Monthly Archives: August 2017

It’s Time To Ditch The Concept Of ‘100-Year Floods’

Photos of water-covered neighborhoods and families riding floating refrigerators to safety have made clear the scale of Hurricane Harvey’s wrath. But the risks that coastal Texans faced before the storm hit — and the probability that others will be dealt a similar fate — are still a confusing mess. Surveys have shown that even people who live across the street from the bureaucratically determined risk zones known as floodplains don’t understand how those boundaries were drawn or what the risk metric that defines them really means.

That’s no surprise to experts, who say the concept of the “100-year flood” is one of the most misunderstood terms in disaster preparedness. In the wake of catastrophic flooding on the Texas coast, the media has been working hard to explain the term, turning out dozens of articles explaining that a “100-year flood” is not a flood that you should expect to happen only once every 100 years. Instead, it refers to a flood that has a 1 percent chance of happening in any given year. Over the course of a 30-year mortgage, a house in a 100-year floodplain has a 26 percent chance of being inundated at least once.This number is derived using probability theory. First, we calculate the probability of there not being a flood over a 30-year period. Since for each year, there is a 99 percent chance of there not being a flood, the chance that there is no flood over 30 years is 74 percent (or .99^30). The probability of a house in a 100-year floodplain being inundated at least once, then, is just the complement, so 26 percent.

“>1

Stories that emphasize this fact are “doing the Lord’s work,” said Wesley Highfield, professor of marine sciences at Texas A&M University at Galveston. But there are still more holy offices to perform. The concept of using a “100-year flood” as a benchmark for risk isn’t just misunderstood; it obscures fundamental statistical problems in how we assess flood risks — problems that can lead to residents and homeowners believing themselves to live in a zone of safety that isn’t there. It may be time for us to find a different way of evaluating that risk altogether.

Floodwaters covered this neighborhood near Interstate 10 in Houston on Thursday.

Marcus Yam / Los Angeles Times

The term “100-year flood,” which Harvey has almost certainly caused,The U.S. Geological Survey has not yet determined the scale of the flooding, but the storm now holds the record for greatest amount of rain dropped by a single storm in the continental U.S. ever. And in some places, the waters have exceeded the benchmarks for a 500-year flood — a flood with only a 0.2 percent chance of happening in a given year.

‘>2 entered the American vocabulary in 1973, when the federal government first defined which parts of the country would fall under new flood control regulations and which would not. If a parcel of land fell inside the boundaries of where a 1-percent-annual-risk flood was likely to reach, any new buildings constructed there would have to be elevated and insured — and would therefore be more expensive. And you might not be able to build there at all. Outside the floodplain, there would be no restrictions.

But there’s a gap between the data those maps are built on and the floodplain boundaries themselves. To get from point A to point B, scientists have to make a lot of assumptions and extrapolations, building in layers of uncertainty that mean the final determination of what is and isn’t in the floodplain should never be thought of as exact.

It begins with roughly 8,000 streamgages, sensors that the U.S. Geological Survey has deployed to collect real-time data on the depth and velocity of rivers and streams across the country. Throw that into a mathematical potpourri with other data points — what’s known about the shape of the stream, say — and you can come up with an estimate of flow, water measured in cubic feet per second.

The USGS collects these flow estimates, plotting them over the years to find the normal amount of water that moves down a given stream — and what that flow looks like when it jumps to levels well above average. Finally, a computer model helps turn those high flow rates (a measure that doesn’t tell you much about whether your couch will be underwater) into an estimated flood depth (which does). Plunk the flood depth estimates down on top of maps and you get a floodplain.

These floodplain maps are best understood as estimates — and not necessarily very reliable ones. “Something like 15 to 20 percent of insured flood claims happen outside the floodplain,” Highfield said. In the Houston area, he found that number to be closer to 30 to 40 percent. “Doesn’t seem like it’s performing well,” he said.

There are three big problems happening with this metric, experts told me. First, there’s the streamgage data. Although there are lots of these tools, they aren’t everywhere there’s a flood risk, and some of them haven’t been collecting data for very long. In the Houston area, for instance, some of the streamgage data goes back less than 50 years.

And we don’t know how representative those years were of the region’s full history. The fewer the data points, the more uncertainty gets baked into the risk metrics, and the less reliable the floodplain predictions will be, said Robert Holmes, national flood hazard coordinator with USGS.

Then there’s the fact that the models we use don’t account for the fact that cities aren’t static. Because Harvey is a hurricane and the flooding is happening on the coast, there’s been a lot of discussion about what these floods mean in the context of global climate change. But the bigger factor in flood map uncertainty could be how humans have changed the landscape and what we’ve built there, said Laura Read, associate scientist at the National Center for Atmospheric Research. The flood risk map for the Houston area was updated in late 2016. But the previous map was a decade old, and local emergency management plans were still based on it when Harvey hit. And because maps aren’t updated annually, even newer maps might not reflect changes to urban development — hills that have been flattened, soil that’s been paved over, wetlands filled in. Things change fast in suburban and exurban America, and places that absorbed water four years ago might not do that as well today. That affects what ends up underwater and when. The risks are constantly changing, but the estimates of the risks aren’t.

Finally, because of all the uncertainty, a flood that has a 1 percent annual risk of happening has a high water mark that is best described as a range, not a single maximum point. Experts might say that 10 feet of water represents a 100-year flood, but there’s a margin of error that means an actual flood on that scale could be significantly shallower — or deeper. Holmes said he has dealt with lots of elected officials over the years who thought of the depth of a 100-year flood as an exact point. “But then I show them the uncertainty boundaries, and they see the 2 or 3 feet on either side, and all of a sudden their eyes become opened,” he said. Decisions about zoning and flood mitigation would be more informed if they were based on the range of possible flood heights.

All of this means that floodplain maps, and the regulations and insurance rates that come from them, are being handicapped by the metric they’re built on. But Highfield and other experts are still grappling with the question of how to change it. The truth is there’s no perfect way to measure the risk of flooding — there’s always going to be some kind of gap of extrapolation between observed data and predicted risk.

For instance, an Obama-era rule enacted after Hurricane Sandy (and since eliminated by President Trump) would have asked agencies to adhere to one of a trio of stiffer flood risk standards, one of which would be building to the level of a rarer 500-year flood — one that has a 0.2 percent chance of happening each year. In general, experts said, designing laws and buildings around a 500-year floodplain means fewer people will be harmed and less property damaged even during smaller floods. But it wouldn’t make maps more accurate. A 500-year floodplain still wouldn’t have taken the ongoing change of landscapes into account. Likewise, a 500-year floodplain would still be based on the same, limited, historical data points. And using the same data to estimate the risk of an even rarer event would have created a larger range of possible high-water marks than the already wide spectrum that comes along with the 100-year flood metric.

At left, President Trump receives a briefing on Hurricane Harvey this week. At right, advisers brief then-President Barack Obama on Hurricane Sandy in 2012.

Getty images

Instead, Highfield would like flood risk regulations to be based on less of a “yes/no” dualistic metric — is this inside the floodplain or not? — and more on a spectrum of possibility. That’s not a totally crazy concept. For instance, there’s no single price for car insurance for everybody. Instead, it’s based on a spectrum of risk — younger people and sports car owners pay a little more than grandmas who drive station wagons. A spectrum of risk would make the already complicated job of risk communication more confusing, he said, but the actual decision-making process about development and land use might be better served. Decisions still have to be made, even when the data we’re basing them on is imperfect.

But whether we could or should change the metric is different from whether we will. And Highfield, who has been studying the metric’s usefulness and accuracy since 2011, doesn’t think that’s likely. Both he and Holmes said a change would require Americans to think differently about the way we balance safety with other concerns, such as the cost of constructing flood protection infrastructure or the freedom to build in places we maybe shouldn’t.

Highfield thinks evidence probably isn’t enough to overcome the inertia of tradition, established bureaucratic systems, and money. In the wake of Harvey, some localities will choose to legislate around the safer-but-more-expensive 500-year flood metric. But he doesn’t expect many to take that path. And forcing a better standard would mean forcing decisions that are socially and politically untenable. “Are you going to tell a city or town that you can’t build here?” Highfield said. “You can’t increase your tax base because of a low probability event? That doesn’t fly most places.”

NFL Preseason Found A Way To Get Even Worse

For journeyman players and NFL rookies, the final week of preseason is the last live-action audition. For veteran starters, it’s the last chance to get hurt in a meaningless game. Because of this, NFL teams generally won’t play their established starters this week, and they will almost certainly not risk injury for their franchise quarterbacks. But in recent seasons, NFL teams have taken this preseason risk management one step further: Quarterbacks hardly play any preseason games anymore, let alone the last one.

The preseason pass attempts of the game’s top quarterbacks are down precipitously. Only one starting quarterback is currently listed among the top 10 in preseason pass attempts: Jameis Winston of the Tampa Bay Buccaneers (69 attempts) — perhaps not by coincidence, he’s also the only one with an HBO camera crew documenting his summer.

Of course, taking fewer snaps on the field doesn’t guarantee that a quarterback will avoid injury — the Dallas Cowboys lost Tony Romo for more than half of last year’s regular season even though he saw only six passes’ worth of preseason action. But coaches seem to be placing special emphasis on keeping star quarterbacks out of harm’s way as much as possible in trivial summer games.

To measure this, we looked at the 10 passersDrew Brees, Philip Rivers, Eli Manning, Tom Brady, Ben Roethlisberger, Matt Ryan, Aaron Rodgers, Peyton Manning, Carson Palmer and Joe Flacco.

