fivethirthyeight--2020-08-14--MVP Voting Has Never Been More Boring

MVP Voting Has Never Been More Boring

With the first round of the playoffs set to begin Monday , the NBA will soon announce the winners of this year ’ s regular-season awards , based on pre-bubble performance .
This year ’ s finalists for the MVP have already been announced , with only Giannis Antetokounmpo and LeBron James expected to receive first-place votes .
This fits a pattern that has become the norm in recent years : The number of serious MVP candidates is rarely more than two players .
Since 1981 , the first season that the NBA media voted on the award , there @ @ @ @ @ @ @ two or fewer players received first-place votes .
It ’ s happened in five out of the last seven MVP races — six in the last eight if it happens again this year .
The days when fringe MVP candidates like Peja Stojaković or Jermaine O ’ Neal could siphon off a first-place vote or two are long gone .
In today ’ s NBA , there is a clear consensus around which players are contending for the league ’ s highest individual honor , making it harder to justify a vote for @ @ @ @ @ @ @ have a realistic chance of winning .
One reason for this decline in fringe MVP candidates may be that the NBA now publicizes every voter ’ s ballot , a practice that began in 2014 .
Before then , when a voter wanted to go against the grain and vote for Carmelo Anthony over LeBron James for MVP , that voter did so anonymously .
But since 2014 , every media member ’ s ballot for every award is public record .
This puts pressure on voters to submit ballots that don ’ @ @ @ @ @ @ @ unless they want to be publicly shamed .
The Athletic ’ s Sam Amick , who has an MVP vote , has suggested that the fear of backlash , particularly on social media , leads to fewer dissenting opinions in the MVP race .
In 2019 , Amick told Hoops Hype , “ There are definitely some people who feel that a few years ago , had votes not been publicized , Stephen Curry wouldn ’ t have won unanimously .
In 2015-16 , it kind of reached a point where there were @ @ @ @ @ @ @ on the same level ] so you were going to look like an idiot if you voted for anybody else other than Steph.
” Contrast the MVP race with the NBA ’ s Executive of the Year award , which is voted on by team executives ( they can ’ t vote for themselves ) and the ballots are kept secret .
In each of the past three seasons , the only ones with available voting totals , eight candidates received first-place votes for the award .
Another factor likely contributing to the @ @ @ @ @ @ @ has taken steps to make the panel of media members who vote on the awards less team-focused .
As recently as 2016 , local broadcasters were able to vote on the awards , which may have resulted in some ballots that favored hometown players .
Guys like Ben Wallace in 2003 , Elton Brand in 2006 and Dwight Howard in 2009 may have benefited from this kind of homerism : All three received exactly one first-place MVP vote in their respective seasons .
The panel of voters is also smaller than in years @ @ @ @ @ @ @ voting privileges from local broadcasters in 2017 , the number of voters shrunk from 131 to 101 .
A smaller panel of voters means there are fewer first-place votes to go around .
One final reason for why voters converge on a couple of players each year may be that voters have more information on the candidates than ever before .
Discussions about the MVP race take place year-round and often start before a single regular-season game has been played .
Voters also have access to better information .
Advanced analytics , which @ @ @ @ @ @ @ comparisons , have improved as they ’ ve grown in popularity .
That also means it ’ s harder to make a case for Player C when Player A and Player B rank higher across various impact metrics .
As a result , subjective analysis and narrative , which can favor fringe MVP candidates , play less of a role in determining the top vote-getters than they might have in years past .
This , on its surface , is a good thing .
Advances in accountability and access to more and better @ @ @ @ @ @ @ race in which , more often than not , the player with the most impressive season wins the award .
If there ’ s a downside , it ’ s that the voters are vulnerable to groupthink .
One way to counteract groupthink is to encourage voters to embrace the ambiguity of what “ most valuable ” means .
The NBA has never explicitly defined it , so why start now ?
After all , under an expansive view of “ most valuable , ” there can be more candidates from which to @ @ @ @ @ @ @ think the MVP should be awarded to the best player — and not necessarily who just had the best season — then it is perfectly reasonable to cast a vote for Kawhi Leonard , the reigning Finals MVP .
Or if you feel the MVP should be given to the player who elevated himself and his team the most when compared to preseason expectations , then there ’ s a case for Chris Paul as the MVP .
Or if you want to define “ valuable ” literally and base it on which @ @ @ @ @ @ @ his contract , then a vote for Luka Dončić makes sense .
To be clear , there is nothing wrong with someone voting for either Antetokounmpo or James .
But it ’ s worth considering whether we need a 100-person panel of voters if that ’ s what everyone is going to do .

With the first round of the playoffs set to begin Monday, the NBA will soon announce the winners of this year’s regular-season awards, based on pre-bubble performance. This year’s finalists for the MVP have already been announced, with only Giannis Antetokounmpo and LeBron James expected to receive first-place votes. This fits a pattern that has become the norm in recent years: The number of serious MVP candidates is rarely more than two players.
Since 1981, the first season that the NBA media voted on the award, there have been eight MVP races in which two or fewer players received first-place votes. It’s happened in five out of the last seven MVP races — six in the last eight if it happens again this year.
The days when fringe MVP candidates like Peja Stojaković or Jermaine O’Neal could siphon off a first-place vote or two are long gone. In today’s NBA, there is a clear consensus around which players are contending for the league’s highest individual honor, making it harder to justify a vote for a candidate when he doesn’t have a realistic chance of winning.
One reason for this decline in fringe MVP candidates may be that the NBA now publicizes every voter’s ballot, a practice that began in 2014. Before then, when a voter wanted to go against the grain and vote for Carmelo Anthony over LeBron James for MVP, that voter did so anonymously. But since 2014, every media member’s ballot for every award is public record. This puts pressure on voters to submit ballots that don’t stray too far from the consensus unless they want to be publicly shamed.
The Athletic’s Sam Amick, who has an MVP vote, has suggested that the fear of backlash, particularly on social media, leads to fewer dissenting opinions in the MVP race. In 2019, Amick told Hoops Hype, “There are definitely some people who feel that a few years ago, had votes not been publicized, Stephen Curry wouldn’t have won unanimously. In 2015-16, it kind of reached a point where there were no other candidates [perceived to be on the same level] so you were going to look like an idiot if you voted for anybody else other than Steph.”
Contrast the MVP race with the NBA’s Executive of the Year award, which is voted on by team executives (they can’t vote for themselves) and the ballots are kept secret. In each of the past three seasons, the only ones with available voting totals, eight candidates received first-place votes for the award.
Another factor likely contributing to the trend toward consensus is that the NBA has taken steps to make the panel of media members who vote on the awards less team-focused. As recently as 2016, local broadcasters were able to vote on the awards, which may have resulted in some ballots that favored hometown players. Guys like Ben Wallace in 2003, Elton Brand in 2006 and Dwight Howard in 2009 may have benefited from this kind of homerism: All three received exactly one first-place MVP vote in their respective seasons.
The panel of voters is also smaller than in years past. After the NBA took away voting privileges from local broadcasters in 2017, the number of voters shrunk from 131 to 101. A smaller panel of voters means there are fewer first-place votes to go around.
One final reason for why voters converge on a couple of players each year may be that voters have more information on the candidates than ever before. Discussions about the MVP race take place year-round and often start before a single regular-season game has been played.
Voters also have access to better information. Advanced analytics, which allow for easier and more objective player comparisons, have improved as they’ve grown in popularity. That also means it’s harder to make a case for Player C when Player A and Player B rank higher across various impact metrics. As a result, subjective analysis and narrative, which can favor fringe MVP candidates, play less of a role in determining the top vote-getters than they might have in years past.
This, on its surface, is a good thing. Advances in accountability and access to more and better information has led to an improved MVP race in which, more often than not, the player with the most impressive season wins the award. If there’s a downside, it’s that the voters are vulnerable to groupthink.
One way to counteract groupthink is to encourage voters to embrace the ambiguity of what “most valuable” means. The NBA has never explicitly defined it, so why start now? After all, under an expansive view of “most valuable,” there can be more candidates from which to choose.
For example, if you think the MVP should be awarded to the best player — and not necessarily who just had the best season — then it is perfectly reasonable to cast a vote for Kawhi Leonard, the reigning Finals MVP. Or if you feel the MVP should be given to the player who elevated himself and his team the most when compared to preseason expectations, then there’s a case for Chris Paul as the MVP. Or if you want to define “valuable” literally and base it on which NBA player performed best in relation to his contract, then a vote for Luka Dončić makes sense.
To be clear, there is nothing wrong with someone voting for either Antetokounmpo or James. But it’s worth considering whether we need a 100-person panel of voters if that’s what everyone is going to do.
Check out our latest NBA predictions.


