The analysis of match data using advanced techniques of analysis (analytics) provides coaches with many valuable insights to enrich their analysis of the game and make smarter decisions on how to improve performance.
That’s why coaches need Soccerlogic.
Soccerlogic is a game changer. It can discover useful insights in all types of performance data, such as match events files (Opta, STATS), GPS, fitness, etc. This knowledge will provide coaches with valuable information on how to get an edge on other teams, win games and competitions.
Soccerlogic offers a unique and valuable service to football clubs that want to fully exploit data to gain a competitive advantage. Find out more, or ask for a demonstration by contacting email@example.com or filling the form at the end.
“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
How Soccerlogic helps coaches improve performance
Soccerlogic data-driven analysis is unique. Valuable insights on performance are obtained from analysing play-by-play event data (Opta or similar) of many matches (thousands of events) using advanced data analysis techniques (including machine learning). Applying such techniques is the only way to extract hidden and valuable knowledge from large quantities of data, in business or in football. Such knowledge will enrich a coach understanding of the game and enable smarter decisions on how to improve performance.
Some details of this valuable knowledge are:
- In-depth, contextual performance metrics.. Performance is evaluated broken down by as many contexts (or conditions) a coach may wish to analyse, such as: Home vs. Away, 1st vs. 2nd half; before (or after) a goal, a substitution, a yellow (or red card); before (or after) a change of tactics (or formation), and so on. These contexts can also be combined, for example: Home vs Away and 1st vs 2nd Half, etc. The result is a series of contextual metrics (as opposed to traditional match specific ones) that provide a rich in-depth understanding of how teams and players have performed. These will enable coaches to quickly identify specific performance parameters that affect overall performance and make more appropiate changes.
- Conditional (context rich) performance metrics are compared (by match cumulatively) as the season progresses and signifcant changes identified and reported. All of this is done automaticaly by intelligent computer analysis – no need for coaching staff to spend hours doing it. The results are quckly available few hours after a match ends. These are the valuable insights that enable the coaching staff to make a more accurate evaluation of performance. Soccerlogic = more useful stats/metrics + more time use them and make better decisions on how to improve performance.
- Success Analysis – This context (or conditionl) analysis underpins the discovery of conditional metrics that drivs a team’s successful performance: Soccerlogic highlights what the team does well, significantly better than the opposition when it wins, and what does poorly when it loses. If there is a pattern that explains good or bad performance Soccerlogic will report it! And a coach can then make smarter decison on how to improve performance.
- Know your opposition as well as your team. Scouting reports of a few matches are not sufficient to get a good understanding of how rival teams play. Soccerlogic can enrich this by analysing (in the same way as described above) their latest matches, and gather a wealth of insights on how they play: their tactics, their strong and weak points, etc. It can also provide an objective comparison of their performance with yours. That’s a a lot of valuable inforrmation to help select the best team and tactics for the match. And is all all collected an put together quickly – it is all computer driven.
- Help in decision making. Are you faced with a difficult decision? Do you need to quickly check whether your gut feelings are objectively sound. Soccerlogic’s data-driven analysis can help you make the right decision. Has that player’s performance declined? Does the team plays better when player X or player Y is in the starting lineup? Has that new tactic been more or less successful than the previous one? These are just a few examples of how Soccerlogic helps you, the head coach, make many crucial decisions.
- Get more from fitness or GPS data. In fact, from any data you have or may want to use to monitor performance. Soccerlogic will crunch it with its sophisticated analysis tools and squeeze any useful insights to help you improve performance.
- Help in making the most of new technology or latest innovations. FIFA’s recent ruling that coaches can access match analysis reuslts in real-time is a game-changer! No longer they need to wait hours after the game for that crucial information that they may have missed during the game – they can get it almost immediately in (near) real-time. And by quickly reacting to it, they may be able to make those changes that could improve performance and alter the course of the game. Such action can make the difference between winning or losing. Soccerlogic can provide such information. It constantly analyses data on the game as it is played and report on how teams and players are performing. It can also notify to the coach that crucial information that needs to be quickly digested and acted upon to keep the team on a winning (or not-losing) course. That’s the advantage of computer-driven analysis!
There is more! Please ask for details or a demo at firstname.lastname@example.org
Gianni Pischedda founded Soccerlogic in 2003 to help coaches fully exploit the increasing amount of data available on the game. They would be able to analyse the game to a level of detail not possible with traditional statistics. And be rewarded by a wealth of valuable insights on how to improve performance and win games.
Starting in the early ‘90s, Gianni had been a pioneer of applying advanced data analysis to help businesses become more competitive. Years later, his passion for the beautiful game led him to try to do the same for football clubs. Soccerlogic was the outcome of his effort. Football Intelligence* was born !
Since pioneering this data-driven approach to the analysis of football (and sports in general), Soccerlogic has gained world leading experience of helping coaches make the most of match data for improved performance. It has worked with many clubs around the world: in football/soccer, AFL (Australian football) and Cricket. Soccerlogic has also undertaken research projects in Rugby, Ice Hockey, Tennis and Basketball.
* The terms ‘Football Intelligence” and “Sports Intelligence’” were coined by Gianni from ‘Business Intelligence” which at the time (late ’90s) was used to indicate the new and advanced techniques of data analysis being increasingly adopted by business to exploit data for competitive 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.
… 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 …”
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