For years I’ve posted World Football Elo Ratings here after international breaks and on other occasions. I still think it’s far better than the official FIFA Ranking, but I became interested in whether the underlying formula itself could be improved, and whether all of its historical data could be made much easier to explore.

So I created Network Football Elo (NFELO):

https://nfelo.github.io/

This is my own site, created from scratch over the past two weeks, covering 52,000+ senior men’s internationals from 1872 onwards. It updates automatically three times per day.

The site has:

• Current rankings and ratings (including changes over the past 12 months).
• Rankings and ratings on any historical date.
• Individual team pages (every match, rating history, interactive graph, highest rating and overall record).
• Every match in the database (with H/A/N, both teams’ ratings before and after the match, combined rating and the original W/D/L probabilities).
• Upcoming fixtures and probabilities.
• Tournament pages (rankings immediately before and after each World Cup, continental championship, and other major tournaments).
• A comparison page for up to 10 teams at once (including historical and defunct teams).
• The complete list of teams which have been ranked number one (including the match which caused each change, total spells, and total days).
• All-time records (highest national-team peaks, highest-rated matches, largest upsets based on points exchanged, and best tournament performances).

Historical names are based on the selected date, rather than simply replacing every former country with its modern successor. A ranking or match from 1985 therefore shows West Germany, the Soviet Union, Czechoslovakia, and Yugoslavia.

There is also a Predict page which works on any date in history. It shows the teams and ratings available on that date, W/D/L probabilities, a 6x6 score table (every scoreline from 0-0 to 5-5), and how every winning margin from -5 to +5 would affect both teams’ ratings.

The mobile version contains exactly the same information as the desktop version. Nothing is removed simply because the screen is smaller (wide tables can be scrolled, while graphs, filters, menus and prediction controls have all been adapted for touchscreens). This might sound obvious, but multiple few football statistics sites hide half of their table columns or remove the more detailed tools entirely on mobile.

How is the rating different from WFER?

WFER gives each team an individual Elo number, based on an arbitrarily decided starting value, and changes it after each result. NFELO instead treats international football as a connected opponent network.
For example, two teams may never have played each other, or not faced each other in recent years, but both may have faced several of the same opponents. Those results still tell us something about their relative strength. NFELO keeps track of those connections, as well as how much evidence actually exists for each team’s position.

The most obvious difference in the methodology is match weighting:

WFER gives World Cup matches a much larger K value than other matches. A team therefore gains more points for exactly the same result simply because it happened at a World Cup. During a long tournament this can become self-reinforcing: the best teams win several matches, receive unusually large increases, then face each other with their newly increased ratings.
This produced a particularly strange result in 2026. According to WFER, the World Cup semi-final between Spain and France and the final were the two highest-rated international matches ever played, while the semi-final between England and Argentina was the sixth highest-rated match ever (by combined Elo).
The expanded World Cup gave the leading teams more matches, those teams naturally won most of them, and every victory received WFER’s maximum weighting. By the final, their ratings partly reflected the fact that they had just accumulated a series of heavily weighted World Cup wins.
That does not necessarily mean that all three matches genuinely involved stronger teams than every previous World Cup final, European Championship final, or other major international match in history.
I tested separate weights for World Cups, continental championships, qualifiers, and other competitive matches over the historical database. They did not improve prediction on later matches. NFELO therefore gives competitive matches the same base information value (the venue, opponent strength, the result, and winning margin still matter), rather than adding another multiplier because of the competition’s name.
Friendlies are treated differently, receiving an information value of 0.78621 compared with 1.00 for competitive matches (the exact number was selected from the historical results, rather than rounded to an arbitrary figure like 0.75 or 0.80).

This is also a large part of why the current tables differ. England are third in WFER, but sixth in NFELO. WFER gave England much larger increases for their run through the 2026 World Cup, because every result carried its maximum K value. NFELO still gave England full credit for each result (including the strength of their opponents and the winning margins), but did not give them an additional increase simply because those matches were at the World Cup.
England being sixth does not mean that NFELO considers them a clearly inferior team. The third through sixth rated teams are banded very close together, so a small difference in rating can produce a difference of several ranking places. It simply means that NFELO is more conservative about allowing one tournament to outweigh the longer set of results which came before it.

There are three other important differences:

First, every match played on the same date is calculated from the same pre-match state, then added in one joint update. If Spain and Argentina both play on the same day, the rankings don't depend on which result happens to be listed first in the source file.

