Case Study 1: The 2016-17 Triple-Double Season of Russell Westbrook

Executive Summary

Russell Westbrook's 2016-17 season stands as one of the most statistically remarkable individual performances in NBA history. He became the first player since Oscar Robertson in 1961-62 to average a triple-double for an entire season, finishing with 31.6 points, 10.7 rebounds, and 10.4 assists per game. This case study examines Westbrook's box score statistics in depth, exploring what they reveal about his performance, what context is necessary for proper interpretation, and what limitations exist in evaluating his season through traditional statistics alone.

Background and Context

The Situation

Following Kevin Durant's departure to the Golden State Warriors in the summer of 2016, the Oklahoma City Thunder faced a fundamental question: could they remain competitive with Russell Westbrook as their sole superstar? Westbrook had always been an elite player, but now he would carry an unprecedented offensive load without another MVP-caliber teammate to share responsibilities.

Historical Context

Oscar Robertson's 1961-62 triple-double season (30.8 PPG, 12.5 RPG, 11.4 APG) had stood alone for 55 years. The feat was considered unrepeatable in the modern era due to: - Faster pace in the 1960s creating more statistical opportunities - Different rebounding patterns (guards grabbing fewer rebounds) - Evolution of positional roles - Changes in how assists were credited

Westbrook's Pre-2016-17 Profile

Season PPG RPG APG FG% TS%
2013-14 21.8 5.7 6.9 43.7% 54.6%
2014-15 28.1 7.3 8.6 42.6% 53.6%
2015-16 23.5 7.8 10.4 45.4% 55.4%

Westbrook had already established himself as an elite playmaker and scorer. The question was whether he could elevate further while maintaining efficiency.

Box Score Analysis

Scoring Production

Westbrook's 31.6 points per game led the NBA and represented a career high:

Scoring Breakdown: - Two-point field goals: 7.2 made per game (14.4 attempts) - Three-point field goals: 1.8 made per game (7.2 attempts) - Free throws: 8.2 made per game (10.4 attempts)

Efficiency Metrics: - Field Goal Percentage: 42.5% - Three-Point Percentage: 34.3% - Free Throw Percentage: 84.5% - True Shooting Percentage: 55.4%

Context: The league average TS% in 2016-17 was 55.3%, meaning Westbrook scored at exactly league-average efficiency while carrying an enormous volume. For comparison:

Player PPG TS% Usage Rate
Westbrook 31.6 55.4% 41.7%
Harden 29.1 61.3% 34.2%
LeBron 26.4 61.9% 30.0%
Durant 25.1 65.1% 27.7%

Westbrook's 41.7% usage rate was the highest in NBA history at that point, meaning he used over 4 in 10 of Oklahoma City's possessions when on the court. Maintaining league-average efficiency at this usage level was a significant accomplishment.

Rebounding Production

Westbrook's 10.7 rebounds per game ranked 10th in the NBA and first among guards:

Rebounding Breakdown: - Offensive rebounds: 1.7 per game - Defensive rebounds: 9.0 per game

Positional Context: | Position | League Avg RPG | Westbrook | |----------|---------------|-----------| | Point Guards | 3.8 | 10.7 | | Shooting Guards | 4.2 | - | | Small Forwards | 5.1 | - | | Power Forwards | 6.8 | - | | Centers | 8.4 | - |

Westbrook's rebounding was 181% above the point guard average, an extraordinary deviation from positional norms.

Critical Analysis: Uncontested Rebounds

Video analysis and tracking data revealed important context: - 73.8% of Westbrook's defensive rebounds were uncontested - League average for uncontested DRB%: approximately 65% - Thunder teammates frequently boxed out and allowed Westbrook to secure defensive boards

This strategy served multiple purposes: 1. Allowed Westbrook to initiate fast breaks immediately 2. Reduced turnover risk from outlet passes 3. Maximized Westbrook's playmaking opportunities

The Trade-Off Question: Did the Thunder sacrifice team offensive rebounding for Westbrook's rebound totals?

