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.

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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.

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.

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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.

(1)

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.

(2)

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.

(3)

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.

(4)

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.

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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!

 

May the odds be in your favor — March Mathness begins

Let the games begin! After the excitement of Selection Sunday, brackets are ready for “the picking.” Have you started making your picks?

Check out the full schedule of teams selected yesterday, and join the fun by submitting a bracket to the official Princeton University Press March Madness tournament pool.

Before you do, we recommend that you brush up on your bracketology by checking out PUP author Tim Chartier’s strategy:

 

 

For more on the math behind the madness, head over to Dr. Chartier’s March Mathness video page. Learn three popular sport ranking methods and how to create March Madness brackets with them. Let math make the picks!

Be sure to follow along with our March Mathness coverage on our blog, and comment below with your favorite strategy for making March Madness picks.

The math behind March Madness

It’s almost that time again. The beginning of the March Madness basketball tournament is a few days away, and here at PUP, we cannot wait!

We’re marking our calendars (find the schedule here) and going over our bracketology, with a little help from PUP author Tim Chartier.

To kick off the countdown, we bring you an article from the Post and Courier, who checked in with Dr. Chartier about how numbers can be the best strategy in bracketology.

College basketball fans seeking to cash in on March Madness need to turn on their calculators and turn off their allegiances.

That was the message Dr. Tim Chartier, a math professor at Davidson and published author, brought to cadets at The Citadel on Monday night.

“The biggest mistake people make in bracketology is they go with their heart no matter what the data says,” said Chartier, who has made studying the mathematics of the NCAA basketball tournament part of his students’ course work at Davidson. “They just can’t let a certain team win or they just have to see their team do well.

“It’s hard not to do that, because that is part of the fun.”

Chartier has made it easier for the average fan to use math in filling out their own brackets at the March Mathness website marchmathness.davidson.edu. The site will get a lot of traffic after the NCAA tournament field is announced on March 15.

 

Read the full article on the Post and Courier website.

Dr. Tim Chartier is a numbers guy, and not only during basketball season. He likes to show students how math can apply outside of the classroom. How can reposting on Twitter kill a movie’s opening weekend? How can you use mathematics to find your celebrity look-alike? What is Homer Simpson’s method for disproving Fermat’s Last Theorem? Dr. Chartier explores these and other questions in his book Math Bytes.

(Photo courtesy of Davidson College)

(Photo courtesy of Davidson College)

 

As Dr. Chartier and others gear up for basketball lovers’ favorite time of year, PUP reminds you to mark your calendars for these key dates.

Check back here soon for more hoop scoop!

• Selection Sunday, March 15, ESPN

• First and Second Rounds, March 20, 22 or March 21, 23

• Greensboro Regional, March 27, 29, Greensboro Coliseum (Greensboro, North Carolina)

• Oklahoma City Regional, March 27, 29, Chesapeake Energy Arena (Oklahoma City, Oklahoma)

• Albany Regional, March 28, 30, Times Union Center (Albany, New York)

• Spokane Regional, March 28, 30, Spokane Veterans Memorial Arena (Spokane, Washington)

• National Semifinals, April 5, Amalie Arena (Tampa Bay, Florida)

• Championship Game, April 7, Amalie Arena (Tampa Bay, Florida)

Calculus predicts more snow for Boston

Are we there yet? And by “there,” we mean spring and all the lovely weather that comes with it. This winter has been a tough one, and as the New York Times says, “this winter has gotten old.”

snow big[Photo Credit: John Talbot]

Our friends in Boston are feeling the winter blues after seven feet of precipitation over three weeks. But how much is still to come? You may not be the betting kind, but for those with shoveling duty, the probability of more winter weather may give you chills.

