Which College Statistics Are Most Correlated With NBA Success?

The NBA draft is almost upon us! For the sake of curiosity, I decided to dig into the data to determine which college statistics have been most correlated with NBA success.

I will first give a few words on my methodology. I considered all players drafted in the year 2000 to the present whose age 25 season could have happened in 2017-18 or earlier. The age 25 season criteria is included because I measured player value in terms of Win Shares (a stat provided by basketball-reference) produced in the age 25 season. I chose to measure performance at this age rather than something like prime years (age 26-29) to allow more recent draft picks into the sample.

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Introducing 3-PIGS, a New Way of Understanding Three-Point Variability in NBA Games

The rise of the three-point jump shot in the NBA has been well documented. Over the past five regular seasons the average team three-point attempt rate has spiked from 24.3% to 33.7%, per basketball-reference. The analytically driven Houston Rockets actually attempted more three-pointers than two-pointers this past season!

In this three-point happy league, analyzing an individual game after removing the effects of “hot” or “cold” three-point shooting can be very informative. It allows us to see how well the teams played in all aspects of the game except three-point accuracy. We can strip aside a particularly unusual shooting performance and observe the “fundamentals” of a game, in a certain sense.¬† This is what Three-Pointer Independent Game Score (abbreviated as 3-PIGS) does.

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Are Past Games in a Playoff Series Predictive of the Next Game?

I recently was building a simple model to forecast an NBA playoffs series. As I was building the model, I realized that I was not taking into account the possibility that each team’s strength could change over the course of the series. If a team dominates games 1 and 2, we might reasonably expect them to have a higher likelihood of winning game 3 than we did at the beginning of the series.

But perhaps we should not alter out initial belief too much. After all, the 2 games in the example above is not a very large sample. Sheer randomness and recency bias may be causing us to shift our thinking too much. My initial hunch was just that; the general public overreacts too much to a few performances.

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An Analysis of the 2-for-1 Strategy in the NBA

The appropriately named “2-for-1” is a strategy utilized at the end of a quarter in which a team tries to time their shot attempts so that, as the name suggests, they get two attempts while their opponents only have one. To execute the strategy, a team usually pushes the ball up the floor to take a shot with about 30 seconds left in the quarter, thus ensuring that their opponents cannot hold for the last shot.

Intuitively, this strategy seems perfectly reasonable. Just like holding for the last shot, a team which executes the 2-for-1 is gaining one extra possession. Who would not want an extra possession? Well, we could imagine a scenario where the two possessions are so rushed that their expected value is less than the value of the one “normal” possession the opponent is allowed. For example, suppose we value a conventional NBA possession at 1.09 expected points but the two rushed possessions usually generate bad shots and are only worth 0.5 expected points each. Then it might make more sense to execute a “1-for-1” strategy and simply grant the opponent the last shot.

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Which Team Statistics are Most Stable From the 1st Half of the Season to the 2nd?

In my work on the Threes and Layups NBA Net Rating Calculator, I have focused on 14 statistics (the same 7 on offense and defense) which capture a team’s performance across all areas of the game:

  1. 2-Point %
  2. 3-Point %
  3. 3-Point Attempt Rate
  4. Free Throw Attempts Per Field Goal Attempts
  5. Free Throw %
  6. Turnover Rate
  7. Rebound Rate (Offensive and Defensive)

The NBA Net Rating Calculator allows you to see how much a team’s regular season performance would be expected to change if you changed one of these statistics. But this leads to a natural question: which of these statistics is most likely to change?

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