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Basketball Data Analytics Data Science Mathematics NBA NBA Playoffs Sports Statistics Toronto

Live Metrics for NBA Games

Yesterday for the first time, I took the playoff game between Cleveland and Toronto as an opportunity to test out a script I wrote in R that keeps track of key statistics during a game in real time (well, every 30 seconds). Based on previous work, it is evident that championship-calibre teams are the ones that have excellent 2PT-FG% and the ability to draw fouls, so I tracked these during the game, and I came up with the following plot of several time series:


One sees for example that while Toronto started off the game with a much higher 2PT FG%, towards the end Cleveland ended up winning that battle.

A video of this animation is as follows (set the YouTube player to 1080p + FullScreen for Max Quality!)

An interesting question to ask is how are these series correlated? Well, let’s see:

corrplot
In this correlation plot, “pd” indicates point difference, “PF” indicates personal fouls, “2PFG.” indicates 2-Point field goal percentage.

One sees immediately from the correlation plot above that there is a very strong correlation between Cleveland’s point difference  and Toronto’s personal fouls, with some strong correlations attributed to Cleveland’s 2-Point FG% as well.  The equal and opposite is true for Toronto’s point difference. It seems that during a game of this intensity in the playoffs, drawing fouls is a very important factor in determining which team leads and eventually wins in the game combined with 2-Point field goal percentage.

By Dr. Ikjyot Singh Kohli

Sikh, Theoretical and Mathematical Physicist, main research in the structure and dynamics of Einstein's field equations.

1 reply on “Live Metrics for NBA Games”

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