Makeover Monday 2019 Week 1: NHL Attendance

If you've not heard of Makeover Monday before, it's a weekly social data project run by Andy Kriebel and Eva Murray. They've recently released a book, and the assorted blogs over the last 3 years are an excellent repository of knowledge.

Here's some notes on my take on this weeks data.

Data can be complicatedly simple.

When approaching a dataset, I always find myself zipping around to try and find out what's interesting to me. In a project such as Makeover Monday, this is as important as creating something insightful, or beautiful, or interesting. As ever, understanding the data and what's what played a big part in the first moments of playing around with this dataset.

Highlighted in blue are the original fields in the dataset. The rest are bits I've calculated. As you may note, after creating one calculation (Weighted Total Attendance), I got lazy and duplicated, then renamed the fields in this dataset.

But - at a row level, the data is each team, each season with seven data points.

Tableau Shuffle.

My approach of the Tableau Shuffle ended up leading me to a connected scatter plot - I was going to try an approach which looked at home and away attendances, and how they've changed season on season. However - two issues here. One, Vegas' team has only existed for a season and two, the overall league level view was a straight line. Not useful at all.

Maybe one to revisit another time.

So I reverted back to one of my favourite dashboard types - the one chart dashboard. Colour, ordering and visual cues were to be my ally here.
Here's what I ended up with.

Here are some notes on what we see.

• The colour scheme was a random choice - I picked a colour I liked then fiddled with the colour wheel (I used this https://color.adobe.com/create/color-wheel/) until I had a few which I liked. The font is Tableau which works fine.
• Sorting in this case is the average total attendance (both home and away) for the each team, highest to lowest. I used the subtitle to indicate the colour legend in this case, highlighting the red as the 2017-18 value.
• Given I'm a fan of context, the natural next question if you only have the most recent data is, 'how does this compare to previous seasons?' - so they're also visible, but in a less light format.
• The other piece of information I've added is the average of averages - showing the average attendance for all attendances in the data set for each team.

Finally, one part which Mark Edwards liked was the labelling of the maximum and minimum values. This was a bit fiddly - Tableau allows you to only label the maximum and minimum like so:

But, it only allows you align each one way. So if I wanted the top to align to the right of the mark, and the bottom to align to the left - I can't do that. Well, not easily.

I had two options.. I could add in reference lines and make them invisible (this is a trick I've used previously to give breathing room), or in this case I simply used a dual axis.

So each axis is identical, but the top mark section (the collapsed one) shows the maximum value, and is aligned Middle Right, whilst this one which you can see above, the bottom mark section shows the minimum value, aligned Middle Left.

The reason for this is so that I don't have to have a visible axis - so at a glance, the user can quickly see the diverging axis range.

Is this the best way? It depends. View the full viz here: http://tabsoft.co/2R13aXG

Would love to hear any thoughts & feedback! Drop a comment or tweet me, @Scribblr_42.

Thanks for reading!

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Introducing Project Griffon

This post was originally posted on The Information Lab: https://www.theinformationlab.co.uk/2018/10/30/introducing-project-griffon/

For more on Project Griffon, please follow this Tableau Community page: https://community.tableau.com/docs/DOC-22175

When I think about the Tableau platform - by this I mean all the products from Desktop through to Mobile - I think about the richness of information which can be garnered. I think about the wealth of information sitting within Tableau. In the same breath of talking about self-service, we often talk about automation and measuring impact. And as data people, the best way to do this is using the information and the tools we have in front of us.

Introducing Project Griffon.

With Project Griffon, we want to share example with you - tshe Tableau Community. The objective here is to share tools which use what you have in front of you to help explain more about what’s going on.

Let’s start with the basics.

A few months ago, I wrote about the beginnings of my journey to learn Tableau Server - and how it’s not as intimidating as it seems. My colleague (and Server Mentor!) Jonathan MacDonald wrote this awesome post about exploring the Tableau Server repository as a getting started, when it was first released.

