#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.

@TLMAnalytics

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:

https://statsbomb.com/

#10 Match Report: Man City 2 – 1 Liverpool

Liverpool head into their first game of 2019 still unbeaten and 7 points clear of arguably the best ever Premier League side, reigning champions Manchester City. Manchester City were on course for another incredible year, and still are by anyone else’s standards, however losing at home to Crystal Palace and then Away to Leicester in 2 of their last 3 games was not in the script for their next documentary.

Up to Christmas, City had been unbeaten too, sitting top of the league and had already played all of the other ‘Top 6’ sides away from home. it was looking like the question was whether City could go unbeaten, with Liverpool doing amazing to just keep up. A severe dip in form, a key injury and some incredible shooting against them saw City relinquish the lead in the title with Liverpool not looking like slowing down at all.

A Liverpool win at the Etihad and the gap becomes 10 points, arguably the title race is over without a Liverpool collapse (not impossible). A draw would maintain the 7-point gap, but would also give Liverpool hope that they can continue in their excellent season since the champions couldn’t beat them at their own ground. Whilst a win for City would reduce the gap down to 4 points, which means City are still relying on Liverpool messing up, but it also means that Liverpool are no longer untouchable and City will have put doubt in Liverpool’s minds.

Considering City finished champions 25 points ahead of Liverpool and won 5-0 in this fixture last season, if I were to say to you that this was the most even game of the season so far would be surprising to say the least. It shows how far Liverpool have come in such a short space of time that that is indeed the case, this game was incredibly even and almost any result could’ve happened if repeated.

City did end up winning 2-1, however the Expected Goals (xG) from Understat suggest it wasn’t an easy win. The xG score was City 1.18 – 1.38 Liverpool, suggesting arguably Liverpool would win this game more often than City if repeated and a draw is most likely. For a game with two of the highest scoring teams in the league, there were not that many shots or chances created with only 9 – 7 for City – Liverpool respectively. This low shot volume adds to the variance in xG numbers and emphasises that it would be more down to individual skill at finishing or luck to determine this game rather than an overwhelming inevitability that someone would score.

Figure 1: Size of bubble = Expected Goals (xG), Location = Location of shot, Stars = Goals

In terms of finishing and scoring goals, Liverpool were not very clinical however they did create the best chance of the game with a lovely cross field pass followed by a first time cross across the box for a tap in to an empty goal. They also had a ball cleared off the line by centimetres following a scramble after a rebound off the post. Other than those two, Liverpool were limited to shots through crowds of bodies. City managed to manufacture some chances through counter attacks, and also capitalise on the fact that Sergio Aguero is an incredible finisher from tight angles. Whilst Liverpool scored with their highest xG chance (0.62), City missed both of their highest xG chances (0.49, 0.32) and scored from two lower xG chances (0.06, 0.05) which suggests that it was City’s finishing when needed was the difference in goal scoring.

Since not very chances were being made, most of the game and interesting plays were between the two boxes. There are three players I’d like to highlight, all playing central midfield: Fernandinho, Bernado Silva and James Milner. It’s hard to quantify the effect that these players had on the game, but all three were excellent in denying the opposition any space or progression up the pitch.

No player had more ball recoveries than Silva with 10, Fernandinho had 9 and Milner whilst only being on the pitch for about an hour had 7. In of itself ball recoveries doesn’t mean much, however especially for City players it’s the area of the pitch that they win the ball back that’s so great.

Figure 2: 25/65 Man City recoveries in Liverpool’s half, 10/56 Liverpool recoveries in Man City’s half

https://thelastmananalytics.home.blog/2018/11/06/3-are-man-city-better-without-the-ball-defensive-analysis/

5 out of the 10 ball recoveries for Silva and 4 out of 9 for Fernandinho were in Liverpool’s half, which suggests that City were winning the ball back high up the pitch and not allowing Liverpool to progress much further. Compared to other players with high recoveries, this is significant. Not only recoveries, but Silva also completed 3 tackles on the halfway line out of 8 (!) attempts and made 4 interceptions in Liverpool’s half. As you can imagine Silva got around the pitch a lot this game and managed to cover 13.7km which is the most in a game this season. I don’t usually like those kinds of stats since they don’t suggest anything about a player’s involvement in a game but maybe suggest that they’re just out of position recovering for the whole game. However, Silva was definitely involved and sometimes that extra effort you put in makes others do the same.

A lot of Fernandinho’s work is done off the ball, in ways that aren’t quantifiable by tackles or interceptions or distance covered. It’s clear how large an impact he has in City’s midfield since the two games he didn’t play due to injury were the two games they lost so far this season. Fernandinho deserves more than a paragraph of one game to highlight his skills, he’ll be the focus of an upcoming post in the future. But City need to find a replacement quickly for him, or find a way of playing that doesn’t rely so heavily on him sweeping up behind the front 5’s press.

