Rudy Gobert, Derrick Favors, and the Value of a Block

November 17th, 2015 | by Clint Johnson
Photo by Melissa Majchrzak/NBAE via Getty Images

Photo by Melissa Majchrzak/NBAE via Getty Images

As analytics continue to sweep the NBA, a misconception is growing: that improved numbers provide a “right” way to see the game. Analytics don’t offer the correct way to see anything; instead, they offer new ways to see what is familiar. Examining the relative value of a block is a prime example.

Dwight Howard is a three-time NBA Defensive Player of the Year where Tim Duncan has never won the award, despite appearing on 14 All Defensive teams to Howard’s five. This is primarily because of the long-held belief “he who blocks and rebounds the most is the biggest defensive beast.” Howard has led the league in blocked shots twice and rebounding five times. Duncan has never topped the league in either category1. Award voters thus determined their supreme defensive player based almost purely on accumulation of simple defensive statistics.

In 2010, a presentation at the MIT Sloan Analytics Conference challenged that simple2 notion. John Huizinga, a Distinguished Service Professor at the University of Chicago Booth School of Business, argued for Duncan’s superiority as a defensive centerpiece based not on total or per game block numbers but instead on calculations of points prevented via blocked shots.

Huizinga argued that the real value of a block can be measured in terms of points prevented in two ways: “How much did the block contribute to the team’s ability to score points… and how much did the block reduce the opponent’s ability to score points?” He presented findings from a seven-year span of play that placed Duncan repeatedly atop the league in block value while Howard often appeared near the bottom. More recently, Nylon Calculus released similar data for 19 seasons of NBA play.

Rudy Gobert and Derrick Favors are already as celebrated as any shot-blocking tandem in the modern NBA. Their value protecting the rim has already been seen. However, introducing different notions of block value can serve as a lens through which to see the Jazz’s defensive foundation in new, and perhaps useful, ways.

Data for this analysis was (laboriously) mined from last season’s play-by-play data3. For all players averaging 1.7 or more blocks per game given a minimum of 50 games played, it includes the distance at which shots blocked were taken and the resulting possession following the block (retrieved by the offensive team, ball out of bounds to retain offensive possession, or a turnover). That data is then combined with points per field goal attempt, points per possession, and other league averages to calculate a host of statistics that serve as particular ways to view the value of a blocked shot.

Here are definitions for statistics particular to this analysis:

OWN = Own rebound percentage. Probability a player will grab the rebound after blocking a shot.

TO% = Turnover percentage. Probability a block will result in the blocking team gaining possession.

<5ft = Shot blocked comes within five feet of the basket.

OOB = Out of bounds. Probability a blocked shot will result in an out of bounds play for the opponent.

PB = Points Per Block. Average per block points prevented by stopping the shot.

PPB = Points Prevented Per Block: Average per block points prevented both by stopping the shot and the probability of creating a turnover.

PPbB = Points Prevented by Block: Total points prevented per game by stopping shots by block.

TPP = Total Points Prevented. Total points prevented by block in a game, both by stopping shots and creating turnovers.

BIB: Blocks in bounds. Percentage of blocked shots that remain in play.

The data lends itself to presentation in three configurations, each a unique way to see the value of shot blockers. I term these Gross Impact Value, Pure Rim Protection Value, and Block Potency Value.

Gross Impact Value

Gross impact value is a simple ranking average that seeks to measure the overall impact a shot blocker makes on the defensive end of the floor. It does not try to articulate what statistics are of greater importance to competitive play by using weighted values for the categories. Instead, it is a simple overall number that suggests how well a player performs across these various performance statistics: turnovers created via block per game, total points prevented by block per game, fouls committed per game, and opponent field goal percentage at the rim4.

Gross Impact Chart

Gross Impact Chart

Jazz fans should be pleased but not surprised at who heads this chart: The Stifle Tower. Gobert is the only top shot blocker in the NBA last season to perform in the elite5 range in every gross impact category. Fourth in total points prevented, second-most-disciplined in not committing fouls, and a league-leading opponent field goal percentage at the rim — the very definition of gross impact.

Favors’ placement is also encouraging. While Favors ranked only 13th in blocks per game last season6, he jumps to 8th in this assessment. Revealingly, he ranks above DeAndre Jordan, Andrew Bogut, Andre Drummond, and Dwight Howard7.

Gross impact value suggests that two of the ten most impactful shot blockers in the NBA last season, including the Shot Blocker Supreme, played side-by-side for the Utah Jazz.

Pure Rim Protection Value

Pure rim protection value is a way to articulate how much of a shot blocker’s defensive impact actually comes at the rim. Like these other statistics, it is less a descriptive statistic meant to accurately portray the game than it is a quick and easy way to see how these players performed on average at various aspects of rim protection. It considers what percentage of shots blocked come within five feet of the hoop, points per block, fouls committed per game, and opponent field goal percentage at the rim.

Pure Rim Protection Chart

Pure rim protection is an interesting divider for last season’s super-elite shot blockers: Anthony Davis, Serge Ibaka, and Gobert. While each averaged better than 2.2 blocks per game, Ibaka’s total came while heavily protecting the rim while Davis racked up blocks at a clear expense of rim protection. Gobert lands smack in the middle, a respectable placement given that his low percentage of blocks within five feet of the rim and low PB do not detract from his league-best Opp FG%.