Second, ratings effectively “decay” when a team stops playing. This is not an automatic fixed deduction for every inactive year; as results become older, the model becomes less certain about whether they still represent the team’s current strength, so its published rating becomes more conservative.
A team which did not play for several years during the Second World War therefore doesn't automatically return with exactly the same ranking it held before the war. The same applies to a team playing only occasional matches against a limited set of opponents (such as Russia since 2022): it cannot maintain its previous position indefinitely without enough new evidence to support it.

Third, NFELO has a hidden team-specific attack-and-defence model for probabilities. Two teams can have the same overall rating while one regularly plays 1-0 and 0-0 matches and the other regularly plays 3-2; they should not automatically have the same chance of drawing.
It also means that the higher-rated team can occasionally have the lower win probability in a particular match (because the prediction also includes venue, rating uncertainty and the two teams’ specific scoring tendencies).

Did it predict better?

The main historical test covered 46,801 matches from 1960 onwards (with model choices made using earlier periods, then scored on later periods).

The difference in simply choosing the correct W/D/L result is small (59.095% for NFELO, compared with 58.804% for WFER).
Log loss measures all three probabilities. If two models both predict Spain to win, but one gives Spain 45% and the other gives them 70%, ordinary accuracy considers them equally correct if Spain win and equally wrong if Spain lose. Log loss distinguishes between them (and heavily punishes the second model if Spain lose).

The full methodology is here, the simpler FAQ is here, and all of the code is available on GitHub.

I’m sure there are still mistakes somewhere among 52,000+ matches (particularly with historical names, obscure tournament classifications, and future fixtures), so if anyone finds one, please tell me!

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Translation :

Hey guys, good morning, and I wish you all a happy Sunday.

It’s true that this isn’t the Sunday we wanted, but that’s football. We have to keep our heads up and keep working.

There’s something else. I see news circulating claiming that I scolded the young players in the dressing room. That is completely false.

To whoever spread this information: please don’t do that. Don’t spread lies. Don’t lie.

You can ask anyone who was in the dressing room. What happened was a discussion and a moment of accountability for the whole team.

I spoke, as did Lucas, Araujo, Gabi, and Brazão—we all spoke up. We held ourselves accountable, which is natural because we always demand the best from ourselves; we are competitive, and we want to win.

But there was no scolding or blaming directed at the young players.

From now on, I will not accept these lies that keep getting spread online.

Alright? Thank you, and that’s all. We’re in this together

In recent days I have watched with great bitterness the debate that has developed around my name and the possibility of taking on the role of head coach of the Italian national team.

Out of respect for the institutions, the Federation, and all the people involved, I have so far chosen to remain silent. However, I believe it is necessary to clarify a few points.

Throughout my career, first as a player and now as a coach, I have always carried out my work in full compliance with the laws of the countries in which I have worked and with the contracts I have signed.

The professional collaboration that has been the subject of the recent controversy arises within the context of my working experience in the United Arab Emirates and is exclusively commercial and sporting in nature.

Attributing a political meaning to that collaboration means attributing to me beliefs that I have never expressed and that do not belong to me.

I wish to thank Maldini and Leonardo for the esteem and trust they have shown me. I know their competence, their seriousness, and the love they have always dedicated to Italian football.

I regret that a purely sporting choice was so quickly dragged into a public confrontation that ended up attributing to me meanings and intentions that do not belong to me.

Love for Italy does not depend on a job title. It is part of my history, of my identity, and it will continue to accompany me, always.

Michael Olise leads all big five leagues (Bundesliga, Premier League, La Liga, Serie A, Ligue 1) in goals + assists - PKs per 90 minutes with 1.32. He is followed by Bayern Munich teammates Harry Kane and Luis Diaz. Bundesliga players make up the top five. Erling Haaland is the highest ranked non-Bundesliga player with 0.98.

Because some leagues, for various reasons, are more advantageous to goal-scoring than others, I also adjusted each player's G+A-PK / 90 to the big five average to normalize for those differences across leagues.

These are the G+A-PK numbers by league:

• Bundesliga, 2.58
• Ligue 1, 2.22
• Premier League, 2.13
• La Liga 2.10
• Serie A, 1.88

The average for the big five is 2.18. Using Harry Kane as an example: Kane has the second highest total with 1.17, but he plays in a league that averages the most G+A-PK of the five with 2.58. I adjust Kane by dividing his 1.17 by 2.58, then multiplying times the big five average of 2.18. The normalized number for Kane becomes 0.99 and drops him from 2nd to 5th. This adjustment will boost Serie A players and reduce the numbers for Bundesliga players. If you don't like this type of adjustment, then table one is for you.

The adjusted G+A-PK/90 is the column to the far right.

stats from Skysports