About the Data

Player Talent Grades

To read about our offensive talent grades, go here.

To read about our defensive and rebounding talent grades, go here.

To read about the difference between talent and impact metrics, go here.

To read about our talent optimization ratings, go here.

Player Impact Plus-Minus

Player Impact Plus-Minus is a metric that combines traditional boxscore value with luck-adjusted on/off player data to estimate how much value a player adds to their team.

Luck-adjusted data, developed by Nathan Walker, is used to adjusted for factors that are out of an individual team or player’s control. For instance, free throw shooting and three point shooting can cause wide variance in the specific ratings, but in studies it has been shown that teams and players have limited control over makes or misses. Another example is adjusting for rebounding and turnovers to attempt to limit the noise from the final values.

The boxscore component is calculated off a regression from a 15-year RAPM sample. Especially on offense, there is real value to be found in the traditional boxscore. Combining that with more advanced play by play data, PIPM is able to see who is adding value that the boxscore is unable to capture.

Points Over Expectation

Points Over Expectation (POE) is a metric that tries to explain the following question: How well does a player perform compared to how we’d expect an average player to perform?

To do this, POE turns to data on the NBA’s website about Synergy play types and uses a regression or two along with some more basic math to calculate values. League average points per possession data is used to set the bar for expected performance.

POE = Offense (positive values good) – Defense (positive values bad)

Offense – Created Points Over Expectation

The offensive part of points over expectation is simple, and is called Created Points Over Expectation. It looks just as scoring. Positive values are good.

Here’s the 30 second version: If a Spot Up possession yields 1 point per possession on average, and Kevin Durant has 5 spot up possessions in a game, he’d be expected to score 5 points. If he scores 7, he has a +2 spotting up CPOE. If he scores 4 points in those 5 possessions, his spotting up CPOE is -1.

To get your total game CPOE, do that for every play type and add them together. That’s it.

CPOE is simple and adjusts for role in a way that overall points per possession does not. 1 point per possession could be great or horrible efficiency compared to expectations, depending on role. If it’s on all putbacks, you’re slacking. If it’s all in the pick and roll as the ball handler, you’re doing well. CPOE accounts for role in that way.

It’s not precise, as there are many factors that would ideally go into expectation other than just play type, but it’s what can be done with that simple data and does a decent job at evaluating scoring performance vs expectation.

Defense – Defensive Points Over Expectation

On the defensive end, we use total Defensive Points Over Expectation (tDPOE). It’s similar to CPOE but with a slight difference in calculation. It also different from CPOE in that positive values are indicative of below average performance.

Synergy play type defensive data accounts for primary defense, which allows primary defense to be calculated the same as CPOE. This is Defensive Points Over Expectation.

Help defense can’t be captured in that data, and thus a regression with PIPM is used to help identify residual values by player, which are used as inputs to calculate help defense. This component of defense was recently added, and may be referred to as help defensive DPOE.

Throw DPOE and help DPOE together and you get total defensive points over expectation. Just like CPOE, tDPOE adjusts for role, allowing for a more detailed look at performance by adding an expectation.

Adjustments are in the works to account for team situation and look at play types more granularly for more specificity.