The 2026/27 season is almost here so, just in time, I have some 2026/27 predictions.
I’ve analysed the last 20 games of 2025/26 using a) an xG model (I call it the “0.25 0.25” xG model, the first 0.25 refers to all chances having a maximum possible value of 0.25 and the 2nd refers to a bonus xG value for a goal being scored) and b) market inferred ratings from the closing betting lines.
I added some adjustments for the team’s motivation in those last 20 games, compared how much European distraction they had last season vs. this season and taken an educated guess at the positive or negative influence of player transfers.
Arsenal lead the way in my predictions, but Manchester City follow closely behind. I think Arsenal are a known quantity, and 80.6 points is quite a standard prediction. On the other hand, I am more positive about the chances of Manchester City than most people I did not measure a massive impact for Rodri last season when comparing City’s performances with him on versus off the pitch; plus, Andersen looks like an excellent replacement. Furthermore, I am not overly fussed about Guardiola leaving. I have posted studies on the impact of managers changes before on this page and could not find evidence in the data that these changes had an important impact. Admittedly, those studies were for mid-season manager changes at teams more likely to cycle average managers. However, without any empirical data to demonstrate Guardiola’s ability as a manger, I remain sceptical. I have not factored in any potential punishment for Manchester City’s ‘115 charges’ case, though looking at their relegation odds of 12/1 it looks like we should.
Chelsea and Liverpool are closely matched in 3rd and 4th but sit quite a long way behind the top two. Without putting weight on market-inferred ratings from last season, Liverpool would be even lower, as I did not rate their performances very highly. Unless some big transfer activity happens (Barcola?), I don’t see a reason to be too bullish on them. Chelsea have made plenty of signings (Rogers and Lacroix being the biggest) and may well be poised to take advantage of being the best Premier League team with no European competition.
I have Manchester United as underdogs for the top four. I think Casemiro is an important loss, while Sancho and Tielemans don’t move the needle much. United fans may end up fairly disappointed once again.
At first, I was surprised to see Tottenham at 1.2 (1/5, -500) for a top half finish, but I have decided they are indeed likely to make a big jump. Van Hecke and Tonali are likely to make a big difference and no Europe should help them have a less chaotic season than the previous two.
Next comes the Brighton andBrentford pair. Owned by sports bettors, these clubs may well have by far the best analytics in football. Brentford have made one key signing – Sangare – who may have been a big factor in Lens making Ligue 1 a title race last season. I have both teams pushing 80% to fit into the top 10.
Newcastle have lost a lot of key players, but I think they can do better than many expect. Nearly everything went wrong for them last season, including plenty of injuries, yet they still were far from bad. I think they will surprise if the new signings have a good impact.
I think Villa’s key departures means they will struggle to meet their standards of last season.
Despite liking Leeds last season, I am not on their side this time (relative to betting odds). Generally, I’m not convinced goalkeepers are one of the most important position. Even if that’s wrong, it is incredibly hard to rate a goalkeeper accurately enough to call a goalkeeping signing ‘key’. In a comprehensive study I’m working on about ‘parking the bus’, I noticed Leeds standing out for some reckless and poor play while leading games.
Palace/Forest/Fulham are all fairly close, but there’s plenty of scope to be wrong here. Forest feel like the most likely of the three to have the highest ceiling and Palace feel like they have a higher ceiling than Fulham.
Sunderland occupy 17th in my rankings so it will be interesting to see how they follow up their brilliant 7th place from last season.
I rated Ipswich and Coventry quite equally in the Championship last season. I prefer Ipswich’s signings plus they have a recent season of experience in the PL so they are my pick to finish higher. I rated Hull like a bottom half team last season so it’s no secret they might struggle. The 22.7 pt total I have above gives them about a 77% chance of finishing rock bottom. Given the strength in depth of the Premier League it will not be a massive surprise if they flirt with Derby’s 11pt ‘record’.
That’s all on the PL for now. I have been working a lot on a large study on game state tactics and ‘parking the bus’ over the last few weeks so look out for that one coming soon!
