Offensive Rating measures how the offense of a team performs. This can be at the team level - for example, the Duke Blue Devils had a 118.4 Offensive Rating in the 2018/2019 NCAA season - or for a specific lineup, player, or situation within a team. This is accomplished by choosing a criteria (i.e. when James Harden is on the floor in the 4th Quarter or all Syracuse offensive possessions) and dividing the number of points scored by the total possessions by the offensive team. This provides a repeatable method for comparing and understand how an offense performed or, if the sample is large enough, will perform in certain situations.
OffRtg = 100 * [ (Points) / (POSS) ]
However, Offensive Rating is the surface level of offensive performance. How those points were scored can vary significantly.
Shot types, ability to draw fouls, reducing turnovers, and grabbing offensive rebounds all play significant factors in how a team generates points. As you may have noticed, those are the ever popular Four Factors and every offensive rating has a unique and easily understood signature.
For example, at this moment, Luka Garza(Iowa) and Vernon Carey(Duke) have very similar offensive ratings, both around 121.5, but when we look at the four factors we see how different the paths can be to get there.
When compared side by side, it's clear that Duke(when Carey is on the floor) has a slightly more efficient shot profile(54.6 > 53.5) and is better on the offensive glass(35.7 > 33.5), while Iowa(when Garza is on the floor) gets to the line more often(26.2 > 24.8) and turns the ball over less(14.3 < 15.3).
The resulting Offensive Ratings are the same but the stats underlying stats are almost always different.
Defensive Rating in basketball is very similar to Offensive Rating in its creation and usefulness. You can read more about that here.
At Pivot, our focus is on understanding the impact a player or combination has on the entire team's performance, rather than his or her individual statistical prowess. Whether in our single player on/off data, the player combination matrix or the lineup explorer, Pivot Analysis provides players and coaches the ability to measure the effects of each individual and player combination on the whole team's performance. In the coming months, we will be adding functionality enabling the user to measure and analyze rebounds, turnovers, and shooting percentages (four factors and more) in the same fashion. The Pivot Analysis application will tell you when Player X is on the floor how many points a team scores, how often they turn the ball over, how well they shoot threes, how often they shoot free throws, and how well they crash the offensive boards.