fivethirthyeight--2020-08-17--When Conventions Have Mattered

When Conventions Have Mattered

With both the Democratic National Convention and Republican National Convention going virtual this year , there are once again questions about what it is exactly that the national party conventions do .
For instance , conventions haven ’ t really picked the nominees for president and vice president since party reforms in the 1970s — arguably even earlier — so it raises the question : At this point , are they just really expensive events with lots of balloons and TV cameras ?
If we look at the history of modern conventions , it ’ s tempting to dismiss the @ @ @ @ @ @ @ from all around the country as pageantry .
But if you look closer , you ’ ll notice that conventions have played an important role for different wings of the party , who may disagree with party leadership and want to generate their own media attention .
Granted , this has become harder to do over the years , especially as conventions have become more TV-centric and prepackaged .
And that will likely be particularly true this year given the pandemic .
But that doesn ’ t mean conventions have lost all meaning @ @ @ @ @ @ @ at the conventions , they still shine a spotlight on the parties , illuminating emerging factions and up-and-coming politicians , setting the stage for — and creating — the future of each party .
That ’ s why it ’ s unfortunate that this year ’ s format has to be virtual — and why one should hope that it won ’ t be the new normal .
It ’ s hard to imagine that there will be a big clash in the parties this year given the virtual aspect of the conventions @ @ @ @ @ @ @ that the physical environment of the conventions has , at times , created memorable confrontations between conflicting wings of the party .
Take the 1948 “ Dixiecrat walkout , ” in which delegates from Mississippi and Alabama walked out of the Democratic convention in protest of the party ’ s embrace of the civil-rights movement in both its platform and floor speeches .
The delegates didn ’ t just walk out either .
Several in the party broke away and formed the States ’ Rights Party , which ran its own presidential ticket @ @ @ @ @ @ @ s true that this infighting didn ’ t actually affect the convention all that much .
For instance , it didn ’ t stop Democrats from including language on civil rights in its platform .
Nor did it prevent the nominee , Harry Truman , from winning the actual election .
But it did foreshadow the gradual loss of power of the Southern faction within the Democratic Party .
Similarly , in 1964 , the Republicans went into their convention deeply divided , between moderate and liberal Republicans on the one hand and @ @ @ @ @ @ @ Sen. Barry Goldwater on the other .
When New York Gov .
Nelson Rockefeller , one of the liberal old guard , spoke on the floor against extremism , convention delegates booed .
Historian Geoffrey Kabaservice reports the impressions of those who had been on the floor : one moderate Republican activist , Tanya Melich , said it “ felt like Nazi Germany , ” while former president Dwight Eisenhower commented on the anti-democratic spirit of the moment .
Of course , Goldwater was resoundingly defeated , but the lasting impact of the @ @ @ @ @ @ @ unity in post-election discussions , and in the selection of the next RNC chair .
( This clash also foreshadowed the party ’ s movement in a more Goldwater direction . )
Over time , conventions have become more scripted and less spontaneous , making moments like 1948 and 1964 harder to see .
But that doesn ’ t mean that dissension is completely hidden now .
The 2016 Democratic convention had some tense moments between Sen. Bernie Sanders supporters and Hillary Clinton supporters , for example , with Sanders supporters chanting and @ @ @ @ @ @ @ s first night .
Of course , this didn ’ t change the result of the already-determined floor vote — Clinton still walked away the nominee — but it did serve as a vivid reminder of the fissures within the party .
And after Clinton ’ s loss in November , the idea that the party lacked cohesion became part of the explanation for why she lost .
This engendered some resentment among Clinton supporters , but it also led to some fairly large concessions to the Sanders faction .
Party leaders , @ @ @ @ @ @ @ of superdelegates in the conventions , a change Sanders supporters had long advocated , and which the party adopted in 2018 .
Fights over convention rules can be more than just procedural Although fights over convention rules might seem arcane , they are often motivated by larger , and more meaningful , party disagreements .
Let ’ s go back to 1964 for a moment .
The Democratic Party was in a moment of great upheaval over civil rights , with members from some states , like Mississippi , actively fighting against the @ @ @ @ @ @ @ s state party had banned African Americans from party meetings , and thus its delegation lacked any Black members .
This angered some Democrats in the state , and so as a way of protesting segregation and the years of intimidation and disenfranchisement that Black voters had faced , a group of activists organized the Mississippi Freedom Democratic Party .
Their intention was to highlight the injustice and illegitimacy of the official delegation , and they hoped to take their places on the convention floor .
Party leadership offered the group two non-voting @ @ @ @ @ @ @ not be reached .
Instead , the MFDP occupied empty seats on the convention floor and sang freedom songs in protest when the chairs were removed .
As historian Sam Rosenfeld writes in “ The Polarizers , ” the MFDP actually had a pretty weak case under convention rules , but the moral force of their stance left a lasting mark on the party .
The national party moved to ban racial discrimination among state delegations , and some of the members of the MFDP were seated at the convention in 1968 .
@ @ @ @ @ @ @ Republicans had their own — albeit very different — skirmish over rules .
Then-Rep. Ron Paul hadn ’ t won a single primary , but thanks to states that choose delegates through caucuses and conventions , his supporters were able to take over several state delegations .
Although the nomination of Mitt Romney was not in question , there was still some conflict on the floor — fights over the seating of Paul delegates and a push to place his name officially in contention for the nomination , which would have given him @ @ @ @ @ @ @ Party leaders , however , wanted to present a unified party and direct media attention toward Romney .
So they changed the rules to thwart the Paul movement to prevent something like this from happening again : In order to be eligible to be nominated at the convention , a candidate needed to win the majority of delegates in at least eight states .
This was a substantial change — previously , a candidate needed only a plurality of delegates in only five states — and it had unintended consequences .
One of @ @ @ @ @ @ @ in her book on primary rules , was that the new rule would have prevented party leaders dissatisfied with the possibility of a Trump nomination from entering any other names into consideration .
Accordingly , the party changed the rule , again — back to a candidate needing only a plurality of delegates in five states — and leaders talked about whether it could be changed for 2016 so that it could place a more mainstream Republican , like Ted Cruz or John Kasich , into contention .
But ultimately , this didn @ @ @ @ @ @ @ up securing the nomination before the convention anyway .
Convention speeches can also shape our politics Let ’ s face it , convention speeches can be a little stilted .
They ’ re usually pretty safe and predictable .
But a few have proven memorable — maybe even influential .
Former New York Gov .
Mario Cuomo ’ s 1984 convention speech certainly deserves at least a brief mention here .
It tackled economic inequality and challenged incumbent president Ronald Reagan ’ s rhetoric about American individualism .
In the words of historian @ @ @ @ @ @ @ the things that Democrats wished themselves to be but no longer felt they were as a party.
” And when Cuomo died in 2015 , some commentators connected his legacy , including the ideas expressed in this speech , to reemerging progressive politics in the Democratic Party , and to figures like Elizabeth Warren .
Eight years later , Pat Buchanan , a former Richard Nixon and Reagan aide and political commentator , made his own splash at the 1992 GOP convention after his somewhat successful challenge to incumbent President George H.W .
@ @ @ @ @ @ @ conservative and more populist wing of the party , Buchanan had long had reservations about Bush , so he used his speaking slot at the convention to talk about the impending “ culture wars.
” His line , “ this election is about more than who gets what .
It is about who we are , ” helped usher in a new form of politics , in which compromise was outdated , and outrage the dominant mode of discourse .
But perhaps the most consequential convention speech of the modern era was delivered @ @ @ @ @ @ @ .
Delivering his remarks in Boston , Barack Obama famously said , “ [ T ] here is not a liberal America and a conservative America , there is the United States of America.
” By emphasizing the common ties and values across a divided nation , Obama became , if not a household name , a national political figure .
His bid for the presidency four years later still required several hard-fought campaigns , but the speech certainly made Democratic voters and party leaders alike take notice .
Live conventions do not @ @ @ @ @ @ @ nominee .
They may not even persuade very many people .
But in-person proceedings , and the ability to disrupt them , offers an opportunity for voices within the party that are not easily amplified on Zoom .
The story of jeers , walkouts , and credential fights is also the story of how the two parties have transformed themselves and sorted out their distinct and sometimes competing ideological identities .
The television era , the end of contested nominations at the convention , and the emphasis on party unity have all changed @ @ @ @ @ @ @ place to resolve conflict within parties .
And in an era with no shortage of political conflict — whether over race , class , gender , or public health — we ’ ll have one fewer venue for conflict to play out in public view .
Conventions may not be worth the cost or the time , and during a pandemic they ’ re certainly too risky , but moving to a virtual format forever would involve risks , and losses , of its own .

