Sunday, February 19, 2017

The Oscars Really Do Spread the Wealth Around

If it's February, it's time to indulge in hobby-making over a different kind of election here at Baseballot: the Academy Awards. Notoriously difficult to predict due to a lack of hard data and polling, the Oscars often force prognosticators to resort to fickle historical precedents, working theories, and, worst of all, gut instinct.

One of those working theories is that the Academy of Motion Picture Arts and Sciences likes to "spread the wealth around." The idea is that the Academy has many favorite people and movies that it would like to honor, but it only has a limited number of awards to bestow, so it tries to sprinkle them around strategically so that everyone deserving gets a prize for something. At first, it seems a stretch of the imagination to think that a disparate voting bloc could be so coordinated and strategic in its thinking. Academy members aren't making decisions as a unit; their results are an aggregation of thousands of individual opinions. However, the Academy is still a smaller and more homogenous electorate than most, and their preferences may reflect the groupthink of the Hollywood insiders who dominate their ranks. Sure enough, a dive into the data shows that the Academy may indeed lean toward distributing the wealth evenly.

One of this year's hottest Oscar races is that for Best Actor. Will Casey Affleck join the club of Oscar winners for his portrayal of a Boston janitor in Manchester by the Sea, or will Denzel Washington take home his third trophy for hamming it up in Fences? If you believe that the Oscars spread the wealth around, you'd probably lean toward Affleck—and history suggests that's a good bet. At the last 15 ceremonies, a past Oscar winner has gone up against an Oscar virgin 47 times in one of the four acting categories (Best Actor, Best Actress, Best Supporting Actor, and Best Supporting Actress). A full 32% of nominees in those 47 races were past winners, so, all things being equal, past winners should have won at around an equal rate. However, all things clearly were not equal, as those past winners prevailed just seven times out of 47: 15%. Looking at the same data another way, they won just seven times out of their 75 nominations—a 9% success rate. That's far lower than a nominee's default chances in a five-person category: one out of five, or 20%.

Some caution is still warranted, though. Oscar history is littered with famous counterexamples: the time two-time winner Meryl Streep (The Iron Lady) stole Best Actress from underneath the overdue Viola Davis's (The Help) nose at the 2011 ceremony, or when Sean Penn (Milk), a prior winner for Mystic River, edged out Mickey Rourke for The Wrestler at the 2008 awards. In addition, the converse of spreading the wealth isn't necessarily true. Even if the Oscars make a concerted effort to keep extra statuettes out of the hands of people who already have them, they probably don't bend over backward to reward undecorated artists who are "due."

Finally, there may be a difference in wealth-spreading between the acting categories, where the nominees and their records are well known, and the craft categories, where the awards are thought of as going to films, not people. This can explain the wide inequalities in categories like Best Sound Mixing. Sound mixer Scott Millan has won four Oscars in his nine nominations, and legendary sound engineer Fred Hynes won five times in seven tries. By contrast, Greg P. Russell, nominated this year for mixing 13 Hours, has compiled 17 nominations and has never won. And the all-time record for most Oscar nominations without a win goes to mixer Kevin O'Connell, who is a 21-time nominee, including his bid this year for Hacksaw Ridge. Unfortunately, both will almost certainly lose this year to La La Land mixer Andy Nelson, who has two wins in his previous 20 nominations. Even if this isn't because of the longstanding trophy inequality in Best Sound Mixing, it is certainly consistent with it.

Within each ceremony, the Academy also appears to spread the wealth fairly evenly among movies. Of the last 15 Oscars, seven can be considered "spread the wealth" ceremonies, while only three can really be considered "sweeps" (2013 for Gravity, 2008 for Slumdog Millionaire, and 2003 for The Lord of the Rings: The Return of the King); five are ambiguous. Granted, this is a subjective eyeballing of the data, but I have some hard numbers too. The median number of Oscars won by the winningest film of the night over the last 15 years is five—a respectable total, but not one that screams "unstoppable juggernaut." Twelve of the 15 ceremonies saw the winningest film take six Oscars or fewer. Eight of the 15 years were below the average mode of 5.4 Oscars, represented by the black line in the chart below:


More stats: during the same 15-year span, anywhere between nine and 14 feature films (i.e., not shorts) per year could go home and call themselves an Oscar winner—but 10 out of 15 of the years, that number was 12, 13, or 14 films. Just five times was it nine, 10, or 11 films. Finally, at nine of the 15 ceremonies, the standard deviation of the distribution of Oscars among feature films was less than the average standard deviation, again represented by the black line. (A low standard deviation means that the values in a dataset tend to cluster around the mean—so a year when four films each won three awards has a low standard deviation, but a year when one film won 11, two films won two, and the rest won one has a very high standard deviation.)


With only three ceremonies in the past 15 years boasting significantly above-average standard deviations, it's clear that a low standard deviation is the norm—which means it's typical for more films to get fewer Oscars. In other words, spreading the wealth.

Of course, the pattern is only true until it isn't. Every so often, a juggernaut of a movie does come along to sweep the Oscars: think Titanic, which went 11 for 14 at the 1997 ceremony. This year appears poised to deliver us another of these rare occurrences. La La Land reaped a record 14 nominations, and according to prediction site Gold Derby, it is favored to win 10 trophies next Sunday. Don't let overarching historical trends override year-specific considerations, like the runaway popularity of Damien Chazelle's musical, when making your Oscar picks.

Wednesday, February 15, 2017

After Three Special Elections, What We Do and Don't Know About the 'Trump Effect'

Let's get one thing out of the way—we can't possibly know with any certainty what's going to happen in 2018. The elections are just too far away, too much can change, and right now we're operating with a very small sample of information.

With that said, three partisan special elections have taken place since the January 20 inauguration of President Donald Trump. These races are as hyperlocal as you can get, and they have not drawn much attention or voter interest. But these obscure elections have followed an interesting pattern with respect to Trump.

