Math Drives Careers: Author Louis Gross

Gross jacketLouis Gross, distinguished professor in the departments of ecology, evolutionary biology, and mathematics at the University of Tennessee, is the author, along with Erin Bodine and Suzanne Lenhart, of Mathematics for the Life Sciences. For our third installment in the Math Awareness Month series, Gross writes on the role mathematics and rational consideration have played in his career, and in his relationship with his wife, a poet.

Math as a Career-builder and Relationship-broker

My wife is a poet. We approach most any issue with very different perspectives. In an art gallery, she sees a painting from an emotional level, while I focus on the methods the artist used to create the piece. As with any long-term relationship, after many years together we have learned to appreciate the other’s viewpoint and while I would never claim to be a poet, I have helped her on occasion to try out different phrasings of lines to bring out the music. In the reverse situation, the searching questions she asks me about the natural world (do deer really lose their antlers every year – isn’t this horribly wasteful?) force me to consider ways to explain complex scientific ideas in metaphor. As the way I approach science is heavily quantitative, with much of my formal education being in mathematics, this is particularly difficult without resorting to ways of thought that to me are second nature.

The challenges in explaining how quantitative approaches are critical to science, and that science advances in part through better and better ways to apply mathematics to the responses of systems we observe around us, arise throughout education, but are particularly difficult for those without a strong quantitative bent. An example may be helpful. One of the central approaches in science is building and using models – these can be physical ones such as model airplanes, they can be model systems such as an aquarium or they can be phrased in mathematics or computer code. The process of building models and the theories that ultimately arise from collections of models, is painstaking and iterative. Yet each of us build and apply models all the time. Think of the last time you entered a supermarket or a large store with multiple checkout-lines. How did you decide what line to choose? Was it based on how many customers were in each line, how many items they had to purchase, or whether they were paying with a check or credit card? Did you take account of your previous experience with that check-out clerk if you had it, or your experience with using self-checkout at that store? Was the criterion you used some aspect of ease of use, or how quickly you would get through the line? Or was it something else such as how cute the clerk was?

As the check-out line example illustrates, your decision about what is “best” for you depends on many factors, some of which might be quite personal. Yet somehow, store managers need to decide how many clerks are needed at each time and how to allocate their effort between check-out lines and their other possible responsibilities such as stocking shelves. Managers who are better able to meet the needs of customers, so they don’t get disgusted with long lines and decide not to return to that store, while restraining the costs of operation, will likely be rewarded. There is an entire field, heavily mathematical, that has been developed to better manage this situation. The jargon term is “queuing models” after the more typically British term for a waiting line. There is even a formal mathematical way of thinking about “bad luck” in this situation, e.g. choosing a line that results in a much longer time to be checked out than a different line would have.

While knowing that the math exists to help decide on optimal allocation of employee effort in a store will not help you in your decision, the approach of considering options, deciding upon your criteria and taking data (e.g. observations of the length of each line) to guide your decision is one that might serve you well independent of your career. This is one reason why many “self-help” methods involve making lists. Such lists assist you in deciding what factors (in math we call these variables) matter to you, how to weight the importance of each factor (we call these parameters in modeling) and what your objective might be (costs and benefits in an economic sense). This process of rational consideration of alternative options may assist you in many aspects of everyday life, including not just minor decisions of what check-out line to go into, but major ones such as what kind of car or home to purchase, what field to major in and even who to marry! While I can’t claim to have followed a formal mathematical approach in deciding on the latter, I have found it helpful throughout my marriage to use an informal approach to decision making. I encourage you to do so as well.

Check out Chapter 1 of Mathematics for the Life Sciences here.

Alan Turing’s handwritten notebook brings $1 million at auction

turing jacket

Alan Turing: The Enigma

Old journals can be fascinating no matter who they belong to, but imagine looking over the old notebook of the mathematician credited with breaking German codes during WWII.

The Associated Press and other venues reported that a handwritten notebook by British code-breaker Alan Turing, subject of the 2014 Oscar-winning film “The Imitation Game,” a movie based on our book, Alan Turing: The Enigma, brought more than $1 million at auction from an anonymous buyer on Monday. Originally given to Turing’s mathematician-friend Robin Gandy, the notebooks are thought to be the only ones of their kind, and contain Turing’s early attempts to chart a universal language, a precursor to computer code. (In an interesting personal wrinkle, Gandy had used the blank pages for notes on his dreams, noting that, “It seems a suitable disguise to write in between these notes of Alan’s on notation, but possibly a little sinister; a dead father figure, some of whose thoughts I most completely inherited.”)

