An Efficient-Market Advanced Metric for the NBA
Any basketball discussion involving analytics and statistics boils down to the pro-analytics camp versus the anti-analytics camp. My mission: to help both sides realize they both fall under the same squad—Team Ignorant (which is okay, I’m the starting point guard). However, my primary objective is to introduce what I consider the most complete, all-encompassing GOAT data metric of all time.
From the above matrix of completely fabricated data, I’ve carved the population into four quadrants. Quadrant 4 is where you’ll find professional basketball players, coaches, assistant coaches and whoever else that’s closely involved with the team during games and practices. Quadrant 1 houses the mathematically inclined, folks heavy into data whether it be data scientists, actuaries, quants and those excited by Kaggle competitions. The Quadrant 2 unicorns are even more rare, and you can bet whatever they’re doing is considered proprietary and will remain a well-kept secret (and not in r/NBA).
Quadrant 3 is where you live, where I live, and where Team Ignorant thrives. It doesn’t matter how many streams you watch, how many pick-up games at LA Fitness you got next, or the number of queries you pull from basketball-reference.com, we should all get comfy because this is where we’ll live out the rest of our lives. The difference between you and me though, is that I know what I don’t know.
Players and former players will cluck about the overuse of analytics in today’s game, and to a certain extent, they’re right. No one wants to be reduced (or optimized) to just a corner-three specialist, but that decision ultimately comes down to the front-office and playing style. Analytics isn’t telling you how to run an inbound play, it tells you a bit about probability and how you can expose advantageous situations. If you’ve ever been on the court and taken advantage of a mismatch, guess what, you’re now an analytics-head! The difference is that computers and models can do this with many more games/situations/schemes/players than you could ever compile from your Tuesday-night run. Players shouldn’t blame math because they don’t understand it, they should take a hard long look in the mirror and realize they’re not all James Harden.
On the flip side, there are folks that will die on their hill of advanced metrics and think PERs, WARs, RAPTOR (or to a lesser extent, box scores) mean anything once the ball is in the air. The data that we pore over is only a measurable outcome from a fraction of all things happening on the court. Even at the lowest levels of basketball, there are nuances on both an individual and team level that don’t get tracked (and shared for free) in the NBA. I haven’t found a good metric for team chemistry, or how well a player matches up against a personal rival, or how LeBron’s field of vision and eye movements correlate to the probability of a made basket off the next pass.
These numbers satiate what a lot of fans crave, order. Team Ignorant wants to either argue at a bar, or sort ‘by descending’ to determine who’s best. All of this because we want to live in an orderly world when in reality, we’re bathing in various shades of grey.
The Criteria
The answer is simple, find a metric that blends the best of both worlds. What type of data do we have access to, that not only encompasses concrete and irrefutable numbers, but also the intangibles of the game. Here are my criteria of what makes a player great:
1. A player must exhibit at least some level of winning, but not necessarily championships
2. A player must be a valuable contributor to the team (tangibly or intangibly)
That’s it.
On Winning
Yes, championships are the hallmark of a winner. LeBron James and John Salley—both have won four championships. Both have won on three different teams. Both need to watch out for Patrick McCaw. So how else can winning be quantified?
Teams and people change in the playoffs, everything becomes a little bit harder and strategies are always adapting. The more progression a team is able to achieve, the more difficult the remaining games become. “They were never able to get out of the second round” is pretty disparaging, so let’s start there as the baseline requirement.
On Contribution
In the investment world, there is a hypothesis called ‘efficient markets’, meaning the price of a stock reflects and encompasses all available information. This is why you’ll see prices swing wildly as soon as some new news hits the news (barring any insider trading). In basketball, I believe that coaching decisions have loads more inputs than we’re privy to, and they are taking this into account with who they put out on the floor.
This is what all other ‘all-in-one’ metrics get wrong. It’s a fool’s errand to quantify what a player means to a team when you (or me) don’t even understand all of the inputs that go into it. We see the surface stats: points, rebounds, assists, but do those even matter?
Sixth-men of the year preach that it doesn’t matter who starts a game, but instead who’s on the court when it matters. Shouldn’t a player’s sheer presence during the most critical games of the entire season be a metric we value above all else? Whether you’re a scorer, lock-down defender, floor-spreader, mind-fuckerer, if you’re out on the floor during the conference finals or NBA finals, there’s probably a pretty good reason why.
The Methodology
I started by calculating all players that have logged any minutes in a Conference Finals or NBA Finals win, and charted them by the sum of their total minutes played in every victorious contest. Minutes played in losses were not calculated so as not to penalize a player for rolling over teams in gentlemanly or non-gentlemanly sweeps. This opens up the discussion to whether we should not only hate on a lack of championships, but also a lack of significant podium finishes.
TOTAL MINUTES = (CONF FINALS WIN * MINUTES PLAYED) + (NBA FINALS WIN * MINUTES PLAYED)
This yielded 1,189 results and a very long tail. Only considering the crème de la creme, I further narrowed this down to three standard deviations from the mean (σ = 335.81 mins, μ = 191.8 mins), which means that if you weren’t checked into the game for more than 1,200 total minutes (sorry Wilt), you didn’t make the cut.
I was thinking of just stopping there, rewarding the longevity of Kareem Abdul-Jabbar and LeBron James, but that didn’t sit quite right. Although both are very deserving of my fictitious award, my list was rewarding too many Robins riding on the coattails of Batmans. So I then normalized the total minutes played by the total games played and charted it below.
Most all-time lists are going to see the same names come up over and over, and this list is no different. But what I was hoping to unveil are some surprises, some folks that don’t get the respect they deserve because they don’t show up in advanced metrics. The guys that lead in inconspicuous ways and always seem to be on the floor when it matters most.
Takeaways
For those without domain-knowledge, this list includes some of the greatest offensive players the game has ever seen. Amongst these giants is a guy like Dennis Johnson, who played significant minutes but was largely overshadowed by the forwards on his team. DJ was known for smothering opposing guards every time he was on the court and coming up big in clutch situations. On the opposite end of the spectrum you have a player like Allen Iverson, who didn’t make the list because of limited post-season success, but played an average of 48 minutes per game in later-round playoff wins and carried his team on his shoulders into the NBA Finals.
There are no fancy algorithms or predictive power in these figures, players aren’t stack ranked to give a GOAT list or a Mount Rushmore. Instead, it provides a different way to evaluate players and giving kudos to the under-appreciated Dennis Johnsons of the world and all the numbers they put up on the invisible scoreboards.
Notes
There were some limitations in this dataset (sourced from stathead.com), specifically the lack of Conference Final data (games played and minutes played) for the 40s, 50s and 60s. Seems to make Bill Russell’s total minutes that much more impressive since they’re only from NBA Finals games.
