Case Study 2: Mike Conley and the Mid-2010s Memphis Grizzlies Pace Outliers
Executive Summary
The Memphis Grizzlies from 2010-2017 represented a deliberate counter-strategy to the league's increasing pace. While most teams accelerated their offenses, Memphis built consistent playoff success around the slowest pace in the NBA. This case study examines how rate statistics properly contextualized the Grizzlies' production, why their players' traditional statistics were systematically undervalued, and what pace-adjusted analysis revealed about their true quality.
Background: Going Against the Trend
The League-Wide Pace Increase
| Season | League Average Pace |
|---|---|
| 2010-11 | 92.1 |
| 2011-12 | 91.0 |
| 2012-13 | 92.0 |
| 2013-14 | 93.9 |
| 2014-15 | 93.9 |
| 2015-16 | 95.8 |
| 2016-17 | 96.4 |
From 2010 to 2017, league pace increased by 4.3 possessions per 48 minutes (4.7% increase).
The Grizzlies' Contrarian Approach
| Season | Grizzlies Pace | League Avg | Rank (lowest=30th) |
|---|---|---|---|
| 2010-11 | 89.2 | 92.1 | 30th |
| 2011-12 | 88.0 | 91.0 | 30th |
| 2012-13 | 89.4 | 92.0 | 30th |
| 2013-14 | 90.7 | 93.9 | 30th |
| 2014-15 | 91.5 | 93.9 | 30th |
| 2015-16 | 92.5 | 95.8 | 30th |
| 2016-17 | 93.9 | 96.4 | 29th |
For seven consecutive seasons, the Grizzlies ranked 29th or 30th in pace—consistently 3-4 possessions slower than league average.
Why Play Slow?
The Grizzlies' roster supported this strategy: 1. Elite interior scoring (Zach Randolph, Marc Gasol) 2. Dominant post defense (Gasol, Tony Allen) 3. Limited perimeter shooting (below-average 3PT%) 4. Physical style that wore down opponents over 48 minutes
Playing slow maximized their strengths and minimized possessions where their weaknesses would be exposed.
Mike Conley: The Undervalued All-Star
Traditional Statistics Didn't Tell the Story
Mike Conley's career with Memphis produced modest-looking numbers:
| Season | PPG | APG | SPG | FG% | 3P% |
|---|---|---|---|---|---|
| 2012-13 | 14.6 | 6.1 | 2.2 | 43.6% | 36.2% |
| 2013-14 | 17.2 | 6.0 | 1.5 | 44.9% | 36.2% |
| 2014-15 | 15.8 | 5.4 | 1.3 | 44.8% | 38.6% |
| 2015-16 | 15.3 | 6.1 | 1.6 | 42.5% | 36.8% |
During this stretch, Conley never made an All-Star team. His counting stats ranked him outside the top 30 point guards by PPG.
Pace-Adjusted Analysis
Adjusting Conley's numbers to league-average pace revealed different conclusions:
| Season | Raw PPG | Pace Factor | Adjusted PPG | Adjusted Rank |
|---|---|---|---|---|
| 2012-13 | 14.6 | 1.029 | 15.0 | - |
| 2013-14 | 17.2 | 1.035 | 17.8 | - |
| 2014-15 | 15.8 | 1.026 | 16.2 | - |
| 2015-16 | 15.3 | 1.036 | 15.9 | - |
The adjustment was modest (2-4% increase) but consistent.
Per-100 Possession Statistics
The clearer picture emerged in per-100 possession statistics:
| Season | Conley Pts/100 | League Avg PG | Percentile |
|---|---|---|---|
| 2012-13 | 20.4 | 18.9 | 68th |
| 2013-14 | 23.4 | 19.8 | 79th |
| 2014-15 | 21.8 | 19.5 | 74th |
| 2015-16 | 21.3 | 19.6 | 71st |
Conley's per-possession production consistently ranked in the 70th-80th percentile among point guards—significantly higher than his raw PPG suggested.
Efficiency Context
Conley's efficiency metrics were elite despite modest counting stats:
| Season | TS% | League Avg | Rank among PGs |
|---|---|---|---|
| 2012-13 | 54.2% | 53.5% | 12th |
| 2013-14 | 56.2% | 54.0% | 7th |
| 2014-15 | 57.8% | 54.0% | 5th |
| 2015-16 | 55.4% | 54.0% | 10th |
His True Shooting Percentage consistently exceeded league average while playing on a team that created fewer opportunities per game.
