#12 Statsbomb Event Data – Fernandinho Replacements

Manchester City find themselves once again top of the Premier League, with the chance to retain the title for the first time in 10 years since Manchester United in 2008/09. However they also find themselves without Fernandinho, the only seemingly irreplaceable player in their squad that overflows with talent. Fernandinho has missed four Premier League games so far this season, the two at the end of December in which they lost and left the league title in Liverpool’s hands and the two most recent games which were both dominating 1-0 wins. Even if their performances were no worse off and just lacked some luck, no doubt there is nobody else in their squad who can do exactly what Fernandinho does.

Even Guardiola has commented that there is no doubt they will be looking to bring in a replacement:

“I think with the way we play we need a guy who has of course physicality, is quick in the head and reading where our spaces to attack are”


In this post I will try to scout a replacement for Fernandinho using Statsbomb’s 2018 FIFA World Cup Event data. This is a small sample size, so will only include players and their performances in the World Cup. I will define some metrics that could be used to describe the type of player that would fit the role that Fernandinho plays and identify those players that performed best during the World Cup.

Guardiola talks about physicality, quickness of thought and reading where the spaces will be to attack. It is hard to quantify those qualities, however using adapting some simpler metrics could give a good shortlist.

We know that Manchester City will have the ball a lot and want to get the ball forwards to their more attacking players in attacking areas, relying on Fernandinho to progress the ball. Using Statsbomb’s passing events, with the start and end location in x, y coordinates, I have defined a ‘Progressive Pass’ to be one that moves up the pitch more than 10m. Players who have the ability to progress the ball forwards are desired. It could be argued that we also want to only include players who progress the ball from deeper positions so as to more accurately emulate Fernandinho’s role, however we have a small sample as it is and the ability to play progressive passes is what we are looking for.

Whilst lots of players are great at passing, what makes Manchester City so special and Fernandinho so hard to replace, is their ability or willingness to win the ball higher up the pitch. Check out a previous post in the link below where I show how many more times they win the ball back in the opposition’s half. In the same vein, using Statsbomb’s ball recovery event with the x, y location I create a count of times that a player has recovered the ball in the opponent’s half. This tries to emulate the ability to win the ball back quickly after losing it and pinning the opposition back.


The combination of progressive passes and high ball recovery is used as a proxy for the type of skills that Fernandinho portrays and can be used to get a shortlist of players that perform similarly. Looking at only the players who played positions considered as central midfield or defensive midfield, the top 10 is below.

Figure 1: Midfield Progressive Passes and Opponent Half Recoveries Top 10 from 2018 FIFA World Cup

One thing to note is that these are pure counts and not per game or per 90min. It would be worth taking a look at that to account for the differences in games and minutes played. For example, Croatia making the Final and Germany getting knocked out in Group Stage is a difference of four games, so Toni Kroos making it to 2nd on the absolute list is incredible.

Initially it looks like the list makes sense, players like Kroos, Modric, Rakitic are all players who you could see being able to play in a deeper midfield role. Mascherano is also in the same mould, even more so considering he has played at Centre Back most of the time for Barcelona and Fernandinho has begun to slot in there to bring the ball out.

Those players are all 30+ years old so no better than Fernandinho in terms of potential replacements. Granit Xhaka and Marcelo Brozovic are two that are just entering their prime midfield years at the age of 26. This is where it’s important to note that when scouting, context is important and large sample sizes are encouraged. Xhaka may have the progressive passing ability and love of yellow cards, but probably wouldn’t have the discipline.

This post has looked at outlining a way to narrow down a shortlist of potential replacements for Fernandinho, the methods can be used to find similar players for any player as long as you can identify what you are looking for. Ideally you would get a much larger sample size of games and could look at a player’s contribution per game or per 90mins to get a more stable shortlist. In the future I would like to look at some unsupervised methods which don’t require you to specify or create the similar fields as I have done here.

I have included the total passing heatmaps and the recovery maps of selected players; if you want to see any players specifically from the World Cup from any position then give me a shout!

Once again, massive shout out to Statsbomb for providing the free source of event level data, it’s hard to come by and even harder to collect so it’s much appreciated!


#11 Normalizing xG Chain – Are all actions created equal?

In this post I will be taking a look at the concepts of xG Chain (xGC) and xG Buildup (xGB), why they are useful and how we can develop these concepts to get even more use from them. Both of these concepts further the expected goals (xG) and expected assists (xA) metrics, allowing the contribution of players not directly involved in a goal to be accounted for.

xG is a likelihood attached to each shot that attributed the chance of that shot being a goal. This metric is only really useful for players who take lots of shots, such as forwards.

xA is attached to a pass that immediately precedes a shot, the xA measures the likelihood that a pass will become an assist from the following shot.. This metric aims to widen the influence of the xG metric and attribution of play to the creative players who create the shots that the xG provides information for.