Andrew Hodges, author of Alan Turing: The Enigma, commented that “the notebook sheds more light on how Turing ‘remained committed to free-thinking work in pure mathematics.'” To learn more about the life of Turing, check out the book here.

Math Drives Careers: Author Oscar Fernandez

We know that mathematics can solve problems in the classroom, but what can it do for your business? Oscar Fernandez, author of Everyday Calculus, takes a look at how knowledge of numbers can help your bottom line.

Why You Should Be Learning Math Even If You Don’t Need It for Your Job

I want to tell you a short story about epic triumph in the midst of adversity. Okay, I’m exaggerating a bit, but hear me out.

A couple of years ago, I approached Boston Scientific—an S&P 500 component—with a crazy idea: let me and a team of students from Wellesley College (a liberal arts college for women) and Babson College (a business school) do consulting work for you. It was a crazy idea because what could I—a mathematician who knew nothing about their business—and some students—who hadn’t even graduated yet—possibly offer the company? Plenty, it turns out, all thanks to our common expertise: mathematics.

Mathematics, often depicted in movies as something pocket-protector-carrying people with less than stellar social skills do, is actually quite ubiquitous. I’d even say that mathematicians are the unsung heroes of the world. Alright, that’s a bit of hyperbole. But think about it. Deep in the catacombs of just about every company, there are mathematicians. They work in low light conditions, hunched over pages of calculations stained with days-old coffee, and think up ways to save the company money, optimize their revenue streams, and make their products more desired. You may never notice their efforts, but you’ll surely notice their effects. That recent change in the cost of your flight? Yep, it was one of us trying to maximize revenue. The reason that UPS truck is now waking you up at 6 a.m.? One of us figured out that the minimum cost route passes through your street.

But we’re do-good people too. We help optimize bus routes to get children to school faster and safer. We’ve spent centuries modelling the spread of disease. More recently, we’ve even reduced crime by understanding how it spreads. That’s why I was confident that my team and I could do something useful for Boston Scientific. Simply put, we knew math.

We spent several weeks pouring over data the company gave us. We tried everything we could think of to raise their revenues from certain products. Collectively, we were trained in mathematics, economics, computer science, and psychology. But nothing worked. It seemed that we—and math—had failed.

Then, with about three weeks left, I chanced upon an article from the MIT Technology Review titled “Turning Math Into Cash.” It describes how IBM’s 200 mathematicians reconfigured their 40,000 salespeople over a period of two years and generated $1 billion in additional revenue. Wow. The mathematicians analyzed the company’s price-sales data using “high-quantile modeling” to predict the maximum amount each customer was willing to spend, and then compared that to the actual revenue generated by the sales teams. IBM then let these mathematicians shuffle around salespeople to help smaller teams reach the theoretical maximum budget of each customer. Genius, really.

I had never heard of quantile regression before, and neither had my students, but one thing math does well is to train you to make sense of things. So we did some digging. We ran across a common example of quantile modelling: food expenditure vs. household income. There’s clearly a relationship, and in 1857 researchers quantified the relationship for Belgian households. They produced this graph:

fernandez 1

That red line is the linear regression line—the “best fit to the data.” It’s useful because the slope of the line predicts a 50 cent increase in food expenditure for a $1 increase in household income. But what if you want information about the food expenditure of the top 5% of households, or the bottom 20%? Linear regression can’t give you that information, but quantile regression can. Here’s what you get with quantile regression:

fernandez 2

The red line is the linear regression line, but now we also have various quantile regression lines. To understand what they mean let’s focus on the top-most dashed line, which is the 95th percentile line. Households above this line are in the 95th percentile (or 0.95 quantile) of food expenditure. Similarly, households below the bottom-most line are in the 5th percentile (or 0.05 quantile) of food expenditure. Now, if we graph the slopes of the lines as a function of the percentile (also called “quantile”), we get:

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(The red line is the slope of the linear regression line; it doesn’t depend on the quantile, which is why it’s a straight line.) Notice that the 0.95 quantile (95th percentile) slope is about 0.7, whereas the 0.05 quantile (5th percentile) slope is about 0.35. This means that for every $1 increase in household income, this analysis predicts that households in the 95th percentile of food expenditure will spend 70 cents more, whereas households in the 5th percentile will spend only 35 cents more.

