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  • Predictive thinking – Shot types      

    Unfortunately, a bad weekend last time out where a lot of my line predictions were wrong (I did a PL week 27 preview).  A lot of it was bad line-up news but that couldn’t explain all of the error.  More work needed!

    In today’s quick post we are going to consider whether teams can perform different shot types more repeatably. 

    I have split shots up into

    • Foot shots assisted with passes
    • Shots assisted with crosses
    • Shots assisted by a throughball
    • Counter attacking shots
    • Unassisted shots

    I am totalling the xG in each category for each team across 16 leagues and calculating the R-square (Rsq) value for these totals between the 1st and 2nd half of the season.

    Figure 1

    Interpreting these numbers

    Foot shots assisted with a pass are the most common shot.  The proportions are approximately 1 : 0.5 : 0.06 : 0.12 : 0.59 for passes : crosses : throughballs : counter attacks : unassisted.  This is relevant because smaller sample sizes affect how variable the rsq value will be.  I hope that taking the average across many leagues has helped smooth out these anomalies. 

    Passes (foot shots)

    Pass assisted shots see the highest correlation.  This implies they are the bread and butter of good teams and may be the most skill dependent category of football.  The correlation is higher on the attacking side than the defensive side – this could imply teams are generally more in control of their attacking performance than their defensive performance.

    Crosses

    Cross assisted shots see lower repeatability between half seasons than pass assisted.  I think this makes sense – finding a teammate with a cross is more subject to randomness than shorter passes.  We do not see the same attack/defense split as we did for pass assisted shots.  I also find this intuitive – crosses are a weaker form of attack where defensive strength/ability often is an important factor.  Strong defences consistently snuff out crosses; weaker defences do not. 

    Throughballs

    The large attack/defence split for throughballs is very interesting.  It means throughballs may be largely to the credit of the attack than a result of poor defending.  (Remember the low Rsq value on the defensive side means teams conceding a lot of xG via throughballs in the 1st half of the season do not go onto concede a lot of xG via throughballs in the 2nd half of the season).

    Counter Attacks

    Correlation here shows as low on both sides.  This implies counter attacks are situational and not a play style teams can rely on too strongly. 

    Unassisted shots

    Unassisted shots being a little luck based is an idea I’ve (rightly or wrongly) used for a decade now so not too surprised to see a low correlation.  This is the only category where the defensive side shows a significantly higher correlation which makes sense owing to the fact unassisted shots often come about from defensive errors (and not attacking skill).

    I hope you found this short piece interesting today.  The idea was to help people think more about things that could help drive predictive analysis.  Please subscribe using the box on my home page if you want to read more!

  • Premier League Week 27 – can I predict the closing prices?

    Last time I did this for week 25 my predicted prices were a little less accurate than the early prices.  The biggest miss was Manchester City as money came in for Liverpool.  By kick off these teams lined up as approximately equal strength.  It seems like Doku & Gvardiol are seen as important and maybe Liverpool were seen to be finding form again vs. City losing it. City played well in the game but I don’t really believe closing prices are beatable (making money while backing at kick off).

    All the prices are derived from season performances (in depth metrics) and expected lineups for the game.  I will discuss points of interest/contention for each game.

    Aston Villa vs. Leeds

    Market price (home -draw away) (Wednesday 18th February)  1.915 3.725 4.85

    It looks like Stach and Struik are fit for this game (this is very important).  Villa have a question mark with Matty Cash.  I have Villa priced up right around the evens mark here.

    Predicted 2 3.8 4.25

    Brentford vs. Brighton

    Market price (home -draw away) (18th February)  2.05 3.925 3.875

    My biggest position of the week.  Brentford still seem a little unfashionable but should be back to full health (with Schade back) and Brighton are quite ordinary.  Brentford should go odds on here; this is one I will be disappointed to get wrong. 

    Predicted: 1.9 3.9 4.5

    Chelsea vs. Burnley

    Market price (home -draw away) (18th February)  1.245 7.9 14.75

    My spreadsheet is saying Chelsea 1.3 just based on 25/26 performance.  However, once you add Chelsea being relatively fresher (no Europe/cups this week compared to the rest of the season), Burnley having poor form (since Cullen injury seemingly) and Chelsea’s lineup between stronger than their average (Palmer, Caicedo) this season I agree with the current price.

    Predicted: 1.245 7.9 14.75

    West Ham vs. Bournemouth

     Market price (home -draw away) (18th February)  2.52 3.825 2.92

    A tricky one in my opinion.  West Ham’s recent form is substantially different from the whole season so it’s a question of how much weight to put on this.  2.7 was available early in the week on West Ham which I did not take.  Bournemouth’s performances were expecting to drop more since they lost Semenyo and had other injuries, but they have been playing respectably.  I’m sticking with the current market price.

    Predicted: 2.52 3.825 2.92

    Manchester City vs. Newcastle

    Market price (home -draw away) (18th February)  1.45 5.85 7.4

    Newcastle will be injured and tired for this game.  This is already well factored into the price as City are only a tick or two longer than their starting price vs. Fulham.  Their performance against Fulham was ok, but you can’t ignore the chances they conceded just because they were 3-0 up. 

