# Umpires, Referees, and Other Sports Officials

> Officiate at competitive athletic or sporting events. Detect infractions of rules and decide penalties according to established regulations. Includes all sporting officials, referees, and competition judges.

- **SOC code:** 27-2023.00
- **Canonical URL:** https://singulariki.com/roles/role-27-2023-00
- **Also known as:** Horse Show Judge, Major League Baseball Umpire (MLB Umpire), Referee, Sports Official, Basketball Referee, Diving Judge, Dressage Judge, Football Referee
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Officiate at sporting events, games, or competitions, to maintain standards of play and to ensure that game rules are observed.
- Judge performances in sporting competitions to award points, impose scoring penalties, and determine results.
- Inspect game sites for compliance with regulations or safety requirements.
- Resolve claims of rule infractions or complaints by participants and assess any necessary penalties, according to regulations.
- Verify scoring calculations before competition winners are announced.
- Signal participants or other officials to make them aware of infractions or to otherwise regulate play or competition.
- Teach and explain the rules and regulations governing a specific sport.
- Start races and competitions.
- Inspect sporting equipment or examine participants to ensure compliance with event and safety regulations.
- Compile scores and other athletic records.
- Verify credentials of participants in sporting events, and make other qualifying determinations, such as starting order or handicap number.
- Keep track of event times, including race times and elapsed time during game segments, starting or stopping play when necessary.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Oral Expression _(ability)_
- Far Vision _(ability)_
- Oral Comprehension _(ability)_
- Near Vision _(ability)_
- Speaking _(essential_skill)_
- Problem Sensitivity _(ability)_
- Speech Clarity _(ability)_
- English Language _(knowledge)_
- Critical Thinking _(essential_skill)_
- Active Listening _(essential_skill)_
- Judgment and Decision Making _(transferable_skill)_
- Deductive Reasoning _(ability)_

**Skills in demand:**
- English Language _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Deductive Reasoning _(Common Skill)_
- Active Listening _(Common Skill)_
- Social Perceptiveness _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Learning Strategies _(Specialized Skill)_

**Tools & technology:**
- Adobe Acrobat _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Word _(hot technology)_
- Database software
- Email software
- Video editing software
- Web browser software

## AI exposure & outlook

- **AI task-overlap index:** 34th percentile (Moderate) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 47th percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 36th percentile (Moderate) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 25th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 97th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 5.7% growth (About average); 4.6k annual openings; 19.3k → 20.4k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $38,820; 15,080 employed.

## How people actually use AI here

Anthropic Economic Index — measured AI conversations mapped to this occupation's tasks:

- **Automation vs augmentation:** — automation, 61% augmentation (usage-weighted).
- **Autonomy median:** 3.0 (higher = AI acts more independently).
- **Dominant collaboration mode:** learning.

**Tasks most handed to AI here:**
- Teach and explain the rules and regulations governing a specific sport. _(0.4% of measured AI use; learning)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me teach and explain the rules and regulations governing a specific sport.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-27-2023-00_