Use as a copilot
Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.
- Teach and explain the rules and regulations governing a specific sport. · 0.4%
Umpires, Referees, and Other Sports Officials
Occupation · SOC 27-2023.00
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.
Also called: Horse Show Judge · Major League Baseball Umpire (MLB Umpire) · Referee · Sports Official · Basketball Referee · Diving Judge · Dressage Judge · Football Referee · Soccer Referee · Softball Umpire · Athletic Events Scorer · Baseball Coach
Job family: Arts, Design, Entertainment, Sports, and Media Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-27-2023-00/context.md directly.
AI work map
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Keep a human in the loop
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
- Teach and explain the rules and regulations governing a specific sport. · 97.7% need a human
Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Market signal
Independent published positions, read together — not a forecast.
- About average employment outlook (+5.7% by 2034)
- 4,600 openings/yr
- Low AI exposure
- Observed AI use leans augmentation
- Median pay $38,820/yr
- Work is mostly on-site (not teleworkable)
- 2013 flagged this for computerization (97th); today's AI task-overlap is comparatively low (34th)
34th-percentile task overlap — yet about 4,600 openings a year (+5.7% projected, BLS), and observed AI use leans 6136% copilot, not hand-off (AEI) . What exposure means →
AI & job outlook
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Exposure to current AI
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) Moderate | 47th | -0.0 | |
| LLM task exposure, γ (OpenAI / Eloundou) Moderate | 36th | 0.4 | |
| AI assistant applicability (Microsoft) Low | 25th | 0.1 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.2), with simple added tooling (β 0.3), and including AI-powered software (γ 0.4). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.
Mixed signals. Today's AI/LLM studies show relatively low exposure for this job, but the older (2013) Frey–Osborne work rated it higher for computerization and robotics. Different eras, different technologies — the AI measures above reflect the current state.
Historical automation estimate (2013)
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 1.0 · 97th percentile among occupations · High
How AI is actually used in this job
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Teach and explain the rules and regulations governing a specific sport. | 0.2% |
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +5.7% by 2034 |
| Projected annual openings | 4,600 |
| Employment 2024 → 2034 | 19,300 → 20,400 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
Where this work sits on the global GenAI gradient
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
Umpires, Referees, and Other Sports Officials sits at the 71st percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure rose from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
37% mean task exposure (2025)
71st percentile of 427 placed occupations
+1 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Sports Coaches, Instructors and Officials · 3422 | 37% | Minimal |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
Working with AI in this job
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 61.4% working with AI · — handed to AI |
| Most common way people use AI here | Learning · you ask AI to explain or teach |
| Typical AI autonomy | 3.0 / 5 · higher = AI acts more independently |
What people delegate to AI
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Teach and explain the rules and regulations governing a specific sport. | Learning | 0.4% |
Where a human is still needed
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Teach and explain the rules and regulations governing a specific sport. | 97.7% |
What people most often hand AI here
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
-
Help me teach and explain the rules and regulations governing a specific sport.From: Teach and explain the rules and regulations governing a specific sport. · 0.4% of measured AI use · learning
Tasks
All 16 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Officiate at sporting events, games, or competitions, to maintain standards of play and to ensure that game rules are observed. Core · importance 5.0
- Judge performances in sporting competitions to award points, impose scoring penalties, and determine results. Supplemental · importance 4.9
- Inspect game sites for compliance with regulations or safety requirements. Core · importance 4.6
- Resolve claims of rule infractions or complaints by participants and assess any necessary penalties, according to regulations. Core · importance 4.5
- Verify scoring calculations before competition winners are announced. Supplemental · importance 4.4
- Signal participants or other officials to make them aware of infractions or to otherwise regulate play or competition. Core · importance 4.4
- Teach and explain the rules and regulations governing a specific sport. Core · importance 4.3
- Start races and competitions. Supplemental · importance 4.3
- Inspect sporting equipment or examine participants to ensure compliance with event and safety regulations. Core · importance 4.2
- Compile scores and other athletic records. Supplemental · importance 4.1
- Verify credentials of participants in sporting events, and make other qualifying determinations, such as starting order or handicap number. Supplemental · importance 4.1
- Confer with other sporting officials, coaches, players, and facility managers to provide information, coordinate activities, and discuss problems. Core · importance 4.0
- Direct participants to assigned areas, such as starting blocks or penalty areas. Supplemental · importance 4.0
- Keep track of event times, including race times and elapsed time during game segments, starting or stopping play when necessary. Supplemental · importance 4.0
- Report to regulating organizations regarding sporting activities, complaints made, and actions taken or needed, such as fines or other disciplinary actions. Core · importance 4.0
- Research and study players and teams to anticipate issues that might arise in future engagements. Supplemental · importance 2.9
Work activities
- Inspect work sites to identify potential environmental or safety hazards.