“>1 who threw the most yards over the past 10 seasons — nine of whom are still active and starting on NFL teams.

From 2007 to 2012, these quarterbacks — as a group — generally averaged around 45 attempts each preseason. But from 2013 to 2017,The 2017 data includes all but the final week of preseason, which, if history holds, should not affect the numbers much. In 2016, 29 of 32 starting quarterbacks for Week 1 did not throw a pass in the last preseason game, and the three who did (Drew Brees, Robert Griffin III and Russell Wilson) collectively only threw 17 passes.

“>2 the number has steadily dropped. In 2015, the group averaged 26.9 preseason pass attempts. In 2017, that number has fallen to 18.8.This doesn’t include Joe Flacco’s zero passing attempts this preseason because of injury.

“>3

Among that group, Aaron Rodgers has thrown only 22 passes in the past two Green Bay preseasons combined. The Steelers’ Ben Roethlisberger has just 26 pass attempts in that span. The preseason throws for those two quarterbacks over the 2007 and 2008 preseasons were 113 and 74, respectively. And changing teams doesn’t seem to have an impact: Jay Cutler, who briefly retired in the offseason before getting a job in Miami, has thrown just 14 passes for the Dolphins. By comparison, he threw 42 for the 2014 Chicago Bears.

One logical assumption may be that this group of quarterbacks is getting older and thus playing less each preseason, but that theory doesn’t hold. To test this, we looked at the 10 best seasons by a quarterback age 38 or older since 2001Based on yardage.

“>4 — excluding the two recent ones by Tom Brady. The average number of preseason pass attempts among this group was 40.1, including 54 by Peyton Manning in 2014, 49 by Brett Favre in 2007 and 58 by a 41-year-old Vinny Testaverde in 2004. This suggests that the trend of star QBs throwing less in preseason is less a matter of age and more a philosophical shift in the league.

NFL teams are clearly aware that the most prized assets need to be protected. The Minnesota Vikings did not have Adrian Peterson log a single preseason carry for five years. And the Denver Broncos this year have barely played star pass rusher Von Miller, the key to their fearsome defense. But that hasn’t stopped preseason injuries from derailing seasons before they start. Last weekend saw severe injuries to New England Patriots top wide receiver Julian Edelman, Bears No. 1 wideout Cam Meredith and Kansas City Chiefs top running back Spencer Ware. On the defensive side of the ball, the Cowboys reportedly lost starting middle linebacker Anthony Hitchens for eight weeks with a fractured knee. And last week started with Odell Beckham Jr.’s status for the regular season becoming a question mark when the New York Giants’ biggest star sprained his ankle.

NFL coaches seem to be enacting unofficially at the quarterback position what some around the game are advocating for league-wide — cutting the preseason in half. NFL team owners and the league’s player union have been stuck on whether that means essentially trading a reduction in preseason games for more contests in the regular season. But this month, NFL Commissioner Roger Goodell told Giants fans that the league would consider cutting preseason games unconditionally.

Risking a quarterback’s health seems especially unwise given that teams today are unwilling to even open up their regular-season playbook out of fear of revealing secrets — in other words, the tactics being practiced are as irrelevant as the final score. So it’s no surprise that the predictive value of preseason performance has been steadily declining for nearly a quarter of a century. Some teams have stopped charging regular-season prices for preseason games, largely because of the poor quality of play.

Perhaps the best approach is for teams to treat all preseason games, no matter the number, how they do the final exhibition — use them as a proving ground for backups and the bottom of the roster. That way, starters at all positions, not only quarterbacks, are protected from injury. But then, with the uniforms and stadiums the only NFL-like things about these games, all teams would have to price preseason tickets accordingly.

CORRECTION (Aug. 29, 5:20 p.m.): An earlier version of this story incorrectly said Adrian Peterson did not log a carry for five postseasons with the Minnesota Vikings. It was five preseasons.

Politics Podcast: Trump’s Biggest Domestic Crisis Yet

 

With catastrophic flooding in Texas, President Trump is facing the biggest domestic crisis of his presidency so far. The FiveThirtyEight Politics podcast talks about the political challenges that natural disasters can pose to local officials, governors and presidents.

Plus, as Trump hovers around his all-time lowest job approval rating, Nate Silver discusses the kinds of events that have the most bearing on the president’s popularity. Harry Enten also explores the world of fake and shoddy polling.

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.

The Arpaio Pardon Encapsulates Trump’s Identity Politics

The trio of major announcements made by President Trump’s administration on Friday night — the departure of national security aide Sebastian Gorka, the pardon of former Maricopa County, Arizona, Sheriff Joe Arpaio, and the release of a formal memo from the president ordering the Pentagon not to accept transgender people as new recruits in the armed forces — illustrate two important things about the president’s governing style.

First, one of the defining features of the Trump administration is that he embraces a kind of conservative identity politics, in which he promotes policies supported by groups that he favors and that may have felt marginalized during Barack Obama’s presidency. The second is that Trump’s support for those policies is not contingent on the presence of ousted aides like Gorka and Steve Bannon, who agree with him on these positions.

The memo banning transgender recruits and barring the Pentagon from paying for future sex reassignment surgeries delighted conservative Christian activists, a core part of Trump’s base. Similarly, during his campaign, Trump had strong support from unions that represent police officers, border security agents and other law-enforcement personnel, a group that until recently included Arpaio.

And Arpaio has long been a hero to groups strongly opposed to illegal immigration, which were vital to Trump winning the GOP nomination. Arpaio was convicted last month of criminal contempt of court for ignoring a 2011 federal court order that barred him and his department from considering race when making law-enforcement decisions. Arpaio argues that his tactics, which a court ruled illegally targeted Latinos, were simply an effort to enforce existing immigration law.

“So proud of you, Mr President!” author and conservative activist Ann Coulter said on Friday.

Obama, in contrast, ended the ban on openly transgender people serving in the military, strongly defended the Black Lives Matter movement as it questioned police tactics across the country, and pushed for citizenship rights for undocumented immigrants.

It’s still not clear what other actions Trump will be able to take to please his base on immigration — whether he will be able, for example, to build a wall on the United States-Mexico border or get rid of the DACA program, which effectively protects roughly 1 million young immigrants from deportation — as the courts and Congress also have a say.

But the two moves Trump made Friday illustrate that the president himself is likely to continue to govern using this brand of conservative identity politics. It is perhaps his most consistent governing philosophy, a kind of unifying theory for understanding a president who frequently seesaws back and forth in other policy areas.

On economic issues, for instance, he has abandoned many of his campaign promises that angled in a more populist direction. Trump has not yet dramatically overhauled NAFTA, declared China a currency manipulator or defended Medicaid against budget cuts proposed by congressional Republicans. On foreign policy, he has also bowed to more establishment-friendly stances; this week, he reversed himself on a major campaign position when he called for extending the war in Afghanistan.

But on identity issues, it seems, the president is determined to push forward with his campaign promises. He is threatening a government shutdown if Congress does not fund the border wall and refusing to abandon the travel ban on people from some majority-Muslim countries, even after it was repeatedly struck down in the courts. In a recent speech, he staunchly defended law-enforcement officials, noting that he supported giving them military equipment.

A week before Trump pardoned Arpaio and enacted the transgender military ban, Bannon, one of the leading White House voices advocating for a confrontational, identity-politics-style approach, left the administration. On Friday night, Gorka, a Bannon ally and a major administration advocate for blunt rhetoric on identity issues, such as using the phrase “radical Islamic terrorism,” also departed abruptly. If Trump really wanted either of these men to remain in the administration, it is likely they would have.

So what we dubbed the “Bannon Wing” of the administration earlier this year has lost its namesake and, in Gorka, one of its most prominent voices. Gorka, in a letter to Trump that was quoted in The Federalist, wrote, “it is clear to me that forces that do not support the MAGA promise are — for now — ascendant within the White House.” (“MAGA” refers to Trump’s slogan, “Make America Great Again.”)

In his letter, Gorka says he will serve the president from outside the White House because, “Regrettably, outside of yourself, the individuals who most embodied and represented the policies that will ‘Make America Great Again,’ have been internally countered, systematically removed, or undermined in recent months.”

Gorka is right, in that the staffers associated with Bannon are decreasing in number. New chief of staff John Kelly does appear to have the power, with either Trump’s approval or his acquiescence, to make the White House staff more establishment-friendly and less Bannon-like.

In the long run, dumping Bannon, Gorka and the like could move the administration’s policy away from more controversial moves, like the Arpaio pardon.

But right now, the recent staff changes appear to be mostly about easing tensions between various White House staffers and formalizing the processes governing the flow of information to the president. Kelly, according to published reports, is truly in charge of the White House structure in a way previous chief of staff Reince Priebus was not.

But the way the Trump administration governs has not fundamentally changed. And it’s easy to see why. Look at one part of what Gorka wrote — the phrase “outside of yourself.” Trump appears to believe in the MAGA mission. In the eight days since Bannon left, in addition to pardoning Arpaio and moving to block transgender people from joining the military, the president has attacked those calling for the removal of Confederate monuments and suggested that the news media is intentionally trying to increase division in the country.

In a YouGov survey conducted before the pardon was formally announced, opinions on pardoning Arpaio were split along partisan lines: Most but not all Republicans backed it while most Democrats and a plurality of independents opposed it.

Donald Trump plays to the base, example No. 1,345 or so. In other words, Donald Trump doesn’t need Steve Bannon or Sebastian Gorka because he already has Donald Trump.

Will The Champions League Finally Get A New Champion?

