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

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 3^rd 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!

Firstly 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.

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.

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.

It Takes a Team of Individuals to Win the League

There have been some great end of season posts recently about the EPL by the likes of @JamesGrayson and @FootballFactman.  Each has taken a detailed look at the performance of teams as a whole and demonstrated that there are several indicators of success.  However looking at the stats from Who Scored I was amazed to see just how little Manchester United featured at the top of any of the team statistics.  Admittedly they did finish top of what may be considered the two most important metrics; goals scored and passes completed (see table 1).  However, what’s interesting when calculating the Coefficient of Variation (CV%), basically the significant variation between scores, we can see that United score significant lower in a number of key areas including through balls (33.33%), total shots per game (13.45%) and dribbles (25.15%) than the top team, which you would normally associate with a better attacking team.  While they have scored significantly more goals than their nearest rival (6.83%) when breaking the goals down into those scored from open play (1.96%) and from set pieces (1.96%) they are not that far ahead.

This led me to ask the question with United winning the league so convincingly without dominating the top of the ratings are individuals more important than the team?

There is a ME in TEAM

To test this hypothesis I again looked at the Who Scored stats on individual players as surely they must have players dominating at the top of these tables?

Looking at the overall player rating United suddenly started to show more dominance with the only team having 4 players in the top 20.  However both Liverpool and Tottenham both had 3 players who scored a higher average ranking.  Interestingly Arsenal and Chelsea only had 2 players and Manchester City had had a paltry 1.  So looking at this I started to conclude that maybe to be the best you need more individual players to perform at a high level over a season as opposed to a team of steady eddies.

@TedKnutson recent great work on identifying players has targeted the importance of assists.  Looking at the table 11 out of the top 20 players play for top 4 teams however Arsenal (30 in total), Chelsea (29), and Manchester City (23 in total) have 3 players each with United having 2 (18 in total). Interestingly when looking at the key pass metric United didn’t have a single player in the top 20 while Arsenal had 2, Chelsea 2 and Manchester City 2.

Hmm so I wasn’t really sure if this proves the theory or not, so I then went on to compare the number of players each team had that contributed the following:

1. An assist
2. 3 or more assists
3. A goal
4. Scored 3 or more goals

As the results show United had significantly more players contribute an assist and more than 3 assists, in addition they also had significantly more players score a goal however there was no significant variance in the number of players who contributed 3 or more goals.

What is an interesting aside is the number of players who have both scored and create goals for all 4 teams with none being lower than 11 proving how much football is now a squad game.

Conclusion

This piece suggests that to win the league you need both a group of 3-4 top performing individuals throughout a season and a team that can reduce the pressure if one or more of these top players fails to perform.  This is quite clearly a blend the United have managed to create time and time again.  While they might not have had as many players in the top 3 sports as Chelsea or Arsenal across the different metrics they have had a greater depth of players contributing.  This is clearly demonstrated by the number of players who scored and assisted goals significantly out performing their nearest rivals.

This finding is quite intuitive with teams like Arsenal and Tottenham having to heavily rely on the same top performers i.e. Carzola and Bale week in week out in order to stay in touch.  This indicates that to be a top 4 team you need 3 top class players leading the way in several categories but to win you need a full squad (look at how many players contribute to United’s goals and assists) to be able to back them.

DEFF: Do Shots Result in Goals

Building on the data I’ve started collecting on DEFF I thought it prudent to ask the question do shots actually lead to goals?  This question on the surface of it may seem mad however if we examine the stats there is method to the madness.

The first thing to accept is that shots are a rare occurrence in football when taken into account for a 90 minute game, with goals being an even rarer occurance, hence the soaring costs of strikers.  Therefore do the number of shots a team concedes actually relate to the number of goals or is it luck/skill of the specific player taking it?

Method

I have collected the number of total goals conceded (GC), total shots conceded (TSC) and the SC in the box (SCB) for each team playing in the EPL between the 2008/09 season and 2011/12 season (props to @dankennett for the data).  This has provided a sample of 80 teams over 4 seasons and 1520 games, making the sample size reasonably robust in terms of carrying out statistical analysis.

