How a Honduran pastor is using soccer to heal his community He forged a surprising alliance to transform a site of violence into a place for sport

In the outskirts of San Pedro Sula, Honduras, decades of violence and economic dispossession have torn communities apart. Empty houses, stripped for valuable materials, lie in ruin after families fled from the threat of gang recruitment, economic insecurity and, often, the threat of death.

Dany Pacheco, an evangelical pastor in the Planeta district of San Pedro Sula, the country’s second-largest city, decided to do something. He didn’t want the youth in his neighborhood to suffer the way previous generations had.

Pacheco met with the neighborhood’s gang leaders and negotiated a compromise so they could transform a site of violence into a park.

Dany Pacheco, head pastor of the Casa Esperanza congregation, stands in a swampy lot in the Rivera Hernandez district on the outskirts of San Pedro Sula that was once used to dispose of bodies. Pacheco came up with the idea that by getting rid of a space used for violence, traumatized families could break free from the self-perpetuating cycle. Pacheco gained the permission of the local gangs and began to convert the lot into an open space for the community.

Moises Cubas and his friends head to the inaugural match in the park. Even though he and his friends aren’t in a gang, the zero-tolerance policing strategy has led to acts of police brutality against the boys for being residents of a gang-controlled neighborhood.

Pacheco leads a prayer for peace before the game between police and local gang members. Pacheco is betting that by having the boys play the police on a regular basis, the hatred that plagues the margins of the city can be healed.

The neighborhood came out en masse to watch the inaugural match last summer. The district’s reputation for brutal gang violence had cut them off from the rest of the city, and residents were thrilled for an event like this taking place in their part of town.

The sun sets over the Rivera Hernandez district on the outskirts of San Pedro Sula, Honduras. The mosaic of gang-controlled neighborhoods in this part of the city makes the streets difficult to navigate, as each is controlled by a different gang.

Left: A 32-ounce Caguama beer is poured out for a friend lost to a rival gang incursion. Honduran gangs originated in Southern California when refugees who arrived in the 1980s banded together into self-defense groups to protect themselves against established Los Angeles gangs. When some were deported, they brought with them the criminal expertise learned in L.A.’s racially charged gang wars of the early 1990s. Right: Dario, a gang enforcer, wears a cross bearing the Lord’s Prayer. Gang leaders are pushing to recruit younger boys into their ranks. They go after kids from broken homes, offering wealth and respect. Dario raised himself in the streets after his mother was disappeared by the police when he was a boy.

Selvin Ferrufino digs a trench to help drain the marshy grounds in the Rivera Hernandez district on the outskirts of San Pedro Sula. Pastor Dany Pacheco’s vision for peace has brought together grandmothers, children, gang members and preachers in a collective effort to heal their neighborhood.

A police officer paints the sideline decoration at the football pitch in the Rivera Hernandez district on the outskirts of San Pedro Sula. The police were hesitant to participate at first, but because of Pacheco’s rapport with the local precinct, he persuaded them to join the effort.

Two neighborhood youths smoke cigarettes among abandoned row houses in the Rivera Hernandez district. Boys there face stark decisions on whether to join a gang, refuse their recruitment or flee. The calculus behind their choices is different for every individual, but the driving question remains constant: “How do I survive in San Pedro Sula?”

An abandoned house, stripped of its valuable metals, in the Rivera Hernandez district on the outskirts of San Pedro Sula, Honduras. It’s in these physical reminders of violence and collapse that Pacheco found his purpose: Bring back life to these derelict spaces so that the next generation of Honduran youth might have a chance to live in peace.

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Jacob DeGrom Is Breaking The Cy Young Formula

Pitcher wins were already an unpopular metric among the sabermetric set, but Jacob deGrom’s 2018 season may have officially put the final nail in their coffin, even for traditionalists still hanging on to their old-school stats.

DeGrom is currently leading the major leagues in ERA with a microscopic 1.81 mark in 159 innings. Yet, for all his trouble, he sports a meager 7-7 win-loss record, because his New York Mets cannot score (and are generally even more disgraceful than usual) when their ace takes the hill. Among qualified pitchers with a nonwinning record, deGrom has the second-best ERAWe won’t think about Eric Gagne’s weird 2003 win as a reliever — although you have to hand it to the Cy Young tracker for nailing that pick.

‘>5 DeGrom’s candidacy could end up reinforcing that policy, since the Cy tracker’s leader, Max Scherzer, is running second in pitching WAR and has a more traditionally acceptable 15-5 record. The voters could tab him for the award as a (cop-out) way of straddling the line between new- and old-school evaluation methods.

But they could also give it to deGrom as the reward for 2018’s most outstanding pitching performance — which the award’s own language purports to honor. Whether that happens will be another signpost along the mainstream media’s path toward accepting newer statistics and casting aside old relics like wins.

Check out our latest MLB predictions.

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Step 1: Game Theory. Step 2: ???? Step 3: Profit!

Welcome to The Riddler. Every week, I offer up problems related to the things we hold dear around here: math, logic and probability. There are two types: Riddler Express for those of you who want something bite-size and Riddler Classic for those of you in the slow-puzzle movement. Submit a correct answer for either,1 and you may get a shoutout in next week’s column. If you need a hint or have a favorite puzzle collecting dust in your attic, find me on Twitter.

Quick announcement: Have you enjoyed the puzzles in this column? If so, I’m pleased to tell you that we’ve collected many of the best, along with some that have never been seen before, in a real live book! It’s called “The Riddler,” and it will be released in October — just in time for loads of great holidays. It’s a physical testament to the mathematical collaboration that you, Riddler Nation, have helped build here, which in my estimation is the best of its kind. So I hope you’ll check out the book, devour the puzzles anew, and keep adding to our nation by sharing the book with loved ones.

And now, to this week’s puzzles!

Riddler Express

From Freddie Simmons, a guessing game:

Take a standard deck of cards, and pull out the numbered cards from one suit (the cards 2 through 10). Shuffle them, and then lay them face down in a row. Flip over the first card. Now guess whether the next card in the row is bigger or smaller. If you’re right, keep going.

If you play this game optimally, what’s the probability that you can get to the end without making any mistakes?

Extra credit: What if there were more cards — 2 through 20, or 2 through 100? How do your chances of getting to the end change?

Submit your answer

Riddler Classic

From Steven Pratt, use your econ, win some cash:

Ariel, Beatrice and Cassandra — three brilliant game theorists — were bored at a game theory conference (shocking, we know) and devised the following game to pass the time. They drew a number line and placed $1 on the 1, $2 on the 2, $3 on the 3 and so on to $10 on the 10.

Each player has a personalized token. They take turns — Ariel first, Beatrice second and Cassandra third — placing their tokens on one of the money stacks (only one token is allowed per space). Once the tokens are all placed, each player gets to take every stack that her token is on or is closest to. If a stack is midway between two tokens, the players split that cash.

How will this game play out? How much is it worth to go first?

A grab bag of extra credits: What if the game were played not on a number line but on a clock, with values of $1 to $12? What if Desdemona, Eleanor and so on joined the original game? What if the tokens could be placed anywhere on the number line, not just the stacks?