*Virginia holds legislative elections in odd years, so these numbers are for elections in 2011, 2013, and 2015.
†The regularly scheduled election in Minnesota House District 32B was canceled in 2016 and rescheduled for the special election in February 2017.

Each of the districts shifted dramatically toward Democrats when you compare the results of the 2016 presidential election in the district to its special election results this year. Iowa's House District 89 went from 52–41 Clinton to 72–27 for the Democratic State House candidate. Minnesota's House District 32B went from 60–31 Trump to a narrow 53–47 Republican win. And Virginia's House District 71 went from 85–10 Clinton to 90–0 for the Democratic House of Delegates candidate, although it should probably be ignored because Republicans did not contest the special election. (Thanks to Daily Kos Elections for doing the invaluable work of calculating presidential results by legislative district.)

This could be evidence for the "Trump effect"—Trump's stormy tenure and record unpopularity already poisoning Republican electoral prospects as voters react to what they're seeing in the White House. Clearly, many Trump voters in these districts either didn't show up or changed parties in these special elections. However, there is an alternative explanation.

Trump's uniqueness as a Republican candidate—alienating educated whites and minorities but winning over culturally conservative Democrats—meant that the 2016 map was a departure from the previous several elections, especially in the Midwest. The Iowa and Minnesota districts that held special elections this year are two prime examples of areas that gravitated strongly to Trump. And indeed, both districts were a lot redder in 2016 than they were in 2012, when Obama won Iowa House District 89 63% to 36% and Romney won Minnesota HD-32B by just 55% to 43%.

The 2017 special election results were a lot closer to these 2012 presidential results than the 2016 ones—and even closer to the 2012 State House results (67–32 Democratic in Iowa, 51–49 Republican in Minnesota). So an equally valid hypothesis for the meaning of 2017's results is this: maybe Trump was just a one-time deal. Maybe these districts are simply reverting to their old, usual partisanship.

We can't know for sure yet which hypothesis is correct. So far, we have only run our "experiment" (i.e., special elections) to test our hypotheses in two Trumpward-moving districts, no Clintonward-moving ones (e.g., a wealthy suburban district or a minority-heavy one). Therefore, both hypotheses would predict a lurch (or return) leftward for these districts relative to 2016 presidential results. Indeed, that was the result we saw. However, we will have to wait for a special election in a Clintonward-moving district before we can differentiate between the two possibilities. Georgia's Sixth Congressional District is an excellent example, moving from 61–37 Romney to 48–47 Trump. The special general election for this seat, vacated by new Health and Human Services Secretary Tom Price, is in June.

Of course, it is also possible—even probable—that both hypotheses are partly true. A move from 52–41 Clinton to 72–27 for the Democratic State House candidate in Iowa is probably not entirely reversion to the mean when Obama won the district "just" 63–36 and Democrats captured the seat 67–32 in 2012. But rather than assuming—or fervently hoping—that these election results represent a huge anti-Trump wave building, it's good to remember that there are other possible explanations as well.

Thursday, February 9, 2017

Hall of Fame Projections Are Getting Better, But They're Still Not Perfect

You'd think that, after the forecasting debacle that was the 2016 presidential election, I'd have learned my lesson and stopped trying to predict elections. Wrong. As many of you know, I put myself on the line yet again last month when I shared some fearless predictions about how the Baseball Hall of Fame election would turn out. I must have an addiction.

This year marked the fifth year in a row that I developed a model to project Hall of Fame results based on publicly released ballots compiled by Twitter users/national heroes like Ryan Thibodaux—but this was probably the most uncertain year yet. Although I ultimately predicted that four players (Jeff Bagwell, Tim Raines, Iván Rodríguez, and Trevor Hoffman) would be inducted, I knew that Rodríguez, Hoffman, and Vladimir Guerrero were all de facto coin flips. Of course, in the end, BBWAA voters elected only Bagwell, Raines, and Rodríguez, leaving Hoffman and Guerrero to hope that a small boost will push them over the top in 2018. If you had simply taken the numbers on Ryan's BBHOF Tracker at face value, you would have gotten the correct answer that only those three would surpass 75% in 2017.

But although my projections weren't perfect, there is still a place for models in the Hall of Fame prediction business. In terms of predicting the exact percentage that each player received, the "smart" model (which is based on the known differences between public and private voters) performed significantly better than the raw data (which, Ryan would want me to point out, are not intended to be a prediction):


My model had an overall average error of 2.1 percentage points and a root mean square error of 2.7 percentage points. Most of this derives from significant misses on four players. I overestimated Edgar Martínez, Barry Bonds, and Roger Clemens all by around five points, failing to anticipate the extreme degree to which private voters would reject them. In fact, Bonds dropped by 23.8 points from public ballots to private ballots, and Clemens dropped by 20.6 points. Both figures are unprecedented: in nine years of Hall of Fame elections for which we have public-ballot data, we had never seen such a steep drop before (the previous record was Raines losing 19.5 points in 2009). Finally, I also underestimated Fred McGriff by 5.4 points. Out of nowhere, the "Crime Dog" became the new cause célèbre for old-school voters, gaining 13.0 points from public to private ballots.

Aside from these four players, however, my projections held up very well. My model's median error was just 1.2 points (its lowest ever), reflecting how it was mostly those few outliers that did me in. I am especially surprised/happy at the accuracy of my projections for the four new players on the ballot (Rodríguez, Guerrero, Manny Ramírez, and Jorge Posada). Because they have no vote history to go off, first-time candidates are always the most difficult to forecast—yet I predicted each of their final percentages within one point.

However, it's easy to make predictions when 56% of the vote is already known. By the time of the announcement, Ryan had already revealed the preferences of 249 of the eventual 442 voters. The true measure of a model lies in how well it predicted the 193 outstanding ones. If you predict Ben Revere will hit 40 home runs in 2017, but you do so in July after he had already hit 20 home runs, you're obviously benefiting from a pretty crucial bit of prior knowledge. It's the same principle here.

