Offensive Ratings

Last updated 15 days ago

Remember a team's offensive efficiency?

An individual player can be assessed using a similar measurement. This is a player's Offensive Rating (ORtg).

This stat comes from the mind of Dean Oliver. Oliver defines the offensive rating:

Individual offensive rating is the number of points produced by a player per hundred total individual possessions.

This number tells you how many points a player is likely to score when given an opportunity. It answers the question, how efficient is an individual player?

The measurement is complex. We're not going to go into it all here. Detailed explanations can be found in Oliver's book Basketball on Paper or at Basketball-Reference.

The tricky part of the calculation is measuring individual possessions. An individual possession is broken down into made shots, missed shots, missed free throws, and turnovers.

Once you identify each component of an individual possession, you can calculate points produced and a player's offensive rating.

ORtg = (Points Produced / Total Possessions) x 100

Examples

In the 2017-2018 season, Michigan State's Nick Ward posted a 116.8 offensive rating.

What does this mean?

Ward was extremely efficient when given opportunities to score.

Ward produced around 1.168 points per individual possession. Over 100 individual possessions, Ward would be expected to produce around 117 (116.8) points.

This mark was good for 4th in the country amongst players that played at least 40%of their team's minutes.

Why is minutes played important?

Certain players are more involved in their team's offense than others. If a player has more opportunities to score (individual possessions) this will have an impact on their offensive rating.

For example, UCF's Tacko Fall played 27.3% of his team's minutes in 2017-2018.

Fall's offensive rating was 103.2.

Ward played in 47.1% of his team's minutes, which is a lot more than Fall played.

This is why minutes played is important when assessing a player's offensive rating, it's a bigger sample size.