Let’s review the performances & MIRs (betting market inferred ratings) of Spain and Argentina this World Cup and use them to preview one of the biggest games in football.
Figure 1
The first two rows show the performances of each 2 finalists for each of their World Cup games so far. It’s expressed as a ratio (adjusted xg for divided by total xg in match) and is schedule and game state adjusted (I see a larger game state effect in the World Cup than in any domestic league.) The average value at the end doesn’t include performances in their 3rd group game because of questions over motivation.
The 2nd group of rows (GPG) just shows the total adjusted xG in each game. As I’m using ratios, I think it’s relevant to show that the xG score wasn’t something stupidly low like 0.3-0.1 (as this looks very strong for the 0.3 in ratio terms).
The 3rd group of rows show market inferred ratings, i.e. what the expected goal ratio that the betting markets implied at kick-off. I use 3 decimal places here and 2 for the other categories because of the accuracy I think each measure holds. These figures are schedule adjusted so how I rate each team is a potential source of error. The average only uses MIRs from the last 3 knockout games as I think that is a substantial sample for this measure (and the more recent the data, the better.)
Spain’s performances have been relentlessly strong (apart from Uruguay where they had limited motivation). Argentina have also been strong but not quite to Spain’s level. It’s an appropriate Final as I rank them the 1st and 3rd best teams of the tournament (France 2nd).
Argentina posted very consistent MIRs through the tournament until the semi-final in England. In my semi-final preview I showed how I rated Argentina favourite for the game and was slightly conservative in predicting closing odds that rated both teams equally. The market did drift out a few ticks (which gave me a moderately successful pre-game trade) but I still don’t quite understand why Argentina’s MIR for that game seems so out of line. Spain’s MIR did not actually clear Argentina’s but has trended upwards and will continue to do so after their performance against France.
Figure 2
The first columns denote the weight put on MIRs and performances as methods to rate each team. These differing weight correspond to the implied match odds you can see on the right hand side. I’ve fixed the match GPG at 2.2 which corresponds with the current market line (as I haven’t focused on predicting total goals).
I generally feel 60% MIR – 40% performances is an appropriate blend and that matches with the current betting market. The more weight I put on performance the more favoured Spain become. The MIRs doubt they can really be this good and regress their level somewhat (a 5-game sample is no guarantee of anything). I can see a case siding with Spain, owing to their consistent performance level. It is, however, very debateable whether ‘their performances are consistent’ is a valid reason to put more weight on the average performance level.
Predicted closing line 2.32 (Spain) – 3.98 – 3.125 (Argentina)
This is it for the World Cup but please subscribe to this site using the white box on the home page at (syzygyanalytics.co.uk) to be notified whenever I post. I am planning my own version of this paper “Should teams park the bus?”! https://www.sfu.ca/~tswartz/papers/bus.pdf
When considering why I predicted the closing line wrong for Mexico/England and Spain/Belgium it dawned on me I *may* have had a flaw in my approach to football betting for the last 9 years. I’ve put a lot of work into developing my ratings with numerous adjustments, but it was the conversion of the ratings into match odds that I have been somewhat overlooking (which relates to the format of the rating).
I was using team ratings in terms of (adjusted) xG difference. If team A had average scorelines of 1.4-0.6 and team B had average scorelines of 2.0-1.2 I’d view both teams as equal strength given their shared goal supremacy of 0.8. I’d work out the goal line (total goals expected in the match) separately. I gravitated to ratings in terms of (adjusted) xG difference because it seemed convenient to work with. Schedule adjustments, game state adjustments, pricing up a match between two teams, it all worked well.
The problem is there may be a flaw as 2 teams posting similar xG differences may not always be of the same skill level. Team A scores 70% of the goals in their games while team B only scores 62.5% of them (Team A’s matches are much lower scoring). If, instead of expecting an average supremacy from team A of 0.8 goals per game we expect them to score (on average) 70% of the goals this is a dramatic switch-up of approach.