One of the most powerful parts of Tableau Server, the Administrative Views, which reads and generates Tableau dashboards to serve up information about the usage of the Tableau Server. Getting the most out of these dashboards are easy - but somethings you want to build your own custom views. Perhaps you're interested in finding out your users and groups, and their login behaviours or maybe you'd like to follow Pablo Saenz de Tejada's lead and makeover some of these admin views?

Project Griffon's objective is to help you along this journey, by both enabling and supporting the build of reports and tools like this.

In this post I'll be talking about the repository itself, and navigating it to find out more.

Step 1. Make sure you have access to the Postgres Repository.
The method to turn this on depends on the version of Tableau you are using. For 2018.1 with TSM and above, use this post and for before 2018.1 and TabAdmin use this post.
This will allow you to access the Postgres database with readonly access which will enable you to create views.

Then.. Explore!

Using Tableau Desktop - or if you're on the new license types, Tableau Server's Web Edit function, which allows you to connect to Postgres in browser - you're able to connect up to the Repository using:

• The URL or IP address of your Tableau Server
• Setting the port to 8060
• Entering the readonly credentials which were defined earlier in the post

In JMac's post about exploring the Tableau Server repository he talks about some example connections and joins. These are documented on Tableau's website here http://onlinehelp.tableau.com/current/server/en-us/data_dictionary.htm but this can be tedious to scroll up and down.

So - in anticipation for this - I created an interactive visualisation in order to dig into these a little deeper into these tables.

Clicking the above takes you to a fully interactive detailed exploration of the Tableau Server Repository tables and fields. If the table has a join clause, you can click through and find the joins in here.

If you're interested in fields which have changed, use the filters on the left to swap between tables where there has or has not been changes version-to-version.

Next steps.
Getting started with the Postgres tables can be daunting, but what if you want to use the built-in administrative views as a starter for 10?

This is actually available on the Tableau Server machine itself - by this I'm referring to the computer or virtual machine where the Tableau Server application is installed.
Once you access the views in browser, hop across to your machine with Tableau Server installed and navigate to the Program Data folder. The rough URL should look something like
C:\ProgramData\Tableau\Tableau Server\Data\tabsvc\temp
(If you've installed Tableau on a drive not named C:\ you might find it in the D:\ drive.
This temp folder will be full of a lot of random files - but one which has a prefix which has "tabbed admin  views<numbers_Version)twb" - note without the .twb, which when added manually...




Creates a nice little .twb file which we can open in Tableau!

This will contain a user named 'rails' - but replacing that with the read only information that was set earlier when Postgres was turned on as a feature will have you good to go with a whole host of data sources!

Over the next few weeks, I'll be sharing example workbooks which anyone who has access to Postgres will be able to plug and play. Keep an eye out for the next one!

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3 reasons tsm (Tableau Services Manager) is exciting

This blog was originally published on The Information Lab: https://www.theinformationlab.co.uk/2018/07/31/3-reasons-tableau-services-manager-tsm-is-exciting/

As of Tableau 2018.2, we are saying a bittersweet goodbye to tabadmin, and a resounding hello to a Tableau Administrator's new favourite command line interface, Tableau Services Manager, or tsm.

This is one of many changes on Tableau Server in the new latest & greatest version, so be sure to check out my colleague Jonathan MacDonald's post on 15 big changes coming to 2018.2 on Windows.

To get a brief understanding of what tsm brings which is different to tabadmin, this post by  Tableau illustrates the difference in functionality.

1. It's all in browser baby!

One of the huge changes tsm will bring is a new-fangled in-browser experience for local administrators. For example, to complete the install, the page below will be shown - and yes, this means you don't have to be logged into a Remote Desktop in order to finish off an install.

This includes steps such as entering your license details and registering. All now possible to complete in browser.

