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.

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.

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)

Tim Chartier and the Mega Menger

math bytesHow many business cards are needed to complete a level 3 Menger Sponge? What is a level 3 Menger Sponge? Tim Chartier, author of Math Bytes: Google Bombs, Chocolate Covered Pi, and Other Cool Bits in Computing, explains.

In a Huffington Post article, A Million Business Cards Presents a Math Challenge, Chartier asks readers to go in their wallet and check for  business cards. If there aren’t any, “they may be part of a worldwide math challenge. Over the past month, people around the world have been building a mathematical structure out of more than a million business cards.” That mathematical structure is a Mega Menger, but before we get there, let’s discuss what a level 1 Menger Sponge is.

A level 1 Menger Sponge is a fractal consisting of twenty cubes. Each cube is made up of 6 business cards, so a level 1 Menger needs 120 business cards. A level 2 Menger is created using 20 level 1 Mengers, a level 3 Menger is made with 20 level 2 Mengers, so on and so on.

Here at the Press, we don’t have a million business cards to complete a Mega Menger, but we do have a lot of books. Using Chartier’s code (which you can play with here: http://lifeislinear.davidson.edu/sierpinski.html), we were able to create 2-D versions of a Menger Sponge called a Sierpinski Carpet using our jacket images. Below, see if you can figure out which books are featured in the fractal images below! Click the images to see the related title.






Grab your M&Ms and ace math this year with Math Bytes

In this segment from WCCB in Charlotte, NC, Tim Chartier shows how math can be both educational and delicious! This experiment is taken directly from his recent book Math Bytes: Google Bombs, Chocolate-Covered Pi, and Other Cool Bits in Computing. There are lots of other hands-on experiments that are suitable for aspirational mathematicians of all ages in the book.

bookjacket Math Bytes:
Google Bombs, Chocolate-Covered Pi, and Other Cool Bits in Computing
Tim Chartier

Quick Questions for Tim Chartier, author of Math Bytes

Tim Chartier, Photo  courtesy Davidson CollegeTim Chartier is author of Math Bytes: Google Bombs, Chocolate-Covered Pi, and Other Cool Bits in Computing. He agreed to be our first victi… interview subject in what will become a regular series. We will ask our authors to answer a series of questions in hopes to uncover details about why they wrote their book, what they do in their day job, and what their writing process is. We hope you enjoy getting to know Tim!

PUP: Why did you write this book?

Tim Chartier: My hope is that readers simply delight in the book.  A friend told me the book is full of small mathematical treasures.  I have had folks who don’t like math say they want to read it.  For me, it is like extending my Davidson College classroom.  Come and let’s talk math together.  What might we discover and enjoy?  Don’t like math?  Maybe it is simply you haven’t taken a byte of a mathematical delight that fits your palate!

PUP: Who do you see as the audience for this book?

TC: I wanted this book, at least large segments of it, to read down to middle school.  I worked with public school teachers on many of the ideas in this book.  They adapted the ideas to their classrooms.  And yet, the other day, I was almost late taking my kids to school as I had to pull them from reading my book, a most satisfying reason.  In my mime training, Marcel Marceau often said, “Create your piece and let the genius of the audience teach you what you created.”  I see this book that way.  I wrote a book that I see my students and the many to whom I speak in broad public settings smiling at as they listen.  Who all will be in the audience of this book?  That’s for me to learn from the readers.  I look forward to it.

Don’t like math? Maybe it is simply you haven’t taken a byte of a mathematical delight that fits your palate!

PUP: What do you think is the book’s most important contribution?

TC: When I describe the book to people, many respond with surprise or even better a comment like, “I wish I had a teacher like you.”  My current and former students often note that the book is very much like class.  Let’s create and play with ideas and discover how far they can go and, of great interest to me, how fun and whimsical they can be.

PUP: What inspired you to get into your field?

TC: My journey into math came via my endeavors in performing arts.  I was performing in mime and puppetry at international levels in college.  Math was my “back-up” plan.  Originally, I was taking math classes as required courses in my studies in computer science.  I enjoyed the courses but tended to be fonder of ideas in computer science.  I like the creative edge to writing programming.  We don’t all program in the same way and I enjoyed the elegance of solutions that could be found.  This same idea attracted me to math — when I took mathematical proofs.  I remember studying infinity – a topic far from being entirely encompassed by my finite mind.  Yet, through a mathematical lens, I could examine the topic and prove aspects of it.  Much like when I studied mime with Marcel Marceau, the artistry and creativity of mathematical study is what drew me to the field and kept me hooked through doctoral studies.

