Spotlight on…Letter-Writers

Italo Calvino: Letters, 1941-1985

Italo Calvino:
Letters, 1941-1985

For the final post in this series, we turn to the raw materials of biography with two volumes of collected letters. Private letters often give a very different picture from public writings – less guarded, more spontaneous and immediate. They can shed light on the development of ideas and concepts over time, revealing the struggle so often obscured by the perfection of the finished work. These letters are a vital primary source for biographers. It seems certain that the rise of email and decline of letter-writing will profoundly affect the work of future biographers. Will email prove as durable as paper? Will the sheer volume of electronic correspondence defeat even the most dedicated researchers? It may be decades before the answers to these questions are clear. For now, we are still seeing significant collections of letters published, allowing readers to make their own first-hand acquaintance with Carl Jung and Italo Calvino.

Analytical Psychology in Exile collects the correspondence between Jung and one of his most brilliant students, Erich Neumann. The letters span nearly three decades, offering a fascinating insight into the maturing of Jung’s theories as he shares them with, and defends them against, the younger Neumann. Jung has been accused of sympathy with the Nazi regime in Germany, and of anti-semitism, yet here we see him in dialogue with a Zionist Jew who was forced to flee Germany for Tel Aviv in 1934. Inevitably, given the impending catastrophe, these letters touch on complex and controversial issues such as the psychology of fascism and anti-semitism, and the crushing experience of exile. Neumann lived to see the founding of the state of Israel and died there in 1960; although nearly thirty years his senior, Jung outlived him by a year.

While Jung passed the Second World War in the comparative security of Switzerland, Italo Calvino experienced first-hand the dangers of life in Fascist Italy. In Italo Calvino: Letters, 1941-1985, that experience is most profoundly seen in an absence, the lack of any correspondence from his years in hiding as a member of the Italian resistance. Although his letters rarely refer to the war, his time fighting with the resistance resulted in a deep philosophical and personal commitment to communism. We see his disillusion and resignation from the Communist Party following the crushing of the Hungarian revolution of 1956 and his excitement at the fresh hope offered by the événements of 1968 in Paris. The course of his writing, from the autobiographical realism of The Path to the Nest of Spiders to the dazzling metafiction of If On A Winter’s Night A Traveller, perhaps reflects his withdrawal from political life. Nonetheless, Calvino remained an acute critic and his letters are filled with sharp assessments of post-war Italy’s vibrant cultural life.

Mathematics Awareness Month 2015: Math Drives Careers

Internet search, pharmaceuticals, insurance, finance, national security, medicine, ecology. What is the link between these diverse career fields? Students graduating with a mathematical sciences degree can find a professional future in all of these fields, and a wide range of others as well. This year’s Mathematics Awareness Month takes a step out of the classroom to show just where mathematics can lead after graduation.

Mathematics Awareness Month is an annual celebration dedicated to increasing public understanding of and appreciation for mathematics. The event, which started in 1986 as Mathematics Awareness Week, adopts a different theme each year. This year’s theme is “Math Drives Careers,” and PUP is excited to bring you a series of guest posts from our authors. Check back all this month for posts about using math to raise revenues, to understand sports and economics, and to solve complex problems.

The organizers of Mathematics Awareness Month explain the importance of mathematics in today’s workforce:

“Innovation is an increasingly important factor in the growth of world economies. It is especially important in key economic sectors like manufacturing, materials, energy, biotechnology, healthcare, networks, and professional and business services. The advances in and applications of the mathematical sciences have become drivers of innovation as new systems and methodologies have become more complex. As mathematics drives innovation, it also drives careers.”
Check out this official Mathematics Awareness Month poster, which includes career descriptions for 10 individuals who used their love for math to find rewarding careers:



Follow along with @MathAware and take a look at Math Awareness Month on Facebook.

