Apologies to ThickCulture readers for all the sports talk recently. I’ll get more sociological again soon. I promise. I wrote the following as an email to my pal and historian of American sports, Dan Hawkins, but thought I’d post it here to get a wider response.

I’ve been following the latest NBA free agent rumors and pretty much every other sentence on ESPN is “clearing cap space.” I certainly remember a lot of talk about “cap space” going back to 2008 when teams started drooling over LeBron’s availability in 2010. But I don’t recall much talk about it before then. If memory serves, in the 1990s, people tended to talk about “blockbuster trades” more.

I have several hypotheses to help explain this observation:

1) I’m wrong. Perhaps I’m just more tuned into NBA post-2008, but I kind of doubt it. I feel less tuned in to the NBA than I was 1990-2002.

2) It’s a media effect. Maybe cap space was always a big issue, but because ESPN and its ilk have created a bigger “newshole” for sports coverage, they can cover acquisitions issues more closely. It seems like Bill Simmons and others responded to/created market demand for this sort of trade and signing speculation.

3) It’s a product of the superstar era. The modern game relies on superstars to a greater extent and so free agent signings have become more important means by which teams improve. Thus, “freeing cap space” to sign free agent has become a more common tactic.

4) NBA rules have changed. Here, I’m way out of my depth. Have there been changes to the regulations surrounding acquisitions that have made free agent signings more desirable?

Thoughts?

The following post is by Ryan Larson ’14, a senior sociology major at Concordia College. He loves sports of all kinds, plays jazz sax, and will begin a graduate program in sociology in the fall.

With the NCAA’s Men’s Basketball Tournament starting today, the media are alight with predictions as to who will cut down the nets April 7th. The annual phenomenon of penciling in the winners in tens of millions of brackets has a new twist this year: a billion dollar prize. The grand prize is being offered by Quicken Loans, the Detroit mortgage lender, with the backing of Warren E. Buffett, to anyone who fills out a perfect 2014 tournament bracket. The prize money will be paid out in 40 annual payments of $25 million, or a one-time lump sum of $500 million. However, how likely is a perfect bracket to surface?

In all likelihood, it won’t. No record of a perfect bracket has surfaced to date, and the advent of Internet-based bracket filling makes this much easier to track. For example, in the 16 years of the ESPN online bracket challenge, not one has been perfect (this also holds for the other Internet-based hosts). Jeff Berge, Professor of Mathematics at DePaul University says the odds of picking a perfect bracket randomly is 1 in 9,223,372,036,854,775,808 (the probability of getting 63 out of 63 right is the product of the probability of getting each one right, which for a coin flip is 50 percent). If everyone on earth filled out 100 brackets, it would theoretically take 13 million years to get a perfect bracket. In sum, the prediction worth putting much credence in is the notion that Buffett won’t have to part with his billion.

However, not all NCAA March Madness contests are a 50/50 coin flip. A no. 1 seed has never lost to a no. 16 seed, which makes these games easier to predict correctly than the Final Four contests. Incorporating just this one piece of information, University of Minnesota Professor of Biostatistics Brad Carlin put the odds at more like “1 in 128 billion.” This estimate is based solely on the probabilities of correct predictions in each round: the probability of calling a first-round game correctly ranges from 51 percent for the No. 8 vs. No. 9 game to 100 percent for the No. 1 vs. No. 16; and that second-round games can be called with 65 percent accuracy. The figures are 60 percent for Sweet Sixteen games and 50 percent for every game from the Elite Eight through the final. To put this in perspective, your odds of being killed by a vending machine are higher than picking a perfect bracket at even with the incorporation of these conditions.

