Will AI replace Umpires, Referees, and Other Sports Officials?

Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Umpires, Referees, and Other Sports Officials (United States, SOC 27-2023), 19% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 19 out of 100 (range 16–24, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.

Which tasks in “Umpires, Referees, and Other Sports Officials” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Compile scores and other athletic records” (69/100, high); “Research and study players and teams to anticipate issues that might arise in future engagements” (68/100, high); “Report to regulating organizations regarding sporting activities, complaints made, and actions taken or needed, such as fines or other disciplinary actions” (66/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Umpires, Referees, and Other Sports Officials” stay human?

About 81% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Direct participants to assigned areas, such as starting blocks or penalty areas” (0/100, minimal); “Start races and competitions” (0/100, minimal); “Inspect sporting equipment or examine participants to ensure compliance with event and safety regulations” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.

What should someone working in “Umpires, Referees, and Other Sports Officials” do about AI?

Start from the ledger rather than the headline: 19% of this job's weighted core work is exposed, and roughly 81% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.

How is the AI exposure score for Umpires, Referees, and Other Sports Officials calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 16 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

US dataswitch to UK

Umpires, Referees, and Other Sports Officials

officiating at sporting events, games or competitions, signaling participants or other officials to make them aware of infractions or to otherwise regulate play or competition and teaching and explaining the rules and regulations governing a specific sport. If that's your week, this page is about your job.

The honest answer

AI is already taking a real slice of the routine work here: reporting to regulating organizations regarding sporting activities. That is a slice of tasks, not of you.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 19%
changing shape: 0%
staying human: 81%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 19 out of 100 (16–24 allowing for uncertainty): minimal exposure, across 16 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for umpires, referees, and other sports officials is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

That slice is not coming back; the core of the job, officiating at sporting events, games or competitions, stays yours. The tools change, the responsibility doesn't.

Your week, as this page understands it

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. The job title says “umpires”, “referees” or “other sports officials”: officially one job, several names. The real job is the part underneath: officiating at sporting events, games or competitions, to maintain standards of play and to ensure that game rules. That is the thing someone has to be right about.

The exposed part of this job is specific, and we won’t pretend it is coming back. But umpires, referees, and other sports officials is not one task. It is 16 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is officiating at sporting events, games or competitions, to maintain standards of play and to ensure that game rules, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 19%
changing shape: 0%
staying human: 81%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

4 tasks

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

Reporting to regulating organizations regarding sporting activities
This is reading one thing and writing another: regulating organizations regarding sporting activities in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Report to regulating organizations regarding sporting activities, complaints made, and actions taken or needed, such as fines or other disciplinary actions.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Match reports and disciplinary write ups follow a standard format from facts already noted.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Verifying scoring calculations before competition winners
This is reading one thing and writing another: calculations before competition winners in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Verify scoring calculations before competition winners are announced.” (O*NET task statement)
How this row was scored
Exposure score: 61 out of 100 (57–65 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Checking scoring arithmetic is exactly what a spreadsheet does.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Researching and studying players and teams to anticipate issues that might arise in future engagements
This is reading one thing and writing another: players in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Research and study players and teams to anticipate issues that might arise in future engagements.” (O*NET task statement)
How this row was scored
Exposure score: 68 out of 100 (61–75 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Reading up on teams and players beforehand is desk research, though lower level fixtures are poorly documented.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Compiling scores and other athletic records
This is reading one thing and writing another: scores in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Compile scores and other athletic records.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Compiling scores and records is structured data entry software handles.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

0 tasks

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.

Staying human

12 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

Officiating at sporting events, games or competitions
This work happens in the physical world: sporting events, games or competitions, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Officiate at sporting events, games, or competitions, to maintain standards of play and to ensure that game rules are observed.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Officiating means standing in the arena making decisions as events unfold.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Teaching and explaining the rules and regulations governing a specific sport
This work happens in the physical world: the rules, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Teach and explain the rules and regulations governing a specific sport.” (O*NET task statement)
How this row was scored
Exposure score: 25 out of 100 (18–32 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Rulebooks are public and easy to explain from, though teaching sessions usually happen in person.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 4/4.
Conferring with other sporting officials
This work happens in the physical world: other sporting officials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Confer with other sporting officials, coaches, players, and facility managers to provide information, coordinate activities, and discuss problems.” (O*NET task statement)
How this row was scored
Exposure score: 18 out of 100 (11–25 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Sorting out arrangements with coaches and venue staff is mostly conversation at the ground.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Signaling participants or other officials to make them aware of infractions or to otherwise regulate play or competition
This work happens in the physical world: participants, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Signal participants or other officials to make them aware of infractions or to otherwise regulate play or competition.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Signaling calls to players and officials happens in the moment on the field.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Inspecting game sites for compliance with regulations or safety requirements
This work happens in the physical world: game sites, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Inspect game sites for compliance with regulations or safety requirements.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (3–11 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Checking a venue for safety and rule compliance means walking it before the game.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Inspecting sporting equipment or examining participants to ensure compliance with event and safety regulations
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inspect sporting equipment or examine participants to ensure compliance with event and safety regulations.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Checking equipment and competitors before an event means physically examining them.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.