What is De-extinction? #MammothMonday

To celebrate the release of Beth Shapiro’s How to Clone a Mammoth: The Science of De-Extinction, we will be providing a variety behind-the-scenes footage, Q&As, pictures, and videos every Monday. Last week, we posted the wonderful trailer for the book. Since then, the topic of De-extinction has been captivating scholars and animal-lovers alike. From a recent Earth Times piece highlighting De-extinction:

Professor George Church plans to insert these genes into Asian elephant embryos and study how they develop. His viewpoint is that we have caused so much extinction, the means of recreating recently extinct (about 3,300 years only according to remains on Wrangel Island in Siberia) species should be useful technology. The name of the worthy-enough game is “De-extinction.”

Today, we are excited to share an original video of Beth Shapiro explaining what exactly De-extinction is, the first in a series of six original videos tied to her book:

John Nash wins Abel Prize from the Norwegian Academy of Science and Letters

John Nash

Princeton University mathematician, John Nash, has won one of the highest honors in the field, an Abel Prize from the Norwegian Academy of Science and Letters. Nash will share the prize with colleague Louis Nirenberg. The academy stated, “Their impact can be felt in all branches of the theory…[T]he widespread impact of both Nash and Nirenberg on the modern toolbox of nonlinear partial differential equations cannot be fully covered here.”

Read more about Nash’s work and the award, which includes an $800,000 prize, here.

Davidson student hangs onto 97 percent March Madness ranking

Are you still mourning the loss of your perfect bracket after the multiple upsets this March Madness season? Even before the Villanova and NC State match up on Saturday, 99.3 percent of brackets were busted. As experts deem a perfect March Madness bracket impossible, having a nearly perfect bracket is something to brag about. Today, we hear from David College student Nathan Argueta, who argues that knowing a thing or two about math can help with March Madness strategy.


March Mathness: Calculating the Best Bracket

First and foremost… I am far from a Math Major and, prior to this class, the notion that math and sports going hand in hand seemed much more theoretical than based in reality. Now, 48 games later and a 97.2% ranking percentage on ESPN’s Bracket Contest has me thinking otherwise.

In Finite Math, we have explored the realms of creating rankings for teams based on multiple factors (win percentage, quality wins, etc.). Personally, I also take into account teams’ prior experience in the NCAA Tournament. Coaches with experience in the Sweet 16, Final Four, and Championship Game (like Rick Pitino out of Louisville) also factored into my decisions when deciding close games. Rick Pitino has made the Sweet Sixteen for each of the past four years. With a roster whose minutes are primarily distributed amongst second and third year players (players who have had success in the NCAA tournament in the past couple of years) I found it difficult to picture Louisville losing to either UCI, UNI, or even the upcoming battle against upstart NC State (who have successfully busted the majority of brackets in our class’s circuit by topping off Villanova).

In theory, the quest to picking the best bracket on ESPN begins and ends with establishing rankings for each team in the contest. Sure there are four of each seeding (1’s, 2’s, etc.), yet these rankings are very discombobulating when attempting to decide which team will win between a 5th seed and a 12th seed or a 4th seed and a 13th seed. One particular matchup that I found extremely interesting was the one between 13th seeded Harvard and 4th seeded UNC. Gut reaction call—pick UNC. UNC boasts a higher ranking and has ritual success in the postseason. But hold on—Harvard had a terrific record this year (much better than UNC’s, albeit in an easier conference). The difficult thing about comparing Harvard and UNC, however, became this establishment of difficulty of schedule. I nearly chose Harvard, were it not for the fact that Harvard got beaten by about 40 points against UVA while UNC put up more of a fight and only lost by 10 points.

In order to pick the perfect bracket (which mind you, will never happen), categorizing and ranking teams based on their wins against common opponents with prior sports knowledge is imperative. My school pride got the better of me when I chose Davidson to advance out of the Round of 64 against Iowa simply because I disregarded factors like momentum, size, and location. Looking back, it is no wonder that Davidson lost by over 30 points in what many pundits were looking to be a potential upset match. While mathematically our team’s chances could have more than competed against Iowa, in reality our season was spiraling downwards out of control since the second round of the Atlantic 10 Tournament in which we hardly beat out a surprising La Salle team and got annihilated by an injury plagued VCU team that we shut-out just nine days before. Moral of the story… brackets will be brackets and while math can certainly guide you towards a higher ranking in your class pool, you can kiss perfection good-bye. This is March Madness.

