Clearing Cap Space

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?

Is there Such a Thing as a “Tinder for Democracy”?

I have an curiosity about Tinder (strictly academic — I’m happily married), a dating app that lets you find singles (or “singles”) in your immediate vicinity and allows you to quickly zero in on the one you find most attractive.

An article in BetaBeat details how the site works:

You pick a gender (male, female or both), then decide how far or close you want them to be (10 to 100 miles away) and how old (18 to 50+.) It’s like ordering pizza. You can also write a tagline to describe yourself and add a few more photos for people who want to learn more about you(r looks) before making their choice.

Swipe right if you approve of someone’s appearance. Swipe left if you’re not into them. If you reject someone, the poor schmuck won’t be able to contact you. But if you both swipe right, you’ll be able to chat up a storm until you make plans for drinks at a mutually agreeable location.

What fascinates me about Tinder is that it’s a simple, elegant app that does one thing, facilitate hooking up. Across the world, organizations and city governments are engaging in “hackathons” designed to build apps to help solve civic problems. The White house just concluded their National Day of Civic Hacking where programmers/coders in 103 cities set to work on solving civic problems. The coders created an impressive set of apps and sites designed to address pressing local and regional issues. However, none of these projects, as important as they are will have the social impact of a “hookup app.”

I’m afraid our efforts to change political dynamics using social media is still reckoning with a question posted in a tweet by Jeff Jarvis:

Soccer Isn’t Popular in the US Because the Wrong People Watch it

If you listen to traditional media channels, you may be surprised to learn that soccer is actually a pretty big deal in the United States. Take for instance, Stephen Dubner’s usually engaging and informative Freakonomics radio, who trotted out a tired canard about how unpopular soccer is in the United States. The story starts with the ludicrous notion that the World Cup is unpopular because it isn’t American football.

It’s no secret that soccer continues to lag behind other U.S. sports in viewership and enthusiasm. For instance, 111.5 million Americans sat down to watch Super Bowl XLVIII in 2014. Meanwhile, only 24.3 million watched the 2010 World Cup Final.

I believe this is known as a “straw man” argument. Soccer is not as popular as American football? Nothing is as popular as American football! The 24.3. million people tuned in to the final of the 2010 FIFA World Cup (a 41 percent increase over the 2006 cup, by the Way) is comparable to Game 7 of the 2013 NBA finals which captured 26 million viewers and more than the final game of the 2013 World Series which captured 19.2 million viewers. By contrast, it is much great than the 8.2 million that watched the last game of the 2013 NHL Stanley Cup Finals. By Dubner’s ludicrous standard, no sport is popular in the United States because it isn’t American football.

Later on, Dubner cites a Harris poll noting that only three percent of Americans cite soccer as their favorite sport compared to 30 percent who cite Pro Football and 11 percent who cite College Football. More straw man. That same poll reports that only 4 percent cite hockey as their favorite sport and 7 percent cite basketball. Not to mention that this was an online poll conducted in English.. but we’ll get to that.

This is why a more interesting conversation about soccer in the United States has shifted from “soccer isn’t popular” to “soccer is only popular every four years.” Political Scientist Andrei Markovitz talks of the Olypianization of soccer, whereby Americans tune in to the big event (World Cup) every four years and ignore the sport in the interim (kinda like American politics.. sorry couldn’t resist). But even that isn’t true… the landscape is shifting rapidly, only it’s a little hard to tell because soccer is so fragmented.

First, soccer is a global game so it’s played all over the world. Second, the way soccer works is that there are really two leagues, one based on clubs and one based on country. The biggest event for countries is this month’s World Cup, but national teams play in tournaments between World Cups. There are regional tournaments aside from qualification for the World Cup itself. In the Central American, Caribbean and North American region — CONCACAF, there’s a tournament called the Gold Cup. In South American it’s called Copa Libertadores America, in Africa it’s the African Cup of Nations, and so on… In the US, these tournaments do pretty well. The CONCACAF gold cup does respectable, if not spectacular, ratings on TV in the United States. In 2013, 4.9 million people watched the final between the US and Panama. The 2012 Euro Cup averaged over 1 million viewers on ESPN, double that of 2008.

