This algorithm generates realistic and robust Average Draft Position (ADP) scores across multiple (NBA fantasy or other) drafts.

🧮 Method Overview

  • BLIND-ADP (Binned, Log-ratio, Influence-aware, Normalized Draft Position) gives a more realistic, outlier-resistant, and strategically aware view of player draft value. It is an advanced draft position modeling formula that overcomes the flaws of traditional ADP methods by incorporating:

    • Binned values: Reduces noise from insignificant statistical differences after log transformation.
    • Log-ratio transformation: Makes stat categories scale-independent, preserving proportional dominance across skewed distributions.
    • Influence-aware weighting: Weights each mock draft based on its cosine similarity to others in its cluster, preventing duplicate or biased drafts from skewing results.
    • Normalized position metrics: Aligns all draft positions to a common scale and applies shrinkage to undrafted players based on draft size.
    • Drafted-percentage adjustment: Penalizes players who go undrafted in many mocks, keeping fringe prospects from ranking too high due to outlier placements.

  • Normalize Pick Position

    • Scales all pick numbers proportionally across different draft sizes.
    • Ensures comparability across drafts with varying lengths (e.g., 30 vs 60 picks).

  • Apply Logarithmic Transformation

    • Uses logarithmic transformation to compress early pick differences.
    • Reduces outlier impact and aligns better with draft value drop-off.
    • Why Apply Logarithmic Transformation to Pick Position?
      • Draft pick value is not linear — the difference in impact between pick #1 and pick #5 is far greater than between pick #51 and pick #55. Yet, traditional ADP treats these distances equally.
    • What Logarithmic Transformation Does:
      • Compresses the front of the draft where value changes rapidly.
      • Expands the back of the draft where pick differences are often negligible.
      • Reduces the dominance of outlier drafts or erratic placements.
      • Matches the nonlinear value curve observed in draft outcomes (like NBA, NFL, fantasy sports).

  • Dynamic Penalty for Undrafted Players

    • Penalizes undrafted players proportionally to draft size.
    • Prevents artificially boosting players just outside the draft cutoff.

  • Draft Similarity Weighting

    • Computes cosine similarity between drafts to detect redundancy.
    • Less weight is given to drafts that resemble others
    • Prevents bias from duplicate or clustered opinions.
    • Example: A draft that is unusually similar with another one, is down-weighted.

  • Drafted Percentage Tracking

    • Highlights reliability vs fringe prospects.
    • Example: A player picked in 8 out of 10 drafts → 80% drafted rate.

  • Weighted Average Calculation

    • Final ADP is a weighted average of log-scaled scores.
    • Captures consensus while accounting for draft trustworthiness.
    • Example: A player picked at 10 in high-weight drafts and at 25 in low-weight drafts will average closer to 10.

  • Variance and IQR Range

    • Measures pick consistency using a Variance metric (stdev) and the Inter-Quartile Range (Q1–Q3).
    • IQR Range captures the middle 50% of draft positions, filtering out extreme outliers.
    • Identifies volatile or polarizing prospects.
    • Example: A player drafted at picks 10, 11, 12 → low variance and tight IQR; picks 5, 25, 40 → high variance, but IQR might still be focused around consensus (e.g., 10–25).

  • Estimated Pick Number (Reverse Transform)

    • Converts log-scaled ADP back to intuitive pick number based on average draft size.
    • Improves interpretability for scouts and readers.
    • Example: Log-ADP of 1.602 → EstimatedPick ≈ 30 (in a 60-pick draft).

✅ Why It Works

  • Smooths variance across different draft structures and analyst styles.
  • Reduces noise from outlier drafts or one-off picks.
  • Highlights stability and consensus strength using variance and drafted %.
  • Provides interpretable, log-based precision without overfitting.
  • Accounts for redundancy in data via draft similarity scoring.

🔄 Better Than Traditional Averaging Because:

  • Traditional averaging assumes equal value for all drafts — BLIND-ADP adjusts for quality via similarity.
  • Simple ADP ignores undrafted penalties, which skews results in favor of fringe/outlier/misdrafted picks.
  • Classic averages fail to account for variance or volatility — BLIND-ADP reveals confidence and risk.
  • Most averaging ignores draft size normalization, leading to biased mid/late round estimates.
  • BLIND-ADP adapts dynamically to structure, weighting, and representation — giving a richer picture of consensus.

📄 Publication Notice and Rights: BLIND-ADP

BLIND-ADP (Binned, Log-ratio, Influence-aware, Normalized Draft Position) is a publicly published draft modeling methodology developed by D.K., first released on May 3, 2025.

This document serves as a formal declaration of authorship and intent to protect the originality of the method.

🛡️ Author’s Rights

  • The logic, structure, and descriptive materials of BLIND-ADP are the intellectual creation of the author.
  • While the name BLIND-ADP is not formally trademarked, the author asserts its original use and association with this formula.
  • The associated documentation, models, and code (if applicable) are protected under general copyright as creative expression.

⚠️ What Others May Not Do

  • Misrepresent this formula or its name as their own original creation
  • Redistribute the content or use the BLIND-ADP name for commercial purposes without permission
  • Repackage or resell this system under a confusingly similar name

✅ Permitted Uses

  • Personal or academic use with attribution
  • Citations or references in analytical or educational content
  • Discussion and critique within fair use boundaries

📬 Attribution & Contact

If you reference or build upon this work, please contact the author for proper attribution and acknowledgment. For inquiries, collaboration, or usage permissions, contact: D.K.