Welcome to FiveStat
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Data-driven EPL predictions
Correct score heatmaps, win probabilities and xG analytics for Premier League fixtures
Our correct score heatmaps display the probability of every scoreline for each upcoming fixture. The darker the cell, the more likely that scoreline, giving you an instant read on how our model expects a match to play out.
Our model breaks down every fixture into three key metrics: who's most likely to win, whether the game will go over or under 2.5 goals, and the probability of each Team keeping a clean sheet.
Our xG table gives insight to how each team is performing, beyond just points. We use data from our match prediction model to run 10,000 Monte Carlo simulations of the remaining fixtures to show the probability of every team finishing in each position.
What do these results mean?
Are these numbers actually good?
Yes - and they're harder to achieve than they look. Football is one of the most unpredictable sports in the world. A coin flip gives you 50% accuracy; a model that always picks the home team gets around 45%. Consistently beating those baselines with a statistical model is genuinely difficult.
Moneyline accuracy (~67%)
This measures how often the model correctly identifies the winner in matches that have a winner (i.e. excluding draws). Professional betting models and bookmakers typically sit in the 65–70% range for this metric. Reaching 67% on a full season of Premier League fixtures is a strong result.
Over / Under 2.5 accuracy (~57%)
The over/under 2.5 goals market is one of the most efficient in football betting - bookmakers price it very tightly. A model hitting 57%+ on this market over a full season is performing above the level needed to find consistent value against the market.
How was it measured?
These figures come from a walk-forward backtest on the full 2024/25 Premier League season (380 matches). Walk-forward means the model only used data available at the time of each prediction - no future data was used. This mirrors exactly how it works in production. Read the full methodology →
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