By this measure, my accuracy was obviously worse. I overestimated Bonds's performance with private ballots by 13.4 points, Martínez's by 11.5, and Clemens's by 9.8. I underestimated McGriff's standing on private ballots by 12.9 points. Everyone else was within a reasonable 6.1-point margin.


That was an OK performance, but this year I was outdone by several of my fellow Hall of Fame forecasters. Statheads Ben Dilday and Scott Lindholm have been doing the model thing alongside me for several years now, and this year Jason Sardell joined the fray with a groovy probabilistic model. In addition, Ross Carey is a longtime Hall observer and always issues his own set of qualitatively arrived-at predictions. This year, Ben came out on top with the best predictions of private ballots: the lowest average error (4.5 points), the lowest median error (3.02 points), and the third-lowest root mean square error (6.1 points; Ross had the lowest at 5.78). Ben also came the closest on the most players (six).


(A brief housekeeping note: Jason, Scott, and Ross only published final projections, not specifically their projections for private ballots, so I have assumed in my calculations that everyone shared Ryan's pre-election estimate of 435 total ballots.)

Again, my model performed best when using median as your yardstick; at a median error of 3.04 points, it had the second-lowest median error and darn close to the lowest overall. But I also had the second-highest average error (4.8 points) and root mean square error (6.2 points). Unfortunately, my few misses were big enough to outweigh any successes and hold my model back this year after a more fortuitous 2016. Next year, I'll aim to regain the top spot in this friendly competition!

Wednesday, January 18, 2017

Here Are 2017's Final Hall of Fame Predictions

Happy Election Day, baseball psephologists! One of the closest Baseball Hall of Fame elections in memory wraps up this evening at 6pm on MLB Network, when players from Edgar Martínez to Billy Wagner will learn whether they've been selected for baseball's highest honor.

...Except we already know that neither Martínez nor Wagner is going to get in. Each year, Ryan Thibodaux sacrifices his December and January to painstakingly curate a list of all public Hall of Fame votes so that the rest of us know roughly where the race stands. But Thibodaux's BBHOF Tracker is just that: rough. Many Hall of Fame watchers—Scott Lindholm, Ben Dilday, Jason Sardell, and yours truly—have developed FiveThirtyEight-esque election models, treating the Tracker data as "polls," to predict the eventual results. My model, which is in its fifth year, adjusts the numbers in the Tracker up or down, depending on whether a given candidate has historically under- or overperformed in public balloting. You can read my full methodology over at The Hardball Times.

With the big day upon us, it's time for me to issue my final Hall of Fame projections of 2017. While it is possible–even probable—that a couple more public ballots will be revealed before 6pm, the below numbers are accurate as of 5:55pm on Wednesday, when 250 ballots were publicly known. I will update this post as necessary leading up to the announcement.

My model started the election season optimistic—forecasting a record-tying five-player class. However, it ends the campaign predicting only four inductees: Jeff Bagwell, Tim Raines, Trevor Hoffman, and Iván Rodríguez. The other player in serious contention—Vladimir Guerrero—is projected to fall just short.

Bagwell and Raines are virtual locks for election. At a projected 83.9% and 83.5%, respectively, they are so far ahead of the required 75% that it would take a massive error to keep them out. By contrast, Hoffman and Pudge fans should be on the edge of their seats. Although their 75.9% and 76.0% projections are just over 75%, they would require only a single-point error in my forecast to put them under it instead. (This is entirely possible; no predictive model can be perfect. Last year, my model was off by an average of 1.5 points for each candidate, and in one case it was off by 3.5 points.) Hoffman's and Rodríguez's chances are better thought of as too close to call.

Guerrero is also close enough to 75% (72.3%) that he can't be ruled out for election either. As a first-year candidate, my model doesn't have much precedent to go off when making his prediction, so there is greater-than-usual uncertainty surrounding his fate. My model sees Guerrero as the type of candidate who gains votes on private ballots, but the magnitude of that gain could vary greatly. In this way, Guerrero could lift himself up over 75% without a huge amount of effort.

Perhaps the best way to think about this Hall of Fame election is to consider Hoffman, Pudge, and Vlad all as coin flips. (There's not much practical difference between having a 51% chance of winning a game and a 49% chance, even though those two sets of odds indicate different favorites.) On average, if you flip three coins, you will get 1.5 heads (well, OK, either one or two). Add that to the two virtual locks, and you're looking at an over-under of 3.5 inductees. While I am predicting four players elected, three is almost as likely.

Further down the ballot, I'm projecting Barry Bonds to reach 59.3% and Roger Clemens to reach 59.2%. Although this is well short of election, it's an astounding, nearly 20-point gain from last year's totals. If they do indeed break 50%, history bodes very well for their eventual election. Ditto for Martínez, who, at a projected 63.5%, appears to be on deck for election either in 2018 or 2019. Mike Mussina is tipped to hit 53.0% this year, also above the magic 50% threshold. He appears to have overtaken Curt Schilling as the ballot's premier starting pitcher; my model predicts Schilling will drop to 44.2% from his 52.3% showing last year.

Finally, two serious candidates are expected to drop off the ballot. The obvious one is Lee Smith, whose projected 35.3% matters less than the fact that this is his 15th, and therefore automatically last, year on the ballot. At the very bottom of my prediction sheet, Jorge Posada is currently expected to get 4.3% of the vote. Like with Hoffman/Rodríguez/Guerrero and 75%, this is well within the margin of error in terms of its closeness to 5%, the minimum number of votes a player needs to stay on the ballot. Whether Posada hangs on or not is the other major uncertainty tonight; my model currently thinks he's slightly more likely to drop off than stick around.

You can view my full projections on this Google spreadsheet or archived below for posterity. After the election, I'll write an analysis of how my (and others') prediction models did. Until then, all we can do is wait!

Sunday, January 1, 2017

State of the State Schedule 2017

With the imminent inauguration of President Donald Trump, many are turning with fresh hope to state governments. Although Trump's party holds total control over exactly half of them, the states are where Democrats expect their grassroots opposition campaign to start. Twenty-six Republican-held governorships will be on the ballot in 2018, and what the GOP does with its control in the 2017–2018 legislative sessions will set the tone for many of these races. For those on the right, the next two years represent the pinnacle of power for their party; now with the full support of the federal government, what initiatives will Republican governments pursue?