PUP: What is the biggest misunderstanding people have about what you do?

TC: Many think mathematics is about numbers.  Much of mathematics is about ideas and concepts.  My work lies at the boundary of computer science and mathematics.  So, my work often models the real world so often mathematics is more about thinking how to use it to glean interesting or new information about our dynamic world.  Numbers are interesting and wonderful but so is taking a handful of M&Ms and creating a math-based mosaic of my son or sitting with my daughter and using chocolate chips to estimate the value of Pi.  And, just for the record, the ideas would be interesting even without the use of chocolate but that doesn’t hurt!

PUP: What would you have been if not a mathematician?

TC: Many people think I would have been a full-time performer.  I actually intentionally walked away from that field.  I want to be home, have a home, walk through a neighborhood where I know my neighbors.  To me, I would have found a field, of some kind, where I could teach.  Then, again, I always wanted to be a creative member of the Muppet team – either creating ideas or performing!

I pick projects that I believe aren’t just exciting now, but will be exciting in retrospect.

PUP: What was the best piece of advice you ever received?

TC: At one time, I was quite ill.  It was a scary time with many unknowns.  I remember resting in a dark room and wondering if I could improve and get better.  I reflected on my life and felt good about where I was, even if I was heading into my final stretch.  I remember promising myself that if I ever got better that I would live a life that later — whether it be a decade later or decades and decades later — that I would try to live a life that I could again feel good about whenever I might again be in such a state.  I did improve but I pick projects that I believe aren’t just exciting now, but will be exciting in retrospect.  This book is easily an example of such a decision.

PUP: Describe your writing process. How long did it take you to finish your book? Where do you write?

TC: The early core of the book happened at 2 points.  First, I was on sabbatical from Davidson College working at the University of Washington where I taught Mathematical Modeling.  Some of the ideas of the book drew from my teaching at Davidson and were integrated into that course taught in Seattle.  At the end of the term, my wife Tanya said, “You can see your students and hear them responding.  Sit now and write a draft. Write quickly and let it flow.  Talk to them and get the class to smile.”  It was great advice to me.  The second stage came with my first reader, my sister Melody.  She is not a math lover and is a critical reader of any manuscript. She has a good eye.  I asked her to be my first reader.  She was stunned.  I wanted her to read it as I knew if she enjoyed it, even though there would be parts she wouldn’t understand fully, then I had a draft of the book I wanted to write.  She loved it and soon after I dove into the second draft.

PUP: Do you have advice for other authors?

TC: My main advice came from award-winning author Alan Michael Parker from Davidson College.  As I was finishing, what at the time I saw as close to my final draft, Alan said, “Tim, you are the one who will live with this book for a lifetime.  Many will read it only once.  You have it for the rest of your life.  Write your book. Make sure it is your voice.  Take your time and know it is you.”  His words echoed in me for months.  I put the book down for several months and then did a revision in which I saw my reflection in the book’s pages — I had seen my reflection before but never as clearly.

Tim is the author of:


Math Bytes
Google Bombs, Chocolate-Covered Pi, and Other Cool Bits in Computing
Tim Chartier

“A magnificent and curious romp through a wonderful array of mathematical topics and applications: maze creation, Google’s PageRank algorithm, doodling, the traveling salesman problem, math on The Simpsons, Fermat’s Last Theorem, viral tweets, fractals, and so much more. Buy this book and feed your brain.”–Clifford A. Pickover, author of The Math Book

Math Bytes is a playful and inviting collection of interesting mathematical examples and applications, sometimes in surprising places. Many of these applications are unique or put a new spin on things. The link to computing helps make many of the topics tangible to a general audience.”–Matt Lane, creator of the Math Goes Pop! Blog


#PiDay Activity: Using chocolate chips to calculate the value of pi

Chartier_MathTry this fun Pi Day activity this year. Mathematician Tim Chartier has a recipe that is equal parts delicious and educational. Using chocolate chips and the handy print-outs below, mathematicians of all ages can calculate the value of pi. Start with the Simple as Pi recipe, then graduate to the Death by Chocolate Pi recipe. Take it to the next level by making larger grids at home. If you try this experiment, take a picture and send it in and we’ll post it here.