Both of these are intuitive and simple concepts that provide an estimate for specific actions on the pitch. Since goals and assists are key events in a match, it makes sense to focus analysis on them since they are incredibly predictive. xG and xA are very limited however, they only care about a shot and the preceding pass so don’t tell us anything about any of the play that happens leading up to there. It turns out that the majority of football isn’t just taking turns taking shots, so it would be nice to be able to do something like xG/xA for other actions on the pitch.

Just as xA is to xG; attributing the result to the preceding pass, xG Chain is to xA where it aims to do the same thing for the whole preceding possession chain. In this way you can widen the influence of xG to all players that are involved in the preceding possession. Where xG mainly highlights forwards and xA mainly highlights creative players, xG Chain aims to highlight players that make contributions to the possessions that end up with a shot. These could include your ‘assisting the assister’ players, your deep lying playmakers like Jorginho who get criticised for lack of assists or your progressive passing defenders that wouldn’t usually get the credit they potentially deserve for starting effective possessions.

Calculating xG Chain: https://statsbomb.com/2018/08/introducing-xgchain-and-xgbuildup/

  • Find all possessions each player is involved in
  • Find all shots within those possessions
  • Sum the xG of those shots (usually take the highest xG per possession)
  • Assign that sum to each player, however involved they are

You can normalise xGC per 90mins to see contributions per match, however this still highlights forwards and creative players since if they are the players getting the shots then they will get all the credit for their own shots plus any other possession chains they are involved in.

Since the aim is to highlight players that xG and xA don’t directly pick up, you can calculate xGC without including the shots and assists to get xG Buildup. This leaves all of the preceding actions to the assist and the shot, or all of the build up play as it were. By removing assists and shots, the dominance of forwards is removed and the remaining players are heavily involved in all the play up to just before the defining assist and shot. You can also normalize xGB per 90 mins to see contributions per match. Again, each player involved gets equal contribution as long as they are involved in the possession chain in some way.

xG Chain and especially xG Buildup are great metrics that highlight the contributions of players leading up to assists and shots. They allow players that don’t contribute directly to goals to make a case for their own importance. Normalising per 90 mins is an effective way to allow for reduced player minutes due to injury or substitutions, and evaluate all players on the same basis.

As great as the concepts of xGC and xGB are, there is a clear and influential flaw in the calculation when assigning the xG of the possession chain to the players involved. Each player gets equal contribution no matter how involved they were. So player A makes a simple 5 yard pass in their own half gets the same assigned contribution as player B who made the decisive through ball to a player who squared it for an open goal. Neither player would get credit in xG/xA but both would get the same xGC/xGB contribution despite the fact that player A’s contribution was potentially arbitrary and player B’s turned the possession chain from probing to penetrating and a shot on goal.

Another way to consider the contributions of each player is if you were to remove the action of that player, how likely was the possession chain to have still occurred. If you remove player A’s simple pass, it doesn’t take much for the possession chain to maintain its low threat whereas if you remove player B’s decisive through ball then it’s unlikely that the possession chain continues in the same way. In this way, player B’s contribution could be argued to be more important than player A’s.

This leads to considering other ways of normalising xGC and xGB, each method of assigning contribution and normalising will highlight different aspects of the build up.

Since you have all the information of each possession chain, you may have access to the number of passes or touches that each player contributed to the chain. If you proportion the xGC out by the frequency of passes or touches you can get a good idea of the proportion of involvement that each player has in each possession chain. For example, if a possession chain involves two players, C and D, where player C made 3 passes and player D made 4 passes with a resulting shot that has an xG of 0.7. Then player C contributed 3/7 passes so gets an xGC of 3/7 * 0.7 = 0.3 and player D contributed 4/7 passes so gets an xGC of 4/7 * 0.7 = 0.4. Since player D was involved slightly more than player C then player D gets a higher xGC. A similar calculation can be made using touches which will consider players who dribble more than just counting passes.

You aren’t limited to just counting passes or touches of the ball, you can get more creative with the allocations if you want to credit specific types of actions. You could only count progressive passes that move the ball forward by at least 10 yards, try to quantify the most important or necessary actions of a possession chain (decisive through ball/taking on a player in the box) or count the number of opposition players taken out of the game by each player involved, where ‘taking a player out the game’ may be defined as moving the ball closer to the defending team’s goal than the player.

xG Chain and xG Buildup are both intuitive and simple metrics that assign contributions to players that don’t get directly involved in taking shots or assists but are frequently involved in preceding actions to these events. On their own they can already highlight players that seem to contribute well under the ‘eye-test’ when you watch them, but they can be misleading and provide many false positives since all actions are considered equal under xG Chain.


Credit to Statsbomb and Thom Lawrence for introducing concepts and providing clear explanations and examples. They even include free data sets for FAWSL and the 2108 FIFA World Cup if anyone wants to try themselves. Check them out here:


#9 Defensive Metrics [Transitions]

No matter how good a team are at maintaining possession, it is inevitable that you will lose the ball at some point. That’s okay, it happens, it’s not something you can prevent. What you can do something about is how you decide to react. This is where transitions come into play and happen so frequently that it’s important to get them right. Every time possession gets turned over, the attacking team need to change their mentality and positioning to reflect the fact that they are now defending. When considering defensive transitions, I will look at how quickly a team can make that change and what that change actually could be.

