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needsahuman.

Will AI replace umpires, referees, and other sports officials?

A little.

Cameras can measure a pitch or a line call, but game control, disputes and safety rulings still sit with a person. This job scores 78 out of 100 on (higher is safer). Today people do 27% of the work with AI’s help, and 73% still needs a person.

Updated 3 October 2026 27-2023 3432 2026-Q4
Arts, Design, Entertainment, Sports, and MediaUmpires, Referees, and Other Sports Officials27-2023 · 2026-Q4
0% AI does it27% AI helps73% needs a human
Your job's name, lit by the work that still needs a human.Needs a human 73%AI helps 27%AI does it 0%

AI does it: AI can do the task largely by itself. AI helps: a person still does it, faster with AI. Needs a human: AI can do little of it yet.

Why the hardest calls stay with a person

Will umpires be replaced by AI? Not as a whole job, and the reason is simple. Deciding whether a pitch crossed the plate is a measurement problem, and cameras are very good at measurement. Running a contest is not a measurement problem. An official starts play, keeps order, reads intent, and decides what counts as a foul when two players collide at speed.

Look at the task list above and the split shows up quickly. Signaling participants and other officials, inspecting equipment and playing surfaces, keeping time and recording scores: that work is already machine-friendly. Resolving disputes and complaints from coaches, enforcing rules on conduct, and judging contact or intent are judgment calls made in front of an angry crowd. Someone has to own them.

There is also the authority problem. A ruling only works if players accept it on the spot. Tracking systems can feed a decision, but a person still has to deliver it, absorb the argument, and keep the game moving. That is why the human share of task time, printed above as 73%, sits where it does.

Measured, assisted, and left to the official

Automated ball-strike systems and line-call technology have taken over the clean measurement duties. Ball and player tracking fixes whether a pitch or a shot was in or out, and electronic timing and scoring replaced the stopwatch and the paper card. In the split above, this group accounts for 0% of task time, which is roughly what our coverage score measures.

The assist layer is bigger and growing. Video review helps officials check a goal, a catch, or a foot on the line. Rules databases, scheduling tools, and report writing software shorten the paperwork that follows a game, including verifying eligibility and filing results. That assisted group takes 27% of task time.

What is left is the part people picture when they picture an official. Controlling player and coach behavior, ruling on intent and safety, deciding when conditions make play unsafe, and explaining a decision so a game can restart. Those are not slow desk tasks that a model can batch overnight. They happen in two seconds, with no replay, in front of a stadium.

What has actually been tested

No study has put an automated system against trained officials across the full job and published the result. Our parity evidence grade for this occupation is D on an A to D scale, and a D means not measured, so we publish no parity number at all. That is the honest position, and how we grade parity evidence explains why we refuse to guess one.

Ball-strike technology has been trialed in minor league baseball and in preseason play, usually with a challenge format where a player can ask for a review instead of handing every pitch to a machine. That tells us something about accuracy on one call type. It tells us nothing about ejections, injury stoppages, or a disputed rule interpretation in the last minute. A study that settled the question would compare full games officiated by people against games officiated by systems, measuring correct rulings, game flow, and how often a human had to step in.

Good to know: every score on this page comes from open task and labor data, not from a test we ran ourselves, and the full method is published.

How soon this could shift

Most likely between 2038 and 2060 (8 in 10 of our scenarios). The replacement-year method sets out exactly what that window covers.

Two things could pull it earlier. League policy is the big one: once a top league adopts a tracking system for a call type, lower leagues follow fast, and youth and college games follow the leagues. Cheaper camera hardware is the other, because the cost panel above shows the tooling side is far less expensive per season than staffing a crew.

Two things hold it back. The physical share of this job needs the robotics tier labeled above as a dexterous humanoid, and nothing at that tier works on a wet field at game speed. And most officiating happens far from pro stadiums. Youth leagues, high school games, and recreational sports are run on thin budgets with no camera rigs, so a volunteer with a whistle stays the cheapest option by a wide margin.

The labor market adds context. About 15,780 people work as sports officials in the US, median pay is $40,710, and employment is projected to rise 5.2% through 2035 (BLS, 2025). Many leagues report trouble filling crews at all.

Staying needed on the field

Lean into the duties the task list leaves with people. Game management, meaning control of conduct, pace, and tempers. Dispute resolution with coaches and captains, where tone decides whether a protest ends or escalates. And safety judgment, including calling play on conditions, equipment, and injuries.

Two skills compound. First, working alongside review technology: knowing when to accept a tracking call, when to overrule, and how to run a challenge cleanly. Second, training and assessing other officials, which is how experienced crew members move up as leagues professionalize.

