Chapter 22: Case Study 2 - The Decline of Carmelo Anthony: Projecting Aging Superstars

Introduction

Carmelo Anthony's career arc presents a compelling case study in projecting player decline. From perennial All-Star and scoring champion to out of the league by age 35, his trajectory defied some expectations while confirming others. This case study examines how projection systems handled Carmelo's aging process, what signals indicated accelerating decline, and how teams could have better anticipated his diminishing value.

Part 1: Peak Carmelo - The Baseline (2012-2014)

Statistical Profile at Peak

During the 2012-13 and 2013-14 seasons, Carmelo was at the height of his powers:

2012-13 Season (Age 28): - 28.7 PPG (Scoring champion) - 6.9 RPG - 2.6 APG - 56.0% TS - 5.4 Win Shares - 3.0 BPM

2013-14 Season (Age 29): - 27.4 PPG - 8.1 RPG - 3.1 APG - 56.1% TS - 7.1 Win Shares - 3.0 BPM

Historical Comparables at Age 29

To project Carmelo forward, we identify players with similar profiles at age 29:

Selection criteria: - 25+ PPG - Primary scoring option - Similar playing style (mid-range focused, iso-heavy) - Perimeter-oriented forward

Identified comparables: 1. Dominique Wilkins (Age 29: 28.1 PPG, 7.0 RPG) 2. Alex English (Age 29: 27.9 PPG, 5.5 RPG) 3. Bernard King (Age 29: 22.7 PPG, 5.8 RPG) 4. Jerry Stackhouse (Age 29: 16.4 PPG - post-decline) 5. Glen Rice (Age 29: 19.9 PPG)

Standard Projection at Age 29

Using historical aging curves for scoring wings:

Expected production ages 30-35:

Cumulative projection (Age 30-35): - Total Win Shares: 21.7 - Expected remaining All-Star selections: 2-3 - Probability of productive career past 35: 30%

Part 2: Early Warning Signs (2014-2017)

The First Decline Phase

Carmelo's actual production began declining earlier than the median projection:

Detecting Decline Signals

Several metrics signaled faster-than-expected decline:

Signal 1: Efficiency Collapse

Carmelo's True Shooting percentage dropped from 56% to 53%, a meaningful decline for a primary scorer. This suggested: - Declining shot selection - Reduced ability to get to preferred spots - Potentially declining athleticism

Signal 2: Defensive Metrics

Advanced defensive metrics showed significant deterioration: - Defensive Rating: 106 (2013-14) → 112 (2016-17) - DBPM: -1.2 (2013-14) → -2.8 (2016-17)

Signal 3: Load Management Inability

Unlike some aging stars, Carmelo couldn't maintain efficiency while reducing usage: - Usage rate remained high (28-30%) - Efficiency declined with maintained volume - No transition to complementary role

Signal 4: Comparable Divergence

Of his comparables: - Dominique Wilkins: Still productive at 32 (24+ PPG) - Alex English: Productive through age 33 - Bernard King: Career derailed by injury - Jerry Stackhouse: Rapid decline (most similar to Carmelo's path)

Updated Projections (2017)

By 2017, models should have adjusted projections downward:

Revised projection from age 33:

Key adjustment factors: - Efficiency decline steeper than typical - Defensive metrics showing accelerated decline - Style of play not adapting to age

Part 3: The OKC Experiment and Career Unraveling

The Trade Decision

In September 2017, the New York Knicks traded Carmelo (age 33) to Oklahoma City. The Thunder's projection question: What could they expect from an aging Carmelo?

Information available to OKC: - Three consecutive years of declining efficiency - Clear defensive liabilities - High usage rate/iso-heavy style - No demonstrated ability to play complementary role

What a proper projection would have shown:

Optimistic scenario (20% probability): - Reduced role acceptance - 16-18 PPG on improved efficiency - Neutral defensive impact - Win Shares: 3-4

Baseline scenario (50% probability): - Continued iso-heavy play - 15-17 PPG on declining efficiency - Negative defensive impact - Win Shares: 1-2

Pessimistic scenario (30% probability): - Poor fit with team style - 12-15 PPG with poor efficiency - Significant defensive liability - Win Shares: 0-1

Actual Results (2017-18)

Carmelo with OKC: - 16.2 PPG - 50.4% TS (career low) - -0.3 Win Shares - -2.5 BPM

This fell within the pessimistic scenario range. The question for projection systems: Was this predictable?

Projection Failure Analysis

What projections missed: 1. Role adaptation difficulty (projected 20% probability, deserved higher) 2. Impact of surrounding star players on his opportunities 3. Psychological/fit factors not captured in models

What projections got right: 1. Declining efficiency trajectory 2. Defensive limitations 3. Win Shares in the predicted range (pessimistic scenario)

Part 4: The Final Act (2018-2020)

Houston and Exile

The 2018-19 season marked Carmelo's lowest point: - Traded to Atlanta (immediately waived) - Signed with Houston: 10 games, 13.4 PPG, then waived - No NBA employment for remainder of season

The Portland Resurgence

After a year away, Portland signed Carmelo in November 2019:

2019-20 with Portland (Age 35): - 15.4 PPG - 52.8% TS - 1.3 Win Shares (per 82 games pace)

This modest resurgence fell within the range of possibility but highlighted projection challenges:

1. Motivation factor: A humbled Carmelo accepted a reduced role
2. System fit: Portland's spacing worked better
3. Selection effect: Only teams that thought he could contribute signed him

Final Assessment

Career production ages 30-37:

Cumulative variance: Significantly below projections ages 30-34, roughly at projections 35+

Part 5: Lessons for Projecting Aging Stars

Lesson 1: Style-Dependent Aging

Carmelo's iso-heavy, mid-range focused style was particularly susceptible to decline: - No transition to three-point shooting - Relied on physical advantages that erode - Skills didn't transfer to complementary roles

Projection adjustment: Players with adaptable skill sets (shooting, passing) should receive more favorable aging projections than volume scorers.