The Champions League is billed as the international club soccer competition that brings together the 32 best teams from all corners of Europe. But over the past few years, the tournament has grown increasingly predictable. It now looks more like an event in which 29 teams compete across nine months to decide who gets to lose to three incredibly wealthy clubs from Spain and Germany.

The world’s three top teams — Real Madrid, Bayern Munich and Barcelona — have lifted every one of the last five Champions League trophies. The 2017-18 Champions League, which had its draw Thursday, appears to be no different at the onset. FiveThirtyEight’s club soccer predictions have these three teams as the favorites, each with a roughly 1-in-6 chance of winning the tournament.

On the other hand, that also means there is a roughly 50 percent chance that someone new will win the Champions League this season. But even if a dark horse breaks through, we can safely assume that it won’t be from Cyprus or Azerbaijan.

Not only have the same three teams dominated the Champions League final, but the same big leagues have likewise dominated the group stages. The four top leagues in FiveThirtyEight’s global club soccer rankings –– England’s Premier League, Spain’s La Liga, Germany’s Bundesliga and Italy’s Serie A — sent 14 teams to the group stages last year, and 12 of them qualified for the 16-team knockout phase. Since 2012-13, an average of 11 clubs from the big four leagues have qualified for the knockouts.

This year’s Champions League draw seems to offer further evidence of big money dominance. The four top leagues managed to qualify 15 teams for the group stagesThree years ago, UEFA changed the qualification rules to allow leagues a maximum of five teams in the group stage, up from four. This applies if a club finishes out of the top four in its league but won either the most recent Champions League or the Europa League. The Premier League sent five teams this season because sixth-place Manchester United won the Europa League, while Spain sent four and Germany and Italy each contributed three.

‘>1 — which means that nearly half the clubs in the Champions League hail from just four countries.

Of the 15 teams from England, Spain, Germany and Italy, 14 have a better than 50 percent chance of making the knockout phase, according to the FiveThirtyEight model. Only Tottenham, from the English Premier League, comes in lower (at 41 percent) — and by no coincidence, Spurs were drawn into Group H with two of the top six teams in FiveThirtyEight’s rankings, Real and Dortmund. Among the remaining 17 clubs (those not members of the top four leagues), only four have a better than 50/50 shot of making the knockouts: France’s Paris Saint-Germain, which is ranked fourth in FiveThirtyEight’s global rankings, and French league champion Monaco, as well as Portugal’s Porto and Benfica — all of whom made it through group play last season.

So who has the best chance to break the run of Bayern, Real and Barca?

Among the seven with the next best odds of winning the Champions League in the FiveThirtyEight model — PSG, Juventus, Dortmund, Manchester United, Manchester City, Monaco and Napoli — only Manchester United has won a Champions League this century. Those seven have a combined 36 percent chance of pulling it off this time around. But even if a relative outsider doesn’t win it all, the finals should be within reach. In the past five years, Juventus and Dortmund have reached the final but fallen short.

That’s the thing about a knockout tournament: With so few games, outcomes can easily turn on a run of form or a bit of luck. The Champions League may be increasingly stratified, but its structure means that at least a dozen or more quality teams have reason to dream. Beyond the obvious sides that have big money — such as PSG, which invested about $530 million in wages and fees to acquire Brazilian superstar Neymar from Barca — or recent finalists like Juventus and Atletico Madrid, Thursday’s draw has given four teams, from two relatively soft groups, particular cause for hope.

Group F: Napoli and Manchester City

Maurizio Sarri’s Napoli finished last season just 5 points short of a scudetto with a massive plus-55 goal difference. No club in Italy bested it, and across the top four leagues, only Barcelona, Real Madrid, Bayern Munich and Tottenham had a higher one. Expected goals tells a similar story — Napoli’s plus-44 expected goals difference was bettered only by Barca, Real and Manchester City.Expected goals (xG) are a statistic that estimates the quality of scoring chances by considering factors including the location of the shot, the type of assist and the pattern of play leading to the shot. Add this up for all scoring chances created and conceded, and that produced expected goal difference. My method for calculating this plus-44 number can be read here.

‘>2 In the previous Champions League, Napoli was eliminated by Real, the eventual champions.

So far, the Italian club has managed to keep its entire starting 11 intact despite interest from top English sides in players like Piotr Zielinski and Kalidou Koulibaly. This stands in contrast to Napoli’s rivals Roma, which lost star forward Mohamed Salah to Liverpool. By holding together a team that was one of the world’s best last year, Napoli put itself in position to benefit if the draw was friendly, and it was.

City, meanwhile, has used its financial power to solve squad issues at fullback, spending about $165 million on Benjamin Mendy, Kyle Walker and Danilo. Also, the main reason that City had an elite expected goals difference but could not translate it into goal difference in league play last season was the very poor performance of goalkeeper Claudio Bravo. With new $48 million keeper Ederson, City should not concede so many more goals than expected goals this year.

Drawn with Shakhtar Donetsk of Ukraine and Feyenoord of the Netherlands, Napoli and City are favorites in the group, according to the FiveThirtyEight model — with 14 percent and 20 percent chances, respectively, of reaching the semifinals. Neither club has achieved European glory in decades, but excellent squads and a favorable draw should give fans some optimism.

Group E: Sevilla and Liverpool

The sides that met in the Europa League final two seasons ago have found themselves in by far the easiest Champions League group. With Spartak Moscow of Russia and NK Maribor of Slovenia in the other two slots, Group E is the only group without a single team from FiveThirtyEight’s global top 15. Because Sevilla and Liverpool managed only fourth–place finishes in their leagues last season, neither has more than a 1 percent chance of winning the Champions League trophy. However, Liverpool added Salah, whose production numbers last season compared favorably to superstars like Robert Lewandowski and Cristiano Ronaldo.Salah managed 0.97 expected goals and expected assists per 90 and 0.92 non-penalty goals and assists per 90. Lewandowski put up 0.92 and 0.88, while Ronaldo had 0.98 and 1.02.

“>3 Sevilla has not added any signings as expensive as Salah, but the return of Ever Banega, one of the best creative passers in the world, should help the Spanish side.

Because of the highly favorable draw, both teams are strong favorites to get out of the group, and it would take only a short run of form from there to reach the semifinals. Sevilla and Liverpool each have about a 10 percent chance of making it that far.

Which College Football Teams Do The Most With The Least Talent? (And Vice Versa)

College football can feel like a hopelessly deterministic sport sometimes. In this week’s preseason AP poll, for instance, it was revealed that the recruiting machines at Alabama, Ohio State, Florida State and Southern Cal are also the top favorites to win the College Football Playoff. Ho-hum.

But although raw talent has a pretty strong correlation with on-field success, it doesn’t completely guarantee it. Teams with good rosters can always let their fan bases down, while others can achieve far better results than we would expect from their recruiting hauls alone. (Hello, service academies!)

To get a sense of which teams have gotten the most — and the least — out of their talent, I took ESPN’s Football Power Index (FPI) ratings for each FBS program over the past two seasons,Ideally, we’d be able to look at this over a longer timeframe, but the data I’m using for this story only goes back to the 2015 season.

“>1 and plotted them against 247Sports.com’s Team Talent Composite scores. (The latter measures a roster’s strength by tracking how many highly touted prospects a team has at its disposal.) The overall relationship between FPI and roster talent is relatively strong — recruiting scores explain about 65 percent of the variation in team performance — but some teams have managed to rise above college football’s penchant for predestination.

I mentioned the service academies — Air Force, Navy and Army — because they are the biggest outliers here. Although their recruiting process works largely the same as at other schools (with the biggest exception being a lack of scholarships specifically for athletics), they face unique barriers to hauling in top talent, including mandatory military service after graduation, tougher academic requirements and even size restrictions for incoming players.Guidelines for weight and body fat, for instance, can make it difficult for top linemen to qualify.

“>2 That’s why, according to the Team Talent Composite, the academies are mostly filled with players who were lightly regarded coming out of high school. But whether because of their emphasis on character and discipline, or just their predilection for triple-option schemes that can trip up the most formidable defenses, these programs have produced far better results than their talent would suggest.

Among Power Five schools, the top outperformers are a generally unsurprising collection of well-coached programs, such as the perennially overachieving Wisconsin Badgers, the Washington schools (both UW and WSU), plus Bill Snyder’s K-State and Mike Gundy’s Oklahoma State squads. But ahead of them all might be a surprising team: the Oklahoma Sooners. OU got a reputation for losing big games under former coach Bob Stoops, but Stoops probably should have also gotten more credit for putting the Sooners in position to play those games in the first place, given the way they outplayed the expectations of their recruiting classes.

Meanwhile, at the other end of the spectrum, there are programs that recruit like crazy but achieve only modest outcomes, like South Carolina, Texas and Georgia. The latter two in particular are storied programs that recruit off of their prestige, but both teams have found a way to mess up that advantage in recent seasons. There’s also no shortage of teams that field average talent but manage to be awful anyway, like Kansas and Rutgers. All of these schools serve as testament to the importance of coaching and player development in any program’s fate. Although a team like Nick Saban’s dominating Alabama squad can be No. 1 in recruiting and No. 1 in performance on the field, most schools have to make the best out of what they’ve got.

How much bang does your favorite school get for its recruiting buck? Find out in our searchable table below.