Another funky addition due to this in-browser functionality is that you'll be able to administer on the go within your mobile device! Yes, yes -  it was possible using RDP apps in the past - but tsm makes this easier than ever.


As you can see, there is a lot of control, configuration and maintenance which can be tweaked just from the browser. If you have a test server environment, I would highly recommend installing 2018.2 on it, and getting stuck into the new in-browser features which tsm allows.

2. Tableau Server can be elastic!
This is huge.
As you can see from the above screenshot, tsm allows topology changes to not only be made in browser, but also with no downtime. This is impressive in itself, but it gets better.

Say for example you are a business who is running a high number of extract refreshes and background tasks overnight, but during work hours would like to have your server optimised for performance - this is now possible given that you can script topology changes with tsm.

So a batch script may run just after 7PM to re-optimise the Tableau Server processes to have more backgrounder processes, and then once all background tasks (both extract and any subscriptions for example) have completed and the workday begins, another script can execute which ramps up the number of VizQL processes.

Huge.

3. Taking Back Control
Finally, there's a lot more control with tsm. For example, when extracting log files from the browser interface on tsm, you'll be able to choose what to include and exclude, as shown below.

Further, you are now able to upload log files to Tableau Support directly from tsm in browser. Very, very cool! No need to worry about large log files when sharing to troubleshoot.

The method of upgrade is slightly changing as well, and this in turn can reduce the amount of total downtime in a multi-node cluster. The upgrade installer will lay down some groundwork before the full upgrade, and Tableau themselves claim that a 5-node cluster which used to take 75 minutes to upgrade was now done with 15 minutes of  downtime instead.

The full list of tsm commands can be found here - so all you Server Admins can check out what levels of control are now possible with tsm.

But be aware!
The introduction of tsm also means that any of your existing scripts which are running tabadmin (for example, for housekeeping, automated backups etc) will have to be translated to tsm

Luckily, Tableau have a great little translation guide on this here: https://onlinehelp.tableau.com/current/server/en-us/tabadmin_to_tsm_cli.htm

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Consultant's Diary - Calculated Field Pro-tips

This blog was originally published on The Information Lab: https://www.theinformationlab.co.uk/2018/05/08/consultants-diary-calculated-field-pro-tips/

I’m going to try and share some of the techniques and methods I use as a consultant. Using Tableau & Alteryx daily means that I’ve found I take small techniques, tips & tricks for granted. Hopefully this will be useful for you all.

Using Tableau almost everyday means you pick up a few shortcuts. Here's some of my favourites when it comes to calculated fields.
Between placements during my time in the Data School, I sat in on some training. Between creating a Pareto whale and supporting DS4, I wrote a quick blog about the 7 ways to create a calculated field in Tableau. Check out this post - and let me know if I've missed any - you'll find some others in the comments on that post.

What I want to share in this particular post is other shortcuts I typically use as I work on a piece of work in Tableau. Let's get started:

Setting up my calculation window


When you install a fresh new version of Tableau Desktop, you are presented with this fun pop up when you create a calculated field (using one of the 'traditional' methods) - but I personally hate this view. I much prefer clicking the little arrow on the right hand side which pops out a menu:


This is for two reasons; the first that you are able to see the explanation of each type of calculation - which I find super useful when both explaining the logic to others, and working out the logic for myself; and second you are able to see the syntax of the function you will use. The fact that each function is organised into sections is also useful, particularly given different functions have different affects on different field types.

Visual Cues
Tableau is full of visual cues -  From the small icons for field types, and elements on a measure to indicate different manipulations - I'm thinking the Table Calculation triangle, the Set venn-diagram and the Forecast arrow.

Within the calculated field window, this is no different.



Let's take  the above  as an example;

• [State] and [Sales] are orange given they are fields within the data source
• 'New York' is grey given it is a string
• SUM( ) is blue given it is a function

Other examples include:

• Purple for parameters
• Black or grey for 'literal' functions - for example #2018-05-01# returns the ISO formatted date or TRUE/NULL, as well as numeric literals (ie. 1.45 or 12)

Documentation Pro-Tips
When documenting calculated fields there are two pro-tips I'd like to share about adding comments.