With both the Democratic National Convention and Republican National Convention going virtual this year, there are once again questions about what it is exactly that the national party conventions do.
For instance, conventions haven’t really picked the nominees for president and vice president since party reforms in the 1970s — arguably it’s been even longer than that — so it raises the question: At this point, are they just really expensive events with lots of balloons and TV cameras?
If we look at the history of modern conventions, it’s tempting to dismiss the large, in-person gatherings of power players from around the country as pageantry. But if you look closer, you’ll notice that conventions have played an important role for some wings of the party, who may disagree with party leadership and want to attract media attention for themselves.
Granted, this has become harder to do over the years, especially as conventions have become more TV-centric and prepackaged. And that will likely be particularly true this year given the pandemic. But that doesn’t mean conventions have lost all meaning. Even if nothing is actually decided at the conventions, they still shine a spotlight on the parties, illuminating emerging factions and up-and-coming politicians, setting the stage for — and creating — the future of each party. That’s why it’s unfortunate that this year’s format has to be virtual, and why one should hope that it won’t be the new normal.
Convention clashes often underscore big party divisions
It’s hard to imagine that we’ll see big internal clashes in the parties this year given the virtual conventions, but it’s worth noting that the physical environment of the conventions has, at times, created memorable confrontations between conflicting wings of the party.
Take the 1948 “Dixiecrat walkout,” in which delegates from Mississippi and Alabama walked out of the Democratic convention in protest of the party’s embrace of the civil rights movement in both its platform and floor speeches. The delegates didn’t just walk out either. Several members of the party broke away and formed the States’ Rights Party, which ran its own presidential ticket that year.
Now, it’s true that this infighting didn’t actually affect the convention all that much. For instance, it didn’t stop Democrats from including language supporting civil rights in their platform. Nor did it prevent the nominee, Harry Truman, from winning the actual election. But it did foreshadow the gradual loss of power of the Southern faction within the Democratic Party.
Similarly, in 1964, the Republicans went into their convention deeply divided, with moderate and liberal Republicans on the one hand and a growing conservative faction that supported Arizona Sen. Barry Goldwater on the other. When New York Gov. Nelson Rockefeller, one of the liberal old guard, spoke on the floor against extremism, convention delegates booed.
Historian Geoffrey Kabaservice reports the impressions of those who had been on the floor: One moderate Republican activist, Tanya Melich, said it “felt like I was in Nazi Germany,” while former president Dwight Eisenhower commented on the anti-democratic spirit of the moment. Of course, Goldwater was resoundingly defeated in November, but the lasting impact of the 1964 convention was an emphasis on party unity in post-election discussions, and in the selection of the next RNC chair. (This clash also foreshadowed the party’s movement in a more Goldwater-esque direction.)
Over time, conventions have become more scripted and less spontaneous, making it harder to produce unexpected moments like the memorable events of 1948 and 1964. But that doesn’t mean that dissension is completely hidden now. The 2016 Democratic convention had some tense moments between Sen. Bernie Sanders supporters and Hillary Clinton supporters, for example, with Sanders supporters chanting and booing during speeches on the convention’s first night.
Of course, this didn’t change the result of the already-determined floor vote — Clinton still walked away the nominee — but it did serve as a vivid reminder of the fissures within the party. And after Clinton’s loss in November, the idea that the party lacked cohesion became part of the explanation for why she lost. This engendered some resentment among Clinton supporters, but it also led to some fairly large concessions to the Sanders faction. Party leaders, for instance, recommended limiting the role of superdelegates in the conventions, a change Sanders supporters had long advocated, and which the party adopted in 2018.
Fights over convention rules can be more than just procedural
Although fights over convention rules might seem arcane, they are often motivated by larger, and more meaningful, party disagreements.
Let’s go back to 1964 for a moment. The Democratic Party was in a moment of great upheaval over civil rights, with members from some states, like Mississippi, actively fighting the movement. In fact, Mississippi’s state party had banned African Americans from party meetings, and thus its delegation lacked any Black members. This angered some Democrats in the state, and so as a way of protesting segregation and the years of intimidation and disenfranchisement that Black voters had faced, a group of activists organized the Mississippi Freedom Democratic Party. Their intention was to highlight the injustice and illegitimacy of the official delegation, and they hoped to take their places on the convention floor.
Party leadership offered the group two nonvoting delegate seats, but a compromise could not be reached. Instead, the MFDP occupied empty seats on the convention floor and sang freedom songs in protest when the chairs were removed. As historian Sam Rosenfeld writes in “The Polarizers,” the MFDP actually had a pretty weak case under convention rules, but the moral force of their stance left a lasting mark on the party. The national party moved to ban racial discrimination among state delegations, and some of the members of the MFDP were seated at the convention in 1968.
Fast forward to 2012, when the Republicans had their own — albeit very different — skirmish over rules. Then-Rep. Ron Paul hadn’t won a single primary, but thanks to states that choose delegates through caucuses and conventions, his supporters were able to take over several state delegations. Although the nomination of Mitt Romney was not in question, there was still some conflict on the floor — fights over the seating of Paul delegates and a push to place his name officially in contention for the nomination, which would have given him a speaking slot at the convention.
Party leaders, however, wanted to present a unified party and direct media attention toward Romney. So they changed the rules to thwart the Paul movement to prevent something like this from happening again: In order to be eligible to be nominated at the convention, a candidate needed to win the majority of delegates in at least eight states.
[newsletter-politics]
This was a substantial change — previously, a candidate needed only a plurality of delegates in five states — and it had unintended consequences. One of which, political scientist Caitlin Jewitt argues in her book on primary rules, was that the new rule would have prevented party leaders dissatisfied with the possibility of a Trump nomination from entering any other names into consideration. Accordingly, the party changed the rule, again — back to a candidate needing only a plurality of delegates in five states — and leaders talked about whether it could be changed for 2016 so that it could place a more mainstream Republican, like Ted Cruz or John Kasich, into contention. But ultimately, this didn’t matter, as Trump ended up securing the nomination before the convention anyway.
Convention speeches can also shape our politics
Let’s face it, convention speeches can be a little stilted. They’re usually pretty safe and predictable. But a few have proven memorable — maybe even influential. Former New York Gov. Mario Cuomo’s 1984 convention speech certainly deserves at least a brief mention here. It tackled economic inequality and challenged incumbent President Ronald Reagan’s rhetoric about American individualism. In the words of historian Jeff Shesol, “It was all the things that Democrats wished themselves to be but no longer felt they were as a party.” And when Cuomo died in 2015, some commentators connected his legacy, including the ideas expressed in this speech, to reemerging progressive politics in the Democratic Party, and to figures like Sen. Elizabeth Warren.
Eight years later, Pat Buchanan, a former aide to President Richard Nixon and Reagan and a political commentator, made his own splash at the 1992 GOP convention after his somewhat successful challenge to incumbent President George H.W. Bush. As part of the socially conservative and more populist wing of the party, Buchanan had long had reservations about Bush, so he used his speaking slot at the convention to talk about the impending “culture wars.” His line, “This election is about more than who gets what. It is about who we are,” helped usher in a new form of politics, in which compromise was deemed outdated and outrage became the dominant mode of discourse.
But perhaps the most consequential convention speech of the modern era was delivered by an Illinois state senator in 2004. Delivering his remarks in Boston, Barack Obama famously said,“There is not a liberal America and a conservative America, there is the United States of America.” By emphasizing the common ties and values across a divided nation, Obama became, if not a household name, a national political figure. His bid for the presidency four years later still required several hard-fought campaigns, but the speech certainly made Democratic voters and party leaders alike take notice.
Live conventions do not necessarily result in a new platform or nominee. They may not even persuade very many people. But in-person proceedings, and the ability to disrupt them, offer an opportunity for voices within the party that are not easily amplified on Zoom. The story of jeers, walkouts and credential fights is also the story of how the parties have transformed themselves and sorted out their distinct and sometimes competing ideological identities.
The television era, the end of contested nominations at the convention, and the emphasis on party unity have all changed this, but conventions are still a place to resolve conflict within parties. And in an era with no shortage of political conflict — whether over race, class, gender or public health — losing conventions means we have one less venue where conflict can play out in public view.
Conventions may not be worth the cost or the time, and during a pandemic they’re certainly too risky, but moving to a virtual format forever would involve risks, and losses, of its own.


fivethirthyeight--2020-08-17--Our Election Forecast Didn’t Say What I Thought It Would