Usually an attacking team try to set up to maximise the space on the pitch, the players will be positioned high and wide to open up space between defenders. Whilst the defending team usually try to set up more compact, to deny space to the attacking teams and keep them away from key areas on the pitch such as near the penalty area and goal. Moving between these two mindsets efficiently throughout a match will determine games, the best teams are capable of seamlessly navigating between the two. Defensive transitions are moving from an attacking state to a defensive state. Moving from having possession with an intent to score a goal to not being in possession with an intent to get possession back and not concede.

Even in Figure 1 and Figure 2, Football Manager 2019 now acknowledges transitions when creating tactics to add to their realism.

When you lose possession, there are two main ways of trying to win the ball back. You can try to win the ball back immediately, this is commonly known as ‘counter pressing’ or ‘gegenpress’ popularised by Jurgen Klopp at Borussia Dortmund and now at Liverpool. If this isn’t an option then you will revert straight to a defensive set up that aims to deny space to the attacking team in key areas, this may be in your half or just approaching the penalty area. This is usually the default option for a team, especially for lesser teams against more threatening opponents. I’ll take a look at the benefits and drawbacks of each option and when each should be used.

When trying to win the ball back immediately, it usually requires a high burst of energy in the short amount of time after losing possession to swarm the opponent. An example of this is Barcelona under Pep Guardiola and their 6-second rule, where within 6 seconds after losing the ball the Barcelona players blitz the ball and opponent with the aim of forcing an error and retaining possession as quickly as possible.

The clear benefit to this style is that if it works, you minimise the amount of time that the opposition has possession and you maximise the time that you have possession since you win the ball back so quickly. Unfortunately, that’s under the condition that you do win the ball back. If you don’t win the ball back quickly, it’s hard to maintain such a high intensity of effort and pressure so you are forced into the second option and revert to a designated structure.

In that short space of time, the main aim is to win the ball back so defensive structure may be neglected. Again, if you can’t win the ball back quickly then it may take you longer to revert back to a designated structure and a team may capitalise on this extended transition period where players may be out of position. Due to the potential negligence in structure, this type of press is usually only used when losing possession in the opposition’s half. This gives you more time to revert back to a defensive structure if you fail to win the ball back.

When you fail to win the ball back immediately, or if you choose not to even try to, then you need to have a defensive structure that you move to every time your opponent has possession. The main aim is to not concede a goal and a mechanism to do that using a structure is to try to deny space to the opposition in key areas. What you define as a key area can depend on specific matches but generally a structure is constructed to deny space in your penalty area and anywhere within shooting range on goal. Depending on where you lose the ball can reduce the options that you have. Losing the ball in the opposition’s half allows you to try to deny ball progression into your own half before the opponent even get anywhere near your goal. Once they manage to get into your half you can then attempt to stop them progressing near to your penalty area. Whereas if you lose the ball in your own half then you need to immediately assume a structure that denies space near your penalty area.

Compared to turtling, a gegenpress will require certain type of players that are capable of frequent short high intense periods. Not every team has those players so that style isn’t even an option so some teams. The potential drawbacks of failing to win the ball back and neglecting defensive structure puts more emphasis on one on one defending and so is utilised more by teams that have a higher quality of individual players. When you turtle and drop deep to deny space, you are utilising the short spaces between players to cover and as a result you don’t need high quality individuals but those individuals to work as together. It’s a tactical decision whether or not to use the gegenpress and teams that are expected to win will use it as a way to gain an advantage with little risk.


Credit to Football Manager for acknowledging transitions in their tactics page, love the development

#7 Defensive Metrics [Decision Making]

“If I have to make a tackle then I have already made a mistake.”

Paolo Maldini

It’s a famous quote I’m sure you’ll have heard, but you can hear the penny drop in every single person who hears it for the first time. One of the best defenders (if not the best) to have played football couldn’t be wrong could he. Yet defenders and defensive players are judged mainly on statistics such as number of tackles or blocks. Tackles and blocks are usually last-ditch attempts to prevent an opponent from progressing.

Defending is a constant ongoing process that is happening throughout a football match, no matter who has the ball or where the ball is on the pitch. As a collective team, and individually, every player is moving into positions that adhere to a defensive structure with an aim of conceding the least amount of goals possible. Each player will contribute to that by performing defensive actions, these are usually known as tackles or blocks. However, to perform a tackle or block first requires the opposition to have the ball in a potentially dangerous area, or rather first requires you to allow the opposition to have the ball in a potentially dangerous area. More importantly and less easy to quantify would be the actions and ability to prevent a forward getting the ball in dangerous areas in the first place.

It doesn’t seem a stretch to suggest that the something better than blocking every shot on goal is to prevent every shot being taken in the first place.