Clearly quantile regression is powerful stuff. So, my team and I went back and used quantile regression on the Boston Scientific data. We came up with theoretical maximum prices that customers could pay based on the region the product was sold in. As with IBM, we identified lots of potential areas for improvement. When my students presented their findings to Boston Scientific, the company took the work seriously and was very impressed with what a few students and one professor could do. I can’t say we generated $1 billion in new revenue for Boston Scientific, but what I can say is that we were able to make serious, credible recommendations, all because we understood mathematics. (And we were just a team of 5 working over a period of 12 weeks!)

April is Mathematics Awareness Month, and this year’s theme is “math drives careers.” After my Boston Scientific experience and after reading about IBM’s success, I now have a greater appreciation of this theme. Not only can mathematics be found in just about any career, but if you happen to be the one to find it (and use it), you could quickly be on the fast track to success. So in between celebrating March Madness, Easter, Earth Day, and April 15th (I guess you’d only celebrate if you’re due a tax refund), make some time for math. It just might change your career.

Photo by Richard Howard.

Photo by Richard Howard.

Oscar Fernandez is the author of Everyday Calculus. He is assistant professor of mathematics at Wellesley College.

March Mathness 2015: The Wrap Up


The champion has been crowned! After an eventful and surprising March Madness tournament, Duke has been named the new NCAA national champion.

A year of bragging rights goes to PUP paperbacks manager Larissa Skurka (98.6 percent) and PUP executive math and computer science editor Vickie Kearn (98.4 percent), who took first and second place in our ESPN bracket pool. Congrats to both! Check out all of the results here.

As we wrap up March Mathness, here are two final guest posts from basketball fans who used math and Tim Chartier‘s methods to create their brackets.

 Swearing by Bracketology

By Jeff Smith

My name is Jeff Smith, and I’ve been using Tim Chartier’s math algorithms to help with my March Madness brackets for several years now. I met Tim when we were traveling the ‘circuit’ together in creative ministries training. You may only know Tim for his math prowess, but I knew him for his creativity before I knew he was a brilliant mathematician. He and his wife, Tanya, are professional mimes, and his creativity is genius too.

Several years ago, he mentioned his method for picking brackets at a conference where we were doing some training together. He promised to send me the home page for his site and I could fill out my brackets using his parameters and formula. I was excited to give it a shot. Mainly, because I am part of a men’s group at our church that participates in March Madness brackets every year. Bragging rights are a big deal…for the whole year. You get the picture.

Also, I have two boys who did get one of my genes: the competitive edge. I sat down and explained the process. Because they did not know Tim, they were a little more skeptical, but I promised it wouldn’t hurt to try. That year, in a pool of 40+ guys, we all finished in the top ten. We were all hooked!

Since then, I have contacted Tim each year and reminded him to send me the link to his site where I could put in our numbers to fill out our brackets. Generally, the three of us each incorporate different parameters because we have different philosophies about the process. It has become a family event, where we sit around the dinner table; almost ceremonially, and we take our output and place them in the brackets. The submission is generally preceded by trash talking, prayer, and fasting. (Well, probably not the fasting, because we fill up with nachos and chips during the process.)

Jeff post

Men of March Mathness: Jeff, Samuel, Ben Smith.

This year, I was in South Africa on a mission trip during the annual ritual. Thank God for video chatting and internet access. Halfway across the world, we were still able to be together and place our brackets into the pool. It was such a wonderful experience. While my boys veered from the path, picking intuitively instead of statistically, I didn’t stray far. (I was strong!) If it wouldn’t have been for Villanova, whom I will never choose again in a bracket, I would be leading the pack. But, I’m still in the top ten of the men’s bracket at my church, with an outside shot of winning. In the Princeton bracket, I’m doing even better because I stayed away from the guessing game a little more.