This assessment highlights the real value of true defensive anchors like Bogut and Duncan: what they lack in glossy total statistics they make up for in actual rim protection. Serge Ibaka is on a level all his own. From mining the data, I can attest that no shot blocker last season stuffed so many dunk attempts8 as Ibaka. While injuries to Kevin Durant and Russell Westbrook the past few seasons have earned highlights, Ibaka’s own injury struggles were just as key to the Thunder’s competitive struggles as his more well publicized teammates.

Derrick Favors appears 5th in this assessment, borderline elite, with solid numbers across the board and a particularly strong Opp FG%. While neither Favors nor Gobert is the dedicated rim protector possessed by Oklahoma City, Golden State, or San Antonio9, Utah has one top-five tower flanking another top-ten spire. Favors’ steady rim protection is a perfect accompaniment for Gobert’s aggressive, rangy swatting.

Block Potency Value

Block potency value tries to express the overall value of any player’s average blocked shot. In other words, what impact on the game comes as a result from any one block rather than cumulative blocks? Like the values above, it is meant less as an accurately descriptive statistic than a tool to consider overall performance on a number of related values. It takes into account own rebound percentage, turnover percentage, points per block, points prevented per block, and blocks in bounds.

Block Potency

Block Potency

In this category, Favors takes top billing10. Only Ibaka tops Favors in block potency, and then only by a numeric hair. To put in perspective just how potent each blocked shot by Ibaka and Favors is, consider the difference between 2nd (Favors) and 3rd (Bogut) is greater than the difference between 3rd and 7th (Davis). He may not be the most prolific, but last season Favors was a shot blocking artist: rebounding his own blocks frequently, not only stopping shots but ending possessions, and making the opposing team pay a high price in points with every block.

Not surprisingly, Gobert suffers here. When one defends like a natural disaster — explosive, wide-ranging, and with considerable fallout of splayed legs and volleyball-like spikes — controlling the outcome of a block is nigh impossible. There is a kind of symmetry in Jazz players ranking both second and second-to-last on this list. As with pure rim protection, Favors’ excellence here is a fine counterbalance to Gobert’s exuberance. That said, the French Phenom has undeniable growth potential in terms of keeping blocked balls in play. A little less emphasis on swats would allow him to corral more of his own blocks as well, creating greater transition opportunities for the Jazz.

Like any analytics, these numbers don’t define Gobert’s or Favors’ impact as shot blockers; they merely provide new ways to consider that substantial impact. If the duo recreate anything like what they produced last season11And they’re off to a solid start.12, the rest of the league must find the view terrifying.

Clint Johnson

Clint Johnson

Clint Johnson is a professional author, writing educator, and editor. He teaches writing at Salt Lake Community College. A frequent presenter at both writing and educational conferences, he writes about the Jazz as a break from his other writing work.
Clint Johnson

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  1. Don says:

    I don’t see Kanter anywhere on the list. He was a starting center last year.

    Just saying…

    • Clint Johnson says:

      Yeah, my guess is that on the good to bad scale, Enes Kanter would rank “bad” pretty much any way your look at it in this regard. Though I must admit, my guess is the blocks he does get probably stay in bounds pretty frequently.

  2. IDJazzman says:

    I would guess that the pairing of Favors and Gobert leads the league against other team’s Bigs, in most or all of the above categories? Jazz have an impressive duo with Favors and Gobert.

    • Clint Johnson says:

      I assume so as well, though it would take A LOT of work to find out. So far, the only way to get data on blocked shot distance and outcome is to mine it from play-by-play records (so far as I know). The only duos that would be anywhere close may be Dwight Howard and Terrance Jones or Josh Smith in Houston or Andrew Bogut and Draymond Green for the Warriors. But even those likely wouldn’t pose actual challenges to the Jazz bigs’ production last season.

      If Gobert and Favors maintain approximately what they did last year, no other team in the league can match their combination of shot blocking quantity and quality. It’s no surprise the Jazz smother teams at the rim when both their towers are healthy.

  3. Robin Rodd says:

    this is fantastic analysis. love it.

  4. Scott says:

    This is a great analysis and shows how analytics can be used to compare players. But blocked shots are only a portion of the impact a shot blocker has. He also causes shots to be altered or even not taken so as not to be blocked. And what about when a player misses an open shot just worrying about the shot blocker being in the vicinity? I imagine this happens a lot when a player has to worry about two shot blockers at the same time, such as Gobert and Favors. Are you up for another analytics challenge, Clint?

    • Matt says:

      Other than the shots not taken, what you’re talking about is reflected in Opp FG%

      • Scott says:

        In a macro sense you’re right. But it would be interesting to see how many shots were actually altered or not taken instead of just a single Opp FG%. But those numbers are probably not available, especially the shots not taken.

        • Clint Johnson says:

          I agree that altered shots is implied by Opp FG% at the rim, as an altered shot generally has less chance of going in. The lower the overall FG% for a range, the more often the defender is likely altering shots.

          Discouraged shots are a different matter entirely. I don’t think there’s any way to really track that at this point, that I know of. The best option would probably be looking at shot frequency in’s defensive tracking data combined with defended field goals attempted. But then, those stats penalize an aggressive, rangy rim protector who manages to be a factor in many shots, such as Gobert, who led the league in defended field goals within six feet last year (8.5 per game).

          If someone was willing to work at it, it’s possible to compare a team’s (or player’s) season-long shot chart to their shot chart against against a particular opponent to get some sense of how that affects shot distribution. But that has problems to, especially when it comes to identifying cause for discrepancies. Lots of variables at play.

          So at this point, discouraged shots are probably a quantifiable blind spot in the game.

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