From this data as with the previous posts I have used a simple correlation to determine the strength of relationship between the different variables.

Results

Looking at the relationship between TSC and GC there is a moderately strong correlation (r2 = 0.51) which indicates that 51% of GC are as a result of the total number of shots conceded by a team.  As you can see this is quite a strange finding which indicates that because a team concedes a large number of shots it doesn’t necessarily mean they will concede lots of goals.

Presumably however there will be a significantly stronger relationship between the number of shots conceded in the box and the number of GC as this is what we’re told by the experts? Well as you can see the r2 value has increased to 0.64 (rounded up) which shows that 64% of goals conceded are as a result of shots conceded from inside the box.  The relationship is stronger but again you would assume it shots in this key area should account for closer 80%.

Conclusion

So what does this data tell.  Firstly that conceding or having lots of shots does not mean your team will score/concede lots of goals.  This leads to the several potential conclusions:

1. Luck plays a large part in goals being scored

2. The standard of shooting is extremely poor outside of goal scoring experts

3. Only a minority of players have the ability to convert chances

4. Defences are extremely efficient at preventing high quality goal scoring opportunities

5. Teams are extremely wasteful of opportunities or lack the patience to create a ‘genuine’ goal scoring opportunity.

The next step to add more clarity to this position will be to collect total shots on target data, who takes the shots and where are they taken from.  This will then allow us to understand just how much of a waste a time shots actually are.

DEFF: Relationship Between Goals Conceded and Points Dropped

I initially introduced the concept f measuring a teams true defensive efficiency (DEFF) in previous posts based upon several stats such as SoTC and SoTC/GC. This post looks at the relationship between Goals Concede (GC) and the number of points a team drops per season as opposed to wins.  Why look at this you may ask? Well as we know the team who drops the least amount of points wins the league and generally teams that have the worst goal difference get relegated (see Omar Chaudhuri’s excellent 5 added minutes blog for more on that).  However as seen in my last post the teams at the top and bottom of the goal difference table don’t necessarily follow the same pattern GCs. 

Aim

With this in mind I wanted to see if there was a relationship between the number of goals conceded vs the number of points lost during a season and league position. 

Method

Using data from www.statto.com for the 2010/11, 2011/12 and 2012/13 season I collected the number of goals each team conceded (GC) and calculated the number of points dropped (PD) by teams (points won – total points available).  I then divided PD by GC to calculate the average number of points dropped per goal conceded.

Results

The graph below shows the league position vs. average points dropped per goal conceded (PD/GC) (the variables).  The green line indicates the teams that finished in Champions League positions, the orange line shows the teams qualifying for the Europa League, the gray lines shows the half way point and the red line shows the teams in the relegation zone.  The legend on the graph show the different league seasons and the black line of bets fit indicates the basic pattern or distribution of the variables.

As can be seen the is a very clear bell shaped curve with the teams finishing in the top 4 and bottom 3 having, on average, the lowest PD/GC with teams finishing in the mid table table cluster of 8-14th having the highest.

In order to finish in the top 4 it would generally appear that a team needs to have PD/GC -0.2 below the league average PD/GC.  Therefore as shown previously a team finishing higher up the league can afford to concede a relatively higher number of goals per game over the season due to their higher ability to score more goals.  Conversely teams who are in the relegation should maybe not head quite as much on the side of caution as was previously thought.  While keeping a tight defense is an important quality and one which is constantly pointed to by the pundits, the data indicates that a team should look to risk conceding more goals in exchange for increasing their chances of scoring more. 

Southampton are an excellent example of this. In the past 6 games they have the a record of W3 D1 and L2 with a goal difference of 11-9.  This is a PD/GC differential of 0.78 which is top 2 form.   So while Southampton have conceded 1.5 goals per game they have committed to being brave and attacking their way up the table.

