Does Footedness Really Matter?

Shrey Grover
9 min readMar 13, 2021


Analysing influence of centre back’s preferred foot in ball progression in football

— Written by Anuj Singh and Shrey Grover

Till not long ago, centre backs were outlined as players with high aggression, tackling prowess and aerial dominance. But, football of the decade expects them to have a new bow in their skill armoury — high quality ball playing skill. To bring some visual motivation, let’s consider the intelligent long balls in a goal scoring sequence by Virgil Van Dijk (Liverpool) in Champions League and Aymeric Laporte (Manchester City) in FA Cup:

Virgil Van Dijk’s assist against Bayern Munich
Laporte’s long pass against Newcastle United

Both progressive passes find their target men in very attacking positions and lead to stellar goals. However, there is a subtle difference — Van Dijk is right footed while Laporte is left footed.

Left Footed, Right Footed — Does it Make A Difference?

Football community has a divided opinion about this idea — should defenders be made to play on the natural side of the field based on their preferred foot?

One of the most prominent proponents of this idea, Pep Guardiola, emphasised on Laporte being left footed centre back was vital to their quick buildup after signing the latter.

Bleacher Report expounded the idea that to make threatening progressive passes, the central defender cannot be on the wrong side of the defender pairings. This is because the angle would be against the player and would be forced to pass out, in line with his/her weaker foot.

But, having a left footed defender on the natural side is not the case with every successful team. According to an article questioning if left-right combination is a necessity, among the top ten teams in traditional four European leagues, only 12 out of 40 teams (33%) had left footed — right footed centre back pairs in 2019/20 season.

So, does it really make a difference in the way plays are built up from the back and add threat based on the centre back’s preferred foot? Are there any prominent passing characteristics, governed by footedness, that can further give an insight of a team’s overall playing style?

These are some questions that we try to answer using our analysis.


We make use of the publicly available Wyscout’s spatiotemporal match events dataset, which logs events like passes, shots fouls etc. It stores details like event position coordinates, player who performed the event, timestamp etc. for each event for 2017–18 season. To obtain the player positional data in the starting lineup for each match, we relied on the formation data on (on consensual basis). For this exercise, we use data from five leagues — English Premier League, Ligue 1, Serie A, La Liga and Bundesliga.


Before we jumped onto implementation, we made the following assumptions which are relevant for our problem statement.

  1. We focus our study on left centre backs as they provide a good mix of left and right footed players (All right centre backs tend to be right footed).
  2. Since defenders usually tend to progress the ball by passing more than carrying, we have filtered out progressive passes from the data using Wyscout’s progressive pass definition . According to the definition, a pass is classified as progressive if it is at least 30m if starting and ending pass coordinates are within the team’s own half, at least 15m if starting and ending pass coordinates are in different halves and at least 10m if starting and ending pass coordinates are in the opponent’s half.
  3. We consider left centre backs in four defender formation and in three/five defender formation as different entities (L_CB and LCB respectively) to capture the impact of change in formation.
  4. We only consider the ones who have played at least 20 progressive passes at a particular position for a team to avoid including players with less progressive pass data.


Once we have our assumptions defined, we move ahead by working on the following pipeline:

  1. Firstly, we merged events data with player positional data as they come from different sources. Since we had inconsistencies in player and team names in both data sources, we merged these by creating a common reference vocabulary for player and team names, using accent handling and string matching. We had to manually intervene in a limited number of cases.
  2. We further adjusted the Wyscout’s pitch coordinates to the pitch size of 104 x 64 m. We also divided the pitch into eight regions as shown below in Fig 1. For our analysis on ball progression into attack, we concentrated on the four attacking regions.
  3. We then computed the desired metrics — preference and offensive value-added per progressive pass (OVPP) — for each region. For OVPP values, we relied on the training a VAEP framework using our data (as explained in the next section)
Fig 1 : Field Division into eight regions

Metrics Computed — preference and offensive value per progressive pass:

As stated in our methodology, we try to study the relation between preferred foot of a central defender and his/her passing characteristics. To do so, we use preference and understand the passing characteristics of a central defender, offensive value-added per progressive pass (OVPP) as our metrics. In this section, we define our metrics using some equations.
In the below equations, i denotes a particular attacking region of the pitch (as per Fig 1).

a) Preference (in %) — percentage of progressive passes attempted in a particular region as compared to the other regions.

Fig 2 : Equation for Preference

b) Offensive value-added per progressive pass (OVPP) — total offensive value added by every progressive pass attempted in a particular region.
Note: This value is scaled up by 1000 to avoid dealing with small values.

Fig 3 : Equation for OVPP

To compute the offensive values, we relied on the VAEP framework that values on-the-ball actions of players. Every on-the-ball action alters the game state. That is, an action ‘a_i’ moves the game from game state ‘S_(i−1)’ to game state ‘S_i’ . VAEP leverages this by measuring the value of this change in game state:

Fig 4: Equation for value of an action

Further, the value of every game state is defined as —

Fig 5: Equation for value of a game state

where the terms depict the probability that a team in a particular state ‘S_i’ will score or concede a goal in next k game states, respectively. Since our analysis is limited to the offensive contribution made by central defenders through progressive passes, we incorporated only the first term of the equation (Fig. 5).

We also trained the model on our processed Wyscout data as a pre-trained model for the same was not available.

Visualisations and Inferences:

Now that we had our results ready, we plotted the marginal plots based on Preference and OVPP for all left centre backs. (Fig 6.1 — Fig 6.4)
The dotted lines in the figure denote the median values along preference and OVPP.

Fig 6.1 : Preference vs Offensive Value per Progressive Pass in Left Flank region
Fig 6.2 : Preference vs Offensive Value per Progressive Pass in Left Central region
Fig 6.3 : Preference vs Offensive Value per Progressive Pass in Right Central region
Fig 6.4 : Preference vs Offensive Value per Progressive Pass in Right Flank region

From the above visualisations, we can gather some interesting inferences:

Fig 7: Median values for Preference and OVPP across different regions and preferred feet
  1. In the left flank and left central region:
    * Left-footed left centre backs prefer making more progressive passes than right-footed left centre backs.
    * Left-footed left centre backs also add more offensive value per progressive pass than their counterparts.
  2. In the right flank and right central region:
    * Right-footed left centre backs prefer making more progressive passes than the left-footed left centre backs.
    * Right-footed left centre backs also add more offensive value per progressive pass than their counterparts.

There are a few notable exceptions to these inferences:

  1. On the right flank, there are few left-footed left centre backs that offer very high offensive value. These include:
    a. Santiago Selak (LCB — Genoa)
    b. Paul Dummet (LCB — Newcastle United)
    c. Aymeric Laporte (L_CB — Manchester City)
    d. Kevin Wimmer (L_CB — Stoke City)
  2. Similarly, on the left flank, a few right footed LCBs stand out with high offensive contribution. These include:
    a. Sokratis (L_CB — Borussia Dortmund)
    b. Unai Nunez (L_CB — Athletic Club)
    c. Waldemar Anton (LCB — Hannover 96)
    d. Michele Cremonesi (LCB — SPAL)
    e. Josip Elez (LCB and L_CB — Hannover 96)
    f. Javier Mascherano (L_CB — Barcelona)


We further try to utilise our analysis on some specific use cases —

Otamendi and Laporte — Manchester City’s varied left centre back options :
Manchester City broke their bank to sign Aymeric Laporte (left-footed) for £57 million in January 2018. Prior to this signing, Nicolas Otamendi (right-footed) was the primary left centre back choice for City. In that Premier League season, Otamendi played 22 matches as L_CB and 3 matches as LCB while Laporte featured for 7 matches as L_CB. Let’s have a look at the difference Laporte brought to Manchester City in terms of his progressive pass preference and offensive value added in different attacking regions.

Fig 8: Comparing Laporte’s (left-footed) and Otamendi’s (right-footed) preference and OVPP across different regions

The plots depict some differences in the way Laporte and Otamendi prefer regions and add offensive value.

  • Otamendi’s distribution of progressive passes is more uniform in Left Central, Right Central and Right Flank region while Laporte heavily prefers Left Flank and his preference drastically drops towards the Right Flank (roughly getting halved at each region) as shown in Fig 9.
Fig 9: Otamendi’s and Laporte’s Region Wise Preference
  • Also, Laporte provides significantly higher threat than Otamendi on the Right Flank (roughly four times) as per Fig 8.

Ake’s signing — another centre back similar to Laporte?
Manchester City signed a long term deal to bring Nathan Ake (left-footed) to the squad from Bournemouth in August 2020 to provide cover for Aymeric Laporte (left-footed), beating many top club rivals in the process. It might seem strange for a celebrated club like Manchester City to spend £40 million on a left centre back whose side conceded 65 league goals (third most that season). We try to analyse how Nathan Ake at Bournemouth played as compared to Laporte at Manchester City in terms of progressing the ball through passes and adding threat.

Fig 10: Comparing Laporte’s (left-footed) and Ake (left-footed) preference and OVPP across different regions

These players showcase certain similarities in terms of building up.

  • Laporte and Ake highly prefer Left Flank to progress the ball and their preference drops drastically as they move to the Right Flank (roughly getting half at each region) as shown in Fig 11.
Fig 11: Ake’s and Laporte’s Region Wise Preference
  • Also, Ake, when playing as an LCB i.e. in a three/five defender formation, offers very similar offensive value distribution as compared to Laporte as shown in Fig 12. Both of these players generate their most threat on the Left Flank.
Fig 12: Ake’s (LCB) and Laporte’s (L_CB) Region Wise OVPP

Conclusion and Future Work

Based on our short analysis and inferences, we do find some strong patterns in ball progression and threat generation by central defenders based on their preferred foot.

We find that the left footed left centre backs prefer and add most value in the Left Flank region while right footed left centre backs contribute the most to the Right Flank region. There are also some players that are notable exceptions to this conclusion. Lastly, we try to use these results in some real world scenarios and further understand the rationale behind some decisions.

To extend this idea, we intend to move beyond left central defenders and try to clusters all defenders that have similar characteristics in building up from the back.


We would like to thank Luca Pappalardo and Paolo Cintia for their valuable inputs and feedback for our analysis.

Code for the analysis is available at the following GitHub link —