College football teams’ success vs. their recruiting programs

Team Talent Composite vs. Football Power Index rating for FBS college football programs, 2015-16

FOOTBALL POWER INDEX

TEAM
CONF.
TALENT
ACTUAL
VS. EXPECTED
1 Air Force MW 57.8 +1.4 +24.3
2 Western Kentucky C-USA 377.7 +11.8 +18.1
3 Navy American 324.8 +6.4 +15.4
4 Appalachian State Sun Belt 323.1 +5.0 +14.1
5 Western Michigan MAC 442.6 +9.1 +11.9
6 Memphis American 416.5 +6.8 +11.0
7 Temple American 449.3 +7.5 +10.0
8 Oklahoma Big 12 776.7 +24.4 +9.8
9 Washington Pac-12 683.5 +19.3 +9.6
10 Toledo MAC 444.9 +6.6 +9.3
11 Army FBS Indep. 159.1 -8.5 +9.2
12 Washington State Pac-12 531.7 +10.6 +8.8
13 Wisconsin Big Ten 639.8 +15.9 +8.4
14 Kansas State Big 12 504.5 +8.7 +8.3
15 Oklahoma State Big 12 641.8 +15.7 +8.1
16 Louisville ACC 647.6 +16.0 +8.1
17 Brigham Young FBS Indep. 525.2 +9.4 +8.0
18 Baylor Big 12 639.8 +15.4 +7.9
19 Clemson ACC 826.8 +25.0 +7.8
20 Iowa Big Ten 565.9 +11.3 +7.7
21 Boise State MW 558.9 +10.9 +7.7
22 Houston American 542.1 +9.9 +7.6
23 Utah Pac-12 577.2 +11.6 +7.4
24 San Diego State MW 488.5 +6.9 +7.4
25 TCU Big 12 648.0 +15.1 +7.2
26 Louisiana Tech C-USA 444.7 +4.4 +7.1
27 West Virginia Big 12 640.2 +14.0 +6.5
28 North Carolina ACC 676.3 +15.5 +6.2
29 Utah State MW 344.3 -2.0 +6.0
30 Tulsa American 406.6 +0.9 +5.6
31 Colorado Pac-12 534.2 +6.8 +4.8
32 USF American 546.8 +7.3 +4.7
33 Ohio State Big Ten 904.9 +25.6 +4.3
34 Georgia Tech ACC 586.4 +8.8 +4.2
35 Georgia Southern Sun Belt 413.3 -0.3 +4.1
36 Alabama SEC 982.3 +29.3 +4.0
37 Minnesota Big Ten 525.8 +5.3 +3.8
38 Pittsburgh ACC 630.5 +10.6 +3.6
39 Michigan Big Ten 852.0 +22.1 +3.6
40 Stanford Pac-12 772.2 +17.9 +3.5
41 Virginia Tech ACC 652.6 +11.6 +3.5
42 Troy Sun Belt 363.0 -3.8 +3.3
43 Northern Illinois MAC 392.0 -2.3 +3.3
44 California Pac-12 613.6 +9.2 +3.2
45 Ohio MAC 351.6 -4.8 +2.9
46 N.C. State ACC 613.3 +8.9 +2.8
47 Arkansas State Sun Belt 439.8 -0.5 +2.5
48 Ole Miss SEC 786.2 +17.4 +2.3
49 Central Michigan MAC 384.3 -3.8 +2.1
50 Mississippi State SEC 679.6 +11.5 +2.0
51 Middle Tennessee State C-USA 423.2 -2.2 +1.7
52 Colorado State MW 400.7 -3.4 +1.7
53 Arkansas SEC 692.2 +11.9 +1.7
54 Tennessee SEC 812.2 +18.0 +1.6
55 New Mexico MW 364.5 -5.7 +1.3
56 Southern Miss C-USA 456.1 -1.0 +1.2
57 Northwestern Big Ten 604.7 +6.4 +0.8
58 Bowling Green MAC 413.1 -3.8 +0.7
59 LSU SEC 903.5 +21.8 +0.6
60 Wyoming MW 315.5 -9.0 +0.6
61 Idaho Sun Belt 259.6 -11.9 +0.5
62 Texas Tech Big 12 623.0 +6.8 +0.2
63 Penn State Big Ten 737.4 +12.3 -0.3
64 East Carolina American 422.9 -4.5 -0.5
65 Florida State ACC 898.3 +20.1 -0.8
66 Duke ACC 581.5 +3.4 -1.0
67 Wake Forest ACC 507.6 -0.6 -1.1
68 Michigan State Big Ten 717.5 +10.3 -1.2
69 Iowa State Big 12 540.6 +0.9 -1.4
70 Boston College ACC 534.0 +0.2 -1.7
71 Miami ACC 759.6 +11.8 -1.9
72 Texas A&M SEC 822.5 +15.1 -1.9
73 Florida SEC 794.1 +13.5 -2.0
74 Indiana Big Ten 562.3 +1.3 -2.1
75 Nebraska Big Ten 695.8 +8.2 -2.1
76 Syracuse ACC 514.5 -1.3 -2.2
77 San Jose State MW 408.5 -7.0 -2.3
78 Georgia State Sun Belt 328.7 -11.3 -2.5
79 Nevada MW 385.0 -8.5 -2.6
80 Old Dominion C-USA 333.4 -11.3 -2.7
81 Illinois Big Ten 527.4 -1.5 -3.0
82 Auburn SEC 865.4 +16.2 -3.0
83 Vanderbilt SEC 614.4 +3.0 -3.1
84 Cincinnati American 518.1 -2.1 -3.1
85 USC Pac-12 931.8 +19.5 -3.2
86 Ball State MAC 357.9 -10.8 -3.5
87 Missouri SEC 638.3 +3.8 -3.6
88 UNLV MW 354.5 -11.4 -3.9
89 Notre Dame FBS Indep. 849.8 +14.4 -4.0
90 Oregon Pac-12 747.9 +9.0 -4.1
91 Arizona Pac-12 611.6 +1.6 -4.4
92 Connecticut American 412.9 -9.0 -4.6
93 Marshall C-USA 487.6 -5.1 -4.6
94 UCLA Pac-12 806.2 +11.3 -4.8
95 Arizona State Pac-12 687.5 +5.1 -4.9
96 Kentucky SEC 643.3 +2.2 -5.4
97 New Mexico State Sun Belt 259.4 -18.0 -5.5
98 Akron MAC 428.2 -9.2 -5.6
99 Kent State MAC 336.2 -14.3 -5.8
100 Oregon State Pac-12 534.8 -4.0 -5.9
101 UTEP C-USA 273.3 -17.9 -6.2
102 Massachusetts FBS Indep. 347.3 -14.1 -6.2
103 Buffalo MAC 330.0 -15.2 -6.4
104 FIU C-USA 361.5 -13.6 -6.5
105 Tulane American 398.1 -11.7 -6.5
106 Texas-San Antonio C-USA 377.0 -13.1 -6.8
107 Eastern Michigan MAC 344.5 -15.1 -7.0
108 Louisiana-Lafayette Sun Belt 425.8 -11.4 -7.6
109 Georgia SEC 874.9 +12.0 -7.7
110 Purdue Big Ten 523.5 -6.6 -7.9
111 Louisiana-Monroe Sun Belt 307.8 -17.9 -7.9
112 Florida Atlantic C-USA 405.0 -12.9 -8.0
113 SMU American 460.4 -10.1 -8.1
114 South Alabama Sun Belt 378.3 -14.7 -8.4
115 Virginia ACC 643.0 -1.1 -8.6
116 Miami (OH) MAC 407.6 -13.4 -8.6
117 Maryland Big Ten 627.9 -1.9 -8.7
118 Hawaii MW 385.3 -14.8 -8.9
119 South Carolina SEC 716.3 +2.0 -9.4
120 UCF American 497.4 -9.7 -9.6
121 Rice C-USA 397.2 -15.1 -9.8
122 Texas Big 12 830.1 +7.3 -10.1
123 Charlotte C-USA 317.3 -19.7 -10.2
124 Fresno State MW 403.8 -15.9 -11.0
125 North Texas C-USA 366.1 -18.1 -11.2
126 Texas State Sun Belt 356.1 -20.6 -13.2
127 Rutgers Big Ten 593.9 -8.6 -13.6
128 Kansas Big 12 495.5 -14.4 -14.2

Sources: 247Sports, ESPN Stats & Information Group

The Celtics Didn’t Mortgage Their Future — They Insured It

Danny Ainge finally made a trade, and now he’s getting killed. The guy can’t win.

The Boston Celtics are sending Isaiah Thomas, Jae Crowder, Ante Zizic and the Brooklyn Nets’ unprotected 2018 first-round pick to the Cleveland Cavaliers. The Cavs are sending back Kyrie Irving.

For Boston, the trade means giving up the the last year of Isaiah’s bargain deal, plus the four seasons of additional surplus value (or cheap labor) created by the Brooklyn draft pick’s rookie deal. To put another way, the Celtics are paying to supercharge that draft pick, essentially turning it from an unknown quantity — in terms of both pick range and player quality — into a proven star. This comes with some downside: Getting an All-Star or All-NBA player on a below-market rookie deal is how modern superteams are made — just ask the Warriors. But given the team’s larger context, the trade doesn’t mortgage Boston’s future, it insures it.

At the star level, a Thomas-for-Irving deal is close to an even swap. The two players share skill sets (scoring off the dribble, creating separation for pull-ups, historically bad defense) and both are likely to earn max deals when their deals are up. But Thomas, 28, is three years older and (at least) 6 inches shorter than Irving, 25. Thomas is also on the final year of his deal, which pays him about $6 million this season. Irving has two seasons remaining on his deal before he can opt out, and he’ll make about $19 million this season and $20 million the next.

The Celtics’ ceiling for the 2017-18 season isn’t necessarily higher today than it was Tuesday morning, and their ceiling four or five seasons out, once the player drafted with the Brooklyn pick has matured, is undeniably diminished.