These are just some of the many reasons to pay attention to this winter's State of the State addresses, as well as its handful of budget and inaugural speeches. As this blog provides every year, here is a full schedule of when each state's governor will speak. This list will be updated as new speeches are announced.

Alabama: February 7 at 6:30pm CT
Alaska: January 18 at 7pm AKT
Arizona: January 9 at 2pm MT
Arkansas: January 10 at 10:30am CT
California: January 24 at 10am PT
Colorado: January 12 at 11am MT
Connecticut: January 4 at noon ET (State of the State); February 8 at noon ET (budget address)
Delaware: January 17 at 11am ET
Florida:
Georgia: January 11 at 11am ET
Hawaii: January 23 at 10am HAT
Idaho: January 9 at 1pm MT
Illinois: January 25 at noon CT (State of the State); February 15 at noon CT (budget address)
Indiana: January 9 at 11am ET (inaugural); January 17 at 7pm ET (State of the State)
Iowa: January 10 at 10am CT
Kansas: January 10 at 5pm CT
Kentucky: February 8 at 7pm ET
Louisiana:
Maine: February 7 at 7pm ET
Maryland: February 1 at noon ET
Massachusetts: January 24 at 7pm ET
Michigan: January 17 at 7pm ET
Minnesota: January 23 at 7pm CT
Mississippi: January 17 at 5:30pm CT
Missouri: January 9 at noon CT (inaugural); January 17 at 7:30pm CT (State of the State); February 2 at 11:15am CT (budget address)
Montana: January 2 at 10am MT (inaugural); January 24 at 7pm MT (State of the State)
Nebraska: January 12 at 10am CT
Nevada: January 17 at 6pm PT
New Hampshire: January 5 at noon ET (inaugural); February 9 at noon ET (budget address)
New Jersey: January 10 at 2pm ET (State of the State); February 28 (budget address)
New Mexico: January 17 at 12:30pm MT
New York: Instead of a traditional State of the State, Governor Andrew Cuomo delivered six slightly different State of the State addresses in six different regions of the state: January 9 at 11am ET (New York City); January 9 at 3pm ET (Buffalo); January 10 at 10:30am ET (Westchester); January 10 at 1pm ET (Long Island); January 11 at 11am ET (Syracuse); January 11 at 2pm ET (Albany); January 17 at 8pm ET (budget address)
North Carolina: January 7 at 10:30am ET (inaugural)
North Dakota: December 7 at 10am CT (Dalrymple budget address); January 3 at 1pm CT (Burgum inaugural and State of the State)
Ohio: April 4
Oklahoma: February 6 at 12:45pm CT
Oregon: January 9 at noon PT
Pennsylvania: February 7 at 11:30am ET
Rhode Island: January 17 at 7pm ET
South Carolina: January 11 at 7pm ET
South Dakota: December 6 at 1pm CT (budget address); January 10 at 1pm CT (State of the State)
Tennessee: January 30 at 6pm CT
Texas: January 31 at 11am CT
Utah: January 4 at 11am MT (inaugural); January 25 at 6:30pm MT (State of the State)
Vermont: January 4 at 2pm ET (Shumlin farewell address); January 5 at 2pm ET (Scott inaugural); January 24 at 2pm ET (Scott budget address)
Virginia: January 11 at 7pm ET
Washington: January 11 at noon PT
West Virginia: January 11 at 2pm ET (Tomblin farewell address); January 16 at 1pm ET (Justice inaugural); February 8 at 7pm ET (Justice State of the State)
Wisconsin: January 10 at 3pm CT (State of the State); February 8 at 4pm CT (budget address)
Wyoming: January 11 at 10am MT

National: January 10 at 8pm CT (Obama farewell address); January 20 at noon ET (Trump inaugural); February 28 (Trump State of the Union)

Thursday, December 22, 2016

What I Didn't Expect in Baseball in 2016

There has been a lot to reflect on in 2016. Some of those year-in-review pieces are serious; others are more light-hearted. This is the latter. Having already shared my thoughts on politics, I now turn to another year-end tradition around these parts: revisiting my preseason baseball predictions. Because predicting ball is an exercise in futility, grading my MLB predictions is always a humbling experience; more often, it's a hilarious one, filled with "boy, was I wrong" moments as well as the occasional "jeez, that wild guess was scary accurate." Let's dig into my 2016 American League and National League predictions to see how I did.

Prediction: The AL playoff teams would be the Blue Jays, Rays, Indians, Astros, and Rangers. The NL playoff teams would be the Mets, Nationals, Cubs, Cardinals, and Giants.
What Really Happened: The Blue Jays, Indians, and Rangers made it, but the Red Sox and Orioles replaced the Rays and Astros. In the NL, I missed only the Dodgers, who replaced the Cardinals. Overall, seven out of 10 playoff teams was pretty good! I also estimated the win totals of 14 teams within five; my average error of 6.4 wins was better than any of the five years I've been making official predictions.


Prediction: The Dodgers would lead baseball in days on the disabled list. The injury bug would be particularly devastating to their starting rotation, with only Clayton Kershaw surpassing 200 innings en route to another Cy Young Award.
What Really Happened: One of my eeriest predictions. The Dodgers' snakebitten starting rotation was the story of the summer in Los Angeles, and not even Kershaw was immune: a herniated disk in his back interrupted an historic season and cost him over 10 starts, enabling Washington's Max Scherzer to steal the Cy Young trophy. Kenta Maeda ended up leading the Dodgers with only 175.2 innings, and LA set new major-league records for most players put on the DL in one season (28) and man-days spent on the DL (2,418).

Prediction: Three Indians pitchers would throw no-hitters: Corey Kluber, Carlos Carrasco, and Danny Salazar.
What Really Happened: Only one pitcher league-wide tossed a no-no in 2016: Jake Arrieta on April 21.