If you are weighing related paths, look at Coaches and Scouts, Athletes and Sports Competitors, and Gambling Surveillance Officers and Gambling Investigators, which pairs camera systems with human judgment in a similar way. You can also see the rest of the entertainers and sports workers family, the wider arts and entertainment sector, or put two of these jobs side by side on the compare tool. For a different angle, what the AI assistants say collects how the major models answer the same question.

Frequently asked questions

Are robot umpires coming to MLB?

Automated ball-strike systems have been trialed in minor league baseball and in preseason play, most often as a challenge system. Players ask for a review of a specific pitch instead of the machine calling every pitch. That design keeps a human umpire behind the plate running the game. Adoption at the top level is a league policy decision, not a technical one.

Will referees in other sports be replaced by AI?

Officiating in soccer, tennis, football, and basketball already uses tracking and video review for specific calls, such as offside, line calls, and goal confirmation. Those systems handle measurement. They do not manage player conduct, judge intent on contact, stop play for injuries, or explain a ruling to a furious bench. The task list above shows how that work divides.

Why do some people think robot umpires are a bad idea?

Common objections are pace of play, the loss of pitch framing as a catching skill, disputes over how a strike zone should be defined for different batters, and the cost of installing systems at every level of the game. Others argue human error is part of the sport’s character. These are rules and culture arguments, not accuracy arguments.

How often are umpires wrong?

Published miss rates vary a lot depending on the pitch type, the count, the camera system used as the reference, and how the strike zone is defined. We do not publish a figure for this, because the measurement depends entirely on those choices. Any number you see should come with the definition of the zone and the season it covers.

What do you call a baseball umpire?

In baseball the on-field official is an umpire, with the home plate umpire calling balls and strikes and base umpires covering the rest. Other sports use referee, judge, linesman, or official. O*NET groups all of them under one occupation, Umpires, Referees, and Other Sports Officials, which is the group scored on this page.

Is officiating still worth getting into?

Many leagues struggle to fill crews, especially at youth and high school level, and federal projections point to modest growth in the occupation through 2035 (BLS, 2025). Pay varies widely by level and by whether the work is part-time. The duties that are hardest to automate, such as game management and conduct enforcement, are also the ones that get you promoted.

Each ridge is a slice of the job's task time.Needs a human 73%AI helps 27%AI does it 0%
The job’s mark

No two jobs leave the same print

Every job gets its own fingerprint, drawn from its code. The amber ridges are the share of task time that still needs a person. Below them, the same ridges are written out in ones and zeros: slate for the work AI helps with, white for the work AI can do.

Umpires, Referees, and Other Sports Officials, O*NET-SOC 27-2023. 73% of the job’s task time still needs a human, so 73 of every 100 ridges are amber; slate is what AI helps with, white what AI can do.

What AI can and cannot do

The tasks that make up the job, from , and where AI stands on each today: , (a person does it, with AI speeding it up) or . 73% of the still needs a human.

Each block is one task; its height is its share of working time.Needs a human 73%AI helps 27%AI does it 0%
The job's task list: the parts AI can do are blacked out.Needs a human 73%AI helps 27%AI does it 0%
Officiate at sporting events, games, or competitions, to maintain standards of play and to ensure that game rules are observed.Needs a human
Inspect game sites for compliance with regulations or safety requirements.Needs a human
Resolve claims of rule infractions or complaints by participants and assess any necessary penalties, according to regulations.Needs a human
Signal participants or other officials to make them aware of infractions or to otherwise regulate play or competition.Needs a human
Teach and explain the rules and regulations governing a specific sport.Needs a human
Inspect sporting equipment or examine participants to ensure compliance with event and safety regulations.Needs a human
Report to regulating organizations regarding sporting activities, complaints made, and actions taken or needed, such as fines or other disciplinary actions.AI helps
Confer with other sporting officials, coaches, players, and facility managers to provide information, coordinate activities, and discuss problems.Needs a human
Judge performances in sporting competitions to award points, impose scoring penalties, and determine results.Needs a human
Verify scoring calculations before competition winners are announced.AI helps
Start races and competitions.Needs a human
Compile scores and other athletic records.AI helps
Verify credentials of participants in sporting events, and make other qualifying determinations, such as starting order or handicap number.AI helps
Keep track of event times, including race times and elapsed time during game segments, starting or stopping play when necessary.Needs a human
Direct participants to assigned areas, such as starting blocks or penalty areas.Needs a human
Research and study players and teams to anticipate issues that might arise in future engagements.AI helps

Is it better than a person? The evidence

No direct test against people in this job yet. Every study is , and vendor studies are labelled as such.

When could it be replaced?

