# The Perfect March Madness Bracket

If you’ve ever filled out a March Madness bracket for the NCAA tournament, you probably realize that you aren’t super likely to pick all of the games perfectly. See how bad your chances are here.

1. How do we arrive at the odds of 1-in -9.2 quintillion? Explain the mathematics that gives that large of an answer.
2. One mathematician actually estimates that your odds are between 1-in-10 billion and 1-in-40 billion. Explain why he feels that you have these slightly better odds than the original 1-in-9.2 quintillion.
3. Using the national accuracy of 66.7% when picking first-round games, and assuming we could continue that success rate over the course of the entire tournament, what are the odds of picking a perfect bracket?
4. Over the past 8 years of bracket challenges, winners have averaged picking 49.8% of games correctly. Using that percentage and the techniques you saw used in the first three questions, what are the odds of a winner predicting a perfect bracket?

Thanks to Hugo W. for suggesting this badge!

# From the NFL’s Big Data Bowl

Alex Stern was a High School student volunteering at a nursing home when he heard about data analytics for the first time. Now, a few years later, he’s finishing college and presenting for NFL teams at the Big Data Bowl. Read about his story here.

1. What was the context of the first model that Alex heard about when volunteering at the assisted living facility? What was the firm measuring about the assisted living facility?
2. What was Alex’s undergraduate degree at the University of Virginia? What is he studying in graduate school (for his Master’s degree)?
3. Explain Alex’s football algorithm and what it measured. Why might NFL teams find this information important?
4. Alex learned a lot about communication when presenting to the Big Data Bowl. Why is it important for statisticians and data analysts to be good communicators?

# Using Math to Cast Space Jam 2

Some mathematicians at 538 examined the original cast of the Monstars in the movie Space Jam to see if they could estimate the closest comparable players in today’s NBA.  See who they chose for the new Monstars team!

Watch the video linked above.  Answer each of the questions in a few complete sentences each.

1.  What does the acronym CARMELO stand for?  Explain in your own words what the CARMELO projections are trying to accomplish.
2.  Look at each of the graphs that compare the original player with the new player  from the modern NBA (pictured below).  Which new cast member appears most similar to the original member they are replacing?
3.  Look at each of the graphs that compare the original player with the new player  from the modern NBA (pictured below).  Which new cast member appears most different to the original member they are replacing?

4.  Fred VanVleet is 9 inches taller than Muggsy Bogues was in the original Space Jam movie.  Explain how they can have the exact same rating on the “height” metric in their comparison graphs.

5.  Pick one of the statistics listed in the comparison graphs that you aren’t sure of its meaning.  Look it up in the basketball-reference glossary and explain what the statistic measures in your own words.

# Women are better than men at free throws

The results of a 30-year study show that women NCAA basketball players shoot free throws more consistently than male NCAA basketball players.  See the findings here.

Before reading the article, make at least three hypotheses that you think might be the reason women tend to be “better” at free throws than men in the NCAA.

Below your hypotheses, write a paragraph explaining what the study found.  What appears to be the major factor in the women’s free throw consistency?  Compare this finding to your hypotheses.

Write a second paragraph stating another athletic comparison that you would like to see studied (for example, “Who makes free throws more consistently, left-handers or right-handers?”).  It doesn’t have to be a basketball comparison.  Explain why you think this comparison could be valuable to athletes and coaches.

# For Teachers: #TMC18 Morning Session Wrap-Up & Reflections

I want to reflect publicly on the amazing experience that was #TMC18.  This was my third Twitter Math Camp, and I get more out of the experience every time I attend.  I am grateful to Dave Sabol, St. Ignatius High School, Lisa Henry, and the entire TMC planning committee for their hard work and dedication.  Thank you all.

# Lindsey Vonn Wants To Race Against The Men. Should the Olympics Allow It?

With the 2018 Winter Olympics in full swing, 538 has published an analysis of Men’s vs. Women’s skiing statistics.  In the history of the Olympics, men and women have always raced separately and received separate medals.  American Olympian Lindsey Vonn wants to be able to race against the men.  What do the numbers say about this?

