The heady heights of last season are a distant memory with Forest having won just once so far this season.
But they are not in the drop zone with one point keeping them clear of West Ham and Burnley.
Wolves are yet to win while promoted Sunderland have enjoyed an encouraging start with three wins already on the board.
So who will be heading to the Championship at the end of the season?
The football statisticians at 247Bet have run their predictive Supercomputer to reveal how the end-of-season table will look.
The Science Behind the Supercomputer
The supercomputer adopts the Monte Carlo method, 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.
Let's break this down using an example of Manchester City playing at home against Arsenal:
In our simulator, we first compute the λ for Man City, using both xG (Expected Goals) and actual goals scored per match based on their last 19 home games. This gives us an 'attacking lambda'. For instance, with an xG of 2.24 and actual goals per match of 3.16 whilst playing at the Etihad Stadium, Man City's attacking λ is: 2.7 – both xG and goals are used to measure both underlying and actual performance.
Next, we calculate Arsenal's 'defensive lambda' based on their last 19 away games from their average goals conceded per game (0.95) and xGA (1.2) whilst on the road, giving a value of 1.08.
To tailor these λ values to the specific match-up, we adjust Man City's attacking λ by factoring in the strength of Arsenal's defence. We do this by calculating a multiplier, which is the ratio of Arsenal's defensive λ to the league average defensive λ (1.54).
Multiplying Man City's λ (2.7) by this factor adjusts it for Arsenal's specific defensive strength. This adjusted λ better represents Man City's goal-scoring likelihood against Arsenal. The same process is replicated for the away team.
In formulaic terms:
Home team λ = home team attacking λ * (away team defensive λ / league average away defensive λ)
Away team λ = away team attacking λ * (home team defensive λ / league average home defensive λ)
Our supercomputer uses the match simulator to predict the outcomes of a full season's fixtures. The simulation is run 10,000 times, following which we calculate average standings and probabilities.