When a forward has the ball, they will have an aim in mind of what they want to achieve with their possession. There will be a hierarchy of aims ranging from scoring a goal down to retaining possession of the ball. Whilst a defender will also have an aim in mind when a forward has the ball. Their hierarchy of aims will be a version of the reverse of the forwards, ranging from not conceding a goal to winning the ball back. The immediate aim of both the forward and the defender will depend on factors such as location of the pitch, time of the game, game state and the perceived abilities of each player by each player.

For example, if the striker has the ball in the penalty area then their primary aim may be to take a shot to score a goal, whilst the defender’s primary aim may be to not concede a goal.

If the fullback had the ball in their own half then their primary aim probably won’t be to score a goal straight away, but rather progress the ball up the pitch either through midfield or down the line to the winger. If those two options are not available then they potentially need to regress their aim down to maintaining possession and recycle the ball back to goalkeeper or centre backs. In this case, the defender may be a striker or a winger who has closed the ball down, the defender’s primary aim here may be to prevent forward progression of the ball towards a more dangerous position.

Figure 1: Davies’ decision making options v Chelsea

These thought processes will be going back and forth between each player at all times throughout a match. Even whilst nowhere near the ball, these are things players need to consider at maybe a more minute level. Furthering the example above with the fullback and winger, the fullback’s aim is to ball progression and the winger’s aim is to prevent ball progression. If possible, the fullback would play the ball straight into the striker so that they could progress the ball up the pitch as far as possible as quickly as possible, however collectively the defence need to negate that as an option. Maybe the defending centre back is marking the striker tightly with the defending central midfielder also blocking off any direct pass, just enough so that the fullback doesn’t consider passing to the striker a viable option.

Figure 2: Chelsea unable to prevent Davies from progressing the ball

If the defending team sufficiently prevent efficient progress into dangerous areas of the pitch then their job is made much easier. As we can see in Figure 1 and Figure 2, Chelsea were unable to prevent ball progression, as a result they are left to defend a more dangerous situation and even resort to tackling or blocking (!).

The decisions that each player has, defender or forward, aren’t limited to just marking or blocking passing options and passing or shooting. Forwards may want to dribble past players, cross the ball from wide or even off the ball may make runs into space to receive the ball. These decisions of the forwards cause defenders to react respectively, how well they deal with the questions asked by the forwards depends on the abilities of the team and players in question.

It would be interesting to look at the decision making of defenders and forwards in different situations by counting the number of times or frequency of a decision overall and whether that depends on who they are facing or where they are on the pitch. A decision here for a forward would be a simple action such as attempt a shot, attempt a dribble, pass the ball up the line or retain possession. Whilst a defensive decision would depend on the decision of the forward, it would be interesting to see if players change their decisions significantly when playing against certain players. It could be a way to measure to what degree a defender can force a forward into uncomfortable positions and into making unfavourable decisions or decisions lower down on the forwards hierarchy of aims.

As always, any feedback or questions are welcome. These are primitive ideas and just looking to provoke thoughts of football analytics from a different perspective.


#6 Defensive Metrics [Optimal Positioning]

Even though the most tracked part of a football match is where the ball is, the most interesting things often happen off the ball. The ball is only in play for 50-60 minutes of a 90-minute match, and each individual player is only on the ball a minimal part of that time. The majority of the game is played off the ball by all players, they need to move about the pitch in relation to their teammates, the opposition and the ball. A forward can move off the ball to find space between defenders to receive a pass, whilst the defenders need to keep an eye on the forwards and track these attempts.

For a specific player, at a given point of the game, there are locations on the pitch that would be considered worse positions to be in than others. For example, if the opposition had the ball on the edge of your penalty area, you would consider your central defender to be at a worse position if they were standing by the opposition’s corner flag than if they were marking the opposition’s forward. Since there is a concept of better or worse positions, that leads to the possibility of there being an optimal position for a specific player at a given point of the game. You could also think of it such as if you were to remove a single player from the game, where would you want to replace them in the game such that you couldn’t move them to a better place.

Several factors could affect the perception of a position at a given time being better or worse. These could be physical states of the game, such as locations of teammates, opposition or the ball. They could also be non-physical, such as score, the aim of the tactics or time on the clock. Considering where the ball is and who is in control of the ball will dictate the general area where your teammates and opposition will set up. Considering the tactics and formation that you and the opposition are looking to play will dictate the general areas of where individual players will set up.

Different tactical styles, scores and time remaining will affect what the aims of a team are. Some teams, such as Manchester City, want to control the largest surface area of the pitch possible. Control of the pitch can be determined by which team is likely to get possession of the ball if the ball was located in that area. Whilst other teams are aware that they can’t afford to try to control the largest surface area of the pitch, but rather look to control the areas of high interest such as around their own penalty area. Individual players need to position themselves with appropriate distances between each other to reflect their tactical style and goal. Certain distances between certain players would be better or worse than others, so again there must be an optimal distance with respect to tactical aim. When each player achieves this optimal distance, the collective team would appear to perform optimally.

