MLB Playoff Probabilities: Automatic Updating

In our recent post, Predicting MLB Playoff Probabilities, we addressed a simple method for estimating the probabilities for each MLB team using a Markov Chain Monte Carlo using historical results from earlier in the season. We introduced the notion of continuity correction to help adjust for small sample sizes and a simple random number generator to…

Who’s Going To Make the MLB Playoffs: Simple Simulation

ESPN recently updated their Hunt For October page and introduced probabilities for teams making the playoffs. While ESPN does not give a detailed analysis on how they obtain these simulations, we can instead create our own method. To do this, let’s take a simple approach: use past results of games and a little statistical machinery to build…

Mapping Crime in Madison, Wisconsin

In 2013, it was reported that Madison, Wisconsin was one of the safest cities in the United States. In the FBI reports that help dictate the “safest cities” and “dangerous cities” lists, statistics on shootings, homicides, burglaries, rapes, assaults, and other various violent crime statistics are taken into consideration. Interest in these numbers typically stem from an…

Musings About Rankings Systems: NCAA Football and Basketball

In light of the continual chest-beating by the SEC against Ohio State’s schedule, we figured to take a moment to look at common methods for ranking teams from a mathematical viewpoint and gain insight on the impact of schedules. While we are accustomed to playoffs and all the drama that it brings to our everyday life,…

Redefining NBA Divisions By Clustering

With the recent announcement that the NBA is changing the format of playoffs by seeding the playoffs based solely on records, we take a look at the complexity and reasoning for dividing out the 30 NBA teams into 6 divisions. For Major League Baseball and the National Football League, divisions serve a major role in scheduling. An…

Proximity of NBA Teams

Many fans of the NBA select their team based on one of four main criteria: they may have grown up in a household that was once within proximity of an NBA team; their favorite player has moved to a particular team; the team is has a history of being a winner; or they have some odd…

Spatio-Temporal Data In the NBA

In our last post we saw that we can use a simple nonparametric statistical method, called the kernel density estimator, to build nice looking scoring charts. As we saw, these charts give a better idea of the distribution of the scorer when compared to the basic shooting chart used for several decades past. In this post,…

NBA Shot Charts via Kernel Density Estimation

Shot charts have been used for decades, used to identify locations where players make field goals and to identify locations where players give up field goals. Decades ago, each player has their own chart with four quarters. Typically if a field goal is made, the player has a ‘o’ is placed on the relative location on…

Score Flows of NBA Teams

Almost every NBA game is played as a game of runs, with teams either piecing together five to ten point runs or going scoreless for a couple minutes at a time. To identify the scoring pace of a team, we can take a look at a simple measurement: the Score Flow. Score flows are popular…