The first one you might perhaps know about - this is where you use two forward slashes (//) to  write a comment within the calculated field. This is also useful for troubleshooting whether a long nested calculation is returning what you are looking for - by using // to 'grey out' parts of the calculation, and testing what value is being returned by each part.

The second is something I found when preparing for my Tableau Desktop Certification;


With this function, you're able to add a comment to your calculated field;


Which looks like this when you hover over the calculation.


A useful example of this is to either give a quick indication of what the calculation is doing, or what is inside it:


What are your favourite calculation shortcuts?

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Consultant's Diary - Wildcard vs. Dynamic Input

I'm going to try and share some of the techniques and methods I use as a consultant. Using Tableau & Alteryx daily means that I've found I take small techniques, tips & tricks for granted. Hopefully this will be useful for you all.

When speaking with clients, customers and friends about Alteryx, I always mention that adding multiple files in bulk with ease is a nice feature which Alteryx does well.
However, there are a couple of ways to do this, and in this post I'm going to explain two of them, and discuss the merits/demerits of each.

Using the * wildcard input.
A neat feature to input multiple files is to use *.<filetype> within the input tool. Let me show this example.

I have multiple CSVs, similar to shown below. I would like to bring these all through, and I know each file has the same data structure - by this, I mean each file has the same column headers.
One solution here could be to bring these all into Alteryx as individual inputs, and then use  the Union tool to bring these all together. But what happens when I have hundreds of csv files, such as below?



The trick here is to use *.csv in the input, and this will union the files as they come in. One thing to be aware of is that Alteryx will use the first file it reads in (which will be based alphabetically) as the one it uses as the 'master' to union the columns)
Below is how we'd normally input a file..



But when we want to bring in multiple files, we replace the text (in this case Transactions 2015 Apr.csv) with a * -- as below.



So this works nicely when you know that each file in your set has an identical structure, and is a godsend when you wish to combine/input/union many files at once within seconds.
However, if my CSVs are not in one place, but in multiple folders (perhaps categorised by country) the *.csv trick will not work.

So let's talk about the Dynamic Input tool.
The Dynamic Input tool isn't as straightforward as the *.<filetype> trick. In order to input multiple files, it requires the Directory tool (to read in file names from a folder) and a 'master' file to compare data structures - similar to the *.<filetype>, but more explicitly.
For this example, I am sharing the process I use when I input data from a folder which contains two seasons worth of football data. I'm interested in 2 things;

• The 'chances' file as this gives me shot level data BUT
• Only for the current season

The data I have is in this structure, and I receive it in the same format each week;


So the tool I need here in Alteryx to read all of these fields in is the File Directory tool and the Dynamic Input tool. These two in tandem can help me bring in ONLY the data I'm interested in seeing.



In order to only look at the .csv files, we use a similar syntax to the asterisk trick, and add a *.csv for the wildcard. In this case, we also want SubDirectories included. The output is below:



The output will look like something like this, with the full path, directory, file name - etc all in the output. We're interested in the 'FullPath' field here - but in my case I want to exclude all filepaths which don't include "2016-17 Data" - so a filter tool comes in before my Dynamic Input tool.



There's a few configuration points to make here:

• The Dynamic Input tool has to have a 'Source Template' - this is basically the .csv file you want to dynamically input with a guideline on the columns, and any other input configs you would like to make (ie. delimiters, whether the file name should be outputted as a field etc.)
• We want to set the tool to 'Read a List of Data Sources' - which in this case is the chances dataset for this season we've filtered to.
• The 'Action' drop down should then change  the Entire File Path (of the Input Source) and the field we're using to do this is FullPath

And let's see it work it's magic below..

