Part 1: Can Analytics help Talent ID and Quantify Creativity in Sport?

6 Feb

This is the first in a series of blogs I’ll be writing for the Sports Analytics Innovation Summit on London which is one of the leading meet ups in the world.

Analytics is Child’s Play

Analytics at Work

Anyone who has played or coached sport at any level will have constantly analyzed what theirs and their opponent’s next move is.  A perfect example of this is watching a group of 6 year olds play cat and mouse.  At first both the cat and mouse run round in circles to catch each other, then suddenly the cat will make a prediction based on the information gained form the game: “if I change direction and go the other way will that increase my chance of catching the mouse?” (or words to that effect) and sure enough it works.  Suddenly the 6 year old has started to analyze the concepts of the game based on the knowledge they have (data) and then develop a new strategy to affect a future outcome.  In a sense they have created a feedback loop which will become more advanced with the more data they consume.  This is probably the simplest form of sports analytics and should be the basis of the work we carry out when working with coaches and players.

Keep it Simple and Make it Meaningful

Whether as a fanalysist, researcher or team principle, the amount of data available to analyze performance is eye watering.  For a 90 minute game of football Prozone capture 54,000 data points by tracking each player’s individual movement through a multi camera system.  If you think that’s impressive then IBM’s Slam Tracker uses 39 million data points from 7 years’ worth of Grand Slam tournaments to determine player patterns to predict the outcome of the match.

However it’s not the level of data you collect that’s important but how it’s applied to gain an advantage.  Two excellent recent examples of how to use data to gain an advantage are British Rowing and Leicester Tigers.

British Rowing are without doubt one of this country’s biggest sporting success stories.  In 2008 they won their highest amount of medals at an Olympic games and where the most successful nation. However they knew that to keep ahead of the game they need to progress further.  Enter sports analytics.  To better understand the key physical, mental, tactical and technical factors that underpinned racing success British Rowing undertook a detailed factor analysis.  Based on data collected over the decades from past Olympic cycles they were able to first identify the factors which correlated to winning and through regression analysis developed a training program targeted.  The rest is now history with the team breaking their record medal haul winning 4 gold, 2 silver and 3 bronze at London 2012.

Leicester Tigers recognized that to be able to compete in both the league and European competition it was vital for them to be able to get their best players starting as many games as possible.  They identified the largest preventative factor to this was the occurrence of injuries.  Along with increases in size and physicality of players the number of injuries suffered be players also increased, therefore they had to think smartly about how to reduce the number of injuries being suffered in a season.  A partnership with IBM was struck with the idea of preventing injuries by predicting when they were likely to happen.  By using IBM predictive analytics the clubs sports science team where able to collate all the data collected on players from training load, sleep patterns to past injuries.  Based on past data they are able to input current information through the predictive software to model the risk percentage a certain player is to become injured.  The club are currently 3rd in Guinness Premier League and top of their Heineken Cup group respectively.

TID and Measuring Creativity

So what about the future?  One of the largest area’s which analytics can support is the golden goose of talent identification (TID).  It could be said that a number of sports have extremely inefficient talent identification systems in place.  Football clubs invest up to £5m per year into an academy which they may recoup through the sail of youth products or through savings on not having to pay for new player.  So in terms of a balance sheet academies can and generally do stack up, however if we look at this from a talent conversion quota perspective the story is not so rosy.  The number of academy players being released before the age of 16 is 50% and for those fortunate to progress through to u18 level on average 93 per season gain a 1 year contract.  This works out an average of 4.65 players per team and gives us a 25% success rate for the players who are left in the system.

Young Iniesta

So how can analytics help to solve this?  UK Sport and the English Institute of Sport have already set the ball rolling with several highly successful TID programmes which have focused on converting athletes who may not have progressed at their primary sport but have the potential to transfer and excel at another.  By developing a series of physical performance indicators the programmes have been able to identify a number of athletes who have gone on to win medals at world level.

Sport isn’t just about physicality, it also requires a high level of sporting intelligence.  With most team sports looking to unearth the next Lionel Messi, Tom Brady or Kobi Jones.  So how do we ensure we identify and nurture the right talent from raw potential to genius?