But the worst-case scenarios are now off the table. The Celtics have done away with the risk of losing Thomas (leaving his crucial bucket-making role vacant) for nothing in free agency or close to nothing in a last-minute trade. Maxing out a 29-year-old 5-foot-9 scoring guard would have been a massive risk, and it would have been difficult to find a trade partner other than Cleveland. Contending teams that need a guard with Thomas’s skill set and can offer something in return are rare — practically nonexistent, actually, until Irving requested a trade. And while the Celtics do lose the surplus value they would have gotten from adding a future star on a rookie deal, here’s the crucial thing to remember: Irving is likely better than the player they’d draft with Brooklyn’s pick.

We’d expect a player picked first overall to produce almost 35 win shares over his first five seasons, and a player selected between second and fifth overall will probably produce between 20 and 25:

Irving produced 31.4 in those seasons — a bit less than the average No. 1 overall pick (though his rookie season was shortened by the 2011-12 lockout), but better than the average second through fifth pick. He also did it while missing 85 games over those 5 seasons. That’s concerning in its own way, but it shows you Irving’s ability to fill it up when he’s on the floor. It’s far from certain that the Brooklyn pick will turn out to be No. 1 overall now that the team is no longer openly tanking, and even if the Nets do turn out to be the worst team in the league, their pick would only have a 25 percent chance of being No. 1. So the Celtics lose out on the early, below-market years of an uncertain draft pick, but they get a player entering his prime whose early seasons were better than those of most top draft picks. Irving’s $136 million projected value over the next five years, according to CARMELO, isn’t All-NBA-level, but it’s a solid baseline for a team that needed a new point guard.

Boston was ridiculed earlier this summer for passing on Markelle Fultz, who was taken with the No. 1 overall pick that the Celtics traded to the Philadelphia 76ers. But Fultz’s strengths mirror Irving’s — pull-up jump shooting, pick-and-roll scoring — and remain hypothetical in an NBA setting. The Sixers would be thrilled if Fultz turns out to be as good as Irving. And while Fultz projects to produce like a superstar, there’s almost no chance he plays at Irving’s level this season, which happens to be Al Horford’s age-31 season and Gordon Hayward’s age-27 one. If the Celtics lost Thomas in free agency after next season, leaving them with no ready replacement for his star-level perimeter shot-making while they waited for Jaylen Brown and draftee Jayson Tatum to turn into star performers, they risked taking a step backward during what should be a prime year for their two big free-agent acquisitions.

The argument for holding onto assets is that there’s a better chance to “keep the window open.” But that cuts both ways. A season lost at the front end or in the middle of the contention window is just as damaging as one lost at the end.

Besides, Ainge’s Assets — a stockpile that he’s been building since the infamous Kevin Garnett trade with Brooklyn in 2013 — have been a running joke going back to the days when Kevin Love was a rumored Boston target. He spent the last year targeting All-NBA wings Jimmy Butler and Paul George but declined to include premium assets such as the exact draft pick he just sent to Cleveland. Seeing him now pull the trigger on Irving, a very good player who isn’t quite Butler or George, makes for good meme fodder, sure. Butler and George both went for cheap, but both also went for packages that catered specifically to the teams dealing them (the Bulls really like Kris Dunn, and new Pacer Victor Oladipo played his college ball at Indiana). It’s not really clear what kind of offer it would have taken to move Chicago or Indiana off those deals and keep their stars in the East.

It was important for Ainge to find a deal sooner rather than later. Butler, George and Irving all signed their contracts before the salary cap spiked thanks to the influx of money from a new TV deal. This makes them far easier to trade than star players typically are because their salaries are easier to fit onto their new team’s roster and their original teams have to take back less money that’s tied to inferior players. If the Celtics hadn’t found a suitable place to spend their assets by the time the pre-TV deals had expired, they would have had a difficult time fitting a new star under the cap without also dealing away a star already on their payroll.

Questions remain, including how good the Celtics’ defense can be after they shipped out Avery Bradley this offseason and are now sending Crowder to the Cavs, but these are mundane tactical concerns. Boston’s big, existential unrest finally seems to have come to an end. The Celtics’ core is more or less set. Now they have to actually play the games.

Global Club Soccer Rankings

Global club soccer rankings
men’s soccer teams
Soccer Power Index (SPI)

