Some time ago, I began wondering what the value of certain actions in a game were. How did they translate to points, since points determine who wins based on the rules of the game?
So much goes in to scoring points: first one needs possession of the ball. Well, stop right there. How much is possession of the ball worth? On average, about 1.07 points. How much is a turnover worth, then? If it’s the loss of a possession, it should be worth about -1.07. Then what is the value of a missed field goal? Well, it’s failing to score on a possession, but on average, 26% of the time the offense still recovers the rebound, so it’s not quite as bad as a turnover. So 1.07 * 0.74 means a missed field goal is approximately -0.79 points. And so on…
I call this model “Expected Value,” as a tip of the cap to my poker background. Viewing actions this way isn’t new — PER uses value of possession concepts — but there are stats in play here that haven’t been used before.
A huge component of this rating is introducing defensive statistics in order to ballpark players defensive performances. On the offensive end, its major novel component is accounting for distributing the share of offensive scoring between creators and those they created for. The “helped” elements in the table below are an estimate of how much credit should go to the creator of an open layup, shot or layup foul (one of those fouls in which the player is intentionally fouled from behind to prevent a dunk).
Since EV is using novel stats from my stat-tracking (linked to in the tables below), it’s not even 400 games old (stat-tracking from the 2010 playoffs and 2011 regular season). Nonetheless, on the last round of correlations I tested, the correlation coefficients were as follows:
- Offensive EV to ORtg: 0.97
- Defensive EV to DRtg: -0.80
- Expected Value to Overall Efficiency: 0.91
Interesting correlations given that the model is built around causality. And the correlations are even stronger when including Help Needed, the defensive counterpart to Opportunities Created. Without further ado, here are the marginal values used for EV. First, the defensive values:
|3-point FG Against||-1.93|
|2-point FG Against||-0.93|
|Shooting Free Throw||-0.47|
|Missed FGA Against||0.79|
|Made 3-pt FG||1.93|
|Made 2-pt FG||0.93|
|Helped 2-pt FG||-0.37|
|Helped 3-pt FG||-0.35|
Astute observers will notice there is absolutely no accounting for screens. Most players set similar screens, with a few outliers on both ends, generally determined by size. Comparably, most defensive players handle screens similarly (I’ve yet to see the player who can run through a screen), although some outliers hedge them better than others.
Which leads to another, difficult to quantify issue: spacing. This is also a small issue in most cases, but great shooters will prevent defenses from collapsing too much into the lane. It’s extremely hard to quantify how often a defender is reluctant to sag off of a shooter, especially since most players will double and rotate appropriately even if they are guarding Ray Allen.
Some other major elements still missing that might be possible to quantify in the future with devices like optical tracking:
- Shots deterred
- Quality of “closeouts”
- How long a player holds the ball and mucks up an offensive possession
A final note: Player ratings and comprehensive metrics are often polarizing. Fans tend to cling to metrics they intuitively like or ones that their favorite players do well in, or they tend to ignore metrics for converse reasons. Both extremes often miss what exactly it is the metric is representing. I’ve written about the major players in the advanced stat community before, and my hope is that people keep that in mind when viewing Expected Value. It is still a work in progress. (For example, an obviously superior model would be “Dynamic EV”, which incorporates how the values of events change as the shot clock changes and how they change based on opponent.)
In the next post I will look at the defensive leaders in this metric from the 2010 playoffs.