A geometric way of viewing the areas of control on a pitch would be to look at Voronoi diagrams. 

Figure 1: Voronoi Diagram Red v Blue

If we look at Figure 1 with team red against team blue, each of the polygons surrounding each player would correspond to the areas on the pitch that they control. If the ball happened to be within their boundaries, they would most likely get to the ball first (considering each player has the same speed). This concept has been around for a while and has been made possible due to the technology available to football clubs, player tracking is everywhere nowadays and is crucial to understanding how your team is performing.

Voronoi diagrams can be used at a team level to understand structure and how well a team can transition between situations, but also is useful at the player level as you can identify which players find the most space or which players are the best at denying space.

In terms of quantifying better or worse positions of an individual player, the surface area of a player’s Voronoi region can be indicative of how well positioned they are. It is important to note that not all spaces on the football pitch are equal, controlling the areas closer to the goals area more beneficial than controlling the centre of the pitch. Perhaps a weighted surface area would be a better quantifier of control of the pitch and would be another contributor to identifying optimal positioning.


Special mention to below for their work already on Football Geometry and Voronoi Diagrams:
@Soccermatics –  https://medium.com/@Soccermatics/the-geometry-of-attacking-football-bee87e7a749
@UTVilla –  http://durtal.github.io/interactives/Football-Voronoi/

 #5 Defensive Metric Concepts [Expected Shot Block]

There are emerging metrics in football such as Expected Goals, Key Passes, Progressive Passes and even now Expected Assists. These are all measuring single events in a football match and quantifying their utility or effectiveness with an indicator or a probability of happening. These are also all measurements of how effective a player is at executing actions whilst on the ball, particularly in offensive positions such as shooting or creating shots from passes. They give us a better idea for which players and teams are most (or least) effective at offensive events. The higher your Expected Goals and the more Key Passes you make, the more goals your team is likely to score. However, there aren’t similar metrics that measure defensive contribution. This may be due to the act of contributing to goal scoring being an objective decision, with each goal scored there is a single player who scored it and it’s easy to allocate contributions. With allocating defensive contribution, it is hard to quantify the presence of a non-event. It is hard to quantify how much of an effect a player or team has on the opposition not scoring. I think that if it’s possible to quantify a sensible defensive metric of any kind then it could be as useful as any of the offensive metrics above, I will use this series to brainstorm some ideas of such defensive metrics and how it would be possible to compute them.

Expected Shot Block:

Where better to start with defensive metrics than with the clearest act of denying a goal, the shot block. This concept is in direct competition to and is inspired by the concept of Expected Goals.

For Expected Goals, each shot is given a probability of being a goal based on historical shots of a similar type. For example, a shot that is taken with the head from a cross may be given 0.1 xG whereas a shot from a counter attack inside the 6-yard box may be given 0.5 xG. It suggests that the shot from a counter is 5 times more likely to go in than the headed effort. These may not be realistic numbers but the concept stands. Some shots are more likely to go in than others depending on a number of factors including where on the pitch the shot was taken, what play led to the shot, what the shot is taken with and game state of the match.

The concept of the Expected Shot Block would be to calculate the probability that the shot is blocked by a defender. This would require more information than just the event data of the shot, it would require the knowledge of the presence of a defender and how likely it is that a defender makes a block in a similar situation based on historically similar shots. The time this decision is made would be at point of contact of the shot. Based on shots from the past, you can categorise them into similar categories as that of Expected Goals but with the added factor of the presence of a defender or defenders between the ball location and the goal. The ball location and the two posts of the goal create a triangle and if there are any defenders in this area then the shot would be identified as having the potential to be blocked. The presence of the goalkeeper is expected and since we are looking at shots being blocked not saved then the goalkeeper’s location can be acknowledged but not required for calculation. The location of the goalkeeper may alter the shot direction of the attacker so may affect shot blocking numbers.

Expected Shot Block - FM

Furthering the concept of an Expected Shot Block would be to calculate the percentage of the goal that is open to the shot at the point of contact. When identifying if a shot has the potential to be blocked, you can calculate the percentage of the goal that is available to be shot at where the defenders wouldn’t make a block. This calculation could be done in either 2D or 3D. You can assume an average area for the defender’s body and block out the area of the goal that the defender is in front of. In 2D this would be less accurate than in 3D since it would be assumed that the defender can block a shot of any height. Whereas in 3D, you could create silhouettes of the defender’s limbs and create a more accurate percentage that way.

There are some problems with this concept but I think it has potential. You need more than just event information, you also need player location data which is harder to get. It also assumes that the shot is a direct hit straight at goal, whereas many players attempt to bend and curl the ball around defenders. Just because a defender is in the way of the goal, doesn’t guarantee a blocked shot in that location. There are many times where a shot goes through a defender’s legs or just past a limb, defenders and players aren’t perfect.