For this, we turn to mathematician Oscar Fernandez, professor at Wellesley College. Professor Fernandez uses calculus to predict the probability of Boston getting more snow, and the results may surprise you. In an article for the Huffington Post, he writes:

There are still 12 days left in February, and since we’ve already logged the snowiest month since record-keeping began in 1872 (45.5 inches of snow… so far), every Bostonian is thinking the same thing: how much more snow will we get?

We can answer that question with math, but we need to rephrase it just a bit. Here’s the version we’ll work with: what’s the probability that Boston will get at least s more inches of snow this month?

Check out the full article — including the prediction — over at the Huffington Post.

Math has some pretty cool applications, doesn’t it? Try this one: what is the most effective number of hours of sleep? Or — for those who need to work on the good night’s rest routine — how does hot coffee cool? These and other answers can be found through calculus, and Professor Fernandez shows us how in his book, Everyday Calculus: Discovering the Hidden Math All around Us.

This book was named one of American Association for the Advancement of Science’s “Books for General Audiences and Young Adults” in 2014. See Chapter One for yourself.

For more from Professor Fernandez, head over to his website, Surrounded by Math.

 

Photo Credit: https://www.flickr.com/photos/laserstars/.

Q&A with the authors of The Fascinating World of Graph Theory

The fascinating world of graph theory goes back several centuries and revolves around the study of graphs—mathematical structures showing relations between objects. With applications in biology, computer science, transportation science, and other areas, graph theory encompasses some of the most beautiful formulas in mathematics—and some of its most famous problems. For example, what is the shortest route for a traveling salesman seeking to visit a number of cities in one trip? What is the least number of colors needed to fill in any map so that neighboring regions are always colored differently?

Princeton University Press catches up with Arthur Benjamin, Gary Chartrand, and Ping Zhang, authors of The Fascinating World of Graph Theory, to discuss just what it is that makes graph theory so fascinating.

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PUP: What is graph theory?

AB, GC & PZ: Graph theory is the study of objects, some pairs of which are related in some manner. Since there are no restrictions on what the objects might be and no restrictions on how two objects might be related, applications of graph theory are only limited by one’s imagination.

PUP: Why is graph theory important?

AB, GC & PZ: There are problems and questions that occur in a wide variety of settings that can be visualized with the aid of graphs and which can often be understood more clearly. Understanding the theoretical nature of graph theory can, in many instances, lead us to solutions of these problems and answers to these questions.

PUP: Where do you see graph theory in action in the real world?

AB, GC & PZ: Because graph theory has been shown to be so useful with problems in transportation, communication, chemistry, computer science, decision-making, games and puzzles, among other things, there are few aspects of life where graphs do not enter in.

PUP: Who needs to understand graph theory? And why does understanding the theoretical underpinnings help us?

AB, GC & PZ: Whether it’s mathematics or some other scholarly endeavor, a key element to understanding is not only becoming aware of what others have accomplished but developing a knack of being curious and asking relevant questions. Because graph theory has applications in so many areas, it is an ideal area within mathematics to become familiar with.

PUP: Why did you write this book?

AB, GC & PZ: There have been numerous reports of American students doing poorly in mathematics in recent years. Furthermore, we believe that mathematics has acquired an under-served reputation of being boring and difficult. While gaining a good understanding of any subject requires effort, we know that many aspects of mathematics are interesting. Since we felt it was likely that many people are not familiar with graph theory, we decided to illustrate how interesting and useful mathematics can be by writing a book on graph theory with this goal in mind. While we wanted to include some real mathematics, showing how certain facts can be verified, we primarily wanted to show where mathematics comes from, discussing some of the people responsible for this, and how mathematics can assist us, often in many unexpected and fascinating ways.

Read the preface of The Fascinating World of Graph Theory here!

Andrew Hodges honored with Scripter Award

 

Andrew Hodges, author of ALAN TURING: THE ENIGMA

Andrew Hodges, author of Alan Turing: The Enigma

Congratulations to PUP author Andrew Hodges, who along with The Imitation Game screenwriter Graham Moore, has been awarded the USC Libraries Scripter Award. Hodges’s book, Alan Turing: The Enigma, was used as the basis for the screenplay of the Oscar-nominated film.