Consider pitting team A against team B. We’ll say they’re both from a league that has an average of 2.6 goals per game. From team A’s perspective, team B will add 0.6 goals to this game compared to team A’s average game. Using my original method, these 0.6 goals will be distributed equally to team A and team B. This means we rate both teams equally and treat the higher game total as separate.
However, now consider team ratings as the ratio of goals scored. Team A is rated better (0.7 vs. 0.625). This means when we match them up, team A will be the favourite. They’ll be even more favoured when they are awarded more than a fair share of the 0.6 goals added to the game as well.
So, is team A the better team in this scenario? I think we can say they are definitely *some* amount better. An average scoreline of 1.4-0.6 is worth almost 3 expected points more than an average scoreline of 2-1.2 over a 38-game season. Their defence is twice as good, but their attack is more than half as good. Their games are not so low scoring that they will suffer too many draws.
I need to put more effort into thinking about this. Is the expected scoreline for this game 1.3-1.3, is it 1.53-1.07 (using ratios ) or it is somewhere between the two? Do teams who score 70% of the goals in their low scoring games still score 70% of the goals in higher scoring games?
Today I’ll use ratios to preview the two semi-finals.
France vs. Spain
Figure 1(‘Perf1’ ‘Perf2’ ‘Perf3’ in the top table should say Last 32, Last 16, Quarter-Final)
All performance and MIR (market inferred performance) figures are schedule adjusted.
For the 2nd table I only used the last 2 games for MIR and excluded the 3rd game for performance (motivation questions in that game for both teams).
The more we weight towards performance the further things swing towards Spain -Spain’s performance have bene consistently strong. The markets opened with France around 2.4 and they have now drifted out to 2.63. France are coming off a very strong performance vs. Morocco. My draw price is a bit off; I will work on that.
We’re quite close to kick off now as I write this so I’m not going to predict a move from now as between 50% and 75% MIR weight seems reasonable.
Closing line 2.62-3.325-3.175
Argentina vs. England
Figure 2(‘Perf1’ ‘Perf2’ ‘Perf3’ in the top table should say Last 32, Last 16, Quarter-Final)
Ok I have Argentina as the favourite here across all MIR/Performance blends.
As I write at 6:18pm UK time on Tuesday the market is 2.81 – 3.025 – 3.175
I’m going to predict a closing line of 3.0-3.0-3.0! If I’m wrong, I’ll hopefully learn why.
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I am writing previews for the last 3 quarter finals. First up, Spain vs. Belgium then the next two will follow very soon! I will review the betting odds implied levels of the teams and as well as World Cup performances so far. I want to compare what I think the betting line should be to its current number.
Figure 1
The MIRs (market-inferred team rating) contain various adjustments. For example, for Spain’s RD3 game against Uruguay, Spain were not strongly motivated as they had already won the group. This will result in the MIR pushing down Spain’s overall rating when it is not deserved. This can be corrected by adding some small amount. Also, I improved the schedule adjustment for each team by varying the MIR rating of their opponents for each game. For example, Portugal’s lacklustre performances reduced their rating as the tournament went on so Spain are considered to play an opponent with a rating of +0.94 instead of, for example, a rating of +1.16 had they played Portugal in their opening match.
These adjustments help us make a more direct comparison of how the global sports markets rate each team. These MIRs will be very accurate as it’s only professional syndicates placing very large bets that significantly move the markets. We can see Spain are rated consistently highly across the tournament. It appears there may be a small drop off since the first two games but as they were so strongly favoured in those games (against Cape Verde and Saudi Arabia) it stresses various parts of the process (e.g., are they more favoured against these teams because there’s less likely to be a game state effect where they hold on for a narrow victory). Similarly for their round 3 game against Uruguay, it’s hard to quantify the impact of reduced motivation.
Spain’s MIR for the 2 knockout ties so far is +1.58. This means they would be expected to win by an average of 1.58 goals against an average world cup team (24th best team of 48). Spain’s performances have been consistently solid, between 1.5-2.0 goals better than their opponents each time (except for Uruguay, which has been rated upwards but even so, I think I should discount that match). Spain have not spent a minute losing so far and plenty winning which results in a game state adjustment of around +0.27. I am using a simple game state adjustment where I expect leading teams to underperform by about 0.55 goals per game. I am intentionally using one decimal place for performances and 2 for MIRs as I think it represents how much information each rating system contains (over these sample sizes).