My editors are forever asking me to take the long Twitter threads I write and turn them into articles here at FiveThirtyEight .
So I ’ m actually going to give that a try !
What follows are some follow-up thoughts on our election model , which was originally composed in the form of a V E R Y L O N G tweetstorm that I never published .
( See if you can guess where the 240-character breaks would have been . )
In this thread … err , article … I ’ ll try to walk you through @ @ @ @ @ @ @ of our model and respond to a few thoughtful critiques I ’ ve seen elsewhere .
Before you dive in , it may help to read our summary of the state of the race , or at least skim our very detailed methodology guide .
But the basic starting point for a probabilistic , poll-driven model ought to be this : Is polling in August a highly reliable way to predict the outcome in November ?
Polling in August is somewhat predictive .
You ’ d much rather be ahead than behind .
@ @ @ @ @ @ @ wild swings .
You can see that in the daily threads that Nathaniel Rakich , one of our elections analysts , puts together .
Here is what a national polling average would have looked like in elections dating back to 1976 : OK , I cheated a bit .
I ’ m using a version that Nathaniel published last week , partly because this was the exact moment in the campaign when Michael Dukakis , the 1988 Democratic nominee , started to blow his large lead , which he never regained .
Still @ @ @ @ @ @ @ there !
John Kerry led at this point in 2004 .
George W. Bush had a 10-point lead at this point in the 2000 race , but , as we know , he didn ’ t win the popular vote that year .
In other cases , the leading candidate won , but the margin was off by as much as 20 points ( Jimmy Carter in 1976 ) .
Now , as I wrote last week , there are some caveats here .
Several of these polling averages were taken while one @ @ @ @ @ @ @ , and although there are ways to correct for those , every time you correct for something so your model fits the past data better , you raise the possibility that you ’ re overfitting the data and that your model won ’ t be as accurate as claimed when applied to situations where you don ’ t already know the outcome .
There are also decent arguments that polling averages have become more stable in recent years .
In that case , the wild fluctuations in the polls from , say , @ @ @ @ @ @ @ relevant .
Our model actually agrees with these theories , up to a point !
The fact that voters are more polarized now ( more polarization means fewer swing voters , which means less volatility ) is encoded into our model as part of our “ uncertainty index , ” for instance .
But we think it ’ s pretty dangerous to go all in on these theories and assume that poll volatility is necessarily much lower than it was before .
For one thing , the theory is not based on a @ @ @ @ @ @ @ most recent elections , for instance .
The 2004 and 2012 elections featured highly stable polling — 2012 especially so .
But 2000 and 2016 ( ! )
did not , and 2008 election polling was not especially stable , either .
Small sample sizes are already an issue in election forecasting , so it seems risky to come to too many firm conclusions about polling volatility based on what amounts to two or three examples .
Meanwhile , other people have pointed out that the most recent two presidents , Trump and @ @ @ @ @ @ @ approval ratings .
But the president just before them , George W. Bush , did not .
His approval rating went through some of the wildest fluctuations ever , in fact , even though polarization was also fairly high from 2000 to 2008 .
That said , polls have been stable so far this year .
Indeed , that ’ s another factor that our uncertainty index accounts for .
But don ’ t get too carried away extrapolating from this stability .
Case in point : Polls were extremely stable throughout most @ @ @ @ @ @ @ the voting started , we saw huge swings from the Iowa caucuses through Super Tuesday .
Poll volatility tends to predict future volatility , but only up to a point .
Remember , too , that voters haven ’ t yet been exposed to the traditional set pieces of the campaign , namely the conventions and the debates , which are often associated with higher volatility .
Now , suppose that despite all the weirdness to come in the general election campaign , Biden just plows through , leads by 6 to 9 @ @ @ @ @ @ @ wins by that amount on Nov. 3 ?
If that happens , then we ’ ve got more evidence for the hypothesis that elections have become more stable , even when voters are confronted with a lot of surprising news .
But , crucially , we don ’ t have that evidence yet .
So some of the models that are more confident in Biden ’ s chances seem to be begging the question , presuming that polls will remain stable when I ’ m not sure we can say that yet .
@ @ @ @ @ @ @ COVID-19 .
Sometimes — though people may not say this outright — you ’ ll get a sense that critics think it ’ s sort of cheating for a model to account for COVID-19 because it ’ s never happened before , so it ’ s too ad hoc to adjust for it now .
I don ’ t really agree .
Models should reflect the real world , and COVID-19 is a big part of the real world in 2020 .
Given the choice between mild ad-hockery and ignoring COVID-19 entirely , @ @ @ @ @ @ @ However , I also think there are good ways to account for COVID-19 without being particularly ad hoc about it .
If you ’ re designing a model , whenever you encounter an outlier or an edge case or a new complication , the question you ask yourself should be , “ What lessons can I draw from this that generalize well ?
” That is : Are there things you can do to handle the edge case well that will also make your model more robust overall ?
As an aside , @ @ @ @ @ @ @ think people should pay a lot of attention to edge cases and outliers .
For instance , I pay a lot of attention to how our model is handling Washington , D.C. Why Washington ?
Well , if you take certain shortcuts — don ’ t account for the fact that vote shares are constrained between 0 and 100 percent of the vote — you might wind up with impossible results , like Biden winning 105 percent of the vote there .
Or when designing an NBA model , I may pay a @ @ @ @ @ @ @ Russell Westbrook , who has long caused issues for statistical systems .
I don ’ t like taking shortcuts in models ; I think they come back to bite you later in ways you don ’ t necessarily anticipate .
But if you can handle the outliers well , you ’ ve probably built a mathematically elegant model that works well under ordinary circumstances , too .
But back to COVID-19 : What this pandemic encouraged us to do was to think even more deeply about the sources of uncertainty in our forecast @ @ @ @ @ @ @ the aforementioned uncertainty index , which has eight components ( described in more depth in our methodology post ) : • The number of undecided voters in national polls .
More undecided voters means more uncertainty .
• The number of undecided plus third-party voters in national polls .
More third-party voters means more uncertainty .
• Polarization , as measured elsewhere in the model , which is based on how far apart the parties are in roll call votes cast in the U.S. House .
More polarization means less uncertainty since there @ @ @ @ @ @ @ volatility of the national polling average .
Volatility tends to predict itself , so a stable polling average tends to remain stable .
• The overall volume of national polling .
More polling means less uncertainty .
• The magnitude of the difference between the polling-based national snapshot and the fundamentals forecast .
A wider gap means more uncertainty .
• The standard deviation of the component variables used in the FiveThirtyEight economic index .
More economic volatility means more overall uncertainty in the forecast .
• The volume of major news , @ @ @ @ @ @ @ New York Times headlines in the past 500 days , with more recent days weighted more heavily .
More news means more uncertainty .
Previous versions of our model had basically just accounted for factors 1 and 2 ( undecided and third-party voters ) , so there are quite a few new factors here .
And indeed , factors 7 and 8 are very high thanks to COVID-19 and , therefore , boost our uncertainty measure .
However , we ’ re also considering several factors for the first time ( like polarization @ @ @ @ @ @ @ .
In the end , though , our model isn ’ t even saying that the uncertainty is especially high this year .
The uncertainty index would have been considerably higher in 1980 , for instance .
Rather , this year ’ s uncertainty is about average , which means that the historical accuracy of polls in past campaigns is a reasonably good guide to how accurate they are this year .
That seems to me like a pretty good gut check .
It might seem counterintuitive that uncertainty would be about average @ @ @ @ @ @ @ accounting for multiple types of uncertainty means that some can work to balance each other out .
We don ’ t have a large sample of elections to begin with ; depending on how you count , somewhere between 10 and 15 past presidential races had reasonably frequent polling .
So your default position might be that you should use all of that data to calibrate your estimates of uncertainty , rather than to try to predict under which conditions polls might be more or less reliable .
If you are going to @ @ @ @ @ @ @ , though , then we think you need to be pretty exhaustive about thinking through sources of uncertainty .
Accounting for greater polarization but not the additional disruptions brought about by the pandemic would be a mistake , we think ; likewise , so would be considering the pandemic but not accounting for polarization .
I ’ ve also seen some objections to the particular variables we ’ ve included in the uncertainty index .
For instance , not everybody likes that our way of specifying “ the volume of major news ” @ @ @ @ @ @ @ .
I agree that this isn ’ t ideal .
The New York Times takes its headlining choices very seriously , but as we learned from thumbing through years of its headlines , it also makes some idiosyncratic choices .
However , I don ’ t think anybody would say there hasn ’ t been a ton of important news this year , much of which could continue to reverberate later in the race .
Nor should people doubt that poll volatility is often news-driven .
Polls generally don ’ t move on @ @ @ @ @ @ @ to major political events ( such as debates ) and news events ( such as wars starting or ending ) .
Even before COVID-19 , we were trying to incorporate some of this logic into our polling averages by , for instance , having them move more aggressively after debates .
Other people have suggested that we ought to have accounted for incumbency in the uncertainty index , on the theory that when incumbents are running for reelection , they are known commodities , which should reduce volatility .
That ’ s a @ @ @ @ @ @ @ I ’ d thought to look at , although after taking a very brief glance at it now , I ’ m not sure how much it would have mattered .
The 1980 and 1992 elections , which featured incumbents , were notably volatile , for instance .
So if it ’ s too soon to be all that confident that Biden will win based on the polls — not that a 71 percent of winning the Electoral College ( and an 82 percent chance of winning the popular vote ) are anything @ @ @ @ @ @ @ else that might justify that confidence ?
In our view , not really .
I ’ ll be briefer on these points , since we covered them at length in our introductory feature .
But forecasts based on economic “ fundamentals ” — which have never been as accurate as claimed — are a mess this year .
Depending on which variables you look at ( gross domestic product or disposable income ? )
and over what time period ( third quarter or second quarter ? )
you could predict anything from the @ @ @ @ @ @ @ of elections to a big Trump win .
Furthermore , FiveThirtyEight ’ s version of a fundamentals model actually shows the race as a tie — it expects the race to tighten given the high polarization and projected economic improvement between now and November .
So although we don ’ t weigh the fundamentals all that much , they aren ’ t exactly a reason to be more confident in Biden .
What about Trump ’ s approval rating ?
It ’ s been poor for a long time , obviously .
And @ @ @ @ @ @ @ part of their fundamentals calculation .
But I have trouble with that for two reasons .
First , the idea behind the fundamentals is that they ’ re … well , fundamental , meaning they ’ re the underlying factors ( like economic conditions and political polarization ) that drive political outcomes .
An approval rating , on the other hand , should really be the result of those conditions .
Second , especially against a well-known opponent like Biden , approval ratings are largely redundant with the polls .
That is to @ @ @ @ @ @ @ approval rating ( favorable rating minus unfavorable rating ) is -12 or -13 in polls of registered and likely voters , then his being down 8 or 9 points in head-to-head polls against Biden is pretty much exactly what you ’ d expect .
( Empirically , though , the spread in approval ratings are a bit wider than the spreads in head-to-head polls .
A candidate with a -20 approval rating , like Carter had at the end of the 1980 campaign , wouldn ’ t expect to lose the election by @ @ @ @ @ @ @ that include a lot of highly correlated variables can have serious problems , and approval ratings and head-to-head polls are very highly correlated .
I ’ m not saying you couldn ’ t work your way around these issues , but unless you were very careful , they could lead to underestimates of out-of-sample errors and other problems .
One last topic : the role of intuition when building an election model .
To the largest extent possible , when I build election models , I try to do it “ blindfolded , @ @ @ @ @ @ @ as many decisions as possible about the structure of the model before seeing what the model would say about the current year ’ s election .
That ’ s not to say we don ’ t kick the tires on a few things at the end , but it ’ s pretty minimal , and it ’ s mostly to look at bugs and edge cases rather than to change our underlying assumptions .
The process is designed to limit the role my priors play when building a model .
Sometimes , though @ @ @ @ @ @ @ model run , the results come close to my intuition anyway .
But this year they didn ’ t .
I was pretty sure we ’ d have Biden with at least a 75 percent chance of winning and perhaps as high as a 90 percent chance .
Instead , our initial tests had Biden with about a 70 percent chance , and he stayed there until we launched the model .
Why was my intuition wrong ?
I suspect because it was conditioned on recent elections where polls were fairly stable — @ @ @ @ @ @ @ close , making Biden ’ s 8-point lead look humongous by comparison .
If I had vividly remembered Dukakis blowing his big lead in 1988 , when I was 10 years old , maybe my priors would have been different .
But as I said earlier , I ’ m not necessarily sure we can expect the polls to be quite so stable this time around .
And when you actually check how accurate summer polling has been historically , it yields some pretty wide margins of error .