Presenting the New Trailer for Beth Shapiro’s “How to Clone a Mammoth”

Should we clone extinct animals? Evolutionary biologist and “ancient DNA” researcher Beth Shapiro’s highly anticipated How to Clone a Mammoth: The Science of De-Extinction takes apart an idea that not so long ago seemed more fiction than science. Now, several teams of researchers are working to reconstruct the mammoth genome. How to Clone a Mammoth is making its debut with an array of coverage, including a feature in yesterday’s Sunday Times. From the article:

What excites some scientists, and disturbs others, is that the genome could one day become a template to recreate real mammoths — or something like them.
In her new book, How To Clone a Mammoth, Beth Shapiro of the University of California, an expert on ancient DNA, said: “If we really want to bring mammoths back to life, then we’re in luck, as far as DNA preservation goes. Some mammoths lived in places where their bones and carcasses were buried in permafrost, like being stuck in a freezer for 30,000-plus years.
“It’s in pretty shoddy condition, so hard to piece together, but if we sort through these tiny pieces, finding where they fit along the elephant genome, then we can slowly build a lot of the mammoth genome.”

We are delighted to share the book’s wonderful new trailer:


Sunny, Spring Book List

The Bee
Say goodbye to cold, dreary winter and hello to spring. Welcome the season by checking out Princeton University Press’s selection of natural history books. Wondering what bird you hear chirping? Download BirdGenie Backyard Birds East and West and find out. Some choices to get you out of the house and into nature:


The Bee:
A Natural History

Noah Wilson-Rich
With contributions from Kelly Allin, Norman Carreck & Andrea Quigley
“The natural history of solitary, bumble, honey and stingless bees is as gripping as our lengthy alliance, as urban beekeeper Noah Wilson-Rich and contributors show in this charming compilation. They cover evolution, biology (including a unique proboscis made of two organs), behaviours (such as honey bee ‘quacking’), the causes of catastrophic die-offs, and more.”–Barbara Kiser, Nature



Birds of Australia:
A Photographic Guide

Iain Campbell, Sam Woods & Nick Leseberg
With photography by Geoff Jones
“Written primarily for the visiting birder, reading a copy of this book will fire up anyone’s desire to get out into the field to see more of our fabulous birds.”–Sean Dooley, Australian Bird Life


bookjacket Britain’s Butterflies:
A Field Guide to the Butterflies of Britain and Ireland

Fully Revised and Updated Third edition
David Newland, Robert Still, Andy Swash & David Tomlinson
“The images in this pocket-sized photo-guide are excellent and include pictures of eggs, chrysalids and caterpillars of all breeding species. Comparing very similar species can be difficult, but computer mock-ups helpful place specimen in situ. Clear text and page design make the book easy and fun to use.”BBC Wildlife magazine


bookjacket Britain’s Hoverflies:
A Field Guide

Revised and Updated Second edition
Stuart Ball & Roger Morris
Praise for the previous edition: “The latest field guild from the excellent Wildguides. . . . Beautifully and clearly laid out.”–Charlie Moores, Talking Naturally


Spotlight on…Scientists

Nikola Tesla, by W. Bernard Carlson

Nikola Tesla
by W. Bernard Carlson

Genius is no guarantee of public recognition. In this post we look at the changing fortunes and reputations of three very different scientists: Alan Turing, Nikola Tesla, and Albert Einstein.

With the success of the recent movie, the Imitation Game (based on Andrew Hodges’ acclaimed biography Alan Turing: The Enigma), it’s easy to forget that for decades after his death, Turing’s name was known only to computer scientists. His conviction for homosexual activity in 1950s Britain, his presumed suicide in 1954, and the veil of secrecy drawn over his code-breaking work at Bletchley Park during the Second World War combined to obscure his importance as one of the founders of computer science and artificial intelligence. The gradual change in public attitudes towards homosexuality and the increasing centrality of computers to our daily lives have done much to restore his reputation posthumously. Turing received an official apology in 2009, followed by a royal pardon in 2013.