Prediction: The Orioles would slug 250 home runs as a team—a number not seen since the 2010 Blue Jays—but would be led in OBP by slap hitter Hyun Soo Kim.
What Really Happened: Baltimore hit 253, 28 more than the next most powerful team. Kim's .382 OBP led all Orioles with at least 16 plate appearances.

Prediction: Julio Teheran would struggle his way to a 4.00 ERA, clearing a path for Ender Inciarte to be the most valuable Brave. He would even be better than the man he was in part traded for: Shelby Miller. Patrick Corbin and maybe even Robbie Ray would allow fewer runs than their new Arizona teammate.
What Really Happened: Inciarte put up 3.8 WAR, third-best on the Braves. Freddie Freeman (6.5 WAR) led the squad, and Teheran righted the ship to the tune of a 3.21 ERA, 4.07 K/BB ratio, and 4.9 WAR. As for Miller? He was worth just −0.8 WAR for the Diamondbacks. Corbin (5.15 ERA) and Ray (4.90) both had terrible seasons, but not nearly the disaster that was Miller's (6.15).

Prediction: The Royals would have a better record when Raúl Mondesí Jr. starts at shortstop than when Alcides Escobar does.
What Really Happened: Trick question: Mondesí never started at shortstop but instead played almost exclusively second base. Meanwhile, Escobar and his .261/.292/.350 batting line inexplicably started every single game the Royals played. Yet sure enough, Kansas City's record in just Mondesí starts was 23–17, and overall (i.e., in Escobar starts) it was just 81–81.

Prediction: Jon Gray would be dominant on the road—posting a 2.80 ERA—but would be unable to solve Coors Field, stumbling to a 5.20 home ERA.
What Really Happened: The Rockies phenom actually struggled more on the road: a 4.91 ERA. At home, he was a surprisingly good 4.30 ERA pitcher, limiting hitters to an excellent .241/.291/.383 line at altitude.

Prediction: Kevin Cash and Bruce Bochy would be voted 2016's Managers of the Year. Terry Francona would be runner-up in the American League. Buck Showalter would have such a disappointing season he would be shown the door.
What Really Happened: Neither Cash nor Bochy came close to sniffing the award, which went to Francona in the Junior Circuit. Showalter kept his job, but he did take heat after failing to use Zach Britton in a key situation in the Wild Card Game.

Prediction: Age would catch up to Justin Verlander and Ian Kinsler in Detroit. Verlander's fastball velocity would tick down until he led the Tigers with the highest WHIP on the team. Meanwhile, Mike Pelfrey would boast the league's highest ERA.
What Really Happened: Verlander's WHIP did lead the team—in a good way. It was the lowest in the entire AL, in fact. According to PITCHf/x, his fastball averaged 93.7 miles per hour, his best mark since 2013. Likewise, the highest ERA in the American League didn't belong to Pelfrey—but rather his teammate, Aníbal Sánchez. Far from deteriorating, Kinsler upped his WAR for the fourth year in a row (to 6.1) and won a Gold Glove.

Prediction: Bryce Harper would disappoint in the follow-up to his insane MVP season of 2015. He would maintain the same beastly rate stats, but he would miss a third of the season due to injury.
What Really Happened: Bryce stayed healthy for the second full season—or at least that's what he claimed. His OPS mysteriously dropped a staggering 295 points to .814, perhaps the result of playing through a right shoulder injury for… yep, a third of the season.

Prediction: Three Twins would finish in the top five for AL Rookie of the Year: José Berríos, Byung Ho Park, and eventual winner Byron Buxton. In the NL, Corey Seager would waltz home with the trophy.
What Really Happened: None of the three Twins even got a single vote. Berríos started 14 games with a hellish 8.02 ERA, Park hit just .191, and Buxton notoriously scuffled through his first two cups of coffee with a .193/.247/.315 slash line before coming to life (.287/.357/.653) in September. The Tigers' Michael Fulmer, of course, ended up winning the award in the AL. In the Senior Circuit, was there ever any doubt? Seager's .877 OPS and 6.1 WAR made him an easy choice.

Prediction: Jay Bruce would be one of the only good hitters on the Reds, earning him a trade out of town. Brandon Phillips, meanwhile, would slip to a .300 OBP and cease to be an asset on defense. His final WAR: 0.0.
What Really Happened: Bruce put up an .875 OPS for Cincinnati, better than anyone not named Joey Votto, and on August 1 he was traded to the Mets. Phillips held on in the OBP department (.320), but for the first time since 2006 he put up a negative DRS (−7) to finish with a 0.8 WAR.

Prediction: The penny-pinching Astros would keep Ken Giles out of the closer's role in an effort to suppress his salary in arbitration—but this would also allow them to use him in the highest-leverage situations.
What Really Happened: Indeed, the Astros handed Luke Gregerson, then Will Harris the closer's role before giving Giles a crack; he finished with 15 saves. Giles didn't lead the AL in leverage index like I predicted, but his 1.83 LI was higher than any other Astro.

Prediction: Marcus Semien would be one of the Athletics' best players and would even be a net positive on defense.
What Really Happened: Semien slugged 27 home runs and was worth 3.0 WAR, both tops among Oakland hitters. On defense, he was barely an asset (0.1 dWAR), thanks to the positional adjustment of playing shortstop.

Prediction: The worst infield defense in the AL would doom Rick Porcello's second season with the Red Sox. Meanwhile, in Chicago, Chris Sale would sail to his first Cy Young Award with a sub-2.50 ERA.
What Really Happened: The Boston infield actually saved nine runs defensively, helping Porcello along to a 3.15 ERA and the Cy Young. Sale was no slouch either, posting a 3.34 mark for the Pale Hose. By December, of course, who was better is a bit of a moot point: they're now teammates in Boston.

