A graph search solution to this week’s scrabble-related riddle from fivethirtyeight.

About a week ago, I saw this post on the subreddit /r/dataisbeautiful. After watching Redditor FDTAEV’s animation, I found myself wishing there was a visualization better suited for comparing the careers of baseball players who found themselves in the top 10 home run leaders. So using the same data, I set out to make one.

Short post today– just wanted to share these charts that I made following this weekend’s Superbowl game between the Denver Broncos and the Carolina Panthers. What I’ve done is grab estimated play-by-play win probabilities from here and rank plays according to how much win probability they added. Denver’s defense came out as the star of the show, with Manning’s offense only generating 5 of the top 25 most decisive plays. Plays in the top 25 include forced turnovers, critical third-down stops, and long returns on special teams.

So I’m reviewing data structures and I’ve found some good implementation examples in Python for most of the big ones. However, try as I might, I couldn’t find a good example of a trie implemented in Python that used object-oriented principles. Several examples that I did find recommended using a list of lists, or a dictionary of dictionaries to represent a trie. I found those to be too sloppy and hard to read, so I made this.

Lately I’ve been obsessed with fivethirtyeight’s sports coverage. Their projects range from complex interactive data products like CARMELO (which attempts to probabilistically forecast the career of NBA players by identifying predictive statistics and comparing current players with players from history with similar statistics) to short studies like this one which shows that fast pitchers are more effective in October, and that fast balls were particularly effective against the Cubs during this year’s Leauge Championship series.