How to make smarter decisions in football clubs

Utilising next-generation data management to increase your club’s return on information

Within my last article (Startup FC - The opposite of what we have always done), I wrote about a fictional example of a different way to think about the football club as a functional organisation. The idea was to find best practices from the startup world in order to adapt them to the football industry. I am still overcome with joy about the positive feedback and the great appreciation which found me with regards to this article through various channels. I also received some questions about my notion of the practical implementation of such concepts in a football club.

"That's all well and good, but how the hell are we supposed to do that?"

Of course, I am aware that not all of my thoughts were realisable and some may turn out to not work at all in the real world. But still, I strongly believe that the underlying mindset would generate a positive impact for a football club and that there are some parts that are certainly worth considering and evaluating in practice.

To answer these questions, I picked a fraction of the provided concept and tried to carve out how to deploy this in the real world. While the first section of this article aims to provide a kind of brief introduction into data-driven processing, the second part offers a “how-to” guide for building a data infrastructure in football clubs that fosters smart decision-making.

What does it mean to be data-driven and why is it the foundation for smart decision-making?

From my point of view, being data-driven describes a decision-making process that involves collecting data, analysing it to extract patterns and facts from that, to finally utilise those facts to make conclusions that impact your decision-making. These are the main ingredients to create factual discussion cultures. By following this process, the decision-making of an organisation is based on actual data rather than intuition, gut-feel, or the “apparent” experience of a few people. Without data or with bad data processing, biases, deceptions, and flawed assumptions may intrude judgements and lead to poor decision-making. 

Data-driven decision-making can be seen as a six-step-process (loop). First of all, the right understanding of the decision-making process is mandatory. This means that all involved people should agree to the deployed processes to get the maximum of trust and commitment among all people.

Personal vanities, know-it-alls, and the urge to distinguish oneself are harmful to any decision-making process.

Personal vanities, know-it-alls, and the urge to distinguish oneself are harmful to any decision-making process. Once the club’s people share the same vision, they can define the missions. In this decision-making context, the missions can be understood as the questions that need to be answered within the club - now and in the future. So, the first step for implementing a data-driven culture is to develop the missions and to get everyone on board as well as defining the KPIs to monitor.

Within the second step, the whole thing gets a little more technical - now it is time to identify the data sources in your club. Data can appear in various forms with different volumes, varieties, and velocities. Depending on the project’s scope, a club could, for example, start examining just a single department’s data like marketing data or performance or scouting data.

The third step is dedicated to data cleansing and organising it efficiently. Here, the raw data is connected, centralised, and prepared for the actual magic.

The fourth step is about analysing the raw data and performing calculations on those numbers. You can utilise any kind from simple to sophisticated statistical models or even machine learning approaches. Once you have set up a solid raw data foundation, your infrastructure is ready for any kind of analytics to be built on top of it. In many, many cases, simply having all the numbers calculated consistently and being available centrally at any time is already a major improvement and by far sufficient for a football club’s regular use cases. Besides performing math, the translation of the numbers is another important aspect to consider.

Here, the fifth step comes into play - the data visualisation. This can be done in the form of interactive dashboards, which are automatically refreshed (especially useful for regularly used KPIs) or based on ad hoc requests with brief presentations or reports. The information always needs to be digestible for the audience and communicated in an understandable way. There are many tools out there that help to create visually compelling and easy to grasp dashboards and reports.

The sixth step is finally about drawing conclusions. Here, you can merge your qualitative knowledge and experience with quantitative data to put old assumptions to test and formulate and falsify hypotheses. Do not only trust the data blindly but use it as a common foundation to build passionate and factual discussions upon it.

Below you can find a brief illustration of the presented process.

How to implement data-driven structures in your club?

Now, let’s take a look at our Startup FC and its data infrastructure before and after its metamorphosis.

In mediaeval times:

In the past, Startup FC had no single source of truth when it came to data. Scouts and coaches handled their own Excel spreadsheets, from time to time a database provided by the FA was queried opportunistically by the video analyst and performance reports or medical data were sent by email. Moreover, they had a subscription with a scouting platform provider but no solution for integrating the data with their other existing sources. This absence of smart data management had several impacts:

• Inconsistent KPIs due to missing central logic
• No possibility to analyse data across sources
• Data silos, no data enrichment 
• Limited use of data because of cumbersome manual processes
• No objective data foundation for meetings and discussions
• Disastrously expensive mistakes

The modern era:

With the establishment of a dedicated data department a new data management structure was introduced as well. The club started to literally centralise all their data into a virtual layer by exploiting a technology called data virtualization combined with automated ETL processes for higher performance. By virtualizing their data the club is able to access all the data in real-time and deploy their logics on it consistently. The virtual layer connects and centralises any data source and enables SQL-based transformations and calculations. Based on the raw data, several core views can be easily prepared to virtually join and cluster the data into logical blocks or units with respect to its purposes. Now it is easy to distribute the data to the right consumers and/or build analytics on top of consistent metrics since any kind of front-end, like BI-tools or even Excel/Google Sheets, can be connected in seconds. The virtual layer also acts as a governance tool and guarantees that sensitive data is protected against unauthorised access.

The heavily improved accessibility alone already led to cultural changes within the whole organisation. Data moved into the center of the club and its departments, simply because it was available now and people were curious to take advantage. Scouting now takes into account data from all internal and external sources, resulting in complete data sets, generating actionable insights and 360° view on all potential signings and own players from academy to first team. Marketing is able to steer their activities much more efficiently since they now know exactly how each campaign performs and what any Euro spent on marketing returns. Even the training sessions on the pitch are better coordinated because the coaching staff has the most recent tracking and performance data as well as information from the medical department right at their fingertips in an intuitive dashboard. This helps, on the one hand, to field the best starting eleven at the weekend as well as, on the other hand, to prevent muscle injuries due to fatigue. A better monitoring can also help to show the players their improvement path and to warrant the best possible training.

All that was needed to achieve such a high-performance data landscape was a clear vision, a finger twist of determination and an investment in the amount of probably less than the star player’s weekly salary into technology and a person who knows a little bit of SQL (I think, 1 FTE should be enough to operate the system, depending on the demand and the number of data use cases, more can be expedient).

The club was able to massively leverage their Return on Information.

The club was able to massively leverage their Return on Information. Keep in mind, there was actually no additional data added, it is just the combination and exploitation of already existing data that skyrockets the productivity within the club and heavily increases the share of good decisions made. 

As always, I thank you for reading and hope you had some fun and maybe some interesting insights. Whenever you have some feedback or questions please do not hesitate to drop me a message. I am always happy about new input and the exchange with like-minded people.

Cheers!

_______________________________

*Icons from icons8.com

** SQL (pronounced "ess-que-el") stands for Structured Query Language. SQL is used to communicate with a database. According to ANSI (American National Standards Institute), it is the standard language for relational database management systems. SQL statements are used to perform tasks such as update data on a database, or retrieve data from a database. (Source: SQLcourse.com)