Calling bookworms and movie-goers alike — this award has something for all of you. Established in 1988, the USC Libraries Scripter Award is an honor that recognizes the best adaptation of word to film. The award is given to both the author and the screenwriter.

Alan Turing: The Enigma — a New York Times–bestselling biography of the founder of computer science — is the definitive account of an extraordinary mind and life. Capturing both the inner and outer drama of Turing’s life, Andrew Hodges tells how Turing’s revolutionary idea of 1936 — the concept of a universal machine — laid the foundation for the modern computer and how Turing brought the idea to practical realization in 1945 with his electronic design.

The book also tells how this work was directly related to Turing’s leading role in breaking the German Enigma ciphers during World War II, a scientific triumph that was critical to Allied victory in the Atlantic. Turing’s work on this is depicted in The Imitation Game, which stars Benedict Cumberbatch and Keira Knightley.

Benedict Cumberbatch plays Alan Turing in THE IMITATION GAME © 2014 THE WEINSTEIN COMPANY

Benedict Cumberbatch plays Alan Turing in THE IMITATION GAME © 2014 The Weinstein Company

At the same time, Alan Turing: The Enigma is the tragic account of a man who, despite his wartime service, was eventually arrested, stripped of his security clearance, and forced to undergo a humiliating treatment program — all for trying to live honestly in a society that defined homosexuality as a crime. Alan Turing: The Enigma is a gripping story of mathematics, computers, cryptography, and homosexual persecution.

Check out Chapter 1 of Alan Turing: The Enigma for yourself here.

The other four finalists for the Scripter award included:

  • Gillian Flynn, author and screenwriter of Gone Girl
  • Novelist Thomas Pynchon and screenwriter Paul Thomas Anderson for Inherent Vice
  • Jane Hawking, author of Travelling to Infinity: My Life With Stephen, and screenwriter Anthony McCarten for The Theory of Everything
  • Screenwriter Nick Hornby for Wild, adapted from Cheryl Strayed’s memoir Wild: From Lost to Found on the Pacific Crest Trail

 

“A Brief History” of Stephen Hawking’s work

As we near February, and Oscars month (our calendars are marked for Feb. 22!), PUP takes a look at The Theory of Everything. The best-picture nominee, which stars Eddie Redmayne and Felicity Jones, depicts the love story and life story of Stephen Hawking and Jane Wilde. The beginning of the film is set in Cambridge, where Hawking is a brilliant graduate student. For movie-goers looking for a deeper look at Hawking’s scholarly work, PUP brings you a “Brief History” of books by Stephen Hawking.

Liam Daniel / Focus Features Eddie Redmayne stars as Stephen Hawking in THE THEORY OF EVERYTHING.

Liam Daniel / Focus Features
Eddie Redmayne stars as Stephen Hawking in THE THEORY OF EVERYTHING.

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A Brief History of Time

“A landmark volume in science writing by one of the great minds of our time, Stephen Hawking’s book explores such profound questions as: How did the universe begin—and what made its start possible? Does time always flow forward? Is the universe unending—or are there boundaries? Are there other dimensions in space? What will happen when it all ends?

Told in language we all can understand, A Brief History of Time plunges into the exotic realms of black holes and quarks, of antimatter and “arrows of time,” of the big bang and a bigger God—where the possibilities are wondrous and unexpected. With exciting images and profound imagination, Stephen Hawking brings us closer to the ultimate secrets at the very heart of creation.”

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On the Shoulders of Giants

“In On the Shoulders of Giants, Stephen Hawking brings together the greatest works by Copernicus, Galileo, Kepler, Newton and Einstein, showing how their pioneering discoveries changed the way we see the world.