Submit your answer

Solution to last week’s Riddler Express

Congratulations to 👏 Jonathan Hegarty 👏 of Cedar Grove, New Jersey, winner of last week’s Riddler Express!

Where on Earth can you travel 1 mile south, then 1 mile east, then 1 mile north, and arrive back at your original location?

You can do this at the North Pole, for starters — that’s the easy one. But there is also an infinite number of other such points on the planet that allow for this paradoxical navigation. Specifically, any point that is 1+1/(2nπ) miles from the South Pole, where n = 1, 2, 3, …

Our winner, Jonathan, explained how this works. At the North Pole, after walking a mile south (which could be any direction, since you’re as far north as possible) and a mile east, you would still be exactly 1 mile south of where you started. Therefore, when you walk north, you end up at your original position.

However, you can also do something similar if you are near the South Pole. We’re looking for a place that, after walking our first mile south, leaves us in position to walk 1 mile east and have that mile be a perfect circle around the South Pole. In other words we’re looking to start in a place that makes the circumference of the circle around the South Pole 1 mile, making the radius of that circle (and the distance to the South Pole) 1/2π. Once completing this circle, you are free to walk back north 1 mile to your starting point, which would be 1+1/2π miles in any direction from the South Pole. But you’re not limited to just taking a single circle around the South Pole. What if you want to walk a half-mile circle around the South Pole twice? In that case, you would want the radius of that circle to be 1/4π, so you would be starting 1+1/4π miles from the South Pole. Continuing that logic, you can really start at any place that is 1+1/(2nπ) miles from the South Pole, where n is any positive integer and the number of times you wish to walk around the South Pole.

Brrr.

Solution to last week’s Riddler Classic

Congratulations to 👏 Marissa James 👏 of Berkeley, California, winner of last week’s Riddler Classic!

Last week found us in the factory of Riddler Rugs, where 100-by-100-inch random rugs are crafted by sewing together a bunch of 1-inch squares. Each square is one of three colors and is chosen for the final rug randomly. Riddler Rugs also wants its rugs to look random, so it rejects any finished rug that has a four-by-four block of squares of the same color. What percentage of rugs should we expect to be Riddler Rugs rejects? (Say that 10 times fast.)

Riddler Rugs will reject about 0.066 percent of its rugs, or roughly 1 rug in every 1,500.

We can arrive at that number in two different ways: a simple approximation and then a somewhat more involved one. My editor could barely stomach the former, let alone the latter, so tread carefully.

Let’s start with the approximation. Consider the fact that each of the squares in the top 97 “rows” and the left-most 97 “columns” of the quilt define an upper left corner of a four-by-four block. The squares are almost all part of other blocks, too, but what matters here is their role in the upper left of a block. (There are 97 of them because the three bottom rows and three right-most columns “start” blocks that extend off the edge of the rug, so we don’t count those.) Multiply 97 by 97, and you get 9,409 four-by-four blocks to use as a baseline for calculations.

The chances that any one of these blocks is all one color are the same as the chances that 15 of the squares in the four-by-four block match the color of the first upper-left square. With our three colors, that chance is equal to ((1/3)^{15}). Therefore, the chance that there are no one-color blocks out of the 9,409 is ((1-(1/3)^{15})^{9,409}), so the chance that there is a one-color block (and thus that the rug is rejected) is (1-((1-(1/3)^{15})^{9,409})), or about 0.0006555, or about our 0.066 percent.

The other way to get at this solution is to think about the whole universe of possible rugs. To do that, we have to determine the numerator (how many rejectable rug combinations there are) and the denominator (the number of total possible rug color combinations). The latter is easy to calculate: There are ({3^{100}}^2) possible rugs (three colors to choose from, and 100 rows and columns of squares in each rug).

Figuring out the numerator is where it gets trickier. We need to consider every possible one-color block that will cause the rug to be rejected, along with every possible rug that includes that “bad” block. To do that, consider that there are (97^2) possible bad blocks, and three ways they each could be bad. Those bad blocks automatically make a rug a reject, but it matters what the rest of the rug looks like, since we are trying to do a full accounting of every possible rug. We can use our old denominator formula here, with one small tweak: (3^{(100^2-16)}), with the “16” representing the number of squares we know the color of already (the 4×4 reject block).

With all that in hand, we’re nearly done. Multiply the two elements of our numerator, and divide it over the denominator, and you get a formula for the proportion of rejected rugs: ((97^2cdot 3)cdot (3^{(100^2-16)})/({3^{100}}^2)). That simplifies to (97^2/3^{15}), which is about 0.0006557, or again about our estimate of 0.066 percent.

And, as usual, you could also turn to a computer simulated approximation. Stephanie Valenzuela was kind enough to share her Python-based approach.

So how does this process scale? Riddler Rugs, I’m not ashamed to say, is in it for the money and has plans to produce way more than just one measly random rug. One rug’s not cool. You know what’s cool? A million rugs. And we’d prefer to not reject any, if we can help it.

Laurent Lessard plotted the probability of rejecting no rugs out of a million produced. With our initial three colors, we’re nearly sure to reject at least some rugs. But if we expanded our fabric palette to six or even seven colors — ROYGBIV, say — Riddler Rugs would have an excellent chance to produce a million rugs without rejecting even a single one.


Want to submit a riddle?

Email me at oliver.roeder@fivethirtyeight.com.

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The 5 Big Takeaways From Our House Forecast

Democrats are favored to gain control of the House of Representatives in this year’s midterm elections, according to the FiveThirtyEight forecast model. But — a very FiveThirtyEight-ish sentence follows — the range of possible outcomes is wide and Democrats’ prospects are far from certain. Relatively small shifts could allow Republicans to keep control of the House, or could turn a blue wave into a tsunami.

What’s behind all of this? Our methodology post goes into a lot more detail about how our forecasts are calculated. But it’s rather abstract — so in this article, I’m going to focus on how these factors are playing out given what we know about the political environment this year.

Theme No. 1: A broad consensus of indicators point toward Democrats performing well

In contrast to our presidential forecasts, which are heavily dependent on polling, our House model uses a broad mix of polling and non-polling indicators, including factors such as fundraising totals and historical trends in midterms. Those indicators look both pretty good for Democrats and are remarkably consistent with one another:

  • The Lite version of our forecast, which focuses as much as possible on district-level and generic ballot polls, projects Democrats to win the popular vote for the House by 7 or 8 percentage points.
  • The Classic version of the model, which incorporates a lot of non-polling metrics such as fundraising and past voting in each district, also shows Democrats winning the popular vote by 7 or 8 points.
  • The generic ballot, which influences all three versions of our forecast, has generally shown Democrats with a lead of … 7 to 8 points percentage points.
  • And finally, our model calculates a starting assumption about the race based on historical trends in midterms since 1946 and presidential approval ratings. It also implies that Democrats “should” win the House popular vote by about 8 percentage points — just what the other metrics show.

So you’d expect Democrats to do pretty well based on the historical propensity of opposition parties to gain ground in midterm elections, especially under unpopular presidents. And Democrats are doing roughly as well as you’d expect them to in most indicators of the national environment.