It’s hard to quantify defensive actions and shot blocking seemed to have the most relevant as it’s related to the current set of offensive metrics, it’s not perfect. If anyone has any thoughts or comments regarding other issues I may have missed, please do let me know!


#4 Team Analysis: AS Monaco – Realistic Expectations

In this piece I will take a look at AS Monaco’s Ligue 1 performance to see why they are sitting in 19th after 13 games. Looking at their recent transfers, I’ll investigate what their expectations would have been compared to what their expectations should be.

This is a team that only two years ago won the Ligue 1 title, holding top spot for most of the season, and was a team full of emerging young talent. Players such as Kylian Mbappe, Bernado Silva, Thomas Lemar, Benjamin Mendy, Tiemoue Bakayoko and Fabinho all contributed greatly alongside veterans such as Radamel Falcao and Joao Moutinho. This season the only player still at the club is still Falcao, Monaco were a club that thrived off the talent of these rising stars and cashed in. Larger European clubs such as Paris Saint Germain, Manchester City, Atletico Madrid, Chelsea and Liverpool all came in and stripped Monaco of their title winning team.

Since the system worked so well previously, it’s not hard to see why they have tried to replicate their success and have looked to reinvest in a new crop of youngsters to compliment the likes of Falcao once again. They have brought in the likes of Youri Tielemans, Pietro Pellegri, Willem Geubells, Benjamin Henrichs and Aleksandr Golovin who are all under 22, with Pelegri and Geubells both 16 at the time they were brought in. The concept which they are trying to repeat is to build the team around giving these promising youngsters lots of playing time, hoping to accelerate their development and mature early, therefore prolonging their careers at the highest level.

This transition couldn’t happen overnight, and has taken two years for these recruitment changes to occur. Last year Monaco managed a 2nd place finish behind a resurgent PSG team, which is respectable considering they loaned Paris their best asset in Mbappe for the year and lost Bernado Silva and Mendy to Manchester City. That’s a lot of attacking threat to lose, however they managed to keep hold of Lemar, Fabinho and Moutinho. Fabinho and Moutinho are two competent central midfielders who can take control of any given game, allowing Monaco the foundation to let their forwards do their thing. It could be suggested that losing Fabinho to Liverpool and Moutinho to Wolves in the summer before this season are the losses that were hardest to replace. Fabinho has proved himself worthy of a spot in a Jurgen Klopp midfield three which is saying something and Moutinho is part of the Portuguese midfield duo at a Wolves team proving themselves already a competent Premier League team.

Out of those youngsters brought in over the last two years, only Youri Tielemans has the suggested promise to be able to replace either. However, Tielemans has been playing in a more offensive midfield role previously at Anderlecht and Belgium, relying on a young player who’s still getting used to controlling a game from deep may not be the best idea.

That moves us on to Monaco’s current crisis, they sit 19th in Ligue 1 after 13 games and just been thrashed 4-0 for the second time by PSG [PSG have won their opening 13 games and sit 13 points clear]. After 9 games, Leonardo Jardim, who was in charge of their title winning season, mutually agreed with the club to leave and has been replaced by Thierry Henry in his first managerial role.

Monaco v PSG 11Nov18

A team that has finished 1st and 2nd in the previous two seasons shouldn’t be anywhere near the bottom of the league at this point of the season. They have underperformed their xG and xGA across the 13 games, so they haven’t scored as many as they should and have conceded more than they should have. Though not by a huge margin, they have 12 goals from 16.31 xG and conceded 22 from 17.47 xGA. Regressing to the mean, we can expect Monaco to perform better than current standings suggest, but that is nowhere near challenging for the title. Based on previous seasons, expectations would be to dominate most games by creating lots of chances and giving away few. Their goal difference of -10 and xGD of -1.16 shows a difference in expectation versus reality but compared to 2nd place Lille’s xGD of +7.28 [PSG’s xGD = 23.66] shows how far away from pre-season expectations they are.

Except, there doesn’t seem to be anything clearly wrong. Their defence is as leaky as suggested, conceding 1.69 goals per game. They aren’t creating enough good chances to score the goals to win games, scoring 0.92 goal per game. They have had 150 shots, creating 15 big chances but conceded 154 shots and 19 big chances so far. Most worrying is that they aren’t controlling games, they aren’t putting the opposition under pressure and they seem to be playing in matches on a level playing field with many of the teams in the league. So far this season they are performing like an unlucky mid table side, nowhere near their expectations of European qualification.

When I say there doesn’t seem to be anything clearly wrong, I mean that there doesn’t seem to be anything immediately fixable wrong. It’s not the case that they can change just one thing and go back to being the title challenging side they used to be. That’s because they are literally a completely different team to that one, even if the expectation hasn’t changed, the players definitely have and they are not as good as those who left. Unfortunately, it seems as though Monaco’s attempts to recruit a new group of young title winners, or at least challengers haven’t worked so far. Which isn’t surprising. It will take time for the players to get used to playing in a top 5 European league and playing with each other and handling the expectations all at once. They aren’t suddenly a bad team, just not what they were last year and the expectations surrounding the team need to reflect that. It is also worth noting that they have had some serious injury concerns which has forced them to play maybe more youngsters than planned.