It’s a shame that James Milner had to be the one to come off early in the second half, Milner plays similarly to Bernado Silva when Liverpool have the three in midfield and was as effective as Silva defensively until he got taken off. Moving to 4-2-3-1 since they needed to score was probably a sensible move, however needing a goal and leaving Jordan Henderson on the pitch alongside Fabinho (better version of Henderson) doesn’t always end well. It worked out since Liverpool scored an amazing team goal but they may have been more of a threat if Milner was alongside Fabinho. Also, doesn’t help pushing Wijnaldum out to left wing with several wingers sitting on the bench but hey.

Come the end of the season, this game will be regarded as a turning point whatever happens. Whether Liverpool collapse and City come back to win their second title in a row or Liverpool brush it off and continue in the same manor we will find out, but Manchester City have showed their hand and they are here to stay until the end of the season. We have our first real title race in years, take it in and enjoy it.

Thanks to @StatsZone and Understat for images, stats and xG numbers.

@TLMAnalytics

#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.

@TLMAnalytics

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

#8 Player Partnerships and Compatibility

Throughout the years there have been many partnerships between players that seem to stand out and are more memorable than others. The classic Yorke and Cole strike partnership for Man Utd’s treble winning season, Xavi and Iniesta passing rings round everyone for fun and Ferdinand and Vidic who became arguably the Premier League’s best defensive pairing. The main thing in common with these players is that they were successful, playing at the highest level of the game for years and so were the best of their eras. As they were successful, they must have been pretty good. Individually you need to be very good to make it at the top, however since football is a game that requires eleven players all working towards the same goal it’s how well you can fit into a system alongside other talented players which can make a good team a great team. If the aim of a team is to be greater than the sum of its parts, when that occurs very good things tend to happen, eg. Leicester City in 2015/16.

The problem arises at every football club of how to fit together all of your best, talented players into the same team. If you can get those players working well together then you will be working near the optimal level that your team can achieve, and that’s the aim. However, this frequently doesn’t turn out to be the case. You have two-star players who both play as strikers, however each prefers to lead the line on their own and so when you force them to play together, both of their performances drop and as a result the team’s performance drops. It feels like a lose-lose situation, either you drop one of your star players who will then be unhappy or you play them both and suffer bad performances. The ideal solution as suggested is to get them both playing together, however sometimes players are incompatible and so the next best solution is to work out which of those players is more compatible with your team and offload the other.

On a football pitch there are eleven players and so you want to get optimal performance from the whole team rather than just having your right full back and right winger link up well every now and then. Each player has a partnership to some degree with every other player on their team, those players that are in frequent contact and close proximity will usually have stronger partnerships since they interact more. These are pairings such as the two centre backs, two centre midfielders, full back and their respective winger and two strikers. This of course will depend on formations, if you are playing three central midfielders then it’s how well those three can play together which is important.

Figure 1 – Football Manager represents player partnerships with green lines

One way to measure the compatibility of all eleven players would be to compare the results and performances of each combination of eleven players on the pitch. So, one starting line-up would have a set of results, whilst if you just changed one player you assume that is a completely different starting line up and have a separate set of results for that group. It’s debatable how much of an impact changing one player could be, it depends how influential the player you replaced was. The problem with this is that there won’t be a large enough sample size since rarely do the exact same players play every week due to injury or rotating players due to tactics.

Another option could be to compare the results and performance of specific partnerships that you are interested in, such as when your central defenders are the same or when your strikers are the same. In looking at specific partnerships, it allows you to look into more specific areas of performance when assessing. For example, when comparing two sets of central midfielder partnerships, you may want to compare how much possession or passes you had in those matches whereas if you’re comparing two sets of striker partnerships you may want to compare how many goals were scored in those games.

As mentioned earlier, there aren’t just partnerships between those players in positions in close proximity but between players all across the pitch. Maybe a right full back has an understanding with the left winger and likes to make a long diagonal pass or maybe the winger likes to come inside and receive passes from the central defenders.

So far, we have looked at how to compare sets of partnerships with each other, using metrics such as goals or passes as proxies for compatibility since they are seen as productive outputs. Without realising, these are all on the ball metrics. There is nothing wrong with looking at those, however they need to be just part of the answer. Most of the game for every player is played off the ball so it’s arguably more important to assess their performance off the ball than on it.

When assessing partnerships off the ball performance, as I’ve discussed in my Defensive Metrics posts, it’s more about how you can be in the right place at the right time and what decisions you make. If it so happens that when you are playing with a certain player that collectively you are able to maintain correct distances, are capable of covering for mistakes and therefore force the opposition to make worse decisions as a result then you would be more compatible with that player than with another. The problem is that it’s hard to quantify these, I have attempted to outline a few ways in which you can start to get some insights in previous posts however it’s still very early. If we can effectively measure player performance of the ball and interactions between players that don’t involve the ball then we will get more of an insight into what makes some partnerships work so well and why some others don’t.

