Bias Trends

Who keeps getting bad calls — over time?

Per-month trend of AI-judged incorrect calls for the top 8 players and teams. Rising lines mean a pattern of bias is accumulating.

2 buckets
Whistle Consistency

How closely does each official match the AI's "Truth"?

100 means every judged call matched the AI verdict. The blue → violet gradient highlights where calls deviate most.

  • #1
    Ben Taylor
    12 judged calls58% deviation
  • #2
    Jacyn Goble
    10 judged calls40% deviation
  • #3
    Tony Brothers
    235 judged calls37% deviation
  • #4
    Scott Foster
    314 judged calls32% deviation
  • #5
    Ed Malloy
    21 judged calls29% deviation
  • #6
    Zach Zarba
    38 judged calls24% deviation
  • #7
    Marc Davis
    43 judged calls23% deviation
  • #8
    Ken Mauer
    10 judged calls20% deviation
Executive Summary · AI-generated

Flagged game: New York Knicks @ Cavaliers

50% of 4 reviewed calls in this game (2026-05-24) were judged incorrect.

Pressure Rating

Does crew accuracy hold up on the road?

Pressure = home accuracy − away accuracy. Positive values mean the crew's "Truth Accuracy" drops in hostile environments.

  • Ed Malloy
    H 80% · A 50%
    +30
    Pressure
  • Ken Mauer
    H 100% · A 75%
    +25
    Pressure
  • Jacyn Goble
    H 75% · A 50%
    +25
    Pressure
  • Kane Fitzgerald
    H 100% · A 80%
    +20
    Pressure
  • Zach Zarba
    H 81.8% · A 68.8%
    +13.1
    Pressure
  • James Capers
    H 87.5% · A 78.6%
    +8.9
    Pressure

Players

Top 8 players by total incorrect calls

Wrong calls per game

Teams

Top 8 teams by total incorrect calls against

Wrong calls per game

Wrong calls are the AI's incorrect verdicts on judged calls. Inconclusive judgements are excluded. Only entities with ≥ 5 total calls are charted.