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Five Tips for Football Data Analytics

Data itself is not valuable to people. Its the ability to use data who generates insights about things the people care about.

The pandemic has taught us how to deal with data. To trust it and use it to make valuable decisions. The collection of football match data is now so advanced that data is available from almost all semi-professional and professional leagues.

Until a few years ago, club officials remained very sceptical, even dismissive. Meanwhile, the data analytics "early adopters" are reaping the competitive advantage that investment in people and data analytics is beginning to give them: Liverpool, Midtjylland, AZ Alkmaar or Brentford are just a few in the fast growing list.

As for many companies, digital transformation is a big challenge for many football clubs. Before you can realise the great potential, you need time and investment in people, technology, processes and culture.

The most important thing first.

Start from your Strategy and nor from your data.

Don't start by asking yourself what data can benefit you. Ask yourself what data you need to measure and improve your strategy.

Here are our five additional best practices from football to get the most long-term value from your data strategy and analytics.

1. form context - data remain numbers without human interpretation

Data does not lie. But data doesn't talk either.

Whether you are a football club or a company from another industry, the same principles apply. Data alone is not enough to discover something new. Data does not automatically lead to insights. No coach can work with data alone. Analysing and interpreting data is hard work. You will always need skilled people with the right know-how to work with the data. To build the all-important context, to interpret the meaning, to process and communicate the findings in a way that is suitable for the recipient. Don't think these skills can be acquired along the way. Get an expert and make sure that the expert, in addition to analysing, also empowers your team professionally and culturally to deal with data.

It is also important to decide which data is relevant and which is not. Often data is collected just because you can. In football, too, an exaggerated obedience to technology/data can lead to an approach that measures everything measurable but leads to poorer decisions overall.

Without relevance, no context.
Without context, no insights.
Without insights, no improvement.

Copernicus had the same data as everyone else, he saw, heard and learned the same things as his contemporaries. But he came to completely different conclusions. Because he questioned assumptions that were not open to discussion for everyone else.

2. invest in people and data analytics skills

Invest in people and data and in football analytics skills. Build up know-how in your club. Don't delegate analytics to one person who is already overloaded with a thousand other tasks. Make analytics a priority on the agenda, even if the budget for it is small.

Anchor analytics in your club as a fixed topic and not just so that you can't be accused of not having done it. Dealing with data is a culture that you have to build and promote within the association. Be open to new things and don't see analytics as a threat, but as a complement.

Analytics does not take anything away from anyone. Analytics is neither the enemy of the coach. Nor of the scouts, nor the enemy of intuition.

Start with Analytics before Analytics starts with you.

Jürgen Klopp showed with Liverpool how wonderfully these skills complement each other. He was open to new things and combined his emotional intelligence based leadership style with the capabilities of Ian Graham's data analytics team.

If you have limited funds and resources to do this, seek expert advice. So that you can start in a targeted and focused way to get the most out of your resources.

3. analytics is not a consumer good but a team sport

Don't just focus on presenting results to your stakeholders on a silver platter. Your stakeholders have limited time for analytics and would prefer to simply consume the most valuable results unidirectionally.

But analytics "fast food" gives away a lot of its potential. It stays with the "I" and never gets into the "we". One-sided production, communication and consumption misses the chance to become better, more sustainable and more valuable.

Only when you manage to involve your recipients in the questions, hypotheses, interpretations and validations, you are on a cooperative path with more chances to generate valuable insights and competitive advantages.

Analytics is a teamsport.

As a positive side effect, you also continuously build up more know-how in the club and create more understanding for data . Successful clubs increasingly rely on cooperation and teamwork for analysis, hypothesis generation and definition of measures.

Nevertheless, you should not simply make the data available to everyone. The handling of data must be learned. Data should only be used for analysis. Otherwise, you will soon have a player in the sports director's office who demands a salary increase because he, as the second-best dribbler, earns less than the fourth-best.

Whether in sports, analytics or business:

To survive, the members of any system must be able to cross-fertilise. A system without diversity and cross-fertilisation stops, becomes stupid and is doomed to decline.
Not competition, but cooperation and communication are the most important drives in evolution.

4. use key messages and storytelling

"We hunger for knowledge and drown in information" John Naisbitt

One of the most important tasks and skills of the analyst is to analyse broadly, to validate creatively, but then to be as compact, simple and clear as possible in the statement.

The ability to interpret mountains of data and break it down and reduce it to a simple and compact message is priceless.

The clearer and simpler the message, the more likely it is that it will be understood by your stakeholder and that the measures will be implemented correctly. Simple statements are also easier to critically review and improve or correct.

But consider, that a wrong or poor implementation of the measure does not mean that the analysis was wrong.

Despite BigData, it is still the storytellers who fascinate us.

In order to be understood at all, we have to speak the language of the recipients in the communication and processing of the findings. The bait must please the fish and not the angler.

In the preparation of the message, it is also important to connect the purely analytical "cold" facts and figures with a goal/meaning that also has an emotional value.

The storytelling essence:

Every good story needs a hero, involves a conflict, is emotional and has a meaningful motive.

As a datanalyst or datascientist, if your clients or recipients are less curious after your analysis than they were before, you've done something wrong.

5. deliberately engage in insecurity

Football is: Large pitch, high number of players, many different player skills, small goal, few goals, high unpredictability, many random factors. Football is complex. Countless possibilities arise from many variables. Variety (number of distinguishable states) is the measure of complexity (diversity).

In complex systems there is no trivial decomposition. Complex things do not become linear by decomposition, they remain complex. This means that we often cannot draw simple conclusions with pure reflection and that we have to work with uncertainties, hypotheses and experiments in the analysis.

Consciously dealing with hypotheses is an important competence. This includes not only the formulation of hypotheses, but also their revision. Or the formation of new hypotheses if experiments provide data that contradict the initial hypothesis.

In medicine and science, experimentation is the standard procedure in complex systems to gain new knowledge. Learning through experimentation (trial&error) is the logic of evolution. It has been a sustainable, proven method of further development for about four billion years.

The evolutionary human challenge for innovation:

Humans feel more protected in the cave than on the open flat field.

Dealing with hypotheses and uncertainty is very difficult for many people, as adults have been trained and conditioned thousands of thousands of times in school and in business not to fail.

It becomes even more difficult when the experimentally obtained data contradict one's own expectations.

In order to find something new, we have to consciously place ourselves in uncertainty. And also endure the uncertainty.

People strive for credibility. Aborting a plan is a contradiction. In doing so, we admit to having thought differently in the past than we do today. We need to break this pattern in the complex world.

"Madness is always doing the same thing and expecting different results" Albert EIinstein

Be courageous. Be open to new things. Be open to chance. Accept that failure is part of it. Openly discuss how to deal with uncertainties and failure. In a complex system, no new knowledge emerges without experimentation. Just make sure that you fail quickly.

Be also brave enough to express concerns, even if they are just a feeling and not yet provable. Because if they are provable, they no longer need to be explained.

Successful people fail more often than unsuccessful ones.

Meanwhile, everyone has access to data. But few have a clear, end-to-end, intelligent and adaptive decision-making process.

Can you keep your cool despite public opinion and make your own decisions with conviction, regardless of external pressure? If you can, then #DataAnalytics is for you. If you can't, then why bother? (Oliver Seitz)

Smart #DataAnalytics is self-financing and saves money! If you invest a few percent of players' wages, you will be able to hire much cheaper & better players.

footballytics - we know how to make the data talk

We combine the competences of football tactics, scouting and data analytics and support clubs in interpreting and using data to make better decisions in scouting and match analysis based on data validation.

Football and data, not data and football.


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