### Solving IMO 1989 #6 using Probability and Expectation

IMO 1989 #6: A permutation $$\{x_1, x_2, \ldots , x_m\}$$ of the set $$\{1, 2, \ldots , 2n\}$$, where $$n$$ is a positive integer, is said to have property $$P$$ if $$| x_i - x_{i+1} | = n$$ for at least one $$i$$ in $$\{1, 2, ... , 2n-1\}$$. Show that for each $$n$$ there are more permutations with property $$P$$ than without.

Solution: We first observe that the expected number of pairs $$\{i, i+1\}$$ for which $$| x_i - x_{i+1} | = n$$ is $$E = 1$$. To see this note if $$j$$, $$1 \leq j \leq n$$, appears in position $$1$$ or $$2n$$ it's adjacent to one number, otherwise two. Thus the probability it's adjacent to its partner $$j+n$$ in a random permutation is \eqalign{ e_j &= \frac{2}{2n}\cdot \frac{1}{2n-1} + \frac{2n-2}{2n}\cdot \frac{2}{2n-1} \\ &= \frac{2(2n-1)}{2n(2n-1)} \\ &= \frac{1}{n}. } By linearity of expectation we overall have the expected number of $$j$$ adjacent to its partner $$j+n$$ is $$\sum_{j=1}^{n} e_j = n\cdot\frac{1}{n} = 1$$.

More is true. By the same argument, if we remove any partner pair $$\{j,j+n\}$$, the expected number of partner pairs in a random permutation of the remaining integers is still 1. This is the critical observation.

Conditional on the partner pair $$\{j,j+n\}$$ appearing in a random permutation, what is the expected number of partner pairs $$e$$? Observe that if $$n>1$$ it must be less than 2, since as before the expected number of partner pairs ignoring $$\{j,j+n\}$$ is 1, and the probability the $$\{j,j+n\}$$ pair where they appear has separated another partner pair is greater than 0.

Putting this together, if $$n=1$$ the property $$P$$ obviously holds. For $$n>1$$, note the expected number of partner pairs $$E = p\cdot e$$, where $$p$$ is the probability that a random permutation has property $$P$$ and $$e$$ is as before. But we already know $$E=1$$, and by the previous argument if $$n>1$$ we have $$e<2$$, hence $$1 = p\cdot e < 2p$$ and we conclude $$p > \frac{1}{2}$$.

### A Bayes' Solution to Monty Hall

For any problem involving conditional probabilities one of your greatest allies is Bayes' Theorem. Bayes' Theorem says that for two events A and B, the probability of A given B is related to the probability of B given A in a specific way.

Standard notation:

probability of A given B is written $$\Pr(A \mid B)$$
probability of B is written $$\Pr(B)$$

Bayes' Theorem:

Using the notation above, Bayes' Theorem can be written: $\Pr(A \mid B) = \frac{\Pr(B \mid A)\times \Pr(A)}{\Pr(B)}$Let's apply Bayes' Theorem to the Monty Hall problem. If you recall, we're told that behind three doors there are two goats and one car, all randomly placed. We initially choose a door, and then Monty, who knows what's behind the doors, always shows us a goat behind one of the remaining doors. He can always do this as there are two goats; if we chose the car initially, Monty picks one of the two doors with a goat behind it at random.

Assume we pick Door 1 and then Monty sho…

### Mixed Models in R - Bigger, Faster, Stronger

When you start doing more advanced sports analytics you'll eventually starting working with what are known as hierarchical, nested or mixed effects models. These are models that contain both fixed and random effects. There are multiple ways of defining fixed vs random random effects, but one way I find particularly useful is that random effects are being "predicted" rather than "estimated", and this in turn involves some "shrinkage" towards the mean.

Here's some R code for NCAA ice hockey power rankings using a nested Poisson model (which can be found in my hockey GitHub repository):
model <- gs ~ year+field+d_div+o_div+game_length+(1|offense)+(1|defense)+(1|game_id) fit <- glmer(model, data=g, verbose=TRUE, family=poisson(link=log) ) The fixed effects are year, field (home/away/neutral), d_div (NCAA division of the defense), o_div (NCAA division of the offense) and game_length (number of overtime periods); off…

### Notes on Setting up a Titan V under Ubuntu 17.04

I recently purchased a Titan V GPU to use for machine and deep learning, and in the process of installing the latest Nvidia driver's hosed my Ubuntu 16.04 install. I was overdue for a fresh install of Linux, anyway, so I decided to upgrade some of my drives at the same time. Here are some of my notes for the process I went through to get the Titan V working perfectly with TensorFlow 1.5 under Ubuntu 17.04.

Old install:
Ubuntu 16.04
EVGA GeForce GTX Titan SuperClocked 6GB
2TB Seagate NAS HDD