Bring your sports analytics into focus – better data visualization means better understanding of player performance
A picture says more than a thousand words – that is as true in professional football as it is in traditional industries who now rely on data for reporting. After all, those pictures can represent game changing moments, on pitch emotions, wins and losses. But crucially, they also show meaning behind results – and what your team, club or organization needs to do to improve performance and tactics.
Why better data visualizations matter in football
Football is fast paced. Analysis and insights must keep up – especially when information is provided during a live game and there’s no time for second-guessing, for deciphering and Q&A.
That’s why data visualization in football is an excellent tool to help you bring a wealth of information to those who need to make decisions: managers, strength and conditioning coaches, the medical team, and beyond.
Visualizing data is critical as it allows analysts to provide a true shortcut for decision-making by reducing the time to insight. And that is where charts and diagrams play such an important role.
How it works
The inputs into this process are numerous and stem from a wide variety of sources. There’s game data, which clubs can purchase. There’s training data they own and bring to the process. And then there’s a lot of player information – along with data about opponents, about previous seasons and other leagues.
In short: there is complex and vast well of data to tap into.
What you need though, isn’t just the data – you need insights. Precise, concise and accessible information that is available when you need it to make decisions that matter.
So what’s missing?
Often the reports in football – just as in any business – don’t make it as easy as it could be to get to the essential insights. What’s often missing is the most important and actionable information.
Why? Because this information is often obscured by too many colors, too much detail on a single page – and by graphics that can lack clarity and description.
As you might expect, this can lead to a whole world of trouble. At best, the stakeholders in teams are left scratching their heads as they seek to understand the reports. At worst they make assumptions or end up ignoring these reports altogether.
Right now, you might be thinking this sounds all too familiar. It seems that data and data visualization in football and in sports generally, suffers from the same challenges that many businesses in other industries face: there’s lots of data available, lots of smart people working with it and making every effort to share critical information, but not enough application of best practices. But the good news is, this is easy for you to fix
3 simple steps for creating better visualizations in football:
Designing visualizations that are simple and have a clear message is key. Simplicity can be achieved by first removing all the unnecessary ‘chart junk’, those lines, labels and borders that don’t add any value.
Choosing simple but tried and tested charts is an effective way to communicate information. The two charts you will likely use most are bar charts and line charts. Bar charts are used for comparing categorical data, for showing clear rankings from highest to lowest and vice versa and for representing metrics in a shape that is easy to understand.
Line charts are used for time series data, showing trends over time, seasonal data, peaks and troughs and for revealing patterns across teams or players. Line charts are also easy to understand and can be annotated with callouts to highlight particularly interesting and noteworthy data points.
Simplicity also relates to the overall layout of a report or dashboard. Once you have simplified the individual components, be sure to reflect the same approach when putting everything together.
Create a layout that follows a logical flow, e.g. left to right, top to bottom or a z pattern and is intuitive to consume. Leave some space between different charts so that the information has an impact on the reader.
I have noticed with football visualizations (and in the reports they’re part of), that sports scientists and analysts tend to include multiple measurements or indicators in a single chart. This then means they have to use two different or even three different axes on one chart, making it difficult to understand.
A chart should provide a very clear picture and summary of the data it represents. If your audience has to go back and forth between the visualization, keep referring to the legend (e.g. what color or shapes represent), turn it sideways to read and understand labels, then some adjustments are in order.
Focusing on a single message per chart is one way to stick to the idea of simplicity and to achieve the ultimate outcome of making data and information accessible – giving you a strong foundation for decision making.
When you want to compare multiple measurements but they’re on (very) different scales and potentially in different units, it’s time to pull out your trusted bar chart. Simply make some enhancements and you have a great visual that clearly shows everything in one place.
How do you do this?
Divide your data into the different measurements and then place horizontal bar charts side by side or ‘stack’ vertical bar charts on top of one another.
The chart below shows very clearly which player covered the most distance at >14.4km/h. It also allows sorting by the other measurements.
The layout lets report users compare results across players (vertically) as well as across measurements for an individual player (horizontally).
Colors can take visualizations to the next level by adding context, by highlighting and grouping items that belong together – and by indicating whether data points are below, at or above target.
Most of the time I see color being overused in data visualization. I have yet to come across dashboards, reports and charts where I would recommend using more color.
I don’t want to lock away your creativity or advocate a lifeless and stark approach to visualization where every report is in black and white. But what I want to encourage you to do is to use colors to their maximum effect.
Use it to highlight data points in scatterplot, for example, and remove color from all those points that are only there for context. Immediately the essential information will stand out much more clearly.
By highlighting something you’re not removing details. In fact, you are helping your stakeholders to focus on exactly that information that they need.
Color is also very effective for grouping similar items together. For example, you can show a certain player formation on the pitch in color – again removing color from the other players. Or if you identify a cluster of related data points, color them the same to show that they belong together.
If in doubt: remove and reduce color. Try starting your visualization without colors, just a simple black and white or grey chart. Be very selective in what you add color to, so that you develop a clear message for your audience.
Where do you go from here?
In theory these suggestions are always straightforward. They sound simple and logical, right? So how do you put them in practice?
Sharing best practices
It’s important that the concept of best practices in data visualization is understood but also shared. What are some of the best practice recommendations? Which ones are missing in your current work? And how can you make that information accessible to the wider team?
Sharing knowledge and information but also leading by example are critical if you want to initiate change that results in better, more effective data visualizations that form the basis for decision-making.
Working with before and after examples (before vs once best practices are applied to a chart or report) can help convince sceptics and gives you the opportunity to create a common understanding.
Finding inspiration from the outside will also help develop new skills and ideas for your work. Look at disciplines like education, the arts, engineering, fintech and others who have been working with data for a very long time or where people have a different focus for design and communication.
Take advantage of the tools that exist in the community, such as the visual vocabulary created in Tableau by Andy Kriebel.
And when creating charts and reports that are intended to communicate information clearly, be sure to always start with a question. Don’t start with the intention of creating a radar chart. Instead be very clear on what question you are trying to answer. Do you want to identify different characteristics for a player? And do you want to overlay them with those of an opponent? If so, do you really need a radar chart or could your question be addressed with something that is much simpler to create and understand, such as… a bar chart?
Connecting with the community
There is a large enthusiastic and passionate community out there that features many data analysis and visualization experts. Accessing these people is easier than you think, and I encourage you to do so. You will find a hefty dose of inspiration, plenty of new ideas, great people to have engaging conversations with and a very easy way to connect with leading experts.
Some examples in the data visualization community include projects such as MakeoverMonday and Sports Viz Sunday, where a passionate and an ever-growing community tackles different datasets and builds visualizations.
Taking things even further – with our analytics database
As experts in sports science, football and data analysis, you’re unlikely to be working with simple data sets. There are multiple data sources with different structures and it’s probably a challenge to align these and have consistency. I’ve heard many frustrated sighs from analysts working with sluggish and insufficient systems that make their lives difficult at times.
And it shouldn’t be that way. The tools you use should enable and empower you to focus your knowledge and talents and experience on the task at hand and to add value in the process of going from raw data to insights for decision-making.
We have an efficient, simple setup that let’s you access more data, faster, so that you can deliver insights to the people who need them – when they need them.
The following architecture example shows how you can combine your data sources (such as Opta’s F24 data feed for live analysis during games) with an Exasol analytics database to provide a live link to the source as well as to your reports.
This results in an analytics platform that can be used for simple acceleration of existing processes or be extended into a platform that unites all of your sources in a single place – and enables advanced analytics and data science, reporting and ad hoc analyses with the tools that are right for you.
Ultimately, it doesn’t matter whether you simply want to tackle a few colourful charts and apply best practices, want to connect with others in the community, or you’re planning on optimizing your current infrastructure to get even more value from your data. I just hope I’ve given you enough to get more from data visualization – and find ways to go even further and change the game for your team, your club or organization.