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Former Member

Being able to make data driven decisions has become a necessity rather than having an option for businesses. Lots of sectors adapted or adapting to transformations but football sector could not pick up the pace even though it has a huge market and its universal popularity is preeminent.

We wanted to use Analytics Designer to provide ground step for football clubs for digital transformation.

Application consists of 3 main sections:

  1.       Main page displaying the overall performance of our team and team players as well as league standings.

  2.       Match Detail page to analyse matches individually for gaining insights.

  3.       Player Detail page to compare and analyse players.

Data Source: European Soccer Database, Kaggle[1]


Application description

For the application, we choose to analyse 2014/2015 and 2015/2016 seasons of Manchester United FC from Premier League.

On the main page we can see the overview of our team in the current season. Average overall rating of the players of the team can be seen in the tiles at the top of the page by their roles.

Below the tiles has been divided into three parts;

  1.       A field shows top 11 on our team for the current moment.

  2.       All of our players in the team with their overall ratings.

  3.       League standings.

On the second page, we have 2 sections with 2 custom developed cascaded dropdown filters using advanced scripting functionality of the tool. Everything dynamically changes when user interacts with dropdown filters including team logos and R Visualization. 

On the first tile we have created an R visualization. Using coordinate based shoot events information and R integration of Analytics Designer. We filled the empty matrix with shoot events from the dataset and plotted with ggplot2 package.

On the second tile. We have the match results with details including scores, attempts, possession, accuracy, cards and logos of the teams which also dynamically changes when user selects the week and season from the dropdown filter.

On the player details(3rd) page users can compare performances of the players from the radar chart visualization also they can see the best performing players by overall rating and observe their other metrics from the table.

The filtering can be done using the custom developed dropdowns for enhanced analysis which provides cascaded filtering. So when user selects the team and position only players of that position in that team shown.


We aim to deliver an application that provide support to the football coaches and executives for analyse their team. Usage of JavaScript and R in the Analytics Designer helps for getting out of most from your data.

Even though analytics designer functionality has just been added freshly we noticed a lot of new implementations are possible when you dig into features. Also learning curve was very impressive especially with the improvements on the new script editor.

Special Thanks to my colleagues from Itelligence TR Analytics Team; Halil Savas, Vahit Kuruosman, Ridvan Gurhan Barisik, Cagatay Cevik, Muhammed Mutlu.


[1] Overall ratings has been retrieved from FIFA


PS. This project is part of SAP's Analytics Designer Hackathon. You can find more information here.
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