The other type of competition in world soccer is league competitions. Here, soccer is gaining ground as well. If you compare the TV ratings of any one soccer league to traditional US sports, they don’t fare well. In the 2012 regular season, the NBA average a rating of 3.3 (roughly between 3-4 million US households). That’s a pretty strong compared to the ratings of our domestic soccer league (Major League Soccer – MLS’s). MLS’s meager ratings of between 100,000 and 300,000 households seems small. But the soccer space in the US is divided between a number of leagues. So to be fair, you add MLS’ 200,000 viewers to the 500,000 to 700,000 that watch the English Premiere League on Saturday mornings and the 800,000 to 1,000,000 that watch the Mexican League (LIGA MX) and soccer on a regular basis begins to approach the NBA in magnitude.

So why the view that the sport is irrelevant, even among people who should know better? The perception that soccer is “small time” in the US sports landscape is driven by two key factors. One, its popularity is fragmented as I’ve already discussed, so there’s not one league to focus on, bur rather a multitude of “foreign” leagues to discuss. But I think the other explanation is more pernicious, its perception comes for society’s sustained marginalization of “foreigners,” particularly Mexican immigrants in the United States. It is a means of drawing boundaries of “Americanness” around sports. Unwittingly, it is a way of identifying based on identity groups that suggest race and ethnic categorization, but do not explicitly state it.

Most telling in the Freakonomics radio piece is this throwaway line where Dubner’s doubts the prospect of soccer becoming as popular as American football.. as if that were the standard:

let’s be honest, it probably won’t. Many of the people who are most fanatical about the sport in the US have some kind of tie to Europe or South America or Africa.

This is intended to suggest that only those with close ties to “foreigners” appreciate the game.. a fallacy that need it’s own unpacking. But let’s take this at face value. Does he realize how many people he is talking about? There are roughly 50 million Latinos in the United States, many of whom “have strong ties” to soccer loving countries, primarily Mexico. I’m sure a smart guy like Dubner knows that Mexico is actually in North America so the exclusion of Mexico must be because it doesn’t fit the narrative they are trying to tell about the unpopularity of the sport.

Here’s the problem: Soccer is enjoyed by people who inhabit the United States, but because many of those people may be first or second generation immigrants, and in many cases many not speak English or have English as a primary language, it’s not culturally relevant to include in debates about the popularity of sport. Close to 5 million people in the US watched the Liga MX (Mexican soccer league) final between Leon and Pachuca, a number that compares favorably with the ratings for MLB playoff games, but it’s irrelevant because either it was watched in Spanish or watched by Spanish-speakers, I’m not sure which.

Sports media constantly refer to a “big four” American sports (Football, Basketball, Baseball and Hockey). Soccer when mentioned is still talked about as a foreign entity. A few days ago ESPN commentator Michael Wilbon opined that US National Soccer Team coach Jurgen Klinsman to “get the hell out of America” because he suggested Kobe Bryant should not be given a contract extension based on past performance. The inference was that this foreigner shouldn’t be commenting on American games.

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So if a person on US soil watches a game in Spanish, are they a foreigner? Are they tuning in to a sport broadcast in a foreign language and that’s what makes it foreign? This narrative of a “big four” underscores a troubling assumption. A sport is only truly “popular” in the United States if English-speaking, native born people follow it. When they do, then we can call it an “American sport.” I’d argue that there is a deep cultural marginalization going on when the preferred sport of the largest-minority ethnic group in the United States is viewed as marginal because it’s not viewed by “the wrong people.” To say people don’t follow soccer in the United States is a veiled way of saying that it’s not viewed by people that matter.

The sociologist Eduardo Bonilla-Silva has a great term for what I think is going on: white habitus. This is the idea that the “separate residential and culture life” (103) of Whites creates a:

“racialized, uninterrupted socialization process that conditions and creates whites’ racial taste, perceptions, feelings, and emotions and their views on racial matters” (104)

A habitus that reinforces notions of what cultural norms and tastes are “American” and which are “foreign” is reinforced by this social and cultural isolation. To personally not like the game isn’t evidence of cultural bias, but arguing that the sport isn’t popular even when there is evidence to the contrary, suggests an ignorance derived from cultural isolation. Commentators on traditional media outlets (ESPN and FOX, for instance) as a space of cultural life reinforces the idea that to be American means to follow some sports and not others. Mike Wilbon is paid to “act a fool” for lack of a better term, but that doesn’t mean that he’s being culturally arrogant when he claims to know what constitutes an “American” sport. Things are changing however and I suspect that if four year’s time, when the 2018 World Cup kicks off in Russia, I won’t be compelled to write a post like this.