*credit to Understat and @Statszone for the numbers and figure


#2 Team Analysis: The Rise (and Rise?) of Deportivo Alaves

I will take a look at the intriguing situation that Deportivo Alaves have found themselves in. Comparing where they were a year ago to where they are now, I will look to identify whether or not their current results are sustainable.

In 2016/2017, Mauricio Pellegrino managed Alaves to 9th place in their first season back in La Liga for 6 seasons. This was a great achievement and quickly drew the eyes of the Premier League where he went on to manage Southampton. Luis Zubeldia, an Argentine who had never managed in Europe before, took over for the start of the 2017/2018 season before being sacked after losing and failing to score in each of the first four games. Gianni De Biasi, who was previously the Albania coach who managed to qualify them to their first major tournament, replaced Zubeldia. Though he managed to get them their first goals and wins, it wasn’t enough as after only two months in charge his contract was terminated as Alaves sat rock bottom of the La Liga table on 6 points. They had 2 wins and 11 losses from 13 games, only scoring 7 goals and conceding 22. After a great first season back in La Liga finishing 9th the year before, this wasn’t exactly how they’d hoped the start of their second season would go. Since then, Abelardo Fernandez has been in charge and has won 19 out of 35 La Liga games, winning 1.74 points per game. Alaves would’ve finished 5th in the 2017/2018 season had they maintained that across a season and qualified for Europe. After 10 games in the current 2018/2019 season, Alaves find themselves 2nd only behind Spanish giants Barcelona.

Apart from the set back early in 2017/2018, Alaves have proved they are well worth their place in La Liga. In only their 3rd season back to La Liga they are sitting in 2nd place, I will take a look at the stats behind their recent fixtures to judge how sustainable their recent results have been.

So far Alaves have won 6 games, drawn 2 games and lost 2 games, meaning they are at 2 points per game and results are better than average across Fernandez’ tenure. They have scored 14 goals and conceded only 9, however their expected goals (xG) is 10.41 and expected goals against (xGA) is 13.59. Alaves are outperforming their expected returns at both ends of the pitch, they are scoring more than and conceding less than is expected based on the shots that have occurred. When a team is outperforming their expected goals, it is usually not sustainable, elite level finishing is the exception. We can expect that Alaves will regress back to the mean, wherever that mean is.

Even though Alaves have outperformed their xG/xGA as a total, looking at each match they’ve played individually tells a different story. Since they appear to be over performing expectation, you may expect that they over perform in each game. This is not the case as there is only one game that they have won where they had a lower xG than their opponent (1.10 – 0.85 vs Real Valladolid [away]). This includes their win against Real Madrid in which they snatched victory in the last-minute to earn a 1-0 win with xG of 0.95 – 0.84.

Two games in particular highlight the importance of looking at individual games, their first game away to Barcelona and their away trip to Rayo Vallecano. Barcelona thrashed Alaves 3-0 on the opening day after receiving a guard of honour for winning the title last year, with xG of 3.27 – 0.25. So 3.27/13.59 of xGA and 3/9 of their goals against all came in the first game, in the nine games since then they have had an xG of 10.32 and conceded 6. They are still out performing xGA, however it’s a much more representative view. Several games later, Alaves won 1-5 away to Rayo Vallecano with xG of 1.08 – 1.25. Helped out by a Rayo Vallecano red card and some excellent finishing in the first half, they created some higher xG chances in the second half with more space to counter into and ran away with the game. There were 5/14 of Alaves’ goals but only 1.25/10.41 xG in this game, meaning that in the other nine games they had and scored nine goals from 9.16 xG which looks much more reasonable and sustainable.

Rayo Vallecano v Deportivo Alaves

Across each individual game the xG prediction for a winner is correct 70% of the time (2 draws and 1 loss), if Alaves carry on putting up these numbers for xG and xGA then there’s no reason why their success isn’t sustainable. The only question is whether they can keep it up. Of course, xG doesn’t win you games, actual real-life goals do. Let’s delve into what type of goals and when these goals are scored.

Alaves haven’t scored a single goal in the middle 15 minutes of any half (15-30/60-75mins). This means all of their goals have come at the start or the end of a half, these are very good times to score. A goal at the start of a half will put you on the front foot and a goal at the end of the half gives the opposition little time to react, if it’s just before half time you can go and regroup whilst if it’s just before the end of the game there’s usually no time for reply. It is not a conscious decision when they decide to score but provides insight into the flow of how Alaves try to play. Explosive starts to each half with a quieter middle to relax before ending strongly. Alaves are very good at finishing halves but whether that style of play is sustainable is another matter, out of the 10 games they’ve played in they have scored in 90+ minutes five times. This has won them three games and drawn one meaning that they have gained seven points from last-minute goals. That is not sustainable.