- Inspect facilities, equipment or supplies to ensure conformance to standards.
- Coordinate athletic or sporting events or activities.
- Evaluate skills of athletes or performers.
- Monitor activities of individuals to ensure safety or compliance with rules.
- Determine operational compliance with regulations or standards.
- Verify accuracy of data.
- Investigate work related complaints to determine corrective actions.
- Discuss performance, complaints, or violations with supervisors.
- Coach others.
- Observe individuals' activities to gather information or compile evidence.
- Investigate personal characteristics or activities of individuals.
- Compile technical information or documentation.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Abilities
| Oral Expression | 3.9 | |
| Far Vision | 3.9 | |
| Oral Comprehension | 3.8 | |
| Near Vision | 3.8 | |
| Problem Sensitivity | 3.6 | |
| Speech Clarity | 3.6 | |
| Deductive Reasoning | 3.3 | |
| Speech Recognition | 3.3 | |
| Flexibility of Closure | 3.1 | |
| Perceptual Speed | 3.1 | |
| Selective Attention | 3.1 | |
| Time Sharing | 3.1 | |
| Written Comprehension | 3.0 | |
| Inductive Reasoning | 3.0 | |
| Memorization | 3.0 | |
| Speed of Closure | 3.0 | |
| Trunk Strength | 3.0 | |
| Written Expression | 2.9 | |
| Information Ordering | 2.9 | |
| Category Flexibility | 2.9 | |
| Stamina | 2.9 |
Essential skills
| Speaking | 3.6 | |
| Critical Thinking | 3.4 | |
| Active Listening | 3.3 | |
| Monitoring | 3.1 | |
| Reading Comprehension | 3.0 | |
| Active Learning | 3.0 | |
| Learning Strategies | 3.0 | |
| Writing | 2.8 |
Knowledge
| English Language | 3.4 | |
| Education and Training | 2.9 | |
| Administration and Management | 2.9 |
Transferable skills
| Judgment and Decision Making | 3.3 | |
| Social Perceptiveness | 3.0 | |
| Coordination | 3.0 | |
| Complex Problem Solving | 3.0 | |
| Instructing | 2.9 | |
| Time Management | 2.9 | |
| Persuasion | 2.8 | |
| Negotiation | 2.8 |
Skills in demand
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Tools & technology
| Example | Category | |
|---|---|---|
| Adobe Acrobat | Document management software | Hot technology |
| Microsoft Excel | Spreadsheet software | Hot technology |
| Microsoft Office software | Office suite software | Hot technology |
| Microsoft Outlook | Electronic mail software | Hot technology |
| Microsoft PowerPoint | Presentation software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| Database software | Data base user interface and query software | |
| Email software | Electronic mail software | |
| Video editing software | Video creation and editing software | |
| Web browser software | Internet browser software |
Work context
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
How to get in
- Job zone
- Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
- Education
- Usually requires a high school diploma or GED, though some occupations may not.
- Typical entry-level education
- High school diploma or equivalent · BLS, the typical path — not a requirement
- Related experience
- Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
- Preparation level
- SVP (Below 6.0) — total schooling plus on-the-job experience.
Education of current workers
Share of people in this occupation at each level of education.