Lesson 2: Efficiency Trends Are Predictive

The 3-point drop in TS% from ages 29-31 was a significant signal. Projection systems should: - Weight recent efficiency trends heavily - Distinguish between volume and efficiency decline - Flag accelerating efficiency deterioration

Lesson 3: Defensive Decline Precedes Offensive Decline

Carmelo's defensive metrics collapsed before his scoring did. This is common for aging wings: - Reduced lateral quickness - Lower effort/stamina on defense - Overall value (combining offense and defense) declined rapidly

Projection adjustment: Incorporate defensive aging curves that typically precede offensive decline.

Lesson 4: Role Flexibility Is Valuable

Players who demonstrate ability to play multiple roles age better: - Three-point shooting addition - Playmaking expansion - Defensive versatility

Carmelo showed limited role flexibility, which projections should have penalized.

Lesson 5: Context Matters More for Declining Players

As players decline, their remaining value becomes more context-dependent: - System fit - Complementary teammates - Defined role - Coaching relationship

For borderline players, context uncertainty should widen projection intervals significantly.

Part 6: Building a Better Aging Model

Quantitative Warning Signs Checklist

Based on the Carmelo case, flag the following for accelerated decline:

Modified Aging Curve for At-Risk Players

For players showing 2+ warning signs, apply accelerated decline rates:

Standard decline (age 32-35): -0.8 WS per year Accelerated decline (at-risk players): -1.5 WS per year

Projection Model Pseudocode

function project_aging_scorer(player, current_age):

    # Calculate base projection from standard aging curves
    base_projection = standard_aging_curve(player, current_age)

    # Identify warning signs
    warning_signs = count_warning_signs(player)

    # Calculate efficiency trend
    efficiency_trend = calculate_ts_trend(player, years=2)

    # Calculate defensive trend
    defensive_trend = calculate_dbpm_trend(player, years=2)

    # Assess role flexibility
    flexibility_score = assess_flexibility(player)

    # Apply adjustments
    if warning_signs >= 2:
        base_projection *= 0.85  # 15% reduction

    if efficiency_trend < -2.0:
        base_projection *= 0.90  # Additional 10% reduction

    if defensive_trend < -1.0:
        base_projection *= 0.95  # Additional 5% reduction

    if flexibility_score < 50:
        # Widen uncertainty for inflexible players
        uncertainty_multiplier = 1.3

    return base_projection, uncertainty_multiplier

Part 7: Discussion Questions

1. Contract Implications: Based on the analysis, what would have been a fair contract for Carmelo in 2017? How should projection uncertainty affect contract structure?

2. Team Fit: How should projection systems incorporate team-specific fit, particularly for declining players?

3. Player Agency: Could Carmelo have extended his career by making different choices (accepting reduced roles earlier, adding three-point volume)? How should player agency be incorporated into projections?

4. Comparable Selection: Should iso-heavy scorers be compared only to other iso-heavy scorers, or to all similar statistical profiles?

5. Front Office Decision-Making: Given appropriate projections, should OKC have traded for Carmelo? What was the risk-reward calculation?

Exercises

Exercise 1: Real-Time Projection Update

Using Carmelo's 2014-15 statistics (first year of decline), manually calculate an updated projection using: - Original 2014 projection - Actual 2014-15 results - Bayesian updating with appropriate weighting

Exercise 2: Comparable Analysis

Identify three modern players (current or recent) with similar aging trajectories to Carmelo. What characteristics do they share?

Exercise 3: Alternative History

Model a scenario where Carmelo had added a three-point shot (36%+ on 5+ attempts per game) by age 32. How would this have changed his projected career arc?

Exercise 4: Contract Valuation

The Knicks gave Carmelo a 5-year, $124M contract starting at age 29. Using proper projection methods, what would the expected value of this contract have been at signing? At what point did the contract become negative value?

Conclusion

The Carmelo Anthony case demonstrates both the value and limitations of player projection. Key takeaways:

1. Standard aging curves provide reasonable baselines but miss player-specific factors
2. Early warning signs (efficiency decline, defensive deterioration) are predictive
3. Playing style significantly affects aging trajectory
4. Context becomes increasingly important as players decline
5. Projection systems should identify at-risk profiles and adjust accordingly

The difference between Carmelo's peak value and his rapid decline illustrates the importance of sophisticated projection methods for roster construction, contract allocation, and trade evaluation. Teams that correctly projected accelerated decline could have avoided significant opportunity costs.