From Copernicus’ revolutionary claim that the earth orbits the sun and Kepler’s development of the laws of planetary motion to Einstein’s interweaving of time and space, each scientist built on the theories of their predecessors to answer the questions that had long mystified humanity.

Hawking also provides fascinating glimpses into their lives and times – Galileo’s trial in the Papal inquisition, Newton’s bitter feuds with rivals and Einstein absent-mindedly jotting notes that would lead to his Theory of Relativity while pushing his baby son’s pram. Depicting the great challenges these men faced and the lasting contributions they made, Hawking explains how their works transformed the course of science – and gave us a better understanding of the universe and our place in it.”

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The Nature of Space and Time

Princeton University Press

By Stephen Hawking and Roger Penrose

“Einstein said that the most incomprehensible thing about the universe is that it is comprehensible. But was he right? Can the quantum theory of fields and Einstein’s general theory of relativity, the two most accurate and successful theories in all of physics, be united in a single quantum theory of gravity? Can quantum and cosmos ever be combined? On this issue, two of the world’s most famous physicists–Stephen Hawking (A Brief History of Time) and Roger Penrose (The Emperor’s New Mind and Shadows of the Mind)–disagree. Here they explain their positions in a work based on six lectures with a final debate, all originally presented at the Isaac Newton Institute for Mathematical Sciences at the University of Cambridge.

How could quantum gravity, a theory that could explain the earlier moments of the big bang and the physics of the enigmatic objects known as black holes, be constructed? Why does our patch of the universe look just as Einstein predicted, with no hint of quantum effects in sight? What strange quantum processes can cause black holes to evaporate, and what happens to all the information that they swallow? Why does time go forward, not backward? In this book, the two opponents touch on all these questions.”

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The Universe in a Nutshell

“In this new book Hawking takes us to the cutting edge of theoretical physics, where truth is often stranger than fiction, to explain in laymen’s terms the principles that control our universe.

Like many in the community of theoretical physicists, Professor Hawking is seeking to uncover the grail of science — the elusive Theory of Everything that lies at the heart of the cosmos. In his accessible and often playful style, he guides us on his search to uncover the secrets of the universe — from supergravity to supersymmetry, from quantum theory to M-theory, from holography to duality.

He takes us to the wild frontiers of science, where superstring theory and p-branes may hold the final clue to the puzzle. And he lets us behind the scenes of one of his most exciting intellectual adventures as he seeks ‘to combine Einstein’s General Theory of Relativity and Richard Feynman’s idea of multiple histories into one complete unified theory that will describe everything that happens in the universe.'”

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The Grand Design

“When and how did the universe begin? Why are we here? What is the nature of reality? Is the apparent ‘grand design’ of our universe evidence for a benevolent creator who set things in motion? Or does science offer another explanation? In The Grand Design, the most recent scientific thinking about the mysteries of the universe is presented in language marked by both brilliance and simplicity.

The Grand Design explains the latest thoughts about model-dependent realism (the idea that there is no one version of reality), and about the multiverse concept of reality in which there are many universes. There are new ideas about the top-down theory of cosmology (the idea that there is no one history of the universe, but that every possible history exists). It concludes with a riveting assessment of m-theory, and discusses whether it is the unified theory Einstein spent a lifetime searching for.”

See more books by Stephen Hawking here. Which of these have you read, and which are on your “to-read” list?

PUP News of the World — November 19, 2014

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Each week we post a round-up of some of our most exciting national and international PUP book coverage. Reviews, interviews, events, articles — this is the spot for coverage of all things “PUP books” that took place in the last week. Enjoy!


The Original Folk and Fairy Tales

of the Brothers Grimm

These are not the bedtime stories that you remember.

When Jacob and Wilhelm Grimm published their Children’s and Household Tales in 1812, followed by a second volume in 1815, they had no idea that such stories as “Rapunzel,” “Hansel and Gretel,” and “Cinderella” would become the most celebrated in the world. Yet few people today are familiar with the majority of tales from the two early volumes, since in the next four decades the Grimms would publish six other editions, each extensively revised in content and style.