| High School Diploma | 42.9% | |
| Less than a High School Diploma | 23.8% | |
| Bachelor's Degree | 14.3% | |
| Post-Secondary Certificate | 9.5% | |
| Associate's Degree (or other 2-year degree) | 4.8% | |
| Doctoral Degree | 4.8% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Interest areas
| Athletics | 6.8 | |
| Public Speaking | 3.2 | |
| Management/Administration | 2.8 | |
| Teaching/Education | 2.6 | |
| Protective Service | 2.6 |
Work styles
| Dependability | 6.0 | |
| Attention to Detail | 5.0 | |
| Integrity | 4.0 | |
| Self-Control | 3.0 | |
| Stress Tolerance | 2.4 | |
| Self-Confidence | 2.4 | |
| Cautiousness | 2.0 |
Career interests (Holland / RIASEC)
| Enterprising | 5.8 | |
| Conventional | 4.5 | |
| Realistic | 4.3 | |
| Social | 3.4 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $25,070 |
| 25th percentile | $30,920 |
| Median (50th) | $38,820 |
| 75th percentile | $53,560 |
| 90th percentile | $93,180 |
| People employed | 15,080 |
Industries that employ this occupation
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Arts, Entertainment, and Recreation · Sector | 6,440 | $42,350 |
| Fitness and Recreational Sports Centers · National industry | 1,560 | $40,840 |
| Other Services (except Public Administration) · Sector | 1,550 | — |
| Educational Services · Sector | 1,320 | $38,320 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 130 | $34,440 |
| Temporary Help Services · National industry | 130 | $34,440 |
| Health Care and Social Assistance · Sector | 80 | $35,240 |
| Accommodation and Food Services · Sector | — | $48,610 |
Where this work is most concentrated
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Fitness and Recreational Sports Centers · National industry | 25.3× | 1,560 |
| Arts, Entertainment, and Recreation · Sector | 24.92× | 6,440 |
| Other Services (except Public Administration) · Sector | 3.58× | 1,550 |
| Educational Services · Sector | 0.99× | 1,320 |
| Temporary Help Services · National industry | 0.5× | 130 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 0.15× | 130 |
Related occupations
Part of the Arts, Entertainment, & Design career cluster.
- Coaches and Scouts · 27-2022.00 Compare →
- Athletes and Sports Competitors · 27-2021.00 Compare →
- Amusement and Recreation Attendants · 39-3091.00 Compare →
- First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services · 39-1014.00 Compare →
- Exercise Trainers and Group Fitness Instructors · 39-9031.00 Compare →
- First-Line Supervisors of Police and Detectives · 33-1012.00 Compare →
- Animal Trainers · 39-2011.00 Compare →
- Compliance Officers · 13-1041.00 Compare →
- Self-Enrichment Teachers · 25-3021.00 Compare →
- Bailiffs · 33-3011.00 Compare →
- Athletic Trainers · 29-9091.00 Compare →
- Recreation Workers · 39-9032.00 Compare →
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
What you can do with this
Options the data surfaces for Umpires, Referees, and Other Sports Officials — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Skills that travel
Capabilities this work builds that are used across many other occupations.
Adjacent roles
Occupations O*NET rates as related — the nearby moves on the map.
Paths in
How people typically prepare for this work.
- High school diploma or equivalent · typical entry level
- Job zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed · preparation tier
Zoom out
On the global GenAI exposure gradient this work sits around the 71st percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Umpires, Referees, and Other Sports Officials show 34th-percentile AI task overlap — and about 4,600 annual U.S. openings
- Umpires, Referees, and Other Sports Officials rank in the 34th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
- The occupation is projected to see about 4,600 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
- BLS projects employment to be about average (+5.7%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $38,820, across about 15,080 U.S. workers.BLS OEWS (May 2024)
- Of the AI use actually observed for this work, 61% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census.2026-01-15-v4-plus-2025-03-27-v2
Copy the whole kit
Umpires, Referees, and Other Sports Officials show 34th-percentile AI task overlap — and about 4,600 annual U.S. openings • Umpires, Referees, and Other Sports Officials rank in the 34th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 4,600 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be about average (+5.7%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $38,820, across about 15,080 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 61% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Umpires, Referees, and Other Sports Officials". https://singulariki.com/roles/role-27-2023-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Sources for this page
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
- Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
- Microsoft “Working with AI” working-with-ai Microsoft Research
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
- AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
- ILO / Gmyrek et al. GenAI exposure gradient 2025 International Labour Organization
- IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022 Institute for Structural Research (IBS)
- Frey & Osborne (2013) frey-osborne-automation academic
- Dingel & Neiman (2020) dingel-neiman-workathome academic
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Umpires, Referees, and Other Sports Officials." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-27-2023-00
APA
Singulariki. (2026). Umpires, Referees, and Other Sports Officials. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-27-2023-00
BibTeX
@misc{singulariki-role-27-2023-00,
title = {Umpires, Referees, and Other Sports Officials},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-27-2023-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.