    EDIT:  Thursday 19th.  1.515 5.35 6.7.  City have drifted after Newcastle’s performance mid-week in Azerbaijan.  I’ve been surprised about the money against City recently, I suppose poor form and the absence of Doku and Gvardiol.  City get quite a large positive game state adjustment in my ratings – they have spent so many minutes ahead.  Their performance at tied is comfortably top of the league.  Haaland is a fitness doubt which is a big issue as well.

    Predicted: 1.515 5.35 6.7

    Crystal Palace vs. Wolves

    Market price (home -draw away) (18th February)  1.64 4.35 6.3

    Wolves must be close to checking out.  They were dreadful against Forest and are potentially not that fresh for this game after an FA Cup game on Sunday and Arsenal on Wednesday.  Palace have a UECL match on Thursday so I will want to see how many players they rest before being confident of a Palace shorten.

    Predicted: 1.55 4.59 7.3

    Nottingham Forest vs. Liverpool

    Market price (home -draw away) (18th February)  4.55 4.15 1.855

    Forest are in Turkey on Thursday night which really feels like not what they need right now.  It complicates this game as Liverpool will be fresh and raring to go.  I don’t know how seriously Forest will take the EL game so I’m not making a play for now.

    Predicted: 4.235 4.15 1.87

    Sunderland vs. Fulham

    Market price (home -draw away) (18th February)  2.79 3.375 2.87

    Sunderland will still be without Xhaka but otherwise both sides are quite healthy.  Sunderland look big currently in my eyes.  I rate the performance of the 2 teams this season about equally.  The situation then tips in Fulham’s favour because of Fulham’s fresh lineup and Sunderland missing Xhaka.  They both missed players for AFCON so really the difference is just Xhaka and I can’t get all the way from Sunderland 2.25 (given my comment on the teams being equal strength) to 2.8 so I’m siding with Sunderland here.

    Predicted: 2.5 4.2 2.76

    Tottenham vs. Arsenal

    Market price (home -draw away) (18th February)  6.9 4.35 1.605

    I agree with this line

    Predicted: 6.9 4.35 1.61

    Everton vs. Manchester United

    Market price (home -draw away) (18th February)  3.825 4.02 2.03

    2 healthy sides except for Grealish.  I have a small lean towards Everton.

    Predicted: 3.67  4.02  2.09

  • How to ‘beat’ xG using simpler quantities

    Introduction

    This week I’m want to test a variety of different metrics to compare their predictive power.  I have always focused on the predictive and I think providing value as an analyst is almost solely a function of predictive analysis than descriptive.  For this reason I am less interested in a descriptive idea such as xG (a measure of how often a chance is scored) but rather what is the correct value of a chance today to predict the success of the team tomorrow.

    Most xG models use complex calculations to arrive at values for each shot.  A lot of data is used such as shot type, distance from goal, distance from centre line and even parameters such as speed of attack or defensive coverage.  This could present a problem for analysts – this data may not be readily available, or they may not have the expertise required to turn the data (e.g. X/Y coordinates) into information (xG value).  In this post I am going to be comparing the predictive performance of some simpler shot count metrics with some xG metrics.

    Today the the metrics I will be comparing are as follows

    • Understat.com npxG difference (non penalty expected goal difference)
    • Understat.com xPts

    (Expected points is how many points a team would be have scored on average for the season so far based on simulations of the xG value of their chances.)

    • Total shot difference per game

    (The difference of shots attempted and shots conceded)

    • Total shot difference per game (game-state adjusted)

    (Teams that lead have a shot difference per 90 of at least -5 (so teams that trail have a shot difference of at least +5).  This makes leading/trailing teams appear worse/better than their real level.  I compare total minutes leading to minutes trailing for each team to adjust for this.  For example, a team that has 5.5 more 90s leading vs. trailing in the first half of the season will on average have had their shot difference reduced by ~28 shots owing to game state.  As this is due to game state and not their ability I add 28 shots on to their total shot difference.  I have written a few articles so far on game state before on my blog so check them out if you want to read more:)

    https://syzygyanalytics.co.uk/2025/08/01/soccer-team-rating-iii-game-states-i/  (part 2 linked at the bottom)

    • Total shot difference (game-state & big chance adjusted)

    (A shot that Opta has tagged as a big chance counts double)

    • Total shot difference (game-state, big chance & goals adjusted)

    (A goal is worth a bonus shot)

    • Market implied team ability

    (The betting odds at kick off are potentially an excellent measure of every team’s ability as a lot of smart money has shaped these markets.  This makes the team ratings we can derive from betting odds an interesting metric for comparison)

    • My own team rating metric

    I have developed this over about 10 years and included it for the sake of comparison.

    ***Thanks to Joseph Buchdale at footballdata.co.uk for the betting market odds, whoscored for shot data and understat.com for xG data.***

    I will be measuring the performance of the above 8 metrics in 2 different ways. 

    1. How well does performance in the 1st half of the season predict goal difference in the 2nd half of the season and
    2. How well does performance in the 1st half of the season predict betting odds at kick off for the 2nd half of the season.

