Tinkering with motorsport data again has, as is the way of these things, also got me thinking about (sports) journalism again. In particular, a portion of what I’m tinkering with relates to ideas associated with "automated journalism" (aka "robot journalism"), a topic that I haven’t been tracking so much over the last couple of years and I should probably revisit (for an previous consideration, see Notes on Robot Churnalism, Part I – Robot Writers).
But as well as that, it’s also got me thinking more widely about what sort of a thing sports journalism is, the sensemaking that goes on around it, and how automation might be used to support that sensemaking.
My current topic of interest is rallying, most notably the FIA World Rally Championship (WRC), but also rallying in more general terms, including, but not limited to, the Dakar Rally, the FIA European Rally Championship (ERC), and various British rallies that I follow, whether as a fan, spectator or marshal.
This post is the first in what I suspect will be an ad hoc series of posts following a riff on the idea of a sporting event as a crisis situation in which fans want to make sense of the event and journalists mediate, concentrate and curate information release and help to interpret the event. In an actual crisis, the public might want to make sense of an event in order to moderate their own behaviour or inform actions they should take, or they may purely be watching events unfold without any requirement to modify their behaviour.
So how does the reporting and sensemaking unfold?
Three Phases of Sports Event Journalism
I imagine that "event" journalism is a well categorised thing amongst communications and journalism researchers, and I should probably look up some scholarly references around it, but it seems to me that there are several different ways in which a sports journalist could cover a rally event and the sporting context it is situated in, such as a championship, or even a wider historical context ("best rallies ever", "career history" and so on).
In Seven characteristics defining online news formats: Towards a typology of online news and live blogs, Digital Journalism, 6(7), pp.847-868, 2018, Thorsen, E. & Jackson, D. characterise live event coverage in terms of "the vernacular interaction audiences would experience when attending a sporting event (including build-up banter, anticipation, commentary of the event, and emotive post-event analysis)".
More generally, it seems to me that there are three phases of reporting: pre-event, on-event, and post-event. And it also seems to me that each one of them has access to, and calls on, different sorts of dataset.
In the run up to an event, a journalist may want to set the championship and historical context, reviewing what has happened in the season to date, what changes might result to the championship standings, and how a manufacturer or crew have performed on the same rally in previous years; they may want to provide a technical context, in terms of recent updates to a car, or a review of how the environment may affect performance (for example, How very low ambient temperatures impact on the aero of WRC cars); or they may want to set the scene for the sporting challenge likely to be provided by the upcoming event — in the case of rallying, this is likely to include a preview of each of the stages (for example, Route preview: WRC Arctic Rally, 2021), as well as the anticipated weather! (A journalist covering an international event may also consider a wider social or political view around, or potential economic impact on, the event location or host country, but that is out-of-scope for my current consideration.)
Once the event starts, the sports journalist may move into live coverage as well as rapid analysis, and, for multi-day events, backward looking session, daily or previous day reviews and forward looking next day / later today upcoming previews. For WRC rallies, live timing gives updates to timing and results data as stages run, with split times appearing on a particular stage as they are recorded, along with current stage rankings and time gaps. Stage level timing and results data from large range of international and national rallies is more generally available, in near real-time, from the
ewrc-results.com rally results database. For large international rallies, live GPS traces with update refreshes of ervy few seconds for the WRC+ live tracker map, also provide a source of near real time location data. In some cases, "champaionship predictions" will be available shwoing what the championship status would be if the event were to finish with the competitors in the current positions. One other feature of WRC and ERC events is that drivers often give a short, to-camera interviews at the end of each stage, as well as more formal "media zone" interviews after each loop. Often, the drivers or co-drivers themseleves, or their social media teams, will post social media updates, as will the official teams. Fans on-stage may also post social media footage and commentary in near real-time. The event structure also allows for review and preview opportunities througout the event. Each day of a stage rally tends to be segmented into loops, each typically of three or four stages. Loops are often repeated, typically with a service or other form of regroup, (including tyre and light fitting regroups), in-between. This means that the same stages are often run twice, although in many cases the state of the surface may have changed significantly between loops. (Gravel roads start off looking like tarmac; they end up being completely shredded, with twelve inch deep and twelve inch wide ruts carved into what looks like a black pebble beach…)
In the immediate aftermath of the event, a complete set of timing and results data will be available, along with crew and team boss interviews and updated championship standings. At this point, there is an opportunity for a quick to press event review (in Formula One, the Grand Prix + magazine is published within a few short hours of the end of the race), followed by more leisurely analysis of what happened during the event, along with counterfactual speculation about what could have happened if things had gone differently or different choices had been made, in the days following the event.
Throughout each phase, explainer articles may also be used as fillers to raise general background understanding of the sport, as well as specific understanding of the generics of the sport that may be relevant to an actual event (for example, for a winter rally, an explainer article on studded snow tyres).
Fractal Reporting and the Macroscopic View
One thing that is worth noting is that the same reporting structures may appear at different scales in a multi-day event. The review-preview-live-review model works at the overall event level, (previous event, upcoming event, on-event, review event), day level (previous event, upcoming day, on-day, review day), intra-day level (previous loop, upcoming loop, on-loop, review loop), intra-session level (previous stage, upcoming stage, on-stage, review stage) and intra-stage level (previous driver, upcoming driver, on-driver, review driver).
One of the graphical approaches I value for exploring datasets is the ability to take a macroscopic view, where you can zoom out to get an overall view of an event as well as being bale to zoom in to a particular part of the event.
My own tinkerings will rally timing and results information has the intention not only of presenting the information in a summary form as a glanceable summary, but also presenting the material in a way that supports story discovery using macroscope style tools that work at different levels.
By making certain things pictorial, a sports journalist may scan the results table for potential story points, or even story lines: what happened to driver X in stage Y? See how driver Z made steady progress from a miserable start to end up finishing well? And so on.
The above chart summarises timing data at an event level, with the evolution of the rally positions tracked at the stage level. Where split times exist within a stage, a similar sort of chartable can be used to summarise evolution within a stage by tracking times at the splits level.
These "fractal" views thus provide the same sort of view over an event but at different levels of scale.
Such are the reporting phases available to the sports journalist; but as I hope to explore in future posts, I believe there is also a potential for crossover in the research or preparation that journalists, event organisers, competitors and fans alike might indulge in, or benefit from when trying to make sense of an event.
In the next post in this series, I’ll explore in more detail some of the practices involved in each phase, and start to consider how techniques used for collaborative sensemaking and developing situational awareness in a crisis might relate to making sense of a sporting event.
2 thoughts on “Idle Reflections on Sensemaking Around Sporting Events, Part 1: Three Phases of Sports Event Journalism”
Thanks for this interesting article. I was looking at your 2021 Monte Carlo chart and had these thoughts and questions:
– Displaying the final value of “overallPosition” at the tail of its sparkline would be useful.
– For “Times rebased relative to car 69”, how was car No. 69 chosen for time-based gaps? This seems odd to me unless this is a tailored report for team 69.
– Does SS_15 Overall = 153.6 for car No.1 mean that by the end he is 153.6 seconds ahead of the reference car? If so, I would suggest possibly switching the colors so green=ahead and red=behind.
– According to https://en.wikipedia.org/wiki/2021_Monte_Carlo_Rally#Classification, the class difference between No. 1 and No. 33 is +32.6. This agrees with SS_15 Overall 153.6 – 121.0. Inserting another column showing the +32.6 gap would be interesting to me.
– The final set of SS_1_stages combination of numeric values and horizontal differences feels like an alternative experimentation with you SS_ Overall and its companion Gap chart presentation. I think I would prefer just having the Gap chart with numeric labels.
I’m looking forward to your next posts.
One of the things I keep tinkering with is where on the chart each column should be positioned. I can relocate columns, but in code rather than interactively. If I had an app to build the charts, being able to drag columns to new positions would be a handy feature to have to hand…
Re: why car 69: the code I have gives me a simple widget to select which car I generate the view for; the image in the post is just one I had to hand… On my to do list is a thing to generate reports for each crew showing all results from their perspective. Again, it would be nice to create an app to let an end user choose their own view but my code is a bit too fragile for that…
Re: red and green – the sense of the colors is in part determined by whose perspective you take; eg is the leader 20s ahead (green from the leader’s point of view) or is the reference car 20s behind (from their perspective a red gap). I have switches somewhere deep in code that should let the user choose the sense depening on how they prefer to interpret / read the columns. You can actually train yourself to read the chart with different colour senses depending on column (eg whose perspective you read the column from). The overall ranking sparklines, for example, take a global perspective showing evolution of ranking, rather than being rebased to a reference car.
Re: showing the 32.6 overall gap between first and second ranked cars, that starts to get confusing becuase the sense of the numbers in the charts is that they are referenced to a selected car. You can get the 32.6 by subtracting 121.0 from 153.6. To show the actual 32.6 in the chart, you’d select car 1 or car 33 as the reference.
Re: experimentation – the whole thing is experimentation! See some earlier discussion of the evolution of the chart eg here and here.
I’ve also done some really experimental things in the past, eg here; see also this summary of various rally chart types.
Thanks for the comments – any and all discussion welcome:-)
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