Marc Gasol: The Pace Context for MVP Voting
2014-15: The Pace Problem in MVP Discussions
Marc Gasol finished 5th in MVP voting in 2014-15 with these statistics:
| Stat | Gasol | Curry (MVP) | Harden (2nd) |
|---|---|---|---|
| PPG | 17.4 | 23.8 | 27.4 |
| RPG | 7.8 | 4.3 | 5.7 |
| APG | 3.8 | 7.7 | 7.0 |
| Team Pace | 91.5 | 98.3 | 96.4 |
Raw PPG comparisons made Gasol seem far less impactful. But pace context changed the picture:
Pace-Adjusted Offensive Production
| Player | Raw PPG | Possessions Gap | Adjusted PPG |
|---|---|---|---|
| Gasol | 17.4 | -6.8 vs Curry | 18.7 |
| Curry | 23.8 | baseline | 23.8 |
Even adjusted, Curry outproduced Gasol offensively—but the gap narrowed from 6.4 to 5.1 points.
Defensive Context Rate Statistics Revealed
The MVP case for Gasol rested primarily on defensive impact:
| Metric | Gasol On | Gasol Off | Differential |
|---|---|---|---|
| Defensive Rating | 97.2 | 104.6 | -7.4 |
| Opponent eFG% | 47.8% | 51.2% | -3.4% |
| Opponent At-Rim FG% | 54.2% | 62.8% | -8.6% |
Gasol's on-court defensive presence improved the Grizzlies by 7.4 points per 100 possessions—an enormous differential that raw statistics couldn't capture.
Net Rating: The Complete Picture
| Player | On-Court Net Rtg | Off-Court Net Rtg | Differential |
|---|---|---|---|
| Gasol | +8.2 | -4.1 | +12.3 |
| Curry | +11.1 | +0.8 | +10.3 |
| Harden | +9.4 | +4.2 | +5.2 |
By on/off differential, Gasol actually exceeded Curry and substantially exceeded Harden. The slow pace suppressed his counting statistics while net rating revealed his true impact.
Team Offensive Efficiency: Slow But Effective
The Grizzlies' Offensive Paradox
Memphis ranked consistently low in PPG but solid in Offensive Rating:
| Season | PPG | PPG Rank | ORtg | ORtg Rank | Gap |
|---|---|---|---|---|---|
| 2012-13 | 93.4 | 26th | 104.0 | 15th | +11 |
| 2013-14 | 97.6 | 22nd | 107.0 | 10th | +12 |
| 2014-15 | 97.4 | 18th | 106.7 | 13th | +5 |
| 2015-16 | 99.9 | 21st | 107.3 | 14th | +7 |
The team that looked offensively limited by PPG (26th, 22nd, 18th, 21st) was actually roughly average-to-solid by ORtg (15th, 10th, 13th, 14th).
Why the Discrepancy?
PPG = ORtg * (Pace/100)
Memphis had low pace, so even average ORtg produced below-average PPG: - 107.0 ORtg at 90.7 pace = 97.1 PPG - 107.0 ORtg at 93.9 pace = 100.5 PPG
Same offensive efficiency, 3.4 fewer points per game simply due to pace.
Defensive Excellence in Rate Terms
The Core Strength
The Grizzlies' identity was defensive:
| Season | DRtg | Rank | Opp PPG | PPG Rank |
|---|---|---|---|---|
| 2012-13 | 98.7 | 2nd | 88.5 | 1st |
| 2013-14 | 101.6 | 7th | 92.3 | 2nd |
| 2014-15 | 100.1 | 3rd | 91.5 | 1st |
| 2015-16 | 102.3 | 7th | 94.7 | 3rd |
Why Opponent PPG and DRtg Rankings Differed
2012-13 provides a clear example: - DRtg: 98.7 (2nd in NBA) - Opponent PPG: 88.5 (1st in NBA)
The Grizzlies held opponents to the fewest points primarily because they played at the slowest pace. Their per-possession defense was elite (2nd), but their counting stat defense (1st) benefited from fewer possessions.
This is why defensive analysts prefer DRtg to opponent PPG—it isolates defensive quality from pace effects.
Tony Allen: The Pace-Era Defensive Specialist
Traditional Stats Looked Minimal
Tony Allen's traditional statistics were extremely limited:
| Season | PPG | RPG | APG | SPG | MPG |
|---|---|---|---|---|---|
| 2012-13 | 8.9 | 4.6 | 1.6 | 1.8 | 26.4 |
| 2013-14 | 9.0 | 4.9 | 1.3 | 1.6 | 25.8 |
| 2014-15 | 9.1 | 5.5 | 1.3 | 1.8 | 26.0 |
By counting statistics, Allen appeared to be a replacement-level player at best.
