## Solving last week’s L.A. Math challenge

We’re back with the conclusion to last week’s LA Math challenge, The Case of the Vanishing Greenbacks, (taken from chapter 2 of the book). After the conclusion of the story, we’ll talk a little more with the author, Jim Stein. Don’t forget to check out the fantastic trailer for LA Math here.

Forty‑eight hours later I was bleary‑eyed from lack of sleep. I had made no discernible progress. As far as I could tell, both Stevens and Blaisdell were completely on the up‑and‑up.   Either I was losing my touch, or one (or both) of them were wasting their talents, doctoring books for penny‑ante amounts.   Then I remembered the envelope Pete had sealed. Maybe he’d actually seen something that I hadn’t.

I went over to the main house, to find Pete hunkered down happily watching a baseball game. I waited for a commercial break, and then managed to get his attention.

“I’m ready to take a look in the envelope, Pete.”

“Have you figured out who the guilty party is?”

“Frankly, no. To be honest, it’s got me stumped.” I moved to the mantel and unsealed the envelope. The writing was on the other side of the piece of paper. I turned it over. The name Pete had written on it was “Garrett Ryan and the City Council”!

I nearly dropped the piece of paper. Whatever I had been expecting, it certainly wasn’t this. “What in heaven’s name makes you think Ryan and the City Council embezzled the money, Pete?”

“I didn’t say I thought they did. I just think they’re responsible for the missing funds.”

I shook my head. “I don’t get it. How can they be responsible for the missing funds if they didn’t embezzle them?”

“They’re probably just guilty of innumeracy. It’s pretty common.”

“I give up. What’s innumeracy?”

“Innumeracy is the arithmetical equivalent of illiteracy. In this instance, it consists of failing to realize how percentages behave.” A pitching change was taking place, so Pete turned back to me. “An increase in 20% of the tax base will not compensate for a reduction of 20% in each individual’s taxes.   Percentages involve multiplication and division, not addition and subtraction. A gain of 20 dollars will compensate for a loss of 20 dollars, but that’s because you’re dealing with adding and subtracting. It’s not the same with percentages, because the base upon which you figure the percentages varies from calculation to calculation.”

“You may be right, Pete, but how can we tell?”

Pete grabbed a calculator. “Didn’t you say that each faction was out \$198,000?”

I checked my figures. “Yeah, that’s the amount.”

Pete punched a few numbers into the calculator. “Call Ryan and see if there were 99,000 taxpayers in the last census. If there were, I’ll show you where the money went.”

I got on the phone to Ryan the next morning. He confirmed that the tax base in the previous census was indeed 99,000. I told Pete that it looked like he had been right, but I wanted to see the numbers to prove it.

Pete got out a piece of paper. “I think you can see where the money went if you simply do a little multiplication. The taxes collected in the previous census were \$100 for each of 99,000 individuals, or \$9,900,000. An increase of 20% in the population results in 118,800 individuals. If each pays \$80 (that’s the 20% reduction from \$100), the total taxes collected will be \$9,504,000, or \$396,000 less than was collected after the previous census. Half of \$396,000 is \$198,000.”

I was convinced. “There are going to be some awfully red faces down in Linda Vista. I’d like to see the press conference when they finally announce it.” I went back to the guesthouse, called Allen, and filled him in. He was delighted, and said that the check would be in the mail.   As I’ve said before, when Allen says it, he means it. Another advantage of having Allen make the arrangements is that I didn’t have to worry about collecting the fee, which is something I’ve never been very good at.

I wondered exactly how they were going to break the news to the citizens of Linda Vista that they had to pony up another \$396,000, but as it was only about \$3.34 per taxpayer I didn’t think they’d have too much trouble. Thanks to a combination of Ryan’s frugality and population increase, the tax assessment would still be lower than it was after the previous census, and how many government agencies do you know that actually reduce taxes? I quickly calculated that if they assessed everyone \$3.42 they could not only cover the shortage, but Allen’s fee as well. I considered suggesting it to Ryan, but then I thought that Ryan probably wasn’t real interested in hearing from someone who had made him look like a bungler.

My conscience was bothering me, and I don’t like that. I thought about it, and finally came up with a compromise I found acceptable. I went back to the main house.

Pete was watching another baseball game. The Dodgers fouled up an attempted squeeze into an inning‑ending double play. Pete groaned. “It could be a long season,” he sighed.

