One explanation to Hillary Clinton’s surprising loss in the 2016 election that seems particularly plausible to me is that Trump simply Out-Campaigned Clinton by 50 Percent in Key Battleground States in Final Stretch. In this blogpost I explored the strategies the two candidates used to address states of different strategic importance.

First, what makes a state ‘key battleground’. This article explained how each individual’s voting power differs by state:

Electoral votes per capita

There’s still a good-sized gap between the states. As a function of general population, Wyoming offers by far the most valuable ballots: Under 600,000 people with 3 electoral votes works out to 5.32 electoral votes per million. The District of Columbia, Vermont, North Dakota, and Alaska come next, each of them over 4 electoral votes per million residents. California stumbles down to last, with 1.48 electoral votes per million, followed closely by New York and Texas.

Decisive voters

The math of such statistical analyses is complicated and much disputed, but at the root lies the fairly simple fact that when a state with a large population is closely contested, it can multiply its voters’ electoral power: up to 3.312 times in a random election, Banzhaf claimed.

I aloso found this interesting article that quantifies voting power in each state with the following methodology:

Presidential Election Voter Power Score: [(Win Probability Score x Number of Electors) / Total Population Aged 18 & Older] x 1,000,000

Senate Election Voter Power Score: [(Win Probability Score x Number of Senators) / Total Population Aged 18 & Older] x 1,000,000

The next thing would be to measure the candidates’ resource allocation to each state. The better way to look at it is to gather a dataset of their speeches/visits. This proved to be harder than I thought; no one has published any curated events log of that. The other way is to compare the states mentioned in their tweets/speeches. Albeit not as good an indicator, tweet mentions could reflect more personal opinions as they are probably less dictated by campaign strategies. Maybe I am just lazy and making up excuses but that’s what I got: the two candidates’ Tweets dataset from Kaggle.

As some abbreviations can be confusing such as IN, OR, VA, to simplify things I extracted only full state names from each tweet. Here is the mashed-up table:

| State | Clinton | Trump | Rank | Vote Power |

|----------------|---------|-------|------|------------|

| Arizona | NaN | 10 | 1 | 207.05 |

| Iowa | 1 | 32 | 2 | 189.88 |

| Ohio | 4 | 22 | 5 | 141.06 |

| Nevada | NaN | 10 | 6 | 138.24 |

| New Hampshire | 1 | 16 | 7 | 122.18 |

| North Carolina | 2 | 6 | 8 | 119.15 |

| Georgia | NaN | 1 | 9 | 109.12 |

| Florida | 2 | 20 | 10 | 105.82 |

| Missouri | NaN | 2 | 11 | 94.65 |

| Maine | NaN | 1 | 13 | 87.19 |

| South Carolina | NaN | 14 | 15 | 78.02 |

| Kansas | 1 | 4 | 16 | 69 |

| Indiana | 3 | 10 | 17 | 60.24 |

| Colorado | NaN | 9 | 19 | 53.15 |

| Texas | 1 | 10 | 20 | 52.14 |

| Wisconsin | 1 | 6 | 21 | 48.67 |

| Pennsylvania | 2 | 6 | 22 | 43.52 |

| New Mexico | NaN | 1 | 23 | 40.99 |

| Delaware | 2 | 1 | 24 | 37.21 |

| Nebraska | NaN | 2 | 25 | 32.27 |

| Michigan | 1 | 4 | 26 | 31.94 |

| Virginia | 3 | 12 | 28 | 29.94 |

| Utah | NaN | 2 | 31 | 26.49 |

| Kentucky | 4 | 4 | 33 | 20.62 |

| Louisiana | 3 | 4 | 35 | 17.1 |

| Oregon | NaN | 1 | 36 | 15.47 |

| Arkansas | 3 | 1 | 37 | 11.09 |

| New Jersey | 2 | 1 | 38 | 10.86 |

| Idaho | NaN | 2 | 39 | 10.47 |

| Washington | 5 | 16 | 40 | 10.36 |

| West Virginia | NaN | 1 | 41 | 8.88 |

| Connecticut | 1 | NaN | 42 | 8.42 |

| Oklahoma | 1 | 4 | 43 | 5.22 |

| Illinois | 1 | 1 | 44 | 3.64 |

| Alabama | NaN | 1 | 45 | 2.88 |

| Hawaii | NaN | 2 | 46 | 2.86 |

| Massachusetts | NaN | 2 | 47 | 1.22 |

| New York | 7 | 20 | 48 | 1.12 |

| Maryland | NaN | 1 | 50 | 0.43 |

| California | 2 | 3 | 51 | 0.37 |

The table, where I bolded ‘must-win’ states according to this fivethirtyeight article, shows whether the candidates’ tweets are targeted according to their strategic importance. And to put these data points in graphs: