We have described the benefits of how new sports data formats could engage mass-market audiences. In this section, we look at some tangible strategies that media, rights holders, and sponsors can enact to realise the value of engaging mass-audiences in visual sports-data formats.
These strategies are, in summary:
- The inclusion of more tracking data in sports and sports analytics.
- More research should be conducted on the visualization of prediction or speculation.
- Rights holders should release more videos as open source for SMEs to experiment with.
- We would like to see more qualitative metadata in sports.
- Extensive research into what visual data engages mass-market fans.
Tracking data is the missing ingredient
More sports should create tracking data.
Tracking data shows the activity of every element, every player and as a result action away from the key focus. The chance to show playback of interesting sub-games within competitions and games such as player-versus-player content is one example.
Gathering this data, however, is hard. It is often not gathered at all or it is collected in a way that requires significant post-processing. For instance, live tracking data in Premier League soccer would only become available if players were allowed to carry GPS or location trackers, which FIFA currently disallow for B2C consumption.
The result is tracking data being used by top-tier clubs who are able to afford expensive-high definition cameras. Tracking data is also generally hard to gather through video, with the disambiguation of entities (players) being done by hand.
The result is that the data is not live and thus not used by media or sponsors who need live data to power their real-time products. If tracking data were more available it could be used to enrich the event data gathered from OPTA or STATS to create better talking-points for fans.
The design of expectation
We would like to see more research on the visualization of prediction or speculation.
The drama of “what if” is central to the experience of sport. Observing experiments that capture and convey these ideas live during games would reinforce this. The visualisation of expectation (not prediction) allows for many intriguing possibilities—giving fans the ability to understand the reasoning behind possible outcomes, creating useful and shareable talking points.
Quantitative event data fails to tell a meaningful story about the ebb and flow in sports such as ice hockey and rugby. Simply showing exactly what happened fails to reflect a larger narrative and story around the possibilities of what might have happened. Using real-time or tracking would allow for longevity for team support and fan engagement.
In-video data
Rights holders should release more video as open source for SMEs to experiment with.
Innovation is hard to foster in video graphics because many content rights are held by a single party that does not allow other parties to use or experiment with alternative formats. We believe this denies fans the opportunity to view varying layers of the game, one that supplements, not replaces, live action with augmented, maybe overlaid, quantitative analysis.
Metadata beyond taxonomies
We would like to see more qualitative metadata in sports.
We would like to see more research into metadata that goes beyond the taxonomic approach adopted across various business domains (which are loosely based on Semantic Web concepts).
This library, science-based approach to organising concepts and data is often more useful in academic contexts than it is to the needs of a mass audience for sports media. Examples of this are typically players tagged by country, position and associated events such as goals or awards. We would like to see more metadata that is created with the mass-market fan in mind. This could tag players by style (attack or defence prone), rivalries, predictability, creativity and so on. These are the things fans talk about. They want information that resonates with them emotionally, not just dry facts.
Engaging new audiences
We would like to see more research into what visual data engages mass-market fans.
The visualisations presented by Peris and colleagues target expert audiences which is unlikely to drive engagement for new fans or occasional followers. Very few graphics in the paper have the mass-market appeal of something as simple as the 10-yard-line in the NFL.
We would like to see research into the barriers to adoption, data-literacy, and learning styles of the mass-market audience for sports. We could then begin to see how much they understand about concepts such as teamwork, tactics, or tempo of the sport and how data could help surface these ideas better.
Conclusion
We believe striking a balance between well-formatted sports data visualisation and strategic insight can strengthen fan engagement and sports industries alike.
However, sports data visualisation is as crucial in the art of storytelling as it is in deciphering patterns.
Data storytelling, for example, should focus on clear messages and easy to understand graphics. This goes beyond simple narratives such as Team X played Team Y in week 32. It should provide an opportunity for novices and experts alike to understand different hypotheses and trajectories of game outcomes. To succeed, the visualisation of expectation presented live during games and the drama of “what if”, should allow fans the ability to understand the reasoning behind possible outcomes, creating shareable talking points and fan interaction.
Finally, the visualisations presented in the paper target expert audiences, which is unlikely to drive engagement for new fans or occasional followers. Inclusion of metadata such as player style, team rivalries, and creativity would augment play signature and provide finer details to the discussion of sports.
We hope that future research will explore how to engage new audiences thereby strengthening the art of sports data visualisation.