Soccer fans: Get ready for the month-long feast of international football that is the UEFA European Football Championship. Here at Sisense, we’re particularly excited because the tournament is more than just a festival of skill and athleticism; it’s a clash of analytics insights.
Like pretty much everything else in the world, football has become more data-driven than ever, so when the 24 teams set out to win the championship on 11 June , you can bet your bottom Euro that each team’s tactics, formation, and training will be shaped by a mountain of data. We can’t wait!
In the modern game, analytics is an essential part of a winning formula that has revolutionized football teams and the way they play. Of course, at Sisense, we see this as a striking example of how companies of all kinds can also be transformed by embracing analytics and applying actionable intelligence derived from multiple data sources.
Infusing analytics into workflows and products is the key to data utilization.Learn why
Football clubs adopt analytics to seek an advantage
Until the mid-’90s there was little more than pen and paper, then video (VCR and DVD), that coaches could use to analyze play. FIFA didn’t even start counting assists until 1994! The 1990s also saw Manchester United adopt analytics and become a dominant force in English and European football. It’s no surprise that rivals followed suit and that by 2010 analytics were widely used by top teams in leading international leagues.
Like every other business, football has experienced rapid technological advances that generate and capture data from training and match play. And also like their counterparts in the business world, coaches are relying on metrics to guide their decision-making.
Gleaning actionable intelligence from disparate data sources
Football teams rely on huge amounts of data drawn from countless sources to take their play to the next level: Internet of Things sensors and other devices connected to the internet use GPS to track players and the ball’s movement in real time. Optical tracking can pinpoint the position of players on the pitch 25 times a second, in relation to the ball, opposition, and teammates.
In training, wearable devices measure players’ workload, movement, and fatigue levels to manage their fitness and positioning and optimize their performance during play. Sensors in these devices connect to cellular phone transmitters or the club’s Wi-Fi network to monitor the data feeds.
The data collected by these devices is used to design personalized training plans. Coaches can also see in real time during matches how each player is performing to help guide strategic substitutions. This is infused analytics at work: Wearable devices deliver data and insights directly to the coaches, enabling them to make decisions and transform teams’ performance without technical data expertise.
Plus, modern data storage on the cloud gives teams the ability to collect and mash up the vast volume of data points from these devices. Big data analytics and artificial intelligence enable the simultaneous processing and analysis of data from many sources to measure and even predict performance. These developments have added a whole new dimension to data analysis.
The same trend has happened in business. Technology now offers leading companies more ways to generate data than ever before. As top companies began to benefit from the analytics they had adopted, increasing numbers of other organizations followed their lead. Analytics has proven to be a winning formula, both on and off the pitch.
How analytics makes winners: Possession and intensity are 9/10ths of the score
The most valuable intelligence for coaches shows what happens in real time, so they can change the shape of their teams and behavior to increase their likelihood of winning. These types of insights are mainly gathered from playing logs, video and GPS tracking, and spatially related data.
Heat map data visualizations have shown teams that keep possession of the ball and maintain high intensity are most likely to score goals and win games. To illustrate this, let’s look at one of the most shocking scores in international football. On 8 July 2014, Brazil suffered its worst ever defeat at the hands of Germany, losing 7-1 in the World Cup semifinal. It was a national humiliation, driven by the Germans’ highly disciplined, possession-based, pressing football.
The heat map shows that Germany swarmed the pitch, with intense movement and possession in critical areas down the wings and directly in front of the Brazilian goal. Their intense pressing denied Brazil time and possession. This analysis shows how possession is a requisite for victory.
Other views show coaches not just how the team operates as a unit, but how effective individuals are playing in any time frame — whether it’s a single passage of play, a half, a full game, or even a whole season. Let’s look at a heat map of Robert Lewandowski’s play for FC Bayern Munich in its imperious 2019/2020 Bundesliga and Champions League winning season.
The map shows how Lewandowski roams around the attacking half so he can get into scoring areas that have a higher success rate. The vast majority of his goals come from inside the penalty area (almost always from a central area), but the way in which he arrives in the space varies, and that’s due to his positional flexibility.
