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

NBA officiating summary

NBA officiating accuracy stands at 76% across 1000 total calls reviewed. Several officials show higher rates of incorrect calls, impacting game integrity. The league should monitor these trends to ensure fair play.

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.