Ground breaking research by Andreas Grunz, Daniel Memmert and Jurgen Perl has looked at quantitatively capturing game intelligence.  By applying artificial neural networks (ANN) to learn repeated behaviours, Memmert and Perl have develop a Dynamical Controlled Network (DyCoN) which can be used to capture unique movements or solutions to pre-set problems.  Where normal ANN’s are trained to only recognize frequent actions (i.e. the norm) ignoring deviations from the norm (i.e. creativity), a DyCoN ANN can be trained to recognize infrequent but important changes from the norm.

So How Does This Work?

Put very basically a sequence can be converted from film to a series of X and Y coordinates with specific coding to describe actions such running, throwing catching etc… This data is then fed into the DyCoN which is trained to recognize patterns of interactions as described above.  This then allows actions to measured and mapped out creating a neural network of all the actions observed. The system can the identify unique responses which indicate creativity.

Example of a trajectory containing a creative neuron (grey circle), representing a rare and adequate action. From ANDREAS GRUNZ, DANIEL MEMMERT, JÜRGEN PERL

Example of a trajectory containing a creative neuron (grey circle), representing a rare and adequate action. From ANDREAS GRUNZ, DANIEL MEMMERT, JÜRGEN PERL

From this it is possible to identify what may be considered the normal response and what a unique solution is.  While the research is still in its infancy it does shed light into a new area that sports analytics can be used for.

As previously mentioned this is the first in a series of blog for the Sports Analytics Innovation Summit. I will be building on this concept of quantifying creativity and introducing you to more of the research in hopefully an understandable way.


Measuring Player Season on Season Performance – Part 1

8 Jan

There has been a lot of work recently looking at teams and players performances based over a full season or several seasons however how do we measure a players in season performance to determine if they have actually improved game on game or just had a one off?  This is probably one of the key aspects for a manager in order to determine who to play, sell and buy.  It can also act as an indicator as to whether specific training programmes are working or if there are specific weaknesses that need to be worked on.  Finally by being able to quantify performance it allows the manager and coach to be more objective in their decisions.


To develop a method of quantifying a players season on season performance level.


A method which is applied in sports science to assess an athletes performance level is to measure their mean (average) performance (time, passes complete) over a number of tests (races, matches etc…) and then work out what an individuals Typical Error (TE) is in their performance.  By finding the mean we can now quantify what an athletes average performance should look like over a number of races or matches.  Once we have the mean and have started to plot the athletes performance over a season it is more than likely you will see some performance above and below the mean.  Some performances will massively fluctuate from the mean while the majority will probably hover around it.

To determine if a performance was a fluke or something to get either very excited or deeply worried about we need to understand the athletes TE of performance?  We calculate the TE by dividing the Standard Deviation (SD) of the performances (the deviation of scores from the mean) by Square Root (SR) of 2.

TE = SD/(SR2)

Now we know the TE we can determine if a performance which deviates from the mean is just an expected variance in performance or something that is above what we would expect to be the norm.  For more detail on this look at Will Hpokins website or check out this presentation here.

For our example we are going to look at the passing completion rate (PC) of Sebastian Larsson to determine if he has had an improvement in the number of passes completed between the 2011/12 and 2012/13 season.  To do this I collected Larssons completed passing stats via StatsZone for last season and this.

To determine if there had been an improvement in Larsson’s pass completion rates from the corresponding games I calculated the difference in performance (i.e. PC vs Liverpool Away 2011/12 – PC Liverpool Away 2012/13).   Once we have the differences for each game we can now plot them into a Bland Altman Plot to visualize the difference in performance.

Seb Larsson TE

While this provides with a good game on game overview of Larssons’s passing performance it doesn’t allow the coach to see whether his performance has been of a greater benefit to the team. To help this we can then look at individual matches and use a spider-graph to show the players performance in specific key performance indicators.

Larsson Player Web

This now provides the coach with both a season long view and in game view to make a more objective assessment of Larsson’s performance improvement.