Our Election Forecast Didn’t Say What I Thought It Would Why we factored in COVID-19, why we ignored approval ratings, and other deep cuts from our election model
My editors are forever asking me to take the long Twitter threads I write and turn them into articles here at FiveThirtyEight. So I’m actually going to give that a try!
What follows are some follow-up thoughts on our election model, which was originally composed in the form of a V E R Y L O N G tweetstorm that I never published. (See if you can guess where the 240-character breaks would have been.)
In this thread … err, article … I’ll try to walk you through my thought process on a few elements of our model and respond to a few thoughtful critiques I’ve seen elsewhere. Before you dive in, it may help to read our summary of the state of the race, or at least skim our very detailed methodology guide.
But the basic starting point for a probabilistic, poll-driven model ought to be this: Is polling in August a highly reliable way to predict the outcome in November?
The short answer is “no.”
Polling in August is somewhat predictive. You’d much rather be ahead than behind. But there can still be some very wild swings.
You can see that in the daily threads that Nathaniel Rakich, one of our elections analysts, puts together. Here is what a national polling average would have looked like in elections dating back to 1976:
The @FiveThirtyEight nat'l polling average with 84 days until E-Day: 2020: Biden+8.3
2016: Clinton+6.6
2012: Obama+0.5
2008: Obama+2.6
2004: Kerry+2.5
2000: Bush+10.0
1996: Clinton+11.3
1992: Clinton+20.1
1988: Dukakis+5.6
1984: Reagan+16.0
1980: Reagan+22.1
1976: Carter+26.6 — Nathaniel Rakich (@baseballot) August 11, 2020
OK, I cheated a bit. I’m using a version that Nathaniel published last week, partly because this was the exact moment in the campaign when Michael Dukakis, the 1988 Democratic nominee, started to blow his large lead, which he never regained. Still, there’s some wild stuff there! John Kerry led at this point in 2004. George W. Bush had a 10-point lead at this point in the 2000 race, but, as we know, he didn’t win the popular vote that year. In other cases, the leading candidate won, but the margin was off by as much as 20 points (Jimmy Carter in 1976).
Now, as I wrote last week, there are some caveats here. Several of these polling averages were taken while one or both candidates were experiencing convention bonuses, and although there are ways to correct for those, every time you correct for something so your model fits the past data better, you raise the possibility that you’re overfitting the data and that your model won’t be as accurate as claimed when applied to situations where you don’t already know the outcome.
There are also decent arguments that polling averages have become more stable in recent years. In that case, the wild fluctuations in the polls from, say, 1976 or 1988 might not be as relevant.
Our model actually agrees with these theories, up to a point! The fact that voters are more polarized now (more polarization means fewer swing voters, which means less volatility) is encoded into our model as part of our “uncertainty index,” for instance.
But we think it’s pretty dangerous to go all in on these theories and assume that poll volatility is necessarily much lower than it was before. For one thing, the theory is not based on a ton of data. Take the five most recent elections, for instance. The 2004 and 2012 elections featured highly stable polling — 2012 especially so. But 2000 and 2016 (!) did not, and 2008 election polling was not especially stable, either. Small sample sizes are already an issue in election forecasting, so it seems risky to come to too many firm conclusions about polling volatility based on what amounts to two or three examples.
Meanwhile, other people have pointed out that the most recent two presidents, Trump and Barack Obama, have had highly stable approval ratings. But the president just before them, George W. Bush, did not. His approval rating went through some of the wildest fluctuations ever, in fact, even though polarization was also fairly high from 2000 to 2008.
That said, polls have been stable so far this year. Indeed, that’s another factor that our uncertainty index accounts for. But don’t get too carried away extrapolating from this stability. Case in point: Polls were extremely stable throughout most of the Democratic primaries … but when the voting started, we saw huge swings from the Iowa caucuses through Super Tuesday. Poll volatility tends to predict future volatility, but only up to a point.
Remember, too, that voters haven’t yet been exposed to the traditional set pieces of the campaign, namely the conventions and the debates, which are often associated with higher volatility.
Now, suppose that despite all the weirdness to come in the general election campaign, Biden just plows through, leads by 6 to 9 points the whole way … and then wins by that amount on Nov. 3? If that happens, then we’ve got more evidence for the hypothesis that elections have become more stable, even when voters are confronted with a lot of surprising news.
But, crucially, we don’t have that evidence yet. So some of the models that are more confident in Biden’s chances seem to be begging the question, presuming that polls will remain stable when I’m not sure we can say that yet.
Then there’s the issue of COVID-19. Sometimes — though people may not say this outright — you’ll get a sense that critics think it’s sort of cheating for a model to account for COVID-19 because it’s never happened before, so it’s too ad hoc to adjust for it now.
I don’t really agree. Models should reflect the real world, and COVID-19 is a big part of the real world in 2020. Given the choice between mild ad-hockery and ignoring COVID-19 entirely, I think mild ad-hockery is better.
However, I also think there are good ways to account for COVID-19 without being particularly ad hoc about it. If you’re designing a model, whenever you encounter an outlier or an edge case or a new complication, the question you ask yourself should be, “What lessons can I draw from this that generalize well?” That is: Are there things you can do to handle the edge case well that will also make your model more robust overall?
As an aside, when testing models on historical data I think people should pay a lot of attention to edge cases and outliers. For instance, I pay a lot of attention to how our model is handling Washington, D.C. Why Washington? Well, if you take certain shortcuts — don’t account for the fact that vote shares are constrained between 0 and 100 percent of the vote — you might wind up with impossible results, like Biden winning 105 percent of the vote there. Or when designing an NBA model, I may pay a lot of attention to a player like Russell Westbrook, who has long caused issues for statistical systems. I don’t like taking shortcuts in models; I think they come back to bite you later in ways you don’t necessarily anticipate. But if you can handle the outliers well, you’ve probably built a mathematically elegant model that works well under ordinary circumstances, too.
But back to COVID-19: What this pandemic encouraged us to do was to think even more deeply about the sources of uncertainty in our forecast. That led to the development of the aforementioned uncertainty index, which has eight components (described in more depth in our methodology post):
The number of undecided voters in national polls. More undecided voters means more uncertainty. The number of undecided plus third-party voters in national polls. More third-party voters means more uncertainty. Polarization, as measured elsewhere in the model, which is based on how far apart the parties are in roll call votes cast in the U.S. House. More polarization means less uncertainty since there are fewer swing voters. The volatility of the national polling average. Volatility tends to predict itself, so a stable polling average tends to remain stable. The overall volume of national polling. More polling means less uncertainty. The magnitude of the difference between the polling-based national snapshot and the fundamentals forecast. A wider gap means more uncertainty. The standard deviation of the component variables used in the FiveThirtyEight economic index. More economic volatility means more overall uncertainty in the forecast. The volume of major news, as measured by the number of full-width New York Times headlines in the past 500 days, with more recent days weighted more heavily. More news means more uncertainty.
Previous versions of our model had basically just accounted for factors 1 and 2 (undecided and third-party voters), so there are quite a few new factors here. And indeed, factors 7 and 8 are very high thanks to COVID-19 and, therefore, boost our uncertainty measure. However, we’re also considering several factors for the first time (like polarization and poll volatility) that reduce uncertainty.
[newsletter-politics]
In the end, though, our model isn’t even saying that the uncertainty is especially high this year. The uncertainty index would have been considerably higher in 1980, for instance. Rather, this year’s uncertainty is about average, which means that the historical accuracy of polls in past campaigns is a reasonably good guide to how accurate they are this year. That seems to me like a pretty good gut check.
It might seem counterintuitive that uncertainty would be about average in such a weird year, but accounting for multiple types of uncertainty means that some can work to balance each other out. We don’t have a large sample of elections to begin with; depending on how you count, somewhere between 10 and 15 past presidential races had reasonably frequent polling. So your default position might be that you should use all of that data to calibrate your estimates of uncertainty, rather than to try to predict under which conditions polls might be more or less reliable. If you are going to try to fine-tune your margin of error, though, then we think you need to be pretty exhaustive about thinking through sources of uncertainty. Accounting for greater polarization but not the additional disruptions brought about by the pandemic would be a mistake, we think; likewise, so would be considering the pandemic but not accounting for polarization.
I’ve also seen some objections to the particular variables we’ve included in the uncertainty index. For instance, not everybody likes that our way of specifying “the volume of major news” is based on New York Times headlines. I agree that this isn’t ideal. The New York Times takes its headlining choices very seriously, but as we learned from thumbing through years of its headlines, it also makes some idiosyncratic choices.
However, I don’t think anybody would say there hasn’t been a ton of important news this year, much of which could continue to reverberate later in the race. Nor should people doubt that poll volatility is often news-driven. Polls generally don’t move on their own, but rather in response to major political events (such as debates) and news events (such as wars starting or ending). Even before COVID-19, we were trying to incorporate some of this logic into our polling averages by, for instance, having them move more aggressively after debates.
Other people have suggested that we ought to have accounted for incumbency in the uncertainty index, on the theory that when incumbents are running for reelection, they are known commodities, which should reduce volatility. That’s a smart suggestion, and something I wish I’d thought to look at, although after taking a very brief glance at it now, I’m not sure how much it would have mattered. The 1980 and 1992 elections, which featured incumbents, were notably volatile, for instance.
So if it’s too soon to be all that confident that Biden will win based on the polls — not that a 71 percent of winning the Electoral College (and an 82 percent chance of winning the popular vote) are anything to sneeze at — is there anything else that might justify that confidence?
In our view, not really.
I’ll be briefer on these points, since we covered them at length in our introductory feature. But forecasts based on economic “fundamentals” — which have never been as accurate as claimed — are a mess this year. Depending on which variables you look at (gross domestic product or disposable income?) and over what time period (third quarter or second quarter?) you could predict anything from the most epic Biden landslide in the history of elections to a big Trump win.
Furthermore, FiveThirtyEight’s version of a fundamentals model actually shows the race as a tie — it expects the race to tighten given the high polarization and projected economic improvement between now and November. So although we don’t weigh the fundamentals all that much, they aren’t exactly a reason to be more confident in Biden.
What about Trump’s approval rating? It’s been poor for a long time, obviously. And some other models do use it as part of their fundamentals calculation. But I have trouble with that for two reasons. First, the idea behind the fundamentals is that they’re … well, fundamental, meaning they’re the underlying factors (like economic conditions and political polarization) that drive political outcomes. An approval rating, on the other hand, should really be the result of those conditions.
Second, especially against a well-known opponent like Biden, approval ratings are largely redundant with the polls. That is to say, if Trump’s net approval rating (favorable rating minus unfavorable rating) is -12 or -13 in polls of registered and likely voters, then his being down 8 or 9 points in head-to-head polls against Biden is pretty much exactly what you’d expect. (Empirically, though, the spread in approval ratings are a bit wider than the spreads in head-to-head polls. A candidate with a -20 approval rating, like Carter had at the end of the 1980 campaign, wouldn’t expect to lose the election by 20 points.)
Also, models that include a lot of highly correlated variables can have serious problems, and approval ratings and head-to-head polls are very highly correlated. I’m not saying you couldn’t work your way around these issues, but unless you were very careful, they could lead to underestimates of out-of-sample errors and other problems.
One last topic: the role of intuition when building an election model. To the largest extent possible, when I build election models, I try to do it “blindfolded,” by which I mean I make as many decisions as possible about the structure of the model before seeing what the model would say about the current year’s election. That’s not to say we don’t kick the tires on a few things at the end, but it’s pretty minimal, and it’s mostly to look at bugs and edge cases rather than to change our underlying assumptions. The process is designed to limit the role my priors play when building a model.
Sometimes, though, when we do our first real model run, the results come close to my intuition anyway. But this year they didn’t. I was pretty sure we’d have Biden with at least a 75 percent chance of winning and perhaps as high as a 90 percent chance. Instead, our initial tests had Biden with about a 70 percent chance, and he stayed there until we launched the model.
Why was my intuition wrong? I suspect because it was conditioned on recent elections where polls were fairly stable — and where the races were also mostly close, making Biden’s 8-point lead look humongous by comparison. If I had vividly remembered Dukakis blowing his big lead in 1988, when I was 10 years old, maybe my priors would have been different.
But as I said earlier, I’m not necessarily sure we can expect the polls to be quite so stable this time around. And when you actually check how accurate summer polling has been historically, it yields some pretty wide margins of error.