Rate Statistics Told Different Story
| Metric | Allen On | Allen Off | Differential |
|---|---|---|---|
| DRtg (2014-15) | 96.8 | 102.4 | -5.6 |
| Opp Pts at Rim | 8.2 | 11.4 | -3.2 |
| Opp eFG% | 46.2% | 50.8% | -4.6% |
Allen's defensive on/off differential (-5.6 DRtg) was among the best in the NBA. The team was 5.6 points per 100 possessions better defensively with Allen on court.
Per-Possession Defensive Impact
Allen's steals per 100 possessions: - 2012-13: 2.8 STL/100 (league-leading pace among regular rotation players) - 2013-14: 2.5 STL/100 - 2014-15: 2.8 STL/100
His 1.8 SPG looked modest, but per-possession rates revealed elite ball-hawking.
Playoff Success: Rate Stats Predicted It
Regular Season Net Rating vs. Playoff Success
| Season | Grizz Net Rtg | Playoff Result |
|---|---|---|
| 2010-11 | +1.2 | Lost 1st round (vs eventual champion) |
| 2011-12 | +4.2 | Lost 1st round (4 v 5 seed matchup) |
| 2012-13 | +5.3 | Lost WCF |
| 2013-14 | +5.4 | Lost 1st round (7 game series) |
| 2014-15 | +6.6 | Lost WCSF |
Net rating consistently indicated a strong team (top 8-10 in NBA), even when PPG/PPG Allowed made them seem mediocre.
Why Rate Stats Predicted Better Than Counting Stats
- Net rating captured efficiency, not volume
- Pace effects neutralized, revealing true quality
- Playoff pace typically slows, favoring teams built for efficiency
- Defensive rating properly valued their elite defense
Free Agency Implications
The Mike Conley Contract
In 2016, Mike Conley signed a 5-year, $153 million contract—the largest in NBA history at the time. Critics cited his "modest" statistics: - Never made an All-Star team - Career high 17.2 PPG - Not a national media presence
Rate-Based Justification
Conley's value showed in rate statistics:
| Metric | Conley | League Avg PG |
|---|---|---|
| ORtg | 118.4 | 110.2 |
| On/Off Net | +7.8 | +2.1 |
| AST/TOV | 3.2 | 2.4 |
| TS% | 57.2% | 53.8% |
By efficiency metrics, Conley was one of the 5-7 best point guards in the NBA—despite counting stats ranking him outside the top 15.
The Grizzlies' low pace systematically undervalued their point guard in the free agent market... until teams doing rate-based analysis identified his true value.
Analytical Lessons
Lesson 1: Pace Systematically Affects Player Valuation
Players on slow-pace teams have deflated counting statistics. This affected: - All-Star voting for Conley - MVP voting for Gasol - Public perception of Allen - Free agent valuation
Lesson 2: Team Strategy Must Inform Individual Evaluation
The Grizzlies chose to play slow. This strategy affected all their players' statistics. Evaluating individuals without team context produced systematically biased conclusions.
Lesson 3: Defensive Value Requires Rate Statistics
Tony Allen's defensive impact was invisible in counting stats. Only rate-based on/off analysis revealed his true contribution.
Lesson 4: PPG and Opponent PPG Are Pace-Corrupted
Team offensive and defensive quality are better measured by ORtg and DRtg than by points scored and allowed.
Lesson 5: Rate Stats Predict Playoff Success Better
Playoff basketball typically slows down. Teams built around efficiency (like Memphis) perform relatively better in playoffs than regular season PPG would suggest.
Conclusions
The Memphis Grizzlies' deliberate slow-pace strategy created a natural experiment in statistical valuation:
-
Mike Conley was consistently undervalued by media and fans who focused on PPG rather than per-possession efficiency.
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Marc Gasol's MVP case made more sense when defensive rate statistics revealed his enormous on/off impact.
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Tony Allen's First Team All-Defense selections were justified by rate-based defensive metrics despite minimal counting stats.
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The team's playoff success was predictable from net rating even when PPG/PPG Allowed seemed modest.
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Rate statistics provide fairer evaluation for players whose teams employ unconventional pace strategies.
The Grizzlies' era proves that analysts must consider pace context when evaluating any player or team. Counting statistics without rate adjustment produce systematically biased conclusions.
Discussion Questions
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Should All-Star and award voting require voters to consider pace-adjusted statistics?
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How might Conley's career narrative differ if he had played on a league-average pace team?
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What are the risks of a team deliberately playing at extreme pace (either direction)?
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How should rate statistics influence contract negotiations for players on outlier-pace teams?
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Did the analytics revolution accelerate the end of Memphis's strategy by convincing other teams to play faster?
Data Sources
- NBA.com official statistics
- Basketball-Reference.com
- Cleaning the Glass
- ESPN Hollinger statistics
- Memphis Grizzlies media guides