WPP #2 - Follow-Up

Intro

Since the last time I checked, the UI hasn't posted the answers to the challenge questions.  I couldn't save the code for our solutions either, but I'll do a writeup with the gist of how to solve the problems.

Intro to a project I actually did on Sunday

So I actually did a project on Sunday. I wrote a NHL playoff predictor. It's fairly rudimentary and only based on the relative regular-season strengths (% of games won in the regular season), but it does 10,000 series simulations with 100 single-game simulations to predict the outcome of a series with reasonable accuracy.

The Maths

So the way the winner is calculated is by a HUGE series of averages.

First, the user inputs the strengths of each team over the regular season. The difference between the two is calculated (using Math.abs(strength1 - strength2)) and stored in a global variable.

In the series() method, it first enters a loop which will individually run 10k times, simulating a full playoff series 7 times. After this, it enters the individual series loop, which terminates after one team reaches 4 wins. Each game is simulated 100 times, and goes like this:

So each team's win is calculated by the difference in strengths applied as a handicap for the worse team. If one team has a strength of, say, .665 (the St. Louis Blues) and another has a strength of .610 (the Minnesota Wild), the difference of these strengths is .055. The method generates a random number between 1 and 0. This actually determines the winner of the game, as it is entirely random. If the teams have the same strength (in the case of the Washington Capitals and the New York Islanders), each team has odds of .5.  If the random number is in the 0 - .49 range, the Capitals win. Otherwise, the Islanders win. In the case of the Blues vs the Wild, however, the range of winning numbers increases by the difference in strength. For the Blues/Wild series, the blues can win in the range of 0 - .555, and the Wild have the other portion. This seems like a small and insignificant change in range, but when run, the program predicts a 4-1 series victory for the Blues almost 99% of the time.

Next comes the calc() method, which looks like this:

The aggregating portion works through the entire array and compiles the scores and the amount of wins per team. The winning score is calculated by taking the average of all the elements in the array, and then the % won and #/10000 win is calculated by counting the number of 4's in the winning team's array and dividing by 10000.

So there it is, the whole project. It can be found in my Github repo under the name 'Predictor', and I'll keep it updated.  Here are the projected scores for each series:

WEST

Blues vs Wild
Blues win 4-1 @98.54%

Predators vs Blackhawks
Predators win 4-3 @62.19%

Ducks vs Jets
Ducks win 4-1 @99.24%

Canucks vs Flames
Canucks win 4-2 @80.77%

EAST

Canadiens vs Senators

Montreal wins 4-1 @99.69%

Lightning vs Red Wings

Lightning win 4-1 @96.82%

Rangers vs Penguins
Rangers win 4-1 @99.99%

Capitals vs Islanders

Islanders win 4-3 @58.58%