Introduction
Over the years I have focused on team performance metrics using passes/shots/goals to make predictive team ratings. International football is a different beast to domestic football because nations play far fewer games than domestic teams. On top of this schedule adjustments are much harder than domestic leagues as between world cups nations nearly only compete within their own continents.
Towards the end of the most recent domestic season, I started developing some individual player ratings by measuring team performance when each player is on-pitch or off-pitch. I began to feel there was more potential in this method than I had previously realised. This encouraged me to try and build individual-based team ratings for the World Cup.
The following outlines my process in order to come up with team ratings and a simulation for the winner of the world cup.
If you are not interested in the method, you may wish to skip to ‘Team Ratings’.
Methodology
Domestic Team Strength
Take the 15 players most likely to feature for each nation in the world cup and calculate the average strength of the domestic teams they play for. One of the challenges in calculating the strength of domestic teams is it requires knowing the strength of the domestic league they compete in. For the leagues that have teams in the Champions League/Europa League I can assess league strength by the performances of teams from each league in these competitions.
Calculating league strength from the CL/EL
For each match in the CL&EL I use the following formula:
Expected home team performance = Domestic league rating of home team – Domestic league rating of away team + Home advantage +/- domestic league strength of the away team. (All in units of expected goals per game)
The domestic league strength is the output we are working towards, but we also need it as input. This is typical of the nature of schedule adjustments. For this first iteration I used league ratings based on the average player market values of each league.
This adjustment is necessary as different leagues will have teams with skewed schedules. E.g. strong leagues with teams progressing deep into the competition will on average play teams from stronger leagues than weaker leagues whose teams exit earlier.
The league rating implied for each game is then Observed performance – Expected home team performance. Complete this for every match in the EL and CL will give us the league strength. I then use these improved league ratings to do a 2nd iteration of this calculation to get a final set of league ratings.
I can correlate these ratings to the average market value of the players in the league. This is shown below in figure 1.
I used the linear correlations displayed into the key in order to create the schedule adjustment for the 1st iteration as just described and also to estimate the league ability of all the leagues that do not play EL/CL.
Figure 1
Individual player impact
The average level of domestic teams that the national players compete for will give us a rudimentary ability level, but we can vastly improve it by considering the impact of each player on their domestic clubs. Some players may be far above or below the average level of the domestic club they play for. One of the most clearcut examples of why this would be necessary is Saudi Arabia. Most of the team plays in the domestic league of their home country. Some of the teams in that league have paid big money for foreign imports and therefore rate quite strongly on a global scale (by my reckoning they would not get relegated in the Premier League). This means the Saudi Arabian players are generally far from being key players for their domestic sides. If we rate the Saudi Arabian national team based only on the ability on the domestic sides the squad plays for, we will significantly overrate them.
I have a few approaches for calculating the player impacts. Arguably the best/most unique one is what I briefly mentioned at the start. I have calculated player impacts based on how their teams perform when they are on/off pitch.
This is fairly involved and includes a performance metric focused on removing noise and multiple adjustments to cover different circumstances for when players are on/off the pitch (for example average minute on/off, gs adj on/off, schedule faced on/off.) I reduced the on/off comparison by a factor equal to the variance of the performance differences. This variance will be large if either minutes on the pitch or minutes off the pitch is a small number. I also weight the calculation more to attack or defence depending on whether attacking contributions imply the player’s position is based on attack or defence. An adjustment I wanted that has proved extremely challenging is considering how strong the rest of the team was when the player is on/off the pitch. For example, a player may coincidentally miss the same games that other key players missed.
This kind of player impact analysis is only possible (for me) for leagues where advanced data is readily available online (such as whoscored.com).
Player Impact Calculations B
To calculate the impacts players had on their domestic team for leagues where less data is available, I rely on market values. I adjust these market values by age to try and correlate them more strongly to player ability. The more different the player value rating the larger the adjustment to the domestic team ability rating will be.