Considering that Alaves appear to be over performing their expected returns at both ends at a total level, the fact that they have scored goals in 90+ minutes in half of their La Liga games and they are currently sitting at 2 points per game which is above their average in the last year, it doesn’t appear that their current results are sustainable. That’s not to say that they will revert back to the relegation battling side a year ago, but they will regress back to their mean somewhere in between.

Credit to understat.com once again for their amazing site and xG models. Check them out.


#1 Match Report: BVB 4 – 0 Atletico Madrid

Hello world,

After reading and consuming lots of amazing pieces of analysis that’s out there in the football analytics community, I found myself inspired. Watching football is now also an evaluation of performance rather than just for pure entertainment, here’s a place for me to keep tabs of some of the thoughts I have. Match reports, individual performances and defensive structure will all be on the agenda. Constructive criticism is welcome, please feel free to get in touch!

What this report aims to do is to look back on the game and highlight the key areas of importance. That will be looking at team structures and how the team has been set up to play, individual performances and suggested changes that may (or may not) have improved performance.

Initial Thoughts:

  • Dortmund have started the season exceptionally, unbeaten after 9 games in the league with 29 goals scored from only 18.42 xG. Mainly due to Paco Alcacer’s 7 goals from 2.4 xG
  • Atletico have been unspectacular, not really been tested since beating Real Madrid in the Super Cup.
  • The extreme result is what drew my attention to this game, it’s not every day that Atletico concede four.

Match Analysis:

We have an unexpected extreme score line, let’s see if there’s anything clear to explain why.

Number of shots, completed passes, attacking third passes, tackles, interceptions, fouls and possessions are all even. The main difference is in big chances and big chances created, where Dortmund have four and three respectively compared to Atletico’s zero. Considering Atletico only conceded two big chances to Real Madrid at the Bernabau at the end of September, four could be seen as significant.

Checking the attacking dashboards on Statszone for both teams explains where Dortmund were successful and Atletico weren’t. All of Dortmund’s goals were scored from shots between the width of the posts, this means the chances they created to shoot were optimal. Shooting from between the width of the goal produces the largest angle available of the goal to aim at. They favoured attacking down their left-hand side. Atletico lacked penetration into the box, with many incomplete passes and crosses into the box. They crossed the ball from deep into the box aiming for their forwards, if defences are expecting a cross they are easier to defend and attempts at goal resulting from crosses are harder to convert than others.

There is a clear distinction in styles of chances created between the two teams. Dortmund’s chances were of higher quality as they had shots from between the posts, whilst Atletico resorted to attempts from crosses.

How does a team that’s so rigid and robust in defence such as Atletico give up higher quality chances? If they did so on a consistent basis they wouldn’t be known for having that rigid and robust defence.

The context of how the goals were scored and when is important. At half time, Dortmund had only created one big chance from a corner and had scored from a long-range, deflected Axel Witsel shot. Not too much to split the sides. Considering both have won their opening two games, these two teams will be fighting for the top spot of Group A to get the best possible draw in the knockout rounds. This means that this is a game that Atletico would like to win, and after the uneventful first half, was probably a game that they felt they could win or at least get a draw out of the game.

In the second half, Atletico attempted 16 crosses compared to just 8 in the first half. Only 7 of the 24 were completed. Dortmund only attempted 6 all game. The increased frequency of crosses also came with an increase in clearances from 10 (8 in the box) to 14 (all 14 in the box) for Dortmund. This suggests that despite the increased frequency, Atletico weren’t creating any more or clearer chances. Atletico don’t play with clear wingers, this is part of what gives them such a rigid base, so their full backs have to provide the width. When losing possession getting back into that defensive shape quickly is important. Due to the nature of their attack, Atletico lost possession many times from attempted crosses which gave Dortmund many chances to counter attack. Producing chances from counter attacking against a team that is trying to recover defensive shape will produce good chances since there are fewer defenders and these defenders aren’t always set up properly. Not even Atletico are exempt from this. Dortmund completed less passes in the attacking third in the second half, but completed more passes into the opposition box. This suggests that Dortmund were able to be more productive with fewer opportunities. The difference is Atletico were set up defensively in the first half and were recovering from incomplete crosses in the second.

The resulting half boiled down to whether Atletico could break Dortmund down or whether Dortmund would extend their lead on the break. One well worked move, one perfectly executed counter attack and two assists later for Achraf Hakimi and Dortmund are 3-0 up. An extremely poor decision and pass from Felipe Luis gave Dortmund their fourth and the unpredictability of football wins again.

Ultimately, Atletico are still the defensive unit that we see them as. They just need to decide that’s the way they want to play the game. They did for 45 mins and were unlucky to be behind, they decided not to for 45 mins and conceded three late goals. Atletico will be okay as long as plan A works. Dortmund a tad fortunate, but we seem to be saying that so much recently that it’s getting uncomfortably like that’s just what they do.


Credit to understat.com for the xG numbers and Statszone for the graphics. Please go check them out they are amazingly useful resources.