(Note: If you are here to play with the cool interactive plots and want to skip the lengthy chit chat, scroll down!)<\/p>\n
Now that the NBA regular season is over and the playoffs are well under way, I thought I’d share some data analysis I did recently about the largest comebacks in NBA history.<\/p>\n
The idea of looking at this came up this past March, during a conversation with Tommy Powers\u00a0from UW (who will be spending the summer with us as an intern) at ICASSP 2017 in New Orleans. The Boston Celtics at Golden State Warriors game was for some strange reason shown on TV in a bar where we were\u00a0listening to some great blues band. The Celtics were ahead in Q4 and ended up winning with a large margin, but we were wondering at which point it would become hopeless for the Warriors to stage a comeback.<\/p>\n
I thought it’d be fun to have a plot that showed, for any given time in a game, the largest score deficit that a team was in and still ended up winning. For example, at half-time, what was the largest score deficit that a team ended up overcoming? You could imagine the curve starting at 0 at the beginning of the game (games usually start at 0-0), going down as minutes passed to some minimum, and then creeping back up, because with less time left to play, it becomes harder and harder to overcome a large deficit and win.<\/p>\n
I poked around on the internet for play-by-play data logs, and thought I’d have to write a scraper to get the data from some sports website, when I stumbled upon some reddit thread mentioning that\u00a0stats.nba.com<\/a>\u00a0 had an API from which the data could be accessed. Further googling with the magic word (“github”)\u00a0quickly showed that (of course!) several people had already written some Python wrapper to do\u00a0the heavy lifting. I decided to use\u00a0statsnba-playbyplay<\/a>, as it seemed to have the appropriate features.<\/p>\n A few (read: way too many) hours later, I was able to get the plots I wanted. They only cover the seasons from 1996-97 to 2016-17, because play-by-play data is not available for earlier seasons. I also arbitrarily decided to only consider regular season games, and to not show \u00a0overtime periods (of which I realized some games had\u00a0many!).<\/p>\n Without further ado, here are the results for the largest comebacks overall from 1996 to 2017, with separate charts for games where the home team eventually wins and games where the away team eventually wins. You can hover over the plots (made with plot.ly<\/a>) to see the list of games that correspond to the largest comebacks at each second.<\/p>\n