Initially put 75% weight on the on/off pitch playing impact and 25% weight on the player market value adjustment. This then shifts on favour of the player market value adjustment the lower the % of players each nation has for whom I have calculated on/off pitch impact.
Team Ratings
Figure 2
Here you can see Germany’s world cup squad play for the strongest average domestic teams (first data column). This is partly because quite a few of them play for Bayern Munich. They aren’t however Bayern’s best players (a lot of competition there) and you can see this in the 2nd column – they have one of the lowest on/off player impacts of the top teams.
You can see from the 2nd data column how many sides have positive player impacts. This is what we’d expect to see – the international players all improve their domestic sides. When there is minimal player impact data the player’s impact counts as zero.
Saudi Arabia’s squad containing players who are far from key players for their domestic squads is visible by the red cell in the 3rd data column. Qatar has a similar thing going on.
Figure 3
These are the most finalised ratings I have so far (tweaks always ongoing). The unit is such that if for example, France played Spain I would have France as 0.4 goal favourites (around 11/10 shots). The ratings have been edited such that the average team has a rating (expected goal difference) of zero.
Simulation Results
Next, I used some python script (with credit to google gemini) to simulate the entire world cup. One shortcoming of the simulation so far is I don’t account for high/low scoring games. Each game is assigned around 2.7 total goals. I don’t think this majorly affects the results, but it is something I want to incorporate soon.
Figure 4
- Compared to the global betting markets I’m low on Spain. Players from Sociedad and Athletic Club knock them down their team ability portion of their rating and I’m not measuring a fantastic impact for players like Rodri and Laporte in their clubs this season.
- Brazil and Germany also somewhat favoured. I have Argentina as overrated, alongside Spain.
- I have a lot of underdogs with realistic shots. The USA, Turkey, Austria, Ivory Coast and Senegal are all favoured in my simulations over global sentiment.
- Most of my disagreements are quite likely due to actual performances that the teams have put out in World Cup qualifying. For example, I am low on South American teams like Ecuador and Colombia but they looked exceptionally solid in South American qualifying. I give Ghana almost a 1% chance of winning the tournament despite them not managing a friendly win in 6 attempts. Time will tell how severe the shortcomings of my player-based only rating system are.
Betting
Outright winner
I have backed the following sides (decimal odds)
Brazil 11.0
Germany 17.0
Austria 220
Sweden 210
Ivory Coast 250
USA 90
Turkey 95
Some of these odds have changed (mostly for the worse…).
Individual matches
My current group game bets (all based on my team ratings) are as follows:
USA vs Paraguay – USA win 2.02
Australia vs. Turkey – Turkey win 1.755
Sweden vs. Tunisia – Sweden win 1.96
Ivory Coast vs. Ecuador – Ivory Coast +0.25 1.93
Ghana vs. Panama – Ghana win 2.16
As usual I expect reviewing the closing odds will be key to determining the strength of these bets.
Fifa Golden Ball
Here are my ‘highest rated players’ in the World Cup. The calculation here is the ability of the team they play for vs. their impact on/off the pitch. It’s quite a crude calculation but still a potentially useful one. To describe the meaning of the rating it’s something along the lines of ‘This player would be an average player in a team of this level (expected goal difference per game)”. Arsenal/Bayern/PSG/Man City have an approximate skill level of 1 on this scale. One issue is the players who have played almost every minute for their clubs can only have an impact rated close to zero.
Figure 5
Going off these ratings I am backing the following players to win the Golden Ball:
Raphinha 34.0
Jeremy Doku 81
Bukayo Saka 170
Conclusion
I have studied an international tournament like this before, so I expect to learn quite a few things during the tournament. I look forward to considering how much a team’s true level can differ from the sum of its parts.
It is very likely that I will learn the outright market and betting lines are very accurate already, more accurate than my ratings at least.
Thanks for reading today, I hope you enjoy the World Cup 2026.
Please get in touch in the comments below or at x.com/samh112358 about anything you’d like.
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