    Results

    Figure 1

    Figure 2

    Interpreting the results

    Figures 1 and 2 shows R2 values when each metric for the 1st half of the season is correlated to each team’s goal difference in the 2nd half of the season. 

    • xPts and npxGD hold similar value.  Personally, I slightly prefer npxGD as every chance is credited with its xG value.  xPts counts similar chances different because a chance created in a close game is worth more than a chance created in a game where one team is dominant (think about how many xPts a 0.5xG chance is worth in a game with an xG total of 3.0xG – 0.2xG vs. a game that’s 0.9xG – 0.9xG).  This is potentially a complex point and not the topic of today’s article.
    • It is up to you whether you consider figure 1 (goal difference) or figure 2 (market derived ratings) as a better indicator of team strengths.  I think goal difference is more susceptible to randomness but it’s also more authentic.
    • As the 1st half metrics are based off 17-19 games (quite a big sample), we see goal difference alone predict to a respectable level.
    • Each adjustment we make to the raw shot difference is valuable. Each extra adjustment may have diminishing returns.  This is because playing in leading game states makes it easier to create a higher proportion of big chances.  Similarly, more big chances mean more goals.  I tried making the adjustments in a different order and found the game adjustment was the best single adjustment followed by big chances then goals.
    • All 3 adjustments combined outperform the understat xG metric.
    • My metric shows what extra is still possible (although calculation complexity is on a par with xG, for example it includes shot location). 

    Conclusion

    The conclusion here is fairly clear – you can make a very solid metric with simple quantities like shots, goals, big chances, minutes leading/trailing.  This test was based off a sample of 17-19 games, if we were to use a smaller sample, xG would fare even worse against these shot count metrics.  I think xG is a fairly disappointing performer in terms of complexity vs. performance and arguably even outright performance.

       

    Please send feedback if you found this interesting today, have any questions or any requests on what content you’d like to see!

  • Premier League Week 25 – can I predict the closing prices?

    This week I am writing down my thoughts on week 25 of the Premier League.  My goal is to beat the closing lines, so let’s try and nail down what I think the true price for every game should be!  I am working on the assumption that the closing price = the true price.

    Leeds vs. Nottingham Forest

    Market price (home – draw – away) (3pm GMT Friday 6th February): 2.29 – 3.425 – 3.675

    A tricky one because there is some disagreement between previous market prices for Leeds and my ratings.  My spreadsheet is spitting out 2.07 Leeds (an advanced metric using team performance from this season and predicted lineup strength compared to averages for each team).  That doesn’t consider whether this game has a draw bias – both teams may feel like a point is a reasonable result?  Leeds have much better form in the last half of the season so if you have a read that’s signal rather than noise then this could mean Leeds should be shorter.  Leeds’ rating this season might be slightly hot and Forest’s might be slightly cold.  Leeds underperformance this season relative to shooting metrics is included a bit in my rating but possibly it deserves a little more weight.

    Quite torn but I’m going to go with a true price of the same as the market

    Predicted Closing price: 2.29 – 3.425 – 3.675

    Manchester United vs. Tottenham

    Market price (home – draw – away) (3pm GMT Friday 6th February): 1.615 – 4.95 – 5.65

    Tottenham’s injuries are a little shaky but if we can be confident that Romero/Van de Ven and Solanke are all good to start I’m predicting a small move back towards Tottenham.  Tottenham are still quite injured but have been all season so we can potentially call Spurs’ line-up ‘average’ while the added freshness of not having CL (not a factor for United who never had CL) is also key. 

    Predicted Closing price: 1.67 – 4.63 – 5.41

    Arsenal vs. Sunderland

    Market price (home – draw – away) (3pm GMT Friday 6th February): 1.245 – 7.1 – 17.75

    Arsenal minus Saka and plus Havertz while Sunderland still have no Xhaka are all relevant considerations but in the end, I have no disagreement with the current line.

    Predicted Closing price: 1.245-7.1-17.75

    Bournemouth vs. Villa

    Market price (home – draw – away) (3pm GMT Friday 6th February): 2.76 – 3.725 – 2.7

    Another difficult one as the lineups for these teams have not been consistent recently.  Bournemouth have lost Semenyo to Manchester City and Tavernier to a hamstring injury, but their last few performances have remained competitive.  Villa have numerous injury issues but also some good news like Onana is back, and Luiz/Abraham signings.  In the end I am currently going for:

    Predicted Closing price: 2.86 – 3.78 – 2.60

    Burnley vs. West Ham

    Market price (home – draw – away) (3pm GMT Friday 6th February): 3.475 – 3.675 – 2.27

    Burnley have been very poor in their last few games.  Josh Cullen appears to be a very important loss.  Surprisingly I estimate both teams to field starting lineups about level with their season averages (using transfermarkt.com player values).  This would show dramatic value on Burnley and even adjusting for Burnley’s recent poor performances I put enough stock in a few bad performances to justify the starting price we see now.

    Predicted Closing price: 3.1 – 3.62 – 2.43 

    Fulham vs. Everton

    Market price (home – draw – away) (3pm GMT Friday 6th February): 2.21 – 3.425 – 3.925

    I rate Everton slightly better and additionally think their predicted lineup is better than their average this season.  This is because of valuable African players they missed during AFCON, Branthwaite’s return and an otherwise fit lineup.  The loss of Grealish is obviously disappointing but more than made up by these factors. 

