Football Analytics
Football analytics has risen as an exciting university course for data analysts. Although it might seem like a new field, the discipline extends back to Charles Reep, an accountant in the British Royal Air Force.
Charles Reepâs football observations gave us the idea of the âlong ballâ. Reep was convinced that a small change could make a team score three goals per match instead of two. A team’s chance of promotion depended on this extra goal. He deduced from football match data that the majority of goals were scored from fewer than three passes, and that it was therefore advantageous to move the ball forwards quickly. Reepâs long ball strategy worked, allowing teams to get that all important third goal. This was the humble beginnings of football analytics.
As anyone who has watched football knows, the score at the end of the match does not provide an effective summary for most matches. Football Analytics goes deeper than the score. After all the goal itself is not all that interesting, itâs the series of events that built up to it. This is where football analytics truly excels. For instance, some football analysts have estimated that a third of goals are scored after a set piece (where the ball is stopped, and returned to play). Therefore, itâs advantageous being able to analyse a teamâs history of free kicks and corner kicks to choose who should take the kick, and who should be ready to receive it.
Recruiting the best team with Football Analytics
When constructing a team, you would be wise to accept the assistance of football analytics. Utilising data-driven tools for opposition scouting and player recruitment entered the publicâs awareness following the book (and consequent film) Moneyball, by Michael Lewis. The book dove into the use of analytics in baseball, called Sabermetrics (Saber is short for the Society for American Baseball Research). Lewis showed that baseball game data could answer specific questions. The key question explored was how Oakland scouted and recruited a competitive team, despite operating on a shoestring budget. Using data instead of only reputation and scoutâs intuition, was literally a game changer.
Having video footage of a player youâre scouting and making an educated guess about their ability is one thing. A better approach is to use a playerâs past statistics, as well as machine learning to deconstruct your footage to create a more complete picture. Data captured from tools (such as trackers and cameras) should be analysed and augmented with other data. Football analytics allows you to take the guessing out of recruitment.
However, football analytics is not just for on the pitch…
Football clubs have benefited from analytics on the business side. Building a fan profile, and using web analytics has helped sponsorship departments to fill seats and sell merchandise. However, the use of actual football analytics outside of matches is not as well known.
Clubs gather large databases of their own subjective information on players. With a data literate team this will allow them to plan for the future and to budget.
Charles Reep used a pencil and paper to collect his findings. Thanks to the Internet of Things, we now have a much wider range of data available to us. This allows us to even predict injuries before they happen…
A football teamâs training loads can be monitored, measured, and managed more precisely than ever before. Clubs can use velocity tracking with a GPS to measure how much work is performed (the external load). The playersâ work loads in training and matches can be recorded. This includes their accelerations, durations running at different paces, and distances. Using these measurements football analytics can throw up a red flag (or card if you prefer) when a playerâs external load is setting them on a highway to the injury zone.
The goal for the future
The first goal is to become more future focused. Reep used descriptive analytics. He leveraged the data of past matches to create a sound strategy. However, this approach does not allow us to be tactical, and to anticipate the changing circumstances and challenges from match to match. We now need forecasting, predictive, and prescriptive analytics to have an edge.
The second goal is improving data literacy. Having a dedicated football analyst is one thing…but ideally the managers, coaches, and even the players need to be able to ask questions of the data they have available.
Proposing that the players become data literate might raise a few eyebrows. However, itâs the players themselves who are in charge of making the decisions during the match. Even the best plan can crumble when a player makes poor decisions. By understanding the data, the player can hone their decision making by seeing what outcomes will result from different choices.
Data informed decision making becomes most efficient once all of the decision makers are data literate. Unless this happens, there will most likely be friction between the data literate, and illiterate when leveraging data in decision making. When the managers, coaches, and players can all understand the data then they can all be on the same page.
Qlik for Football Analytics
Our solution is a fully customisable Football Analytics dashboard application, Smarter.Football. Smarter.Football is a dashboard application powered by Qlik, which has been tailored for Football Clubs, Football Scouts, Agents, Analysts, and Managers to address their need to report from dial to detail on a timely basis. The application is supported by a Self-Service BI data centric Qlik Sense architecture. This affords accelerated time-to-report, time-to-decide, and time-to-value. Delivering operational excellence and competitive advantage to your team.