When AI could largely do this job: 2038–2060

Most likely between 2038 and 2060 (8 in 10 of our scenarios). A range from our of how fast AI improves, how fast employers take it up and what holds it back, not a forecast that the job ends. “” has a strict meaning here. Today’s answer is at the top of the page; this is how it could change.

The sand is the human working years left, measured in the same 40-year glass for every job, so a safe trade starts nearly full and an exposed job with a thin layer.

The sand is the human working years left, in the same 40-year glass for every job.Years still needing a humanYears run out

How this job could shift, year by year

Where the job could sit on our scale each year to 2060, across the ten behind its .

Today
Will AI replace this job?
A little.
By 2045
60%
of our scenarios have AI largely doing this job by 2045 (Largely.)
0% still have it mostly needing a person (A little. or Nah.)
By 2060
100%
of our scenarios have AI largely doing this job by 2060 (Largely.)
0% still have it mostly needing a person (A little. or Nah.)

We run this job as ten scenarios spread across its replacement range. In each, the score moves towards the bottom band (Largely: AI could largely do the job) by the year that scenario reaches it, slowly at first and faster later, as adoption usually goes. Each bar splits the ten by the band they put the job in. The model stops at 2060. How the timeline works

Share of this job's scenarios in each verdict band, today to 20600%25%50%75%100%2026: 100.0% of scenarios: AI could do a little of this job (A little.)100%Today2030: 100.0% of scenarios: AI could do a little of this job (A little.)100%20302035: 30.0% of scenarios: AI could do a little of this job (A little.)30%2035: 40.0% of scenarios: AI could partly do this job (Partly.)40%2035: 30.0% of scenarios: AI could mostly do this job (Mostly.)30%20352040: 30.0% of scenarios: AI could partly do this job (Partly.)30%2040: 30.0% of scenarios: AI could mostly do this job (Mostly.)30%2040: 40.0% of scenarios: AI could largely do this job (Largely.)40%20402045: 10.0% of scenarios: AI could partly do this job (Partly.)10%2045: 30.0% of scenarios: AI could mostly do this job (Mostly.)30%2045: 60.0% of scenarios: AI could largely do this job (Largely.)60%20452050: 20.0% of scenarios: AI could mostly do this job (Mostly.)20%2050: 80.0% of scenarios: AI could largely do this job (Largely.)80%20502055: 10.0% of scenarios: AI could mostly do this job (Mostly.)10%2055: 90.0% of scenarios: AI could largely do this job (Largely.)90%20552060: 100.0% of scenarios: AI could largely do this job (Largely.)100%2060
Will AI replace the job?Largely.Mostly.Partly.A little.Nah.
Share of this job's scenarios in each band, year by year. Updated with every release.
Show the data
YearLargelyMostlyPartlyA littleNah
Today (2026)0.0%0.0%0.0%100.0%0.0%
20300.0%0.0%0.0%100.0%0.0%
20350.0%30.0%40.0%30.0%0.0%
204040.0%30.0%30.0%0.0%0.0%
204560.0%30.0%10.0%0.0%0.0%
205080.0%20.0%0.0%0.0%0.0%
205590.0%10.0%0.0%0.0%0.0%
2060100.0%0.0%0.0%0.0%0.0%

What’s stopping AI taking over?

The things that keep this work with people, strongest first. Each is scored 0 to 100 from work context, licensing and the evidence we have.

Evidence gapNo study yet compares AI with people doing this job, so employers have no proof it is good enough.
LiabilityMistakes are rated 2.2 out of 5 for consequence and decisions 3.3 out of 5 for impact; someone has to answer for them.
Clients want a personFace-to-face contact is rated 4.0 and physical closeness 3.9 out of 5; caring for or serving people is 2.2 out of 5 in importance.
Physical work28% of the task time is physical; robots have been shown on 50% of that time.
RegulationWorkers rate responsibility for others' health and safety 2.6 out of 5.
LicensingUsual entry requirement (BLS): high school diploma or equivalent, then moderate-term on-the-job training.

What would it cost to hand the work to AI?

The share of the year AI could handle (372 of 2,080 hours a year), priced two ways. Both are ranges, not quotes.

AI model usage, a year
$40–$3,720
A person’s wage for the same hours
$4,750–$14,850

AI cost covers model usage only: no integration, licences, oversight or the human time still needed to review the work. Human cost is the wage for the same hours, without benefits or overheads. As of 2026-10.

Robots and humanoids

AI software can only take the work at a screen. The rest needs a robot that can do it.

28%
of the task time is physical work
Dexterous humanoid
the kind of robot the physical work would need
Not commercial: no cited robot does most of this work; humanoids are at demonstration and pilot stage.

Source: Anthropic Economic Index, 'What work can robots do?' (30 September 2026); O*NET 31.0 task weights.

Which AI skills does this job lean on?

