The Science of Consistency: A Multi-Year COM Framework for 2026 Fantasy Quarterbacks

Quarterbacks occupy a unique place in fantasy football. They generate the highest absolute point totals, yet the position features a steep drop-off after the top tier and significant week-to-week volatility driven by game script, offensive line play, and dual-threat opportunity. Consistency—reliable weekly floors rather than pure ceiling—often separates championship teams from also-rans. This article applies the identical Consistency of Metrics (COM) framework previously developed for wide receivers, running backs, and tight ends—now fully adapted to quarterbacks—for the 2026 season. 

We fuse two years of observed consistency data with historically weighted averaging, layer in current PPR Average Draft Position (ADP), and use a full Gaussian Mixture Model (GMM) to generate both hard tiers and soft probabilistic memberships. The result is a transparent, data-driven system that quantifies not only how consistent a quarterback has been, but how confidently we can place him in a given tier.

The COM Framework for Quarterbacks

COM measures the percentage of games in which a player met a predefined consistency threshold (typically a fantasy-point floor relative to positional expectations in PPR formats). We begin with two seasons of observed data:

• 2024 COM — prior-year baseline

• 2025 COM — most recent observed consistency

A simple average treats both years equally and ignores the natural decay of relevance. Instead, we apply a historically weighted average that privileges recency while still incorporating last year’s strength as a reliability prior:

Weighted COM=w24⋅COM2024+w25⋅COM2025\text{Weighted COM} = w_{24} \cdot \text{COM}_{2024} + w_{25} \cdot \text{COM}_{2025}

\text{Weighted COM} = w_{24} \cdot \text{COM}_{2024} + w_{25} \cdot \text{COM}_{2025}

where the weight on 2024 is itself a mild function of that year’s consistency:

w24=0.35+0.10×(COM2024100),w25=1−w24w_{24} = 0.35 + 0.10 \times \left(\frac{\text{COM}_{2024}}{100}\right), \quad w_{25} = 1 - w_{24}

w_{24} = 0.35 + 0.10 \times \left(\frac{\text{COM}_{2024}}{100}\right), \quad w_{25} = 1 - w_{24}

Special cases are handled explicitly:

• Only 2025 available →
  0.8×COM20250.8 \times \text{COM}_{2025}0.8 \times \text{COM}_{2025}
  (discount for missing history)

• Only 2024 available →
  0.5×COM20240.5 \times \text{COM}_{2024}0.5 \times \text{COM}_{2024}
  (steep discount for stale data)

• Both zero → 0

This produces a single scalar that balances recency, historical strength, and data completeness—critical for a position where one strong dual-threat season or injury can dramatically alter a player’s profile.

Soft Clustering with Gaussian Mixture Models

Hard cut-offs (e.g., “Tier 1 = 60+”) are convenient but brittle. Real consistency distributions are continuous and overlapping. We therefore fit a one-dimensional Gaussian Mixture Model with five components via the Expectation-Maximization algorithm. 

Each component is a Gaussian characterized by mean

μk\mu_k\mu_k

, variance

σk2\sigma_k^2\sigma_k^2

, and mixing weight

πk\pi_k\pi_k

After convergence and sorting by descending mean, the five components become approximately:

Tier Approximate Center Interpretation

1 ~60 Elite weekly floor (true difference-makers)

2 ~50 High-floor QB1 / strong QB2 options

3 ~35 Solid streaming / matchup-dependent starters

4 ~25 Fringe / low-floor backups

5 ~15 Speculative / low sample

For every quarterback, the model returns a soft probability vector

(γ1,γ2,γ3,γ4,γ5)(\gamma_1, \gamma_2, \gamma_3, \gamma_4, \gamma_5)(\gamma_1, \gamma_2, \gamma_3, \gamma_4, \gamma_5)

— the posterior probability that the player belongs to each tier. The hard tier is simply arg⁡max⁡kγk\arg\max_k \gamma_k\arg\max_k \gamma_k

Soft probabilities surface uncertainty: a player who is 85 % Tier 1 / 15 % Tier 2 is meaningfully different from one who is 98 % Tier 1.

Integrating Market Price: PPR ADP

Consistency without cost is incomplete. We overlay current PPR ADP (Fantasy Football Calculator consensus from the early-to-mid August 2026 mock-draft window). The juxtaposition immediately highlights relative value:

• Elite COM + early ADP → justified premium (Josh Allen, Drake Maye, Lamar Jackson, Joe Burrow).

• High COM + later ADP → potential bargains (Baker Mayfield, Bo Nix, Sam Darnold, Jared Goff, Jacoby Brissett).

• Rising 2025 profiles + mid-round ADP → breakout candidates still available at reasonable cost (Jaxson Dart, Tyler Shough, Caleb Williams).

Selected Insights from the 2026 QB Landscape

• The true Tier-1 core is compact and powerful. Joe Burrow, Jalen Hurts, Josh Allen, Lamar Jackson, Brock Purdy, Baker Mayfield, Drake Maye, Jayden Daniels, Trevor Lawrence, Bo Nix, Sam Darnold, Jacoby Brissett, Jared Goff, Dak Prescott, and Patrick Mahomes all carry soft probabilities above 95 % (and in many cases 100 %) on the highest component.

• Strong 2025-only risers (Jaxson Dart, Tyler Shough, Jacoby Brissett) receive the 0.8× multiplier and land cleanly in Tier 1 or high Tier 2, preventing over-reaction while still elevating them above pure zero-history players.

• High-2024 / lower-2025 profiles (certain veterans) retain solid Weighted COM but show probability leakage into lower components—the model correctly signals increased uncertainty around durability or role changes.

• Mid-round value densifies around Weighted COM 48–55 (Matthew Stafford, Caleb Williams, Jaxson Dart, Tyler Shough, Daniel Jones). Soft probabilities here are often 70–90 % Tier 2, giving drafters a clear “safe starter or high-floor flex” cluster.

• Depth and streaming options with sudden 2025 spikes receive partial credit, while pure historical holdovers are appropriately discounted.

Practical Draft Application

1. Early rounds — Prioritize players whose soft probability on Tier 1 exceeds 0.95 and whose ADP is not dramatically inflated relative to the model. Securing one of the top options creates a massive weekly advantage in a position defined by scarcity at the elite level.

2. Middle rounds — Target high Tier-2 probability players whose ADP lags their Weighted COM (classic “value” zone). Look especially at quarterbacks with strong 2025 consistency who are being drafted as QB2s.

3. Late rounds / bench — Use soft probabilities on Tier 3–4 as a ranking among speculative upside plays and handcuffs; prefer those with at least modest 2025 COM over pure historical holdovers.

4. Risk management — When two players have similar ADP, the one with higher probability mass on the upper components is the safer selection—especially valuable at a position where dual-threat upside and injury risk coexist.

Data Availability

The complete ranked list—every quarterback with 2024 COM, 2025 COM, Weighted COM, current PPR ADP, hard tier, and the five soft-probability columns—is available as a full CSV generated from the model. The underlying GMM parameters (means, variances, mixing weights) are fully reproducible, allowing analysts to re-fit with different component counts or alternative weighting schemes.

Closing Thought

Quarterbacks will always carry higher absolute scoring and dual-threat variance than other skill positions. What the Science of Consistency offers is a disciplined reduction of that uncertainty: a transparent fusion of multi-year performance, statistical clustering that respects continuous distributions, and market pricing that reveals relative value. By replacing gut-feel tiers with soft probabilistic memberships, we move closer to treating consistency as a measurable, optimizable asset rather than a post-hoc narrative. 

Draft accordingly—and may your QB1 deliver double-digit points every single week.