Introducing the Opta Power Rankings, the new way to tell which club is actually “by far the greatest team the world has ever seen.”
During every transfer window, players move all over the world to their new teams. Between divisions. Between countries. Between continents. Measuring the ability of these teams across the global footballing system is a challenging task.
Comparing teams that play in the same domestic league is somewhat straightforward given the number of times they play one another, but how do we compare teams that have never played each other and may never do so? How do we know how good Lionel Messi’s Inter Miami are compared to Cristiano Ronaldo’s Al Nassr?
You would have to watch an awful lot of football to even begin to understand the quality of each team.
But don’t worry, we’ve now got a model that does all the hard work for you.
What Are The Opta Power Rankings?
Opta’s Power Rankings is a global team ranking system that assigns an ability score to over 2,500 women’s and 13,500 men’s domestic football teams on a scale between zero and 100, where zero is the worst-ranked team in the world and 100 is the best team in the world.
The Power Rankings are updated daily and currently rank teams from 190 different countries and over 600 unique domestic leagues, providing a truly global rating system in men’s football.
The latest Power Rankings, as of July 2026, rank Arsenal as the best men’s team in the world. Each team is ranked relative to Arsenal’s rating of 100, with Bayern Munich (99.96) close behind them before a drop to the other teams in the top 10.
On the women’s side, there is a larger gulf between our top-rated team, Barcelona, and the next best team, Bayern Munich (95.95).
How Can We Use the Opta Power Rankings?
Beyond the ability to simply rank or compare teams across the world, the Opta Power Rankings can be applied to several different scenarios.
Let’s look at the FA Cup from last season. By comparing the relative ability of each team in the Opta Power Rankings, we can quickly find that the underdogs won 42 out of the 123 fixtures in the FA Cup in 2025-26.
Macclesfield recorded the statistically most impressive result with a 2-1 home victory over Premier League side Crystal Palace in the third round. Five divisions and 5,789 global ranking spots separated the two teams on the day, but try telling that to The Silkmen. The magic of the cup lives on.
That’s a nice feel-good story but what about a more analytical angle? The Opta Power Rankings give us a new way of quantifying the strength of the opposition in a game. We can look at the goals scored by the highest scorers in the top five European leagues last season and compare the proportion of them that were scored against top-ranked opposition:
It’s clear to see that the strength of the Premier League is reflected in these numbers with Erling Haaland and Igor Thiago scoring a high proportion of their goals against opposition teams that were ranked in the top 100 in the world at the time.
To make it a fairer comparison, we can compare their scoring rates relative to the time they played against teams from both inside and outside the top 100 teams.
Despite facing top opposition every week in the Premier League, Brentford’s Igor Thiago scored 1.21 goals per 90 against stronger opposition compared to 0.27 goals per 90 against weaker teams. What’s the opposite of a flat-track bully?
What about a quick way of scouting youngsters around the world?
Few metrics are more powerful than minutes played in football. Managers placing trust in young players that they see in training all week can often give more insight into their ability than a short cameo at the weekend.
The young players with the most minutes in the 2025-26 season for teams currently ranked in the top 100 highlights a mixture of names. Some of the more established players you would expect. Others you may not have heard of yet.
These examples just scratch the surface of what we’re able to analyse with Opta Power Rankings, though.
They can be used to compare the quality of different leagues, or to create an exchange rate for metrics if players are transferring between leagues. You could quantify the strength of different Champions League groups. You could see who the most in-form teams are every month. Or maybe how a team or league’s rating evolves over time.
How Do We Calculate the Opta Power Rankings?
Now time for the more technical stuff.
The Opta Power Rankings utilise a hierarchical Elo-based rating system to measure the strength of each team based on results and, when available, expected goals (xG).
The Elo rating system is a skill score that has been adapted to many sports since its creation for chess player ratings, including the official FIFA world rankings for both men and women.
The Elo algorithm used here analyses match results from over 260,000 women’s and 3,300,000 men’s games since 2010 and 1990 respectively, assigning a rating to each team that is comparable across leagues, countries, and continents.
Note that the men and women’s algorithms are run independently and therefore you cannot compare ratings between them.
Elo Rating System
The aim of the system is simple. After each game, rating points are exchanged between the teams depending on their performance. Whatever the home team gains in Elo, the away team will lose the exact same value of Elo (or vice versa). If the home team gains 20 Elo points, the away team will lose 20 Elo points.