Results – Assessing a Players Passing Performance

The above graph has put into practice what we discussed above looking at the season on season passing performance of Sebastian Larsson.  Straight away we get a visual indication as to whether Larsson has performed at a higher or lower level to the same match of the previous season.

The red lines show the TE we would expect Larsson’s performances to fluctuate between which is around 12 passes +/-  anything outside the TE would indicate either an exceptionally good or poor performance.  We can also judge whether that performance is a one off or whether the player is consistently out performing their previous season.  Looking at the graph we can see that Larsson is consistently out performing his previous seasons passing stats except for the game against Liverpool which coincided with a 3-0 loss and a slight under performance against Aston Villa which also result in 1-0 defeat, however both of these performances fall within the expected TE.

What is impressive is the number of performances which have seen Larsson out perform his previous season’s passing stats that are over the line of TE indicating on initial inspection that his performance levels have improved.  Looking in slight more detail this may be down to Larsson  now being deployed by Martin O’Neil as a central midfielder rather than winger which you would expect to see more of the ball.

By breaking down Larsson’s performance into a spider-graph we can now determine if his improved passing performance against Newcastle for example has been of a greater significance.  As can be seen Larsson’s forward pass completion (PC) has significantly increased indicating that he has been more influential in creating attacks which is further supported by an increase in the number of chances created and crosses provided.  As a manager O’Neill could deduce that moving Larsson to centre midfield has had both a positive impact on Larsson’s influence on the game and also the performance of the team.


The aim of this post was to introduce a different way to assess a players performance levels using TE and Bland Altman plot which are common place in sports science.  The graph backed up with a more detailed spider-graph seems to be able to provide good data which would be useable by a coach and manager to objectively determine the level of performance of their player.  In this case we looked at Sebastian Larsson who has demonstrated a consistent improvement on his previous seasons performance indicating that he is having a better season.

I plan to revisit this type of analysis more often.

Who is the Most Consistent Striker in the EPL?

7 Jan


This blog has been inspired by the recent set of articles by the excellent Danny Pugsley, Omar Chaudhuri and Mark Taylor who have been looking at measuring player efficiency and teams reverting back to the mean.  As a result of these postings it got me thinking to look at  which strikers have been the most consistent and efficient over the past 3 seasons.


Using data from Who Scored I collated the top 20 goal scorers in the EPL over the past 3 complete seasons (2009/10-2011/12).  From this data I calculated the number of total shots taken (TS) by each player (SpG x Games Started), which allowed me to calculate the % scoring success (SS%) of each player in their respective season to determine the players striking efficiency.


Top Scorer

Out of the three seasons 11 players appeared in the top 20 goal scoring chart at least two times with only Rooney and Lampard appearing in all three seasons.  In total the 20 goal barrier was broken 11 times across all three seasons with Rooney being the only player to score over 20 twice with an excellent 26 goals in 2009/10 and 27 in 2011/12 both times finishing second in the golden boot race.  Only once has the 30 goal barrier been reach which was by Van Persie who scored 30 goals last season for Arsenal.

Table 1: Table to show top scorers in the EPL between 2009/10-2011/12 seasons.

The Most Clinical Striker

Before going into this it should be noted that scores for SS% have been based on the number SpG x Starting Appearances and has not included substitute appearances.  This will obviously have an effect on the outcome however it is my intention to revisit this in future posts.

Looking at the top 20 over the three seasons there is a very different luck to the table and supports the notion of players having an exceptional good season before reverting back to the mean expected performance.  In terms of repeat appearances in the list only Hernandez more than once coming first and third with SS% of 43.33 in 2010/11 and 32.68 in 2011/12 showing high levels of consistency. A few surprising entries to the more established players you may expect to see in the chart are Solomon Kalou and Florent Malouda. Kalou, who has the 5th highest SS% for his 2010/11 season, scored 13 goals at a SS% of 29.76, while Malouda who comes 14th with a SS% of 24.29 when he scored 12 goals.