1.  In what skiing event(s), if any, do men appear to be consistently faster?  In what skiing event(s), if any, do women appear to be consistently faster?  In what skiing event(s), if any, does there not appear to be any discernible difference between men’s and women’s speed?
2. What other factor(s) do we need to take into account about the men’s and women’s skiing events besides their average speed?
3. Using the data provided in the article, write a short paragraph making an argument either FOR or AGAINST women racing against men in alpine skiing events.  (There is no correct stance to take on this issue, but you must use the data to support your claim.  Don’t just state your opinion.)
4. Does Lindsey Vonn think she would win against the men?  What do you think is her motivation for wanting to race against the men?

# The Difference(s) Between Pro and Amateur Soccer Players

With GPS data being used in a variety of sports, we have more data than ever to compare professional athletes.  More than just stats like goals and assists, we can actually measure distance traveled and top speeds.   The folks over at SportTechie have analyzed the data comparing amateur and professional soccer players.  Check out the differences between the players here.

1.  The English Premier League leader in distance covered in the 2016/17 season was Tottenham’s midfielder Christian Eriksen, who covered an average of 11.92 kilometers per match.  If there are approximately 1.62 km. in a mile, how many miles did he average per match?  How does that compare to an average professional midfielder (data included in the article)?
2. Let’s say an amateur attacker and a professional attacker are both racing towards the same spot on the soccer pitch.  Both players are 100 feet from the spot.  Calculate how far away would the amateur player be from that spot when the professional player arrived there, assuming both players ran at their top speed the entire time.  (There are 5280 feet in a mile, and use the speed data from the article linked above).
3. An English Premier League season is 38 games long.  Over the course of a 38-game season, calculate how much farther a professional defender would run when compared to an amateur defender.

# John Urschel’s Problem Sets

Our footballing friend, Baltimore Raven’s Offensive Lineman John Urschel (who we’ve seen before here and here), is now publishing a weekly problem set for The Player’s Tribune.

Take a look at them here:  http://www.theplayerstribune.com/author/jurschel/

Each Wednesday, Mr. Urschel publishes a set of three problems to solve.  Spend approximately 15 minutes per problem working on a solution.  NOTE:  It’s OK if you don’t find a full solution to each problem.  But you need to turn in what looks like approximately 15 minutes worth of work on each problem.  Write out either your solution or your thoughts about this problem (Why was it hard?  Are you close to a solution?  What could you have done differently/better? etc.)

To earn a badge you must turn in your work before the solutions to the problems are published the following Wednesday.  This means you must pick the most current/recent problem set in order to earn a badge for this article.  You are of course welcome to work on these problems just for fun.  Bring them to Mr. Bezaire and he’ll help you!!

# The World’s Fastest Sprinters

Usain Bolt did it again.  Bolt became the first male athlete to win three gold medals in the 100 m dash, cementing his status as the fastest man to ever live.  See the highlights by clicking here or watching the video above.

The Hindustan Times created this cool infographic that details the fastest person from every country. A very interesting way to view some very interesting data points.  Check it out here:  http://www.hindustantimes.com/static/olympics/every-country-fastest-man-in-one-race-100m/

Watch the “race” at both of the links above, and click through the informational pieces that are provided.  Answer the following questions as you click the “NEXT” button.

1.  Use the information about India’s Amiya Kumar Mallick (how far behind he is in distance (m) and time (hundredths of a second) to estimate how fast (meters per second) these world class sprinters are running.
2. Create a new speed estimate based on the distance between Usain Bolt and the runner from Tuvalu (the slowest represented in this list).
3. Are the two speed estimates the same?  Explain why you think this is the case.
4. USN student Morayo ’22 recently won a 100 m. race with a meet-record time of 12.10.  Estimate how many meters she would finish behind Usain Bolt in a race.

# Sumo Analytics

Whoa:  538 (ESPN) has published a huge analytics piece on Sumo wrestling.  Hakuho, a modern great yokozuna in the world of sumo wrestling has drawn comparisons to the historically great Raiden (a great wrestler from the 1700’s — centuries ago!).  They analyze the careers of both wrestlers and try to determine who is the “greatest sumo wrestler of all time”

Check out the article here: http://fivethirtyeight.com/features/the-sumo-matchup-centuries-in-the-making/

Or dive deep into the data yourself here:  http://projects.fivethirtyeight.com/sumo/