Teaching Math with Sports
One of the goals of this website is to use sports as a key application to aid in the teaching of math. The articles below look at many of the most important mathematical topics applied to the world of sports analytics. Click on any of the following images to be taken to an article about the topic at hand.
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Programming and Data Science
We learn a bit about logistic regression and how to implement an Sklearn logistic regression model in Python. Then we apply this to estimate win probabilities given spreads in the NFL.
Calculus
Fermat’s Theorem is all about optimization via differentiation. We look at how this can be used in the sports world to answer some interesting questions.
Statistics
The binomial distribution is one of the most important distributions to understand to do any sports analytics. Especially in discrete games like baseball and golf, the binomial shows up everywhere.
The Poisson distribution is extremely useful in games where play is continuous – hockey and basketball for example. We see how this sometimes difficult to understand distribution is applied to sports analytics.
Hypothesis testing is a simple yet powerful subject. Sometimes it is hard to tell the difference between truth and statistical noise. Hypothesis testing is the answer.
Related closely to the Poisson distribution, the exponential distribution deals with waiting. Oftentimes in sports we are waiting for something to happen – we look at how this distribution can be applied here.
Permutations and combinations are one of the first things we learn about in stats. Usually the examples are uncompelling – picking balls out of a hat or cards from a deck. We look at some examples in sports.
Assorted Topics
The secretary problem is all about making decisions with incomplete information. We look at how this might apply to contracts in professional sports.