This isn’t gut instinct dictating plays; this is analytics.
Team data can also be analyzed as a network, in which nodes represent players and the lines between the nodes represent interactions, such as passes between teammates. Coaches can identify different types of interactions and encode different types of events. This data allows them to identify, change, and test the effectiveness of typical passages of play. In a study of passing networks in football, analysis of a Barcelona versus Real Madrid match in 2018 illustrates this point.
Another recent analysis confirms that the leading teams in Europe’s top five leagues (Bundesliga, La Liga, Ligue 1, Premier League, and Serie A) achieve their success with more passes per match, particularly in the Bundesliga, the Premier League, and Serie A.
More passes also mean less direct, “long ball” play, as the following chart shows.
Here, we see that in all the big leagues, the length of completed passes toward goal has declined in recent years.
Once again by analyzing data, we can identify the winning way to play football, and using the same techniques on team analysis, we can determine which teams do it best and how.
Enhanced coaching: Real-time data and predictive analytics
But the power of analytics doesn’t stop there, because business managers and coaches can now use predictive analytics. In football, this means using analytics to extrapolate what could happen if team formations are altered. Coaches can then tailor training, strategy, and individuals’ roles according to data about their next opponents. Business managers can use data in the same way to tailor their approaches to different customers, staffing levels, inventory, and more.
A metric with predictive power is expected goals (xG), which measures the quality of players’ shots in attacking play and the probability the shots will result in goals. xG uses algorithms that account for factors such as distance from goal, angles, and more. It enables coaches to predict the best positioning of players and patterns of play to optimize scoring opportunities. In this case, analytics provide insights into the most effective strategies to apply in different situations.
With that in mind, data can be put to work to help recruit the right team members for future success or fast-track others.
There’s a wealth of data about players’ strengths and weaknesses, which can be hugely useful to gain deeper insight into individuals’ KPIs. What’s required to harness the power of all this data is an analytics platform that can handle huge and growing sets of data points from a multiplicity of live and cached sources, then visualize them in ways that can provide fast, comprehensible, and actionable insights.
There’s such an appetite for data that it’s not unknown for coaches to even use player data from the EA Sports FIFA video game series to help determine whether a player should be targeted in the transfer market.
Companies have emerged that offer clubs data-driven guidance on recruiting new players. Identifying lesser-known talent in lower-profile leagues can save clubs a fortune on transfers, as it shows what talent exists outside the biggest leagues (think “Moneyball,” but for football).
For example, Serbian champions FC Red Star Belgrade used this kind of service and discovered Lorenzo Ebecilio, a Dutch midfielder playing in Cyprus. An analyst in London studied Ebecilio’s data and recommended him as a fit for Red Star. The club took a chance on him, and he played an important role in the team that unexpectedly made it through to the European Champions League group stage. It’s proof positive that the future of recruitment in football, as in business, can benefit from using analytics.
Data makes winners
The use of analytics in football has exploded as more and more clubs look to data for insights into how they can build winning teams. Football teams’ infusion of analytics into team management, training, performance analysis, and recruitment has become an essential part of achieving success. Analytics in both football and business gives teams a competitive edge.
The forthcoming feast of football at the European Championship will serve up not just piles of sporting excitement, but also heaps of new data. Judging by what we can learn from the qualifying competition, the insights and permutations are fascinating and endless: Will Belgium and Denmark continue their dominance from qualifying and take the top two spots? Or will England maintain its high-score-per-match average and overcome these formidable opponents? Do Spain’s, Germany’s, and Portugal’s high xG numbers dictate who’ll win, or will Turkey’s defensive meanness see them prevail and become champions?
Analytics can help guide our guesses, but the players will tell the tale. We’ll be watching. Will you?
Infusing analytics into workflows and products is the key to data utilization.Learn why
Adam Murray began his career in corporate communications and PR in London and New York before moving to Tel Aviv. He’s spent the last ten years working with tech companies like Amdocs, Gilat Satellite Systems, and Allot Communications. He holds a Ph.D. in English Literature. When he’s not spending time with his wife and son, he’s preoccupied with his beloved football team, Tottenham Hotspur.