## Sunday, June 14, 2015

### How Much Actual Time is Left in the Game?

 The Persistence of NBA Game Time
Close games in the NBA can somewhat of a mixed blessing. As the drama increases, the pace tends to drag, as teams call timeouts to draw up set plays, players are fouled to stop the clock, and officials await decisions from the NBA's centralized review office in New Jersey. On average, the final minute of an NBA game takes over 5 minutes of real time to complete, and that number gets much larger if the game is close.

Here are some cheat sheets I put together that give you a sense as to how much real time is left in an NBA game, as a function of scoring margin and game time remaining. I had hoped to build a real time view of this into my live win probability graphs, but that will likely have to wait for the offseason.

## Monday, June 8, 2015

### New article at FiveThirtyEight: A Win Probability Guide to US vs. Australia

I have a new article up at FiveThirtyEight: A Win Probability Guide to US vs. Australia. Continuing my rather unhealthy obsession with in-game/in-match win probability, I took last year's work on mens soccer probability and applied it to the womens game. The US is a heavy favorite against Australia, and will be for all three of its so-called "Group of Death" matches. Win probabilities for mismatches evolve very differently than for evenly matched teams, and the post is a guide to how the US outlook evolves in the event they don't establish an early lead.

Data for womens sports is not exactly abundant, and soccer was no exception. The data for the model was culled from recent seasons of top league play. It took some effort, as well as some tedious cleaning to compile the data, and in the end, my final dataset consisted of just 950 matches (compared to the 3,000+ matches that all but fell in my lap for mens soccer). So, the model built here may be somewhat more prone to noise (or overly smoothed) than models built from a more robust dataset.

## Thursday, June 4, 2015

### Profiling the Warriors' Free Throw Shooters

Last week's piece on the analysis of shooting arcs (which I pretentiously named "ShArc") received a lot of positive feedback, which was nice. As I indicated at the end of the post, there are about a hundred different directions I can take this, and any meaningful next steps will probably take place in the offseason.

But since it is the eve of the NBA Finals, I thought I'd share some more data, as well as share an approach for creating a simple visualization of each player's free throw accuracy.

The chart below represents some 30,000 free throws taken in the NBA this past season, with each circle representing a shot and where that shot crossed the hoop's threshold. Scatter plots can be a great visualization tool, but when you've got thousands upon thousands of data points, the human eye can only discern so much. To make sense of the this chaotic scatter, I calculated a boundary ellipse around the data, defined as an one that encircles 75 percent of the data points within the smallest area (h/t to Rasmus Baath of R-Bloggers for the idea and code).