If you’ve followed my work, you know I beat the dead horse last season on Twitter criticizing the misuse of Brandon Ingram. A natural wing, he was miscast as a ball-dominant point guard type player. We saw heavy isolation and pick and roll usage, despite the film and data being clear about his relative ineffectiveness in those areas.
Player Optimization Rates
But is there a way to quantify this misuse? Can we see which players are put in the best positions to succeed?
Now we can. In fact, we can get a precise value representing the degree of improper utilization of Ingram’s skill set. By comparing his offensive talent grade with his impact on the offensive end, we see a gap created by a confluence of factors: team scheme, deployment within that scheme, and fit in rotation and in lineups make up most of that gap.
It turns out he was the 3rd least optimized wing of the 206 in our database for last season.
This new angle of analysis is one of the several doors our unveiling of talent grades at BBall Index has opened. By obtaining a grasp on player optimization, we can compare entire teams and even head coaches at optimizing talent.
Here are the Player Optimization Rate top 5 for the 2017-18 season on offense (among the 210 players with 1500+ minutes):
- PJ Tucker
- Darius Miller
- Kyle Korver
- Trevor Ariza
- Al-Farouq Aminu
These are players who have substantially higher impacts than expected based on their offensive talent. They’re playing roles on offenses that augment the impact of their strengths (typically, shooting) and mitigate their weaknesses.
Team Optimization Rates
By taking this a step higher, we can evaluate individual teams over the past five seasons. This is done simply by determining team average talent and impact ratings by weighting player ratings by minutes played.
Here are the teams that got the most out of their roster talent since the 2013-14 season:
- 2017-18 Houston Rockets
- 2015-16 San Antonio Spurs
- 2016-17 San Antonio Spurs
- 2014-15 San Antonio Spurs
- 2013-14 San Antonio Spurs
Gregg Popovich is really good at his job. And I can’t understate how well the Rockets got value out of role players that weren’t necessarily the most talented by putting them in positions to succeed and building an offense around the best skills for their top players.
And if we turn our attention to optimizing defensive talents, here are the top 5 teams:
- 2013-14 Indiana Pacers
- 2015-16 San Antonio Spurs
- 2013-14 Chicago Bulls
- 2016-17 San Antonio Spurs
- 2016-17 Detroit Pistons
And the 2017-18 Utah Jazz ended up sixth. Again, I feel pretty good about this list. Top to bottom, it lines up well with conventional thinking.
Coach Optimization Ratings
The final level of analysis is to compile seasons and attribute them to head coaches. Assistants do play a large role, but for now we’ve stuck to tracking head coaches at the macro level.
Understanding and adjusting for the impact of tanking is another key part of this analysis. It’ll drag down coaches like Brett Brown, who led teams that openly tanked for several seasons during our 5-year database. By removing these years, some additional clarity is provided.
If you read the team optimization section, it’ll come as no surprise to see Gregg Popovich at the top of the coaching ratings. Mike D’Antoni lead the way on offense, with Pop and Snyder at the top on defense.
At the very bottom of the list of 54 coaches who had at least one season coaching a non-tanking team are Luke Walton and Byron Scott (at dead last). Earl Watson isn’t far behind, with Jacque Vaughn and Lionel Hollins trailing him.
Among coaches who’ve coached in every year of the 5-year window, Jeff Hornacek, Jason Kidd, and Dave Joerger grade out the worst.
Utilizing this data in analysis, along with the Player Optimization Rates, will help us better predict how players will perform in new environments.
Where can I see this data?
We’ll have this data available as part of our $10 per month data and tools package. You’ll also see it sprinkled into our free written content and referenced on Twitter.
Can you explain the components that make up the talent-impact gap?
I mentioned above that the major factors accounting for the gap seen in talent and impact grades are team scheme, deployment within that scheme, and fit in rotation and in lineups.
When I say team scheme, I’m referring to the adequacy of organized attack a team is running in terms of set plays and action run more as freelance motion. For example, the Warriors, Jazz, and Hornets run designed plays that are effective in design and result due to their fit with their rosters and soundness in terms of basic tenets of scheme (spacing, multiple actions pressuring a defense, weak side motion occupying potential help defense, etc.).
The levels of scheme will vary across the NBA. Several teams run great or awful offensive schemes, while most are around average. But that impact on this evaluation is clearly seen when comparing the data to general accepted levels of scheme for teams.
Deployment within that scheme is also important. I can draw up a great play, but if I have a player put in a role within that play that is a poor fit with their skill set, I’m holding that player and the offense back. This gets back to my initial complaint about Brandon Ingram. On the opposite end of the spectrum is a player like Trevor Ariza, who is mainly used as a catch and shoot threat, matching his skill levels.
Deployment can greatly impact this evaluation, but is generally a component that coaches get right.
Fit in rotation and in lineups is the third major component. At the team level, it’s rotations/minutes as a whole and construction of lineups. This is where coaches like Steve Clifford take a knock for their rotation lineup and minute construction.
How are you measuring talent?
Player talent is measured using the overall offensive, defense, or overall talent grades derived from our data driven talent metrics that are outlined elsewhere on the site.
How are you measuring impact?
Player impact is measured using an adjusted blend of ESPN’s Real Plus Minus, Jacob Goldstein’s Player Impact Plus Minus, and Regularized Adjusted Plus Minus. These are each impact stats that show how a player’s presence on the court impacted their team, each adjusting for various factors ranging from lineups to luck associated with 3-point and free throw shooting.