I do not follow college basketball during the season. I’m from central Pennsylvania, and Penn State doesn’t have a good basketball team. So, I have no passion for the basketball season. Periodically, I’ll watch a game because my boys are watching, but generally, basketball season is the long wait until baseball season. (Go Pirates!) So, March Mathness has saved my reputation. It makes me look like a genius. Other guys in the group are looking at my bracket for answers. My boys and I are sworn to secrecy about the formula. The only reason I write this is because I’m sure none of them read this blog! But I’m thankful for Tim and the formula and the chance to look good in front of friends. I have never won the pool, however, if you factor my finishes over the course of the years I have been using Tim’s formula, I have the best average of all the guys.


 What Do Coaches Have to Do with It?

By Stephen Gorman, College of Charleston student


It’s that time of year again. The time of year when everyone compares brackets to see who did the best. But if your bracket was busted early, don’t worry — you’re not the only one. In fact, nobody came out of the tournament with a perfect bracket.

The unpredictability of these games is an inescapable fact of March Madness. This tournament is so incredibly unpredictable that some people are willing to give out billions to anyone who can create a perfect bracket; Warren Buffett is one of these people. So is he crazy? Or does he realize your odds of creating a perfect bracket are 52 billion times worse than winning the Powerball. In layman’s terms – if you think playing the lottery is crazy, trying to create the perfect bracket is insane.

However, once you can accept the statistics, predicting March Madness becomes a game of bettering you’re odds – and there are many predictive models that can help you out along the way. Some of these models include rating methods, like the Massey method, which takes into account score differentials and strength of schedule. In addition to this, there are weighting methods that can be applied to rating methods; these take into account the significance of particular games and even individual player statistics. However, I noticed there is one thing missing from these predictive models: a method that quantifies the value of a good coach. In order to take into account the importance of a coach, a fellow researcher (John Sussingham) and I decided to create our own rating system for coaches.

Using data available from, we made a system of rating that incorporated such factors as the coach’s career win percentage, March Madness appearances, and the record of success in March Madness. But before we implemented it, we wanted to justify that it was, indeed, a good way to quantify the strength of a coach. In order to do this, we tested the coach ratings in two ways. The first way being a comparison between how sports writers ranked the top 10 College Basketball coaches of all time and what our coach ratings said were the best coaches of all time. The second way was to test how the coach ratings did by themselves at predicting March Madness.

The comparison of the rankings are shown in the table below:

Rank Our Results CBS Sports Results Bleacher Report Results
1 John Wooden John Wooden John Wooden
2 Mike Krzyzewski Mike Krzyzewski Bobby Knight
3 Adolph Rupp Bob Knight Mike Krzyzewski
4 Jim Boeheim Dean Smith Adolph Rupp
5 Dean Smith Adolph Rupp Dean Smith
6 Roy Williams Henry Iba Jim Calhoun
7 Jerry Tarkanian Phog Allen Jim Boeheim
8 Al McGuire Jim Calhoun Lute Olson
9 Bill Self John Thompson Eddie Sutton
10 Jamie Dixon Jim Boeheim Jim Phelan

It is clear from the table above that there are striking similarities between all three rankings. This concluded our first test.

For the second test, we decided to use the coach ratings to predict the last fourteen years of March Madness. The results showed that over the last fourteen years, on average, coach ratings had 68.4 precent prediction accuracy and an ESPN bracket score of 946. As a comparison, the uniform (un-weighted) Massey method of rating (over the same timespan) had an average prediction accuracy of 65.2 precent and an average ESPN bracket score of 1006. Having a higher prediction accuracy, but lower ESPN bracket score essentially means that you have predicted more games correctly in the beginning of the tournament, but struggle in the later rounds. This comes to show that not only are these ratings good at predicting March Madness, but they stand their ground when compared to the effectiveness of very popular methods of rating.

To conclude this article, we decided that, this year, we would combine both the Massey ratings and our Coach ratings to make a bracket for March Madness. Over the last fourteen years, the combination-rating had an average prediction accuracy of 66.33 percent and an average ESPN bracket score of 1024. It’s interesting to note that while the prediction accuracy went down from just using the Coach ratings, the ESPN bracket score went up significantly. Even more interestingly, both the prediction accuracy and the ESPN Bracket score were better than uniform Massey.

