By: Daniel Tokarz
Updated April 13th
See the bottom of the page for a basic intro to the model as well as directions for its usage.
For a more in depth look at the math behind the model, click here
For a guide on the R script we use to make the model, click here
|3||Golden State Warriors||5.89|
|6||San Antonio Spurs||4.41|
|7||Oklahoma City Thunder||4.10|
|10||Portland Trail Blazers||2.57|
|12||New Orleans Pelicans||1.69|
|18||Los Angeles Clippers||-0.30|
|21||Los Angeles Lakers||-1.73|
|24||New York Knicks||-4.89|
Basic Introduction to the NBA YUSAG Coefficient
Based on the work YUSAG done in creating our NCAA football and basketball rankings, I set out to create a model for ranking NBA teams using the same system. I’ll upload a more thorough explanation of my mathematical model in the next few days, but basically, we create a linear model based upon three variables, the team playing, their opponent and the location. It uses these values to predict the point differential of a game involving a given team and the average NBA team at a neutral location. The game data was taken off of Basketball Reference.
How to use this Model
For a given game between two NBA teams, subtracting the YUSAG coefficient for the road team from the home team and giving the home team a slight boost (~2.1 points) gives the predicted point differential for a given game.
To find the probability that the team will win this game based upon this point differential, plug the differential “d” into this function W(d) which we extracted from our other linear model (more math on this section coming soon as well!)
W(d) = 1/(1+e^(-.1472d))