There are one or two exceptions — indicators that are a little out of the consensus — but both of them fall on the better-for-Democrats side of the consensus. First, Democrats have done really impressively in fundraising. Their candidates have raised more in individual contributions than Republicans in 71 of the 101 districts rated as competitive by the Cook Political Report, despite the fact that about two-thirds of these districts feature Republican incumbents. That’s unusual. Most challengers significantly trail in fundraising at this point in the cycle. Meanwhile, the results of special elections have been very good for Democrats. Our model doesn’t actually use special election results in its forecasts, but they’re part of a coherent alternative narrative in which there’s upside for Democrats relative to what our forecast shows. Donating money and voting in special elections are tangible indicators of voter enthusiasm, and it’s possible that they point toward a Democratic enthusiasm advantage that could become clearer later on in the cycle.

Theme No. 2: However, there’s some feast-or-famine risk for Democrats

It’s much to Democrats’ credit that there are so many districts in play in all corners of the country. (Based on our accounting, Democrats have fielded a nominee in all but three of the 435 congressional districts nationwide). But if you had to pinpoint the exact districts that Democrats will win to gain 23 seats and take the House majority, you’d have a pretty hard time. We have only 215 seats rated as “lean Democrat” or better for the Democrats, fewer than the 218 they need to take the House.

Nonetheless, Democrats are favored to win the majority if current conditions hold because they’ll have a bunch of opportunities, even as underdogs, to win those extra seats: 14 toss-up races, 19 “lean Republican” races and 53 “likely Republican” contests. Those are a lot of lottery tickets to punch, even if Democrats aren’t necessarily favored in any individual race.

The problem for Democrats would come if there’s a shift in the national climate toward Republicans, or even a relatively modest systematic polling error on Election Day in the GOP’s favor. All of the sudden, they’d lose most of the toss-up races along with some of the “lean Democrat” races — and the “lean Republican” and “likely Republican” seats would become an uphill climb at best.

The flip-side to this is that if the political environment gets better for Democrats, their seat gains could pile up at an accelerating rate. There are a plethora of districts that are 10 to 20 points more Republican than the country as a whole, a lot of which were gerrymandered to be “safe” for Republican candidates — but where the gerrymanders could fail in the event of a large enough wave.

Theme No. 3: Incumbents — especially Republican incumbents — are really vulnerable

The first line of defense for a party hoping to maintain its majority is incumbency. Even if the national political climate is unfavorable, its incumbents may be popular enough in their districts to withstand the wave.

The issue for Republicans is that the incumbency advantage has been weakening over time — and it appears to be especially flimsy this year. In the 1990s, incumbents overperformed the partisan baseline of their districts by somewhere on the order of 20 percentage points. (So, for example, a district that might favor Republicans by 2 points in an open-seat race would go to the GOP by 22 points if there were a Republican incumbent running.) In more recent elections, as Congress has become less and less popular, the incumbency advantage has eroded to more like 10 to 12 percentage points. And between anemic fundraising, highly Trump-aligned voting records even for incumbents in purple districts, and reasonably good district-by-district polling for Democratic challengers, our model is projecting only about a 6-point advantage for GOP incumbents this year. Plus, a lot of Republican incumbents have retired.

Our forecast also shows a relatively narrow avantage for Democratic incumbents. But Democratic incumbents have little exposure in the House: Any Democratic incumbent who was strong enough to survive the GOP waves in both 2010 and 2014 probably won’t have any problems this year. (The Senate, where there are lots of vulnerable Democratic incumbents who were last re-elected in the strong Democratic year of 2012, is an entirely different story.)

Theme No. 4: Potential Democratic gains are broad-based, across all regions of the country

One factor helping Trump in 2016 was that he really needed to beat his polls in only one part of the country, the Midwest, to defeat Hillary Clinton in the Electoral College. (Outside of the Midwest, the polls were reasonably accurate and even underestimated Clinton in some states.) By contrast, Republicans are facing a multi-front assault in the House this year:

  • In the Northeast, they have a lot of exposure in New York and New Jersey, which were once bastions of moderate Republicanism but which have become increasingly inhospitable to it — and in Pennsylvania, where court-ordered redistricting resulted in a bad map for Republicans and where a lot of GOP incumbents have retired.
  • In the South, they face pressure because of demographic change in states such as Georgia and Virginia — and increasingly in Texas.
  • In the Midwest, there’s the risk of reversion to the mean with Trump off the ballot, especially as the GOP coalition in these states has come to rely on non-college voters who don’t always participate in midterm elections.
  • And in the West, there are 14 Republican incumbents in California and another four in Washington who are increasingly running against the political current as the Pacific Coast becomes a somewhat literal “blue wall”.

As it happens, projected Democratic gains are almost evenly distributed between the four Census Bureau regions: The Classic version of our model projects them to gain nine seats in the Midwest, nine in the South, nine in the Northeast, and nine in the West. Note that Democrats could completely flop in any one of these regions and yet still (just barely) win enough seats to take the House.

Our forecast shows Democrats gaining House seats all over the country
Democratic-Held Seats
Census Region Total Seats Current FORECASTED* net gain
Northeast 78 51 60 +9
Midwest 94 33 42 +9
South 161 48 57 +9
West 102 63 71 +8

* Forecasts are derived from the Classic version of FiveThirtyEight’s House model as of Aug. 16.

Theme No. 5: Democrats need to win the popular vote by a fairly wide margin

The Classic version of our model gives Democrats a near certainty (about a 98 percent chance) of winning more votes than the GOP in the race for the House — but “only” a 3 in 4 chance of winning the majority of seats. This discrepancy between votes and seats reflects a combination of gerrymandering, voter self-sorting2 and incumbency, all of which favor Republicans to some degree. Thus, in the Classic version of our forecast, Democrats would need to win the popular vote by about 5 percentage points in order to become favorites to win the majority of seats in the House. And in the Lite and Deluxe versions, the breakeven point is closer to a 6-point popular vote win.

Nonetheless, these margins aren’t as bad for Democrats as they might be. At earlier points in the cycle, Democrats had appeared to need more like a 7- to 8-point advantage in the national popular vote to be favored to claim the majority of seats. Since then, the Republican edge has been eroded by retirements, by Pennsylvania’s redistricting, and by the relatively weak GOP incumbency advantage (see Theme No. 3). All of this might seem like splitting hairs, but because so many indicators (see Theme No. 1) point toward Democrats winning the popular vote by a margin of something like 7 or 8 percentage points, these subtle differences are important.


I’ll be on vacation next week — excuse me, I’ll investigating ground-level conditions in Maine’s 2nd Congressional District — but we’re going to be returning to these themes again and again between now and Nov. 6, so let’s call it a day. As a bonus, though, here’s a table put together by my colleague Julia Wolfe showing our Classic forecast in every district throughout the country.