Team Metric Thunder League Avg
Team OREB/G 8.4 9.7
Team OREB% 20.8% 23.4%
Pace 99.6 96.4

Oklahoma City's offensive rebounding was below league average, suggesting some trade-off existed.

Assist Production

Westbrook's 10.4 assists per game ranked second in the NBA behind James Harden:

Assist Quality: - Potential assists per game: 18.2 - Assists per 100 possessions: 14.9 - Assist-to-turnover ratio: 2.0

Turnover Context: - 5.4 turnovers per game (led league) - 7.7 turnovers per 100 possessions - Turnover percentage: 15.8%

Comparative Analysis:

Player APG TOV AST/TO Potential AST
Westbrook 10.4 5.4 1.93 18.2
Harden 11.2 5.7 1.96 17.8
Chris Paul 9.2 2.4 3.83 14.1
LeBron 8.7 4.1 2.12 15.4
John Wall 10.7 4.1 2.61 15.9

Westbrook created more potential assists than any player in the league, but his conversion rate was lower than elite playmakers like Chris Paul. His high turnover total reflected the aggressive, high-risk style required to carry Oklahoma City's offense.

The Triple-Double Achievement

Historical Comparison

Player Season PPG RPG APG Pace TS%
Oscar Robertson 1961-62 30.8 12.5 11.4 ~126 53.2%
Russell Westbrook 2016-17 31.6 10.7 10.4 99.6 55.4%

Pace-Adjusted Analysis: Robertson played in an era with approximately 26% more possessions per game. Adjusting Westbrook's numbers to 1961-62 pace: - Points: 31.6 * (126/99.6) = 40.0 PPG equivalent - Assists: 10.4 * (126/99.6) = 13.2 APG equivalent

By pace-adjusted measures, Westbrook's per-possession production in scoring and playmaking exceeded Robertson's historic season.

Individual Triple-Double Games

Westbrook recorded 42 triple-doubles during the 2016-17 season:

Stat Line Frequency Games
30+ points 29
15+ assists 9
15+ rebounds 2
40+ points 6

In Triple-Double Games: - Record: 33-9 (78.6%) - Average margin: +8.2 points

In Non-Triple-Double Games: - Record: 14-26 (35.0%) - Average margin: -4.1 points

This stark differential raises an important analytical question: were triple-doubles predictive of wins, or were both outcomes (triple-doubles and wins) products of favorable game circumstances?

Advanced Box Score Metrics

Player Efficiency Rating (PER)

Westbrook's PER: 30.6 (led NBA)

Historical context: | Season | Player | PER | |--------|--------|-----| | 2016-17 | Westbrook | 30.6 | | 2015-16 | Curry | 31.5 | | 2008-09 | LeBron | 31.7 | | 1987-88 | Jordan | 31.7 |

Game Score Analysis

Average Game Score: 26.4 (highest in NBA)

Distribution of Game Scores: | Range | Games | Percentage | |-------|-------|------------| | Below 10 | 4 | 4.9% | | 10-20 | 20 | 24.4% | | 20-30 | 31 | 37.8% | | 30-40 | 21 | 25.6% | | Above 40 | 6 | 7.3% |

Westbrook's consistency was remarkable - only 4 games (4.9%) with Game Score below 10.

Usage and Efficiency Relationship

The relationship between Westbrook's usage and efficiency showed interesting patterns:

Game Type Usage Rate TS% W-L
Wins 39.8% 57.2% 47-0
Losses 44.1% 52.8% 0-35

When Oklahoma City lost, Westbrook typically increased his usage while efficiency declined - a pattern common among high-usage players on teams lacking secondary options.

Team Context and Impact

On/Off Court Analysis

Metric Westbrook On Westbrook Off Differential
Offensive Rating 107.8 98.2 +9.6
Defensive Rating 105.4 109.8 -4.4
Net Rating +2.4 -11.6 +14.0

The +14.0 on/off differential was among the highest in the league, suggesting massive impact when on court compared to OKC's performance without him.