    Predicted Closing price: 2.34 – 3.37 – 3.57

    Wolves vs. Chelsea

    Market price (home – draw – away) (3pm GMT Friday 6th February): 5.4 – 4.35 – 1.705

    Some of Chelsea’s key players (Caicedo/Fernandez) have played a lot of minutes recently but Palmer should be good to start here.  Wolves race is nearly run so I have a slight preference to Chelsea

    Predicted Closing price: 6.2 – 4.3 – 1.65

    Newcastle vs. Brentford

    Market price (home – draw – away) (7pm GMT Friday 6th February): 2.15 – 3.725 – 3.775

    Newcastle are fairly injured – Gordon is probably out, Guimaraes is 50/50, Joelinton is out plus various other defenders.  Brentford have a key suspension in Kevin Schade who was sent off against Aston Villa.  Brentford are surprisingly good, so I see some value siding with them at the current prices.

    Predicted Closing price: 2.28 – 3.83 – 3.33

    Brighton vs. Crystal Palace

    Market price (home – draw – away) (7pm GMT Friday 6th February): 2.09 – 3.725 – 3.975

    Palace are recovering from their slump and face a Brighton lineup that is strong as it’s been all season. 

    I’ve changed my mind a few times on how to rate Palace here, so I’ll just settle on no movement

    Predicted closing price 2.09 – 3.725 – 3.975

    Liverpool vs. Manchester City

    Market price (home – draw – away) (9pm GMT Friday 6th February): 2.43 – 3.875 – 3.025

    Manchester City had a cup tie mid-week but were able to rest a few key players.  Liverpool’s line-up is in good shape with Salah poised to find some form again.  City are minus Doku and Gvardiol, both of whom are big misses.  I’m struggling to justify Liverpool being the price they are.  I have City provisionally priced at 2.5 which is quite far from the market price.  I hope Ruben Dias can return but it could be argued that coordinating with a new teammate (Guehi) for the first time away to Liverpool is not ideal.  I must still favour Manchester City here

    Predicted closing price: 2.67 – 3.91 – 2.67  

    I’ll review these predictions next week to see how many I got right.  I will be trading these positions on the Betfair Exchange.

    I’m also working on an ultimate metric comparison to illustrate how metrics like raw xG or shot totals compare to my more complex team rating metric.  I hope to post more on this in the next few weeks.

    You can subscribe using the white box on the home page to be notified on future posts

    Thanks for reading!

  • New Manager Bounce part 2 – another data set

    In my last article I looked for evidence that manager changes had an effect on a team’s performance. I did not see any evidence that sacking a manager had a positive effect on the teams included in that sample (5 leagues from the 2024/25 season.) This week I have done the same analysis for a new sample, namely the top 5 European leagues in the 2023/24 season.

    Results

    The thicker red lines are the average of all the thin team performance lines.

    Table 1

    Table 2

    Conclusion

    The results here add little to what was seen last time out – namely a manager getting sacked had little influence on a team’s performance. The average on the team performance plot is less pretty than for the previous sample – it stays pretty flat. As teams had not suffered a recent performance decay before the managers were changed, their performance did not regress upwards. This is a little surprising but it could be a quirk of the sample. It’s possible if I looked at goal difference it might illustrate better why the managers were removed for the teams involved in the plot. I will include that metric next time I look at managers! Overall my stance has remained the same – I am sceptical that managers have a strong link to the performance of a team.

  • Manager sackings – is the ‘new manager bounce’ real?

    Introduction

    Football managers form a large part of football culture in Europe and beyond with hirings and sacking making up a decent chunk of the column inches written on football. Similarly, they are paid handsomely, with wages of the top managers comparing well with wages of the top players.  Today I am looking at data from about 40 manager sackings in the 2024/25 European season across 5 different leagues. I want to see what the data says about how this manager upheaval influenced the performance of these teams.

    I am going to focus on two different metrics, one is team performance which considers shots, passes and goals and the other is derived by team’s betting odds at kick off.

    The 5 leagues in this study are from the English Premier League, English Championship, English League 1, Serie A and La Liga.  On the following graphs there are 40 lines representing teams that changed manager last season.  There are some teams not included because the manager change was either too early or too late in the season to make a proper comparison of the team’s performance before and after. 

    As you will see every line meets at (0,0) in the centre because the team rating is relative to the team rating at the time of the manager change and 0 refers to zero matches since the manager was fired. (The graphs are high resolution so you should be able to zoom in, if required.)

    Results

    Figure 1

    The ratings here include as many adjustments as possible, such as fixtures, red cards, rebounds e.t.c.  This thick red line (the MEAN of all 40 lines) shows many teams suffered a decline in performances before seeing their manager sacked (not a surprise).  After another 5 games team performance starts to pick up again.

    Figure 2 – (The image is high resolution, please zoom in)

    The thick red line is the mean of all teams in the sample (40 teams).

