It is very possible the NBA has a ‘Moneyball’ Issue

June 15, 2018 |  By Mishka

If you are familiar with the book, ‘Moneyball’, turned movement, turned movie, then you understand how important it is to hold various weights to different statistical categories as not all data is created equal. Moneyball, at its core was an analysis of  the Oakland A’s formidable 2002 season, in which they had one of the smallest payrolls in baseball. The book sought to answer the question of how a team with only $44 Million in total players’ salary can effectively compete with the $125 Million total salary of the Yankees.  The answer is now common knowledge amongst baseball fans. Oakland’s front office was years ahead of the rest of league in analyzing statistics and understanding which statistics were meaningless in certain situations. 

The book, written by Michael Lewis changed the trajectory of the league as baseball statheads underwent a renaissance of understanding. If you subscribe to this new school, RBIs (standing for runs batted in, aka the ability for players to make hits to advance their teammates around the bases) are not nearly as heavily weighted as they once was. In fact, the sport’s most cherished and elusive individual award, the Triple Crown, is awarded when a single player leads the league in RBIs, batting average and home runs. But for ‘Moneyballers’, RBIs are not nearly as important as other stats, and RBI significance seams to diminish with every new season. The reason behind this is because in order for a player to rack up a lot of RBIs he has to have a very good team where his teammates are consistently getting on base. After all, your chances of hitting an RBI are 0% if nobody is already on base. The converse of course, if you are on a great team, where players are consistently getting on base, then your chances of hitting an RBI with each of your individual at-bats goes way up. RBIs are also drastically effected by where a player bats in his team’s lineup. Is he batting behind an All-Star? Then his chances of hitting more RBIs drastically increases. But wait, what if that All-Star happens to be a power hitter that either hits homeruns or strikes out, and nothing in between? In that case, when it comes to hitting an RBI batting after him, he’s as helpful as batting behind the bottom of the barrel players. The point is, RBIs are better in determining a team’s effectiveness to have runners in scoring position, then they are a reflection of an individual’s talents. 

There are so many other stats that have gone to the wayside or have come up from obscurity since baseball’s statistical renaissance. So why haven’t other leagues such as the NBA had any major overhauls in how they manage player stats and data, years after baseball had discovered a lot about what they knew about the game was backwards? They are probably due for a major data disrupt.  

What is going to be the biggest factor that I see effecting the boilerplate stats that basketball has lived by for years? – The Clock. Unlike basketball, baseball is much more of a linear sport by design, which makes it easier to streamline the process from the field to the datacenter initially. Basketball, having a clock and quarters instead of innings, make it a different beast entirely. It is the time management aspect that makes sure coaches will never become obsolete to a stats-driven front office the way they have in baseball. Within the Yankees organization for example, Aaron Boone was hired as coach this year in replacement of Joe Girardi. This wasn’t due to Boone’s coaching prowess, because he doesn’t really have any. It also wasn’t due to any perception of Girardi being incompetent at the position, as he was one of the best managers of the bull-pen in the game. It was because the Yankees General Manger, Brian Cashman, would offer data that suggested a particular line-up or pitching rotation and Girardi would often use his own gut instead of the numbers. Thus, we are now in the Boone era, who is the Yankees first not-actual coach, but more a pawn of Cashman, the front office, and their litany of numbers. This approach is only possible in baseball because as aforementioned, there is a set order and player management, as opposed to clock management. It is also the reason why a baseball coach can essentially run on autopilot, unconscious of his surroundings for long spans during the game with little effect. Basketball and football coaches have to be zoned in ready to react at any moment. 

I would argue that the team-element that makes RBIs a relatively insignificant individual stat in baseball is the same element that works conversely in basketball. To elaborate, in the MLB, each new batter acts as a fresh coin flip, where an individual’s odds of success of hitting the ball have little-to-no correlation with the batter that came before or after him. Conversely, in basketball- how your teammates handle the ball makes a world of difference and a large impact on your individual stats. How well are teammates setting each other up to make shots? Are players given the ball from their peers in a way that allows them to effectively attack the basket? Every second that goes by while an individual has the ball is another second drained from the team as a whole. You only have 12 minutes each quarter for a total of 48 minutes of actual play, so every multi-second span a player has on the court is magnified. 

Efficiency of handling the small amount of regulation time a player has on the court is the most important thing we can gauge. Right now, efficiency is a two dimensional stat, represented only by a fraction- successful shots divided by attempts. I’ll use FG% (Field Goal Percentage) to exemplify why this is a problem. Field Goal Percentage is one of the most used NBA statistics- around top 6ish. The league leader in FG% is Houston Rocket Clint Capela, a solid center but nonetheless probably just cracks my personal top ten, if he makes it at all. Let’s compare him with his teammate and projected 2018 MVP, James Harden. Now by FG% standards, James Harden is not very efficient with a FG% of 44.9%, which ranks him a full 80 spots behind his teammate in the rankings for this metric. But I would argue despite being so far behind, he’s actually a more efficient player then Capela, although FG% would tell you the opposite. The reason is trifold. Obviously, he takes much more difficult shots then Capela due to Capela being a Center and always around the basket for the easy lay-up. Secondly, he makes a lot of three-pointers which maximize a players ability to score the most points in least amount of attempts. But most importantly, he leads the league in one of my favorite stats- Free Throws Attempted. Because what’s the ultimate form of efficiency? Scoring without taking anytime off the clock to do so. And that is why Capela is a no-name outside of NBA fan circles and Harden is a superstar about to achieve MVP status. 




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