Spain’s strong performances push their overall rating into the 1.67-1.83 region.
Belgium have had a bumpier ride. They were very ordinary against Egypt initially but bounced back with stronger performances against Iran and New Zealand. This pumped up their rating, and they went off even-money shots to win in normal time against a strong Senegal side. A poor performance (and an unlikely win) followed, resulted in a much lower MIR against the USA. The performance against the USA was a return to perform suggesting Belgium’s MIR will rise once more.
I don’t think there are any significant injuries or suspensions. This is entirely relatve – we’ve trained our ratings purely on the World Cup so far so it’s only of interest if a player who has been missing is now returning or vice versa. Amadou Onana picked up an unfortunate potentially long-term injury against the USA but had not featured heavily this World Cup regardless. Lamine Yamal missed minutes in Spain’s group games but similarly Doku (a player I rate highly) and De Bruyne have both missed games and could be used as fresh options for this game. I don’t think the potential lineups are a mitigating factor.
My ratings here show Spain as favoured by between 0.63 – 0.67 goals. Currently at 11am BST on Friday the market shows Spain as 0.85 – 0.95 goal favourites. I’m not sure how to explain the difference so I am siding with Belgium here.
Edit: I think there’s a problem with my approach properly respecting differing goal lines and total goals per game. Spain are posting these numbers on a lower total goal count than Belgium. I’ll work on that
I had a good day yesterday predicting the correct direction for price moves in both games. (Both prices moved a bit more than I expected though.)
Let’s look ahead to more Last 16 games. A lot of betting lines have already moved but there is still potential for more movement. Check the previous previews for more information on the graphics shared in this post.
Brazil vs. Norway
Brazil put up good numbers in a 1 goal win against Japan. Norway’s performances really haven’t been all that special except for the game against France. That’s an odd one as it gets up-rated quite significantly because Norway played a weakened team for that game. It seems like a negative that their performance rating is propped up by that game.
Brazil, meanwhile, has lineup concerns. Raphinha won’t be fully fit, although he has been injured for the last 2.5 games anyway. Casemiro is a small doubt and Paqueta is also out. Norway’s performances want to push me more towards the 1 goal fav side of things, but Brazil’s injuries pull me back. Brazil are currently 0.75-0.8 goal favourites which I agree with for the moment.
This is an interesting one. England are currently rated as 0.2 goal favourites which is a long way off what the MIRs and performances would imply. Of course, the home advantage and altitude of the game is a huge deal, but it’s already included in all the above data – Mexico have played ¾ of their games so far in Mexico City (and the other at 1500m in Guadalajara). England have some health concerns – Declan Rice has been playing through a lot of pain and Reece James’ world cup is over. However, there is a chance Saka could make his first start of the tournament.
Mexico have spent a lot of time winning but that’s represented by their large GS adjustment. I just can’t get all the way from the data above to England only being favoured by 0.2 goals. I’ll be aggressive here:
It’s unlikely the line will move this much but we will see.
Portugal vs. Spain
Another interesting dilemma as the two rating methods say different things. Portugal are rated like a strong team but except for against Uzbekistan they really haven’t played like a strong team. There is of course a massive question over whether Christiano Ronaldo is still good enough to play for Portugal. Spain are currently around 0.55 goal favourites which requires a high weight on the performances of Portugal so far and a low weight on the betting markets. If Ronaldo didn’t start, I think Spain would be worth imposing. I can’t put complete stock in Portugal being as poor as some of their performance, so I’ll respect the market rating of Portugal so far and predict:
4.04 (Portugal) – 3.75 – 2.06 (Spain)
USA vs. Belgium
Belgium closed an even money shot against Senegal in the last 32, something many were surprised to see. This was in part because of strong performances against Iran and New Zealand. They were very ordinary against Egypt and Senegal so it feels tricky to gauge which Belgium will show up. The USA miss Balogun after his controversial red card. The markets certainly like Belgium and I think they will again.