For the very first time, The Original Folk and Fairy Tales of the Brothers Grimm makes available in English all 156 stories from the 1812 and 1815 editions. These narrative gems, newly translated and brought together in one beautiful book, are accompanied by sumptuous new illustrations from award-winning artist Andrea Dezsö.

The 156 stories in the Complete First Edition are raw, authentic, and unusual. Familiar tales are spare and subversive: “Rapunzel” ends abruptly when the title character gets pregnant, and in “Little Snow White” and “Hansel and Gretel,” the wicked stepmother is actually a biological mother. Unfamiliar tales such as “How Some Children Played at Slaughtering” were deleted, rewritten, or hidden in scholarly notes, but are restored to the collection here.

The Guardian interviewed author Jack Zipes for a piece on the Grimms and their tales. Here is a sneak peak of the article:

Wilhelm Grimm, said Zipes, “deleted all tales that might offend a middle-class religious sensitivity”, such as How Some Children Played at Slaughtering. He also “added many Christian expressions and proverbs”, continued Zipes, stylistically embellished the tales, and eliminated fairies from the stories because of their association with French fairy tales. “Remember, this is the period when the French occupied Germany during the Napoleonic wars,” said Zipes. “So, in Briar Rose, better known as Sleeping Beauty, the fairies are changed into wise women. Also, a crab announces to the queen that she will become pregnant, not a frog.”

Check out the full article on the Guardian‘s website.

On the other side of the pond, USA Today takes a look at the book in a piece entitled “These Grimm fairy tales are not for the kiddies,”  and cheezburger.com warns that “your kids may never sleep again.” Take a look for yourself — view Chapter One, The Frog King, or Iron Henry.

Our friends at the Times in South Africa and at NRC Handelsblad in Germany also discuss the book this week. Zipes discusses the book on Monocle radio.

now 11.19

 Alan Turing: The Enigma: The Book That Inspired the Film The Imitation Game

 

It is only a slight exaggeration to say that the British mathematician Alan Turing (1912-1954) saved the Allies from the Nazis, invented the computer and artificial intelligence, and anticipated gay liberation by decades–all before his suicide at age forty-one. This year, his story comes to a theater near you — The Imitation Game starring Benedict Cumberbatch and Keira Knightley is due out before the end of the year. And the inspiration for the script sits on a shelf here in Princeton: Alan Turing: The Enigma by Andrew Hodges.

This acclaimed biography of the founder of computer science, with a new preface by the author that addresses Turing’s royal pardon in 2013, is the definitive account of an extraordinary mind and life. Capturing both the inner and outer drama of Turing’s life, Andrew Hodges tells how Turing’s revolutionary idea of 1936–the concept of a universal machine–laid the foundation for the modern computer and how Turing brought the idea to practical realization in 1945 with his electronic design.

The book also tells how this work was directly related to Turing’s leading role in breaking the German Enigma ciphers during World War II, a scientific triumph that was critical to Allied victory in the Atlantic. At the same time, this is the tragic account of a man who, despite his wartime service, was eventually arrested, stripped of his security clearance, and forced to undergo a humiliating treatment program–all for trying to live honestly in a society that defined homosexuality as a crime.

As it is released in the UK, the Guardian takes a look at the film. Hodges provides comments for the piece:

Andrew Hodges, who published the first substantial biography of Turing, Alan Turing: The Enigma, in 1983, suggests that “the production and presentation of the new film [reflects] underlying cultural and political changes” of the last decade and a half – leading to Gordon Brown’s posthumous apology to Turing in 2009, and subsequent royal pardon in 2013.

Hodges said: “Obviously the changes that happened in the UK under the Labour government of 1997-2010, when a robust principle of equality was established in civil society, have made a big difference. Gordon Brown’s 2009 apology was a good example of those changes, and his words seemed to encourage a lot of other people to take the historical question as a serious human rights issue.”