Prediction: Prince Fielder and Shin-Soo Choo would turn in carbon copies of their superb 2015 seasons.
What Really Happened: Fielder had the worst season of his career, hitting just .212/.292/.334 through 370 plate appearances before being forced to retire. Choo had a similarly snakebitten season, going on the disabled list four times with four unrelated freak injuries. On April 10, he was shelved with a calf strain; he returned May 20, and then pulled a hamstring after just three innings. On July 20, he hit the DL again with back inflammation, returning on August 4. On August 15, he was hit by a pitch that broke his forearm and ended his season.

Prediction: Pittsburgh wouldn't need to worry about slipping offensively with the losses of Pedro Alvárez and Neil Walker. John Jaso and David Freese would prove shrewd free-agent signings.
What Really Happened: The Pirates went from a 97 OPS+ in 2015 to a 95 OPS+ in 2016. Jaso hit an above-average .268/.353/.413, and the team also rewarded Freese's .270/.352/.412 line with a two-year, $11 million extension.

Prediction: Mookie Betts would contend for AL MVP, but Carlos Correa would actually win it. The shortstop's 40 home runs would distract voters yet again from Mike Trout. In the National League, Paul Goldschmidt would finally step out of others' shadows and claim his first MVP award.
What Really Happened: Trout deservedly won the AL hardware for just the second time. Correa still turned in a great season, but he slammed just 20 taters. For his part, Betts more than contended for MVP: he came darn close to winning it, with nine first-place votes and a second-place finish. As for Goldschmidt, a down year (for him) doomed him to "just" an 11th-place finish in the NL race.

Prediction: The Diamondbacks would regret the Jean Segura trade. His bat would continue to drag down the lineup, and his poor defense would contribute to Arizona tumbling from 63 DRS to a neutral fielding team.
What Really Happened: Segura had one of the best come-out-of-nowhere seasons in a long time, hitting .319/.368/.499 for a 5.7 WAR. He was average on defense, although the D'backs did fall all the way to −12 DRS.

Prediction: A 5.0 K/BB ratio by Anthony DeSclafani would see him selected to the All-Star Game.
What Really Happened: DeSclafani was injured for much of the first half and did not debut until a month before the All-Star Game, but if he had frontloaded his first 16 starts (8–2, 2.93 ERA, 1.14 WHIP, 4.1 K/BB ratio), he surely would have earned a ticket to San Diego.

Prediction: Marco Estrada and J.A. Happ would fall dramatically back down to Earth, with 9.0 hits per nine innings and a 90 ERA+ respectively. Aaron Sánchez would show he belonged in the bullpen all along by struggling as a starter.
What Really Happened: Estrada followed up a 2015 in which he led the AL with 6.7 hits per nine with a 2016 in which… he led the AL with 6.8 hits per nine. All Happ did was win 20 games with a 135 ERA+. Sánchez led the league with a 3.00 ERA as one of the breakout starters in all of baseball.

Prediction: Under the tutelage of hitting coach Barry Bonds, Marcell Ozuna would take his game to the next level, setting career highs in all three slash categories.
What Really Happened: Ozuna had a good year but not quite a full breakout. His .321 OBP was a career high, but his .266 average and .452 slugging percentages were each three points shy of his historical best. Oh, and Bonds was fired at the end of the season for allegedly losing interest in the team.

Prediction: Andrew Heaney would establish himself as the one sure thing in an injury-plagued Angels rotation, while Jered Weaver would shockingly retire midseason when it became apparent he couldn't throw above 80 miles per hour.
What Really Happened: Heaney got in just one start all year—a six-inning, four-run effort against the Cubs—before feeling elbow discomfort. He had Tommy John surgery in July. Weaver did indeed have a tough time getting outs with his 84.0-mile-per-hour "heater," but he stuck it out the whole year and ended with a 5.06 ERA.

Prediction: Ian Desmond would look so lost in the outfield that the Rangers would bench him. He would enter career purgatory, bouncing around on the free-agent market as a utility man for the rest of the decade.
What Really Happened: With −4 DRS, Desmond wasn't an asset in the outfield, but he ably remade himself into a useful player there, amassing 2.7 WAR. As for his financial future, Desmond just signed an inflated five-year, $70 million deal with the Rockies.

Prediction: Domingo Santana would put up a vintage Adam Dunn season: a .230 average but a .340 OBP, 180 strikeouts but 30 home runs.
What Really Happened: Santana lost significant time to two injuries in 2016, but he still did the following in 281 plate appearances: a .256 average, .345 OBP, 11 home runs, and 91 strikeouts.

Prediction: Milwaukee would be the only team in baseball with zero complete games in 2016.
What Really Happened: Indeed, no Brewer starter pitched a complete game. However, three other teams also shared this ignominious distinction: the Marlins, Yankees, and Blue Jays.

Prediction: Jason Heyward, Kris Bryant, and Anthony Rizzo would all rank among the NL's top 10 position players by WAR. Ben Zobrist, meanwhile, would continue to decline thanks to poor defense.
What Really Happened: Bryant (at 7.7 WAR, the NL MVP) and Rizzo (5.7) ranked first and fifth, respectively, in WAR, but Heyward's contract infamously proved a bust, as he could muster just a 70 OPS+ and 1.5 WAR. Zobrist did cost his team multiple runs defensively for the third straight year, but his excellent hitting (.272/.386/.446) made him Chicago's fifth-best position player (3.8 WAR).

Prediction: The White Sox would be a fountain of youth for Mat Latos, who would be worth 2.0 WAR, and Melky Cabrera, who would begin to justify his $42 million contract.
What Really Happened: Latos bombed out of Chicago, and then Washington, with a 4.89 ERA and 0.1 WAR. Yet in a development no one outside the South Side noticed, Cabrera was great in 2016, putting up an .800 OPS that almost matched his 2014 performance.

Prediction: Sonny Gray's 2015 luck would reverse itself, with a .340 BABIP leading to a 3.95 ERA.
What Really Happened: Gray was unlucky—and then some. He offended A's fans with a 5.69 ERA, far worse than an already poor 4.67 FIP. His BABIP was a not-great .319, but the real culprits were a terrible 64% strand rate and artificially inflated 1.4 home runs per nine innings.