This year, the combination-ratings had three out of the four Final Four teams correctly predicted with Kentucky beating Duke in the Championship. However, the undefeated Kentucky lost to Wisconsin in the Final Four. Despite this, the combination-ratings bracket still did well, finishing in the 87.6th percentile on ESPN.

Math Drives Careers: Author Ignacio Palacios-Huerta

Logical thinking, analytical skills, and the ability to recognize patterns are crucial in an array of fields that overlap with mathematics, including economics. But what does math (or economics, for that matter) have to do with the world’s most popular sport? Economist Ignacio Palacios-Huerta’s recent book, Beautiful Game Theory: How Soccer Can Help Economics  made a splash during the last World Cup, showing how universal economic principles can be understood through soccer. Read on for his thoughts on why the language of modern economics, including behavioral economics, is mathematics.

The Role of Mathematics in my Life as an Economist

To describe the role of mathematics in my life as an economist, I first need to explain what, to me, Economics is all about. So let me take you to one of my favorite books, A Treatise of Human Nature, written almost 300 years ago by David Hume.

Beautiful Game TheoryIn the introduction Hume writes, “‘Tis evident that all the sciences have a relation, more or less, to human nature … Even Mathematics, Natural Philosophy, and Natural Religion, are in some measure dependent on the science of Man, [which is] the only solid foundation for the other sciences”. By the science of man Hume means the understanding of all facets of human nature, including preferences, senses, passions, imagination, morality, justice, and society. This science applies wherever men are making decisions, be it running public institutions or countries, as employees in firms, or as individuals investing in education, taking risks in financial markets, or making family decisions. This science of man is thus what one may initially be tempted to call Economics for, as George Bernard Shaw puts it in my favorite definition, “Economy is the art of making the most of life”.

But of course this definition is incomplete because other social sciences (e.g., sociology, history, psychology, political science) are also concerned with human behavior. So what makes Economics “different”? Here is the difference: the difference is not the subject matter but the approach. The approach is totally different, and a very mathematical one. As such, mathematics plays a critical role in the life of any economist.

Let me elaborate. Continuing with Hume, it turns out that he also anticipated our methodological approach in modern Economics: observation and logical arguments. Which can be translated as: data and data analysis (what we call econometrics), and mathematics, for mathematics is, after all, the language of logic. So in Economics, as in physics, we write down our ideas and theories in mathematical terms to make logical arguments, and then we use more math (statistical, econometrics, etc) to check whether the data appear to be consistent with the theoretical arguments. If they are, the evidence can be said to support the theory; if they aren’t, the theory needs to be refined or discarded. Yes, lots of math and related techniques provide what is our distinct “economics approach to human behavior.” It is not the subject matter but the approach that is different, and it heavily relies on mathematics.

To economists and other social scientists, mathematics has many methodological virtues: it can lend precision to theories, can uncover inconsistencies, can generate hypothesis, can enable concision and promote intelligibility, and can sort out complex interactions, while statistical and econometric analysis can organize and carefully interpret voluminous data.

None of this is obvious when you begin studying Economics (“Why should I take all this math, statistics and econometrics? Why all this pain?”). But I think most of us soon learn to appreciate that the language of modern economics is mathematics, and that it is rightly so. And this is not math for the sake of math (as in pure mathematics), but math with a purpose: modeling human behavior.

Let me conclude by saying that since the economic approach is applicable to all human behavior, any type of data about human activity can be useful to evaluate economic theories. This includes, why not, sports data, which in many ways can be just perfect for testing economic theories: the data are abundant, the goals of the participants are clear, the outcomes are easy to observe, the stakes are high, and the subjects are professionals with experience. If a theory is “correct”, sport is a good setting to check it.

So just as data involving falling stones and apples were useful to Galileo Galilei and  Isaac Newton to test for the first time theories that were important in physics, data from sports can be useful in Economics to do exactly the same. As such in some of my contributions to Economics I have used math to develop theoretical models, and further mathematical tools applied to this type of data to test them.

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.

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.

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.

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!


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.

Pi Day Recipe: Apple Pie from Jim Henle’s The Proof and the Pudding

Tomorrow (March 14, 2015) is a very important Pi Day. This year’s local Princeton Pi Day Party and other global celebrations of Albert Einstein’s birthday look to be truly stellar, which is apt given this is arguably the closest we will get to 3.1415 in our lifetimes.