The odds for all 435 house races

According to the Classic version of FiveThirtyEight’s House model, as of 6 p.m. on Aug. 16, 2018

Odds that…
district Incumbent A Democrat Wins A Republican Wins
AK-1 Don Young 24.31% 75.69%
AL-1 Bradley Byrne 0.05 99.95
AL-2 Martha Roby 2.39 97.61
AL-3 Mike Rogers 0.18 99.82
AL-4 Robert B. Aderholt 0.00 >99%
AL-5 Mo Brooks 0.18 99.82
AL-6 Gary Palmer 0.02 99.98
AL-7 Terri A. Sewell 100.00 0.00
AR-1 Rick Crawford 0.16 99.83
AR-2 French Hill 22.65 77.34
AR-3 Steve Womack 0.08 99.92
AR-4 Bruce Westerman 0.13 99.87
AZ-1 Tom O’Halleran 94.16 5.83
AZ-2 Open Seat 90.01 9.99
AZ-3 Raul Grijalva 99.97 0.03
AZ-4 Paul A. Gosar 0.06 99.94
AZ-5 Andy Biggs 0.20 99.80
AZ-6 David Schweikert 13.39 86.61
AZ-7 Ruben Gallego 99.98 0.00
AZ-8 Debbie Lesko 21.54 78.46
AZ-9 Open Seat 98.82 1.18
CA-1 Doug LaMalfa 13.94 86.06
CA-2 Jared Huffman >99% 0.00
CA-3 John Garamendi 99.87 0.13
CA-4 Tom McClintock 12.50 87.50
CA-5 Mike Thompson 99.95 0.00
CA-6 Doris O. Matsui 100.00 0.00
CA-7 Ami Bera 98.13 1.87
CA-8 Paul Cook 100.00
CA-9 Jerry McNerney 99.86 0.14
CA-10 Jeff Denham 70.77 29.23
CA-11 Mark DeSaulnier >99% 0.00
CA-12 Nancy Pelosi >99% 0.00
CA-13 Barbara Lee 99.99 0.00
CA-14 Jackie Speier >99% 0.00
CA-15 Eric Swalwell >99% 0.00
CA-16 Jim Costa 99.77 0.23
CA-17 Ro Khanna >99% 0.00
CA-18 Anna G. Eshoo >99% 0.00
CA-19 Zoe Lofgren >99% 0.00
CA-20 Jimmy Panetta 99.88 0.00
CA-21 David Valadao 64.34 35.66
CA-22 Devin Nunes 2.23 97.77
CA-23 Kevin McCarthy 0.11 99.89
CA-24 Salud Carbajal 97.27 2.73
CA-25 Steve Knight 77.44 22.56
CA-26 Julia Brownley 99.82 0.18
CA-27 Judy Chu 100.00 0.00
CA-28 Adam Schiff >99% 0.00
CA-29 Tony Cárdenas >99% 0.00
CA-30 Brad Sherman >99% 0.00
CA-31 Pete Aguilar 99.82 0.18
CA-32 Grace Napolitano >99% 0.00
CA-33 Ted Lieu >99% 0.00
CA-34 Jimmy Gomez 99.98 0.00
CA-35 Norma Torres >99% 0.00
CA-36 Raul Ruiz 99.68 0.32
CA-37 Karen Bass >99% 0.00
CA-38 Linda Sánchez >99% 0.00
CA-39 Open Seat 34.57 65.43
CA-40 Lucille Roybal-Allard 99.89 0.00
CA-41 Mark Takano 99.99 0.01
CA-42 Ken Calvert 2.25 97.75
CA-43 Maxine Waters >99% 0.00
CA-44 Nanette Diaz Barragán 100.00 0.00
CA-45 Mimi Walters 58.04 41.96
CA-46 J. Luis Correa >99% 0.00
CA-47 Alan Lowenthal 99.97 0.03
CA-48 Dana Rohrabacher 66.27 33.73
CA-49 Open Seat 74.91 25.09
CA-50 Duncan D. Hunter 8.17 91.83
CA-51 Juan Vargas >99% 0.00
CA-52 Scott Peters 99.80 0.20
CA-53 Susan Davis 99.97 0.03
CO-1 Diana DeGette >99% 0.00
CO-2 Open Seat 99.78 0.22
CO-3 Scott Tipton 40.54 59.46
CO-4 Ken Buck 3.63 96.37
CO-5 Doug Lamborn 2.79 97.21
CO-6 Mike Coffman 64.57 35.43
CO-7 Ed Perlmutter 99.69 0.31
CT-1 John B. Larson 99.97 0.02
CT-2 Joe Courtney 99.93 0.07
CT-3 Rosa L. DeLauro 99.98 0.02
CT-4 Jim Himes 99.84 0.16
CT-5 Open Seat 96.33 3.66
DE-1 Lisa Blunt Rochester 98.77 1.23
FL-1 Matt Gaetz 0.01 99.99
FL-2 Neal Dunn 0.02 99.98
FL-3 Ted Yoho 1.18 98.82
FL-4 John Rutherford 0.03 99.97
FL-5 Al Lawson 99.99 0.01
FL-6 Open Seat 28.17 71.83
FL-7 Stephanie Murphy 97.43 2.57
FL-8 Bill Posey 1.95 98.05
FL-9 Darren Soto 99.86 0.14
FL-10 Val Demings 100.00 0.00
FL-11 Daniel Webster 0.07 99.93
FL-12 Gus M. Bilirakis 1.56 98.43
FL-13 Charlie Crist 99.60 0.40
FL-14 Kathy Castor 100.00 0.00
FL-15 Open Seat 27.57 72.43
FL-16 Vern Buchanan 11.59 88.41
FL-17 Open Seat 0.37 99.63
FL-18 Brian Mast 7.58 92.42
FL-19 Francis Rooney 0.64 99.36
FL-20 Alcee L. Hastings 100.00 0.00
FL-21 Lois Frankel 100.00 0.00
FL-22 Ted Deutch 99.88 0.12
FL-23 Debbie Wasserman Schultz 99.69 0.31
FL-24 Frederica Wilson 100.00 0.00
FL-25 Mario Diaz-Balart 28.18 71.82
FL-26 Carlos Curbelo 37.89 62.11
FL-27 Open Seat 97.11 2.89
GA-1 Buddy Carter 0.21 99.79
GA-2 Sanford D. Bishop Jr. 99.89 0.11
GA-3 A. Drew Ferguson 0.01 99.99
GA-4 Hank Johnson >99% 0.00
GA-5 John Lewis 100.00 0.00
GA-6 Karen C. Handel 4.62 95.38
GA-7 Rob Woodall 29.40 70.60
GA-8 Austin Scott 100.00
GA-9 Doug Collins 0.00 >99%
GA-10 Jody Hice 0.01 99.99
GA-11 Barry Loudermilk 0.06 99.94
GA-12 Rick Allen 1.76 98.24
GA-13 David Scott >99% 0.00
GA-14 Tom Graves 0.00 >99%
HI-1 Open Seat >99% 0.00
HI-2 Tulsi Gabbard >99% 0.00
IA-1 Rod Blum 72.87 27.13
IA-2 David Loebsack 96.99 3.01
IA-3 David Young 66.97 33.03
IA-4 Steve King 17.55 82.44
ID-1 Open Seat 0.04 99.96
ID-2 Mike Simpson 0.31 99.69
IL-1 Bobby L. Rush >99% 0.00
IL-2 Robin Kelly >99% 0.00
IL-3 Daniel Lipinski 99.98 0.02
IL-4 Open Seat >99% 0.00
IL-5 Mike Quigley >99% 0.00