Table 2: Table to show the top 20 players with the highest SS% across 2009/11-2001/12 seasons

Table 2: Table to show the top 20 players with the highest SS% across 2009/11-2001/12 seasons

Surprisingly none of the top scorers from the 3 seasons entered the top 20 most efficient strikers with only Berbatov and Van Persie entering the top 20 who finished in the top three for their respective season. In Van Persie’s most successful season with 30 goals he had a SS% of 17.63 with Drogba’s season best of 29 getting a SS% of 16.71 and  Rooney getting a SS% of 18.34% for his top scoring seasons.  This would suggest that to be top scorer you a team needs to create between 5 – 6 shots per game for a teams top striker to score.


The above initial analysis highlights how difficult it is for a striker consistently finish near the top of goal scoring charts with only 11 players managing to come in the top 20 for at least 2 seasons and only 2, Rooney and Lampard, finishing in the top 20 goal scorers in each of the three seasons. Whilst I have not looked at midfielders it is clear to see that Frank Lampard is the most consistent goals scorer followed by Clint Dempsey. However when looking at strikers whilst Rooney can be considered the  most consistent goal scorer Hernandez, while less prolific, has the highest combined SS%.  This consistency has been further proven with this seasons goal scoring exploits which has seen him with an SS% of 69.56 and 8 goals.

When taking into account the relationship between goals scored and league position Manchester United should rightly be top of the league and the favorites win this season with them containing both the most consistent striker of the past three seasons and the most clinical.  Add into the mix Van Persie who is again topping the goal scoring charts with 16 goalswith his highest SS% of 25.40 it is hard to look past them for the league title.

Building Play from a Throw In

11 May

Set Up

Full size pitch

2 gates 10yrds to be placed  past half way line by the players.


Blue team starts possession from a throw in (let players decide where they want to start) and aim to pass ball to outlet players between the two gates. Red team defends the gates and aims to win the ball to counter attack and score in the goal.

Key Points for the Blue Team

  1. Create an attacking opportunity as quickly as possible
  2. To gain more territory
  3. Retain possession

Starting Position

Anywhere along either touch line allow the players to decide where they want to start.  Change the player who throws the ball in between Full Backs, Wingers and Centre Midfielders to replicate real play.

Allow attacking team to decide where the two gates should be positioned.  E.g. Two wide gates, one wide one narrow.  Ask the players to think where the key spaces will be to break out to.

Remember the position you start will decide the type of throw and the position of the target players.

Allow play to flow to create real situations for the players to react to.

Phase 1: 8 v 5 + 2 outlet players

Start the game with an over load of 7 out fielder players and Goal Keeper vs 5 defenders.  Position 2 outlet players between the gates.  By playing with an over load this should encourage the players to make the pitch big and take risks when receiving the ball to pull defenders tight.

Make sure players on the opposite side of the pitch make big angles to allow a quick switch to score in the gate.


  1. When the ball is played to the outlet can you break quickly into the other half and score from a cross/thought ball etc…
  2. Take away the outlet players and play 8 vs 7.  Attackers have to score by either dribbling between the gates or playing  a team mate into the space.  Let the players decide the situation.
  3. Obvious but change the position of where the ball is thrown in!

Key Points

You may have set ideas for movements you want from your players at the throw in but allow the player to experiment giving time of both the attackers and defenders to discuss how they are going to deal with the situation. DON’T JUST GIVE THEM THE ANSWER

Don’t forget the importance of the Goal keeper as the will often be the outlet player.  Ensure he is comfortable enough to receive the ball and that he and the other players are confident he will be able to pass the ball out.

Are your outfield players confident to receive the ball under pressure.  Remember of your marked there must be a space somewhere else to exploit.

Where is the space to play the ball?

Ensure the team moves as a unit e.g. Goalkeeper pushes up to act as a sweeper. Full backs kuck in when required and provide maximum width at the right time.

6 Goal Game with Sweepers

10 May

Set Up

Pitch size 60 (wide) x 40 (length)

6 gates x 6yrds

2 teams 8 v 8 with 7 outfielders and 1 sweeper behind the 3 gates.


To score a goal in one of the three gates.  Reds attack the 3 gates protected by the blue sweeper and Blues attack the 3 gates protected by the Red sweeper. 

The two sweepers stay behind the gates but can move along the line to act as a blocker to prevent the other team scoring.  Teams can only score in a gate that the sweeper is not stood in. 

Key Points

  1. Can the attacking team move the ball quickly to displace the sweeper from the gates and score quickly.
  2. Can deep players play pass-set with forward players to create shooting opportunities.
  3. Create ‘player gates’ to pass ball through to turn defenders
  4. Can attackers players play of should of defender
  5. Can deep players be brave and attack space to draw in defenders
  6. Can the wide players create 2v1 overload
  7. Can central players inter-change positions in real time to create fluidity in attack.


Biesla proves he is still the master as Bilbao provide a defensive master class against Barcelona

9 Nov

Athletic Bilbao vs Barcelona starting line up

Athletic Bilbao 2 – 2 Barcelona

This was a match dominated by rain and defensive shape which for the majority of the game was won by Biesla.  Athletic Bilbao set up as a 4-5-1 with Llorente playing as the lone striker but supported quickly by the effective Susaeta and the eye catching Muniain. Barcelona lined up in their typical 4-1-2-3 shape with a front three of Messi playing as false 9, Fabregas playing in a free role and Adriano cutting in from the left.

Biesler’s Defensive Shape

Bilbao’s tactics was to play a high pressurising game with Llorente closing from the front on Pique and Mascherano.  The most interesting aspect was how Bilbao dealt with Barcelona’s attacking front 3. Messi is brilliant we all know but key to his game is the ability to pick the ball from deep positions where he has space to run at defenders.  Biesla countered this in two ways, firstly by man marking Messi with Martinez (normally a central midfielder) tracking him into deep positions.  In addition Bilbao squeezed the space for the ball to be played into midfield from defence.
This meant that Messi wasn’t able to get on the ball as much as normal but also when he was in possession there was always at least one player in close proximity.

Athletic Bilbao Defensive Shape

The second risk taken was for Iraola to man mark Adriano.  This was a very brave move by Biesla who has a reputation for innovative tactics, as when tracking players in a man for man system there are opportunities to exploit large spaces that can become vacated by a play being out of position.  Fortunately for Bilbao, Barcelona weren’t able to exploit this.

Bilbao Making Play Predictable

As previously mentioned Bilbao reduced the space for Barcelona to play in, which meant that for the first 25 minutes, attacks were started by Mascherano and Pique.  This would normally come through Xavi and Busquets with a series of quick short passes, however due to Bilbao’s defensive shape the space normally enjoyed by Barcelona was reduced.  This enabled Bilbao to make play predictable and force Barcelona to play the ball where Bilbao dictated. By making play predictable Bilbao were able to anticipate where the ball was being played allowing the likes of De Marcos and Iturraspe to intercept the ball quickly before releasing Susaeta and Muniain.

This game had a number of similarities between the recent friendly between Chile and Spain which ended in Spain winning 3-2.  In this game Chile played a high defensive line closing the Spanish defence from the front.  This was due to Spain having two holding centre midfielders which allowed Chile to push forward and press Spain in their own half forcing long balls to the Chilean defence.

Barcelona Start to dominate

Bilbao looked the better team for the first 25 minutes in both half’s, however no team is able to ‘out work’ Barcelona for the full 90 minutes consequently Barcelona did have large periods of possession.  Interestingly Guadiola’s men looked their most dangerous when aping Bilbao with a high defensive line.   Looking at the below picture you’d be forgiven for thinking Bilbao were playing from right to left with how high Barcelona’s defence is.  This shows just how high they pushed up the field to wrestle control of the game back.  This resulted in Martinez and Amorebieta forced to play the long passes to Llorente, who while being one of the only players in the league to be able to physically dominate Pique, meant the influential Muniain was bypassed.