fivethirthyeight--2020-08-17--Democratic National Convention, Day 1: Live Updates And Analysis

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fivethirthyeight--2020-08-17--FiveThirtyEight Debate Club: Do We Even Need Political Conventions Anymore?

It ’ s time for a very appropriate episode of FiveThirtyEight Debate Club !
Since we ’ ll be live @ @ @ @ @ National Conventions this week and next , we thought it would be a good idea @ @ @ @ @ In this episode , we argue about whether parties should even have political conventions .
@ @ @ @ @ , take a guess as to which side our politics editor , Sarah Frostenson , @ @ @ @ @ are you on ?
Be sure to weigh in on YouTube or Twitter .
And @ @ @ @ @ , let us know what you ’ d like us to debate in the next @ @

It’s time for a very appropriate episode of FiveThirtyEight Debate Club!
Since we’ll be live blogging the Democratic and Republican National Conventions this week and next, we thought it would be a good idea to ask … why? In this episode, we argue about whether parties should even have political conventions. Before you watch the video, take a guess as to which side our politics editor, Sarah Frostenson, is on.
Whose side are you on? Be sure to weigh in on YouTube or Twitter. And while you’re there, let us know what you’d like us to debate in the next episode.
What’s the best way to organize your bookshelf? | FiveThirtyEight Debate Club
Whatâs the best state in the US? | FiveThirtyEight Debate Club


fivethirthyeight--2020-08-17--The Raptors’ Defense Is Almost Never The Same, But It’s Always Really Good