    This graph derives team ability from betting odds at kick off.  While we do see a slide in team ability in the run up to a manager sacking, this slide continues for 6 more games after a manager change.  It’s worth noting the values on the y-axis – this method of rating teams is less subject to fluctuation than the previous method that solely measured on pitch performance. 

    Conclusion

    I had a prior scepticism over the importance of most managers that has not been alleviated in this study.  While the first plot does imply a manager change has a positive influence this can’t be separated from natural variation.

    To prove this, I calculated team performance in groups of 10 games to get the following plot:

    Figure 3

    The x-axis is comparing performance between games 1-10 and 11-20 of a season for the same 5 leagues I have used for the previous games.  This means data points/team to the right of the y-axis have experienced an increase in performance for the 2nd 10 games of the season compared to the 1st 10 games.  The y-axis then compares the team performance for games 21-30 compared to games 11-20.  The correlation and the line of best fit shows that a lot of data points to the right of the y-axis are also below the x-axis – i.e. all the teams that ‘improved’ in the 2nd set of 10 games regressed by 50% in the next 10 games.

    In fact, this is really intuitive!  Consider a team having performance of some number, x, for the first 10 games, and then a performance of 0.5x for the second ten games.  What performance do we expect for the 3rd set of 10 games?  Assuming recent form is negligible we have to answer the average of x and 0.5x, so we get 0.75x. This is exactly what figure 3 demonstrates – performance change in the 2nd set of 10 games regresses in the 3rd set of 10 games. The fact the slope is slightly less than 0.5 likely relates to recent form playing a small role.

    Now let’s go back and reconsider figure 1. We can not conclude there is any evidence that manager changes are affecting team performances.  Team performance for the 10 games after a manager change almost exactly represents the team performance for the 20 games before the manager change (the data point at x=10 is the average of the data point at x=0 and x=-10). The red line simply displays a natural regression.

    The second graph also fails to show any benefit of firing a manager, in fact, it’s the opposite.  Betting odds at kick off are very efficient – I think they have summed up nearly all of the human knowledge of the strength of the 2 teams lining up on the day.  They demonstrate quite a dramatic contradiction of the perceived ‘new manager bounce’.  While this trope holds some weight in terms of results (although likely nothing to do with the manager change itself) the opinion of the smart money in the world often sees a team weaker than before.  In this specific sample the rating recovers after 14 games. It’s possible new managers experimenting with the lineup or caretaker managers with less experience result in the team experiencing a further drop in performance.

    In my opinion it is quite hard to judge a manager in terms of results as it’s hard to say what level a team should be performing at.  So many times, has a manager been credited with a fair chunk of his team’s positive fortunes moved to a different team only to disappoint.  Similarly sacked managers have seen their reputation recover elsewhere. 

    If had to choose a manger I would study his/her opinions and perspective on the game more closely than I would study his/her results. 

    Thanks for reading I hope you found this interesting today!

    Thanks to the following resources that helped me create my post today

    Transfermarkt pages on managerial changes, e.g. https://www.transfermarkt.co.uk/ligue-1/trainerwechsel/wettbewerb/FR1/plus/?saison_id=2024

    Whoscored data for advanced team data www.whoscored.com

    And Joseph Buchdahl’s extensive betting market data at www.footballdata.co.uk

    Please comment or like if you have any questions and subscribe to my blog using the white box on the home page to get notified when I post.

  • Squad value comparisons using Transfermarkt.com

    This week I have been working on incorporating player values into my algorithms that price up football games so I thought I could write a small piece with some related charts.

    Thanks to http://www.transfermarkt.com for their excellent website!

    Which teams in Europe’s big 5 leagues are over or underperforming their squad value?

    I’m using each team’s performance in their national league with a league-by-league adjustment based on each leagues’ performance in the Champions League this season (this adjustment is England 0, Spain -0.55, Italy -0.75, France -0.75, Germany -0.95.  I’m not totally confident about these adjustments but won’t be a mile off.)

    I’m also ‘correcting’ player value for age as younger players are generally not as good as their value implies while older players are better than their value implies.

    Figure 1

    The wealth of the Premier League is on show as 16 of the 20 teams this season have had a lineup valued at over €200 million while Spain, Italy, France and Germany have 4, 6, 2 and 3 respectively.

    ‘Performance level’ can be used to compare teams as it does not have individual meaning after the league strength adjustment (e.g. Bayern are just under 0.5 goals better per game than Barcelona but having a performance level of 0 on this graph doesn’t mean average in any way).

    It will be interesting to check back on some of these numbers at a later data to see if the position of teams relative to the line has changed and whether key players at overperforming clubs are more valuable (whether they are still at that club or not).

    Figure 2

    Lille have had starting line ups average less than €10m per player but have been performing very nicely in Ligue 1.  Hakon Haraldsson and Matia Fernandez-Pardo are 2 young players who may be ones to watch.

    Villarreal and Lens are also around the same area of the graph – Alberto Moleiro of Villarreal and Mamadou Sangare and Samson Baidoo of Lens may be more young players partly responsible for the current overperformance of their clubs.

    Liverpool are big underperformers; this will be in part due to the highly valued Mo Salah not getting close to matching his very high-performance levels of last season.  Real Madrid are playing ok (even in some of the games they have not won) but their position on the graph is a function of their very high squad value and their performance value not being at the level of a few others.  German teams feature quite heavily in the underperformers list partly because of German performances in Europe that imply the league is not especially strong right now (not totally confident on this).

