Why Chris Paul is a Better MVP Candidate than James Harden

I posted this on reddit yesterday, so I figured I’d put it up here as well (with minor edits).

Exhibit A: Points Created

I combined standard points per game stats with the points created by assists numbers from stats.nba.com. I also added points for free throw assists (assuming a league average FT shooter). Then I adjusted for minutes per game and pace to get points created per 100 possessions. Here’s what I got:

Harden Paul points created

So Paul creates 3.3 points per 100 more than Harden, a nontrivial difference. You can argue that points scored (especially unassisted ones, which make up 70% of Harden’s points and 77% of Paul’s, excluding FTs) are more “valuable”, but I think it’s reasonable to conclude that Paul produces at least as much for the Clippers’ offense as Harden does for the Rockets’.

Exhibit B: Efficiency

These guys are neck and neck in shooting efficiency. Paul gets the edge in effective field goal percentage, 54.3% over Harden’s 51.3%. But Harden’s voracious appetite for free throws pushes him above Paul in true shooting percentage, 60.5% over Paul’s 59.5%. Basically, both guys have very good (but not mind-blowing) efficiency.

Turnovers are a different story though. Paul is a freak at not losing the ball considering how much he has it. He’s averaging 3.4 TOs per 100 possessions. Compare that to other “high involvement” players:

  • James Harden – 5.3 TOs per 100
  • LeBron James – 5.7
  • Stephen Curry – 4.7
  • Russell Westbrook – 6.4
  • John Wall – 5.5

Paul effectively buys the Clippers an extra couple shots per game. He’s a huge part of why they have the 2nd least TOs per 100. Meanwhile, Harden turns it over a lot more and the Rockets give up the 3rd most TOs in the league. This difference gives Paul a small edge in efficiency.

If you want to get really fancy, Paul and Harden are very close in true usage, which measures how many possessions a player is involved in by shooting, earning free throws, turning it over, or assisting. Paul has a true usage of 54.4% while Harden is at 53.7%. In other words, Paul is involved in just 0.7 more possessions out of every 100 while creating an extra 3.3 points. If you compute points per true possession, you get 1.158 for Paul against 1.112 for Harden.

Exhibit C: Defense

Chris Paul is a better defender than James Harden.

Oh, you want me to defend that claim? Okay, sure. Harden has certainly improved from last season, but he’s still not good. He’s prone to losing his man away from the ball, especially if someone sets a screen on him. Even when he’s focused, he’s not exactly a lockdown defender.

Paul might not be as impactful as his reputation suggests, and sometimes he coasts, but he doesn’t make many mistakes. His anticipation makes him a great on-ball defender. He’s physically limited as a help defender, but he knows how to make himself a pest. When Paul locks in on D, he’s really tough to beat (this play notwithstanding).

In summary, Harden is still a slight minus on D while Paul is a solid plus.

Closing Argument:

So I’ve established that:

  1. Paul has been at least as productive on offense as Harden.
  2. Paul has a small offensive efficiency edge over Harden.
  3. Paul has played better defense than Harden.

From 1 and 2 I conclude that Paul has been better than Harden on offense, and 3 states that Paul has been better on defense. Let’s do the math.

Better offense + better defense = better season

Even if you’re unconvinced Paul has been better on offense (a reasonable position), I challenge you to make a sound argument that Harden has been better on that end. So as a lower bound I propose:

Equal offense + better defense = better season

If you believe that the MVP should go to the player who has had the best season on a good team (which I think best captures the spirit of the award), then Paul is a better candidate than Harden.

Additional thoughts:

Their teams have almost identical records, so that’s not a factor here.

Paul has played in every game. Harden has missed one. So that’s not a factor here.

The conventional thinking is that Harden has had less help than Paul. I think that’s true, but overstated. The Rockets have a team full of average-ish players who fit well together. The only legitimately poor players to get minutes are Dorsey and Papanikalou. The Clippers have more talent starting, but they’ve also given 1200 minutes to Spencer Hawes, 900 to Glen Davis, and 800 to Austin Rivers (in only 40 games!). These guys have no business in a contender’s rotation.

I also think Paul is at least partially responsible for making DeAndre Jordan a star. Without Paul’s (and Griffin’s) playmaking, DJ wouldn’t be shooting 71%. A full 47% of his shots have been dunks!

In terms of wins, the Rockets have probably overachieved and the Clippers have probably underachieved. Based on their offensive and defensive ratings, we’d only expect the Rockets to win 50 games compared to the Clippers expected 58. Make of that what you will.

Here’s a full table for points created for the MVP candidates besides Anthony Davis. “EFF” is points created per 100 true usage possessions.

Full MVP Points Created

Money, Minutes, and the NBA’s Bias Towards Offense

It’s no secret that NBA fans have a bias towards offense. And they have good reasons for this. First of all, offense is more fun. Aside from the nerdiest basketball wonks, most people would rather watch high-flying dunks and long-range threes than precise rotations and intricate pick-and-roll defense. Furthermore, offense is easier to understand. Whether you’re watching film or looking at stats, offensive contributions are easier to discern than defensive ones.

There’s absolutely nothing surprising or wrong with casual fans having a bias towards offense. However, if this also affects the people running teams–those who make their livings knowing more about basketball than the rest of us—then we have something interesting.

Fortunately, we can study this. In this post, I examine the values GMs and coaches put on offense and defense by looking at how they distribute money and minutes.