Predicted closing line:
2.9 (USA)– 3.55 – 2.68 (Belgium)
Argentina vs. Egypt
I ‘m pretty in line here with the ~1.4 goal current line market line. Argentina have spent a massive amount of minutes winning so far but are coming off a difficult game against Cape Verde.
Predicted closing line: 1.405 – 5.15 – 11.25 (no change)
Switzerland vs. Colombia
No disagreement here either, I think around 60% weight on MIR is about right which puts me right in line with the betting market.
Today I have 2 match previews for the last 16. I will post the rest in due course. I am previewing the games trying to predict the betting odds at kick-off as I don’t currently have a strong belief that bets can be good if they are not ahead of the closing line.
Canada vs. Morocco
Figure 1
MIR = Market Implied (team) Rating
GS ADJ = Game state adjustment (I’m seeing quite a large game state effect in the World Cup. Leading teams so far have performed at a level of around +0.2 adjusted xG per game which is massively offset by leading teams being significantly better to the tune of around +0.8 adjusted xG per game. This makes performance in leading game states around -0.6 xG per game. In domestic leagues the largest game state effect I’ve seen is around -0.4xG or -0.5xG per game.)
BC ADJ = Big chance adjustment (An adjustment for whether teams have logged an unexpectedly good or bad big chance differential relative to their underlying rating).
HOME ADV CORRECTED = As Canada have played 3 of their first 4 matches in Canada and this match will be played in Houston, Canada get downrated by 0.75 (3 of their first 4) x 0.3 (estimated home advantage for games in Canada) = 0.225 (see Home Adv Corrected) column.
My player-based team ratings (from my World Cup preview) are now ditched and I’m using a combination of previous market closing lines and performance ratings for the 4 World Cup games each team has played thus far. Previous market closing lines are adjusted if I think teams had differing motivations or a team began to under/over perform expectations enough across the World Cup such that the rating of each opponent is not correct. For example, in this case Morocco’s MIR vs. Haiti (RD3) is rated down a small amount because of waning motivation for Haiti. (As an example for the second point, Paraguay now appears to have been rated strongly against the USA but that’s because the rating of the USA has since risen. In that case Paraguay’s market inferred rating against the USA would be rated down.)
Both these teams have impressed this tournament – you can see by the MIR values increasing each round. Canada’s is increasing it just appears to have decreased in the last 32 because they lost their home advantage!
Whatever weight of MIRs and performance I use here doesn’t matter – I’m seeing Morocco as ~0.75 favs. Chadi Riad is a major doubt which is a small negative for Morocco, and they also have had 1 day less rest and played for 120 minutes against the Netherlands. Alphonso Davies might be fit enough to start for Canada which really could be a boost to Canada’s hopes.
The market has been up and down a lot but now sits with Morocco around 0.8 goal favourites. 12 hours ago, Morocco was closer to 0.65 (if the Morocco -0.75 line is decimal odds of 2.2 that’s equivalent to being 0.75 goal favourites). I think we might see Morocco drift out a little again before kick-off if my analysis is accurate enough.
My predicted closing odds are 5.5 – 3.575 – 1.86
Paraguay vs. France
Figure 2
Paraguay meets France in Philadelphia for what is expected to be a one-sided affair. Paraguay went to penalties against Germany but have had 1 more day rest than France. As you can see in these tables, Paraguay’s group stage performances were seemingly poor and their MIRs suffered as a result. Interestingly, it’s going to be extremely hot in Philadelphia today which could be a useful confounding factor for Paraguay.
Paraguay’s performance against Germany will have stopped their MIR slide although France looked electric against Sweden. Diego Gomez returns from suspension, but Omar Alderete is injured. France has lost Aurelien Tchouameni to injury.
The market is currently at around France 1.94 goal favs. I have a slight interest in Paraguay because I think the heat might blunt France’s attack enough to make 3 or 4 goal victories less likely.
