Data Visualization - Electoral Overperformance
It seems inevitable that every journalist will have to work with data at some point in their career — and in the 21st century, we’ve seen the sorts of stories that can be extracted from massive, otherwise-incomprehensible data sets. I have very little experience with data visualization myself, but certainly I can see that the niche for data journalism is growing in the areas of economics and public affairs reporting, which I’m particularly interested in exploring. As such, I’ve followed three different sources of data and data journalism: FiveThirtyEight, Propublica (via Twitter), and r/DataIsBeautiful (via Reddit). All of these sites have unique data sets that offer different insights into the political and socioeconomic landscape of our time,. Being a political junkie myself, though, this dataset below caught my eye for how simple, yet comprehensive, a story it tells on the 2022 midterm elections:
A Red Wave In Florida. A Blue Riptide In Michigan. Published Nov. 21, 2022.
This visualization was generated by combining two different datasets: the midterm election results (in this case, as provided by ABC News) and FiveThirtyEight’s own Partisan Lean Index. At first glance, it seems that “partisan lean” is a statistic that is difficult to quantify — how can you assign numbers not to election results, but to popular sentiment? — but the way that the site describes its methodology is pretty straightforward:
We define “partisan lean” as the average margin difference2 between how a state or district votes and how the country votes overall. For example, if a state has a FiveThirtyEight partisan lean of R+5, that means it is 5 percentage points more Republican-leaning than the nation as a whole. Put another way, in an election that’s exactly tied nationally, we would expect Republicans to win that state by 5 points.
These averages are derived from the results of the previous election cycle. For example, my own district, FL-13, has a partisan lean of R+12.2, indicating that in 2020 it was 12.2 percentage points more “Republican leaning” than the nation as a whole. This is despite the fact that the district actually went for Joe Biden in 2020, though it’s also likely that Republicans won most downballot races that cycle. This year, it seemed to me that results for Democrats in my district, and in my state, were catastrophic. Yet, according to the data, Democrats actually overperformed in my district by 4.1 percentage points; another way to look at it might be that Republicans underperformed, winning the district by only 8.1 percentage points. These two conclusions are technically the same, but their importance depends on who is looking at the data. A Democratic strategist on the state or national level, for example, might see the overperformance analysis and feel emboldened, and might conclude that the district can be turned blue despite this year’s election loss — and thus might choose to devote outsized resources to FL-13 for the 2024 election cycle. Republicans might reexamine their messaging to see why comparatively fewer R voters turned out for them this cycle compared to last, especially considering the unpopular Democrats running for state and national office.
This overperformance analysis is fascinating to me, because it is a relatively simple way of conveying thousands of different calculations, and making them all fit into the national picture. Though the actual overperformance index is somewhat complicated at face value, the visualization is easily digestible, and allows us to make conclusions about the election cycle in seconds: Republicans overperformed expectations in South Florida, Democrats overperformed in Arizona, and California and Mississippi don’t seem to be changing partisan allegiances anytime soon. How we interpret these conclusions is rooted in how we see the world, as is how we choose to act based on those conclusions in the future.