Express reviews The Imitation Game, noting that:

Turing should be a national treasure, honoured for his extraordinary achievement in solving the fiendish mysteries of the greatest encryption device in history. He helped turn the tide against the Nazis. Without Turing the age of the computer might never have come to pass as quickly as it did.

Engineering and Technology magazine interviews Andrew Hodges — check out one of the questions below:

Q: The blue plaque at Alan Turing’s birthplace that you unveiled in 1998 describes Turing as ‘code-breaker and pioneer of computer science’. Are these six words a good crystallisation of the man, or do we need to expand upon them?

A: Turing would have described himself as a mathematician. I think it’s fair to unpack that and describe some of the things he did. The two things he did which are most distinctive are that he founded the whole concept of computer science, upon which everything in computer science theory is now based. And the other thing was his work during the Second World War, which was extremely important cryptanalysis.

Although what he did often seems abstruse, he was unusual in that he was very alive to engineering and the concrete application of difficult ideas. The best example of that is in his code-breaking work. But you can see it in everything he did. Computer science is all about linking logical possibilities with the physical reality. There are lots of paradoxes in Turing’s life, but this is the central theme.

Begin cracking the code by reading Chapter One of Alan Turing: The Enigma.

 

 

Wrapping up #UPWeek — Follow Friday

What a week it has been. Wrapping up the university press blog tour are six movers and shakers. These university presses take to their blogs to discuss fields, authors, and research that is on the cutting edge. Check out these posts for insight into what university presses are adding to scholarly and popular discussions right now.

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University of Illinois Press — University of Illinois Press discusses the emerging topics and authors in their Geopolitics of Information series.

University of Minnesota Press — John Hartigan, a participant in the University of Minnesota Press’s new Forerunners series, explains the ways in which he uses social media to enhance scholarly connections and establish social-media conversations with regard to his research.

University of Nebraska Press — How should university pressess be adding to the conversation on social media and who is doing it right? University of Nebraska Press’s marketing department takes a look at the potential for social media use in scholarly publishing.

NYU Press — The folks at NYU Press blog about the forthcoming website for the book Keywords for American Cultural Studies (Second Edition).

Island Press — Island Press takes a look at what is on their editors’ radar these days and why those scholars and fields are important.

Columbia University Press — Every Friday, the Columbia University Press blog runs a post called the University Press Roundup in which they highlight posts from around the academic publishing blogosphere. This blog tour post explains how and why they have made a commitment to a blog series that rarely features their own titles. They discuss how university press blogs generate publicity for individual titles but also provide a much-needed environment where scholarship can be presented for a general readership.

Looking back — a #TBT for #UPWeek

Upress week

 

This afternoon, we head back in time for University Press Week’s Throwback Thursday. Check out these six posts for a look back at the history, recent and not so recent, of university presses.

Temple University Press — The folks at Temple University discuss the development of their influential Asian History and Culture series.

Wesleyan University Press — Learn more about the great Wesleyan Poetry Series with this group of #tbt posts.

Harvard University Press — Late last year, Harvard University Press made roughly 3,000 previously unavailable backlist works available again. These titles go back as far as the late 1800s. (How cool!) While prepping the data, we kept a running list of titles that were really showing their age. This post will give you a few laughs as you are asked to name “Backlist Title from Harvard University Press – OR – Song by Theatrically Erudite Indie Band The Decemberists?”

University of Washington Press — Check out the “then and now” cover designs of these recently reissued Asian American classics.

University of Toronto Press — University of Toronto Press will be looking back at the publications of The Champlain Society, an historical society which publishes primary source archive material that explores Canada’s history. Their post highlights this year’s volume, as well as historical images from past publications.

MIT Press — Up at MIT, they take a look back at former press designer Muriel Cooper. She designed MIT Press’s iconic colophon 50 years ago in 1964.