IL-6 Peter J. Roskam 25.34 74.66
IL-7 Danny K. Davis >99% 0.00
IL-8 Raja Krishnamoorthi 99.89 0.11
IL-9 Jan Schakowsky >99% 0.00
IL-10 Bradley Schneider 99.91 0.09
IL-11 Bill Foster 99.94 0.06
IL-12 Mike Bost 38.37 61.63
IL-13 Rodney Davis 32.50 67.50
IL-14 Randy Hultgren 35.98 64.02
IL-15 John Shimkus 0.03 99.97
IL-16 Adam Kinzinger 2.23 97.77
IL-17 Cheri Bustos 99.86 0.14
IL-18 Darin LaHood 0.02 99.98
IN-1 Peter Visclosky 99.98 0.02
IN-2 Jackie Walorski 7.46 92.54
IN-3 Jim Banks 0.26 99.74
IN-4 Open Seat 0.72 99.28
IN-5 Susan W. Brooks 0.93 99.07
IN-6 Open Seat 0.11 99.89
IN-7 André Carson 99.99 0.01
IN-8 Larry Bucshon 0.77 99.23
IN-9 Trey Hollingsworth 23.53 76.47
KS-1 Roger Marshall 0.00 >99%
KS-2 Open Seat 39.00 60.99
KS-3 Kevin Yoder 21.21 78.78
KS-4 Ron Estes 12.73 87.27
KY-1 James Comer 0.01 99.99
KY-2 Brett S. Guthrie 0.12 99.88
KY-3 John A. Yarmuth 99.35 0.64
KY-4 Thomas Massie 0.06 99.94
KY-5 Harold Rogers 0.00 >99%
KY-6 Andy Barr 47.30 52.70
LA-1 Steve Scalise 0.00 >99%
LA-2 Cedric Richmond 100.00 0.00
LA-3 Clay Higgins 0.09 99.91
LA-4 Mike Johnson 0.20 99.80
LA-5 Ralph Abraham 0.07 99.93
LA-6 Garrett Graves 0.00 >99%
MA-1 Richard E. Neal 100.00 0.00
MA-2 James McGovern 99.98 0.02
MA-3 Open Seat 99.90 0.10
MA-4 Joseph P. Kennedy III 100.00 0.00
MA-5 Katherine Clark >99% 0.00
MA-6 Seth Moulton 99.97 0.03
MA-7 Michael E. Capuano 100.00 0.00
MA-8 Stephen F. Lynch 100.00 0.00
MA-9 William Keating 98.78 1.22
MD-1 Andy Harris 1.10 98.90
MD-2 C. A. Dutch Ruppersberger 99.98 0.02
MD-3 John P. Sarbanes 99.99 0.01
MD-4 Anthony Brown >99% 0.00
MD-5 Steny H. Hoyer 99.99 0.01
MD-6 Open Seat 98.83 1.17
MD-7 Elijah Cummings >99% 0.00
MD-8 Jamie Raskin >99% 0.00
ME-1 Chellie Pingree 99.44 0.56
ME-2 Bruce Poliquin 40.79 59.21
MI-1 Jack Bergman 18.99 81.01
MI-2 Bill Huizenga 5.88 94.12
MI-3 Justin Amash 1.92 98.08
MI-4 John Moolenaar 0.44 99.56
MI-5 Daniel Kildee 99.79 0.21
MI-6 Fred Upton 14.96 85.04
MI-7 Tim Walberg 38.06 61.94
MI-8 Mike Bishop 42.62 57.38
MI-9 Open Seat 98.89 1.11
MI-10 Paul Mitchell 0.24 99.76
MI-11 Open Seat 65.19 34.81
MI-12 Debbie Dingell 99.99 0.01
MI-13 Open Seat 100.00 0.00
MI-14 Brenda Lawrence >99% 0.00
MN-1 Open Seat 45.34 54.66
MN-2 Jason Lewis 76.13 23.87
MN-3 Erik Paulsen 65.79 34.21
MN-4 Betty McCollum 99.98 0.02
MN-5 Open Seat >99% 0.00
MN-6 Tom Emmer 0.19 99.81
MN-7 Collin C. Peterson 85.48 14.52
MN-8 Open Seat 35.44 64.56
MO-1 William “Lacy” Clay Jr. >99% 0.00
MO-2 Ann Wagner 10.17 89.83
MO-3 Blaine Luetkemeyer 0.02 99.98
MO-4 Vicky Hartzler 0.08 99.92
MO-5 Emanuel Cleaver 99.78 0.22
MO-6 Sam Graves 0.06 99.94
MO-7 Billy Long 0.01 99.99
MO-8 Jason Smith 0.01 99.99
MS-1 Trent Kelly 0.07 99.93
MS-2 Bennie G. Thompson 99.53 0.00
MS-3 Open Seat 0.27 99.73
MS-4 Steven Palazzo 0.45 99.55
MT-1 Greg Gianforte 12.18 87.81
NC-1 G.K. Butterfield >99% 0.00
NC-2 George Holding 10.74 89.25
NC-3 Walter B. Jones 0.00 100.00
NC-4 David Price >99% 0.00
NC-5 Virginia Foxx 5.19 94.81
NC-6 Mark Walker 21.51 78.49
NC-7 David Rouzer 7.18 92.82
NC-8 Richard Hudson 14.12 85.88
NC-9 Open Seat 50.35 49.64
NC-10 Patrick T. McHenry 0.30 99.70
NC-11 Mark Meadows 0.34 99.66
NC-12 Alma Adams >99% 0.00
NC-13 Ted Budd 37.21 62.79
ND-1 Open Seat 1.34 98.66
NE-1 Jeff Fortenberry 0.15 99.85
NE-2 Don Bacon 58.20 41.80
NE-3 Adrian Smith 0.00 >99%
NH-1 Open Seat 75.26 24.74
NH-2 Ann Kuster 98.55 1.45
NJ-1 Donald Norcross 99.98 0.02
NJ-2 Open Seat 87.08 12.92
NJ-3 Tom MacArthur 43.91 56.09
NJ-4 Chris Smith 6.66 93.34
NJ-5 Josh Gottheimer 99.02 0.98
NJ-6 Frank Pallone Jr. 99.98 0.02
NJ-7 Leonard Lance 63.22 36.78
NJ-8 Albio Sires >99% 0.00
NJ-9 Bill Pascrell Jr. >99% 0.00
NJ-10 Donald Payne Jr. >99% 0.00
NJ-11 Open Seat 72.34 27.66
NJ-12 Bonnie Watson Coleman >99% 0.00
NM-1 Open Seat 97.92 2.07
NM-2 Open Seat 24.28 75.72
NM-3 Ben R. Lujan 99.98 0.01
NV-1 Dina Titus 99.98 0.02
NV-2 Mark Amodei 1.31 98.69
NV-3 Open Seat 66.86 33.14
NV-4 Open Seat 78.55 21.45
NY-1 Lee Zeldin 10.95 89.05
NY-2 Pete King 18.79 81.21
NY-3 Thomas Suozzi 99.14 0.86
NY-4 Kathleen Rice 99.87 0.13
NY-5 Gregory W. Meeks 100.00 0.00
NY-6 Grace Meng 99.97 0.00
NY-7 Nydia M. Velázquez 99.99 0.00
NY-8 Hakeem Jeffries 100.00 0.00
NY-9 Yvette D. Clarke >99% 0.00
NY-10 Jerrold Nadler >99% 0.00
NY-11 Daniel Donovan 24.32 75.68
NY-12 Carolyn Maloney >99% 0.00
NY-13 Adriano Espaillat >99% 0.00
NY-14 Joseph Crowley >99% 0.00
NY-15 José E. Serrano >99% 0.00
NY-16 Eliot Engel 99.98 0.00
