Calculating PER – Player Efficiency Rating
There is a wealth of NBA datasets out there today. We’re going to be using 2019–2020 statistics courtesy of Basketball Reference. Importing this dataset as a CSV was easy, and we’ve got aggregated regular season stats like Field Goals Attempted, Rebounds, Steals and more that are instantly searchable with natural language. However, one thing that we are missing is Player Efficiency Rating or PER.
PER is the creation of John Hollingworth, a basketball columnist that created this all-in-one metric. PER serves to rate players on a per minute basis, taking into account both their positive and negative accomplishments on the court. Calculating PER is messy — take a look at this formula.
Calculating PER
I’m going to use Tellius data preparation to create a new feature that is PER. One really great part about using a platform with built-in data prep is I don’t need to go somewhere else to create this new dimension. Within Tellius Indicators I can create this feature in seconds:
The idea behind Moneyball is that a lower income team can compete with the big teams with their larger budgets. When you look at total salaries vs PER ratings, it turns out that money doesn’t necessarily buy the best PER. So how can these lower budget teams know who to target?
This is where we introduce salary. I downloaded a salary table of NBA players again from Basketball Reference and joined that easily within Tellius. All it took was a point and click join based on playerID.
Analyzing PER
Now I can take a look at each player on a plot of salary vs PER. There’s a slight correlation between PER and salary, but it’s very slight. So which players should we be looking at?
Let’s split this into quadrants by creating another new feature within Tellius — a feature that identifies those players that are above average PER and below average salary. I do this using Tellius data prep as well, this time using a feature called Signatures:
After dropping our new features into the “Color By” area, we have our quadrant chart!
Identifying Players to Target
Focusing on the “Underpaid High Performer” quadrant, we are now given 109 players with above average PER and below average salary that we can target:
Let’s narrow this list down a little bit more. I’ll convert this to a Detail Table, drag in a few columns and take a look at some specific names. I want the most cost effective, efficient player who has demonstrated they can score some points. I’ll filter for total points > 500 and then sort by Salary divided by PER to find our most cost effective, efficient players to target for acquisition:
Obviously, some of these players are still on their rookie deals and they’ll get paid regardless. As an NBA executive, or any executive allocating spend, wouldn’t you want to focus on those cost effective, high performing individuals in order to find those hidden gems others are overlooking?
Professional basketball is a much smaller ecosystem than professional baseball in the United States. However, there is a place for salary weighted sabermetrics in basketball, especially as the sport continues to grow and more G-League teams are added.
How can you apply this to your day-to-day?
This type of quadrant analysis can help organizations beyond sports to focus on the right prospects, clients, employees and beyond. We moved quickly from visualization to a target list by being able to iterate with Tellius data prep on new features.
If you want to create new features and visualize them as fast as I’ve done here, or if you want to explore this dataset yourself, check out www.tellius.com/free-trial.
