Today’s Football Predictions

Today’s Football Stats

Today’s Football Predictions

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Power Rating (PR). At the heart of our club football forecasts are betStats365’s Power Ratings, which are our best estimate of a team’s overall strength. In our system, every team has an offensive rating that represents the number of goals it would be expected to score against an average team on a neutral field, and a defensive rating that represents the number of goals it would be expected to concede. These ratings, in turn, produce an overall PR, which represents the percentage of available points — a win is worth 3 points, a tie worth 1 point, and a loss worth 0 points — the team would be expected to take if that match were played over and over again. Given the ratings for any two teams, we can project the result of a match between them in a variety of formats — such as a league match, a home-and-away tie or a cup final — as well as simulate whole seasons to arrive at the probability each team will win the league, qualify for the Champions League or be relegated to a lower division. Before a season begins, a team’s PR are based on two factors: its ratings at the end of the previous season, and its market value as calculated by Transfermarkt (a site that assigns a monetary value to each player, based on what they would fetch in a transfer). We’ve found that a team’s market value — relative to their league’s average value — is strongly correlated with its end-of-season PR. Thus, we use these market values to infer each team’s preseason PR. As a season plays out, a team’s ratings are adjusted after every match based on its performance in that match and the strength of its opponent. Unlike with the Elo rating system used in severalothersports, a team’s rating doesn’t necessarily improve whenever it wins a match; if it performs worse than the model expected, its ratings can decline.

Forecasting matches

Given two teams’ Power Rating (PR), the process for generating 1 / X / 2 probabilities for a given match is three-fold:

  1. We calculate the number of goals that we expect each team to score during the match. These projected match scores represent the number of goals that each team would need to score to keep its offensive rating exactly the same as it was going into the match, and they are adjusted for a league-specific home-field advantage and the importance of the match to each team.
  2. Using our projected match scores and the assumption that goal-scoring in football follows a Poisson process, which is essentially a way to model random events at a known rate, we generate two Poisson distributions around those scores. These give us the likelihood that each team will score no goals, one goal, two goals, etc.
  3. We take the two Poisson distributions and turn them into a matrix of all possible match scores, from which we can calculate the likelihood of a win, loss or draw for each team. To avoid undercounting draws, we increase the corresponding probabilities in the matrix to reflect the actual incidence of draws in a given competition.