All hope is not lost (although it’s pretty close to it). Implementing statistical modeling techniques on historical tournament data can help increase your chances of picking games correctly (however, at a very modest rate). Arguably the most popular model is that of former New York Times, now ESPN prognosticator Nate Silver. Silver, and his team at fivethirtyeight, are in their fourth year of building a model to correctly pick the winners of the March Madness contests. The model is primarily based (weighted at 5/7 of the model) of a composite of computer college basketball rankings. These computer based rankings are combined with two human based metrics (2/7 of the model): the NCAA selection committee’s S-Curve and preseason rankings from the Associated Press and the coaches (used as an indicator for “underlying player and coaching talent”). Additionally, Silver and his team adjust for injuries and player suspensions (using a statistic called win shares) and travel distance. Silver then simulates the tournament thousands of times to obtain predicted probabilities of each team advancing in each round (interactive graphic with the final model can be found here).

What other factors influence a win probability? Other inquiry has backed up Silver’s notion that rankings matter, and that season performance (wins (particularly away wins), offensive scoring) and historical team performance (final four appearances, championships) also can lend some predictive insight. Ken Pomeroy’s predictive rankings are also very popular (and also incorporated into Silver’s model), although details of his methods are hidden behind a paywall. His models highlight the importance of strength of schedule as an important factor in the equation. Additionally, ESPN’s Basketball Power Index (BPI), created by Alok Pattani and Dean Oliver, accounts for the final score, pace of play, site, strength of opponent and absence of key players in every Division I men’s game (a new addition to silver’s model this year). However, the inclusion of these metrics into a regression equation rarely gets you more predictive prowess than a coin toss (R2=.5).

Although modeling could help you gain valuable insight into your office bracket pool, it will not lead to a perfect bracket without a large amount of luck coming your way. Although sports do have a large amount of systematic variation, the inclusion of a good amount of random variation is what makes both prediction difficult and athletic contests beloved. When filling out your brackets this year, data driven analysis should give you leg up wouldn’t have had otherwise. Listen to what the fox has to say. (For further reading: predictive analytics are also used to predict which teams will be selected to the tournament on Selection Sunday, with surprising accuracy).

The following is a guest post by Concordia College sociology major Ryan Larson ’14 and continues his series predicting Olympic hockey results.

With the semi-finals set, I have indicated where I got predictions right and wrong. Keep in mind these are probabilities, and upsets are common in Olympic hockey (1980 anyone?). Recall that the models, at best, explained just under a third of the variance in probabilities. Therefore, getting more than 50% of the games correct would be a case of the model outperforming itself. These are probabilistic statements, and in the case of the Finland Russia game, we would expect each team to each win 50 games were 100 games between them to be played (In sum, it is no surprise that Finland won the game). Also, Slovenia’s defeat of Austria would have 30 times if 100 games were played (in theory), and that game Tuesday morning happened to be one of them. On the other hand, Latvia’s win was a bit more impressive considering their lack of NHL talent. Furthermore, the models are built to explain medal wins, not necessarily qualification playoffs.

In the following bracket, correct predictions are highlighted and incorrect forecasts are marked in red. Additionally, teams who were eliminated are crossed out. In terms of the semi-finals, Sweden’s probability of advancing to the gold medal game marginally increased with Finland’s defeat of host nation Russia. Predicting such rare events (Olympic medal wins), off of small sample sizes (only 4 previous games allowed NHL talent to participate), in a game with a lot of randomness is a difficult endeavor.

The following is a second guest post by Concordia College sociology major Ryan Larson ’14. An earlier post describes his models predicting the outcomes of the Olympic hockey tournament. After graduation, Ryan intends to pursue graduate study in sociology and criminology.

With the bracket set, I have decided to apply my models to the bracket to see how well my predictions fare. The previous analysis was completed before the bracket was released. With the bracket seedings now set, there cannot be a 1-2 Canada-USA finish. Therefore, I applied my model predicting whether a team would win any medal to the bracket competitions all the up to the final two games (Gold medal and Bronze medal game). For the two medal games, I used the gold medal model, for reasons discussed in the previous post. Below is the bracket with predictions of each game. Each probability is normalized to each game, so the relayed probability is the team’s chances of winning the game relative to who the team is playing.