Teammate Performance

Thunder teammates shot significantly better with Westbrook on court:

Player FG% w/ Westbrook FG% w/o Westbrook Difference
Steven Adams 57.2% 51.4% +5.8%
Victor Oladipo 46.8% 42.1% +4.7%
Enes Kanter 55.8% 52.3% +3.5%

This suggests Westbrook's playmaking created easier shots for teammates, though sample sizes for "without Westbrook" minutes were limited.

Limitations of Box Score Analysis

What the Numbers Don't Show

  1. Defensive Effort - Westbrook's steals (1.6 per game) appeared positive - However, he frequently gambled on passing lanes - Defensive rating with him on court (105.4) was actually better than off (109.8), complicating the narrative

  2. Off-Ball Movement - Box scores cannot capture whether Westbrook set screens or moved without the ball - His high usage meant limited off-ball contributions

  3. Shot Quality Created - Assists counted equally regardless of shot difficulty - Did not capture screens set or gravity created

  4. Pace Manipulation - Thunder's pace (99.6) was above league average - This created additional counting stat opportunities

  5. Garbage Time Effects - Some statistics accumulated in blowouts (both directions) - Did not distinguish high-leverage from low-leverage performance

The MVP Debate

The 2016-17 MVP race between Westbrook and Harden highlighted box score limitations:

Metric Westbrook Harden
PPG 31.6 29.1
RPG 10.7 8.1
APG 10.4 11.2
TS% 55.4% 61.3%
Team Wins 47 55
On/Off +14.0 +8.2

Harden had superior efficiency and team success. Westbrook had the triple-double narrative and larger on/off differential. The vote came down to how voters weighted these factors - a question box scores alone cannot answer.

Conclusions and Key Takeaways

What Box Scores Revealed

  1. Historic Production Volume: Westbrook's counting statistics were genuinely extraordinary
  2. Efficiency at High Usage: Maintaining league-average efficiency at 41.7% usage was impressive
  3. Playmaking Excellence: 18.2 potential assists showed elite creation ability
  4. Team Dependence: The +14.0 on/off differential suggested massive impact

What Required Additional Context

  1. Rebounding Inflation: Team strategy inflated defensive rebounds
  2. Turnover Tolerance: High turnovers were acceptable given offensive load
  3. Defensive Contribution: Box score defense (steals/blocks) didn't capture full picture
  4. Efficiency Trade-offs: Volume vs. efficiency debate required context

Analytical Lessons

  1. Context Transforms Interpretation: The same statistics meant different things in different contexts
  2. No Single Number Suffices: PER, TS%, triple-doubles all captured partial truths
  3. Process vs. Outcome: Triple-doubles and wins correlated but causation remained unclear
  4. Historical Comparisons Require Adjustment: Pace and era differences mattered enormously

Final Assessment

Russell Westbrook's 2016-17 season demonstrated both the power and limitations of box score analysis. The statistics told a remarkable story of individual production under extreme circumstances. However, proper interpretation required understanding team context, strategic choices (allowing uncontested rebounds), efficiency trade-offs, and the limitations of what counting statistics can capture.

The season stands as a landmark in NBA history - and as a case study in why sophisticated analysts look beyond raw numbers to understand what truly happened on the court.

Discussion Questions

  1. Should the triple-double average be weighted more heavily than efficiency metrics when evaluating player value?

  2. How should analysts account for team strategy that inflates individual statistics (like allowing uncontested rebounds)?

  3. Given the on/off differential, was Westbrook underrated or appropriately rated by traditional metrics?

  4. How might this season look different if evaluated solely by tracking data and advanced metrics?

  5. What does the correlation between triple-doubles and wins tell us about causation?

Data Sources

  • NBA.com official statistics
  • Basketball-Reference.com
  • NBA tracking data (stats.nba.com)
  • ESPN Hollinger statistics
  • Cleaning the Glass (on/off data)