There are many examples of passing networks in football, based on specific matches and representing the distribution of passes between each player. They provide a certain aspect of the partnership between players, it would be interesting to see other examples of networks in football with connecting lines that represent how compatible the two players are. How exactly we can measure that is up for debate.

@TLMAnalytics

#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.

@TLMAnalytics

#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.

@TLMAnalytics


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!

@TLMAnalytics

#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

@TLMAnalytics

#3 Are Man City Better Without The Ball? – Defensive Analysis

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In this piece I will take a look at what makes Manchester City such a good defensive team by looking at the types of recoveries that they make. Comparing games that they have dominated and those they have ‘struggled’ in (used very loosely) suggests there’s a reason why they have looked less clinical in some games, and can identify a potential chink in the City armour.

We all know that City are one of the greatest teams at keeping hold of possession. Pep Guardiola has brought with him and adapted his style of play from Barcelona and Bayern Munich with great success. They are beautiful to watch, passing the ball around the pitch with such patience, precision and ease that it makes you think you could do it watching from the couch. What’s not immediately obvious is the defensive prowess of teams under Guardiola and how they manage this, despite not even training tackling (!).

“I am not a coach for the tackles so I don’t train the tackles.” – Guardiola, Dec 2016

To score a goal, you need to have the ball, and when playing against Manchester City you don’t get the ball for long. This means that you need to make every time you do have the ball count, if you are wasteful then you might not see it again for a while. The problem is that as good as they are in possession, once Manchester City lose the ball they are arguably even better, making it extremely difficult for opposition teams to take advantage.

They have played 14 games this season and only twice have they made less recoveries than their opponents. Considering City are so good with the ball, you may hope that they are at their weakest when they don’t have it. This shows that they are just as good, if not better than most at getting the ball back.

Not only are City at least as good as everyone else at recovering the ball in general, in terms of where they recover the ball, they are by far better than most. Out of 724 total recoveries made so far this season, 231 were made in the opponent’s half (~32%). Whereas only 112/628 recoveries were conceded in City’s half (~18%). This means that City are recovering the ball higher up the pitch more often than against them, which is important since there is less distance to goal and usually fewer defenders the higher up the pitch you win the ball back.  Even more astounding is the fact that the minimum number of times City have recovered a ball in the opponent’s half is 11, which was away to Arsenal in the first game of the season. Every game they make at least 11 recoveries in the opponent’s half, the most so far was against Fulham at home (shock) where they made 25 recoveries in Fulham’s half.

image2

Whilst only on four occasions have their opponents recovered possession in the City half more than 11 times. These games were the three away games against fellow top 6 members (Arsenal, Liverpool and Spurs) and their only defeat, at home to Lyon in the opening Champions League game.

Out of the 14 games City have played, these four were among those where they looked the least clinical version of themselves. They beat Arsenal 2-0 however arguably should have won by more. The 0-0 draw against Liverpool looked like a game with two teams who didn’t want to lose cancelling each other out, hardly any chances were created in that game. They won 1-0 against Spurs in another game where arguably City should’ve score more. In their 1-2 loss to Lyon with Guardiola in the stands, they met a clinical Lyon side and couldn’t create a big chance all game. In each of these games City didn’t look at their best, and in each of them, their opponents recovered the ball in City’s half as much as City recovered the ball in their half (63 v 54). In the other 10 games, City recovered the ball in the opposition half 168 times and conceded recoveries only 58. That’s only four more than the four least clinical games City had.

Not only does recovering the ball high up the pitch prevent the opposition from getting anywhere near your goal and therefore no chance to score, it also instantly puts you on the front foot and creates better scoring opportunities for your own team. We know that City are such a good team with the ball, however this suggests that many of their great chances probably also come from winning the ball back from teams high up the pitch. It may seem obvious and easier said than done, but if you are able to prevent City from doing this to you or if you are able to recover the ball high up the pitch against them then that looks the best way to disrupt them. Limiting the number of times they recover the ball in your half gives you the chance to move the ball further towards their goal and prevents them from exploiting your defensive transitions.

The players with the most recoveries per start for Manchester City seem to be the players who have played the most this season. This suggests that it’s something Guardiola keeps an eye on and favours in his players. I have counted the number of times a player has made 5+ recoveries in a game compared to how many starts that player has had. The top 3 are Mendy (8 times/9 starts), Fernandinho (12/14) and Laporte (10/14). The games which Mendy missed, Delph (3/4) and Zinchenko (1/1) covered for him and appears to be a recovery gold mine at City’s left side.

Credit to @StatsZone for the graphics and recovery numbers.

@TLMAnalytics

#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.

@TLMAnalytics