The following is a guest post by Concordia College sociology major Ryan Larson ’14. After graduation, Ryan intends to pursue graduate study in sociology and criminology. He is also a huge hockey fan.

Hockey is back at the forefront of the national sports consciousness thanks to T.J. Oshie and his Olympic shootout heroics against host team Russia on Saturday morning. Many in the media have made claims as to which country will obtain the coveted title of world hockey dominance (via a gold medal, which isn’t actually solid gold). However, to what extent are these claims mere speculation?

The Claims

Baseball has long been the hallmark choice for sports analytics, due to its large sample sizes (162 game seasons) and relatively independent events (for a more thorough discussion, I highly recommend Nate Silver’s The Signal and The Noise, Ch. 3). Recently, analytics has moved to ice hockey which has been spearheaded by Rob Vollman. Not surprisingly, he has made one of the only claims on who takes home the gold peppered with any quantitative substance. Vollman makes an implicit assumption having many NHL players (also good ones) is an indicator for Olympic success. This makes theoretical sense, as the hegemonic domination of the NHL in the professional hockey market clearly attracts the world’s finest athletic performers. Jaideep Kanungo, in an aptly titled “Hockeynomics” article (following scholarship in Simon Kuper and Stefan Szymanski’s Soccernomics) claims that countries with higher populations (higher likelihood of producing elite talent), gross domestic product (more resources to support player development such as indoor ice and equipment), and experience (proxy of country support) may give clues to a team’s success in Sochi.

The Data

To evaluate these claims, I channeled my inner Nate Silver and constructed a dataset using the Olympic mens hockey teams from 1998-2010 (prior to 1998 NHL players were not allowed to participate). I coded each team’s aggregate NHL games played, goaltender games played, goals, assists, points, and all-stars. Additionally, I appended the NHL data with GDP per capita, population, and IIHF World Ranking in each respective competition year (the IIHF World Ranking was instituted in 2003, so I manually calculated the rankings of each country in the 1998 and 2002 games). The IIHF ranking is utilized as an indicator of international competition success. I also coded if a team won gold, or if any medal was won irrespective of its elemental composition. As could be assumed, the NHL measures are all highly correlated (Table 1). Therefore, in each analysis I chose to use the highest correlated NHL metric with each respective dependent variable (specifically, NHL games played for medal win and all-stars for gold win). For the stats geeks out there, I use of a multilevel random effects probit model structured hierarchically by year. Probit regression models probabilities of outcomes (here, of winning any medal and of winning a gold medal). This model deals with the non-independence of the dependent measure of cases in the same Olympic year, because when three teams medal (or one team obtains gold) all others do not. While these analyses have very few cases (n=52), the dataset is a population of all relevant teams and years (making statistical significance irrelevant).

The Model

Table 2 depicts each predictor’s effect on the change in probability of success in the Winter Olympics.

Looking at Table 2, we can glean three major insights on what best predicts Olympic hockey success:

1. NHL measures are relatively good predictors for Olympic team success. The addition of 1 NHL player increases a team’s probability of winning a medal by 12.9% and the addition of 1 all-star increases a participating country’s probability of winning gold by 13.4%. The NHL measures outperformed other predictors in the models by accounting for about a third of the variation in medal and gold medal wins by themselves. This finding supports the notion that having players with experience in the best league on the planet is crucial for Olympic success. These effects are particularly impressive considering the small size of the population and the fact that these models are predicting relatively rare events.

2. IIHF World Ranking points, GDP per capita, and country’s population prove to be relatively poor predictors of Olympic medal winning. Compared to the NHL metrics, the other factors in the model were not as predictive. The only measure that was associated with any substantial probability change was population size in the gold model – and it decreased the probability of winning a gold! This finding is most likely a statistical artifact of the small sample size, as only 4 gold winners were included in the analysis. A possible explanation for this artifact could be the cultural hockey support (which is outside the scope of this data) present in countries with relatively small populations that tend to fare well in the tournaments (Czech Republic, Sweden). This same explanation most likely holds for GDP per capita as well, and a bigger sample size may show positive effects. For the above theoretical reasons, population and GDP per capita were not included in the final model (brings Pseudo R2 to .25).

