This post will be brief. I’m going to allow the reader to visualize Vantage’s metrics and interpret the correlations in his or her own way. First, let’s look at the correlations between Offensive Efficiency and *some* of the various offensive metrics that Vantage tracks in the form of a correlation matrix.

The positive correlations are shown in blue, while the negative correlations are shown in red. The darker the color, the greater the magnitude of the correlation. For the Pie graphs, the more filled they are, the higher the correlation. In addition, notice the lines in the small graphs go forward for positive correlation and backwards for negative correlation.

In the upper left corner is Offensive Efficiency followed by Received Screen Outcome Efficiency, Open+ FG%, Set Screen Points Per Chance, Set Screens Per Chance, Set Screen Outcome Efficiency, Screens Received Per Chance, Isolation Frequency, Roll-Pop%, Solid Screen%, Contest+ FG%, Open+ Frequency and Cut Efficiency. Please see the table at the bottom of this post for definitions.

Open+ FG% and Contest+ FG% have the highest correlations with Offensive Efficiency while the next highest correlation is Set Screen Points Per Chance. This is not in the least bit surprising since the three of these metrics are directly related to points. Likewise, we also see statistics like Received Screen Outcome Efficiency and Set Screen Outcome Efficiency are highly correlated. However, perhaps most interesting is the correlation between Set Screens Per Chance (or Screens Received Per Chance) and Set Screen Points Per Chance. Does setting a lot of screens make teams more efficient? Or do more efficient teams just set more screens? Which metric causes the other?

Now let’s look at the correlations for some of Vantage’s defensive statistics.

In the upper left corner is Defensive Efficiency followed by Effective Screen Defense Rate, Contest+, Pressure Rate Per 100 Chances, Effective Help Rate, Turnovers Forced Per Chance, Defensive Moves Per Chance (also called Defensive Activity Rate), Inside Shots Against %, Close-Out Points Allowed, Effective Double Team Rate, Keep in Front % and Deflections Per 100 Chances. Again, please refer to the bottom of the post for definitions.

Turnovers Forced Per Chance and Close-Out Points Allowed are the most correlated with Defensive Efficiency. However, Close-Out Points Allowed is also the only metric that is on the scale of points allowed. For example, Effective Help Rate measures the number of help attempts that don’t end in a score, assist+ or a missed Open+ shot. It is not directly correlated with points allowed. This does not mean it’s not a useful statistic, as it’s more likely to be *predictive than reflective. *

Now let’s look at the relationship between switching on screens and how effective teams are at defending screens (Effective Screen Defense Rate).

For teams that have more points (which are just individual games) in the upper right of the graph, they will be switching on screens a lot while still remaining effective defending screens. Theoretically, these teams will need versatile defenders who can guard multiple positions to be able to effectively switch on screens. As you can tell from the graph, not many teams switch on screens very often. One team that is pretty interesting is the Knicks, who might come the closest to having a number of points in the upper right corner. They certainly appear to switch on screens more than most teams while still playing effective defense on screens. Another interesting team is the Nuggets, who appear to have a number of random points all over the place. Their graph appears to be the most spread out (they switch sometimes, other times they don’t switch, they also play good and bad defense on screens). Finally, it’s worth remembering the scale of this graph which goes from 0 to 0.4 with some occasional games near or above 0.5. However, for almost all of the games, switching on screens is the *less *likely event.

Let’s take a closer look at the graph above with a subset of 6 teams (the Bulls, Celtics, Clippers, Heat, Lakers and Thunder).

Each team is fit with a regression line as well as a shaded region that includes the 95% confidence interval for the fit. For most teams, we see that an increase in switch% on screens leads to a decrease in Effective Screen% (Effective Screen Defense Rate). However, what this graph is really great for is that we get an idea of the magnitude of the decrease in Effective Screen% (Effective Screen Defense Rate). For example, the Lakers are significantly worse defending screens the more they switch but a team like the Thunder plays pretty consistent screen defense whether they switch or not. In fact, we can see a slight *increase *in their regression line when they switch on defense (meaning they play better screen defense when they switch).