The difference in ratings between the two teams serves as a predictor for the outcome of a match. Each team’s Elo rating will update on a game-by-game basis based on the likelihood of each team winning and the actual outcome of the match. In simple terms, the following tends to happen in the traditional Elo rating system:
If the higher-rated team wins, fewer points will be exchanged. If the lower-rated team unexpectedly wins, more points will be exchanged. If the match ends in a draw, the lower-rated team will gain a few points. The greater the margin of victory, the greater the number of points exchanged is. Remember whatever one team gains, the other team loses!However, actual scorelines do not always reflect the underlying quality of a team’s performance. A team can dominate a match on chances created yet still lose due to poor finishing or bad luck. To address this, we also incorporate xG into our rating system.
xG Enhanced Elo
Instead of relying solely on the actual scoreline, our Elo rating system also uses xG to generate a probability distribution over all plausible scoreline outcomes based on the chances created by both teams. For each possible scoreline, the corresponding Elo change is computed and then weighted by the probability of that scoreline occurring. This produces an xG-based Elo change that reflects what could have happened based on chance creation.
Our final Elo adjustment is then taken as a weighted average of the traditional Elo change (based on actual score) and the xG-based Elo change. We optimised this weighting to maximise the predictive performance of our Elo ratings on future games, which results in our algorithm taking 80% of the Elo change from the actual scoreline and 20% from xG (when available). When no xG data is available for a match, we simply take 100% of the Elo change from the actual score.
Example
Let us demonstrate our xG enhanced rating change with an example match between West Ham and Manchester City. In this game West Ham hold on for a 1-1 home draw, which due to their lower rating than City increases their Elo rating by 4.65 points using the traditional goals-based Elo method. Manchester City consequently lose 4.65 Elo. However, City created significantly better chances (2.03 vs 0.54 xG) and our xG enhanced Elo takes this into account. The xG enhanced method reduces West Ham’s gain to 2.57 Elo, while softening City’s loss, providing a more accurate reflection of performance. Although these changes are relatively minor, they add up over the course of thousands of games played.
If a team wins but created fewer chances (lower xG), fewer points will be exchanged compared to traditional Elo rating system. If a team loses but created better chances (higher xG), fewer points will be lost than traditional Elo suggests. If a match ends in a draw, the team that dominated on xG will benefit, the higher-rated team loses fewer points (or even gains points), while the lower-rated team gains fewer points. The system rewards chance creation, not just the final score, a deserved performance gains more Elo even in defeat or a draw. Goals are still the most valuable commodity, at 80% of the final Elo contribution (when xG data is available).Over the long term, the Elo rating system is self-correcting. A team will gain or lose points relative to their corresponding Elo rating until the ratings reflect their true strength. If a team is undervalued, they will gain more points in each game until their strength is accurately represented by the system.
Hierarchy Structure
The biggest difficulty with comparing teams globally is that some teams have very little crossover with other leagues and countries. To account for this, we’ve used a hierarchy structure that lets Elo points circulate much quicker. This approach adjusts a team’s rating based on their individual within-league team rating, their league rating, their country rating, and their continent rating. For example, Arsenal’s final Elo rating would be a sum of four separate Elos: (Arsenal, Premier League, England, and Europe).
The team level of the hierarchy has its Elo adjusted for every game. However, the other levels are only adjusted when a game takes place between teams in different groups of that hierarchy (e.g., Premier League team vs. Championship team OR English team vs. Spanish team). If a game affects hierarchies other than just the team level, a proportion of the Elo change from the match is applied to the given hierarchy.
We only adjust the highest level of the hierarchy affected by a game (where the lowest level is the league ratings, and the highest level is the continent). For example, if Manchester City (Premier League) played against Vasco da Gama (Campeonato Brasileiro Série A), both teams’ Elo scores would be updated along with the European and South American Elo scores. We do not touch the league or country Elo.
Transformation to Power Rankings
Once we have the underlying Elo ratings, they are then transformed into the Power Rankings. To do this, we use a Power Transformation (we promise this is a technical term and totally unrelated to the model’s name) and a min-max scaler to smooth the distribution of the Elo ratings and ensure a fair application of the zero to 100 values, where zero is assigned to the lowest-ranked team in the world, and 100 is the highest-ranked team.
Summary
The Opta Power Rankings provide team ratings across the entire global footballing ecosystem. They allow us to compare teams in different countries or continents to inform decisions on a comparable scale.
Some of the applications here may seem intuitive but the key is being able to apply them at scale and to automate solutions from both a storytelling and an analytical perspective.
There will always be complexities and nuances when trying to rank nearly 13,500 teams. Leagues with no relegations, statistical outliers within leagues, domestic cup competitions, and new competitions are all examples of new hurdles that we are continuously updating the model for.
So what of Cristiano Ronaldo’s Al Nassr? They are currently ranked 86th in the world. Inter Miami, on the other hand, are down in 102nd.
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Opta Power Rankings Explained: How Our Global Football Rating System Works Opta Analyst.
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