Despite enjoying celebrity in his own lifetime, Nikola Tesla’s reputation declined rapidly after his death, until he became regarded as an eccentric figure on the fringes of science. His legendary showmanship and the outlandish claims he made late in life of inventing high-tech weaponry have made it easy for critics to dismiss him as little more than a charlatan. Yet he was one of the pioneers of electricity, working first with Edison, then Westinghouse to develop the technology that established electrification in America. W. Bernard Carlson’s Nikola Tesla tells the story of a life that seems drawn from the pages of a novel by Jules Verne or H. G. Wells, of legal battles with Marconi over the development of radio, of fortunes sunk into the construction of grandiose laboratories for high voltage experiments.

By contrast, the reputation of Albert Einstein seems only to have grown in the century since the publication of his General Theory of Relativity. He is perhaps the only scientist to have achieved iconic status in the public mind, his face recognized as the face of genius. Children know the equation e=mc2 even though most adults would struggle to explain its implications. From the publication of the four 1905 papers onwards, Einstein’s place in scientific history has been secure, and his work remains the cornerstone of modern understanding of the nature of the universe. We are proud to announce the publication of a special 100th anniversary edition of Relativity: The Special and the General Theory, and the recent global launch of our open access online archive from the Collected Papers of Albert Einstein, the Digital Einstein Papers.

Q&A with Ian Morris, author of Foragers, Farmers, and Fossil Fuels: How Human Values Evolve

Princeton University Press recently had the opportunity to talk with Ian Morris about his new book, Foragers, Farmers, and Fossil Fuels: How Human Values Evolve.

Foragers, Farmers, and Fossil Fuels

In your book you look at the evolution of human values over tens of thousands of years. Can you briefly say why and how values change? Isn’t morality universal and unchanging?

The answer to the last part of this question is easy: yes and no. I say yes because in one sense, morality certainly is universal and unchanging. Our human values are the outcome of millions of years of evolution. Animals that were born with genes that predisposed them to value fairness, love, honor, decency, and a host of related virtues tended to flourish, while animals that did not value fairness, etc., tended not to flourish. As a result, a disposition toward these prosocial attitudes spread through the gene pool, and almost all humans share these same core values. The reason I also say no, though, is because the ways people have interpreted fairness, etc., have varied wildly through time. Few historians dispute this; but fewer still have seen that what causes values to change is not the deep thoughts of philosophers but the most basic force of all–energy. As humanity has moved from foraging through farming to fossil-fuel use, we have found that different levels of energy capture call for different kinds of social organization, and that these different kinds of organization favor very different interpretations of human values. To foragers, fairness often means that everyone should receive equal shares of food, respect, and other good things, but to people in farming society, fairness often means that people should receive very different shares, because they are felt to deserve different shares. Men deserve more than women, the rich deserve more than the poor, the free deserve more than the enslaved, and so on through too many categories to count. Foragers and farmers feel the ways they do not because the former are all saints and the latter all sinners, but because it would be almost impossible to run a foraging society like a feudal monarchy and almost impossible to run a farming society as a band of equals. Foragers who lean toward equality and farmers who lean toward hierarchy itend to outperform and replace foragers and farmers who do not. In our own age of fossil fuels, values have continued to mutate. We tend to believe that fairness means that everyone should receive somewhat equal–but not too equal–shares of food, respect, and other good things. Anthropologists who spend time in foraging or farming societies often feel as if they have stepped into alien worlds, where values are upside-down; and people from most periods in the past would have felt exactly the same way about us.

In our current Fossil Fuel age of values, you argue that violence and inequality have diminished greatly from past periods. That seems very counter-intuitive. Can you elaborate?

A lot of people today are nostalgic for a simpler, vanished, preindustrial world, and there are ways in which they are right to be so; but not if they value peace, prosperity, or (on the whole) equality. Across the last fifty years, social scientists have accumulated data that allow us to measure wealth, inequality, and rates of violence in the past. The results are surprising–so much so that they can seem, as you suggest, counterintuitive. Foraging societies tended to be quite equal in wealth, if only because almost everyone was desperately poor (by one calculation, the average income was the equivalent of about $1.10 per day). They also tended to be very violent (by many calculations, more than 10 percent of foragers died violently). Farming societies tended to be less violent than foraging societies (5 percent rates of violent death were probably not uncommon) and not quite so poor (average incomes above $2.00 per day were common); but they were also massively unequal, regularly having tiny elites that owned thousands of times more than the ordinary peasant Fossil fuel societies, by contrast, are the safest and richest the world has ever seen, and are also more equal than all but the simplest foraging groups. Globally, the average person earns $25 per day and stands a 0.7 percent chance of dying violently, and in some countries progressive taxation has pushed income inequality down close to levels not seen since the simplest foraging societies (even if it is now again on the rise). In every era before AD 1800, life expectancy at birth averaged less than 25 years; now it is 63 years. Despite all the things we might not like about our own age, it would have seemed like a magical kingdom to people in the past.

