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We help coaches discover valuable insights in match data –
to improve performance and win games





Today, coaches have access to a wealth of data on how teams and players perform on the pitch, and computers can quickly analyze it to reveal valuable insights. However, computers alone cannot replace the knowledge and experience of coaches. They are needed to filter the information discovered by computers and integrate it into their analysis. This is the concept behind the founding of SoccerLogic, which uses artificial intelligence and machine learning algorithms to analyze match data and provide coaches with better intelligence for improving performance. The use of AI in football and team sports is something all coaches should explore, and SoccerLogic is dedicated to sharing this technology with them.

Soccerlogic’ computer analysis discovers valuable insights in performance stats.  These will assist coaches to make smarter decisions on how to get an edge on the competition. Soccerlogic can analyze various types of performance data, including match events files, GPS tracking, and fitness data. In addition, it provides coaches with a sophisticated visual analysis tool, allowing them to fully utilize match data for competitive advantage.


SoccerLogic is a very powerful Performance Analysis software package that will enable the coaching staff to both determine and improve the level of performance. Having worked with the product, the capabilities and extent of the analysis available is very impressive, allowing the coaching team to assess the interactions occurring within the game to an unprecedented level.”  Dan Bishop


The Soccerlogic advantage


  • Computer analysis (AI, machine learning) that quickly finds valuable insights in vast amount of performance data (events, GPS, fitness).
  • The ability to discover useful insights in various types of performance data, such as match events files, GPS tracking, and fitness data.
  • A sophisticated tool of visual analysis of match data, which can show graphically the position of the players and the movement of the ball between them at any time during the match.
  • The ability to compute performance stats not only by match but by various contexts or conditions, such as home vs. away, 1st vs. 2nd half, and before/after a goal or substitution.
  • Automatic and quick analysis of performance stats, freeing up coaches’ time to study and use the results to improve performance.
  • The ability to quickly identify and report significant changes in performance in all specified contexts, with results available minutes after the match ends.
  • Help in making difficult decisions by verifying if gut feelings are objectively sound, by relating match outcome to performance.
  • The ability to discover what drives a team’s winning (or losing) performance and highlight the team’s weak and strong points, by finding patterns and trends in many matches.

Sports Intelligence

Soccerlogic was founded by Gianni Pischedda in 2003, with the goal of assisting coaches to make sense of the increasing amounts of data on performance being made available to them. Its innovative data-driven approach to the analysis of football provides coaches with fresh and valuable insights to improve performance.

Before starting Soccerlogic, Gianni had already been assisting business companies in improving performance with computer analysis of their data, and had gained much experience in this field. He saw the potential for using these powerful tools of analysis to improve performance in football and other sports, which led to the creation of Soccerlogic and the concept of “Football Intelligence” or “Sports Intelligence.” The company has gained world-leading experience in helping coaches make the most of match data for improved performance, working with many clubs around the world in football, AFL, Cricket, Rugby, Ice Hockey, Tennis and Basketball.

*The term “Football Intelligence” and “Sports Intelligence” were coined by Gianni from “Business Intelligence” which at the time (late 90s) was the name given to the advanced techniques of data-driven analysis being widely adopted by businesses for competitive advantage. 



Does scoring before halftime gives an advantage?

…  Only in the Champion league such goals appear to give a small advantage, but only for away teams.  In contrast, there is a small but significant gain for home ones in the Top 5.  But, the overall picture shows that goals in the last 5 minutes do not affect outcomes, and when they do, the result is more often negative than positive.


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!

Possession chains and passing sequences

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

How good is xG at predicting match 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.

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 time intervals, and look at changes in activity (ball touches) between a time interval and the next,

Mohamed Salah at Roma and Liverpool

The shot performance of M Salah at Roma and 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.

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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