Development and Application of the AMG Index: 2026 Position-Specific Projections and Player Stratification in Fantasy Football

Abstract

Building on the validated Age-Missed Games (AMG) Predictive Index, this study presents 2026 fantasy football projections derived from consensus ADP lists and realistic player profiles. Box-plot distributions of projected AMG scores reveal clear position-specific patterns: tight clustering in the Low-to-Mid range for young skill players (e.g., RBs and WRs) contrasted with elevated scores and high outliers among veterans (especially QBs and aging RBs/WRs). 

Player-level tables stratify hundreds of relevant athletes into Low, Mid, and High AMG tiers, with color-coded risk indicators. For wide receivers, additional 2025 advanced metrics (Target Share, Yards per Route Run [YPRR], Routes run, and a separate PCA efficiency/volume Index) are integrated to contextualize AMG risk. 

Results demonstrate that Low-AMG players dominate the upper echelons of ADP while High-AMG veterans carry measurable decline risk, providing actionable stratification for drafting and roster management. The AMG Index complements volume/efficiency metrics and offers a robust framework for 2026 fantasy decision-making. 

Keywords: AMG Index, fantasy football projections 2026, position-specific distributions, box plots, player stratification, advanced metrics, PCA

1. Introduction

The AMG Predictive Index integrates age, position risk, career volume, and missed-game history into a single, interpretable score for forecasting next-season fantasy performance. Prior validation confirmed strong statistical separation in projected FPG across Low/Mid/High AMG groups, with the most pronounced effects among RBs and WRs.This follow-up analysis applies the index to comprehensive 2026 ADP-derived player lists across all skill positions. We visualize distributions via box plots (Figures 1–4) and provide detailed player tables with AMG scores and tiers. For wide receivers, we incorporate 2025 advanced metrics (Target Share, YPRR, Routes, and a composite PCA Index) to explore how AMG risk interacts with volume and efficiency. These projections offer fantasy managers a data-driven lens for identifying value, risk, and upside heading into the 2026 season.  

See Part 1 https://www.scienceoffantasyfootball.com/development-and-validation-of-the-age-and-missed-games-amg-predictive-ind

2. Methods

AMG scores were calculated for players appearing in 2026 consensus ADP rankings using the established weighted formula (informed by PCA loadings):

AMG Score=(Age×1.0)+(Pos_Risk×9)+(Career_Touches_k×3.5)+(Missed_Avg_Prev2×1.8)+(Missed_Last×0.7)\text{AMG Score} = (\text{Age} \times 1.0) + (\text{Pos\_Risk} \times 9) + (\text{Career\_Touches\_k} \times 3.5) + (\text{Missed\_Avg\_Prev2} \times 1.8) + (\text{Missed\_Last} \times 0.7)

\text{AMG Score} = (\text{Age} \times 1.0) + (\text{Pos\_Risk} \times 9) + (\text{Career\_Touches\_k} \times 3.5) + (\text{Missed\_Avg\_Prev2} \times 1.8) + (\text{Missed\_Last} \times 0.7)

Position-specific risk weights were applied (RB = 1.0; WR/TE ≈ 0.6; QB ≈ 0.3). Players were binned into Low/Mid/High tiers using consistent thresholds. 

Box plots summarize central tendency, spread (IQR), and outliers for each position. 

WR analysis additionally incorporated 2025 Target Share, YPRR, total Routes run, and a separate PCA-derived efficiency/volume index.

3. Results

3.1 Running Backs (RBs)

Figure 1 (Projected AMG Indexes for RBs in 2026) shows a median AMG score near 44–45, with an interquartile range (IQR) approximately 41–48. A clear cluster exists in the Low-to-Mid range, while several high outliers exceed 60 (e.g., 64.9, 63.2, 60.5), reflecting older, high-volume backs.

Table 1 (selected RBs, full data in supplementary materials) illustrates the stratification:

• Low AMG (green): Ashton Jeanty (34.3), De’Von Achane (40.5), James Cook (40.1), Breece Hall (38.7), Kyren Williams (36.6), TreVeyon Henderson (39.2), etc. — predominantly young or low-miss players.

• Mid AMG: Many established backs in the mid-40s.

• High AMG (red/pink): Christian McCaffrey (56.2), Jonathan Taylor (49), Saquon Barkley (49), Derrick Henry (60.6), Aaron Jones (63.2), Joe Mixon (64), Najee Harris (50.3), etc.

High-AMG RBs are heavily concentrated among players aged 29+, consistent with known age cliffs and injury accumulation.

3.2 Wide Receivers (WRs)

Figure 2 (PCA Index for WRs Distribution 2025) and Figure 3 (Projected AMG Index Scores for WR 2026) show distinct patterns. 

