Occupation · SOC 27-2021.00
Compete in athletic events.
Also called: Baseball Player · Golf Professional · Hockey Player · Race Car Driver · Baseball Pitcher · Basketball Player · Major League Baseball Player · Minor League Baseball Player · Professional Athlete · Professional Golf Tournament Player · All Terrain Vehicle Racer (ATV Racer) · Archer
Job family: Arts, Design, Entertainment, Sports, and Media Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-27-2021-00/context.md directly.
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.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
24th-percentile task overlap — yet about 2,100 openings a year (+5.5% projected, BLS) . What exposure means →
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.
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.) Low | 1st | -1.8 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 3rd | 0.0 | |
| AI assistant applicability (Microsoft) High | 74th | 0.2 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.0), and including AI-powered software (γ 0.0). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
Most of this job's tasks can be done remotely (Dingel–Neiman), which tends to track with higher digital and AI exposure.
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 0.3 · 38th percentile among occupations · Moderate
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +5.5% by 2034 |
| Projected annual openings | 2,100 |
| Employment 2024 → 2034 | 19,100 → 20,200 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
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.
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Athletes and Sports Players · 3421 | 23% | Not exposed |
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.
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.
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
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 |
|---|---|---|
| Assess performance following athletic competition, identifying strengths and weaknesses and making adjustments to improve future performance. | — | 0.5% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Assess performance following athletic competition, identifying strengths and weaknesses and making adjustments to improve future performance. | 88.9% |
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 assess performance following athletic competition, identifying strengths and weaknesses and making adjustments to improve future performance. From: Assess performance following athletic competition, identifying strengths and weaknesses and making adjustments to improve future performance. · 0.5% of measured AI use
All 9 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Oral Comprehension | 4.0 | |
| Oral Expression | 3.9 | |
| Problem Sensitivity | 3.9 | |
| Static Strength | 3.9 | |
| Stamina | 3.9 | |
| Explosive Strength | 3.6 | |
| Dynamic Strength | 3.6 | |
| Speech Clarity | 3.6 | |
| Gross Body Coordination | 3.5 | |
| Near Vision | 3.5 | |
| Deductive Reasoning | 3.4 | |
| Inductive Reasoning | 3.4 | |
| Multilimb Coordination | 3.4 | |
| Extent Flexibility | 3.4 | |
| Far Vision | 3.4 | |
| Speech Recognition | 3.4 | |
| Written Comprehension | 3.3 | |
| Information Ordering | 3.3 | |
| Arm-Hand Steadiness | 3.3 | |
| Trunk Strength | 3.3 | |
| Gross Body Equilibrium | 3.3 | |
| Selective Attention | 3.1 | |
| Manual Dexterity | 3.1 |
| Speaking | 3.9 | |
| Active Listening | 3.8 | |
| Critical Thinking | 3.8 | |
| Monitoring | 3.3 | |
| Reading Comprehension | 3.1 | |
| Active Learning | 3.0 |
| Coordination | 3.6 | |
| Judgment and Decision Making | 3.3 | |
| Social Perceptiveness | 3.1 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
| Example | Category | |
|---|---|---|
| Adobe Photoshop | Graphics or photo imaging software | Hot technology |
| Web page creation and editing software | Hot technology | |
| Microsoft Excel | Spreadsheet software | Hot technology |
| Microsoft Office software | Office suite software | Hot technology |
| Microsoft PowerPoint | Presentation software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| Oracle PeopleSoft | Enterprise resource planning ERP software | Hot technology |
| Email software | Electronic mail software | |
| Motion analysis software | Analytical or scientific software | |
| Instant messaging software | ||
| Web browser software | Internet browser software | |
| YouTube | Video creation and editing software |
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.
What to study: Parks, Recreation, Leisure, Fitness, and Kinesiology . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| Less than a High School Diploma | 34.8% | |
| Bachelor's Degree | 24.5% | |
| High School Diploma | 23.3% | |
| Associate's Degree (or other 2-year degree) | 8.7% | |
| Post-Secondary Certificate | 5.5% | |
| Some College Courses | 3.3% |
The interests and personal qualities O*NET associates with people who do this work.
| Dependability | 7.0 | |
| Achievement Orientation | 6.0 | |
| Self-Control | 5.0 | |
| Stress Tolerance | 4.0 | |
| Perseverance | 3.0 | |
| Self-Confidence | 2.6 |
| Athletics | 6.9 | |
| Physical/Manual Labor | 5.0 | |
| Public Speaking | 3.7 | |
| Marketing/Advertising | 3.0 | |
| Management/Administration | 2.5 | |
| Sales | 2.4 |
| Realistic | 5.2 | |
| Enterprising | 4.5 | |
| Social | 4.3 | |
| Conventional | 3.0 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $24,960 |
| 25th percentile | $36,750 |
| Median (50th) | $62,360 |
| 75th percentile | $130,770 |
| 90th percentile | — |
| People employed | 14,370 |
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 | 13,850 | $62,270 |
| Educational Services · Sector | 280 | $84,460 |
| Other Services (except Public Administration) · Sector | 90 | $67,480 |
| Fitness and Recreational Sports Centers · National industry | — | $61,190 |
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 |
|---|---|---|
| Arts, Entertainment, and Recreation · Sector | 56.24× | 13,850 |
| Educational Services · Sector | 0.22× | 280 |
Part of the Arts, Entertainment, & Design career cluster.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Athletes and Sports Competitors — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
On the global GenAI exposure gradient this work sits around the 42nd percentile of 427 international occupations.
Athletes and Sports Competitors show 24th-percentile AI task overlap — and about 2,100 annual U.S. openings
Athletes and Sports Competitors show 24th-percentile AI task overlap — and about 2,100 annual U.S. openings • Athletes and Sports Competitors rank in the 24th percentile (Low 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 2,100 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 (+5.5%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $62,360, across about 14,370 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Athletes and Sports Competitors". https://singulariki.com/roles/role-27-2021-00 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.
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.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Athletes and Sports Competitors." 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-27-2021-00
Singulariki. (2026). Athletes and Sports Competitors. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-27-2021-00
@misc{singulariki-role-27-2021-00,
title = {Athletes and Sports Competitors},
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-27-2021-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.