    Premier League Squads

    I have lined up each Premier League squad by value to compare them.

    Figure 3

    I find a good way to compare to make this more readable is to look at the rank of the value of each team’s highest value player, 2nd highest value player e.t.c

    Figure 4

    Arsenal’s top few stars are not quite at the value of Liverpool’s and Manchester City’s, but their squad looks very deep and solid – they have the most valuable 5th ‘best’ player, the most valuable 6th ‘best’ player e.t.c

    Brentford are solid performers so far this year given their lack of high value key players. 

    Chelsea surprised me a bit, I thought their squad was stronger on paper than this.  I suppose they have a lot of players out on loan.  Everton may have one of the weaker benches (relative to starting 11) while Leeds looks like the opposite.

    Tottenham are quite big underperformers compared to other teams given their squad (it’s quite deep) while Sunderland are the opposite.  Evaluating player value is a difficult thing to do so it may be that some teams have mis-valued players rather than they are coached better.

    I hope to have more material using transfermarkt player values in the future so stay tuned for that.

    I hope you have a good holidays wherever you are (if you have them!) and please subscribe using the white box on the main page if you’d like to be updated whenever I post.

    Thanks for reading!

  • Premier League thoughts on all 20 teams

    Arsenal

    They really need to get fit because the Manchester City of old has remerged.

    Aston Villa

    5 games into this season I remember calculating that it was about a 1 in 500 shot for Villa to be the same strength as last season and yet have performed like they did (or worse) for those first 5 games.  Sounds unlikely right – that’s a ‘p value’ of 0.002 for the hypothesis ‘are Villa worse this season?’.  However, I believe that 1 in 500 is only really 1 in 500 in this sense if we singled out Aston Villa, started measuring from that point on and then found it was a 1 in 500 chance for luck to explain their performances.  As this discussion of a freakish opening few games could have been about many different teams, 1 in 500 starts to become less significant. 

    Villa are still receiving attention for being bad to this day as it seems their raw xG and xG derived expected points totals are still not favourable.  https://x.com/xGPhilosophy is currently saying they should be 15th ‘based on their xG performances.

    xG is a fairly noisy metric and the midfield of the league is so tight that I suspect the gap between 7th and 15th isn’t very much.  Expected points from xG may be even noisier than xG difference but that’s a topic for another time.  As they have a similar squad to last season their xG numbers arguably deserve some regression to last season, so I think purveyors of xG expecting a Villa collapse are going to be wrong with this one.  Using my own ratings (shots/passes/goals) they have now recovered from that strange start to sit as an above average league team. 

    Bournemouth

    They lost most of their defence over the summer so there was a possibility they would struggle.  This hasn’t come to fruition and they’re another strong mid table team to add to the mix. 

    Brentford

    They lost Mbeumo, Wissa and Norgaard over summer – 3 fantastic performers who played nearly every minute last season.  Things looked a little bleak for them early on but new players like Outtara/Henderson/Kayode seem to have settled in now and they’re a rock-solid Premier League side once again. 

    Brighton

    I was thinking the other day a previous version of Brighton could have been well placed to take advantage of other teams struggles.  Unfortunately, this year Brighton are looking exceedingly mid table.   Chelsea and Liverpool have had troubles but in terms of a top 4 spot there are few stand out candidates to displace them.  

    Burnley

    With no signs of their ability last season in the Championship to stop opponents scoring shots/big chances Burnley face an uphill battle with their relegation chance sitting right around 90%. 

    Chelsea

    With Palmer back they’re going to be pretty decent but still comfortably below the level of a Premier League title contender.

    Crystal Palace

    The FA Cup champions are still good but maybe not quite as good as I’ve seen some xG numbers imply.

    Everton

    Another solid mid table side

    Fulham

    It would be great for them if Antonee Robinson can get back to full fitness.  Regardless though – still a solid side.

    Leeds

    Leeds have received a lot of attention from me this season as I’ve strongly rated their chances of staying up.  I currently have the Relegation betting markets as being the most wrong of any futures market.  I have West Ham around 67% of going down and Leeds just 15% while betting markets rate it as West Ham 45%, Leeds 28%. 

    For the previous 2 championship seasons many analysts would have been surprised that Leeds did not cruise the title.  They may have some issue with style over substance?

    They have been much better home than away which is also something to keep an eye on.  I wrote about this topic recently and found about 90% of the variability of half season home/away performances is down to chance (with the remaining 10% being something real).  For Leeds this would mean they may be ~0.05 goals better per game at home than away (which is worth a few ticks in decimal odds, e.g. 1.97 vs. 1.94)

    Liverpool

    We were saying their performances were pretty ordinary when they were getting last minute winners in the last 5 games and yet they’ve been consistently even more ordinary since then.   

    Manchester City

    Wow, this team is really starting to roll – Arsenal are going to be under so much pressure.  Arsenal and City should be joint favs for the Champions League for me.

    Manchester United

    They’re mere just ok and that’s without Europe.  As teams start getting knocked out of Europe in the second half of the season they will lose that slight advantage.

