Last week, ESPN unveiled a new advanced statistic called Real Plus-Minus (RPM). In short, it attempts to give a single number that can define a player's overall production. Currently, LeBron James leads the NBA with a 7.72 RPM, and the Boston Celtics only have one player in the top 100, Avery Bradley.
It's actually a pretty nifty tool, but I've held off on writing about it because I wanted to let the dust settle first. When RPM was first revealed, tons of analytics aficionados were arguing back and forth on Twitter about its validity, and rightfully so, because not much was outlined about the stat itself.
Yesterday, Ian Levy wrote a piece for Hardwood Paroxysm that captured all of my thoughts. This section of the article in particular jumped off the page:
On Real Plus Minus: Pieces, Pieces Everywhere – Hardwood Paroxysm
My issue with RPM, and really all of the various plus/minus models, is that they are increasingly complex methods for stripping away the context of a player’s production, trying to measure it in a vacuum. It’s an admirable pursuit to some degree and these intricately designed techniques have become, in many ways, the basketball analytics arms race. The problem is that I’m just not that interested in the result. The context and the noise, which these models work so hard to control for, are exactly the things I’m interested in. I don’t just want to know which player is better. I want to know why and in what ways. I want to know what that implies about both the player and team, his teammates and opponents, and basketball as a whole. As constructed and presented, I typically find precious little of that information in plus-minus statistics.
This problem is not unique to RPM, or even to the entire family of plus/minus models. Win Shares, Wins Produced, PER, also chase the same goal–generalizing the "why" to highlight the "what." But the "why" is what I find most interesting, the "why" is the reason I watch and write about basketball.
I couldn't have said that better myself. Levy wrote the words that have been scrambling around in my brain throughout the past week.
CelticsBlog readers who have been keeping up with my articles over the last year know that I've rambled on about how important it is to look at sports from the "why" perspective. That's why I was ecstatic when Brad Stevens was hired because he shares the same philosophical view of life and sports that I do.
Many advanced stats, including RPM, don't answer "why" the numbers are the way they are. As Ian said, "why" matters just as much or more as the "what."
But this is exactly my personal major issue with almost all of these "models" that claim to help us answer which players are the best in the league or which college players will best translate their games to the pros.
It just doesn't work that well. This isn't a knock on anyone who attempts to find those answers, it's just the nature of the sport of basketball. There are so many variables that can't be accounted for, try as we might to figure them out. Numbers never lie, but they can be misleading, which could be the case with RPM.
Companies like Vantage Sports are working diligently to quantify certain "invisible" plays in basketball (like close outs and screens), but it'll be years before that data is publicly available. But once it is, it'll be game-changing.
Until then, I'd love to see more emphasis placed on the technical aspect of basketball. I think stats combined with film study can help answer the "why," in addition to the "what."
I first started tinkering with this idea last December with my "Driving Jeff Green" article, and then with my stupidly long pick-and-roll series from early March. In both of these articles, I wanted to answer "why" and "how" the Celtics performed the way they did by using a combination of film and stats, and not just the numbers themselves, which is the "what."
How Kelly Olynyk found the flow with his three-point jump shot
Rookie center Kelly Olynyk struggled from three-point range to start the season, but he has since become a consistent threat. How has he done it?
But I think I really figured out how I wanted to attack this with my recent Kelly Olynyk and Rajon Rondo studies. On my Olynyk piece, I watched all of his three-point jumpers and recorded if he used "the hop" or "the one-two" on his attempts.
I recommend reading the entire article, but I basically found that he has used the hop more during the second half of the season, which has played a significant role in increasing his production from three. I believe this fact helps answer why Olynyk has been so damn good offensively as of late.
Is it definite? No, of course not, but even Kelly was intrigued by the stats when I showed him before a game in mid-March.
And with my Rajon Rondo article, I found a large disparity between his first free throw attempt and any tries after that. His stats have been consistently terrible over the years on his first attempt, but he has gradually gotten better on his second or third tries. Why? Well, I think it might be because of his constantly changing routine, and the stats support that theory.
These are the types of stats that interest many players and teams, though the fans may not care for them since they are "too nerdy" or "too technical."
But the public does care about seeing their favorite team's players improve, and they certainly care about proper transactions being made. I think these types of "technical statistics" should be more widespread, as they may help player development or certain personnel changes.
We've seen bits and pieces of this with Synergy (for example, if a player is coming off a screen or spots up), but not so much with the technical aspect (for example, the hop or the one-two). Perhaps some NBA teams do track this, but it requires a large amount of time, which currently may be dedicated elsewhere.
I am taking this conversation away from the root of what Ian was writing about, but combining film study and statistics could be more useful to NBA organizations than the current road advanced analytics seems to be going down. It's fantastic that ESPN is pushing advanced stats with Real Plus-Minus, but we're still so far away from having one single be-all and end-all statistic.
With that said, I'd love to see some progression with technical stats that attempt to answer the "why" more than the "what." As Ian Levy said, the reason why I watch and write about basketball is because of the "why."