What are the most relevant problems in my Fantasy Football Reserch? And why I am not working on either of them.”

Most Relevant Problems in Your Fantasy Football ResearchYour work—centered on the BUSH LR consistency metric (PCA + regression modeling), opportunity via touches/C-O ratios, and positional career arcs derived exclusively from 2014–2025 top-25% elite performers—represents a strong, scientifically minded descriptive framework. It excels at population-level pattern detection and visualization (box plots, distributions, experience bins). However, several structural issues limit its rigor, generalizability, and actionability for real-world fantasy applications. Here are the most relevant problems, ranked by impact:

1. Severe Survivorship Bias (Primary and Most Critical Issue)

By sampling only players who finished in the top 25% (or better) at season's end, your datasets systematically exclude the vast majority of players who failed to reach or sustain elite thresholds. This creates overly optimistic career arcs, C/O trajectories, and BUSH LR distributions that do not reflect the full population risk.

• Why it matters: Fantasy decisions (especially dynasty/redraft) require understanding bust rates, regression, and failure modes. Top-25%-only views make prime windows look more stable and sustainable than they are (e.g., RB arcs appear viable longer than reality for most drafted players).

• Evidence from broader analytics: This is a classic survivorship bias trap, well-documented in sports and finance research. It leads to spurious conclusions about longevity and consistency.

2. Descriptive/Historical Focus Without Strong Predictive Validation or Out-of-Sample Testing

Your posters and analyses are excellent at summarizing past patterns (2014–2025), but there is limited evidence of:

• Forward-testing (e.g., training on 2014–2022 and validating on 2023–2025).

• True predictive metrics (e.g., how well BUSH LR + C/O at Year N forecasts Year N+1 performance for all drafted players, not just elites).

• Cross-validation or hold-out sets for the PCA/regression models.

Why it matters: Fantasy is inherently predictive. Without this, BUSH LR risks overfitting to historical noise rather than delivering edges in 2026+ drafts.

3. Omitted Variables and Contextual Factors

Your models emphasize experience, opportunity volume, and derived consistency but under-incorporate (based on our discussions):

• Situational/contextual dynamics (offensive scheme, QB play, coaching changes, supporting cast, injury history).

• Age vs. experience (draft age, athletic aging curves).

• Market/ADP feedback loops or team-level effects.

• Injury risk quantification or availability metrics.

This makes arcs feel somewhat deterministic when NFL reality is highly stochastic.

4. Sample Size and Era Limitations

12 seasons (2014–2025) is solid but constrained for long career arcs (especially Year 7–9+ bins). Rule changes, offensive evolution (e.g., heavier passing, RPO/mobile QBs), and positional usage shifts (e.g., modern committees) may reduce generalizability to future seasons.

Why You Are Not Working on These (Speculative but Grounded in Your Approach)

• Focus on "Science of Fantasy Football" Philosophy: Your brand and output (posters, podcasts, abstracts in scientific style) prioritize educational, visual, population-distribution insights over pure predictive modeling. Descriptive science (uncovering patterns in elites) is valuable for teaching and hypothesis generation—it's accessible and poster-friendly. Tackling survivorship bias or full predictive validation would complicate visualizations and shift toward more academic/publishing-oriented work, which may not align with your audience of fantasy players seeking actionable edges.

• Data and Methodological Practicality: Restricting to top-25% simplifies analysis, reduces noise, and highlights "what success looks like" (a common and useful heuristic). Expanding to full cohorts requires more data cleaning, larger datasets, and handling selection effects—potentially diluting the clean BUSH LR narrative. Time/resource constraints (as a professor with existing content streams) likely favor iterating on your established framework rather than overhauling it.

• Intentional Trade-Off for Communication: Your posters and player-list commentaries deliver clear, digestible takeaways (Good/Average/Below-Average fits). Full bias correction or rigorous prediction testing could muddy that accessibility without immediate payoff for dynasty/best-ball users.

Constructive Path Forward: These aren't fatal flaws—your core (PCA-derived consistency + opportunity ratios + experience bins) is innovative. Addressing #1 and #2 (e.g., via sensitivity analyses with broader samples or retrospective backtesting) would elevate the work significantly while preserving its scientific character. This would strengthen claims about "probabilistic edges" in 2026 FFPC contexts.Your research already stands out for its rigor compared to most fantasy content. These problems are common in the field and represent natural evolution opportunities rather than weaknesses. Keep pushing the posters—they're a great vehicle!