Half-Court Math: Hack-a-Whoever, Isolation and Long 2’s

In my upcoming book, Thinking Basketball, I allude to certain instances where “low efficiency” isolation offense provides value for teams. Most of us compare a player’s efficiency to the overall team or league average, but that’s not quite how the math works, because the average half-court possession is worth less than the average overall possession.

In 2016, the typical NBA possession was worth about 1.06 points. That’s a sample that includes half-court possessions against a set defense, but also scoring attempts from:

  • transition
  • loose-ball fouls
  • intentional fouls
  • technical fouls

Transition is by far the largest subset of that group, accounting for 15% of possessions for teams, per Synergy Sports play-tracking estimations. Not surprisingly, transition chances, when the defense is not set, are worth far more than half-court chances. As are all of the free-throw shooting possessions that occur outside of the half-court offense.

Strip away those premium opportunities from transition and miscellaneous free throws and the 2016 league averaged 95 points per 100 half-court possessions. (All teams were between 7 and 14 points worse in the half-court than their overall efficiency.) Golden State, the best half-court offense in the league this year, tallied an offensive rating around 105, far off its overall number of 115 that analysts are used to seeing.

Transition vs Half Court Efficiency

This has major implications for the math behind “Hack-A-Whoever.” If the defense is set, then, all things being equal, fouling someone who shoots over 50% from the free throw line is doing them a favor. One might think that a 53% free throw shooter (1.06 points per attempt) at the line is below league average on offense because of the overall offensive efficiency. But it’s actually well above league average against a set, half-court defense. (Other factors, like offensive rebounding and allowing the free-throw shooters team to set-up on defense complicate the equation.)

Said another way — fouling a 53% free throw shooter is similar to giving up a 53% 2-point attempt…which is woeful half-court defense.

There could be other viable reasons to “Hack-A-Whoever,” such as breaking up an opponent’s rhythm or psychologically disrupting the fouled player. (These would be good strategic reasons to keep the rule, in my opinion.) But assuming he was a 50-60% foul shooter, coaches would still be making a short-term tradeoff, exchanging an inefficient defensive possession for other strategic gains.

This also has ramifications for isolation scorers and long 2-point shots. Isolation matchups that create around a point per possession in the half court — or “only” 50% true shooting — are indeed excellent possessions. If defenses don’t react accordingly, they will be burned by such efficiency in the half-court. As an example, San Antonio registered about 103 points per 100 half-court possessions this year, and combined it with a below-average transition attack to still finish with an offensive rating of 110, fourth-best in the league.

The same goes for the dreaded mid-range or long 2-pointer — giving these shots to excellent shooters from that range (around 50% conversion) is a subpar defensive strategy. And even a 35% 3-point shooter (1.05 points per shot) yields elite half-court offense.

So, when we talk about the Expected Value of certain strategies, mixing transition possessions together with half-court ones will warp the numbers. Sometimes, seemingly below-average efficiency is actually quite good.

 

How 2016 NBA Teams Differentiated Themselves on Offense

Dean Oliver’s Four Factors uses box score data to determine how teams are successful in key elemental areas. Instead of looking at box stats like turnovers and rebounding, what if we used different types of plays to determine a team’s offensive strengths? Synergy tracks a number of play types, but not all have a large impact on the game. Based on the 2016 data on nba.com, the following were the most common play types this year:

  • 25% were pick-n-roll plays
  • 20% were spot-ups
  • 15% were in transition

Naturally, teams differentiate themselves from the pack based on the plays they run the most; The Lakers led the league in isolation plays, but their efficiency was below-average on those plays, so they lost lots of ground on the average offense. The five categories from Synergy with the largest degree of differentiation were:*

  1. Pick-n-Roll (PnR)
  2. Spot Up
  3. Transition
  4. Post Up
  5. Off Screen

Below is a visual of how every team in the NBA this year fared in these five factors.

2016 Differentiation by Play Type

The y-axis represents the per-game differentiation based on efficiency of a given play type (relative to league average). For instance, if a team ran 820 post ups (10 per game) and averaged 0.10 points per play more than league average, they would generate an extra point per game.

Not surprisingly, the most differentiating play type during the 2016 season was a Golden State spot-up shot. Of the 203 players with at least 100 spot-ups, Steph Curry was 2nd in efficiency at 1.49 points per play and splash brother Klay Thompson 15th at 1.18 points per play. (League average was 0.97 points per spot-up.) Let’s simplify the above visual and just focus on the final eight teams left in this year’s playoff field:

2016 Differentiation Final 8

Now it’s easier to see how the remaining teams stack up. The Warriors don’t really have a post-up game, but so what? They excel at everything else and created the most differentiation of any team in the league in three major categories (PnR, Spot Up and Off Screen.) On the other hand, the Spurs were dominant in the post and excellent in their own right at spot-up plays, but they don’t do damage in transition. (San Antonio also led the league in “put backs” by a large degree, generating over a point of separation alone in that category.) The East’s best team, Cleveland, was above-average at everything.

*Isolation plays would be the 6th major play type. However, no team in 2016 created a point of positive or negative differentiation from isolation plays, which accounted for 8% of all plays tracked during the season.