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We find valuable insights in match data to help football coaches make smarter decisions on how to improve performance



Vast amount of data are now available on performance in all major football competitions.  It’s a game-changer!  Many fresh and valuable insights on how teams play are hidden in this data. This information will enrich the analysis from traditional methods such as video analysis, and enable coaches to make smarter decisions on how to improve performance.

However, finding these precious insights is not simple: advanced analytic methods must be used, such as machine learning algorithms, and visualisation.  And nobody knows better than us how to do this. We pioneered data-driven analytics in football!  And for many years we have been analysing match data as provided by Opta, STATS, and Wyscout.

Soccerlogic have helped many football coaches discover those precious insights that drive successful performance. And can help you too improve performance by finding such insights  in your data.  Get an edge on the competion by contacting us at or by filling the form at the end.


Soccerlogic has the knowledge and experience to help you get the most from match data.

Sport Intelligence

After many years professional experience as a Business Intelligence (BI) consultant, Gianni Pischedda founded Soccerlogic (2013).  It was the result of his passion for football, and his knowledge and experience of the first (machine learning driven) data-mining tools.

In the late 90’s, he discovered that such tools could analyse match data as effectively as business data.  In practice, this meant that the performance of team and players could be analysed to an unprecedented level of detail. As in business, this made possible the discovery of many insights that could drive improvement in performance.  Taken together with the fact that the analysis could be done easily and quickly, and on what then was big-data, such tools gave users a major competitive advantage.  Football Intelligence was made possible and Soccerlogic soon followed.

Since pioneering this modern approach to the analysis of football, Soccerlogic has gained world leading experience of helping football clubs improve performance by exploiting match data.  It has undertaken many projects around the world, delivering performance improvements for football/soccer, AFL (Australian football) and Cricket. Soccerlogic has also carried out succesful application research of its novel method to Rugby, Ice Hockey, Tennis and Basketball.

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You only see what you know.” Goethe

Video analysis and coaches’ intuition are no longer enough to analyse the game and improve performance. There is data! Thanks to advances in technology, a lot of data is available on teams/players’ activity on the pitch.  And there are computers which can quickly analyse it to find many valuable insights on performance.  Such insights will enable coaches to better understand the technical and tactical performance of teams/players, and thus make smarter decisions on how to improve it.

Only computers driven by powerful machine-learning algorithms can find such precious insights.  Football is a complex game, and when it comes to analysing data, big-data, computers have an edge on humans. However, the knowledge and experience of coaches and analysts are crucial in translating these insights into actions that will lead to performance improvement in play.

That computers should play a leading role in helping football clubs improve performance was the key concept underpinning Soccerlogic’s Football Intelligence.  An intuition born out of its founder many years experience of using data mining software to drive performance gains in business.

This was back in 2003, when artificial intelligence (AI) only got a mention in academic papers and science fiction books. How times have changed!  Computer technology has evolved, and a faster rate than anyone had anticipated.  Artificial intelligence is now a practical reality, and its many applications bringing benefits to all areas of human enterprise.

This unpredictable development has not escaped our attention, nor have its implication for football and team sports in general. For a few years now Soccerlogic has been focusing on understanding how the deep learning algorithms that drive AI can provide more and better ‘intelligence’ for improving performance in sports.   It is something all coaches should be keen to explore, and that we are eager to share with them.


“People don’t know what they want (or can get – Ed.) until you show it to them.” Steve Jobs


How Soccerlogic can help you

Soccerlogic can provide valuable insights on the performance of a team/players; precious information that will enable you to make smarter decisions to improve performance and get an edge on rivals.  Some of the benefits we can provide are:

  • Identifying significant changes in performance metrics during the season
  • Success Analysis – identifying tactics that drive successful performance
  • Highlighting key stats – save time, focus on what matters!
  • Knowing the strong and weak points of your team and opposition
  • Quickly verifying whether your observations and intuitions are evidence based
  • Monitoring performance (yours and opposition) during a match in near real-time
  • Fast results – more time to understand, find solutions and implement them in training


We analyse match data to find those key insights that will enrich your tactical and technical knowledge of a team.
We help you make smarter decisions on how to improve performance, and gain a comptetive advantage.