NY-17 Nita Lowey 99.95 0.00
NY-18 Sean Patrick Maloney 98.26 1.74
NY-19 John Faso 52.13 47.87
NY-20 Paul D. Tonko 99.98 0.02
NY-21 Elise Stefanik 7.38 92.62
NY-22 Claudia Tenney 71.17 28.83
NY-23 Tom Reed 4.86 95.14
NY-24 John Katko 36.66 63.34
NY-25 Open Seat 99.72 0.28
NY-26 Brian Higgins >99% 0.00
NY-27 Open Seat 24.50 75.49
OH-1 Steve Chabot 58.23 41.77
OH-2 Brad Wenstrup 5.50 94.50
OH-3 Joyce Beatty >99% 0.00
OH-4 Jim Jordan 4.48 95.52
OH-5 Robert E. Latta 0.31 99.68
OH-6 Bill Johnson 0.04 99.96
OH-7 Bob Gibbs 11.38 88.62
OH-8 Warren Davidson 0.06 99.94
OH-9 Marcy Kaptur 99.97 0.02
OH-10 Michael Turner 6.42 93.58
OH-11 Marcia L. Fudge >99% 0.00
OH-12 Troy Balderson 48.95 51.05
OH-13 Tim Ryan 99.95 0.05
OH-14 David Joyce 18.93 81.07
OH-15 Steve Stivers 2.63 97.37
OH-16 Open Seat 4.99 95.01
OK-1 Open Seat 0.75 99.25
OK-2 Markwayne Mullin 0.01 99.99
OK-3 Frank Lucas 0.00 >99%
OK-4 Tom Cole 0.02 99.98
OK-5 Steve Russell 23.67 76.33
OR-1 Suzanne Bonamici 99.96 0.04
OR-2 Greg Walden 0.26 99.73
OR-3 Earl Blumenauer 100.00 0.00
OR-4 Peter DeFazio 99.17 0.83
OR-5 Kurt Schrader 99.61 0.39
PA-1 Brian Fitzpatrick 21.52 78.48
PA-2 Brendan Boyle >99% 0.00
PA-3 Dwight Evans >99% 0.00
PA-4 Open Seat 99.63 0.37
PA-5 Open Seat 99.98 0.02
PA-6 Open Seat 97.60 2.40
PA-7 Open Seat 86.77 13.22
PA-8 Matt Cartwright 93.52 6.48
PA-9 Open Seat 0.90 99.10
PA-10 Scott Perry 14.85 85.15
PA-11 Lloyd Smucker 27.28 72.72
PA-12 Tom Marino 0.18 99.82
PA-13 Open Seat 0.01 99.99
PA-14 Open Seat 1.23 98.77
PA-15 Glenn W. Thompson 0.08 99.92
PA-16 Mike Kelly 4.76 95.24
PA-17 Conor Lamb 92.96 7.04
PA-18 Mike Doyle 100.00 0.00
RI-1 David Cicilline 99.98 0.02
RI-2 Jim Langevin 99.97 0.03
SC-1 Open Seat 15.00 85.00
SC-2 Joe Wilson 0.85 99.15
SC-3 Jeff Duncan 0.15 99.85
SC-4 Open Seat 0.05 99.95
SC-5 Ralph Norman 12.36 87.64
SC-6 James E. Clyburn >99% 0.00
SC-7 Tom Rice 1.06 98.94
SD-1 Open Seat 1.44 98.56
TN-1 Phil Roe 0.00 >99%
TN-2 Open Seat 0.03 99.97
TN-3 Chuck Fleischmann 0.13 99.87
TN-4 Scott DesJarlais 0.76 99.24
TN-5 Jim Cooper 99.97 0.03
TN-6 Open Seat 0.00 >99%
TN-7 Open Seat 0.03 99.97
TN-8 David Kustoff 0.02 99.98
TN-9 Steve Cohen >99% 0.00
TX-1 Louie Gohmert 0.00 >99%
TX-2 Open Seat 7.84 92.16
TX-3 Open Seat 0.77 99.22
TX-4 John Ratcliffe 0.00 >99%
TX-5 Open Seat 0.08 99.92
TX-6 Open Seat 6.64 93.36
TX-7 John Culberson 49.32 50.68
TX-8 Kevin Brady 0.00 >99%
TX-9 Al Green 100.00 0.00
TX-10 Michael T. McCaul 2.62 97.38
TX-11 K. Michael Conaway 0.00 >99%
TX-12 Kay Granger 0.01 99.99
TX-13 Mac Thornberry 0.00 >99%
TX-14 Randy Weber 1.75 98.25
TX-15 Vicente Gonzalez 99.90 0.10
TX-16 Open Seat 99.98 0.02
TX-17 Bill Flores 0.52 99.48
TX-18 Sheila Jackson Lee >99% 0.00
TX-19 Jodey Arrington 0.00 >99%
TX-20 Joaquin Castro 99.94 0.00
TX-21 Open Seat 17.81 82.17
TX-22 Pete Olson 14.17 85.83
TX-23 Will Hurd 72.36 27.64
TX-24 Kenny Marchant 4.37 95.62
TX-25 Roger Williams 7.09 92.91
TX-26 Michael Burgess 0.12 99.88
TX-27 Michael Cloud 0.48 99.52
TX-28 Henry Cuellar 99.95 0.00
TX-29 Open Seat 99.99 0.01
TX-30 Eddie Bernice Johnson 99.99 0.00
TX-31 John Carter 20.11 79.88
TX-32 Pete Sessions 11.74 88.25
TX-33 Marc Veasey 99.99 0.01
TX-34 Filemon Vela 99.95 0.05
TX-35 Lloyd Doggett 99.99 0.01
TX-36 Brian Babin 0.02 99.98
UT-1 Rob Bishop 0.03 99.97
UT-2 Chris Stewart 2.53 97.43
UT-3 John R. Curtis 0.03 99.97
UT-4 Mia Love 19.01 80.99
VA-1 Robert J. Wittman 1.50 98.50
VA-2 Scott Taylor 8.14 91.86
VA-3 Robert C. Scott 100.00 0.00
VA-4 A. Donald McEachin 99.86 0.14
VA-5 Open Seat 48.78 51.22
VA-6 Open Seat 0.34 99.66
VA-7 Dave Brat 32.08 67.92
VA-8 Don Beyer >99% 0.00
VA-9 Morgan Griffith 0.16 99.84
VA-10 Barbara Comstock 74.71 25.29
VA-11 Gerald E. Connolly 99.99 0.01
VT-1 Peter Welch 99.98 0.02
WA-1 Suzan DelBene 99.87 0.13
WA-2 Rick Larsen 99.88 0.00
WA-3 Jaime Herrera Beutler 10.00 90.00
WA-4 Dan Newhouse 0.80 99.20
WA-5 Cathy McMorris Rodgers 28.14 71.86
WA-6 Derek Kilmer 99.93 0.07
WA-7 Pramila Jayapal >99% 0.00
WA-8 Open Seat 44.36 55.64
WA-9 Adam Smith 100.00 0.00
WA-10 Denny Heck 99.90 0.10
WI-1 Open Seat 20.76 79.24
WI-2 Marc Pocan 100.00 0.00
WI-3 Ron Kind 99.27 0.73
WI-4 Gwen Moore >99% 0.00
WI-5 F. James Sensenbrenner 2.01 97.99
WI-6 Glenn Grothman 29.23 70.77
WI-7 Sean P. Duffy 0.89 99.11
WI-8 Mike Gallagher 0.76 99.24
WV-1 David McKinley 0.30 99.70
WV-2 Alex Mooney 3.65 96.35
WV-3 Open Seat 6.03 93.97
WY-1 Liz Cheney 0.02 99.98