Barcelona pressure as Bilbao tire

With Bilbao starting to drop deeper larger gaps appeared for the first time in the game gave Messi and co the space they craved for to run at the defence.  For the first time Barca looked like they might just over run Biesler’s over stretched team however due to poor decision making Guadiola’s team just couldn’t get the better.

Barcelona’s Mistakes

Bilbao were brilliant let’s make that clear they’re work rate was exceptional they used the ball intelligently for most of the game and stuck to Biesler’s tactics to the very end.  However for all the strengths Bilbao showed they were equally vulnerable.  With the man marking system put in place with the back four this left huge holes and at times confusion with who was doing what.  The two largest gaps appeared on Bilbao’s right with Iraola man marking Adriano this left space for Abidal to provide the overlap but outside of the first goal he barely broke forward seemingly being kept deep to keep Barcelona compact in defence.  The second frustration was the way the midfield seemed to try and force a killer ball to catch the high Bilbao defensive line.


This game represented  a more of a typical Premier League high tempo low possession, full of action game due to the high pressure both teams
put on each through the defensive lines.  Biesla will have been disappointed that his team didn’t take all three points and will rue the last minute mistake which left and easy finish for Iniesta.  However as Guadiola said in his post match embrace with Biesla “your team are beasts”. Yes they are and they provide hope for the future, whether they have a large enough squad to cope with the physical demands that will be placed upon them playing this game is still to be seen but Bilbao will be a match for anyone based on this performance.

Conte’s counter attacking Juventus is to decisive for Ranieri’s narrow Inter Milan

31 Oct

This was an important game for both managers, Juventus were looking to maintain their excellent start to the season and climb to the top of Serie A while Inter who are already onto their second manager or the season were in desperate need of 3 points to help kick start their stuttering season and prevent Inter from being dragged into an unthinkable relegation fight.

Starting Tactics

Inter Milan vs. Juventus Starting Line Up

Being the home team and in need of the win the most, Ranieri went for a more attacking midfield picking the attacking talents of Obi over the more pragmatic Stankovic.  While Inter lined up in the typical 4-3-1-2 formation with Sneijder sitting in the hole, when in possession Inter looked to stretch Juventus and pin back the attacking threats of Pepe and Vucinic with Zanetti and Obi both pulling out as orthodox wingers supported by Nagatomo and Maicon who pushed forward for the overlap as we see the modern full back do.

Unlike at home to Fiorientina during the week, Conte set up Juventus with a slight more defensive 4-2-3-1 as opposed to  the attacking 4-2-4. As the game went on Pepe and Vucinic were both required to play as orthodox wingers to track the runs of the Inter fullbacks to support Chiellini and Lichtsteiner making Juventus 4-4-1-1 when in defence.  This left Matri as the lone striker who was more often than not support by the tireless Marchisio who provided the link from midfield.

Inter’s All Out Attack

Considering the atmosphere and importance of the ‘Derby
d’Italia’ both teams looked incredibly comfortable on the ball.  Even with both teams looking to pressurise
the ball and reduce the space for each other to play in the first thought was always to pass rather than panic.  Juventus started quickly attacking down the right in the first minute but it was Inter who dominated possession in the opening 10 minutes.  Ranieri must be commended for the bravery of his tactics in the first half which saw his teams shape move to an attacking 2-3-3-2  with Zanetti and Obi providing width depending which side Inter were attacking down with the full backs acting as a second winger on the overlap.  While initially the attacks came down Juve’s right hand side with Cambiasso sweeping the ball the ball to Obi, most of Inter’s success came through the combined
brilliance of Zanetti and Maicon.  One thing you do have to admire is the speed that Zanetti has managed to maintain even at the age of 38 and again he made some mesmerising dribbles against a defensively poor Chiellini and Pepe.

While Inter had most of the possession and territory in the opening 10 minutes, outside of Cambiasso’s wasted chance from a Sneijder free kick Inter struggled to create any clear cut chances.   The key issue was the inability to displace defenders from key areas .This was down the attacking formation form Ranieri.  While Inter had large numbers of players forward by having two centre forward the space for late runs into the box is reduced.   Ranieri would do well to learn from Roberto Manchini’s Manchester City, who play a similar system to Inter, but use the full backs as wingers allowing the wide players to play a free role to occupy positions in the middle of the park.  This allows City to stay solid in the middle whilst also being able to have interchange (pardon the pun) between the likes of Silva, Aguero and Dzeko. Instead the attacking play down the wings while fast was too predictable for the Juventus defenders to deal with as a result it was easy for Juventus to clear their lines from defence.