3. NHL all-stars are what drive gold medal wins. Olympic play is characterized by preliminary round robins followed by a bracket single-elimination tournament. As far as the NHL metrics are concerned, getting to be one of the select teams on a podium come tournament end is best predicted by the number of NHL players present on a country’s team. However, when predicting the rare event of a gold medal, all-stars take the predictive lead. In other words, when only 4 teams remain in the bracket (most likely littered with many NHL players) it is the team with the most all-star players that has the greatest probability to take home the title of world champion.

In sum, the models support Vollman’s notion that NHL players matter, and having very good players (all-stars) is key to winning the gold. However, the impacts of GDP per capita, IIHF ranking, and population were relatively weak. However, to fully investigate this notion a larger sample would be ideal (which may soon become impossible).

Predicting Sochi 2014

Using the above models, I entered the 2014 Olympic teams’ data into the equation (excluded GDP per capita and population from the gold model for reasons discussed above). Table 3 relays each team’s probability of winning any medal as well as taking home the gold in Sochi. As illustrated by the pseudo R2 values in Table 2, these predictive models do not account for the majority of variation in probabilities, but model fits of .329 (medal) and .25 (gold) are far from nothing. In spite of the small historical sample size and attempting to predict who will win out of the 12 very best international squads (tight competition), the predictors included should allow us to get a better idea of who will “bring home some hardware” in Sochi above and beyond the speculation rampant in the media.

Much to my chagrin given my love for the Yanks, my models predict that the medalists for the 2014 Winter Olympics are as follows:

No time for a think piece today — I have too many buffalo wings to eat, watery beers to drink, and hours of pre-game coverage to pass before my glazed eyes. But I thought I’d share some worthwhile readings for Super Bowl Sunday.

Trying to decide who to root for? Perhaps the political contributions of team owners will sway you? Broncos lean Right, Seahawks lean slightly Left.

Is the NFL ruining football with an ever-more complex rulebook? Yes. Is it to make more money? Most likely.

Is that shiny new stadium going to help your community? Is it worth public money? Al Jazeera, says no and no. Sociology Lens reviewed the scholarship on the subject back in November.

The Super Bowl is a festival of gendered marketing. What can sociology tells us about that?

We’ve all heard about the concerns about concussions in the NFL (if you haven’t seen Frontline on the subject yet, you must). The Grey Lady’s Frank Bruni has done a great job following this issue and connecting with larger concerns about violence, greed, and bloodlust. But I was also very fond of the introspective contribution by ThickCulture’s own Jose Marichal.

Good news yesterday out of New Mexico where the state’s Supreme Court ruled in favor of full marriage equality (they previously had only civil unions). On MSNBC, one commentator remarked, “We’re now 17 out of 50 on our way nationwide same-sex marriage” — a reference to the fact that New Mexico is the 17th state to have full marriage equality. But “17 out of 50” is misleading because New Mexico has less than 1% of the U.S. population (rather than the 2% suggested by 1 out of 50). Moreover, we shouldn’t assume that gays and lesbians are evenly distributed across the country. I began to wonder, how many people will actually be affected by the decision***?

I threw together this table, showing the state population sizes, the percentages of residents who identify as as gay or lesbian (according to Gallup’s largest state-by-state study to date), and then by multiplying those together, the estimated numbers of gays and lesbians in each state. Green shading means full marriage equality; yellow means domestic partnerships or civil unions.

As you’ll see, though the news from New Mexico is positive, it represents a very small percentage of the U.S. population. There are only 60,000 gays and lesbians in NM, which is about half of one percent of the national LGBTQ population. To have an impact on millions of gays and lesbians, we would need to see changes in populated rust-belt states like OH, PA, and MI.