In their second game of the 2019-20 season , the defending champion Toronto Raptors played the division rival Boston Celtics .
Boston had struggled offensively in its season opener , scoring only 91.4 points per 100 possessions in a loss to the Philadelphia 76ers , and once the game got underway , Toronto replicated much of Philly ’ s defensive strategy .
The Raptors did not send a single double-team on any of the Celtics ’ 19 isolation plays , per Second Spectrum ( the Sixers had doubled on zero of 20 isolations ) .
They played soft coverage , @ @ @ @ @ @ @ or ice coverage , with the on-ball defender forcing the ball-handler to the sideline or the baseline , on 59 of Boston ’ s 73 actionable pick and rolls , and they did not send an extra defender to blitz the ball-handler a single time ( the Sixers played soft or ice coverage on 62 of 71 actionable pick and rolls and sent just one blitz ) .
They were similarly passive against off-ball screens and dribble handoffs , sending zero blitzes on 62 such actions while playing soft or drop coverage 47 @ @ @ @ @ @ @ drop on 48 of 72 off-ball screens and did not blitz at all ) .
But the Raptors did not replicate the Sixers ’ results .
Jayson Tatum , Jaylen Brown , Kemba Walker and Gordon Hayward combined for 87 points while hitting 11 of 26 3-point attempts , Boston scored 110.7 points per 100 possessions , and the Raptors lost by 6 .
About a month later , the Raptors had gotten more aggressive .
Against the Dallas Mavericks , they blitzed Luka Dončić on 27 of his 34 pick and rolls @ @ @ @ @ @ @ times any player has been blitzed in a game this season .
The Raptors succeeded in holding Dončić , as he shot only 5 of 14 from the field , but they also fouled him enough to send him to the line 19 times — and they ended up losing the game by 8 .
Not long after that , the Raptors employed a game plan against the Houston Rockets so outlandish that , according to ESPN ’ s Zach Lowe , Second Spectrum doesn ’ t even have the capability to categorize @ @ @ @ @ @ @ reporters the day before the game with hints of a mystery defense , and what he unveiled was pretty unusual indeed .
Just about every time James Harden crossed half-court , the Raptors had Fred VanVleet come up and double-team him to get the ball out of Harden ’ s hands before any action could even start .
Harden had been on an absolute tear through the first 20 games of the season , racking up a completely ridiculous 39.5 points and 7.8 assists per game while taking 24.8 shots and 14.9 free @ @ @ @ @ @ @ per contest coming in , Harden was practically impossible to stop : Any Houston trip including a Harden iso was producing 1.322 points per possession .
That is the stuff of legends .
But Harden didn ’ t come close to replicating any of those figures against Toronto .
He got only four chances to isolate , with those possessions creating a total of zero Rockets points .
He ended the evening with just 23 points , three assists , 11 shots and six attempts from the charity stripe .
Mission accomplished , @ @ @ @ @ @ @ .
The attention the Raptors paid Harden meant they sprung massive leaks everywhere else in their defense .
Harden ’ s teammates went 19 of 50 from beyond the arc , and the Rockets won the game .
After the game , Nurse called his gambit an “ interesting experiment.
” VanVleet , for his part , went a bit further .
“ If we would ’ ve scored 11 more points , we ’ d be geniuses and the defense was the best in the world and we would ’ ve been @ @ @ @ @ @ @ But we lost and so it didn ’ t work and you go back to the drawing board.
” The Raptors live at the drawing board .
Though their defensive experiments might occasionally end in losses , they exemplify what has made this year ’ s squad a serious threat to get back to the NBA Finals even after losing arguably the best player in the league in Kawhi Leonard .
Because while the schemes themselves were outliers , the aggressiveness with which they were deployed and the wild swing between strategies were @ @ @ @ @ @ @ are among the most aggressive defenses in the NBA — not just this season , but at any time in the past seven seasons .
But how can we compare this defensive strategy to others in the league ?
We did it by building two new metrics : Aggression+ and Variance+ .
Using Second Spectrum ’ s tracking data to identify which coverages teams play against five offensive actions — pick and rolls , isolations , post-ups , off-ball screens and dribble handoffs — we created Aggression+ : an index statistic that shows @ @ @ @ @ @ @ is relative to the average team in the league that season , both against every individual action and overall .
The league average is set at 100 , and each point above or below indicates that team is 1 percentage point more or less aggressive than the average team in the league .
Teams that blitz pick and rolls and dribble handoffs , double-team isolations and post-ups , and jam or switch off-ball screens are considered more aggressive , while those that drop their big man back into the paint on screens and @ @ @ @ @ @ @ considered less so .
The pre-hiatus Raptors employed the most aggressive post-up defense of any of the 210 team-defense seasons over the past seven years , doubling 12.4 percent more often than the average squad .
They were similarly aggressive against isolations ( 14th ) , off-ball screens ( fifth ) and dribble handoffs ( eighth ) .
Only against pick and rolls were they outside of the top decile , ranking 48th out of those 210 team seasons .
Taken together , the 2019-20 Raptors ’ aggregate score is the 14th-most aggressive @ @ @ @ @ @ @ .
The Raptors are something of an outlier among the most aggressive units .
Of the 15 most aggressive defenses of the past seven years , only two others ranked inside the top five in defensive efficiency during that season , per NBA Advanced Stats .
On average , the non-Toronto teams ranked 17th in the league , indicating below-average overall units .
But employing that level of aggression is not the only thing that makes the Raptors defense unique .
It ’ s that they are also among the most willing to @ @ @ @ @ @ @ deep , versatile roster stocked with hyper-intelligent defenders , Nurse is the NBA ’ s most inventive defensive coach , tinkering night to night and even on the fly within games .
He broke out both a box-and-one and triangle-and-two in last year ’ s NBA Finals against the Golden State Warriors .
He ’ s willing to use zone defenses more often than most other coaches .
He ’ ll double at half-court in one game , blitz every pick and roll in another and drop the big man nearly every time @ @ @ @ @ @ @ to get creative with Kyle Lowry , Serge Ibaka and Marc Gasol to anchor his schemes , plus an emerging wing stopper in OG Anunoby ; a rangy , kinetic menace in Pascal Siakam ; a feisty , do-whatever-is-necessary pest in VanVleet ; and multi-positional reserves like Norman Powell , Rondae Hollis-Jefferson and even Terence Davis and Chris Boucher .
With a group like that , Nurse can easily go big or small .
He can aim to control the pace or to make things more hectic .
He can play fast or @ @ @ @ @ @ @ or out .
He can play man or zone or a hybrid of both and switch freely on any trip down the floor .
He essentially uses the season as a long-running experiment , throwing defenses against the wall to find out what works and what doesn ’ t .
And this , too , shows up in the tracking data .
By calculating the variance in each team ’ s Aggression+ for each play-type and overall in every game they played , we created Variance+ : an index statistic that shows how @ @ @ @ @ @ @ on a night-to-night basis , both against every individual action and overall , relative to the average team in the league that season .
Again , the league average is set at 100 , and each point above or below indicates a team is 1 percentage point more or less willing to vary defensive strategies than the average team .
Teams like the Raptors that are willing to swing from one extreme to the other and hit every point in between have high Variance+ scores , while teams like the Orlando Magic , @ @ @ @ @ @ @ every night , have very low scores .
Here is where the Raptors really stand out .
First off , they are an outlier among this year ’ s top defenses .
Of the top-10 point-prevention units in the NBA this season , six of them rank 189th or lower since 2013-14 in Variance+ .
The Raptors rank 12th , changing their schemes night-to-night 77 percent more often than the league-average team this season .
The next-closest top-10 unit is the Lakers , who rank 99th .
And among the teams most willing @ @ @ @ @ @ @ , the Raptors are again a bit of an outlier .
Only one of the other top 15 teams in Variance+ in the past seven seasons sported a defense that ranked inside the top 10 during that season , per NBA Advanced Stats , while nine ranked in the bottom 10 ( three were in the bottom five ) .
On average , those 14 other teams had the 20th-best defense in the NBA that season .
That gets at a key contrast between this year ’ s Raptors and most of the @ @ @ @ @ @ @ one night to the next : Those bad defenses are doing it because nothing works , and so they ’ re searching for anything that can help them get a stop .
The Raptors do it as a proactive strategy , both to throw opponents off their game and because they can .
And the Raptors are not just willing to shift strategies based on that night ’ s opponent .
They ’ ll junk their plan against a certain adversary and throw something completely different at them if whatever they did the @ @ @ @ @ @ @ .
Again , almost all teams are willing to do this if necessary , but the degree to which the Raptors are stands out .
In their second matchup against the Mavericks , for example , the Raptors dialed things back considerably from the level of aggression they showed in that early-season contest .
Conversely , the Raptors got more aggressive the second time they played the Portland Trail Blazers , as well as with each successive game they played against the Charlotte Hornets .
There were several teams against whom Toronto ’ @ @ @ @ @ @ @ , but how much they were willing to swing between tactics even against repeat opponents is notable .
Inside the NBA bubble at Walt Disney World , the Raptors have seemingly been the one team able to consistently get stops .
During the seeding games , Toronto led the league in defensive efficiency , and it wasn ’ t particularly close .
Nurse and company remained unsurprisingly willing to vary their level of aggression within and outside of their base schemes from night to night , against every type of action and overall @ @ @ @ @ @ @ bench .
This , more than anything else , is the key to the team ’ s title defense .
Toronto ’ s offense checks in just outside the top 10 for the full season , and it ’ s been dreadful during the restart .
According to Cleaning the Glass , the Raptors rank a disappointing 15th in half-court points per non-garbage-time play on the year , but they minimize the impact of that ranking by playing in the half-court less often than any other team .
Their defense is a large @ @ @ @ @ @ @ , which helps make up for the poor performance against set defenses .
However you slice it , this year ’ s team does not have the elite , top-five offense last year ’ s had .
They also don ’ t have the best defender from last year ’ s squad , nor one of its other top perimeter stoppers .
But as they open their playoffs tonight against the Brooklyn Nets , the Raptors still have Nurse , plus Lowry , Gasol , Ibaka , Siakam , Anunoby and VanVleet .
@ @ @ @ @ @ @ any defense they want on any given possession , on any given night .
If they ’ re going to find a way to repeat their incredible run from 2019 , that shapeshifting is what ’ s going to carry them across the finish line .

In their second game of the 2019-20 season, the defending champion Toronto Raptors played the division rival Boston Celtics. Boston had struggled offensively in its season opener, scoring only 91.4 points per 100 possessions in a loss to the Philadelphia 76ers, and once the game got underway, Toronto replicated much of Philly’s defensive strategy.
The Raptors did not send a single double-team on any of the Celtics’ 19 isolation plays, per Second Spectrum (the Sixers had doubled on zero of 20 isolations). They played soft coverage, with the big man hanging back, or ice coverage, with the on-ball defender forcing the ball-handler to the sideline or the baseline, on 59 of Boston’s 73 actionable pick and rolls, and they did not send an extra defender to blitz the ball-handler a single time (the Sixers played soft or ice coverage on 62 of 71 actionable pick and rolls and sent just one blitz). They were similarly passive against off-ball screens and dribble handoffs, sending zero blitzes on 62 such actions while playing soft or drop coverage 47 times (the Sixers played soft or drop on 48 of 72 off-ball screens and did not blitz at all). But the Raptors did not replicate the Sixers’ results. Jayson Tatum, Jaylen Brown, Kemba Walker and Gordon Hayward combined for 87 points while hitting 11 of 26 3-point attempts, Boston scored 110.7 points per 100 possessions, and the Raptors lost by 6.
About a month later, the Raptors had gotten more aggressive. Against the Dallas Mavericks, they blitzed Luka Dončić on 27 of his 34 pick and rolls — tied for the greatest number of times any player has been blitzed in a game this season. The Raptors succeeded in holding Dončić, as he shot only 5 of 14 from the field, but they also fouled him enough to send him to the line 19 times — and they ended up losing the game by 8.
Not long after that, the Raptors employed a game plan against the Houston Rockets so outlandish that, according to ESPN’s Zach Lowe, Second Spectrum doesn’t even have the capability to categorize it. Raptors coach Nick Nurse teased reporters the day before the game with hints of a mystery defense, and what he unveiled was pretty unusual indeed. Just about every time James Harden crossed half-court, the Raptors had Fred VanVleet come up and double-team him to get the ball out of Harden’s hands before any action could even start.
Harden had been on an absolute tear through the first 20 games of the season, racking up a completely ridiculous 39.5 points and 7.8 assists per game while taking 24.8 shots and 14.9 free throws. Averaging a league-high 19.3 isolations per contest coming in, Harden was practically impossible to stop: Any Houston trip including a Harden iso was producing 1.322 points per possession. That is the stuff of legends.
But Harden didn’t come close to replicating any of those figures against Toronto. He got only four chances to isolate, with those possessions creating a total of zero Rockets points. He ended the evening with just 23 points, three assists, 11 shots and six attempts from the charity stripe. Mission accomplished, right? Well, not so much. The attention the Raptors paid Harden meant they sprung massive leaks everywhere else in their defense. Harden’s teammates went 19 of 50 from beyond the arc, and the Rockets won the game.
After the game, Nurse called his gambit an “interesting experiment.”
VanVleet, for his part, went a bit further. “If we would’ve scored 11 more points, we’d be geniuses and the defense was the best in the world and we would’ve been Harden-stoppers,” he said. “But we lost and so it didn’t work and you go back to the drawing board.”
The Raptors live at the drawing board. Though their defensive experiments might occasionally end in losses, they exemplify what has made this year’s squad a serious threat to get back to the NBA Finals even after losing arguably the best player in the league in Kawhi Leonard. Because while the schemes themselves were outliers, the aggressiveness with which they were deployed and the wild swing between strategies were anything but.
Nurse’s Raptors are among the most aggressive defenses in the NBA — not just this season, but at any time in the past seven seasons. But how can we compare this defensive strategy to others in the league? We did it by building two new metrics: Aggression+ and Variance+.
Using Second Spectrum’s tracking data to identify which coverages teams play against five offensive actions — pick and rolls, isolations, post-ups, off-ball screens and dribble handoffs — we created Aggression+: an index statistic that shows how aggressive a team’s defense is relative to the average team in the league that season, both against every individual action and overall.
The league average is set at 100, and each point above or below indicates that team is 1 percentage point more or less aggressive than the average team in the league. Teams that blitz pick and rolls and dribble handoffs, double-team isolations and post-ups, and jam or switch off-ball screens are considered more aggressive, while those that drop their big man back into the paint on screens and rarely send help at one-on-one plays are considered less so.
The pre-hiatus Raptors employed the most aggressive post-up defense of any of the 210 team-defense seasons over the past seven years, doubling 12.4 percent more often than the average squad. They were similarly aggressive against isolations (14th), off-ball screens (fifth) and dribble handoffs (eighth). Only against pick and rolls were they outside of the top decile, ranking 48th out of those 210 team seasons. Taken together, the 2019-20 Raptors’ aggregate score is the 14th-most aggressive overall defense of the past seven seasons.
The Raptors D is really aggressive (and really good) Aggression+ — showing how aggressive a team’s defense is compared to league average — and season defensive ratings for the 15 most aggressive team defenses since 2013-14 Season Team Aggression+ Def. Rating 2013-14 Miami Heat 111.45 11 2019-20 Chicago Bulls 109.61 13 2018-19 Houston Rockets 107.09 17 2019-20 Houston Rockets 106.83 16 2017-18 Houston Rockets 105.96 7 2015-16 Houston Rockets 104.98 21 2017-18 Cleveland Cavaliers 104.91 29 2015-16 Milwaukee Bucks 104.76 23 2017-18 Chicago Bulls 104.74 28 2016-17 Milwaukee Bucks 104.68 19 2014-15 Los Angeles Clippers 104.65 16 2014-15 Milwaukee Bucks 104.42 4 2019-20 Charlotte Hornets 103.98 25 2019-20 Toronto Raptors 103.90 2 2013-14 Oklahoma City Thunder 103.88 5 The 2019-20 season only includes games played prior to the coronavirus shutdown. Sources: second spectrum, nba advanced stats
The Raptors are something of an outlier among the most aggressive units. Of the 15 most aggressive defenses of the past seven years, only two others ranked inside the top five in defensive efficiency during that season, per NBA Advanced Stats. On average, the non-Toronto teams ranked 17th in the league, indicating below-average overall units.
But employing that level of aggression is not the only thing that makes the Raptors defense unique. It’s that they are also among the most willing to alter their tactics.
Armed with a deep, versatile roster stocked with hyper-intelligent defenders, Nurse is the NBA’s most inventive defensive coach, tinkering night to night and even on the fly within games. He broke out both a box-and-one and triangle-and-two in last year’s NBA Finals against the Golden State Warriors. He’s willing to use zone defenses more often than most other coaches. He’ll double at half-court in one game, blitz every pick and roll in another and drop the big man nearly every time in the next.
Nurse can afford to get creative with Kyle Lowry, Serge Ibaka and Marc Gasol to anchor his schemes, plus an emerging wing stopper in OG Anunoby; a rangy, kinetic menace in Pascal Siakam; a feisty, do-whatever-is-necessary pest in VanVleet; and multi-positional reserves like Norman Powell, Rondae Hollis-Jefferson and even Terence Davis and Chris Boucher. With a group like that, Nurse can easily go big or small. He can aim to control the pace or to make things more hectic. He can play fast or slow, focus his defensive efforts inside or out. He can play man or zone or a hybrid of both and switch freely on any trip down the floor. He essentially uses the season as a long-running experiment, throwing defenses against the wall to find out what works and what doesn’t.
And this, too, shows up in the tracking data. By calculating the variance in each team’s Aggression+ for each play-type and overall in every game they played, we created Variance+: an index statistic that shows how often a team changes up its coverages on a night-to-night basis, both against every individual action and overall, relative to the average team in the league that season.
Again, the league average is set at 100, and each point above or below indicates a team is 1 percentage point more or less willing to vary defensive strategies than the average team. Teams like the Raptors that are willing to swing from one extreme to the other and hit every point in between have high Variance+ scores, while teams like the Orlando Magic, who almost always employ the same coverages every night, have very low scores.
Here is where the Raptors really stand out. First off, they are an outlier among this year’s top defenses. Of the top-10 point-prevention units in the NBA this season, six of them rank 189th or lower since 2013-14 in Variance+. The Raptors rank 12th, changing their schemes night-to-night 77 percent more often than the league-average team this season. The next-closest top-10 unit is the Lakers, who rank 99th.
And among the teams most willing to change schemes from game to game, the Raptors are again a bit of an outlier. Only one of the other top 15 teams in Variance+ in the past seven seasons sported a defense that ranked inside the top 10 during that season, per NBA Advanced Stats, while nine ranked in the bottom 10 (three were in the bottom five). On average, those 14 other teams had the 20th-best defense in the NBA that season.
The Raptors D locks down even when it changes up Variance+ — showing how often a team changes its defensive strategy on a night-to-night basis — and season defensive ratings for the 15 teams that shifted strategies the most since 2013-14 Season Team Variance+ Def. rating 2018-19 New York Knicks 267.87 26 2013-14 Miami Heat 250.78 11 2017-18 Chicago Bulls 227.78 28 2013-14 Cleveland Cavaliers 223.61 20 2019-20 Chicago Bulls 222.46 13 2018-19 Miami Heat 208.29 7 2014-15 Cleveland Cavaliers 202.99 18 2017-18 Cleveland Cavaliers 194.02 29 2014-15 Orlando Magic 191.02 24 2019-20 New York Knicks 183.89 23 2017-18 Atlanta Hawks 182.92 24 2019-20 Toronto Raptors 177.01 2 2019-20 Houston Rockets 171.02 16 2014-15 Detroit Pistons 168.32 20 2014-15 Miami Heat 165.72 21 The 2019-20 season only includes games played prior to the coronavirus shutdown. Sources: second spectrum, nba advanced stats
That gets at a key contrast between this year’s Raptors and most of the other teams that shift defensive strategies from one night to the next: Those bad defenses are doing it because nothing works, and so they’re searching for anything that can help them get a stop. The Raptors do it as a proactive strategy, both to throw opponents off their game and because they can.
And the Raptors are not just willing to shift strategies based on that night’s opponent. They’ll junk their plan against a certain adversary and throw something completely different at them if whatever they did the first time around didn’t work. Again, almost all teams are willing to do this if necessary, but the degree to which the Raptors are stands out.
In their second matchup against the Mavericks, for example, the Raptors dialed things back considerably from the level of aggression they showed in that early-season contest. Conversely, the Raptors got more aggressive the second time they played the Portland Trail Blazers, as well as with each successive game they played against the Charlotte Hornets. There were several teams against whom Toronto’s strategy remained consistent, of course, but how much they were willing to swing between tactics even against repeat opponents is notable.
You might never see the same Raptors defense twice Raptors’ Aggression+ — showing how aggressive a team’s defense is compared to league average — in matchups against the same opponent Opponent First Game Second Game Third Game Fourth Game Atlanta Hawks 102.7 109.0 107.8 — Charlotte Hornets 100.6 104.5 111.4 — Chicago Bulls 97.8 107.8 102.6 — Cleveland Cavaliers 102.8 100.6 106.8 — Dallas Mavericks 116.6 103.8 — — Indiana Pacers 96.5 101.2 104.5 101.7 Los Angeles Clippers 116.8 106.8 — — Oklahoma City Thunder 109.7 101.1 — — Orlando Magic 101.6 101.8 107.3 — Phoenix Suns 102.4 111.7 — — Portland Trailblazers 100.3 109.9 — — Washington Wizards 99.8 106.2 — — Opponents were included when the standard deviation in the Raptors’ Aggression+ scores in multiple games against that opponent exceeded 3. Only games played prior to the coronavirus shutdown are included. Source: second spectrum
Inside the NBA bubble at Walt Disney World, the Raptors have seemingly been the one team able to consistently get stops. During the seeding games, Toronto led the league in defensive efficiency, and it wasn’t particularly close. Nurse and company remained unsurprisingly willing to vary their level of aggression within and outside of their base schemes from night to night, against every type of action and overall, with the starters and with the bench.
This, more than anything else, is the key to the team’s title defense. Toronto’s offense checks in just outside the top 10 for the full season, and it’s been dreadful during the restart. According to Cleaning the Glass, the Raptors rank a disappointing 15th in half-court points per non-garbage-time play on the year, but they minimize the impact of that ranking by playing in the half-court less often than any other team. Their defense is a large part of what fuels the transition attack, which helps make up for the poor performance against set defenses.
However you slice it, this year’s team does not have the elite, top-five offense last year’s had. They also don’t have the best defender from last year’s squad, nor one of its other top perimeter stoppers. But as they open their playoffs tonight against the Brooklyn Nets, the Raptors still have Nurse, plus Lowry, Gasol, Ibaka, Siakam, Anunoby and VanVleet. And they have the capability to play any defense they want on any given possession, on any given night. If they’re going to find a way to repeat their incredible run from 2019, that shapeshifting is what’s going to carry them across the finish line.
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