Leading up to the publication of the forthcoming The Proof and the Pudding: What Mathematicians, Cooks, and You Have in Common by Jim Henle, we’re celebrating the holiday with a recipe for a classic Apple Pie (an integral part of any Pi Day spread). Publicist Casey LaVela recreates and photographs the recipe below. Full text of the recipe follows. Happy Pi Day everyone!

Notes on Jim Henle’s Apple Pie recipe from Publicist Casey LaVela

The Proof and the Pudding includes several recipes for pies or tarts that would fit the bill for Pi Day, but the story behind Henle’s Apple Pie recipe is especially charming, the recipe itself is straightforward, and the results are delicious. At the author’s suggestion, I used a mixture of baking apples (and delightfully indulgent amounts of butter and sugar).


All of the crust ingredients (flour, butter, salt) ready to go:


After a few minutes of blending everything together with a pastry cutter, the crust begins to come together. A glorious marriage of flour and butter.


Once the butter and flour were better incorporated, I dribbled in the ice water and then turned the whole wonderful mess out between two sheets of plastic wrap in preparation for folding. The crust will look like it won’t come together, but somehow it always does in the end. Magical.


Now you need to roll out and fold over the dough a few times. This is an important step and makes for a light and flaky crust. (You use a similar process to make croissants or other viennoiserie from scratch.)


I cut the crust into two (for the top crust and bottom crust) using my handy bench scraper:



The apples cored, peeled, and ready to be cut into slices. I broke out my mandolin slicer (not pictured) to make more even slices, but if you don’t own a slicer or prefer to practice your knife skills you can just as easily use your favorite sharp knife.


Beautiful (even) apple slices:


Action shot of me mixing the apple slices, sugar, and cinnamon together. I prefer to prepare my apple pie filling in a bowl rather than sprinkling the dry ingredients over the apple slices once they have been arranged in the bottom crust. I’m not sure if it has much impact on the flavor and it is much, much messier, but I find it more fun.



The bottom crust in the pie plate:


Arrange the apple slices in the bottom crust:


Top with the second crust, seal the top crust to the bottom with your fingers, and (using your sharp knife) make incisions in the top crust to allow steam to escape:


The apple pie before going into the oven (don’t forget to put a little extra sugar on top):


The finished product:


There was a little crust left over after cutting, so I shaped it into another pi symbol, covered it in cinnamon and sugar, and baked it until golden brown. I ate the baked pi symbol as soon as it had cooled (before thinking to take a picture), but it was delicious!


Apple Pie

The story of why I started cooking is not inspiring. My motives weren’t pure. Indeed, they involved several important sins.

I really am a glutton. I love to eat. As a child, I ate well; my mother was a wonderful cook. But I always wanted more than I got, especially dessert. And of all desserts, it was apple pie I craved most. Not diner pies, not restaurant pies, and not bakery pies, but real, homemade apple pies.

When I was six, I had my first homemade apple pie. It was at my grandmother’s house. I don’t remember how it tasted, but I can still recall the gleam in my mother’s eye when she explained the secret of the pie. “I watched her make it. Before she put on the top crust, she dotted the whole thing with big pats of butter!”

Several times as I was growing up, my mother made apple pie. Each one was a gem. But they were too few—only three or four before I went off to college. They were amazing pies. The apples were tart and sweet. Fresh fall apples, so flavorful no cinnamon was needed. The crust was golden, light and crisp, dry when it first hit the tongue, then dissolving into butter.

I grew up. I got married. I started a family. All the while, I longed for that pie. Eventually I set out to make one.

Success came pretty quickly, and it’s not hard to see why. The fact is, despite apple pie’s storied place in American culture, most apple pies sold in this country are abysmal. A pie of fresh, tart apples and a crust homemade with butter or lard, no matter how badly it’s made, is guaranteed to surpass a commercial product.

That means that even if you’ve never made a pie before, you can’t go seriously wrong. The chief difficulty is the crust, but I’ve developed a reliable method. Except for this method, the recipe below is standard.

