Via the twitterz, a caution: is data analysis bad for rallying?
I’ve been tinkering with bits and bobs or rally related data for the last few weeks, and I can see how easy it might be for teams and sports data analysts to fall into the trap of always looking to the data to find fixes for a poor performance or a poor result, and perhaps losing sight of the human element and challenge that provides the basis of any sport.
The particular context I’m interested in is using data to support human storytelling, or explanation around, what happened in particular event. A part of this might involve using natural language generation (“data to text”) strategies to generate summaries. But my intention would not be to automate out human reporters. It would be to provide possible storypoints or observations much in sthe same way a not totally reliable or judicious witness might feed observations in that may, or may not, be useful to a journalist creating a report.
Similarly, any automatically generated reports or commentary (such as it might be) might also be views by fans as an enthusiastic, well meaning, or even pub bore level champion of “the data”: fine, in moderation, and perhaps okay to spend some time with for a bit of inside baseball level of (potentially misunderstood) geekiness, but not a replacement for a well crafted piece of sportswriting from a specialist sports journalist.
Even the visual reports I produce are not intended to mean anything in and of themselves. They are throwaway sketches over the data intended as visual cribs that provide a stylised macroscopic view of a large amount of data to help the reader spot stories that might hinted at by the data and act as a starting point for a more considered human level interpretation.
“Because the data…” is not what I’m looking for; “ooh, does that mean…?” and then looking for corroboration elesewhere, and at a far more human(e) level of interpretation, is much more what I’m after…