Vast amount of data are now available for all matches in major football/soccer competitions.
It’s a game-changer! Many precious insights on how teams play can be found in this data.
These enable a coach to enrich his analysis of the game, and make smarter decisions on how to improve performance.
However, finding such insights is not easy – using advanced analytic methods based on machine learning algorithms is a must, as well as visual analysis. What Soccerlogic has been doing for years! Nobody can match our experience – we pioneered data-driven analytics in football. We were first, and are still best!
So, if you wish to make the most of your data, trust us to do it. We have helped many coaches drive improvement in performance by finding fresh and valuable insights in their data, as well as that from Opta, STATS, and others. We can help you too! Find out how by contacting us via email@example.com or sending the form at the end of this page.
“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
After working for many years as Business Intelligence (BI) consultant, Gianni Pischedda started Soccerlogic in 2003. It was the result of his passion for football, and his discovery that analytics software developed for business could also analyse match date very effectively. This meant that he could provide coaches with many fresh and valuable insights on how to improve performance.
Football Intelligence was born!
Since pioneering this modern approach to the analysis of sports data, Soccerlogic has gained world leading experience of helping coaches improve performance. 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.
“You only see what you know“, Goethe
Video analysis and coaches’ knowledge/intuition no longer are enough to analyse the game and improve performance.
Of course, spending millions on buying good players can help. There is a cheaper alternative – make the most of data!
Thanks to advances in technology, a lot of data is available on teams/players’ activity on the pitch. And there are software 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.
Computers driven by powerful machine-learning algorithms must be used for finding such precious insights. Football is a complex game, and when it comes to analysing match data, humans are no match to computers. Only coaches, however, have the knowledge and experience to translate their valuable findings into training decisions that will lead to performance improvement in play.
That computers should play a leading role in helping coaches 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 studying how the deep-learning algorithms that drive AI can provide coaches more and better ‘intelligence’ to improve performance. That will certainly come in the near future. Meanwhile coaches can count on Soccerlogic to continue to stay ahead in the game of exploiting existing technologies to provide them with those insights that enable smarter decisions on how to improve performance, and gain an edge on the competition.
“People don’t know what they want (or can get – Ed.) until you show it to them.” Steve Jobs
You can trust Soccerlogic
We pioneered the analysis of football with advanced data mining tools, and over the years have gained unrivalled experience of using them to find valuable insights in match data.
> We have the educational qualifications: higher degrees in computing and statistics
> We have the skills: verifiable experience of using advanced analytical tools in business and sports
> We have the specific know-how: over 15 years of analysing football data – much longer than anyone else!
> We are familiar with the data: we have been analysing match data (Opta and others) for years
> We have a methodology: a unique, proven method to help clubs quickly benefit from data mining/science
> We have the right tools: we use certified software – what banks and other financial organizations trust
> We won’t make you wait long to get the first results: few days, a week at most
We have the tools, the know-how and experience to help you make the most of your data!
… 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!
“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….”
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 ((www.stratagem.com), I have been doing some work on it.
… 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,
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.
“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 …”
After university, a passion for computers drove Gianni to a career in IT. His interest in Expert System (an early development of Artificial Intelligence) was crucial in his appreciation of the potential of analysis tools based on this technology. Among the first to employ machine-learning algorithms, these software could analyse large business databases very quickly. The result – much valuable information on how to improve performance could be discovered.
After becoming highly skilled in using these (so-called) data mining tools on business data, his passion for football drove him to experiment on match data. His discovery that they could be very effective in analysing performance in football, as well as other sports, led him to found Soccerlogic.
Gianni 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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