“It’s early in the year.” I handed him a piece of paper. “Maybe this will console you.”

“What’s this?” He was examining my check for \$1,750. “Your rent’s paid up.”

“It’s not for the rent, Pete. It’s your share of my fee.”

“Fee? What fee?”

“That embezzling case in Orange County. It was worth \$3,500 to me to come up with the correct answer. I feel you’re entitled to half of it. You crunched the numbers, but I had the contacts and did the legwork.”

Pete looked at the check. “It seems like a lot of money for very little work. Tell you what. I’ll take \$250, and credit the rest towards your rent.”

A landlord with a conscience! Maybe I should notify the Guinness Book of Records. “Seems more than fair to me.”

Pete tucked the check in the pocket of his shirt. “Tell me, Freddy, is it always this easy, doing investigations?”

I summoned up a wry laugh. “You’ve got to be kidding. So far, I’ve asked you two questions that just turned out to be right down your alley. I’ve sometimes spent months on a case, and come up dry. That can make the bottom line look pretty sick. What’s it like in your line of work?”

“I don’t really have a line of work. I have this house and some money in the bank. I can rent out the guesthouse and make enough to live on. People know I’m pretty good at certain problems, and sometimes they hire me. If it looks like it might be interesting, I’ll work on it.” He paused. “Of course, if they offer me a ridiculous amount of money, I’ll work on it even if it’s not interesting. Hey, we’re in a recession.”

“I’ll keep that in mind.”   I turned to leave the room. Pete’s voice stopped me.

“Haven’t you forgotten something?”

I turned around. “I give up. What?”

“We had a bet. You owe me five bucks.”

I fished a five out of my wallet and handed it over. He nodded with satisfaction as he stuffed it in the same pocket as the check, and then turned his attention back to the game.

What made you include this particular idea in the book?

JS: The story features one of the most common misunderstandings about percentages.  There are innumerable mistakes made because people assume that percentages work the same way as regular quantities.  But they don’t — if a store lowers the cost of an item by 30% and then by another 20%, the cost of the item hasn’t been lowered by 50% — although many people make the mistake of assuming that it has.  I’m hoping that the story is sufficiently memorable that if a reader is confronted by a 30% discount followed by a 20% discount, they’ll think “Wasn’t there something like that in The Case of the Vanishing Greenbacks?

There are 14 stories in the book, and each features a mathematical point, injected into the story in a similar fashion as the one above.  I think the stories are fun to read, and if someone reads the book and remembers just a few of the points, well, I’ve done a whole lot better than when I was teaching liberal arts math the way it is usually done.

James D. Stein is emeritus professor in the Department of Mathematics at California State University, Long Beach. His books include LA Math, Cosmic Numbers (Basic) and How Math Explains the World (Smithsonian).

## Anna Frebel on the search for the oldest stars

Astronomers study the oldest observable stars in the universe in much the same way that archaeologists study ancient artifacts on Earth. Stellar archaeologist Anna Frebel is credited with discovering several of the oldest and most primitive stars, and her book, Searching for the Oldest Stars is a gripping firsthand account of her work. Recently she took the time to answer some questions:

What is your main research topic and what is stellar archaeology?

AF: My work is broadly centered on finding the oldest stars in the universe and using them to explore how the first stars and the first galaxies formed soon after the Big Bang. This works because these ancient stars are about 13 billion years old and they are still shining. The universe itself, by comparison, is 13.8 billion years old. I find these ancient stars in the outskirts of the Milky Way galaxy, using a large telescope. I’m also researching how the chemical elements heavier than hydrogen and helium were first created in those early stars, which ultimately allowed Earth to form and to bring about life in the universe.

AF: I have been fortunate enough to discover several “record holding stars”. In 2007, I found a 13.2 billion year-old star, which is incredibly old. This followed the 2005 discovery of the chemically most primitive star – a star of the second generation of stars to have formed in the universe. Since then, I have analyzed some incredible ancient stars in dwarf galaxies that orbit the Milky Way galaxy, and together with my team, we have recently beaten said 2005 record, which was enormously exciting.

Why do people say we are made from stardust?