My predicted closing odds for the 2-goal Asian handicap market is
Finally, the group stage has finished, one of the 495 possible permutations of 3rd place finishers that could happen, has happened, and we arrive at a 32-team straight knockout. Let’s preview the last 32!
All ratings are in the unit of expected goals per game
Betting market inferred ratings and 2026 WC performances are schedule adjusted to the best of my ability.
Check out my World cup preview for a lot more information on the player-based team ratings
My odds vs. Market odds
Figure 1
Game by game
Brazil vs. Japan
I rate Brazil as 0.78 goal favourites here which is currently quite close to the global betting markets. As you can see by the performance ratings, Brazil have been mediocre at times during this World Cup so far. Brazil likely miss Raphinha (a player I rate very highly) while Ko Itakura will likely not be fit to start for Japan. Raphinha played 1.5 games in the groups stages which influences all 3 constituent parts of my overall team rating (which have a 2x Betting Market inferred : 1x WC Performance : 1x Player Rating weighting). Therefore, no Raphinha means Brazil should be downgraded a little bit. Itakura is a loss for Japan also but not quite as significant.
I think a small lean to Japan can be justified. Just as I’m writing this Brazil have drifted from 1.78 -> 1.81. Just putting the finishing touches to this post and lineups are out and Brazil are now 1.84. Hopefully this is not the only correct lean I have in the post!
Germany vs. Paraguay
This is right in line with the betting market again for Germany vs. Paraguay. Germany are strongly favoured here to secure a last 16 spot. Paraguay’s group performances were not very impressive at all.
Netherlands vs. Morocco
2 solid teams face off here in Mexico. Morocco’ s performances have been at least on a par with the Netherlands so far, but other factors give Netherlands the edge here.
Ivory Coast vs. Norway
There’s a bit of a disagreement with each rating system but overall, I think Norway as half goal favourites is reasonable.
France vs. Sweden
It’s interesting how little France rotated in the group stage – not what you’d expect from a team with a squad like theirs! Sweden has some players to be weary of (I have had a slight Sweden lean this tournament) so if I did lean a side it’s definitely Sweden.
Mexico vs. Ecuador
I have this as an equal matchup, which is very interesting. Mexico are once again playing in Mexico City (over 2000 metres above sea level), something which is key to consider. The first two parts of the rating already include said home advantage (markets obviously consider it, and on pitch performance boosted by the home advantage) but the player-based team rating is not. If Mexico’s home advantage is worth 0.5 goals, then this adds ~0.13 on to their rating. It could be argued Mexico’s home advantage is slightly lower in this game as Ecuador are used to playing games at high altitude (because of their own high-altitude stadium in Quito).
Why are the betting markets so much higher on Mexico? If I’m wrong, I could be undervaluing just how much home advantage there is. Even the ball flies and bounces different in the high altitude so maybe Mexico are able to enjoy a huge advantage in this way (an advantage that will keep increasing as they play more games in Mexico City). My ratings favour Mexico by only 0.08 goals after the adjustment which does make me keen to get Ecuador on side we just have to be wary of how large the home advantage could really be. I’m disappointed to read Ecuador are only travelling to Mexico City the day before…
England vs. DR Congo
England are strong favourites here, I agree with the market rating of 1.7 goals. Going just be performances so far this tournament it could well be another grind for England.
Belgium vs. Senegal
An intriguing matchup takes place in Seattle on Wednesday. I have it as a bit closer to a 50-50 match than the betting markets currently depict. Belgium played fairly well in their final 2 group games and missed Jeremy Doku (a player I rate highly) for parts of that. If my betting market inferred ratings are correct, I think the betting lines will move a little bit towards Senegal
USA vs. Bosnia & Herzegovina
The USA are rated very strong favourites in the betting market, and, like Mexico, I don’t have see them as quite such a strong favourite. Home advantage is only worth around 0.1 goals on top of the overall team ratings shown here (as the player-based rating is the only rating to not already include the home advantage). Pulisic will be fit which is very important for the USA so I only have a slight preference to Bosnia.
