Stattleship! Sport Stats API

I’ve been in contact with the team over at Stattleship. They have a cool API that allows you to get various stats for basketball, football and hockey. I used data from that API to create the following data visualization for their blog. The visualization shows the offensive and special team yards gained by each team […]

Twitter Sentiment — Penguins VS. Rangers Gm 4

Game 4 of the Penguins-Rangers series featured a brief overtime period that overshadowed the rest of the game as far as tweet volume goes. Rangers fans were more negative at the beginning of the game after the Penguins scored their first goal. Twitter volume picked up for both teams during the overtime period and Rangers […]

Twitter Sentiment — Penguins vs. Rangers Gm 3

Unfortunately, my Twitter scraper wasn’t looking for the most viral story of the Penguin’s loss to the Rangers. [I’m not linking to it, but it involves a columnist and the Penguins’ GM.] I was able to get general sentiment over the course of the game. There isn’t too much to analyze. There are more Rangers […]

2015 Steelers-Ravens Playoff Twitter Infographics

The Steelers-Ravens playoff game gave me a chance to test out a new analytics server and some of the tools I’ve been working on to make Twitter analysis easy using ad hoc Python scripts. So here goes: There were a lot of Steelers or Ravens colored emojis, black and gold hearts or buttons and the […]

Getting Lucky in a Playoff Series

Sports have a constant uncertainty and randomness in every aspect of the game including determining champions. This is one area you wouldn’t expect to have a lot of variability, since you would want the team that has the best roster composition and played the hardest to win the championship. This concept is usually brought up […]

Twitter Analysis – Penguins Game 7

I’ve been listening to 93.7 The Fan while running the analysis for this, and I never realized that people can say the same thing over and over again but in slightly different ways. Also all tweets were captured AFTER THE CONCLUSION OF THE 1st PERIOD. Everyone knows Twitter is the best venue to vent your […]

Against All Odds — Upsets

It’s a great time to be a Dayton fan!  It’s the first time the school has reached the Sweet Sixteen since before all the Dayton fans I know where born…and they did it as an 11 seed!  Their game against Ohio State was the first game of the tournament to tip.  A little over two […]

2014 NCAA Tournament Predictions — Monte Carlo

The process of simulating the NCAA tournaments involves two-steps.  The first is determining what statistical prediction model to use to determine the outcome of a game.  The second step is to simulate the entire tournament.   Simulating the tournament multiple times and keeping track of each outcome is called a Monte Carlo simulation. This simulates […]

NCAA Tournament — Seeding

All of the analysis is only looking at the 64 team field from the 1985 tournament through 2013.  Before 1985 there were less than 64 teams invited.  Opening Round games are also ignored.   The NCAA Selection Committee just released the seeding for the upcoming tournament, and everyone over the next few days will be […]

2013 AFC Playoffs and Bayesian Statistics

How does a team like the Steelers go from 0-4 to a dark horse for the final AFC playoff spot to inches away from clinching to the birth?  Then the Chargers who were a dark horse themselves go on an secure the 6th seed? Philip Rivers mentioned, in an endorphin-high interview, that no one gave […]

2012 Basketball Scoring Correlation

  Below are correlation graphs illustration a significant (albeit slightly weak) correlation between how many points one team will score and how many points their opponents will score in a given game. This isn’t anything novel, but rather an illustration and confirmation about what you might surmise about teams that play faster score more and […]