Let’s start in the front office. The easiest way to determine what’s important to GMs is to look at how much they pay different types of players. I ran regressions to predict player’s salaries based on three different methods of measuring offensive and defensive contributions.

  1. First, I used ESPN’s Real Plus-Minus, which I believe provides the best measure of a player’s impact on each side of the ball1.
  2. Since RPM comes with certain biases, I also used individual offensive and defensive ratings, courtesy of NBA.com. These stats simply measure how many points a player’s team scores and gives up when he’s on the court. Because they’re unadjusted, they have lots of noise, but they’re also as unbiased as it gets.
  3. Finally, I used win shares as calculated by Basketball-Reference.com as a totally different way to measure impact.

All data sets were from the 2013-14 regular season. I ran regressions with and without adjustments for position, and report position-adjusted results when they were meaningfully different.

Using RPM, a one-point increase in ORPM2 predicted a salary boost of $990 thousand, while an equal improvement on defense corresponded to a raise of only $660 thousand. This difference became magnified when I adjusted for position. A point on offense increased to $1.16 million against just $430 thousand for a point on defense.

Efficiency ratings saw a similar pattern. An extra point added to offensive rating predicted a salary boost of $180 thousand compared to $100 thousand for a point subtracted from defensive rating.

The win shares data gave less definitive results. A hundredth of a win share per 48 minutes was valued at $350 thousand, while the same amount of defense cost $320 thousand (a nonsignificant difference). However, adjusting for position widened the gap, with a hundredth of an offensive win share priced at $370 thousand and the same quantity of defense at $260 thousand3. It’s worth noting that the box score-based metric gave the most equal values for offensive and defensive contributions.

To give you a visual sense of this, I plotted player salaries against ORPM and DRPM.



Overall, these results make a strong case that GMs pay a higher price for offense–maybe even twice as much as they pay for equivalent defensive contributions. It’s tempting to point to that and scream “MARKET INEFFICIENCY!”, but there might be good reasons why this discrepancy exists.

First, I suspect it’s easier to have confident beliefs about a player’s abilities on offense. GMs and scouts can watch film to study either end, but only on offense can they use numbers to confirm what they’ve observed. Defense is still mostly opaque to stats. If GMs feel more uncertainty investing in a player’s defense, they won’t pay as much for it. Certainty comes at a premium, so offense will earn more money.

It could also be the case that future offensive performance is more predictable. GMs might believe that defensive contributions have more to do with a player’s fit within a team and system, while offense is consistent regardless of other factors. Again, this would lead to more certainty about what kind of impact a player’s offense will have.

Both of these explanations are mostly conjecture and might deserve more rigorous investigations of their own. But it’s important to remember that the fact that offense gets paid more than defense doesn’t automatically prove there’s a market inefficiency.

While GMs are in charge of money, coaches get to distribute minutes. It turns out that offense not only gets a player paid, it also gets him on the court.

Using RPM, an extra point of ORPM corresponded to 2.6 additional minutes per game. Meanwhile, a point of DRPM earned just 0.8 more minutes. This means the marginal playing time gain for offense was over three times that of defense.

The results from the other data sets were even more surprising. Using both efficiency ratings and win shares, increases in defensive contributions did not have a statistically significant effect on playing time4. An additional point of offensive efficiency predicted an increase of 0.6 minutes per game, while an equal improvement in defensive efficiency corresponded to a reduction of 0.2 minutes5.

Likewise, an extra hundredth of an offensive win share per 48 minutes predicted 0.8 additional minutes per game, while the same contribution on defense anticipated a loss of 0.1 minutes. After adjusting for position, the negative effect of defense disappeared and an additional hundredth of a defensive win share corresponded with an increase of 0.2 minutes.

Again, for visualization’s sake, here are graphs of minutes per game against ORPM and DRPM.



Let’s think about this for a moment. Markets are complicated, so GMs might have good reasons to consciously pay more for offense. But many of the factors that affect a dynamic market are absent or reduced for a coach. His goal is essentially to optimize his player’s minutes to build the best team possible6. It’s hard to come up with good reasons why coaches’ playing time distributions should make them appear apathetic towards defense. This data suggests they either don’t understand it as well as offense or don’t value it as much they should.

As one final note, it’s worth considering how these two phenomena might interact. For starters, coaches and GMs certainly talk about what players they do and don’t like. Some coaches have a say in how their rosters are built, and some GMs have influence over what happens on the court (and usually hire their coach). Each of these groups influences how the other thinks.

There could also be indirect effects. NBA coaches are smart people who want to keep their jobs. If a GM shells out for an offensively-focused player, his coach might feel pressure to play him more than he’d like, even if that hurts his team. On the flip side, coaches control which players GMs get to watch. If a good defender can’t get on the court because he hurts his team’s offense, the narrative surrounding him will likely focus on his deficiencies. No one will get a chance to see what he does well. Overall, it seems likely that there’s a relationship between the biases we see in each of these groups.

1. You can read more of my thoughts about Real Plus-Minus here.

2. In other words, an increase of one point of offense per 100 possessions.

3. Random note: Using all of these methods, a league average player would expect to make $5.3 million per year.

4. This was true both with and without positional adjustments.

5. Again, this reduction wasn’t statistically significant (p = 0.18). I don’t think being better at defense would actually cause a player to play less.

6. I’m not trying to say coaching is easy. They have to consider tons of factors in every decision. It’s just that their problems are more isolated from other teams than those facing a GM, and it’s more clear what a coach is optimizing for.