Sports analytics and football
You would be forgiven for thinking that the analysis of data and sports don’t mix. But as for any industry, data does indeed play a big part in sports, regardless of the discipline. But what is it for?
The basics
Data analytics in sport is all about providing pertinent insights on teams, players, performance and more. For example, you may have the finances to assemble the finest footballers, but without something in place that goes beyond football technique, such as improving individual and team performance, your money will be wasted and the fans disappointed.
Football in Europe
Football in Europe, according to market and consumer data specialists Statista, is valued at some €25 billion and growing. The universal appeal of some teams, whether local (such as Tottenham) or national (for example England) reaches even the most remote parts of the world, not to mention the participation of teams from outside Europe.
How did data become involved?
This dates back to the 1960s, and in 1966, a data company, ChyronHego, provided data from the live broadcast of various games, which then began to further develop with time and the introduction of new technology. In essence, the penny for sports data analysis and its relevance began to drop.
Further development as time passed
By the early 2000s, with the arrival of more technologically-advanced cameras, data was now being analysed for tracking ball movements in games. At the same time, analytics and data from basketball were beginning to help coaches with their player scouting and recruitment, while at the same time using data to better evaluate and understand their audiences for the better of their enjoyment of the sport.
The arrival of wearable technology to gather data as players partook in their sport was a complete “game changer” across the sporting spectrum. It provided information about performance, whether in training or in competition, that was of incredible value to coaches and managers alike. It helped managers to develop strategies and programmes that could help their players to avoid pitfalls and perform at their best. The data can also help players and trainers alike understand the ramifications of fitness, diet and injury regimes.
It was then realised that data was a valuable means to help change existing approaches to sport and how it was being played.
Krossover, the intelligence, web-based video indexing platform, was the first to generate and analyse insights from video clips. They were able to analyse the playing strategies of teams (for both organisations’ own as well as its competitor sports teams) to identify strengths and weaknesses. With this, the professional approach to sport would change forever.
When did analytics really take off in sports?
In 2011, “Moneyball” starring Brad Pitt was all about a baseball team using data and predictive analytics for its training and new team-member scouting activities. The lead characters used data analytics to gather player statistics and develop competitor insights. They then strategically used predictive analytics on their game. They started winning. And professional sports people worldwide who saw the film started to take notice of this.
Now widely used, analytics in sports has become a business in its own right, on course, according to Built In, the online community for start-ups and tech companies, it is on course to reach more than $3.2 billion by 2028.
Football analytics
Data analysis in football has two strands.
For players - team management, player performance, competitor analysis, sports facilities, training equipment, scientific sport aspects, etc.
For spectators – event management, audience participation, merchandising, media and viewing rights, all-important revenue, etc.
It’s therefore about gathering all the raw data to enable the complete football experience, develop and monitor the required actions resulting from gathered insights and subsequently generate more revenue for the game.
The gathering and interpretation of data have without a doubt been made easier by artificial intelligence and machine learning to process it. This includes the development of algorithms for predicting essentially smarter, winning decisions. So, data analytics in football is not something that simply cannot be ignored.
Off the field
Sports analytics off the field are as important as they are on the field. Business intelligence can track and analyse consumer/fan behaviours, with some even based on sentiment and cognitive behaviours. These are used by those involved in the ownership and management of not only the teams, but the leagues the teams play in.
Needless to say, there is only so much these data tools can do. The football team management needs to know they have to make the required physical decisions for their investments and the required actual manpower. And they are no doubt ever mindful that fans take a very active part in football that goes beyond attending matches or watching on TV. Teams can increase their revenue by using data on other associated revenue streams – their player statistics, merchandise, other branded equipment, the betting industry, fantasy football and so on.
Potential new players
All the data connected with an individual player can be used to analyse the value and worth of a potential new team member. Performance, consistency, results and even fan appreciation can all be included in the analysis of this data.
Brand building and marketing to increase revenue
With all the sponsorship and branding in football, having an insight into their fans and followers through data analytics and machine learning assists with the formulation of the most ideal marketing strategy.
Match tickets
As no two football games are the same, predictive data analysis will help provide the most cost- and revenue-effective analysis and insight to help ensure healthy ticket sales and maximise revenue.
In summary
As sports boom, so does the collection and analysis of data from it. The big teams are already reaping the benefits, and this can only continue. It can only improve both the player and fan experience alike.