Odds may not sum to 100 percent due to rounding and third-party candidates.

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Significant Digits For Friday, Aug. 17, 2018

You’re reading Significant Digits, a daily digest of the numbers tucked inside the news.


74.7 percent chance

FiveThirtyEight launched its 2018 House forecast yesterday. According to our “Classic” model (there’s also “Lite” and “Deluxe”), as I write, Democrats are projected to pick up an average of 35 seats, giving them a roughly 3-in-4 chance to win control of the United States House of Representatives in November. [FiveThirtyEight]


$98,000 of ramen noodles

In Georgia, a 53-foot tractor trailer filled with $98,000 worth of ramen noodles was stolen from a gas station. The brand of ramen was not specified, but if we’re talking Maruchan here, that’s like 29¢ a pack in bulk or about 340,000 packs of ramen, which would keep me happily sated for at least like a year, I’d guess. There are no suspects at this time. Local Georgia distributors of chopsticks and soup spoons remain on high alert, one assumes. [ABC News]


More than 300 news publications

Last week, The Boston Globe’s editorial page proposed a coordinated response by newspapers to President Trump’s attacks on the news media. By yesterday, more than 300 outlets had signed on, running editorials “promoting the freedom of the press.” These papers ranged in readership from The New York Times to the Griggs County Courier in Cooperstown, North Dakota. [The Boston Globe]


20 No. 1 R&B hits

Aretha Franklin, the Queen of Soul and widely regarded as the greatest postwar pop singer, died yesterday at age 76. Her talent and career generated any number of significant digits: 100 Billboard-charting singles, 20 No. 1 R&B hits, 17 Top 10 pop singles, 18 Grammys, three presidential inauguration concerts, and the best-selling gospel record of all time. Rest in peace. [The New York Times]


59 strokes

In the first round of the Wyndham Championship at Sedgefield Country Club in Greensboro, North Carolina, pro golfer Brandt Snedeker shot a 59, which is like a good score for me in mini golf. Snedeker is only the ninth different player to shoot a 59 on the PGA Tour. [ESPN]


9 months in Earth’s orbit

The Moon, it turns out, is not our only moon, as we, meaning Earth, are likely orbited by multiple “mini-moons.” These are small asteroids, just a few meters long, that were sucked into our gravity from an asteroid belt between Mars and Jupiter. They are thought to orbit our planet for about nine months before falling to the surface (yikes) or shooting back out into space. Only one has been officially observed, but a new telescope, the Large Synoptic Survey Telescope in Chile, is going to try to hunt some more down. [Astronomy]


If you see a significant digit in the wild, please send it to @ollie.

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Politics Podcast: Let’s Talk About The 2018 House Forecast

Our 2018 House forecast is now live! That also means that the FiveThirtyEight Politics podcast is resurrecting “Model Talk,” in which Nate Silver answers questions about what goes into the forecast and how it’s reacting to new developments. This is the inaugural episode of the 2018 midterm season.

You can listen to the episode by clicking the “play” button above or by downloading it in iTunes, the ESPN App or your favorite podcast platform. If you are new to podcasts, learn how to listen.

The FiveThirtyEight Politics podcast publishes Monday evenings, with occasional special episodes throughout the week. Help new listeners discover the show by leaving us a rating and review on iTunes. Have a comment, question or suggestion for “good polling vs. bad polling”? Get in touch by email, on Twitter or in the comments.

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25 Districts That Could Decide The House In 2018

One of my favorite FiveThirtyEight concepts is the “tipping-point state,” first introduced by Nate more than 10 years ago. A tipping-point state is a literal swing state: the state that swings the result of a presidential election from one candidate to another. For example, if you put all 50 states in order by the margin that separated Donald Trump and Hillary Clinton in 2016, from most Republican to most Democratic, then went down that list and began counting up the electoral votes for Trump, Wisconsin3 was the state that gave Trump his 270th electoral vote — and therefore the White House.

In the same way, the biennial battle for the U.S. House always has a tipping-point district that determines who gets majority control: the 218th-most-Democratic or most-Republican congressional district in the country (in other words, the median district). Whoever wins the tipping-point district wins the House; it’s that simple.



In addition to all its other bells and whistles, our new U.S. House model can help us estimate the chances that any given congressional district will be the tipping-point district, just like our presidential model estimates which states are most likely to be the tipping point for that race. In first place, the likeliest 2018 bellwether: Minnesota’s 3rd Congressional District. The suburban Minneapolis district went for Hillary Clinton by 9 percentage points in the 2016 election, but it’s home to a strong incumbent in Republican Rep. Erik Paulsen, who won his last election by 14 points and is sitting on a $2.7 million war chest.

But since there are 435 congressional districts, the odds that any particular district is the tipping point are pretty small. (There’s actually only a 1.86 percent chance that Minnesota’s 3rd District will be the tipping-point district.) That’s why we’re better off looking at a bunch of potential tipping points and thinking about them as a group: Where is the campaign for House control being fought, and what does the battleground look like?

