Analysing Brazil’s 3rd Goal: Understanding the Impact of Players Movement

17 Jun

A number of bloggers have really started to up the ante on the standard of data being collected and analysed recently.  In an attempt to support the now seemingly ever expanding quality and depth of analytical writing I have decided to attempt to apply some of my current research to goals scored in the Confederation Cup.

For this piece I have generated my own XY coordinates by basically being very sad and freeze framing every 0.3 and then capturing the positions of each player involved in the move using a simple X Y grid which runs 50 to -50 on the Y axis and 25 to -25 on the X axis.  As a result I’ve been able to produce a crude 2D representation of player’s movements.

For the purpose of this blog I have concentrated on Brazil’s 3rd goal against Japan scored by Jo.  The justification for this is that the goal allows the use of certain statistical tests, in this case running correlation, to determine the impact certain players have on others and at what point a defence moves from being stable to unstable (perturbation).

Running Correlation

I’m sure most people are now well familiar with correlations and will have seen these along with an r or r2 value quoted in a number of blogs which indicates the strength of relationship between different variables.  Just as a reminder ±1 indicates an almost perfect relationship (+ representing one that goes in the same direction and – showing one which goes in opposite directions) with 0 showing there is no relationship whatsoever.

A running correlation does the same, however, like a moving average takes a few samples of data and follows these over a period of time.  In this case I will be tracking the relationship between Jo (the striker) and Yoshida (the marking centre back) X coordinates (across the pitch) and Y coordinates (towards goal).

The reason for this is if we have the ability to capture type of movement that will lead to a quality goal scoring opportunity; we can then start to identify specific movement patterns to train with greater certainty which will hopefully lead to more success.  Also we can start to capture who has a higher level of movement intelligence in attackers and which defenders are able to recognise and prevent this type of movement.

Hopefully You’re Still With Me!

JoFirstly I plotted the movement of the 6 players involved; 3 attackers and 3 defenders.  As we can see straight away there is a general relationship between the 3 pairs of players which we can term a dyad:

  1. Oscar vs. Uchida
  2. Jo vs. Yoshida
  3. Lucas vs. Konno

As we know Oscar was the initial protagonist picking up the ball in his own half and running towards Uchida.  If we isolate this dyad to start we can see that Oscar is making a straight run while Uchida has to check to the left before the pair form a similar relationship moving towards the goal.

Oscar Uch

This is evidenced through the running correlation.  As the red line shows very quickly the relationship between the players movement in the X axis goes from one of stability (+1) to instability (0).  This represents the fact that both players are traveling in completely different directions on the X axis.  This is further backed up with the Y axis which moves for +1 to -0.7 which indicates that the two players are moving towards each other or to put it another Oscar’s affecting the movement of Uchida.  For the rest of the move we see what we call an anti-phase relationship between the movement in X and Y plains which means that the players are moving in and out of synch as Oscar tries to disturb the dyad to his favour while Uchida attempts to maintain stability.

As shown on the graph the Y correlation is near perfect 1 from movement 7 (toward goal) but is in the X plain you can see Oscar is trying to move left and right to win the battle.  Eventually he does by movement 10 were the relationship is totally destroyed (0 correlation).  This matches when he plays his sublime pass to Jo.

Jo vs. Yoshida

Ultimately a great pass is only possible if the supporting players provide great movement to a) create the space and b) actually receive the ball.  The Jo/Yoshida dyad is a fantastic example of a top class movement and unaware defending.

Jo Ypshida

This time we see a different relationship.  As in Fig 1 this dyad starts in the defenders D on the halfway line.  The correlation shows that there is a near perfect relationship between Jo and Yoshida as they both move toward the goal area (Y axis) all the way through until movement 9 where we see the correlation move from +1 to +0.76.  This represents the moment when Yoshida loses Jo and stops to see where he is, and where Jo then takes advantage and gets ahead to allow the pass from Oscar (remember Oscars relationship with Uchida plummets at 9 and stops at 10).

This isn’t the whole story though.  If we look at the X axis we can see that there is a very definite anti phase relationship with the correlation moving between +/- 1 up until the goal is scored at movement 14.  The most important part is at movement 6 where the correlation drops of -1.  This is where Jo moves away from Yoshida while the CB continues to move in the same direction unaware of Jo’s movement. (If you watch the goal you will actually clearly see this).  The next significant movement is at 10 (when the ball is passed) where get a correlation of -0.45.  This represents Jo making his move into the space the space created with Yoshida to close him down realising its too late.

What happens next is obvious with Jo scoring his first goal for Brazil.

A Pointless Exercise

I’m sure some of you will look at this and go well what’s the point of doing that when you can just see it.  Well the point of performance analysis and analytics is to provide objective data to either prove or disprove gut feeling.  By being able to demonstrate scientifically how movement influences the outcomes of attacks we will be able to collect this data for all successful attacking and defending outcomes.  Used properly we can then develop better suited training programme not just for senior but more importantly for junior players.

This is just a microcosm of some of the work I’m interested in but I feel very passionately about what it can help us achieve in the future.


One Response to “Analysing Brazil’s 3rd Goal: Understanding the Impact of Players Movement”

  1. Martin June 18, 2013 at 5:28 pm #

    This is fascinating. What do you see as the long term application of the x and y data in this context? Is there the possibility of using this to ascertain which players are proficient in creating space for their team-mates, particularly in fast break situations?

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