Unfortunately, the two states without marriage equality with the largest number of gays and lesbians — FL and TX — seem like too steep a hill to climb politically in the near-future. However, the New Mexico case is telling as the change came through judicial decision rather than legislative action. A so-called “activist court” can produce rapid changes in policy in many places.

***Of course, other people, like children and other family members, can also benefit from same-sex marriage. I’m referring to those who might potentially marry.

Maybe others have picked up on this, but I’m just getting around to hearing, by way of Slate’s Hang Up and Listen podcast, Chicago Bears receiver Brandon Marshall* comment on the Richie Incognito bullying scandal. In a press conference, he sounded a bit like a sociologist as he commented on gender socialization:

“Take a little boy and a little girl. A little boy falls down and the first thing we say as parents is ‘Get up, shake it off. You’ll be OK. Don’t cry.’ A little girl falls down, what do we say? ‘It’s going to be OK.’ We validate their feelings. So right there from that moment, we’re teaching our men to mask their feelings, to not show their emotions. And it’s that times 100 with football players. You can’t show that you’re hurt, can’t show any pain. So for a guy to come into the locker room and he shows a little vulnerability, that’s a problem. That’s what I mean by the culture of the NFL. And that’s what we have to change. So what’s going on in Miami goes on in every locker room. But it’s time for us to start talking.

While his insight is unsurprising to most sociologists, it might be worth using in class alongside, say, Bill Pollack’s The Boy Code. But I think there’s also a point to be made here about how gender inequality affects us all. Marshall — a professional athlete — embodies hegemonical masculinity and is at the tippy-top of a hierarchy that privileges men and masculine behavior. And, yet, his comment reveals the brutality of that system for even the supposed beneficiaries.

*Despite his eloquence on this issue, Marshall is not necessarily an ideal role model of feminist conduct. He has had a number of run-ins with the law for assault and domestic violence. He was subsequently diagnosed with borderline personality disorder (BPD) and seems to be doing quite well with the treatment. As the Hang Up and Listen hosts mention, one can hear a bit of the language of therapy in his statement, something more NFL players (and people in general) could benefit from.

University of Minnesota Sociology Ph.D. student Tim Ortyl died last week of natural causes related to epilepsy. I didn’t know Tim, but, by all accounts, he was a smart, fun, and kind guy and I know his family, friends, and colleagues will mourn his loss for some time to come. While others will commemorate Tim, as a sociologist and an epileptic myself, I would like to use this space to briefly discuss the lived experience of those with epilepsy.

Not all epilepsy is the same. People ranging from Harriet Tubman to Vladmir Lenin to Prince all have had epilepsy and, according to the Epilepsy Foundation, it affects 2.2 million Americans and 65 million people worldwide. However, “epilepsy” is a social construct and a false binary (i.e., one is “epileptic” or not) that refers to a wide spectrum of conditions. People are diagnosed as “epileptic” when they have had “two or more unprovoked seizures,” but the condition can range from those who have 2-3 seizures in their lifetime to those who have a hundred per day. As The Institute of Medicine writes in a report on the prevalence of epilepsy, “the 2.2 million prevalence estimate is most accurately viewed as approximating a midpoint in a wide potential range of 1.3 million to 2.8 million people with epilepsy.” Personally, I have had fewer than ten seizures in my life and it is “controlled” if I make sure to take a prescription, get adequate sleep, and remember to eat. Others have it much worse.

Depending on their severity of their condition, epileptics – like others with medical issues or disabilities – experience a range of challenges that may make life more difficult. These challenges include regular medical appointments, submitting to a range of unpleasant medical tests, and adjusting to new medications. And, of course, that is the condition for those who are privileged enough to have medical coverage. Many Americans and people in the developing world have to cope with epilepsy without the resources of medical care. In addition to the medical side, depending on state laws*, having a seizure can lead to have one’s driver’s license revoked. Outside of major metropolitan areas with extensive public transportation, not having a driver’s license puts you at a major disadvantage in terms of getting to work, picking up groceries, and performing the basic tasks of everyday life. In two different states, I have experienced having my license revoked. Throughout both experiences, I felt great embarrassment for not feeling like full-fledged adult and guilt for having to ask friends for rides all the time. Even though I knew rationally that I shouldn’t feel that way about something that was beyond my control, I did. Because of the practical and social consequences of a loss of license, some epileptics will choose to fight the ban in court, incurring major legal costs.