The 25 districts most likely to be the House’s tipping point
District Incumbent Partisan Lean Non-Hispanic White Median Household Income Bachelor’s or Higher
MN-03 Paulsen D+5.0 79% $81,804 48%
CO-06 Coffman D+4.1 62 72,470 42
CA-48 Rohrabacher R+6.7 56 83,894 44
NY-19 Faso R+4.9 85 58,698 28
NY-22 Tenney R+12.9 88 50,059 24
IA-03 Young R+2.4 84 60,261 32
NJ-07 Lance R+3.3 72 104,987 51
CA-45 Walters R+4.3 52 93,995 54
OH-01 Chabot R+9.4 70 53,393 33
CA-10 Denham R+0.8 44 56,256 17
VA-10 Comstock D+1.6 63 116,069 54
NE-02 Bacon R+3.9 73 59,906 38
NC-09 Open seat R+13.6 61 53,630 33
NV-03 Open seat R+5.7 58 64,740 31
CA-25 Knight R+0.1 43 73,819 27
NJ-11 Open seat R+4.5 75 103,419 52
MI-11 Open seat R+6.4 80 76,851 46
IA-01 Blum D+0.7 89 55,366 26
NJ-03 MacArthur R+4.6 76 74,644 33
KY-06 Barr R+10.5 83 48,170 30
OH-12 Balderson* R+14.0 86 66,774 40
CA-49 Open seat R+1.1 61 77,558 43
CA-21 Valadao D+10.1 17 38,462 8
TX-07 Culberson R+11.9 44 71,343 49
VA-05 Open Seat R+14.3 73 50,005 27
Median R+4.5 72 66,774 33
U.S. EVEN 62 55,322 30

* Balderson currently leads balloting in the Ohio 12th District special election, but the race has not yet been called.

A district’s “partisan lean” is FiveThirtyEight’s measurement of how much more Republican- or Democratic-leaning a district is than the nation as a whole. It is based on 2016 and 2012 presidential results within the district, plus an adjustment for state-legislative results.

Sources: Daily Kos Elections, U.S. Census

As of the launch of our House forecast on Thursday, these are the 25 districts most likely to strike the decisive blow for House control. They’re a varied group, reflecting the fact that this year’s playing field is quite broad and Democrats have more than one path to a majority. Eight of them are in the West, including six in California, hinting at that region’s potential to serve as the linchpin of a new-look Democratic majority coalition. But seven are in the Midwest, providing Democrats with the option of returning to the embrace of an old flame. The SouthIn the tradition of PECOTA and CARMELO, we created a stupid backronym for it: Congressional Algorithm using Neighboring Typologies to Optimize Regression.

‘>7 to fill in the blanks. It uses polls from districts that have polling, as well as national generic ballot polls, to infer what the polls would say in districts that don’t have polling.

The Classic version also uses local polls8 but layers a bunch of non-polling factors on top of it, the most important of which are incumbency, past voting history in the district, fundraising and the generic ballot. These are the “fundamentals.” The more polling in a district, the more heavily Classic relies on the polls as opposed to the “fundamentals.” Although Lite isn’t quite as simple as it sounds, the Classic model is definitely toward the complex side of the spectrum. With that said, it should theoretically increase accuracy. In the training data,9 Classic miscalled 3.3 percent of races, compared with 3.8 percent for Lite.Specifically, (i) that there is some method to verify the geographic location of the respondent and (ii) each state or district in the poll is weighted individually. (This is an evolution in our policy since 2016.) For instance, in a national poll with 2,000 respondents, we wouldn’t use a 150-person subsample of Texas responses as a Texas poll unless the above conditions were met. We do treat congressional district breakouts of single-state polls as individual polls of those congressional districts, provided that the pollster intends them to be used in this way and changes the names of candidates in the poll to reflect the ones the voter will see on the ballot in her district.

‘>12

  • We don’t use “polls” that blend or smooth their data using methods such as MRP. These can be perfectly fine techniques — but if you implement them, you’re really running a model rather than a poll. We want to do the blending and smoothing ourselves rather than inputting other people’s models into ours.
  • These cases are rare — so if you don’t see a poll on our “latest polls” page, there’s a good chance that we’ve simply missed it. (House polls can be a lot harder to track down than presidential ones.) Please drop us a line if there’s a poll you think we’ve missed.

    Polls are weighted based on their sample size, their recency and their pollster rating (which in turn is based on the past accuracy of the pollster, as well as its methodology). These weights are determined by algorithm; we aren’t sticking our fingers in the wind and rating polls on a case-by-case basis. In a slight change this year, the algorithm emphasizes the diversity of polls more than it has in the past; in any particular race, it will insist on constructing an average of polls from at least two or three distinct polling firms even if some of the polls are less recent.

    There are also three types of adjustments to each poll:

    • First, a likely voter adjustment takes the results of polls of registered voters or all adults and attempts to translate them to a likely-voter basis. Traditionally, Republican candidates gain ground in likely voter polls relative to registered voter ones, but the gains are smaller in midterms with a Republican president. Furthermore, some polls this year actually show Democrats gaining in likely voter models. The likely voter adjustment is dynamic; it starts with a prior that likely voter polls slightly help Republicans, but this prior is updated as pollsters publish polls that directly compare likely and registered voter results. (If you’re a pollster, please follow Monmouth University’s lead and do this!) In mid-August, for example, the model treats likely-voter and registered-voter polls as roughly equivalent to each other, but this could change as we collect more data.
    • Second, a timeline adjustment adjusts for the timing of the poll, based on changes in the generic congressional ballot. For instance, if Democrats have gained a net of 5 percentage points on the generic ballot since a certain district was polled, the model will adjust the poll upward toward the Democratic candidate (but not by the full 5 points; instead, by roughly half that amount — 2.5 points — depending on the elasticity score13 of the district). As compared with the timeline adjustment in our presidential model, which can be notoriously aggressive, the one in the House model is pretty conservative.Latitude, longitude, population density, urban/rural and geographic region.

      ‘>17 and political18 factors. For instance, the district where I was born, Michigan 8, is most comparable to other upper-middle-income Midwestern districts such as Ohio 12, Indiana 5 and Minnesota 2 that similarly contain a sprawling mix of suburbs, exurbs and small towns.

      The goal of CANTOR is to impute what polling would say in unpolled or lightly polled districts, given what it says in similar districts. It attempts to accomplish this goal in two stages. First, it comes up with an initial guesstimate of what the polls would say based solely on FiveThirtyEight’s partisan lean metric (FiveThirtyEight’s version of a partisan voting index, which is compiled based on voting for president and state legislature) and incumbency. For instance, if Republican incumbents are polling poorly in the districts where we have polling, it will assume that Republican incumbents in unpolled districts are vulnerable as well. Then, it adjusts the initial estimate based on the district-by-district similarity scores. For instance, that Republican incumbent Carlos Curbelo is polling surprisingly well in Florida’s 26th Congressional District will also help Republicans in similar congressional districts.

      All of this sounds pretty cool, but there’s one big drawback. Namely, there’s a lot of selection bias in which districts are polled. A House district usually gets surveyed only if one of the campaigns or a media organization has reason to think the race is close — so unpolled districts are less competitive than you’d infer from demographically similar districts that do have polls. CANTOR projections are adjusted to account for this.

      Overall, CANTOR is an interesting method that heavily leans into district polling and gets as close as possible to a “polls-only” view of the race. However, in terms of accuracy, it is generally inferior to using …

      The fundamentals

      The data-rich environment in House elections — 435 individual races every other year, compared with just one race every four years for the presidency — is most beneficial when it comes to identifying reliable non-polling factors for forecasting races. There’s enough data, in fact, that rather than using all districts to determine which factors were most predictive, I instead focused the analysis on competitive races (using a fairly broad definition of “competitive”). In competitive districts with incumbents, the following factors have historically best predicted election results, in roughly declining order of importance: