Data in sports
Tableau: The use of advanced analytics is widespread in baseball and becoming more common in American football. What about football?
Brian Prestidge, Head of Analytical Development for Bolton Wanderers Football Club: Analysis in football has been around for a long time, but real data analytics? It's only in its early stages now.
It could be from Access databases, Excel databases, SQL, some from internal, some from external data providers. And all that information is related to injury, training intensity, training loads, performance in matches, where events happen on the pitch, recovery methods. There's so many vast amounts. We've got over 50 databases all collecting different information. We collect thousands of data points every single week. Prior to Tableau, it was just an absolute mess, if I'm honest.
Tableau: So you're collecting all this data. How does Tableau help you analyze that information? Have you found that the software has made player assessment more efficient?
Brian: One of the key things that we use Tableau for is our mapping of our player recruitment. So we need to identify where our targets and markets are. And Tableau's mapping functionality is fantastic for that. It enables us to actually identify where players are that suit our player profile, and more importantly, to monitor those players and how they're doing. And to evaluate, perhaps, the percentages of players that are getting A and B ratings, again, higher ratings of players that we sort of want to recruit compared to countries that perhaps, actually, it's a waste of resources, both human and financial, to send scouts into that area to recruit players when, actually, we should be going to this area or that area. And Tableau Maps are fantastic for that because they've guided us.
Tableau: Which Tableau products are the most useful for you?
Brian: We've got one Tableau Desktop, which I myself create the dashboards after consultations with various members of staff so we can really make sure that the dashboards are designed around them.
And then we have Tableau Server, and absolutely everyone's involved. I'm talking from coaches who have never touched computers before down to our sports scientists. So we've got a vast range of people. And that was something we took into consideration when bringing in Tableau because we had to bring in a product that was suitable for all people, and not only the staff we've got now, but the staff we might have this time next year or the year after that. It needs to be agile to adapt with our company.
Tableau: Has Tableau made any difference to your daily work?
Brian: So, personally, I mean, I've absolutely loved it. It's been fantastic. It's made my job so much easier in terms of the, what I'd call, the "boring work." And it's actually allowed me to get more hands on with data.
Not only that, actually get other people more hands on with data. Which means if they're hands on with it, they're the experts in their field, they can look at the data that's related to their field. And, therefore, I can have communications with them about, right, where are we going to go now? What questions do you want to answer and how can we we be a bit more predictive in what we're doing? A bit more objective in what we're doing? Actually be at the forefront of data analytics in football.
Tableau: You have several different people using the software. What's been the reaction to Tableau throughout the club?
Brian: I was astounded. I thought, “This is exactly what we need.” The variety of dashboards was the key thing. Everyone's a different learner. So we need different ways of visualizing our data.
Tableau: Has Tableau changed the way you use and interpret data?
Brian: Tableau's enabled us to get deeper insights a lot quicker. We're able to actually understand our data. So rather than just being information that's just stored in the database, we're actually able to utilize this information to make daily decisions, even hourly decisions, and that's key in football. We need to be able to make decisions quickly and they need to be objective. And It's enabled us to work smarter, definitely.
Tableau's enabled us to get deeper insights a lot quicker. We're able to actually understand our data. So rather than just being information that's just stored in the database, we're actually able to utilize this information to make daily decisions, even hourly decisions.
Tableau + Alteryx: A winning combination
Tableau: How much data are you collecting and what were you doing with it?
Brian: We've been taking unstructured and structured data in from various sources for a long time now. The problem has been most of it has been unusable. So we've had this massive amount of data just collected, and we've not been able to do anything with it.
We're collecting over 6,000 XML files every single season, and to make any sense of that, to actually make decisions from a player recruitment perspective, we needed the power of Alteryx, and the user-friendliness of it as well, to be able to actually parse the data. And then not just parse it but actually use predictive analytics tools to actually gain deep insight and to actually understand which players are going to benefit our club.
Tableau: What kind of support have you gotten in using the software?
Brian: The help we've had, the hands-on help, I suppose, we've had from the Information Lab, The support we've had from both Tableau and Alteryx has been fantastic, both across the phone, e-mail, and in person. It's been great because it's enabled us to create not just the basic-level dashboards that perhaps we would have done if we didn't have that help, but we've been able to advance our work and adapt it to make it very relevant to the individuals and to be able to deliver the insights that we actually want as a club.
Tableau: George, you had a few questions for Brian?
Alteryx president George Mathew: So what are some of the things that you're getting from that XML data itself? What's the source content look like these days, particularly for the sports analytics that you're starting to drive?
Brian: We get location information. So that's based upon, you know, where are events happening, what the type of event is, whoever is challenged or unchallenged, who's involved with it, and the outcome of that.
Now, that, to us, provides a very basic level of information, but we need to delve deeper, look at the relationships, look at where on the pitch things are actually happening, so not just an X/Y coordinate but actually is that X/Y coordinate within a penalty area. Is it within a six yard box? Is it within a zone that's actually a good goal score in efficiency zones? That's a benefit that the special analytic tool has allowed us within Alteryx.
George: You mentioned that the pitches are different in every situation. How do you normalize that? How do you make it seamless to blend that information as you're then doing the visual analysis in Tableau?
Brian: Every single pitch in football is different. So when we get these XML files, and you can't just say, “Right, if the ball is here, that means it's in the penalty area, if the ball is there it's in the sort of on the halfway line,” because everything is different.
Tableau: How do you turn those basic coordinates into something more relevant?
Brian: We have to use the sort of some generic coordinates, which show the sides of the pitch, so we can then draw a pitch using the tools within Alteryx. Not only then draw the pitch but draw the halfway line, draw the 18 yard box, draw the six-yard box and any of the zone that we may want to. Like, we split the pitch into left, central and right, as well as into thirds, going up the pitch. So we're able to do that using the spatial tools, and then use spatial match just to identify where events are happening within those areas.
Tableau: How did you manage all this information before you adopted Tableau and Alteryx? Where you able to digitally recreate the pitch?
Brian: Prior to having the XML is we'd get CSV file, and that basically provides us with very generic information about what's going on on the pitch.
The key thing it doesn't provide is locations, locations, X/Y coordinates. We don't get that, which means that realistically we don't know how effective a pass was, how effective a cross was. Whereas actually having that location, having those coordinates and making sense of them in Alteryx means that we can start to create strategies for training, right? Have the team done what we were asking of them? Is the pattern of play what we wanted? When we're looking at opponents, what's their pattern of play, how can we counteract that?
And it's all actionable insight, it's all things that actually influence training on a daily basis and influence not only team selection, but team strategy for match day.