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Consultant's Diary - Filter Customisation in Tableau

I'm going to try and share some of the techniques and methods I use as a consultant. Using Tableau & Alteryx daily means that I've found I take small techniques, tips & tricks for granted. Hopefully this will be useful for you all.

One of the things I love about Tableau is that I never stop learning new things. Recently, I was supporting my colleague Luke Stoughton teach Tableau to a group of university students - and I learned something about filters I'd never used or heard about.

When on a dashboard, there are a few methods to add interactivity - you are able to show different filters from your respective views/sheets, and apply them across multiple worksheets. Alternatively, and perhaps more obviously, you are able to use dashboard actions.

Reminder!

There are 3 kinds of dashboard action; Highlight, Filter and URL. These can be set to be triggered when the end user hovers, selects or 'clicks the menu' of the respective dashboard element. This can then be set across source sheets (ie. The sheet which you click) and target sheets (ie. The sheets which will filter upon these actions). A step further is to select the fields (dimension or measure) to filter.

So what did I not know?

When you create a filter a action, there is an arrow to right - which I've never paid much attention to. When you click this arrow, you are presented with a number of fields.

So let's take this really simple dashboard - Superstore Sales, a map and a bar.

There are two ways of creating actions - on a dashboard using the little funnel icon (only for filter though, with no customisation) or using the menu bar - Dashboard > Actions..

This then pops up a window, and if we select Filter, for example, this window pops up.

The arrow alongside the 'Name' field is similar to adding a <Parameter> field to your titles - it dynamically updates depending on the field. I'd never heard of this or even thought to use it before. In terms of user interface, what it can do - for example if you select your Filter action to be run on a "Menu" selection - it will update depending on the field you've selected. Here's an example:

This level of customisation can really help your visualisation be more user friendly and dynamic. A great example of this in the wild is Luke's Hygiene Rating visualisation (here: https://public.tableau.com/profile/luke.stoughton8750#!/vizhome/FoodHygieneRatings_0/FoodStandards). In this, Luke allows the user to click a URL action which changes depending on the establishment you select:

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The BBC Sport Chronicles: Part 3

Friends, I regret to inform you that BBC Sport is at it again. [http://www.bbc.co.uk/sport/football/43632176]

The above charming graphic is indeed proof that a bar chart, while the doyen of all charts, is not always the best choice. Let's talk about why.

What we see here is a comparison between Mohamed Salah and Kevin de Bruyne - easily the two best players in the Premier League this season. The chart attempts to depict Goals (a whole number), Assists (a whole number) and % shots on target (a percentage) on the same axis.

 Similar to the previous chart from BBC Sport which I deconstructed, this is ripe for a makeover.

A fellow data viz connisseur, Charlie Hutcheson, mentioned that while he 'could' read it, he wasn't sure he liked it. My thoughts were similar, if not erring on the stronger side of non-appreciation of this chart.

So let's go through my makeover steps.

Name	Value	Metric
Salah	37	Goals
Salah	10	Assists
Salah	62	Shots on Target %
Salah	38	Shots off Target %
de Bruyne	11	Goals
de Bruyne	20	Assists
de Bruyne	58	Shots on Target %
de Bruyne	42	Shots off Target %

I created my data table in Excel, and added an additional field called shots off target % - more on that later.

 

I then took this one a step further, creating a mini visual which is an alternative view to the currently published one.

Is it better? You decide. Have a go creating your own version!

 

My viz can be found here: https://public.tableau.com/profile/scribblr.42#!/vizhome/BBCChroniclesPartIII/FinalDashboard?publish=yes

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The BBC Sport Chronicles: Part 2

Just wanted to write a quick blog on the gift which keeps on giving, the BBC Sport data visualisation team.

Drawing from this article by Phil McNulty (http://www.bbc.co.uk/sport/football/43463551), there is a section where McNulty focusses on the conundrum around England goalkeepers, and who should wear the number 1 jersey at the World Cup.