TEAM RATING
RANK1-WEEK CHANGETEAMLEAGUECOUNTRYOFF.DEF.SPI
1

Real Madrid
La LigaSpain3.50.593.5
2

Bayern Munich
BundesligaGermany3.40.592.6
3

Barcelona
La LigaSpain3.30.592.6
4

PSG
Ligue 1France3.00.589.5
5

Juventus
Serie AItaly2.60.587.0
6

Dortmund
BundesligaGermany2.80.686.5
7

Man. City
Premier LeagueEngland2.70.684.8
8
+1

Man. United
Premier LeagueEngland2.40.584.3
9
+3

Chelsea
Premier LeagueEngland2.50.782.0
10
-2

Atlético Madrid
La LigaSpain2.20.581.9
11

Napoli
Serie AItaly2.60.781.7
12
-2

Roma
Serie AItaly2.60.881.5
13

Monaco
Ligue 1France2.50.781.5
14

Tottenham
Premier LeagueEngland2.30.680.4
15

Arsenal
Premier LeagueEngland2.50.978.0
16

Inter Milan
Serie AItaly2.50.878.0
17
+3

Benfica
Primeira LigaPortugal2.10.776.7
18
+1

Liverpool
Premier LeagueEngland2.30.876.5
19
-2

Athletic Bilbao
La LigaSpain2.00.676.2
20
-2

Sevilla
La LigaSpain2.10.776.0
21
+5

AC Milan
Serie AItaly2.20.875.3
22
-1

Villarreal
La LigaSpain2.00.775.0
23

Hoffenheim
BundesligaGermany2.10.774.8
24
+1

Leverkusen
BundesligaGermany2.30.974.5
25
-3

RB Leipzig
BundesligaGermany2.10.874.0
26
+1

Real Sociedad
La LigaSpain2.10.873.8
27
+1

Porto
Primeira LigaPortugal2.00.773.8
28
-4

Lyon
Ligue 1France2.20.973.4
29
+2

Schalke 04
BundesligaGermany2.00.872.5
30

Zenit
Premier LeagueRussia2.00.872.4
31
-2

Gladbach
BundesligaGermany2.10.872.3
32
+3

Valencia
La LigaSpain2.00.871.1
33
-1

Alavés
La LigaSpain1.80.771.0
34

Eibar
La LigaSpain1.90.771.0
35
-2

Lazio
Serie AItaly2.10.970.5
36
+1

CSKA Moscow
Premier LeagueRussia1.80.770.4
37
+2

Leganés
La LigaSpain1.80.770.3
38
-2

Marseille
Ligue 1France1.90.870.1
39
+1

Málaga
La LigaSpain1.80.770.0
40
-2

Atalanta
Serie AItaly1.90.869.9
41
+6

Sporting
Primeira LigaPortugal1.90.869.6
42
-1

Werder Bremen
BundesligaGermany2.21.069.5
43

1. FC Cologne
BundesligaGermany1.90.869.3
44

Espanyol
La LigaSpain1.70.768.8
45
+5

RB Salzburg
BundesligaAustria2.11.068.2
46
-4

Wolfsburg
BundesligaGermany1.80.868.2
47
+4

Eintracht
BundesligaGermany1.70.867.5
48

Everton
Premier LeagueEngland1.90.967.1
49

Real Betis
La LigaSpain1.80.867.0
50
-4

Celta Vigo
La LigaSpain1.90.966.5
51
-6

Mainz
BundesligaGermany1.80.966.5
52
+3

Bordeaux
Ligue 1France1.80.966.4
53
+1

Hertha BSC
BundesligaGermany1.80.966.0
54
-2

Deportivo
La LigaSpain1.70.865.4
55
+1

Ajax
EredivisieNetherlands2.11.165.1
56
-3

Fiorentina
Serie AItaly1.91.064.8
57
+7

PSV
EredivisieNetherlands2.21.364.6
58
-1

Rostov
Premier LeagueRussia1.50.764.3
59

Beşiktaş
Süper LigTurkey1.91.164.2
60
+3

Nice
Ligue 1France1.70.964.1
61

Celtic
PremiershipScotland2.11.264.0
62
-2

Southampton
Premier LeagueEngland1.70.964.0
63
-5

SC Freiburg
BundesligaGermany1.81.063.6
64
+27

Rubin Kazan
Premier LeagueRussia1.70.963.6
65
-3

Torino
Serie AItaly1.91.163.4
66
+9

Lokomotiv
Premier LeagueRussia1.60.963.0
67
+5

Leicester City
Premier LeagueEngland1.81.062.9
68
-2

Las Palmas
La LigaSpain1.81.062.9
69
-1

Hamburger SV
BundesligaGermany1.70.962.8
70

FC Augsburg
BundesligaGermany1.70.962.7
71

Krasnodar
Premier LeagueRussia1.60.962.6
72
-7

Sassuolo
Serie AItaly1.81.162.1
73
+1

Angers
Ligue 1France1.60.961.7
74
-7

Spartak Moscow
Premier LeagueRussia1.81.161.6
75
-6

Basel
Super LeagueSwitzerland1.91.261.2
76
-3

Burnley
Premier LeagueEngland1.71.060.8
77
-1

Stoke City
Premier LeagueEngland1.60.960.7
78
+14

Galatasaray
Süper LigTurkey1.91.260.6
79
+3

Getafe
La LigaSpain1.60.960.4
80
-2

Sampdoria
Serie AItaly1.71.160.3
81
-2

Boca Juniors
SuperligaArgentina1.61.059.8
82
+1

West Ham
Premier LeagueEngland1.81.159.5
83
+1

Sporting Gijón
La Liga 2Spain1.61.059.4
84
-7

Ural
Premier LeagueRussia1.50.959.4
85

St Étienne
Ligue 1France1.50.959.3
86
+3

Feyenoord
EredivisieNetherlands1.91.358.9
87
+1

Toulouse
Ligue 1France1.50.958.8
88
-7

Corinthians
BrasileirãoBrazil1.40.958.5
89
-2

Udinese
Serie AItaly1.61.057.8
90
+3

Cagliari
Serie AItaly1.71.257.7
91
+11

West Brom
Premier LeagueEngland1.50.957.5
92
+23

Watford
Premier LeagueEngland1.61.157.5
93
+2

VfB Stuttgart
BundesligaGermany1.71.257.5
94
+9

Nantes
Ligue 1France1.51.057.3
95
+16

Girona
La LigaSpain1.61.157.2
96

Rennes
Ligue 1France1.40.956.8
97
-11

Lille
Ligue 1France1.51.056.7
98
+10

Cruzeiro
BrasileirãoBrazil1.40.956.7
99
+10

Genoa
Serie AItaly1.51.056.7
100
-2

Fenerbahçe
Süper LigTurkey1.71.256.6
101

Crystal Palace
Premier LeagueEngland1.61.156.6
102
+3

Levante
La LigaSpain1.51.056.6
103
+4

Flamengo
BrasileirãoBrazil1.51.056.5
104
-4

Montpellier
Ligue 1France1.51.056.4
105
-11

Grêmio
BrasileirãoBrazil1.40.956.3
106
-2

Guingamp
Ligue 1France1.51.156.0
107
+3

River Plate
SuperligaArgentina1.51.055.5
108
+4

Bologna
Serie AItaly1.41.055.5
109
-12

Bournemouth
Premier LeagueEngland1.61.255.4
110
-30

Ingolstadt
2. BundesligaGermany1.61.255.3
111
+3

Young Boys
Super LeagueSwitzerland1.61.255.3
112
+4

Akhmat Grozny
Premier LeagueRussia1.61.155.3
113
+6

Chievo
Serie AItaly1.51.155.3
114
-15

Swansea City
Premier LeagueEngland1.51.154.8
115
+24

Lorient
Ligue 2France1.41.054.8
116
+15

Caen
Ligue 1France1.41.054.7
117
+5

Dijon FCO
Ligue 1France1.51.154.6
118
+3

Tigres UANL
Liga MXMexico1.41.054.4
119
+9

Hannover 96
BundesligaGermany1.51.154.4
120
-2

Independiente
SuperligaArgentina1.30.954.2
121
-15

Palmeiras
BrasileirãoBrazil1.41.054.2
122
+2

Darmstadt 98
2. BundesligaGermany1.61.254.2
123
-33

Malmö
AllsvenskanSweden1.71.354.1
124
-1

Racing
SuperligaArgentina1.51.253.4
125
-5

Dinamo Moscow
Premier LeagueRussia1.41.153.4
126
-1

Lanús
SuperligaArgentina1.30.953.4
127
+2

Monterrey
Liga MXMexico1.51.153.2
128
+4

Braga
Primeira LigaPortugal1.51.153.1
129
-2

Santos
BrasileirãoBrazil1.30.952.8
130
-17

Başakşehir
Süper LigTurkey1.51.252.8
131
-1

Estudiantes
SuperligaArgentina1.31.052.7
132
+3

Ufa
Premier LeagueRussia1.10.852.5
133
-16

Rosenborg
EliteserienNorway1.71.452.3
134

Akhisar Belediye
Süper LigTurkey1.51.352.2
135
-2

Defensa y Justicia
SuperligaArgentina1.20.952.1
136
+1

Empoli
Serie BItaly1.51.251.5
137
+18

Austria Vienna
BundesligaAustria1.71.551.4
138

Atlético Mineiro
BrasileirãoBrazil1.41.151.2
139
+1

São Paulo
BrasileirãoBrazil1.41.251.2
140
+1

Newcastle
Premier LeagueEngland1.41.151.2
141
-15

Crotone
Serie AItaly1.31.151.2
142

Middlesbrough
ChampionshipEngland1.31.150.7
143
+9

Atlético-PR
BrasileirãoBrazil1.31.150.6
144
-8

Osasuna
La Liga 2Spain1.51.350.5
145
+3

Kasımpaşa
Süper LigTurkey1.51.350.4
146
+4

Utrecht
EredivisieNetherlands1.61.450.2
147
-1

San Lorenzo
SuperligaArgentina1.31.150.2
148
+6

AZ Alkmaar
EredivisieNetherlands1.81.650.0
149
-5

Granada
La Liga 2Spain1.41.250.0
150
+3

SPAL
Serie AItaly1.31.149.9
151
-6

Metz
Ligue 1France1.41.249.7
152
+11

Sturm Graz
BundesligaAustria1.61.449.0
153
-4

Sport Recife
BrasileirãoBrazil1.31.248.9
154
+5

Rio Ave
Primeira LigaPortugal1.21.148.6
155
+1

Rosario Central
SuperligaArgentina1.21.148.5
156
-5

Le Havre
Ligue 2France1.21.048.5
157
+4

Fluminense
BrasileirãoBrazil1.31.248.4
158
-15

Sunderland
ChampionshipEngland1.41.248.4
159
+8

Trabzonspor
Süper LigTurkey1.41.248.3
160
-2

Parma
Serie BItaly1.41.348.2
161
-1

Olimpo
SuperligaArgentina1.21.147.9
162
+12

Thun
Super LeagueSwitzerland1.61.547.9
163
+23

Estoril
Primeira LigaPortugal1.31.247.5
164
+12

Vitesse
EredivisieNetherlands1.61.547.5
165
-18

Hull City
ChampionshipEngland1.51.447.4
166
+5

Rayo Vallecano
La Liga 2Spain1.41.347.3
167
-1

Vélez Sarsfield
SuperligaArgentina1.21.147.1
168
-4

Huddersfield
Premier LeagueEngland1.31.247.0
169
+40

AIK
AllsvenskanSweden1.21.147.0
170
-13

Rapid Vienna
BundesligaAustria1.51.546.9
171
+18

Toronto FC
Major League SoccerUSA1.51.446.8
172
+5

Botafogo
BrasileirãoBrazil1.21.246.7
173
-4

Banfield
SuperligaArgentina1.31.246.6
174
+17

Brann
EliteserienNorway1.51.546.5
175
-5

Pescara
Serie BItaly1.41.446.5
176
-11

Zürich
Super LeagueSwitzerland1.31.346.5
177
+11

Sivasspor
Süper LigTurkey1.31.346.4
178
+2

Tenerife
La Liga 2Spain1.11.146.4
179
-4

Cruz Azul
Liga MXMexico1.21.246.3
180
-12

Troyes
Ligue 1France1.41.346.3
181
-8

Verona
Serie AItaly1.21.246.2
182
+3

Club América
Liga MXMexico1.31.346.1
183
+4

Vitória
BrasileirãoBrazil1.31.346.0
184
-3

Marítimo
Primeira LigaPortugal1.21.145.8
185
-6

Antalyaspor
Süper LigTurkey1.41.445.7
186
+15

Chapecoense
