- Hi.
My name is Justin Middleton, and I'm
a graduate student at North Carolina State University.
My colleagues and I have been studying
some of the challenges of software development
work in communities.
And in general, that's a pretty good gig, because software
has effects in nearly every major community of our lives,
either as a visible actor, or as part
of the invisible infrastructures that
shape the work that keep things going.
At the same time, we as a research culture
are still figuring out the best ways
to generate, curate, and act on the massive amounts of data
that our technologies produce.
These two knowledge areas, software and data analysis
intersect and diverge at various points providing
new opportunities for progress just
as they provide new problems for us to overcome.
But we believe that by studying areas where both manifest,
we can get a better handle on the processes
of either knowledge area.
This research community in particular
has already explored pressures in traditional sciences,
for example, and like ecology and astronomy, or in business.
But we chose a slightly different approach.
Instead, we looked at baseball, one of the most popular sports
in the United States and internationally.
It is at once athletic, commercial,
and cultural, but most importantly for our purposes,
analytic.
From its origin and its been a sport for statisticians
as well popularizing formats for debate over player performance,
or for a good strategy for moving players
around the bases to get more points.
And for us, as researchers, it's been especially appealing,
because much of the discussion about it
is thoroughly public with books, and shows, and websites,
and other public spaces that incorporate
both analytical and developmental tools.
So then we get to our question, which
is how does the community of baseball analysts
employ software techniques to support their data work?
Or how is software development integrated into a context
specific skill set?
And we ask this through two broad questions.
First, how do they acquire and develop
their developmental skills?
And second by what processes do they apply and realize
software development skills in their data work?
And we got at this through conversations and surveys
with a number of different people
from professional employees of Major League
teams to independent consultants to researchers
for sports statistics to hobbyists
dealing with fantasy leagues.
We analyze the patterns to find continents between literature
and practice, which for the breadth of topics that we
uncovered, was arduous enough in a single publication,
not to mention the five minutes that I have
to summarize it all right now.
This is in part, because the questions detail
a microcosm of analytic practice full of variety.
Our materials address a number of questions,
such as those pictured here now.
And they depict various forms of doing
the work oriented around a community center of interest.
To be sure, despite the variety, there
are commonalities which bind the community.
For example, they share similar programmatic and intellectual
toolkits.
The skills of which can be acquired individually
but must be synthesized with the other knowledge areas
through deliberate and targeted practices and resources.
Yet, despite the low cost of entry into participation
within this field, there is room for those from nearly
any background to contribute.
In terms of application, the overall approach
of the analysts we talk to prefers a flexible posture
towards defining or discovering the analytic question
encouraging a practice, where exploratory coding,
for example, is preferable to the precise designs
and complex frameworks that might be more appropriate later
or elsewhere.
Nevertheless, there are challenges in the coherence
of the community as well.
For one thing, referring to it all as
analytics doesn't give the full credit
to the variety of specializations
that participants can inhabit within a work, including
the focuses of statisticians, of developers,
and data curators all to focus on different targets, which
support a common work altogether.
And the work of software as well it
can be shaped very differently based
on the context in which it occurs.
For example, of the work of a Major League employee,
who has to respond to over 162 games in a regular baseball
year is going to be fundamentally
different in terms of stakeholders and calendar,
for example, than academic researchers and fantasy league
hobbyists.
And in these situations of collaboration,
inform negotiation can be challenging.
And concepts that have meaning in different spheres
such as in software, such as testing, for example,
can be muddled or contested.
And there be challenges in coming together and trying
to share a practice around those.
Now there is a lot more that I couldn't talk about here.
So if you're interested in topics like these,
or if you want to hear more about this,
I encourage you to read the paper
or contact me at the email here.
Nonetheless, I thank you for your time,
overall, and wish you the best conference to come.