Data experts predict Premier League finishes for Wolves, Liverpool, Arsenal, Tottenham, West Ham and rivals - gallery

Wolverhampton Wanderers head into the 2023/24 Premier League season with an almighty late shake-up following the departure of manager Julen Lopetegui and the replacement appointment of Gary O’Neil. The Old Gold have had merely days with their new head coach and a baptism of fire awaits against Manchester United.

Things could be so much worse at Molineux, though, as when Lopetegui came in relegation was almost a certainty. The fact Wolves are still in the Premier League is a huge plus and now they’ll be eager – with a talented squad – to surpass expectations.

Many are expecting Wanderers to be relegated due to the chaos that has unfolded and a lack of transfer activity, but that isn’t necessarily the case according to a new data expert model from BetVictor. An updated predicted table has emerged following simulations for each team’s 38 matches.

The data experts adopt the Monte Carlo method and at its heart is a Python-based match simulator that uses two Poisson distributions – one for the home team and one for the away team – to anticipate the number of goals each team could score in a match.

A Poisson distribution is a powerful mathematical concept that predicts the probability of a given number of events (in this case, goals) happening in a fixed interval of time. The key input to a Poisson distribution is the ‘lambda’ (λ) value, which represents the average rate of an event’s occurrence.

BetVictor’s system uses the match simulator to predict the outcomes of a full season’s fixtures. The simulation is run 10,000 times, following which the average standings and probabilities are calculated which leads to a final table and full range of ‘average’ points of each side.

With all of that explained, it’s time to flick through the pages to see what the data simulations expect the Premier League standings to look like after 38 matches played for each team. There are rather a few surprises in here, to say the least.