### Touring Waldo; Overfitting Waldo; Scanning Waldo; Waldo, Waldo, Waldo

Randal Olson has written a nice article on finding Waldo - Here’s Waldo: Computing the optimal search strategy for finding Waldo. Randal presents a variety of machine learning methods to find very good search paths among the 68 known locations of Waldo. Of course, there's no need for an approximation; modern algorithms can optimize tiny problems like these exactly.

One approach would be to treat this as a traveling salesman problem with Euclidean distances as edge weights, but you'll need to add a dummy node that has edge weight 0 to every node. Once you have the optimal tour, delete the dummy node and you have your optimal Hamiltonian path.

I haven't coded in the dummy node yet, but here's the Waldo problem as a traveling salesman problem using TSPLIB format.

The Condorde software package optimizes this in a fraction of a second:

I'll be updating this article to graphically show you the results for the optimal Hamiltonian path. There are also many additional questions I'll address. Do we really want to use this as our search path? We're obviously overfitting. Do we want to assume Waldo will never appear in a place he hasn't appeared before? When searching for Waldo we see an entire little area, not a point, so a realistic approach would be to develop a scanning algorithm that covers the entire image and accounts for our viewing point and posterior Waldo density. We can also jump where we're looking at from point to point quickly while not searching for Waldo, but scans are much slower.

1. Great/important stuff. Would love to see an efficient way to pull/clean CA's xls data!

### 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…

### Gambling to Optimize Expected Median Bankroll

Gambling to optimize your expected bankroll mean is extremely risky, as you wager your entire bankroll for any favorable gamble, making ruin almost inevitable. But what if, instead, we gambled not to maximize the expected bankroll mean, but the expected bankroll median?

Let the probability of winning a favorable bet be $$p$$, and the net odds be $$b$$. That is, if we wager $$1$$ unit and win, we get back $$b$$ units (in addition to our wager). Assume our betting strategy is to wager some fraction $$f$$ of our bankroll, hence $$0 \leq f \leq 1$$. By our assumption, our betting strategy is invariant with respect to the actual size of our bankroll, and so if we were to repeat this gamble $$n$$ times with the same $$p$$ and $$b$$, the strategy wouldn't change. It follows we may assume an initial bankroll of size $$1$$.

Let $$q = 1-p$$. Now, after $$n$$  such gambles our bankroll would have a binomial distribution with probability mass function \[ \Pr(k,n,p) = \binom{n}{k} p^k q^{n-k…