Prediction: Yasiel Puig would rediscover his 2013–2014 form, and Joc Pederson would discover new heights.
What Really Happened: Puig had his worst season in the majors yet, hitting just .263/.323/.416 between hamstring injuries. He fell so out of favor with the club that they demoted him to AAA in August, and they still found room to criticize him for his behavior while in Oklahoma City. Back in California, Pederson continued to blossom, pairing his previous on-base ability and home-run power with more well-rounded hitting: more doubles and more selective baserunning.

Prediction: Mike Leake would be the one weak link in the Cardinals' rotation, with a 100 ERA+, and Jaime García would land on the DL yet again.
What Really Happened: García pitched a full season for the first time since 2011, starting 30 games. Leake had his worst season yet: an 87 ERA+. He wasn't even the worst St. Louis pitcher, though, as Michael Wacha spat out an 81 ERA+ despite a pretty good 3.91 FIP.

Prediction: Trea Turner would grab ahold of the Nationals' shortstop job so surely that Stephen Drew wouldn't even collect 100 plate appearances.
What Really Happened: Turner had nothing to do with Drew amassing just 165 plate appearances for Washington's $3 million investment. The infield prospect remained exiled to AAA until mid-July, when he finally came up… only to be moved to the outfield. He certainly made a statement, though, with his .342 average and 33 stolen bases.

Prediction: Rich Hill would prove to be an illusion, finishing with a 4.00 ERA.
What Really Happened: Hill extended his four-start dominance from the end of 2015 into his first 14 starts of 2016 with Oakland, posting a 2.25 ERA. Then he was traded to the Dodgers as one of the deadline's biggest gets and did even better: a 1.83 ERA. He went into the offseason as the top pitching prize on the free-agent market.

Prediction: Led by Craig Kimbrel and Carson Smith, the Red Sox bullpen would strike out a quarter of the batters it faced, second in the league only to the hated Yankees.
What Really Happened: It took Smith just 2.2 innings to succumb to season-ending injury, but the Sox bullpen still struck out 25.4% of batters. That's not as impressive as it sounds, though; five other bullpens, including the Yankees' (27.1%), fanned more.

Prediction: Tanner Roark would be mediocre, and Noah Syndergaard would go under the knife at midseason.
What Really Happened: Both Roark and Syndergaard garnered downballot Cy Young votes. Roark finished with a 2.83 ERA, and Syndergaard had the game's strongest peripheral stats (a 2.29 FIP) in his 30 starts.

Prediction: Yovani Gallardo and Kevin Gausman would swap 2015 ERAs, with Gallardo finishing at 4.25 and Gausman at 3.42.
What Really Happened: The two Orioles hurlers did undergo a freaky Friday situation, with Gallardo regressing to a 5.42 ERA and Gausman improving to 3.61, establishing himself as the team ace.

Prediction: Ray Searage would not be able to fix what ails Ryan Vogelsong or Jon Niese, but Juan Nicasio would thrive in Pittsburgh.
What Really Happened: All three reclamation projects fell flat. Vogelsong pitched to a 4.81 ERA in 82.1 innings, and Niese mustered just a 4.91 ERA before being traded back to the Mets. Nicasio boasted strong strikeout numbers (10.5 per nine innings), but he had just a 4.50 ERA in a swingman role.

Prediction: PETCO Park would help James Shields to a bounceback season of a 3.30 ERA, a 1.20 WHIP, and 2.0 walks per nine innings.
What Really Happened: Shields limped through 11 starts in San Diego with a 4.28 ERA before getting traded to the White Sox, where he was lit up in the bandbox that is U.S. Cellular Field. He finished with a 5.85 ERA, 1.60 WHIP, and 4.1 walks per nine—all career worsts.

Prediction: Breakout seasons by Steve Pearce, Blake Snell (who would strike out 10 batters per nine innings), Drew Smyly, and Chris Archer would lead the Rays to the World Series.
What Really Happened: Pearce did return to his 2014 self, slashing .288/.374/.492, and Snell struck out 9.9 per nine (so close!). However, Smyly finished with a 4.88 ERA and Archer lost 19 games, the most in baseball. The Rays stunk up the joint to the tune of 94 losses—my biggest whiff on any team.

Prediction: Even-year magic would strike again. New ace Johnny Cueto would make up for free-agent bust Jeff Samardzija (0.5 WAR), and the Giants would win the World Series for the fourth time in seven years.
What Really Happened: Samardzija did not disappoint (2.7 WAR), and Cueto was even better (5.6 WAR), but the Giants were bounced from the playoffs in four games by their atrocious bullpen and the eventual World Series winners—the Chicago Cubs.

Friday, December 9, 2016

What I Didn't Expect in Politics in 2016

This year was a bad one for people in the political prognostication business. I'm not afraid to say that I failed as spectacularly as anyone: throughout the Republican primary, I clung to the orthodoxy that "the party decides" and that the GOP would never let Donald Trump become its nominee. Throughout the general election, I dismissed Trump's chances of winning over a country with sexism, racism, and scare tactics. Now, we're 41 days away from him taking the oath of office.

I believe that any responsible pundit must reflect on such a failure before reengaging in the profession. We—or at least I—make our predictions based on past information. The election of Donald Trump represented a new piece of information that we must add to our calculus going forward. But we also must avoid overreaction. Throwing our hands up and declaring elections unpredictable is an overreaction. Saying that polling and other hard data are useless is an overreaction.

The truth is that Hillary Clinton did win the popular vote by about two percentage points, as predicted by the national polls. Under different rules of the game, that would be enough to hand her the presidency, and none of this Democratic and media handwringing would be happening. That's right—not a single person could have voted differently and everyone who currently looks so dumb would actually have been totally correct. Not a single person could have voted differently and the prevailing narrative would have been the implosion of the Republican Party instead of the meltdown of the Democratic. But because the votes were distributed just right, and because we use the Electoral College, not the popular vote, we got the outcome we did.