Conclusion

The aim of the post was to see if there was a relationship between league position goals conceded and points lost.  The initial data shows that that the top teams have the lowest as would be expected however more interesting is the relationship between PD/GC and finishing in the relegation zone and mid table. 

If anyone would like to look at the data I’ve pulled together or look at collaborating on future work please feel free to get in touch.

Which Team has the Best Defensive Efficiency (DEFF)

Aim

In the last post I introduced the concept of giving a true measure of a team’s Defensive Efficiency (DEFF). For full details of the 6 metrics click here for the last post.

The aim of this post is to rank teams by their DEFF.  This will be done by looking at the number of goals a team concedes (GC), the number of goals a team concedes  from total number of shots they concede (GCTS%) and the number of goals a team concedes from the total amount of shots on target they concede (GCSoT%).

Data Collection

All data has been collected and verified from @StatsZone app and verified against the @Squawka_Sports website both of which are powered by Opta.

A shot on target is defined as a shot that has either been saved by a goalkeeper or resulted in a goal including penalties and free kicks.  A shot off target is defined as one that was blocked, hit the wood work or missed the target including penalties and free kicks.

Results

Goals Conceded

When looking at the GC the table generally follows the same pattern as that of the current standings of the EPL with the4 most efficient defences in terms of goals conceded being in the top 5 of the league with Stoke being an anomaly (5^th vs. 10^th in the league), providing some more objectivity to Stokes reputation of being hard to beat and not giving much away.  A similar pattern can be seen at the bottom of the table with 3 out of the bottom 4 teams (Wigan 51, Aston Villa 50 and Reading 48 GC) with the most GC being in the bottom 4 of the league.  The biggest surprises in the GC Table are QPR who lie 13^th (41 GC) as opposed to 20^th in the league, and Manchester United who lie 6^th (31 GC) in the GC Table which indicates both how poor QPR’s attacking players are (19 goals scored) and how good United’s forwards are (62 goals scored).

Goals Conceded From Total Shots (GCTS%)

This is where the Table starts to become interesting and indicates a team’s true DEFF.  From there being a total goal difference of 19 goals from Manchester city (24GC) to Wigan (51GC) when converting to GCTS% there is only a 5% difference in efficiency from top to bottom clubs.  The first thing to note is the change in make up the top 4 teams; Sunderland, Swansea, WBA and Stoke, who only concede 8% of the total number of shots. What’s interesting is that these 3 teams also concede some of the highest number of shots yet concede very few.  Sunderland in particular show that while they have TSC of 443 they limit teams to low quality chances a good sign of DEFF.

The teams with the highest GCTS% again see’s Wigan as the worst club (13%), however Tottenham have dropped from 4^th in GC to joint bottom with a GCTS% of 13% from 248 TSC which is actually the least number of shots conceded in the league.  This indicates that either Lloris isn’t particularly safe or the chances the team are conceding in highly susceptible to conceding shots in high % goal scoring areas such as the famed double 6yrd box. 

Goals Conceded from Shots on Target (GCST%)

Once again there is quite a change at the top of the table with Chelsea and Swansea conceding the least (24%), Wigan are rock bottom again (38%). Swansea prove an interesting case conceding with one of the highest SoTC rates yet joint 2^nd in GCST% indicating that they may be both lucky and that Vorn is an exceptional goalkeeper.   Suddenly there is bit more context to the DEFF of Manchester City.  Previously coming out top in GC and GCTS% they now lie 14^th conceding 33% out of 77 SoT which for a team in 2^nd place is an abysmal DEFF.  This indicates that while they may not concede many shots the quality of the chances are either very high or Joe Harte isn’t quite as good as we possibly think?

The other major eyebrow raises are Tottenham (34% out of 89 SoT), Liverpool (36% out of 95 SoT) and Everton (32% out of 99 SoT) all showing that while these three teams may be a bit more gun hoe in their attacking play they are extremely susceptible to conceding goals.   Interesting QPR come 9^th (30% out of 138 SoT) reaffirming the previous statement on their inability to score as oppose to conceding goals being the reason for their league position.