What are some of the ways our values might change as we move away from a reliance on fossil fuels?

No one knows what the future will bring, but there are plenty of signs that we are rapidly moving beyond fossil fuels. I argue in this book that changes in the amount of energy humans harvest from the world pushes them into new kinds of organizations which in turn favor different interpretations of core human values; if this is right, we might expect the 21st century to see the biggest and profoundest transformation in values in history. The industrial revolution released a flood of energy in the 19th and 20th centuries, which favored societies that evolved toward democracy, rule of law, peace, freedom, and gender equality; the big question is whether the 21st century will see these trends going even further, or whether it will see them going into reverse. The answer, I suggest, is that it all depends. There are signs that in the short term–roughly the next generation–we will see increasing inequality and increasing acceptance that such inequality is right, along with increasing instability and violence. In the medium term–the next two or three generations–we may see the values of the fossil-fuel age go into overdrive; but in the longer term–say the next century or so–the transformations may become so massive that it no longer makes much sense to speak of human values at all, because what it means to be a human being might change more in the next 100 years than it has done in the previous 100,000.

bookjacket Foragers, Farmers, and Fossil Fuels:
How Human Values Evolve

Updated edition
Ian Morris


Using math for March Madness bracket picks

The countdown to fill out your March Madness brackets is on! Who are you picking to win it all?

Today, we hear from Liana Valentino, a student at the College of Charleston who works with PUP authors Amy Langville and Tim Chartier. Liana discusses how math can be applied to bracket selection.

court chalk

What are the chances your team makes it to the next round?

The madness has begun! Since the top 64 teams have been released, brackets are being made all over the country. As an avid college basketball fan my entire life, this is always my favorite time of the year. This year, I have taken a new approach to filling out brackets that consist of more than my basketball knowledge, I am using math as well.

To learn more about how the math is used to make predictions, information is available on Dr. Tim Chartier’s March Mathness website, where you can create your own bracket using math as well!

My bracket choices are decided using the Colley and Massey ranking methods; Colley only uses wins and losses, while Massey integrates the scores of the games. Within these methods, there are several different weighting options that will change the ratings produced. My strategy is to generate multiple sets of rankings, then determine the probability that each particular team will make it to a specific round. Using this approach, I am able to combine the results of multiple methods instead of having to decide on one to use for the entire bracket.

Choosing what weighting options to use is a personal decision. I will list the ones I’ve used and the reasoning behind them using my basketball awareness.


Winning games on the road should be rewarded more than winning games at home. Because of that, I use constant rates of .6 for a winning at home, 1.6 for winning away, and 1 for winning at a neutral location; these are the numbers used by the NCAA when determining RPI. I incorporate home and away weightings when performing other weighting methods as well.


Margin of victory is another factor, but a “blow out” game is defined differently depending on the person. With that in mind, I ran methods using the margin of victory to be both 15 and 20. This means if the margin of victory if 15, then games with a point differential of 15 or higher are weighed the same. These numbers are mainly from personal experience. If a team wins by 20, I would consider that a blowout, meaning the matchup was simply unfair. If a team loses by 15, which in terms of the game is five possessions, the game wasn’t necessarily a blow out, but the winning team is clearly defined as better than the opposition.

In addition to this, I chose to weight games differently if they were close. I defined a close game as a game within one possession, therefore three points. My reasoning behind this was if a team is blowing out every opponent, it means those games are obviously against mismatched opponents, so that does not say very much about them. On the other hand, a team that constantly wins close games shows character. Also, when it comes tournament time, there aren’t going to be many blow out games, therefore teams that can handle close game situations well will excel compared to those who fold under pressure. Because of this, I weighted close games, within three points, 1.5, “blow out” games, greater than 20 points, .5, and any point differential in between as 1.