The 2025 PCA efficiency/volume index has a wide spread with high outliers (4.14, 3.60). The 2026 AMG distribution is more compressed (median ~36–38, IQR ~33–42) but still features elevated outliers. 

Table 2 (top WRs with extra metrics) and extended tables reveal: 

• Many high-target, high-route players land in the High AMG group when age or missed-game history is factored in (e.g., A.J. Brown 42.0 High, Davante Adams 51.5 High, Terry McLaurin 48.5 High, Mike Evans 53.8 High, Cooper Kupp 54.7 High). 

• Conversely, several young/high-efficiency players score Low AMG despite strong 2025 volume metrics (e.g., Marvin Harrison Jr. 32.3 Low, Xavier Worthy 29.6 Low, Jalen McMillan 32.5 Low, Marvin Mims Jr. 33.9 Low).

Key observation from extra WR metrics: High Target Share and Routes often align with higher PCA_Index, but AMG provides an independent age/injury lens. Some high-volume veterans (e.g., Stefon Diggs, Tyreek Hill) show elevated AMG despite strong efficiency metrics, flagging decline risk.

3.3 Tight Ends (TEs)

Figure 4 (Projected AMG Index Scores for TEs 2026) shows a median near 41–42, an IQR of ~39–45, and a high outlier at 58.4.

Table 3 shows:

• Low AMG (green): Brock Bowers (37.2), Colston Loveland (35.1), Tucker Kraft (38.4), Sam LaPorta (36.9), Kyle Sadiq (38.7), Max Klare (38.5).

• High AMG (red): Travis Kelce (52.1), Hunter Henry (48.7), Mark Andrews (47.2), George Kittle (52.1), Jonnu Smith (45.9), T.J. Hockenson (44.1), Cade Otton (42.9).

Young, low-miss TEs dominate the Low tier; veterans drive the High tail.

3.4 Quarterbacks (QBs)

Figure 5 (Projected AMG Index Scores QBs 2026) shows the tightest central cluster (median ~35–36, IQR ~33–38) but with a high outlier at 52.3.

Table 4 highlights:

• Low AMG (green): Drake Maye (32.8), Caleb Williams (33.9), Bo Nix (33.8), J.J. McCarthy (31.8), Shedeur Sanders (32.5), Michael Penix Jr. (33.9), Bryce Young (33.7).

• High AMG (red): Josh Allen (38.5), Lamar Jackson (37.2), Joe Burrow (36.9), Dak Prescott (41.2), Patrick Mahomes (38.4), Jared Goff (39.7), Baker Mayfield (38.9), Aaron Rodgers (52.3), Matthew Stafford (46.8), Kyler Murray (36.2), Sam Darnold (36.8), Daniel Jones (37.1), Jacoby Brissett (42.5).

Even many “young” QBs land in Mid/High due to the model’s sensitivity to age and any missed-game history.

4. Discussion

The box-plot distributions clearly illustrate position-specific risk profiles. RBs and WRs show the widest spreads and most actionable High-AMG tails, aligning with prior statistical validation showing the largest FPG deficits in these positions. TEs, and especially QBs, exhibit tighter central distributions but still signal clear veteran risk (e.g., Kelce, Rodgers, Stafford).

For WRs, the additional 2025 metrics reveal that high Target Share and Routes frequently co-occur with elevated PCA efficiency/volume scores, yet AMG independently highlights age- and injury-driven risk (e.g., high-target veterans like A.J. Brown or Davante Adams scoring High AMG).

This complementarity allows managers to layer volume/efficiency data with durability projections.High-AMG players across positions (particularly those with scores >50–55) warrant discounted acquisition or careful monitoring, while Low-AMG young stars (e.g., Jeanty, Bowers, Harrison Jr., Maye, McCarthy) represent strong building blocks.

5. Conclusion

The AMG Index, when applied to 2026 projections and visualized through position-specific box plots and detailed tables, provides clear, actionable stratification. 

Low-AMG players dominate the top of ADP lists with favorable durability profiles, while High-AMG veterans carry measurable decline risk.

Integration of advanced WR metrics further enriches the framework. Fantasy managers can use these tools to optimize drafts, trades, and roster decisions with greater confidence in 2026.

References 

(as in prior article    https://www.scienceoffantasyfootball.com/development-and-validation-of-the-age-and-missed-games-amg-predictive-ind)

Additional supporting data drawn from consensus 2026 ADP sources and advanced metric compilations (Target Share, YPRR, Routes). 

The box plots and tables above (derived directly from the attached figures) make the High/Mid/Low distinctions immediately visible.