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Peter Norvig
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<div align=\"right\"><i>Peter Norvig<br>2 June 2019</i></div>\n",
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"<div align=\"right\"><i>Peter Norvig<br>3 July 2019</i></div>\n",
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"\n",
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"# Tracking Trump: Electoral Votes Edition\n",
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"\n",
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"\n",
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"# TL;DR for policy wonks\n",
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"\n",
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"As of 2 July 2019, Trump would expect **167 electoral votes** under these assumptions (recall that you need **270** to win). If you ran states in order of his approval, the key turning-point state is Pennsylvania; he'd need to win that and every state in which he is more popular. He currently is **9% behind in Pennsylvania**. \n",
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"He's been below 270 every month for the last two years, and that remains true even if you factor in an across-the-board 3% swing in his favor (which was the swing above polls that he experienced in 2016).\n",
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"As of 2 July 2019, Trump would expect **167 electoral votes** under these assumptions (you need **270** to win). If you list states in order of his approval, the key turning-point state is Pennsylvania; he'd need to win that and every state in which he is more popular. He currently is **9% behind in Pennsylvania**. \n",
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"\n",
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"\n",
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"# The details for data science nerds\n",
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"\n",
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"- **Movement**: What's the most a state's net approval could be expected to move, due to random fluctuations (that is, assuming there is no big event that changes people's minds)? I define the maximum expected **movement** of a state as 1/5 of the undecided voters (i.e. assume the undecided voters broke 60/40 one way or the other) plus 2 standard deviations in the net approval over the last 12 months. \n",
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"\n",
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"- **Swing state**: I define a swing state as one whose maximum expected movement is greater than the absolute value of the net approval. There are 16 such states now; if Trump won them all, he would still lose the elction. Some states that are traditionally considered swing states are out of reach for Trump under my model: he currently trails in Pennsylvania by 9%, in Wisconsin by %14, and in Michigan by %15. My model says these states can be expected to move by no more than 5%. Some traditionally solid Republican states are swing states in my model, such as Texas, which has Trump at +4%, but has 4% undecided voters.\n",
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"- **Swing state**: I define a swing state as one whose maximum expected movement is greater than the absolute value of the net approval. There are 16 such states now; if Trump won them all, he would still lose the election. Some states that are traditionally considered swing states are out of reach for Trump under my model: he currently trails in Pennsylvania by 9%, in Wisconsin by %14, and in Michigan by %15. My model says these states can be expected to move by no more than 5%. Some traditionally solid Republican states are swing states in my model, such as Texas, which has Trump at +4%, but has 4% undecided voters.\n",
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"\n",
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"- **Margin**: Suppose a future event swings voters in one direction or another uniformly, across the board in all states. How much of a swing would be necessary to change the election outcome? We call that the **margin**. Today **Trump's margin is 7%:** if he got 7% more votes in all states he would be over 270 electoral votes. (This could come, for example, by convincing the 3% of undecided voters to break for him at a 2% to 1% ratio, and then convincing 3% of disapproving voters to switch to approving.)\n",
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"\n",
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" % Total % Received % Xferd Average Speed Time Time Time Current\n",
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" Dload Upload Total Spent Left Speed\n",
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"100 116k 0 116k 0 0 15866 0 --:--:-- 0:00:07 --:--:-- 31316\n"
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"100 116k 0 116k 0 0 286k 0 --:--:-- --:--:-- --:--:-- 286k\n"
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" '1-Jun-18': -11, '1-Jul-18': -10, '1-Aug-18': -12, '1-Sep-18': -14,\n",
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" '1-Oct-18': -11, '1-Nov-18': -11, '1-Dec-18': -10, '1-Jan-19': -12,\n",
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" '1-Feb-19': -16, '1-Mar-19': -11, '1-Apr-19': -11, '1-May-19': -12,\n",
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" '1-Jun-19': -12}"
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" '1-Jun-19': -12, '1-Jul-19': -11}"
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]
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},
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"9"
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# What across-the-board increase in percentage approval would he need to win?\n",
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"# What across-the-board increase in approval would he need to win?\n",
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"margin(states)"
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]
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},
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"source": [
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"We see that:\n",
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"- Trump is currently leading in states with only **167** electoral votes; \n",
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"- The margin is **9%** (if he got 9% more popular in every state, his expected total would be over 270).\n",
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"- The margin is **9%** (if he got 9% more popular in key states, his expected total would be over 277.5).\n",
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"- Swings from 0 to 10% produce electoral vote totals from 167 to 292."
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]
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},
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"source": [
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"# Electoral votes by month\n",
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"\n",
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"The following plot shows, for each month in office, the expected number of electoral votes (based on net approval) with error bars indicating a 3% swing in either direction (Why 3%? That was the [average error](https://fivethirtyeight.com/features/the-polls-are-all-right/) in national presidential polls in 2016: Clinton was predicted by polls to win the popular vote by 6% but actually only won by 3%.) Trump hasn't been above 270 since 4 months into his term, and even with the 3% swing, since 6 months in."
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"The following plot shows, for each month in office, the expected number of electoral votes with error bars indicating a 3% swing in either direction (Why 3%? That was the [average error](https://fivethirtyeight.com/features/the-polls-are-all-right/) in national presidential polls in 2016: Clinton was predicted by polls to win the popular vote by 6% but actually only won by 3%.) Trump hasn't been above 270 since 4 months into his term, and even with the 3% swing, since 6 months in."
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]
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},
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{
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{
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"cell_type": "code",
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"metadata": {},
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"metadata": {
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},
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"outputs": [
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{
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"data": {
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@ -204,7 +204,7 @@ Ride Sun, 12/6/2015 Canada Rd 2:15:05 34.67 mi 1,237 ft
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Ride Wed, 9/13/2017 Healdburg / Jimtown 2:08:03 34.45 mi 912 ft
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Ride Thu, 10/2/2014 10/02/2014 Mountain View, California 3:08:06 34.28 mi 2,864 ft
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Ride Sat, 2/1/2014 OLH to Page Mill 3:07:43 34.26 mi 3,099 ft
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Ride Sat, 10/21/2017 Pescadero 2:47:08 34.25 mi 2,556 ft
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Ride Sat, 10/21/2017 Pescadero 5:20:08 67.05 mi 4,938 ft
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Ride Sat, 7/30/2016 Morning Ride 2:50:15 34.19 mi 2,799 ft
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Ride Mon, 7/10/2017 Morning Ride 2:38:59 34.18 mi 984 ft
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Ride Thu, 7/31/2014 Work commute 2:36:53 34.15 mi 564 ft
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@ -227,7 +227,6 @@ Ride Sun, 5/12/2019 Peninsula Bike Trail to Bay Trail 2:29:05 33.00 mi 846 ft
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Ride Sun, 8/20/2017 Lunch Ride 2:23:02 32.96 mi 1,407 ft
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Ride Sun, 3/6/2016 Lunch Ride 2:57:54 32.86 mi 2,566 ft
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Ride Sun, 3/5/2017 Lunch Ride 2:57:09 32.82 mi 1,713 ft
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Ride Sat, 10/21/2017 Back from Pescadero 2:36:50 32.80 mi 2,382 ft
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Ride Sat, 12/16/2017 Morning Ride 2:48:58 32.72 mi 2,320 ft
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Ride Tue, 8/23/2016 Morning Ride 2:32:15 32.61 mi 354 ft
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Ride Fri, 12/9/2016 Morning Ride 3:04:52 32.55 mi 2,365 ft
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@ -436,3 +435,6 @@ Ride Mon, 9/5/2016 Afternoon Ride 1:42:56 25.00 mi 902 ft
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Ride Sat, 11/8/2014 Atherton - Woodside 1:40:51 25.00 mi 972 ft
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Ride Sun, 12/10/2017 Exercise Bike 1:30:00 25.00 mi 0 ft
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Ride Sun, 6/2/2019 The Sequoia 6:40:43 77.51 mi 6,467 ft
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Ride Sat, 6/8/2019 Morning Ride 2:53:08 34.42 mi 1,824 ft
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Ride Sat, 6/15/2019 Morning Ride 2:29:38 29.98 mi 1,785 ft
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Ride Fri, 7/5/2019 Morning Ride 4:30:55 64.05 mi 1,965 ft
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