To me, the lesson of 2016 was randomness. Sometimes, data have errors. Sometimes, you have bad luck. Sometimes, unlikely things happen. In looking back at my predictions, I have a hard time seeing what I should have done differently. To predict a Trump win (in the general, at least) would have required a series of assumptions that, at the time, would not have been well grounded in fact or theory. Given the information I had before the election, I actually still think I made a sensible call in expecting Clinton to win. But my mistake was being so sure of it. In an effort to draw attention and project confidence, I treated an event that was likely to happen as one that was certain. I didn't fully appreciate that we live in an uncertain world. I didn't properly allow for the possibility of a polling error or something else weird happening. I didn't stop to think that a 90% chance still comes up empty 10% of the time.

On May 21, 2010, with two outs in the third inning, the Los Angeles Angels intentionally walked St. Louis Cardinals outfielder Skip Schumaker to load the bases for pitcher Brad Penny, a career .157 hitter. Six out of seven times, Penny makes the final out of the inning, preserving the 4–4 tie. On this occasion, though, Penny promptly hit a grand slam for what proved to be the winning hit. Was it the wrong decision by the Angels? I would argue that it was the right call; it just didn't work out this time. Sometimes, the right strategy can still lead to a bad result (and vice versa). It doesn't automatically discredit the strategy, as long as the strategy has been proven sound over a larger sample size. Data and political science remain the most accurate tools for predicting elections over the long term; one bad year doesn't change that.

Going forward, I will continue to use factors like polls and demographic trends in predicting elections. But I will change my attitude to be more humble about my own fallibility. Never again will I declare anything as a "slam dunk" or a "sure bet"; the fairest analysis acknowledges that anything can happen while still pointing toward the overall likelihood of one outcome. To those who heeded my political advice in 2016, I apologize for being overconfident. In the future, I promise that I will retain a more open mind and always be aware of my own limitations as a soothsayer.

One way to do that is to move away from the language of "calling" states and toward a probabilistic election forecast. The Cook Political Report uses such a system through its Solid-Likely-Leans scale of handicapping races. Every year, I use the same scale to make my annual constitutional-office election predictions, with much more favorable results than my failed presidential prognosis. Indeed, while I wasn't perfect in calling downballot races this year either, my more nuanced assessments of each campaign stand up better to post factum scrutiny.

In 2016, I issued Cook-esque race ratings for 52 downballot races: lieutenant governors, attorneys general, secretaries of state, treasurers, auditors, superintendents of public instruction, commissioners of insurance, commissioners of agriculture, a comptroller, a commissioner of labor, and a commissioner of public lands. In the 40 races where I gave one party the edge, that party prevailed 37 times. Specifically:
  • Democrats won all 11 races I rated as Solid Democratic, including three that lacked a Republican opponent.
  • Democrats won five of the six races I rated as Likely Democratic.
  • Democrats won five of the seven races I rated as Leans Democratic.
  • Republicans won 10 of the 12 races I rated as Tossup.
  • Republicans won all five races I rated as Leans Republican.
  • Republicans won all three races I rated as Likely Republican.
  • Republicans won all eight races I rated as Solid Republican, including two that lacked a Democratic opponent.
Here is the Democratic margin of victory or defeat in each of the elections (save Washington treasurer, which was a Republican-on-Republican contest). Note that these numbers are unofficial election results from the Associated Press.


As in 2014, I underestimated Republicans across the board, but not egregiously so. Democrats' average 3.2-point margin of victory in Leans Democratic races, for example, is right where you want it to be, and it isn't a shock that one or two (OK, two) of the elections I rated thusly actually flipped the other way. However, the same can't be said of the races I dubbed Leans Republican. The GOP won them all, by at least 7.1 points (an average of 15.5). My Tossup races behaved more like Leans Republican should: the GOP won them by an average of 6.1 points, losing only two of the 12. That's what happens in a wave election, though: close races all tend to break the same way.

I can console myself with the fact that my biggest errors were isolated to just a few states. Misread the political climate in just one state, and you can blow several races, even as you accurately predict the rest of the nation. For me in 2016, that state was North Carolina. The Tarheel State was responsible for my biggest miss: declaring Democrat Wayne Goodwin and Republican Mike Causey's battle for state insurance commissioner a Likely Democratic contest. Instead, Causey defeated the two-term incumbent by 0.9 points. Almost as embarrassingly, Democratic Auditor Beth Wood—whom I deemed Likely to beat Republican Charles Stuber—prevailed by only 0.1 points in initial returns. (The race isn't even officially called yet, as Stuber has requested a recount.) North Carolina also played havoc with my projections by almost ousting Democratic Secretary of State Elaine Marshall (Solid Democratic) and rejecting Superintendent June Atkinson (Leans Democratic).

Oregon and Missouri were two other states that I misjudged. Democrat Tobias Read won the Oregon auditor's race by only 1.9 points, even though I had dismissed the contest as Solid Democratic. Meanwhile, my third and final incorrect "call" came in the Oregon secretary of state election, where Republican Dennis Richardson defeated Democrat Brad Avakian in a Leans Democratic competition. Farther inland, Missouri singlehandedly goosed the average Republican margin of victory in my Tossup and Leans Republican races when it swung much farther to the right than anyone was expecting. Its Democratic candidate for lieutenant governor lost a supposed Tossup race by double digits, and, while I expected its attorney general and secretary of state races to Lean Republican, I didn't anticipate them doing so by 17.4 and 19.5 points, respectively.

Elsewhere, I called most races right on the money. I'm especially proud of my performance in West Virginia, where I correctly foresaw a close race for secretary of state (presumed favorite Democrat Natalie Tennant was stunned by a 1.8-point loss) and GOP wins for agriculture commissioner and auditor, despite the state's ancestral Democratic tone. Clearly, handicapping downballot races is my calling more than presidential forecasting is—and that's OK by me. I'm looking forward to bringing these understudied races even more into the light as we turn the page to 2017 and the busy midterm year of 2018, when the biggest batch—over 140—of constitutional offices are on the ballot.