    Newcastle

    Again, I think they’re a decent above average side but Newcastle fans may have been dreaming bigger.

    Nottingham Forest

    I’ve flip-flopped a few times on where Forest stand.  I think others may be in a similar position – are they on their way up to the top 10 again?  Or are they fighting relegation?

    Sunderland

    Sunderland are bottom half in performance level but looking comfortably better than at least 3 teams and similar to a bunch of others is still a strong start to a new PL era for them.

    Tottenham

    Some dreadful home performances have captured headlines so far for Spurs who were one of the harder teams to predict coming into the season.  Their performances have varied quite wildly (2nd highest variability in the league), but average it out and they are aren’t meeting league average standards so far.

    West Ham

    I think they’re poor and in big trouble (see what I said about Leeds above).  One small point of contention may be how variable their performance level has been game to game (it’s the most of any team).  The worst performance of any team this season may be their home match vs. Brentford but they also have 4 above average performances against Man United, Newcastle, Forest and Everton. 

    Wolverhampton Wanderers

    I don’t have much to add to the discourse on Wolves. I suppose even if they continue to lose they will find some motivation in trying to avoid being called the worst PL team ever (points wise).

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  • Are some grounds fortresses? Do some teams prefer to play away?

    This week I have done some analysis looking at the performance of teams’ home and away.  I want to test whether some grounds really are fortresses and some teams do better than expected when playing away.

    This is a separate conversation to home advantage which is an ever present and significant factor in every professional football match not played at a neutral ground.  Today we are considering whether some teams may enjoy more or less home advantage than overall averages.

    In order to be a good analyst on a sport like football ones needs an excellent perception of what is signal and what is noise. If teams played half their matches with white stickers on their boots and half their stickers with black stickers we would observe many teams experiencing completely different form with the each type of sticker. In this instance you should be able to see all ‘sticker based’ form would just be noise. Home and away games are similarly split 50/50 so should we consider home and away form in the same way? In my opinion an analyst who assumes everything is noise until solid proof to the contrary arrives is a better analyst than one who finds patterns and structure everywhere.

    Let’s see if we can find any evidence of teams maintaining strong home or away form.

    Analysis

    In order to test this, I am going to use goal difference and xG.

    The xG is my own shots & passes metric, but for simplicity I’ll refer to it as xG.

    I am going to use 290 team seasons (the last 3 years of the big 5 leagues in Europe) for this analysis today. Thanks to whoscored.com and Opta for the data.

    For each team I need to calculate the following:

    1. Goal difference/xG at home for the 1st half of the season
    2. Goal difference/xG at home for the 2nd half of the season
    3. Goal difference/xG away for the 1st half of the season
    4. Goal difference/xG away for the 2nd half of the season
    5. Expected goal difference at home based on average home advantage and opponents for the 1st half of the season
    6. Expected goal difference away based on average away disadvantage and opponent for the 1st half of the season.
    7. Expected goal difference at home based on average home advantage and opponents for the 2nd half of the season
    8. Expected goal difference at home based on average away disadvantage and opponents for the 2nd half of the season

    Difference between home/away performance for the 1st half of the season is given by (1-5) – (3-6) while home/away relative performance for the 2nd half of the season is given by (2-7) – (4-8).  If there is a positive correlation between these 2 results it means some of that home or away form skew is being retained and hence not the result of luck.

    For example, say for the first half of the season, a league average team A has an xGD of +0.5 per game at home and -0.4 xGD per game away.  Let’s also say they’ve faced an equivalent easier schedule at home (expected to perform at +0.1xG per game) than away (-0.1xG per game) and home advantage across the league is +0.3 xG per game.  Using the method this gives a home/away skew of (0.5 – (0.3 + 0.1)) – (-0.4 – (-0.3 – 0.1)) = +0.1xG per game.  Team A are therefore performing 0.1xG per game better at home for the first half of the season than away.  We could then do the same calculation using figures from the 2nd half of the season to calculate the teams’ home/away skew for the 2nd half. 

    Results

    Rather than show a lot of messy plots I made a table showing the slope of the relationship between home/away skew as a percentage.  The percentage shows how much of the home/away skew is retained for each item for each season or year.

    Table 1

    League by league we see quite random results (remember we are correlating home/away skew from the 1st half of the season to the 2nd half).  One season we see teams maintaining their superior home or away form through the season (positive percentages) then for another season superior home or away form apparently predicts the reverse.  This wouldn’t make any sense so must be the result of noise.

    Looking towards the bottom of the table starts to paint the picture more clearly.  I have averaged the by each each and for all 3 years. We get a slope of .119 across all the leagues which means about 12% of the home/away performance xG skew for teams in the first half of a season is retained by teams during the second half of the season.  Using the team A example from the previous part, 0.1xG at home means we could expect them to overperform at home in the 2nd half of the season by just 0.012xG per game!

    Goals however just appear to be too infrequent and random to find any correlation between half seasons, even though I have used 290 team seasons. 

    Looking between whole seasons gives some satisfying results as ~8.5% of home over/under performance is retained when using the xG metric while ~3.5% of home over/under performance is retained when just looking at goal difference.