Games played at different points in the season are also weighted differently. Would you say a team is the same in the first game as the last? There are three different methods to weight time, as provided by Dr. Chartier using his March Mathness site, linearly, logarithmically, and using intervals. Linear and logarithmic weights are similar in the fact that both increase the weight of the game as the season progresses. These methods can be used if you believe that games towards the end of the season are more important than games at the beginning.

Interval weighting consists of breaking the season into equal sized intervals and choosing specific weightings for each. In one instance, I weighted the games by splitting the season in half, down weighting the first half using .5, and up weighting the second half using 1.5 and 2. These decisions were made because during the first half of the season, teams are still getting to know themselves, while during the second half of the season, there are fewer excuses the make. Also, the second half of the season is when conference games are played, which are generally considered more important than non-conference games. For the people that argue that non conference play is more important because it is usually more difficult than in conference play, I also created one bracket where I up weight the first half of the season and down weight the second half.


The last different weighting method used was incorporating if a team was on a winning streak. In this case, we would weight a game higher if one team breaks their opponents winning streak. Personally, I defined a winning streak as having won four or more games in a row.

I used several combinations of these various methods and created 36 different brackets that I have used to obtain the following information. Surprisingly, Kentucky only wins the tournament 75% of the time; Arizona wins about 20%, and the remaining 5% is split between Wisconsin and Villanova. Interestingly enough, the only round Kentucky ever loses in is the Final Four, so each time they do make it to the championship, they win. Duke is the only number 1 seed never predicted to win a championship.

Villanova makes it to the championship game 70% of the time, where the only team that prevents them from doing so is Duke, who makes it 25% of the time. The remaining teams for that side of the bracket that make it are Stephen F. Austin and Virginia, both with a 2.5% chance. Kentucky makes it to the championship game 75% of the time, while Arizona makes it 22%, and Wisconsin makes it 3%. However, if Arizona makes it the championship game, they win it 88% of the time. Furthermore, Wisconsin is predicted to play in the championship game once, which they win.

The two teams Kentucky loses to in the Final Four are Arizona, and Wisconsin. During the final four, Kentucky has Arizona as an opponent 39% of the time, where Arizona wins 50% of those matchups. Kentucky’s only other opponent in the final four is Wisconsin, where Wisconsin wins that game only 5% of the time. On the other side, Villanova makes it to the final four 97% of the time, where the one instance they did not was a loss to Virginia. Villanova’s opponent in the Final Four is made up of Duke 72%, Gonzaga 19%, Stephen F. Austin 6%, Utah at 3%. The only seeds that appear in the Final Four are 1, 2, and one 12 seed, Stephen F. Austin one time.

During the Elite 8, Duke is the only number 1 seed that does not make it 100% of the time, with Utah upsetting them in 17% of their matchups. The other Elite 8 member is Gonzaga 97% of the time. Kentucky’s opponent in this round is Notre Dame 47% and Kansas 53% of the time.

In the Sweet 16, there are eight teams that make it every time: Kentucky, Wisconsin, Villanova, Duke, Arizona, Virginia, Gonzaga, and Notre Dame. Kansas is the only number 2 seed not on the list as Wichita State is predicted to beat them in 8% of their matchups. Kentucky’s opponent in the Sweet 16 is Maryland 39%, West Virginia 36%, Valparaiso 14%, and Buffalo 11%. Valparaiso is the only 13 seed predicted to make it to the Sweet 16. Villanova’s opponent is either Northern Iowa 61% or Louisville 38%. Duke appears to be facing either Utah 67%, Stephen F. Austin 19%, or Georgetown 14%.