Download: Simple as Pi [Word document]
Download: Death by Chocolate Pi [Word document]

For details on the math behind this experiment please read the article below which is cross-posted from Tim’s personal blog. And if you like stuff like this, please check out his new book Math Bytes: Google Bombs, Chocolate-Covered Pi, and Other Cool Bits in Computing.

For more Pi Day features from Princeton University Press, please click here.


Chocolate Chip Pi

How can a kiss help us learn Calculus? If you sit and reflect on answers to this question, you are likely to journey down a mental road different than the one we will traverse. We will indeed use a kiss to motivate a central idea of Calculus, but it will be a Hershey kiss! In fact, we will have a small kiss, more like a peck on the cheek, as we will use white and milk chocolate chips. The math lies in how we choose which type of chip to use in our computation.

Let’s start with a simple chocolatey problem that will open a door to ideas of Calculus. A Hershey’s chocolate bar, as seen below, is 2.25 by 5.5 inches. We’ll ignore the depth of the bar and consider only a 2D projection. So, the area of the bar equals the product of 2.25 and 5.5 which is 12.375 square inches.

Note that twelve smaller rectangles comprise a Hershey bar. Suppose I eat 3 of them. How much area remains? We could find the area of each small rectangle. The total height of the bar is 2.25 inches. So, one smaller rectangle has a height of 2.25/3 = 0.75 inches. Similarly, a smaller rectangle has a width of 5.5/4 = 1.375. Thus, a rectangular piece of the bar has an area of 1.03125, which enables us to calculate the remaining uneaten bar to have an area of 9(1.03125) = 9.28125 square inches.

Let’s try another approach. Remember that the total area of the bar is 12.375. Nine of the twelve rectangular pieces remain. Therefore, 9/12ths of the bar remains. I can find the remaining area simply be computing 9/12*(12.375) = 9.28125. Notice how much easier this is than the first method. We’ll use this idea to estimate the value of π with chocolate, but this time we’ll use chocolate chips!

Let’s compute the area of a quarter circle of unit radius, which equals π/4 since the full circle has an area of π. Rather than find the exact area, let’s estimate. We’ll break our region into squares as seen below.

This is where the math enters. We will color the squares red or white. Let’s choose to color a square red if the upper right-hand corner of the square is in the shaded region and leave it white otherwise, which produces:

Notice, we could have made other choices. We could color a square red if the upper left-hand corner or even middle of the square is under the curve. Some choices will lead to more accurate estimates than others for a given curve. What choice would you make?

Again, the quarter circle had unit radius so our outer square is 1 by 1. Since eight of the 16 squares are filled, the total shaded area is 8/16.

How can such a grid of red and white squares yield an estimate of π? In the grid above, notice that 8/16 or 1/2 of the area is shaded red. This is also an approximation to the area of the quarter circle. So, 1/2 is our current approximation to π/4. So, π/4 ≈ 1/2. Solving for π we see that π ≈ 4*(1/2) = 2. Goodness, not a great estimate! Using more squares will lead to less error and a better estimate. For example, imagine using the grid below:

Where’s the chocolate? Rather than shading a square, we will place a milk chocolate chip on a square we would have colored red and a white chocolate chip on a region that would have been white. To begin, the six by six grid on the left becomes the chocolate chip mosaic we see on the right, which uses 14 white chocolate of the total 36 chips. So, our estimate of π is 2.4444. We are off by about 0.697.

Next, we move to an 11 by 11 grid of chocolate chips. If you count carefully, we use 83 milk chocolate chips of the 121 total. This gives us an estimate of 2.7438 for π, which correlates to an error of about 0.378.

Finally, with the help of public school teachers in my seminar Math through Popular Culture for the Charlotte Teachers Institute, we placed chocolate chips on a 54 by 54 grid. In the end, we used 2232 milk chocolate chips giving an estimate of 3.0617 having an error of 0.0799.