And as part of this piece, was this wonderful bar chart;



The data is from opta, and each goalkeeper is grouped by the relevant number used. However;

1. How easy is it for me to compare the goalkeepers for clean sheets?
2. How easy is it for me to remember which colour bar refers to which metric?
3. What is the takeaway message here?

Of course, I then had a go at "re-vizzing" this, using the following data structure.

Metric	Player	Value
Appearance	Butland	27
Clean sheets	Butland	5
Goals conceded	Butland	49
Appearance	Hart	15
Clean sheets	Hart	3
Goals conceded	Hart	33
Appearance	Pickford	30
Clean sheets	Pickford	7
Goals conceded	Pickford	49
Appearance	Pope	27
Clean sheets	Pope	10
Goals conceded	Pope	22

Here's my process of revisualising this data.

The above changes are simple and help clear the purpose of the visualisation up - As well as helping the reader interpret the data more quickly and easily.

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Reflections: 2017

2017 is coming to a close, and I wanted to break my blogging hiatus to reflect on the last 12 months.

Working abroad

One of the best parts of the Data School was having the opportunity to not only work in the UK, but also abroad - and I was lucky enough to spend my final placement working between the UK and mainland Europe. The experience was really good fun, and the work exposure was awesome. I learnt about the trials and tribulations of working remotely, and across time zones - something I again was able to do later on in the year.


I also met these fun dudes while wandering around.

In fact, personally & professionally it’s been a fun year - I’ve visited 8 countries in total (OK, this list also includes Scotland…)

Uncharted territory

I joined the Information Lab permanently after finishing my 2 years as part of the Data School (but being a part of the Data School is forever, right?!) - and Carl Allchin (a great colleague, friend and mentor) encouraged me to shadow our Tableau Server guru, Jonathan McDonald. I’d had barely any experience of managing or administering a Tableau Server on my three placements, and while I knew some basic concepts, it was a whole new world to me. I’d thrown myself in the deep end with Alteryx Server on my final placement, and learned on the fly, but never had had the opportunity to do the same with Tableau.

Met some swans innit.

However, learning from JMac, I got more and more involved with Tableau Server - and I’m proud to say that between April and December, I pushed myself to pass both the Tableau Server Qualified Associate exam AND the Certified Professional exam. If you’d told me this on the 31st of December 2016, I probably would have laughed - Servers were a field and a concept I was alien to.

Marry this up with passing the Tableau Desktop Certified Professional exam, boom - I’m fully Tableau Certified! This is one of my biggest achievements of 2017.

Expeditions & conferences

I also was able to attend the Tableau Conference in Las Vegas this year, sandwiched between some travelling of the US - a bunch of us from DS1 road tripped across from Oakland to San Francisco, down to spend a few nights in Yosemite before driving through Death Valley to Las Vegas. The trip ended after the conference, where we spent some time around the Grand Canyon.

It was stunningly beautiful and super fun - and the Conference did not disappoint. Meeting & conversing with a vast range of people enthusiastic with Tableau was awesome, and developing my own knowledge was incredibly useful. My highlight, though? Meeting and speaking with Devs. Simply awesome.

So what’s next?

I’m never one to goal set across a long period of time - because both personal and professional life is fluid - but guidelines can be fun. And my only guideline to myself is to continually reinvent & develop my own skillset and knowledge. And of course, have a ton of fun on the way.


I hope to pick up my blogging routine and branch out across other media - and most importantly share more about my ideas for Tableau, for Alteryx and as much as I can in this space (with some room for sports analytics, of course)

I hope you, Reader, the very best health, wealth and happiness for 2018!

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Why your understanding of ‘elite mentality’ is false, and how data can help solve this problem.

In recent years, there’s been a rise in the usage of the term ‘elite mentality’ - particularly in the world of football (soccer). The application, in the way the term is used, is to describe someone who is a good player, but not ‘elite’ because they don’t have a winning mentality. This is measured by the number of finals they’ve won, and medals they have etc etc yada yada yada.