Conclusion

Based on this initial analysis it would appear that GC may not tell the whole story of DEFF with GCTS% and SoTC% being a truer measure.  What is clear is that Wigan has by far the worst defence in the league and that QPR were right to invest so heavily in Remy to try and chase those elusive goals. Tottenham may have been a bit hasty in replacing Freidle as first choice keeper and that Liverpool are ‘work in progress’.

Moving forward I will look at the relationship between goals concede and TSC to determine just how much a causal effect conceding shots are on conceding goals.

Measuring Defensive Efficiency (DEFF)

Aim

The aim of this post is to introduce an objective assessment of individual teams Defensive Efficiency (DEFF) to determine if the team that concedes the most goals is actually the worst defense?  While a team may seem to have a relatively high DEFF does this actually hold true when comparing the number of goals conceded to the actual amount of shots they allow an opposing team to have.  For example a team may only concede 20 goals in a season which indicates a high DEFF but if those 20 goals came from 30 shots on target then you would have to question both the quality of the goal keeper and the quality of chances that the defenders are allowing the opposition to have.  It may be that a team doesn’t conceded due to them having such a high percentage of possession however they’re concentration when they don’t have the ball may actually be very poor consequently allowing attackers to find space to exploit or to gain overloads in key areas of the pitch.

Example DEFF for Arsenal

To try and answer this I have looked at developing several metrics to give a truer measure of a defenses true DEFF.

1. 1.       Goals Conceded (GC)

This does exactly what it says on the tin and is the total number of goals a team concedes over a season.  GC will be further broken down to GC at home (GCH) and GC away (GCA) to give an indication of whether a team’s DEFF is higher home or away.

1. 2.       Goals Conceded Difference (GC-D)

This is an indicator of a whether a team’s DEFF is better at home or away and is calculated as follows:

GC-D = GCH-GCA

Where there is a +ive number left (i.e. +5) this indicates that a team has a lower DEFF at home (conceded more goals at home than away).  If there is  -ive number this indicates a team has a lower DEFF in away matches (conceded more goals away than at home). If the number is 0 this indicates that at a team has an equal DEFF home and away (conceded the same number of goals home and away).

1. 3.       Total Shots Conceded (TSC)

This is the total number of shots conceded by a team over the course of a season.

1. 4.       Shots on Target Conceded (SoTC)

This is the total number of shots conceded by a team that were on target over a season

1. 5.       Shots on Target Conceded % (SoTC%)

This is the percentage of shots conceded by a team that were on target and is calculated as follows:

SoTC% = SoTC/TSC

Calculating the SoTC% will give an indication of how effective a defense is at reducing the number of goal scoring opportunities conceded which is a key trait of a strong defense.

1. 6.       Goals Conceded From Total Shots % (GCTS%)

This is the percentage of goals conceded from the total number of shots conceded and is calculated as follows:

GCTS% = GC/TSC

1. 7.       Goals Conceded from Total Shots on Target % (GCSoT%)

This is the percentage of goals conceded from the total number of shots on target conceded as is calculated as follows:

GCSoT% = GC/ToSc

The reason for calculating both GCTS% and GCSoT% is to give an indication of the quality of shots given away by a team i.e. the better the quality of a chance the more chance of it being scored but also gives an indication of quality of the goal keeper by assessing how many shots that they have to save are actually conceded.

Conclusion

The above 7 initial metrics will start to provide a more objective assessment of a defenses true DEFF.  This post has acted as introduction to how DEFF will be measured.  Rather than going straight into the results I will start to post blogs breaking each section of DEFF down to help build up the picture of DEFF in the league and if it can help predict league position and the number of goals a team will conceded in a season.  There are some initial restraints which are worth pointing out from the start.  Firstly the assessment doesn’t take into account the strength of the team or opposition which can change due to injury suspension or team rotation.  There is no weighting for the league standing of the opposition as well for example you expect a top 4 team to have more chances against a bottom 4 team.  Finally TSC and SoTC do not take into account home or away bias.

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

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 3^rd 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

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.