Inter's ineffective attack

Juve’s Counter Attack

As discussed Inter when in possession were brave in committing players forward, however this left their defence widely exposed.  With Juventus having Pirlo and Vidal sitting deep in the centre of midfield against the lonely Cambiasso it was too easy for Juventus counter through the middle. Cambiasso was often out of position as a result and with no one to cover, Inter left wide spaces between the midfield and defence.

Juventus ont he counter attack

As seen in the diagramme below Cambiasso found himself ahead of Pirlo and Vidal to support the attack with only the left back Nagatomo providing any sort of cover to close Vidal.   With both full backs supporting the attack this left a 2 v 1 situation between Lucio, Chivu and Matri. While Inter had the numerical advantage Matri is an expert at running on the blind side of defenders to create space.

When the Juventus defence won the ball the first aim was to find Pirlo or Vidal to enable the team to play out from defence.  As soon as either was in possession the brilliant Marchisio would begin his run from left to right into the open space with Matri making the opposite run from right to left stretching the two defenders.   With Maicon in no man’s land and Marchisio unchallenged Marchisio was able to play a ball between the defenders for Matri to shoot.   Juventus should have scored twice through this move but for some wasteful shooting, however the warning signs were there.

Juventus’ 4 man midfield vs. Inter’s 3

The biggest miss match of the game and ultimately the downfall of Inter was the numerical advantage Juve had in midfield.  Inter went with three centre midfielders to
accommodate Sneijder, as a result they were always struggling to maintain Juventus’s 4 with Pirlo finding space to pull the strings particularly from the 30th minute where Juventus dominated.  Juventus’s first goal was best the example of this.  As shown against Fiorentina Lichtsteiner acts as a wing back providing width on the right which allowed Pepe to tuck inside.   With Obi having to tuck in to mark Pepe this leaves the space for Lichtsteiner to exploit form deep.  This space should of been marked by Nagatomo who had tucked in with the other back 3 to mark the solitary Matri rather than moving out to the left to reduce the space for the wide ball.

Due to Sneijder’s reluctance to mark Pirlo, the play maker had the space to pull the strings linking again with Vidal to switch the ball  wide for Lichtsteiner to play a low cross for Matri who shot against Castellazzi only for Vucinic to drive the rebound into the top of the net.





Ranieri’s Poor Decisions Making

The second half didn’t have the same excitement as the first partly due to Juventus being happy to pull 11 men behind the ball, but mainly due to the tactical decisions by Ranieri. The first mistake was switching Zarate for Castaignos who provided no
attacking threat. The second was introduction of Stankovic for Obi who again
provided no forward penetration and sat deep alongside Cambiasso (which they
needed in the first half).  Added to this Zanetti appeared to stop making wide runs meaning Maicon was up against Chiellini and Vucinic by himself.  This resulted in Inter basically playing direct ball to Pazzini, who did look excellent, which was easy for Bonucci and Barzagli to deal with. Juventus still had chances to go further ahead on the break with Del Piero who scuffed a shot wide and Estigarribia who ran clear but couldn’t convert.


Inter still don’t look settled and need to get back to basic’s before trying to be so expansive in attack.  Ranieri has to decide how he’s going to play Sneijder and whether he can afford to play 2 strikers and a player in the whole.  Conte can be very happy with his side’s
performance but there are defensive issues on the left which need to be looked at if Juventus are to mount a serious title challenge.

The key players for Juventus today were Vidal who works tirelessly for the team and is the perfect foil for Pirlo.  The other key player is Marchisio who is vital to the way Conte plays whether in a 4-2-4pullign of the front line or in a counter attacking 4-4-1-1 being the link between the midfield and Matri.