AF: We humans are made from all sorts of different chemical elements, mostly carbon. We breathe oxygen and nitrogen, we wear silver and gold jewelry. All these elements were once, atom by atom, created inside different kinds of stars and their supernova explosions over the course of billions of years. Studying this evolution of the chemical elements in the universe with the help of ancient stars means that I’m literally studying the cosmic origins of the building blocks of life. So we really are closely connected with the universe, far more than we realize.

How did you decide to become a scientist?

AF: From a young age I knew I wanted to study stars. They were just so fascinating to me, these big spheres of gas, fusing new elements to gain energy to shine for eons in the sky. Fortunately, I received good advice during high school on how to become an astronomer. After studying physics until 2002, I turned to astronomy and the rest is history. Today, I take pride in sharing my story with young people and the general public by telling them what astronomers do on a daily basis, and how scientific results are achieved. I am passionate about conveying the importance of science literacy to the young and the young at heart while inspiring them with the beauty and mystery of the cosmos.

What kind of telescope is used for your astronomical observations?

AF: Astronomers use all kinds of different telescopes on Earth as well as from space to peer deep into the cosmos. It depends on the type of project and the brightness of the objects which telescope is best suited. Space observations are being carried out remotely, whereas ground-based observations are still done by the astronomer who has to travel to the telescope. More and more telescopes are becoming automated to enable remote controlled “office observing”.

Anna Frebel in front of the 6.5m Magellan Telescope in Chile.

Are you traveling to any telescopes?

AF: Yes, I regularly fly to Chile to the Magellan Telescopes to carry out my observations. These are some of the largest telescopes in the world and the dark night sky in the Southern Hemisphere is terrific for studying the cosmos. It’s the favorite part of my job and I love discovering new facts about the universe through these observations!

What does it mean when you say you’re going observing?

AF: To use the telescopes, you have to fly to Chile. First to Santiago, then to La Serena and from there is a 2-3h drive up the mountains of the Atacama Desert where the telescopes are. There are guest rooms there for the observers to sleep during the day and the observatory chefs are cooking delicious meals for everyone. Dinner is eaten together by all observers, including the technical staff. It’s a little community with the sole purposes of caring for the telescopes and obtaining exquisite astronomical observations all night long of a breathtaking sky.

What does a typical night at the telescope look like?

AF: All preparations for the night happen during the afternoon while it’s still light outside. After sunset, I usually choose the first targets from my list, which I begin to observe soon after dark. Each star is observed for 10-30 minutes. We immediately inspect each observation and then decide on the fly whether we need more data or not. If we have found an interesting old star we may choose to immediately observe it for a few more hours.

Did anything ever go wrong at the telescopes?

AF: Of course! Mostly when it’s cloudy because then we can’t observe any starlight. This can be very frustrating because it can mean that we have to come back to the telescope a year later to try again. Clouds spell bad luck. Other times, the air layers above the telescope are often not as smooth as is required. This makes the stars twinkle and appear less sharp, which means less good data and longer exposure times. And sometimes there are technical problems with the telescope too.

How do you get your telescope time? Can I go to your telescope and observe, too?

AF: To obtain telescope time, astronomers have to submit a proposal to a committee that selects the best projects and awards them the time. The proposal contains a detailed description of the project and the technical details on what information is being sought. Telescope use is restricted to professional astronomers because of the considerable expense. The cost is about USD 50,000 to 100,000 per night, depending on the telescope, and often paid by various institutions and universities who jointly operate observatories. While this is a lot of money, it’s actually not that much in comparison to many other research facilities.

Are there any special moments at the telescope that you remember in particular?

AF: Yes, going observing is always magical and memorable. Of course I particularly remember big discoveries and the excited nervousness of checking and checking whether we didn’t make a mistake and that the discovery was really what it appeared to be. Then, there have been the frustrating moments of sitting at the telescopes for nights on end listening to the rain and flying home empty-handed. I have been there when severe technical problems and even a bush fire prevented observing during clear nights. But I always associate observing with the most colorful sunsets, the calm and peaceful atmosphere up in the mountains, and of course the sleepless but exciting nights.

Anna Frebel is the Silverman (1968) Family Career Development Assistant Professor in the Department of Physics at the Massachusetts Institute of Technology. She is author of Searching for the Oldest Stars, and has received numerous international honors and awards for her discoveries and analyses of the oldest stars. She lives in Cambridge, Massachusetts.