For the filling:
5 cooking apples (yielding about 5 cups of pieces)
1/4 to 1/3 cup sugar
2 Tb butter
1/2 to 1 tsp cinnamon
lemon juice, if necessary
1 tsp flour, maybe

For the crust:
2 cups flour
1 tsp salt
2/3 cup lard or unsalted butter (1 1/3 sticks)

The crust is crucial. I’ll discuss its preparation last. Assume for now that you’ve rolled out the bottom crust and placed it in the pie pan.

Core, peel, and slice the apples. Place them in the crust. Sprinkle with sugar and cinnamon. Dot with butter. Roll out the top crust and place it on top. Seal the edge however you like. In about six places, jab a knife into the crust and twist to leave a hole for steam to escape. Sprinkle the crust with the teaspoon of sugar.

Bake in a preheated oven for 15 minutes at 450° and then another 35 minutes at 350°. Allow to cool. Serve, if you like, with vanilla ice cream or a good aged cheddar.

Now, the crust:

Mix the flour and salt in a large bowl. Place the lard or butter or lard/butter in the bowl. Cut it in with a pastry cutter.

Next, the water. Turn the cold water on in the kitchen sink so that it dribbles out in a tiny trickle. Hold the bowl with the flour mixture in one hand and a knife in the other. Let the water dribble into the bowl while you stir with the knife. The object is to add just enough water so that the dough is transformed into small dusty lumps. Don’t be vigorous with the knife, but don’t allow the water to pool. If the water is dribbling too fast, take the bowl away from the faucet from time to time. When you’re done, the dough will still look pretty dry.

Recipes usually call for about 5 tablespoons of water. This method probably uses about that much.

Actually, the dough will look so dry that you’ll think it won’t stick together when it’s rolled out. In fact, it probably won’t stick together, but trust me. This is going to work.

Tear off a sheet of plastic wrap and lay it on the counter. Place a bit more than half the dough on the sheet and cover it with a second sheet of plastic.

With a rolling pin, roll the dough out between the two sheets. Roll it roughly in the shape of a rectangle.

It won’t look great and it probably would fall apart if you picked it up.

Don’t pick it up. Remove the top sheet of plastic wrap and fold the bottom third up, and fold the top third down, then do the same horizontally, right and left.

Now replace the top sheet of plastic wrap and roll the dough out gently into a disk.

This time it should look pretty decent. This time the dough will stick together.

You should be able to remove the top sheet of plastic and, using the bottom sheet, turn it over into the pie pan. The crust should settle in nicely without breaking.

Form the top crust the same way.

This method rolls each crust twice—usually not a good idea because working the dough makes it tough. But remarkably, crusts produced this way are tender and light. I’m not sure why but I suspect it’s because the dough is fairly dry.

• Cooking apples are tart apples. The best I know is the Rhode Island Greening, but they’re hard to find. Baldwins and Jonathans are decent, but they’re hard to find too. The British Bramleys are terrific. I’ve made good pies from the French Calville Blanc d’Hiver. But we’re not living in good apple times. Most stores don’t sell apples for cooking. When in doubt, use a mixture.
• The lemon juice and the larger quantity of cinnamon are for when you have tired apples with no oomph. The cheese also serves this purpose. It should be a respectable old cheddar and it should be at room temperature.
• Consumption of too many commercial pies makes me loath to add flour or cornstarch to pie filling. The flour is here in case you fear your apples will be too juicy. I don’t mind juice in a pie, in moderation. If adding flour, mix the apples, sugar, cinnamon, and flour in a bowl before pouring into the crust.
• Lard is best. Its melting point is higher than butter’s. It successfully separates the flour into layers for a light, crispy crust. Butter is more likely to saturate the flour and produce a heavy crust. Some like half butter/half lard, preferring butter for its flavor. But the flavor of lard is nice too, and its porkiness is wonderful with apple.

This recipe is taken from:


The Proof and the Pudding

What Mathematicians, Cooks, and You Have in Common

Jim Henle

“If you’re a fan of Julia Child or Martin Gardner—who respectively proved that anyone can have fun preparing fancy food and doing real mathematics—you’ll enjoy this playful yet passionate romp from Jim Henle. It’s stuffed with tasty treats and ingenious ideas for further explorations, both in the kitchen and with pencil and paper, and draws many thought-providing parallels between two fields not often considered in the same mouthful.”—Colm Mulcahy, author of Mathematical Card Magic: Fifty-Two New Effects