I disagree.
Like big time.

Cavani, Higuain, the Argentinian national team, Tottenham Hotspur… often are often all called bottlers who don’t have this “elite mentality.” Players who aren’t good enough; aren’t champions; aren’t

Poppycock.

Sport is about winning, ultimately. But sport is wonderful, beautiful and engrosses billions. And for many, an “elite mentality” is where a player, individual or team are able to win regularly, consistently and in style.

However - If an elite mentality exists, in my eyes, and perhaps even in a coach or even player/ex-player, it doesn’t come from what you win. It comes from how you grow through your career.

Goal Setting

[‘Big Brother’ surveillance in elite sport is pushing a culture with a machine mentality](http://theconversation.com/big-brother-surveillance-in-elite-sport-is-pushing-a-culture-with-a-machine-mentality-34214)



The above article talks about how surveillance is focussing on tiny flaws to ridicule managers. This is of course a by-product of any data - that with the right context or framing, it can be used to push a certain narrative.

But data can be incredibly useful - particularly when it can enable sports professionals to begin to quantify the unquantifiable - which in turns adds another layer of information to consider when diving into player or team analysis.

Let’s take an example.

I am a coach of a professional football team’s academy, and I have 6 players I’ve earmarked for progression into the first-team. But all have flaws.

-> The striker attempts to dribble for as long as possible and usually ends up being dispossessed due to trying flair moves
-> The wide midfielder in my academy enjoys cutting inside and shooting speculatively, rather than looking for the pass.
-> The central defender always thumps it clear, rather than starting an attack.

All of the above can be coached, but with the emergence of data, this progress can be tracked to see which of these players possesses the mentality & hunger to continually progress.

By this, I mean a player who not only is looking for coaches to tell him his strengths and weaknesses, but is one who will consistently and constantly look to improve.

Data can be combined with human coaching to show a player situations where they should change their decision making process, and even for coaches highlight areas where approaches can be gamified in training sessions.

Get the first player to pass at least twice and show him the value he can bring when 1-on-1 with more space to exploit.
Coach the second player to think twice before letting fly with the rocket, with a ‘points’ related coach (merits for ‘good’ locations, demerits for ‘less good’)
Work with the third player to improve their own the ball confidence and technical ability.

Talent Identification (TID) is the intricate art of wanting to use data to help highlight the right time to bring a player through the ‘funnel’ to first team football. Based on precedent, it’s then possible to build an understanding of what key factors are in tagging development, and how to best nurture talent.

Harry Kane

The strange rise and rise of Harry Kane was highlighted by the contrasting views - on one hand, you had the Kane hype train which was highlighting the second coming of a true world class striker; the others were saying it was a freak season due to the previous ‘failings’ of the striker on loan spells at Norwich, Leyton Orient and Leicester.



But amongst these loan spells, coaches spotted something in his game which they wanted to nurture. If you recall, Kane often started as a CAM, as well as a CF when he came through the academy - which is strange now looking back given his propensity to be a baller when it comes to finishing chances.

How can we find players with Kane’s mentality? Who have a hunger to develop, and which cannot be spotted in their on-field outputs, but simply from their responses to coaching, viewing how they apply recommended changes and their drive to improve.

What can clubs do now?

A super duper smart analytical approach will combine the expertise, experience and nous of a coach/scout with the vast amounts of data being collected in order to learn to be smarter with decision making.

Are there markers to help spot talent?

Can training session events be logged, and used in tandem with on-pitch output to track progress toward goals?



How can the qualitative match the quantitative to improve conversations had internally? Can this ensure a club doesn’t pay over the odds for an average player by simply bringing a existing expertise together with existing data?

The dream, as they say, could be getting to a level of data and competence where key markers are used to time purchases of players to capture the upward peak of their career.

Now that would be elite.

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