Why choose Soccerlogic?

Soccerlogic pioneered the application of data mining/science in football, and over the years has gained unrivalled experience of extracting  valuable insights from match data. No other company can provide you with more useful data-driven insights to help you improve performance.

  • We are qualified: have higher education qualifications in computing and statistics
  • We have the skills: verifiable experience of working as data miners/scientist.
  • We have the know-how: we have been analysing football for over 15 years – much longer than anyone else!
  • We understand the data: we have been analysing Opta match data for years – the first using data mining tools
  • We have a methodology: a unique, succesfull method to help clubs quickly benefit from data mining/science
  • We use certified software : the same banks and other financial institutions use – you can trust our results!


Soccerlogic is also the only company that can provide you with tools and training to analyse match data.



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Analytics first, sport second

… Of course, anyone involved professionally in Performance Analysis of any sport has to ‘know’ the sport.  But this deep knowledge that Dean advocates is no longer of primary importance if one has an analytics role in a club.  Analytics is about analysing data, lots of data (big data?); hence the primary skill required for this task is knowledge and experience of advanced analytic techniques and tools.  Without this knowledge and experience is not possible for anyone to analyse data efficiently and effectively.  Any data!

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Possession chains and passing sequences

“A major objective of football analysis should be that of identifying event chains; that is to know what chain of single events led up to a specific outcome. For example, if a team scores, it is useful to know what events preceded the goal. For instance, this could have come after five successive short passes, or after a defensive player lost the ball to an attacking player who shot immediately….”

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Is xG any good at predicting game outcomes?

One can’t afford to ignore Expected Goals (xG) now that Match of the Day are giving the metric such a huge profile. I’m not a massive fan of xG, but I thought it was worth further investigation and so, thanks to data from StrataData ((, I have been doing some work on it.

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Finding changes in tactics and their impact on a match

… To discovery tactical changes, we try finding significant changes in performance by the two teams in the many binary contexts of the game; for example between 1st & 2nd half, before/after a goal conceded/scored, before/after substitutions, etc. We also split the match in ten segments, and look at changes in activity (ball touches) between one time interval and the next,

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Mohamed Salah at Roma and Liverpool

Te shot performance of M Salah at Roma an Livepool is compared in this post. The objective is to find any statistically significant (p<0.05) and non significant (but revealing) changes in performance.

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Balls and Runs – an attempt to Cricket analytics

“I am taking a rest from football, and since the battle for the Ashes  is on (England – Summer 2013), I have turned my attention to cricket.  Australia’s bowlers have been criticised by their lack of success, especially in the 2nd Test at Lords.  So here is my attempt at an analysis of their performance, as well as that of the England’s …”

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Gianni Pischedda

Founder/CEO @soccerlogic. A strong interest in computers drove Gianni to a career in IT after leaving university. He soon developed a passion for advanced application of computers, such as Expert System (an early development of Artificial Intelligence).

When the first software of this technology appeared on the market, Gianni was quick to appreciate that these (so-called) data mining tools could provide great benefits to business.  They could analyse large databases very quickly, and provide much valuable ‘intelligence’ to decision makers.  Gianni then spent many years marketing these software, and gaining much experience of how to use them.

His passion for football led him to experiment with analysing match data.  As in business, the objective was to discover valuable insights that could help managers/coaches improve performance.  This happened in the late 90’s, and it was the first time that such advanced method of analysis had been used on match data. The success of his tests prompted him to found Soccerlogic and promote his innovation among leading football clubs.

Gianni has studied at Middlesex Polytechnic, and at Lancaster University, where he was awarded MA degrees in Numerical Analysis & Computing, and Systems Engineering.



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