The Republican’s Latino Problem

Danny Vinik at the New Repubic has an interesting piece that makes the claim that the Republican Party’s problem with Latinos rest less on immigration reform and more on social spending and persistent messaging that is perceived as hostile to Latinos. The problem is more with the party’s base than with the party leadership:

The Brookings/PRRI poll found that 50 percent of Republicans believe immigrants are a burden on the country, compared to just 44 percent who say they strengthen the nation. On the other hand, 73 percent of Democrats say that immigrants strengthen the country.

This statistic highlights a dilemma for the party, appeal to a big chunk of the party base that is hostile to immigrants while attracting those immigrants to begin with. In the past, this dilemma was resolved by parties through patronage. David Roediger’s brilliant book, the Wages of Whiteness, tells of the patronage system as a ladder of opportunity for Irish immigrants, one many found more preferable than partnering with blacks to agitate for better wages. Hence, they chose the “wages of Whiteness” over actual wages. Neither the Republicans or the Democrats have patronage to give. I’m skeptical that an “improvement of manners,” to quote Richard Rorty, would do much to change the political equation.

How Healthy is Your Democracy?

If you’re not busy and are interested in democratic outcomes, you should really read this important piece by Ben Page and Martin Gilens.

The authors test four preeminent theories of democratic influence in which different actors have disproportionate influence in the American political system (average voters, economic elites, general interest groups and business oriented interest groups). Here’s the takeaway:

Economic elite policy preferences strongly correlate with “average” citizen policy preferences, but aggregated interest groups preferences do not. Business interest group influence does not always correlate with economic elite influence (economic elites want all government spending reduced and business interest groups want spending on their areas of influence).

When it comes of policy outcomes, economic elites and interest groups have the most influence…

a proposed policy change with low support among economically elite Americans (one-out-of-five in favor) is adopted only about 18 percent of the time, while a proposed change with high support (four-out-of-five in favor) is adopted about 45 percent of the time. Similarly, when support for policy change is low among interest groups (with five groups strongly opposed and none in favor) the probability of that policy change occurring is only .16, but the probability rises to .47 when interest groups are strongly favorable (see the bottom two panels of Figure 1.)

This is an empirical confirmation of my “NCAA Tournament” view of American politics. The “3 seed” usually beats the “14th seed,” but not always. A good way of measuring democratic health is how often “bracket busters” occur.

Search for a New Journal Editor – Journal of Integrated Social Sciences

The Journal of Integrated Social Sciences (JISS) is searching for a new Political Science editor. The journal is a web-based, peer-reviewed international journal committed to the scholarly investigation of social phenomena.

In particular, JISS aims to predominantly publish work within the following social science disciplines: Psychology, Political Sciences, Sociology, and Gender Studies. A further goal of JISS is to encourage work that unites these disciplines by being either (a) interdisciplinary, (b) holistically oriented, or (c) captive of the transformative (developmental) nature of social phenomena. Aside from the theoretical implications of a particular study, we are also interested in serious reflections upon the specific methodology employed – and its implications on the results. JISS encourages undergraduate and graduate students to submit their best work under the supervision of a faculty sponsor. More details can be found at www.jiss.org.

General responsibilities include:

• The day to day running of the journal political science editorial office, including managing article peer review, liaison with authors, editing of articles, and preparation of editorial copy.
• Contributing to strategic development of the Journal
• Attracting submissions and themed issue proposals to the journal to ensure continued relevance and quality of content
• Promotional activities, including attending conferences

To make an application, you will need to send a statement outlining your reasons for seeking the position, and overall objectives as political science editor of JISS.

To discuss further or submit an application, please contact Dr. Jose Marichal (current Political Science Divisional Editor of JISS) ~ marichal@clunet.edu.

Making Sense of the Madness

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?

Dunkin' Robot

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).

Olympic Hockey Semi-Finals Update

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.

Semi-Finals

Filling in the Bracket (Hockey Predictions, Part II)

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.

Bracket

Predicting Olympic Hockey Supremacy

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?

Oshie celebrates
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).

Table 1

The Model

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

Table 2

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.

Table 3

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

Table 4