BrasileirãoBrazil1.21.245.7
187
-15

Brighton
Premier LeagueEngland1.21.245.6
188
-5

Benevento
Serie AItaly1.21.245.5
189
-5

Godoy Cruz
SuperligaArgentina1.11.145.2
190
-8

Djurgårdens IF
AllsvenskanSweden1.41.445.0
191
+9

Pachuca
Liga MXMexico1.31.345.0
192
+14

Cardiff City
ChampionshipEngland1.21.244.9
193
-15

Amkar Perm
Premier LeagueRussia1.01.044.9
194
-4

Colón
SuperligaArgentina1.01.044.7
195

Luzern
Super LeagueSwitzerland1.51.544.7
196
-3

Gençlerbirliği
Süper LigTurkey1.11.244.6
197
-1

Talleres
SuperligaArgentina1.21.244.4
198
+19

Almería
La Liga 2Spain1.21.244.4
199
+43

Karabükspor
Süper LigTurkey1.31.444.3
200
-2

Reus
La Liga 2Spain0.80.744.2
201
-2

Gimnasia
SuperligaArgentina1.01.044.1
202
+6

IFK Göteborg
AllsvenskanSweden1.41.544.0
203
-11

Ponte Preta
BrasileirãoBrazil1.11.143.9
204
+3

Strasbourg
Ligue 1France1.31.443.7
205
+22

Bahía
BrasileirãoBrazil1.21.343.6
206
-9

Nancy
Ligue 2France1.11.243.5
207
-5

1. FC Nürnberg
2. BundesligaGermany1.31.443.3
208
+13

Arsenal Tula
Premier LeagueRussia1.11.243.2
209
+2

Huracán
SuperligaArgentina1.01.143.0
210
-48

Guimarães
Primeira LigaPortugal1.31.442.8
211
-7

Santos Laguna
Liga MXMexico1.31.542.8
212
+29

BK Häcken
AllsvenskanSweden1.11.242.8
213
-1

Palermo
Serie BItaly1.21.342.8
214
+1

Konyaspor
Süper LigTurkey1.21.342.7
215
+32

Cádiz
La Liga 2Spain1.21.342.6
216
-6

Tosno
Premier LeagueRussia1.11.242.6
217
-14

Toluca
Liga MXMexico1.21.342.6
218
-4

León
Liga MXMexico1.31.542.4
219
+3

Belgrano
SuperligaArgentina1.01.142.3
220
-2

Atlas
Liga MXMexico1.21.342.2
221
-27

IF Elfsborg
AllsvenskanSweden1.51.742.2
222
-2

Sion
Super LeagueSwitzerland1.21.342.0
223
-10

Aberdeen
PremiershipScotland1.31.541.9
224
+7

Newell’s Old Boys
SuperligaArgentina1.11.241.8
225
-6

Huesca
La Liga 2Spain1.11.341.7
226
+10

Alcorcón
La Liga 2Spain1.01.141.7
227
+12

1. FC Union Berlin
2. BundesligaGermany1.41.641.7
228
-3

Norwich City
ChampionshipEngland1.41.641.6
229
-24

Valladolid
La Liga 2Spain1.21.441.6
230
+24

Sheffield Wed.
ChampionshipEngland1.11.341.5
231
-5

Coritiba
BrasileirãoBrazil1.11.241.5
232
-3

Zaragoza
La Liga 2Spain1.11.341.4
233
+17

Aston Villa
ChampionshipEngland1.21.441.2
234
+10

Heracles
EredivisieNetherlands1.51.741.1
235
+8

Paços de Ferreira
Primeira LigaPortugal1.11.341.1
236
-20

Vasco da Gama
BrasileirãoBrazil1.11.241.1
237
-7

Fulham
ChampionshipEngland1.31.541.1
238
-4

FC St. Pauli
2. BundesligaGermany1.21.441.0
239
+23

Derby County
ChampionshipEngland1.21.440.9
240
-3

Portimonense
Primeira LigaPortugal1.21.340.9
241
+8

Arsenal
SuperligaArgentina1.21.440.8
242
-2

BTSV
2. BundesligaGermany1.21.440.8
243
-19

Molde
EliteserienNorway1.31.640.8
244
-11

Moreirense
Primeira LigaPortugal1.11.340.8
245
-13

Kayserispor
Süper LigTurkey1.31.540.7
246
+6

Temperley
SuperligaArgentina1.11.240.7
247
-9

Alanyaspor
Süper LigTurkey1.61.940.6
248
+31

Reims
Ligue 2France1.01.240.4
249
-1

Lugano
Super LeagueSwitzerland1.21.440.3
250
+8

SKA-Khabarovsk
Premier LeagueRussia1.01.240.3
251
-23

Córdoba
La Liga 2Spain1.21.440.2
252
+1

Wolverhampton
ChampionshipEngland1.21.440.2
253
-18

Gimnástic
La Liga 2Spain1.11.340.2
254
+1

Tigre
SuperligaArgentina1.11.340.0
255
-10

Guadalajara
Liga MXMexico1.11.340.0
256
-5

Chaves
Primeira LigaPortugal1.11.439.8
257
-11

Belenenses
Primeira LigaPortugal1.01.339.6
258
+17

Numancia
La Liga 2Spain1.01.239.5
259
+2

NYCFC
Major League SoccerUSA1.41.739.4
260
+7

Atlético Tucumán
SuperligaArgentina1.11.439.1
261
+5

Tondela
Primeira LigaPortugal1.11.439.0
262
+34

Leeds United
ChampionshipEngland1.11.438.9
263
-4

Pumas UNAM
Liga MXMexico1.21.438.9
264
+16

Necaxa
Liga MXMexico1.01.338.9
265
-9

NY Red Bulls
Major League SoccerUSA1.21.538.9
266
+5

Arminia Bielefeld
2. BundesligaGermany1.21.638.8
267
+15

Querétaro
Liga MXMexico1.11.438.7
268
+4

Carpi
Serie BItaly0.91.138.7
269
-1

Boavista
Primeira LigaPortugal0.91.238.7
270
+41

SV Sandhausen
2. BundesligaGermany1.11.438.6
271
+7

Tijuana
Liga MXMexico1.11.338.6
272
-2

Atlético
BrasileirãoBrazil1.11.438.5
273
+31

Grasshoppers
Super LeagueSwitzerland1.31.638.5
274
-14

1. FCK
2. BundesligaGermany1.11.438.5
275
+2

Feirense
Primeira LigaPortugal1.01.338.3
276
+8

QPR
ChampionshipEngland1.11.438.3
277
-1

Brentford
ChampionshipEngland1.31.738.1
278
+16

Puebla
Liga MXMexico1.11.438.0
279
-15

Rangers
PremiershipScotland1.21.638.0
280
+5

Argentinos Juniors
SuperligaArgentina0.91.237.9
281

Seattle
Major League SoccerUSA1.21.537.9
282
-13

Oviedo
La Liga 2Spain1.21.537.9
283
+4

Groningen
EredivisieNetherlands1.51.937.8
284
+2

San Martín
SuperligaArgentina1.01.337.8
285
+4

Unión
SuperligaArgentina1.01.337.7
286
+4

Vitória
Primeira LigaPortugal1.01.337.7
287
-14

Morelia
Liga MXMexico1.01.437.7
288
-31

Twente
EredivisieNetherlands1.41.837.7
289
-26

IK Sirius
AllsvenskanSweden1.21.637.5
290
-2

Nîmes
Ligue 2France1.11.437.5
291

Sarpsborg
EliteserienNorway1.21.537.4
292

Patronato
SuperligaArgentina1.01.337.3
293
+17

Bursaspor
Süper LigTurkey1.21.637.2
294
+19

Fortuna
2. BundesligaGermany1.11.537.2
295
+19

IFK Norrköping
AllsvenskanSweden1.21.637.2
296
+1

Lugo
La Liga 2Spain1.01.437.1
297
-74

Anzhi
Premier LeagueRussia1.11.437.1
298
+1

VfL Bochum
2. BundesligaGermany1.21.637.1
299
+27

Brest
Ligue 2France1.11.537.0
300
-7

Heerenveen
EredivisieNetherlands1.51.936.9
301
+1

Örebro SK
AllsvenskanSweden1.31.836.8
302
-2

Amiens
Ligue 1France1.11.536.8
303
-29

Greuther Fürth
2. BundesligaGermany1.11.536.7
304
+2

Cesena
Serie BItaly1.01.436.6
305
-2

St. Gallen
Super LeagueSwitzerland1.21.636.4
306
-8

Veracruz
Liga MXMexico1.01.436.4
307
+13

Sochaux
Ligue 2France1.01.436.2
308
+7

Sporting KC
Major League SoccerUSA0.91.336.1
309
+3

Perugia
Serie BItaly0.91.336.1
310
-2

Bristol City
ChampionshipEngland1.21.635.8
311
+8

Preston
ChampionshipEngland1.11.535.8
312
+9

Nottm Forest
ChampionshipEngland1.21.635.7
313
-8

1. FC Heidenheim
2. BundesligaGermany1.11.635.7
314
-49

Östersunds FK
AllsvenskanSweden1.21.735.4
315
-20

Mattersburg
BundesligaAustria1.11.635.4
316
+22

Barcelona B
La Liga 2Spain1.41.935.3
317
+23

Lorca
La Liga 2Spain1.11.535.2
318

PEC Zwolle
EredivisieNetherlands1.41.935.2
319
-2

Avaí
BrasileirãoBrazil0.91.435.1
320
-4

FC Dallas
Major League SoccerUSA1.11.534.9
321
+1

Atlanta
Major League SoccerUSA1.11.634.8
322
+7

Portland
Major League SoccerUSA1.31.834.8
323
-40

Chicago
Major League SoccerUSA1.21.734.8
324
+19

Haugesund
EliteserienNorway1.11.534.8
325
+9

Valenciennes
Ligue 2France0.81.234.7
326
-3

Rheindorf Altach
BundesligaAustria1.01.434.7
327
+17

Wolfsberger AC
BundesligaAustria0.91.434.7
328
-4

Hammarby
AllsvenskanSweden1.11.634.6
329
-4

Strømsgodset
EliteserienNorway1.21.734.6
330
-3

Spezia
Serie BItaly0.81.234.5
331
-22

Admira
BundesligaAustria1.21.834.5
332
-31

Dynamo Dresden
2. BundesligaGermany1.21.734.4
333
+6

Erzgebirge Aue
2. BundesligaGermany1.01.534.3
334
-3

Houston
Major League SoccerUSA1.11.634.2
335
-7

Reading
ChampionshipEngland1.11.634.1
336
-3

Ipswich Town
ChampionshipEngland1.11.634.0
337
-2

New England
Major League SoccerUSA1.21.733.9
338
-31

Lens
Ligue 2France1.11.733.9
339
+3

MSV Duisburg
2. BundesligaGermany1.11.633.9
340
-3

Frosinone
Serie BItaly1.01.533.8
341
-9

Ajaccio
Ligue 2France0.91.433.7
342
-6

Lobos BUAP
Liga MXMexico1.11.733.4
343
+6

Lillestrøm
EliteserienNorway1.31.933.1
344
+3

St Johnstone
PremiershipScotland1.01.632.8
345
+13

Albacete
La Liga 2Spain1.01.632.7
346
+19

Holstein Kiel
2. BundesligaGermany1.11.732.5
347
-17

Clermont
Ligue 2France0.91.432.4
348
+5

Vancouver
Major League SoccerUSA1.11.732.3
349
+3

Birmingham
ChampionshipEngland1.01.632.1
350
+5

Novara
Serie BItaly1.01.632.0
351
-1

Göztepe
Süper LigTurkey1.01.531.9
352
+10

AC Ajaccio
Ligue 2France0.91.431.9
353
-12

Cultural Leonesa
La Liga 2Spain1.01.631.8
354
-9

Aalesund
EliteserienNorway1.21.931.7
355
+18

Montreal
Major League SoccerUSA1.21.831.7
356
-10

Yeni Malatyaspor
Süper LigTurkey1.01.631.6
357
-3

Niort
Ligue 2France0.91.431.6
358
-7

Desportivo Aves
Primeira LigaPortugal0.91.531.5
359
+2

Cittadella
Serie BItaly1.01.731.4
360
+3

Bari
Serie BItaly0.91.431.3
361
+17

Orléans
Ligue 2France1.01.631.3
362
+2

Virtus Entella
Serie BItaly0.91.531.1
363
-6

Excelsior