What do you notice is happening to the error as we reduce the size of the squares? Indeed, our estimates are converging to the exact area. Here lies a fundamental concept of Calculus. If we were able to construct such chocolate chip mosaics with grids of ever increasing size, then we would converge to the exact area. Said another way, as the area of the squares approaches zero, the limit of our estimates will converge to π. Keep in mind, we would need an infinite number of chocolate chips to estimate π exactly, which is a very irrational thing to do!

And finally, here is our group from the CTI seminar along with Austin Totty, a senior math major at Davidson College who helped present these ideas and lead the activity, with our chocolatey estimate for π.

March Mathness Winner

Davidson College student, Jane Gribble, was our March Mathness winner this year. Below she explains how she filled in her bracket.




I love basketball – Davidson College basketball. As a Davidson College cheerleader I have an enormous amount of school pride, especially when it comes to our basketball team. However, outside of Davidson College I know little to nothing about college basketball. I knew that UNC Chapel Hill was having a tough season because this is my sister’s alma mater. Also, I knew that New Mexico, Gonzaga, Duke, and Montana were all likely teams for the NCAA tournament because we had played these non-conference teams during our season and these were the most talked about non-conference games around campus. My name is Jane Gribble. I am a junior mathematics major and this is the first year I completed a bracket.

In Dr. Tim Chartier’s MAT 210 – Mathematical Modeling course we discussed sports ranking using the Colley method and the Massey method. We were given the opportunity to apply our new knowledge of sports ranking in the NCAA Tournament Challenge. Since Davidson College was participating in the tournament my focus was on one game, the Davidson/Marquette game in Lexington, KY. When we traveled to KY I thought I had missed my opportunity to fill out a bracket, but one of my classmates was also traveling for the game with the Davidson College Pep Band and had the modeling program on his computer. We completed our brackets in the hotel lobby in Kentucky the night before our game.

My bracket used the Massey method because in previous years it has had better success than the Colley method. I decided to submit only one bracket, a bracket solely based on math (partially because I know little about college basketball). As a cheerleader and a prideful student it upset me to have Davidson losing against Marquette the following night, but I wasn’t going to let a math model crush my personal dreams of success in the tournament.  The home games were weighted as .5 (it would have been 1 if it was an unweighted model) to take into account home court advantage. Similarly, away games were weighted as 1.5 and neutral games as 1. Also, the season was segmented into 6 equal sections. I believe games at the end of the season are more important than games at the beginning of the season because teams change throughout the year and the last games give the best perspective of the teams going into the tournament. There was no real reason for the numbers chosen, other than they increased each segment. The 6 equal sections were weighted: .4, .6, .8, 1, 1.5, and 2. With these weights in the Massey method my model correctly predicted the Minnesota upset, but missed the Ole Miss, LaSalle, Harvard, and Florida Gulf upsets.

After Davidson’s tragic loss I could not watch anymore basketball for a while. I even forgot that my bracket was in the competition. I only started paying attention to the brackets when a friend in the same competition congratulated me on being second going into the Elite 8; my math based bracket was in the top 10 percent of all the brackets. Once he told me my bracket had a chance of winning, I paid attention to the rest of the games to see how my bracket was doing in the competition. After Davidson’s loss against Louisville last year in the tournament I never wanted to cheer for Louisville. To my surprise, I went into the final game this year cheering for Louisville because my model had Louisville winning it all. I was not cheering for Louisville because of any connections with the team, but was cheering to receive a free ice cream cone, a prize that our local Ben and Jerry’s donates to the winner of  Dr. Chartier’s class pool.

Next year I hope to compete in the NCAA tournament challenge again. This year I greatly enjoyed the experience and want to continuing submitting brackets for the tournament. Next year I will submit one bracket that uses the exact weightings of my bracket this year to see how it compares from year to year. This year I wanted to submit a math bracket that looked at teams who had injuries throughout the season. My motivation for this was Davidson’s player Clint Mann. Clint had to sit out many games towards the end of the season because of a concussion, but he had recovered in time for the NCAA tournament. I thought that our wins during the time without Clint showed our strengths as a team. Unfortunately this year I ran out of time to code this additional weighting. Hopefully next year my submissions will include a bracket using the weights from this year, a bracket that includes weights for teams with injured team members, and another bracket with varying weights.