Interview with David Dubas-Fisher, Sports Data Journalist at Trinity Mirror

David Dubas-Fisher is a Sports Data Journalist at Trinity Mirror – he talks to Interhacktives about how for sports journalism,  it’s particularly important to develop data stories that are not overflowing with statistical analysis as for the reader this will detract from the bigger story.

Do you think that sport data journalism and tactical analysis using stats can take away from the spontaneity and passion that is at the heart of sport? Are people really interested in so many facts and figures?

I think it can depend on how its done. As an accompaniment to a traditional sport report then no. Statistical analysis can provide insight into what is actually happening in a match, or in a team’s season in much the same way as analysis of data around a news story can give you a more accurate and in-depth picture as to what is happening in the world.

However, I think too much statistical analysis can detract. Breaking a game down to say “this team had 73% of possession” and “that team hit 23 shots with a 56% accuracy rate” can mean very little to the fans. There’s that old saying “there’s lies, damn lies, and statistics,” and nowhere is that truer than in sport. You may have a stat that shows one team had more shots, but it doesn’t really tell you if any of those ever looked liked they were going in.

You have to have a kind of “news sense” when it comes to stats as well. Does anyone really care that Manchester City’s successful cross rate is 5.2% higher than Arsenal’s? No they don’t. Part of the job is finding a stat or some data that is interesting to the reader.

One thing we try to do is develop data stories that aren’t just about the stats. One example would be when we worked out the birthplace of every footballer to have played for England. We then analysed this to show us which regions had produced the most England players (it was Birmingham and the West Midlands I believe). It’s a data story, but not statsy. Another example would be in the build-up to England’s World Cup draw. Through the use of Tripline we were able to calculate the distance the team would have to travel between games (by way of some Indiana Jones style maps), and how the draw could result in differences from 4k miles to 8.5k miles.

Has the accessibility to data from paid services such as Prozone and Opta made the work of sports data journalists easier? Has it changed sports data journalism for better or for worse?

I don’t believe Opta has made the role of a sport data journalist easier, no. In my role I use Opta very sparingly. It provides you with an enormous amount of information. But often that information isn’t really what we would use in a data journalism story. For instance, I can use Opta to find out how many goals a player has scored with their right or left foot, but that’s not really the kind of story we would produce. That kind of information as well, is really something that you might want to use to back up a story, rather than form the basis of the story itself.

Through its widgets though it does mean that web sites can have an enormous amount of information on a match or a player readily available and embedded in their site. What that means then is that as a sports data journalist I have to look at stories other than just reporting statistics on a match or a player.

The pay wall on it can be a problem as well. Depending on what kind of organisation you’re working for would determine as to whether or not you’ll be able to access the information.

Do Trinity Mirror source their sport data from anywhere else?

Yes, because of the limitations of Opta and how we can use them we use a lot of web scraping from other sources. For instance the Premier League’s website, or soccerbase.com or even statto.com. All of these sites have an enormous wealth of data that helps us build up our stories and get the information we want and easily.

Do you think the UK are behind the US in sport data journalism?

It’s hard to say. While America is famous for having large amounts of stats and data analysis I’ve not properly looked into how they use it. The little I have seen can be a bit too stats driven. I mentioned earlier about too much detailed stats analysis moving away from what the reader is interested in  and too much stats analysis certainly seems prevalent in some of the American sports coverage I’ve seen.

5 players from the England squad play for struggling man Utd pic.twitter.com/AS0kzoLobX

— David Dubas-Fisher (@dubasfisher) February 27, 2014

How did you move into data journalism specifically?

When I was studying journalism I didn’t think to move into data journalism. Certainly from outside of the journalistic world “data journalism” as a type of journalism in its own right wasn’t something I was aware of. It was only when I started looking for work that I came across data journalism roles, and given my background in IT it seemed like a natural fit for me.

What tools do you use the most as a data journalist? What would you recommend that trainee data journalists learn?

Well, number one are spreadsheet tools. When you’re crunching data there’s little option but to use them.

 Then there’s web scrapers. We use Outwit Hub for that. Because so little data is available in a format that you want and isn’t published in the same way that government data is for news stories, scraping sites for the info you want is a vital tool.

What do you think the future of sports data journalism will be like?

I think automation will play a big role in the future. If you can get a program to automatically analyse data feeds and then interrogate and tweet that information, you have a constant and steady stream of information with minimal effort from journalists, which is cost effective for news organisations. However, that’s just the very statsy stuff, like goal scoring records. More complicated stories that require unique ideas will always require journalists.