## #MammothMonday: PUP’s pups sound off on How to Clone a Mammoth

The idea of cloning a mammoth, the science of which is explored in evolutionary biologist and “ancient DNA expert” Beth Shapiro’s new book, How to Clone a Mammoth, is the subject of considerable debate. One can only imagine what the animal kingdom would think of such an undertaking, but wonder no more. PUP staffers were feeling “punny” enough to ask their best friends:

Chester can’t get past “ice age bones”.

Buddy thinks passenger pigeons would be so much more civilized… and fun to chase.

Tux always wanted to be an evolutionary biologist…

Stella thinks 240 pages on a glorified elephant is a little excessive. Take her for a walk.

A mammoth weighs how much?! Don’t worry, Murphy. The tundra is a long way from New Jersey.

Glad we got that out of our systems. Check out a series of original videos on cloning from How to Clone a Mammoth author Beth Shapiro here.

## 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.)

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 SportsReference.com, 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.

## 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.

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

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

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

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

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

(1)

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

(2)

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

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

(3)

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

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

(4)

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

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

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

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

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

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

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

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

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

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

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

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.

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.

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)

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

Check back here soon for more hoop scoop!

• Selection Sunday, March 15, ESPN

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

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

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

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

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

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

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

## Calculus predicts more snow for Boston

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

[Photo Credit: John Talbot]

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

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

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

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

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

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

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

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

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

## Place Your Bets: Tim Chartier Develops FIFA Foe Fun to Predict World Cup Outcomes

Tim Chartier, author of Math Bytes: Google Bombs, Chocolate-Covered Pi, and Other Cool Bits in Computing has turned some mathematical tricks to help better predict the outcome of this year’s World Cup in Brazil.

Along with the help of fellow Davidson professor Michael Mossinghoff and Whittier professor Mark Kozek, Chartier developed FIFA Foe Fun, a program that enables us ordinary, algorithmically untalented folk to generate a slew of possible match outcomes. The tool weighs factors like penalty shoot-outs and the number of years of matches considered, all with the click of a couple buttons. Chartier used a similar strategy in his March Mathness project, which allowed students and basketball fans alike to create mathematically-produced brackets – many of which were overwhelmingly successful in their predictions.

Although the system usually places the most highly considered teams, like Brazil, Germany, and Argentina at the top, the gadget is still worth a look. Tinker around a bit, and let us know in the comments section how your results pan out over the course of the competition.

In the meantime, check out the video below to hear Chartier briefly spell out the logic of the formula.

Happy calculating!

## March Mathness Postscript

The ups and downs of March Madness are slipping into memory, but we have one final postscript to write. Who won the March Mathness challenge put forth by Tim Chartier to his students at Davidson College?

We are delighted to announce that Robin Malloch, a history major who is graduating this month, picked the best brackets out of Dr. Chartier’s class. She has joined Teach for America and will be teaching Middle School math in Charlotte, North Carolina this fall. Thankfully, before she heads off to do the good work of teaching algebra and geometry to eager (or truthfully, not so eager, we’re guessing) 7th and 8th graders, she has provided us with some insight into her bracket strategy during March Mathness:

I will describe my method as best I can. I made several brackets with different methodologies–one based on basketball gut, one on pure math, and others with a combination.  My purely mathematical bracket did not fair that well. My two combination brackets which were a bit more arbitrary fared the best (89th and 92nd percentile). For those, I used the math to inform my basketball knowledge. Any time my basketball bracket contradicted the math ranking or if the rankings were nearly tied, I would look into the game further to see if either team had key players injured, how player matchups, what experience/track record the coaches had, etc.

As for my actual math rankings, I used Colley. I tried to account for strength of schedule, so I broke the season into three parts. The last couple games of the season are the conference tournament, which I gave more value to than any other games of the season. Then I broke the rest of the season in half: the first half being primarily out-of-conference games and the second half being primarily in-conference ones. For teams in a competitive conference (more than 3 teams from the conference in the tournament), I weighted the second half more heavily. For teams in weaker conferences, I weighted the first half more heavily when teams would likely face tougher competition.  This method actually made my rankings closely resemble the 1-16 rankings produced by the NCAA selection committee.  I enjoyed applying math brackets and looking at basketball with a new lens.

For more on the various bracketology methods Tim Chartier teaches his students, please check out our March Mathness page.