Now, for the teams that make it into the third round. I’m not sure how many people consider a 9 seed beating an 8 seed an upset, but the number 9 seeds that are expected to progress are Purdue, Oklahoma State, and St. John’s. In regards to the 10 seed, Davidson is the most likely to continue with a 47% chance to move past Iowa, which is the highest percentage for an upset not including the 8-9 seed matchups. Following them is 11 seed Texas, who have a 42% of defeating Butler. For the 12 seeds, Buffalo is the most likely to continue with a 36% chance of beating Virginia. The 13 seed with the best chance of progressing is Valparaiso with 19% over Maryland. Lastly, the only 14 seeds that move on are Georgia State and Albany, which only happens a mere 8% of the time.

In general, Arizona seems to win the championship when using Massey and linear or interval weighting without home and away. This could be because most of their losses happen during the beginning of the season, while they win important games towards the end. Using the Colley method is when most of the upsets are predicted. For example, Stephen F. Austin making it to the championship game happens using the Colley logarithmic weighting. Davidson beating Iowa in the second round is also found many times using different Colley methods.

Overall, there are various methods that include various factors, but there are still qualitative variables that we don’t include. On the other hand, math can do a lot more than people expect. Considering Kentucky is undefeated, I presumed the math would never show them losing, but there is a lot more in the numbers than you think. Combining the various methods on 36 different brackets, I computed the probabilities of teams making it to specific rounds and decided to make a bracket using the combined data. This makes it so I don’t have to decide on solely one weighting that determines my bracket; instead, I use the results from several methods. Unfortunately, there is always one factor we cannot consider, luck! That is why we can only make estimates and never be certain. From my results, I would predict to see a Final Four of Kentucky, Arizona, Villanova, Duke; a championship game of Kentucky, Villanova; and the 2015 national champion being Kentucky.



Cinderella stories? A College of Charleston student examines March Madness upsets through math

Drew Passarello, a student at the College of Charleston, takes a closer look at how math relates to upsets and predictability in March Madness.


The Madness is coming. In a way, it is here! With the first round of the March Madness tournament announced, the craziness of filling out the tournament brackets is upon us! Can math help us get a better handle on where we might see upsets in March Madness? In this post, I will detail how math helps us get a handle on what level of madness we expect in the tournament. Said another way, how many upsets do we expect? Will there be a lot? We call that a bad year as that leads to brackets having lower accuracy in their predictions. By the end of the article, you will see how math can earmark teams that might be on the cusp of upsets in the games that will capture national attention.

Where am I learning this math? I am taking a sports analytics class at the College of Charleston under the supervision of Dr. Tim Chartier and Dr. Amy Langville. Part of our work has been researching new results and insights in bracketology. My research uses the Massey and Colley ranking methods. Part of my research deals with the following question: What are good years and bad years in terms of March Madness? In other words, before the tournament begins, what can we infer about how predictable the tournament will be?

One way of answering this question is to see how accurate one is at predicting the winners of the tournaments coupled with how high one’s ESPN score is. However, I also wanted to account for the variability of the level of competition going into the tournament, which is why I also looked at the standard deviation of the ratings of those in March Madness. A higher standard deviation implies the more spread out the playing level is. Ultimately, a good year will have a high tournament accuracy, high ESPN score, and a high standard deviation of ratings for those competing in March Madness. Similarly, a bad year will have low tournament accuracy, low ESPN score, and a low standard deviation of the ratings. This assessment will be relative to the ranking method itself and only defines good years and bad years solely in terms of past March Madness data.

I focused on ratings from uniformly weighted Massey and Colley ranking methods as the weighting might add some bias. However, my simple assessment can be applied for other variations of weighting Massey and Colley. I found the mean accuracy, mean ESPN score, and mean standard deviation of ratings of the teams in March Madness for years 2001 – 2014, and I then looked at the years which rested below or above these corresponding means. Years overlapping were those deemed to be good or bad, and the remaining years were labeled neutral. The good years for Massey were 2001, 2004, 2008, and 2009, and the bad years were 2006, 2010 – 2014. Neutral years were 2002, 2003, and 2007. Also, for Colley, the good years were 2005, 2007 – 2009; bad years were 2001, 2006, and 2010 – 2014; neutral years were 2002 – 2004. A very interesting trend I noticed from both Massey and Colley was that the standard deviation of the ratings of those in March Madness from 2010 to 2014 were significantly lower than the years before. This leads me to believe that basketball has recently become more competitive in terms of March Madness, which would also partially explain why 2010 – 2014 were bad years for both methods. However, this does not necessarily imply 2015 will be a bad year.

