive title. But AI could soon factor in disparate data sources, picking up on something the bookies might have missed and identify an unlikely winner.

About the Author(s)

Ben Wodecki

Jr. Editor

Ben Wodecki is the Jr. Editor of AI Business, covering a wide range of AI content. Ben joined the team in March 2021 as assistant editor and was promoted to Jr. Editor. He has written for The New Statesman, Intellectual Property Magazine, and The Telegraph India, among others. He holds an MSc in Digital Journalism from Middlesex University.

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