    Conclusion

    My takeaway from this is while home/away form is largely the result of randomness, there could be rare instances that considering a team’s home/away form could have an impact. If we have a prior belief that a stadium may offer its home team extra home advantage that may be more valuable than any noisy data we have.

    Using just goals was useless over half a season but when comparing whole seasons we saw a small correlation. If your team seems to be getting betting results home or away that trend is likely just the result of randomness! 

    I believe the metric I’ve used for ‘xG’ in this article is a lot less noisy than raw xG models so I would have strong reservations about putting any consideration into home/away skew when using xG unless we are talking about home/away overperformance over a period of 1-2 years or more.

    To finish with I’ll leave this table of home over/under performance for every team. The table is split into 3 parts for display purposes. Does the position of any teams match up to your expectation?   Remember about 90% of the deviation of xG per game is likely random chance over 1 season (maybe less over more seasons) and teams at the extreme ends of the table are likely to have experienced some extra variance home or away. I may revisit the topic with more data, if you have any questions or ideas about this I would love to hear them.

    If you enjoyed this piece today you can subscribe by inputting your email address in the white box on the home page syzygyanalytics.co.uk! Thanks for reading!

  • Points Projections for 8 different leagues during the 2025 November International Break

    Today I am recording some points projections for 7 different leagues using up to 4 different methods.  All 7 leagues are around 30% complete so let’s see at the end of the season which method predicts the remaining 70% the best! 

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    The 4 different rating systems I am using are as follows:

    My ratings

    Developed by me over the last 10 years these ratings are purely using on pitch actions (passes/shots/goals) from this season.  I don’t think the 10-15 game samples for all the leagues I am looking at today are sufficient to put full stock in what they say (for example a regression to last season’s performances would add some value) but they are still very useful for comparison.

    MIR (Market implied odds)

    A market implied/inferred odds algorithm I created recently using betting odds to calculate the performance level of each team.  I’m using the closing odds (odds at kick-off) for each team for their last 4 or 5 matches with an additional factor of their performance level in their latest match (as the betting odds can not have included this yet).

    Opta

    Opta points projections from https://theanalyst.com.  I think their projections use a broader elo based system so I don’t expect their projections to keep up with the predictive performance of other systems in this specific situation.

    Spreadex

    I’m using the mid-point of points spreads for each team at this spread betting website.  They don’t have any spreads for a few of the leagues in this article.

    Let’s get started!

    Points Projections

    Leeds look plenty strong enough to survive although you must wonder if their shooting data is better than their real level and the Opta and Spreadex predictions end up the more accurate ones.  Sunderland are still rated fairly poorly despite performances. 

    My ratings are pretty close to the MIR.  PSG I have rated as a bit lower but they have not been dominating all their games but their lineups are fairly inconsistent, and they are potentially such a dominant team you wonder if they are slightly cruising at times.  Additionally they could deserve a boost for their performance levels last season so all in all I am not confident about their final tally being closer to 75 than 80.

    Injuries and lineups are a complicating factor across all leagues.  For my ratings I could ideally have included factor that compares predicted future lineup strength to the lineup strength each team has had for the season so far (I don’t have that player level analysis quite ready to go yet).  I have quite a few teams performing differently so far to market opinion here.

    Although billed by many as one of the most competitive leagues around Europe Inter really look like the team everyone is trying to catch.  Napoli are unlikely to repeat last season’s success.  Fiorentina have looked really poor on the pitch so let’s see if they can recover.

    I rate Hoffenheim’s performances strongly so far, let’s see if they can keep it up.

    Opta are showing 29.0 pts for Sheffield Wednesday which I think includes their 12pt deduction (normally the Opta predictions have a smaller difference between the top and bottom team).  The other projections ignore this deduction so I’ve removed the deduction from the Opta projection.  Ipswich have good underlying numbers so I think could be well place to get promoted back to the Premier League.

    Quite a few differences of opinion in League 1 here.  I don’t rate Luton’s or Bolton’s performances so far nearly as high as the general opinion of these teams.  Blackpool have struggled and are being slow to improve.  This will be an interesting prediction to look back on at the end of the season.

    I’m not seeing title contending level performances from Chesterfield?  Barnet haven’t been getting results so far, but they should be considered as one of the favourites for the title. 

    Interesting angles for analysis at season’s end

    • Which projections predicted the future performance of each team the best?
    • Combining my rating and the market implied rating could create a more accurate prediction – what weighting for each would have maximised the predictive ability here?
    • At The TransferFlow Justin Worrall/Ted Knutson (https://www.thetransferflow.com/p/outrights-longshot-bias) wrote an interesting piece on favourites being overvalued by models.  My current grasp of this is, as performance levels for teams can wander in both directions through the season, on average this hurts the favourites’ chances more than it will help (the chances of the favourites is hurt more by their performance dropping than it is by their performance improving.  They don’t need it to improve to have a strong chance of winning the league).  Can we see evidence of this across these projections how can we adjust for it in the future? 

    With thanks to

    Joseph Buchdahl and https://www.football-data.co.uk/ for betting odds, Opta and Spreadex for their projections and Whoscored for passing/shooting data.