Spain vs. Austria
This is the last-32 match with the biggest divergence between my opinion and the current opinion of the global betting markets. I have been low on Spain since pre-tournament (too many players who play for mid-table Spanish sides) and a bit high on Austria. Lamine Yamal did not feature too heavily in the group stages which is something that is not properly factored in here. My player ratings measure the difference between Yamal and Ferran Torres as about a quarter of a goal per game. Yamal only played half the minutes in the group stage meaning Spain’s performances were hurt by around 0.13 xG diff per game. As I’m 0.6 goals away from the betting line, this isn’t enough to explain the 1.5 goal line.
Portugal vs. Croatia
This is close to what the betting markets say but I can see a case for leaning towards Croatia when considering performance data only.
Switzerland vs. Algeria
There’s quite a satisfying parity between Algeria’s metrics. Switzerland have been drifting but still look a bit over-valued by my calculations.
Australia vs. Egypt
Another really close game! Again I think it’s slightly closer to 50/50 than the market currently thinks.
Argentina vs. Cape Verde
Despite not being particularly high on Argentina this tournament I am surprised to see them as mere 2 goals favourites for this game. Argentina rested and rotated a fair bit in their group games as well. A potential issue with my approach is lack of focus on the total goal lines. Is this match low scoring enough (both sides have low scoring tendencies for their level) to mean that Argentina are not 2.6 goal favourites after all.
Colombia vs. Ghana
The rating components are quite inconsistent here. I rate Ghana’s players quite highly but performances relative to Colombia leave a lot to be desired. I could see a case for backing Colombia here as it is only my player ratings keeping my ratings as close as they are (just over 1 goal apart).
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.
This was a comparison between 2 prediction methods, my metric and the Spreadex points spreads.
Figure 1
Spreadex’s prediction error was much less. We’d expect this as 6 games is quite a small sample and so it’s difficult for a pure performance metric to match a market line such as spreadex at that point. The error difference is around 1.5 pts per team.
After just 4 games in the premier league my predictions returned a surprisingly impressive result.. Projecting Leeds high and Wolves/Burnley quite low helped my projections perform the best. There will be plenty of variance involved with looking at the results from just 1 league so let’s look at a larger sample.
8 League Comparison during the November international break
Figure 3 – Average for Spreadex leagues counts only the leagues where Spreadex also had spreads for every team. This is so spreadex can be fairly used in the comparison
The MIR (Market implied team ratings) and the spreads at Spreadex are the best. This isn’t so surprising as closing lines and betting markets have a lot of knowledge and wisdom baked into the odds/spreads. I am a little disappointed the prediction error of my ratings did not beat Opta’s predictions. La Liga really hurt me here – I had better predictions in 5 out of 7 leagues but the size of the La Liga prediction error offset this. Girona and Espanyol were the main culprits, Girona’s weak early performances and Espanyol’s strong ones created bad predictions. This does not mean I was often betting against Girona and for Espanyol in the first half of the season – there are other factors involved than simply what numbers my spreadsheet spits out.
In my November article I raised a couple of other points to review come the end of the season. The first one referred to combining my rating and the MIRs to create better predictions. After testing, this did not result in better predictions -> the higher the weight on the MIR the better.
There is no sign the models overvalued the favourites, in fact the favourites over-performed model expectation (this could be due to motivation levels for these teams remaining high until the end of the season). This may add weight to the hypothesis that Ted Knutson discussed in the piece above. I.e. the models are correct, and the bookies do undervalue favourites in long term markets.
Conclusion
Betting odds are accurate indicators and a performance-based metric alone can not keep up, at least for sample of 11 games or less (remember the betting odds can also consider injuries, previous seasons, player ratings e.t.c). Opta’s ratings are fairly weak but they use a unique method (sort of an elo system I believe) which will have it’s own niche (for example comparing teams from different leagues).
If you would like to be alerted to new articles just put your email into the white box on the home page syzygyanalytics.co.uk. Next I hope to preview the world cup, I’m just not sure yet in what capacity it will be.