Finally, beyond the direct medical and legal consequences of being epileptic, there are significant social challenges. As Erving Goffman noted, physical disabilities always bear stigma. To be at risk of seizure means walking around with a sense of vulnerability to a potential failed public performance. In a society where individual control is so prized, to be as profoundly out of control as one is during a seizure carries with it feelings of shame and humiliation. Even well-intentioned people often deliver up negative social sanctions to epileptics. When I was on the job market in graduate school, one faculty member advised me not to mention my epilepsy to anyone (as if I bring it up a lot!), saying, “You don’t want people picturing you flopping around on the floor in the department.” Later, as a faculty member, (after a sleepless night and forgetting to take my medicine and not eating) I had a seizure while advising a student in my office. While I greatly appreciated the kindness shown by my colleagues and the poor student, the sympathy I received for what felt like a public display of mental and bodily weakness was difficult to accept without feeling diminished by pity. Moreover, I think that physical weakness and vulnerability is particularly difficult for men given social expectations of masculine control and physical strength. Stepping out of a basketball game because of an “aura” (the sense of an oncoming seizure) is not just a failed performance; it is a failure to “power through” and properly perform masculinity.

Because of the stigma associated with epilepsy, as Goffman would predict, nearly all of us who are epileptic actively engage in impression management to try to “save face.” To save face, we keep our condition secret if we can, emphasize that it is “controlled,” make self-deprecating seizure jokes, and try to explain how it is beyond our control. Like all emotion work, managing stigma around epilepsy can be exhausting. For people like me who can hide it for years at a time, it is only occasional work. For other epileptics, impression management is full-time work.

To be sure, these circumstances are common among people with many medical conditions. But the complexity of epilepsy and the challenges it poses to people with it are often unseen and may be affecting those close to us.

*In some states, simply having a seizure triggers the loss of a license. In others, it is only having a seizure despite medication. The driving ban can last from 6-12 months.

On Monday night, Jon Stewart said something I’ve heard a lot of lately: “These people are crazy.” The “these people” in question are the Congressional Republicans who have refused to allocate appropriations or enact a continuing resolution for the 2014 fiscal year until Democrats agree to defund Obamacare. As we all know, the disturbing consequence has been the government shutdown due to get worse next week if the current debt ceiling isn’t raised. Of course, Stewart doesn’t mean that they are literally insane. Like most Lefties who use that term, he means that their ideology is radically outside the mainstream and their tactics are unusual and highly risky. But Republicans rarely use “crazy” to describe Democrats, instead using “dangerous,” “radical,” or “socialist.”

Explaining Conservatives

It seems that the world according to Democrats is more comprehensible to Republicans than vice versa (an assertion supported by social psychologist Jonathan Haidt’s controversial studies of the moral foundations of political views). So, in recent months, Left-leaning publications, commentators, and academics have begun to ask, What has happened to the Republican Party? Paul Krugman makes a moral claim, arguing that “an almost pathological meanspiritedness” has infected “the soul” of the GOP. Others have written about the radicalization and politicization of institutions of conservative intellectualism like the Heritage Foundation, once a legitimate think tank and now the propaganda wing of the GOP. Some commentators like Slate’s Dave Weigel and the New Yorker‘s Ryan Lizza have argued that the extremism of the Republican Party stems from gerrymandering that produced disproportionately white, conservative, Christian, low population districts all too willing to elect radical anti-government congressmen. This perspective is certainly supported by a fair amount of empirical evidence showing that most of the partisan polarization of recent years is on the Republican side.