In order to get a feel for how accurate the ranking methods will be for this year, I created a regression line based on years 2001 – 2014 that had tournament accuracy as the dependent variable and standard deviation of the ratings of those in March Madness as the independent variable. Massey is predicted to have 65.81% accuracy for predicting winners this year whereas Colley is predicted to have 64.19%accuracy. The standard deviation of the ratings for those expected to be in the tournament was 8.0451 for Massey and 0.1528 for Colley, and these mostly resemble the standard deviation of the ratings of the March Madness teams in 2002 and 2007.

After this assessment, I wanted to figure out what defines an upset relative to the ratings. To answer this, I looked at season data and focused on uniform Massey. Specifically for this year, I used the first half of the season ratings to predict the first week of the second half of the season and then updated the ratings. After this, I would use these to predict the next week and update the ratings again and so on until now. For games incorrectly predicted, the median in the difference of ratings was 2.2727, and the mean was 3.0284. I defined an upset for this year to be those games in which the absolute difference in the ratings is greater than or equal to three. This definition of an upset is relative to this particular year. I then kept track of the upsets for those teams expected to be in the tournament. I looked at the number of upsets each team had and the number of times each team gets upset, along with the score differential and rating differences for these games. From comparing these trends, I determined the following teams to be upset teams to look for in the tournament: Indiana, NC State, Notre Dame, and Georgetown. These teams had a higher ratio of upsets over getting upset when compared to the other teams. Also, these teams had games in which the score differences and rating differences were larger than those from the other teams in March Madness.

I am still working on ways to weight these upset games from the second half of the season, and one of the approaches relies on the score differential of the game. Essentially, teams who upset teams by a lot of points should benefit more in the ratings. Similarly, teams who get upset by a lot of points should be penalized more in the ratings. For a fun and easy bracket, I am going to weight upset games heavily on the week before conference tournament play and a week into conference tournament play. These two weeks gave the best correlation coefficient in terms of accuracy from these weeks and the accuracy from March Madness for both uniform Massey and Colley. Let the madness begin!


Christopher Bail talks to Salon about “Terrified”

Christopher Bail, author of Terrified: How Anti-Muslim Fringe Organizations Became Mainstream, recently spoke with Paul Rosenberg for a feature in Salon on how anti-Muslim sentiment is fostered by the broader cultural landscape, and the innovative new methodology he has used to study that process. Paul Rosenberg at Salon writes:

It may be hard to fathom or remember, but in the immediate aftermath of 9/11 the American public responded with an increased level of acceptance and support for Muslims. President Bush—who had successfully courted the Muslim vote in 2000—went out of his way to praise American Muslims on numerous occasions in 2001 and 2002. However, the seeds were already being planted that would change that drastically over time.  Within a few short years, a small handful of fringe anti-Muslim organizations—almost entirely devoid of any real knowledge or expertise, some drawing on age-old ethno-religious conflicts—managed to hijack the public discourse about Islam, first by stoking fears, grabbing attention with their emotional messaging, then by consolidating their newfound social capital, forging ties with established elite organizations, and ultimately building their own organizational and media infrastructure.

How this all happened is the subject of a fascinating new book, “Terrified: How Anti-Muslim Fringe Organizations Became Mainstream,” by sociologist Christopher Bail, of the University of North Carolina.  The book not only lays bare the behind-the-scenes story of a momentous shift in public opinion, it employs cutting-edge computer analysis techniques applied to large archives of data to develop a new theoretical outlook, capable of making sense of the whole field of competing organizations struggling to shape public opinion, not just studying one or two the most successful ones. The result is not only a detailed account of a specific, significant, and also very pernicious example of cultural evolution, but also a case study in how to more rigorously study cultural evolution more generally in the future. In the process, it sheds considerable light on the struggles involved, and the difficulties faced by those trying to fight back against this rising tide of misdirected fear, anger and hatred.

Read the full interview with Christopher Bail that follows here.

Terrified, by Christopher Bail