EredivisieNetherlands1.21.930.9
364
+2

Ternana
Serie BItaly0.91.530.8
365
-6

St. Pölten
BundesligaAustria1.11.830.8
366
-10

Lausanne-Sport
Super LeagueSwitzerland1.21.930.8
367
-19

Vålerenga
EliteserienNorway0.91.630.7
368
-8

Osmanlıspor
Süper LigTurkey1.01.730.7
369
-2

Columbus
Major League SoccerUSA1.11.830.7
370
+4

Millwall
ChampionshipEngland1.01.730.5
371
-3

Chacarita
SuperligaArgentina1.11.830.5
372
+11

VVV-Venlo
EredivisieNetherlands1.11.830.2
373
-3

Bolton
ChampionshipEngland1.01.829.9
374
+1

Brescia
Serie BItaly1.01.829.7
375
-3

Real Salt Lake
Major League SoccerUSA1.11.829.6
376
-7

ADO Den Haag
EredivisieNetherlands1.11.929.4
377
-6

Burton Albion
ChampionshipEngland1.01.729.3
378
-1

Philadelphia
Major League SoccerUSA1.01.729.1
379
+3

Sheffield United
ChampionshipEngland0.91.728.9
380
+22

Jahn Regensburg
2. BundesligaGermany1.11.928.8
381
-2

Salernitana
Serie BItaly0.91.628.7
382
-2

LA Galaxy
Major League SoccerUSA1.01.828.6
383
+8

Sevilla Atlético
La Liga 2Spain1.12.028.5
384
-3

Pro Vercelli
Serie BItaly0.81.528.5
385
+14

Stabæk
EliteserienNorway1.22.128.1
386
-1

Ascoli
Serie BItaly0.91.628.1
387
+2

Tours
Ligue 2France1.01.827.9
388
-4

Barnsley
ChampionshipEngland1.01.827.4
389
-3

LASK Linz
BundesligaAustria0.91.727.3
390
+3

Halmstads BK
AllsvenskanSweden0.91.727.1
391
-15

Auxerre
Ligue 2France0.81.627.0
392
-5

Sandefjord
EliteserienNorway1.01.926.9
393
+3

Avellino
Serie BItaly0.91.726.6
394
+3

Odd
EliteserienNorway0.81.626.6
395
-7

NAC Breda
EredivisieNetherlands1.12.126.6
396
-2

Hearts
PremiershipScotland0.81.726.5
397
-7

Willem II
EredivisieNetherlands0.91.826.3
398

Viking
EliteserienNorway1.01.925.9
399
+1

Orlando City
Major League SoccerUSA0.81.725.6
400
-5

Roda JC
EredivisieNetherlands1.02.125.5
401
+8

Sogndal
EliteserienNorway0.81.825.2
402
+2

D.C. United
Major League SoccerUSA0.91.825.1
403
-11

Bourg-en-Bresse
Ligue 2France1.02.025.0
404
+3

Foggia
Serie BItaly0.81.824.3
405
+1

Venezia
Serie BItaly0.81.824.3
406
+13

Eskilstuna
AllsvenskanSweden0.81.924.0
407
-4

Châteauroux
Ligue 2France0.81.823.9
408
+2

Kalmar FF
AllsvenskanSweden0.81.823.8
409
-1

Paris FC
Ligue 2France0.71.723.8
410
+2

San Jose
Major League SoccerUSA0.81.823.7
411
-10

Ross County
PremiershipScotland0.91.923.7
412
+2

US Quevilly
Ligue 2France0.81.823.4
413

Sparta Rotterdam
EredivisieNetherlands1.02.123.4
414
-3

Colorado
Major League SoccerUSA0.71.623.1
415
-10

Kristiansund
EliteserienNorway0.92.122.9
416
+4

Hamilton Accies
PremiershipScotland0.71.822.4
417

Jönköping
AllsvenskanSweden0.81.822.4
418

Motherwell
PremiershipScotland0.92.122.1
419
-4

GIF Sundsvall
AllsvenskanSweden0.71.721.7
420
+1

Tromsø
EliteserienNorway0.92.021.6
421
-5

Hibernian
PremiershipScotland0.72.019.9
422
+1

Minnesota
Major League SoccerUSA0.82.119.5
423
-1

Cremonese
Serie BItaly0.72.019.3
424

Partick Thistle
PremiershipScotland0.61.918.0
425

Kilmarnock
PremiershipScotland0.61.916.4
426

Dundee
PremiershipScotland0.62.214.5

Read more about how these predictions work »

The Ever-Changing U.S. Plan In Afghanistan

President Trump gave a nationally televised speech at Fort Myer in Virginia on Monday to “lay out our path forward in Afghanistan and South Asia.” But he declined to specify exactly how U.S. strategy in the 16-year-old Afghan War would change, saying, “We will not talk about numbers of troops or our plans for further military activities. Conditions on the ground, not arbitrary timetables, will guide our strategy from now on.” Even if he had provided more details, American plans in Afghanistan have rarely followed the script.

Trump’s Populism Isn’t Popular — But That’s On Him, Not Bannon

What if upon taking office in January, President Trump had carefully balanced the insurgent influence of Steve Bannon, his chief strategist (now gone), with the establishment-friendly approach of Reince Priebus, his chief of staff (now gone) — and governed as a kinder, gentler, more media-savvy populist?

It wasn’t so long ago that such an outcome seemed possible. In January, The Atlantic’s David Frum envisioned a scenario in which Trump passed a truly populist program of “big tax cuts, big spending, and big deficits,” along with “restrictive immigration policies.” Such an agenda would prove fairly popular, Frum imagined, leading to Trump’s easy re-election in 2020. Trump would continue to push everyone’s boundaries but would also pick his battles somewhat carefully; there might be a border wall,This is my riff on Frum’s scenario; his article didn’t discuss the border wall either way.

“>1 for instance, but there would be no mass deportations of illegal immigrants.

Instead, almost the exact opposite has occurred. Trump has maintained most of populism’s rough edges — including its tendency to inflame racial resentment, as was evidenced by his comments on the Charlottesville white supremacist rally earlier this week. But he’s adopted few of the policies that actually make populism popular — or, at least, made it popular enough for Trump to win the Electoral College.

This isn’t Bannon’s fault — it’s Trump’s.

Take the various iterations of the Republican health care bill, which Bannon was reportedly lukewarm about. It proposed massive cuts to Medicaid spending and would greatly have reduced subsidies for older, poorer Americans — exactly the people who helped propel Trump to victory in November. And it would have done all of this partly to finance tax cuts that primarily benefited the wealthy. It was one of the least populist bills that one can imagine. And it cost Trump politically; his approval rating fell significantly while the bill was first being debated in March and then again after it finally failed to pass the Senate last month.

Or take Trump’s decision to fire FBI Director James Comey. There was nothing especially populist about the Comey firing, which put Trump — who campaigned as a “law and order” president — at odds with the intelligence community. And like health care, it’s brought nothing but trouble for him, having led to the appointment of special counsel Robert Mueller and having further hurt Trump’s popularity rating. But it wasn’t Bannon’s doing; he reportedly opposed the firing. Instead, more establishment-friendly figures such as Jared Kushner had reportedly advocated for canning Comey.

But when Bannon prevailed and won internal arguments against Kushner or Priebus or new chief of staff John Kelly, it didn’t turn out all that well for Trump, either. The “travel ban” that Trump implemented at Bannon’s urging in January wasn’t all that unpopular, but its implementation was a mess, leading it to be repeatedly struck down by the courts until the Supreme Court reporters finally allowed a narrow version of it in June. Trump’s Charlottesville response, which was reportedly cheered on by Bannon, has also been a disaster, producing a major backlash from the business community and from establishment Republicans.

The overall result is a president who has yet to sign any major legislation into law — and who has a much greater base of opposition than a base of support. (As of earlier this month, 47 percent of Americans strongly disapproved of Trump’s job performance, while just 20 percent strongly approved of it.) That could make it hard for Trump to “pivot”; he may have alienated too many voters to expand his support, but his base isn’t all that large either.

Related: Politics Podcast

Emergency Politics Podcast: What Does Bannon’s Exit Mean?

It’s easy to imagine how things theoretically could turn out better for Trump in the aftermath of Bannon’s firing. Trump could use the firing as an excuse to turn the page on Charlottesville, for example, or to repair relations, with Kelly’s help, with Republicans on Capitol Hill.

But Trump has more often gotten the worst of all possible worlds. He could wind up with Bannon as a dangerous outside antagonist who knows many of the White House’s secrets, for example, while elevating Kushner — who seems to have consistently given Trump bad advice — into a position of greater influence. And Trump’s most self-destructive impulses aren’t likely to be affected one way or another because they come not from Bannon or Kushner or Kelly but from Trump himself.