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Football Stats

Football Predictions

We covered with football predictions 36 leagues from all over the world.


  • UEFA Champions League
  • UEFA Europa League
  • Austria – Bundesliga
  • Belgium – Jupiler League
  • Denmark – Superliga
  • England – Premier League
  • England – Championship
  • England – League One
  • England – League Two
  • France – Ligue 1
  • France – Ligue 2
  • Germany – Bundesliga
  • Germany – 2. Bundesliga
  • Greece – Super League
  • Italy – Serie A
  • Italy – Serie B
  • Netherlands – Eredivisie
  • Norway – Eliteserien
  • Portugal – Primeira Liga
  • Russia – Premier League
  • Scotland – Premiership
  • Spain – La Liga
  • Spain – La Liga 2
  • Sweden – Allsvenskan
  • Switzerland – Super League
  • Turkey – Süper Lig


  • Mexico – Liga MX
  • USA – MLS
  • USA – USL Championship
  • USA – NWSL


  • Argentina – Superliga
  • Brazil – Brasileirão


  • Australia – A-League
  • China – Super League
  • Japan – J1 League


  • South Africa – Premier Division

The forecasts are based on a substantially revised version of ESPN’s Soccer Power Index (SPI). We have updated and adapted the SPI and transformed into Power Rating (PR) to incorporate club soccer data going back to 1888 (from more than 550,000 matches in all) that we’ve collected from ESPN’s database and the Engsoccerdata GitHub repository, as well as from play-by-play data produced by  that has been available since 2010.

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.

Forecasting seasons

Once we have probabilities for every match, we then run Monte Carlo simulations to play out each league’s season 20,000 times using those forecasts. As with our other projections, we run our Monte Carlo simulations “hot,” meaning that instead of a team’s ratings remaining static within each simulated season, the ratings can rise or fall based on the simulated matches the team plays. In effect, this widens the distribution of possible outcomes by allowing a weak team to go on a winning streak and increase its ratings substantially, or providing for the possibility that a strong team loses its first few games of a simulated season and is penalized accordingly.

League strengths

Most club football matches are played against teams from the same domestic league, but some matches — like those in the UEFA Champions and UEFA Europa League — can be played against teams from different countries.

To assess the relative strength of domestic leagues, we use recent matches played between teams from different leagues, supplemented with league market values from Transfermarkt, to assign a strength rating to every league for which we have data.

To generate these league strength ratings, we’ve set up a system where we first assume that all leagues are of equal strength and determine how far above or below expectation each league has performed over the past five years. In order, we:

  1. Run through all domestic matches in history and calculate domestic team Power Rating (PR) ratings throughout time.
  2. Look at each inter-league match from the past five years and calculate the expected score of the match based purely on each team’s domestic rating at the time.
  3. Take the difference between our expected score of the match and the actual score and run these results through Massey’s Method to find a rating for each league, expressed in how many goals better or worse than the global average that league is.
  4. Regress these calculated ratings toward market-value based ratings, weighted by how many inter-league matches we have for each league.
  5. Run through all matches in history one more time, incorporating league strengths into the predictions for any inter-league matches to improve the final team ratings.

After going through that process, our league strengths can be interpreted as a bonus (in goals) given to each team in an inter-league match.

There are club football leagues being played year-round; follow dozens of them on our club football predictions.