Mcfc Analytics | Tips For Excel

Tableau Public Vector Diagrams using the MCFC Analytics dataset

The advanced data set allows for a more detailed look at how players distribute the ball

The data set provided by the MCFC Analytics project so far has excited analysts and football fans alike. Some are using it to help predict team’s chances of winning the Premier League, whilst others are using it to analyse their team’s next opponent.

One of the first tableau visuals I made showed how teams passed the ball. This was done using the standard MCFC Analytics dataset. It showed the number of passes forwards, backwards and left and right.

The advanced dataset however shows the x and y co-ordinates of where the pass took place and also the final x and y co-ordinates of the outcome. This lends itself to a more detailed (therefore more actionable) data visual. d3.js has some great visuals and one of them is this one that shows flight routes across the USA. So can we make a similar visual in Tableau Public using this advanced data set?

The Outputs

Below shows an aggregation of all passes and their direction. It’s pretty, looks like a firework, but makes less sense than having one per player. The green shading indicates the minute that the pass happened (light green indicates the start of the match, dark green towards the 90th minute)

Every successful pass made in the Bolton vs Man City game

Next we split these passes out by position. Now we can start to actually find insights in the data. Goalkeepers’ passing is generally the longest (not a surprise I know). Maybe you’d use this data to see how defenders switch their style of passing once they go a goal up/down. Reassuringly the visual shows Goalkeepers passing forwards only, so I guess I manipulated the data correctly (more on that later).

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MCFC Analytics – Work in progress

Below shows how Tableau can be used to show the movement of the ball over the pitch using the MCFC analytics data. Not much of a data visual this since it shows no more insight than watching the actual footage, but might give some people ideas for what they want to do with this dataset.

Tableau link here:

http://public.tableausoftware.com/views/MCFCTrail/Dashboard1?:embed=y

note – there’s no ‘play’ button with dashboards hence the YouTube video below…

MCFC Analytics Heat Map with advanced data set

The advanced #MCFCAnalytics data set has now been released…

I’ve had a play around and made the below visual using Tableau. It’s fairly self explanatory, showing where events took place for each player.

For those wondering how to view data provided in XML format you can simply use Excel 2007 (or above) and import data from an XML format (Data>From Other Sources>From XML Data Import).

Each row in the data set describes an event (a pass, an assist, a foul etc). It seems that there are multiple rows for the same event however with each row describing a different attribute (i.e. Aguero might start a dribble where in the data one row describes the movement, another describes what part of the pitch it was on etc.). I therefore quickly deduped the dataset based on the event timestamp. I’m certain that this is not the best/correct way to get an accurate picture of where players were performing “events” on the pitch but it should give a pretty good proxy. I’ve jumped straight in whilst only skim reading the documentation so I’m sure all answers are in there as to how to correctly manipulate the data. A lot of the events occurred outside of the (0,0) to (100,100) zone, I’m sure there’s an explanation for those (players and ball do leave the pitch at times I guess and from looking at the goalkeepers in the visual below it may be showing when the keeper kicked the ball directly out).

Tableau is great for making quick visuals that help you understand your data. Already it shows (as expected) that Kolorov hugs the touchline and Barry covers a large area of the pitch. You can add background images to charts that scale nicely, as shown below.

Come back in a few days [...]

How does your football team pass the ball? – MCFC Analytics

A data visual showing how every Premier League team passes the ball around the pitch

This visual (built with Tableau Public) shows the most common ways each team’s players pass the ball about. Once again it uses the brilliant MCFC Analytics data.

Does Michael Carrick only pass the ball sideways or backwards? Did certain teams use more defensive passing in away games? and so on…

Scroll down for more details on how to interpret the visual and how it was made. Notice how Brendan Rodgers had Swansea’s goalkeeper Michel Vorm playing the ball out left and right quite often compared to other keepers.

About Details on how this was made coming soon, next day or so. For now the image below shows that Wayne Rooney passed left more often than right. Against QPR he passed left 45 times. It’s light blue as he only passed left 45 times on one occasion, (the match against QPR).

For more info about this visual comment below or email me at:

What England’s EURO 2012 starting 11 should really have been according to data | Goalkeepers

MCFC Analytics data can reveal what team Roy Hodgson should have really picked for EURO 2012

MCFC have released a football dataset that contains event data for every player in every Premier League game last season. Let’s use this to see who should have REALLY been in the starting eleven for EURO 2012 and who should have been left at home.

For an image of the tool – click here

To download the tool – click here

The process – Goalkeepers: This analysis was all done in Excel. First take the MCFC Analytics dataset and identify the English goalkeepers. Without a lookup list I felt the best way was to limit the data set to players who made at least 1 save in a match (if a goalkeeper went through last season without making at least one save we can safely say they shouldn’t be considered). [edit - the 'Position ID' field can be used instead]

This leaves us with 38 goalkeepers. Next, filter down further to English goalkeepers. From the data we only have 10 English Premiership goalkeepers left:

Amos (Man Utd), Bunn (Blackburn Rovers), Foster (WBA), Hart (Man City), Kean (Blackburn Rovers), Robinson (Blackburn Rovers), Rudd (Norwich), Ruddy (Norwich), Stockdale (Fulham) and Turnball (Chelsea).

You don’t need a large dataset to know that Joe Hart is probably the best choice for goalkeeper, but what does the data tell us? (We’ll ignore the fact some of these goalkeepers don’t make themselves available for selection any more).

Now if we look at the total number of minutes played (below) we’ll see that Amos (90), Bunn (270), Kean (90), Rudd (99) and Turnball (180) have probably played too few minutes to be seriously considered. We’ll make a fancy “which goalkeeper is best” tool at the end of this section where you’ll [...]

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