The job’s task time split by what an AI model would need to be good at, and where models stand today.

Each star is a task, grouped by the AI skill it leans on.Needs a human 73%AI helps 27%AI does it 0%
Writing · 3.9% of time
Strong
Drafts, edits and translates most routine documents at professional quality.
Analysis · 16.9% of time
Strong
Reliable on structured data and rules; uneven on judgement calls with thin information.
Coding · 0% of time
Strong
Agents complete many routine software tasks end to end; larger systems still need people.
Vision and design · 7% of time
Good
Reads documents, images and layouts well; specialist imaging needs dedicated, approved tools.
Speech · 0% of time
Good
Voice agents handle routine calls and live interpreting; complex or sensitive calls still go to people.
Planning and agents · 5.9% of time
Emerging
Multi-step agents work in narrow, well-tooled workflows; open-ended coordination is unreliable.
Physical manipulation · 48.3% of time
Early
Robots handle structured, repetitive handling; general dexterity outside fixed settings is not commercial.
Care and persuasion · 17.9% of time
Limited
Can script, coach and advise; trust, presence and accountability still need a person.
Amber matter holds its orbit, slate circles the inner disc, white falls in.Needs a human 73%AI helps 27%AI does it 0%
How exposed is it?

Still needs a human: 78/100↑ safer

The amber matter on the outside holds its orbit: that is the task time that still needs a person. The slate matter circling the inner disc is the work AI helps with. The white matter is the share AI can do; it spirals in and is gone over the edge.

The matter follows the job’s task split: 73% needs a human, 27% AI helps, 0% AI does it. Still needs a human: 78/100 ↑ safer. Will AI replace them? A little.

People are asking

How often people ask whether AI will replace this job: on Google, and by estimate, in AI assistants.

In the US

20
Google searches a month, 12-month average to August 2026
Google searches a month, September 2025 to August 2026: from 30 to 10
28
estimated questions to AI assistants in September 2026
Estimated questions to AI assistants a month, October 2025 to September 2026: from 20 to 28
1.27
Google searches a month for every 1,000 people in the job
57th of 197 among all jobs we have search data for

In the UK

10
Google searches a month, 12-month average to August 2026
2.86
Google searches a month for every 1,000 people in the job in the UK (estimated)
29th of 197 among jobs we have UK search data for

Source: DataForSEO, US and UK, fetched October 3, 2026. Google figures are Google Ads’ rounded monthly averages. The AI figure is DataForSEO’s estimate from Google’s “People also ask” data, not a count from any AI assistant. UK workers are ONS employment figures matched to this job, so the UK rate per 1,000 is an estimate. Search figures are not part of our open dataset.

What the AIs say

We asked four AI assistants the same question: will AI replace this job in the next 10 years? One word (Yes, Partly or No), then one sentence. Our own answer, from the data: Still needs a human: 78/100 ↑ safer. Will AI replace them? A little.

ChatGPTPartly

AI will likely automate or assist with some calls—like balls and strikes or line decisions—but human umpires will probably remain for game management, judgment calls, and dispute resolution.

gpt-5.5-2026-04-23 · asked 2026-10-03
ClaudePartly

AI/technology (like ball-tracking and automated line-calling) will likely handle many objective decisions, but human umpires will probably remain for nuanced judgment calls and on-field management for at least the next decade.

claude-sonnet-5 · asked 2026-10-03
GeminiPartly

While automated systems will likely take over ball-and-strike calls and boundary rulings, human umpires will still be needed on the field for physical positioning, player management, and complex in-game situations.

gemini-3.8-flash · asked 2026-10-03
PerplexityPartly

AI will likely take over many objective calls, but human umpires will remain for judgment, game management, and accountability.

sonar · asked 2026-10-03

Assistants answer from what they learned in training (Perplexity also searches the web), so they can be confidently wrong, and the same question can get a different answer tomorrow. Our score is built from task data and graded evidence. Answers collected through DataForSEO.

Cite this page

NeedsAHuman.com (2026). Will AI replace Umpires, Referees, and Other Sports Officials? A little. Still needs a human: 78/100, higher is safer; release 2026-Q4. https://needsahuman.com/jobs/umpires-referees-and-other-sports-officials/ (accessed 4 October 2026).

Scores change with each , so cite the release. The data is open under : credit NeedsAHuman.com with a link. Open data · Press

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The badge updates itself with each release and links back to this page.

Sources

  • Tasks and work context: 31.0, ().
  • Jobs, pay and projections: US , and 2025–35.
  • How AI is used today: ; Microsoft Research, .
  • What AI can do: our task ratings ( r1) and the quality evidence register.
  • UK names and employment: coding index and .

How each score is built: methodology. Every figure on this page: open data. Release 2026-Q4.