Conservative intellectuals (who tend to be more moderate) have also gotten in on the act of explaining the current crisis by pointing out the weakening of party power and the simmering resentment of “forty years of failure” in overturning the “New Deal-Great Society Leviathan.” As The New York Times’ Ross Douthat writes in an usually thoughtful op-ed, “So what you’re seeing motivating the House Intransigents today…is not just anger at a specific Democratic administration, or opposition to a specific program, or disappointment over a single electoral defeat … it’s a revolt against the long term pattern.”

What Can Sociology Tell Us?

This mad scramble to understand what’s going on in the Republican Party is reflective of the fact that we (perhaps especially sociologists) have done too little to theorize and document the dynamics of the American conservative movement. That is one of the central claims of an outstanding 2011 review by Neil Gross, Thomas Medvetz, and Rupert Russell. Here are three key take-aways from the article that can help us understand current developments:

1) Be careful defining “conservative”: Gross et al. reject three common definitions of conservative. The first sees the conservative movement as a “backlash” against progressivism (“attempting to stuff a rapidly changing American society back into the box of a white, theologically conservative small-town vision of the good”). They argue this definition assumes static definitions of progressive and conservative, which don’t match the historical reality. The second flawed definition is that conservatives are “supporters of free market capitalism” who simply exploit race and religion to secure working class support. Gross et al. claim that this definition gives short shrift to the sincerity of social claims of the conservative movement. A final definition holds that conservatives have different assumptions about human nature (it’s unchanging) and the moral order (there’s objective morality) from progressives. This definition assumes homogeneity and intellectual coherence within the movement.

Instead, they offer this definition: “conservatism is not a fixed category of belief or practice but a collective identity that evolves in the course of struggles…over meaning…” But it is an identity that is organized through social structures like social networks and formal organizations like the Republican Party or Tea Party groups. So, in analyzing the current situation, we must keep in mind that while conservatives tend to share an identity, ideology is not and never has been uniform either within the movement at any given moment or over time.

2) Framing Matters: One the main points of the article is that sociologists have not sufficiently recognized the contributions of conservative intellectuals to the movement. One of the main tasks for conservative intellectuals was to “[carve] out a viable identity for the movement” that would bridge the divide between libertarians (limited government in both fiscal and social issues) and traditionalists (social issue conservatives with free market concerns). Conservative intellectuals addressed this problem by reframing the concept of “elites” in a way that would satisfy both groups of conservatives:

“The danger in America lay not in great concentrations of wealth but in the growth of a political and cultural elite…that was more cosmopolitan than patriotic, soft on communism, driven to favor ill-fated social engineering schemes, and supportive of pernicious social trends like secularization.”

The success of this particular framing of “elites” help us understand how House Republicans would see themselves as taking a stand against elite power, while their liberal opponents also see themselves as standing up for the little guy.

3) Institutionalization Matters: As a movement almost entirely encased in the Republican Party, unlike, say, Occupy Wall Street, the American conservative movement has typically adopted electoral solutions. In other words, they’ve tried to back candidates and take control of the institutions of government. At the same time, seeking to counter “the dominance of liberal elites” in academia, the media, and policy institutes, conservative intellectuals encouraged the business community to help fund a “conservative counter-establishment.” This meant think-tanks like the Heritage Foundation and media outlets like Fox News. Today, Occupy has left behind some taglines (e.g., “the 99%”), but the radical wing of the conservative movement has a powerful place in Congress. The institutionalization of conservative views in media and think tanks have shaped the prevailing ideas among the conservative movement constituents, but also helped frame debates more widely. In other words, the effort to institutionalize as succeeded.

Taken together, the American conservative movement should be seen as an ever-changing and heterogeneous movement that has adopted effective framing and has been wildly successful in institutionalizing the movement. In other words, the movement has been anything but “crazy.” Holding aside the question of whether the movement’s ideas or tactics are good for society, they have been successful at doing what all movements aim to do: gain power.