Show the other 6 tasks

Verifying credentials of participants in sporting events
staying human
This work happens in the physical world: credentials of participants, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Verify credentials of participants in sporting events, and make other qualifying determinations, such as starting order or handicap number.” (O*NET task statement)
How this row was scored
Exposure score: 33 out of 100 (26–40 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Checking entries and handicaps against records is routine, but credentials get checked at the venue.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Keeping track of event times
staying human
This work happens in the physical world: track of event times, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Keep track of event times, including race times and elapsed time during game segments, starting or stopping play when necessary.” (O*NET task statement)
How this row was scored
Exposure score: 11 out of 100 (4–18 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: A clock can run itself, but knowing when to stop play is a call made on the field.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Judging performances in sporting competitions to award points
staying human
This work happens in the physical world: performances, in a real place. Software cannot follow it there.
importance 5 · Supplemental
Source: “Judge performances in sporting competitions to award points, impose scoring penalties, and determine results.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (2–10 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Judging a performance as it happens needs an official watching it live.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Resolving claims of rule infractions or complaints by participants and assessing any necessary penalties
staying human
This work happens in the physical world: claims of rule infractions, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Resolve claims of rule infractions or complaints by participants and assess any necessary penalties, according to regulations.” (O*NET task statement)
How this row was scored
Exposure score: 4 out of 100 (0–8 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Settling a dispute mid game depends on an official the players accept, right there.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 2/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Starting races and competitions
staying human
This work happens in the physical world: races, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Start races and competitions.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Starting a race means a person at the line.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Directing participants to assigned areas
staying human
This work happens in the physical world: participants, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Direct participants to assigned areas, such as starting blocks or penalty areas.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Directing competitors to their places happens in person at the venue.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 1/4.

What this job pays, and how many people do it

Median pay: $40,710a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
People doing this job: 15,780in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: regulating organizations regarding sporting activities in, a record out. The rows above are exactly that shape: reporting to regulating organizations regarding sporting activities and verifying scoring calculations before competition winners. What it cannot do is be there in the room, and that is still where sporting events, games or competitions get done. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Start with what does not change: officiating at sporting events, games or competitions is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 19% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 81% in rows it is nowhere near. That is the position, measured across 16 scored tasks. It is not a forecast about you.

So the thing worth your attention is not the job going away. It is the layer around it. Reporting to regulating organizations regarding sporting activities is the part turning into software, and being the person who understands that layer is worth money.

This week: one thing

Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to regulating organizations regarding sporting activities, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.

What you end up holding: a straight answer about what is actually being rolled out, and when
How long it takes: ten minutes

If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether sporting events, games or competitions are in scope or not. Nothing to log into, no license needed.

Over the next 90 days

Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near officiating at sporting events, games or competitions. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.

Over the next 12 months

On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

The roads out of here, and why I am not sending you down them

I looked at the obvious moves out of this job, and here is what I found.

I checked the 12 nearest US occupations to umpires, referees, and other sports officials (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was exercise trainers and group fitness instructors: only about 5% of its durable work is work you already do. And on the numbers you do not need one. This job scores 19/100 here, with only 19% of the task list in the top band, and “officiate at sporting events, games, or competitions, to maintain standards of play…” is not work that hands over cleanly. None of them beats deepening what you already have.

How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

Exercise Trainers and Group Fitness Instructors
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already teach and explain the rules and regulations governing a specific sport, and their equivalent is to explain and enforce safety rules and regulations governing sports, recreational activities, and the…. Across both published task lists that is about 5% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 5% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Meeting, Convention, and Event Planners
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already inspect sporting equipment or examine participants to ensure compliance with event and safety…, and their equivalent is to monitor event activities to ensure compliance with applicable regulations and laws, satisfaction of…. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Athletes and Sports Competitors
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already teach and explain the rules and regulations governing a specific sport, and their equivalent is to participate in athletic events or competitive sports, according to established rules and regulations. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 19% of its task weight, across 16 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like officiating at sporting events, games or competitions arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Sports coaches, instructors and officials is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

In UK official statistics this job is counted as Sports coaches, instructors and officials. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Anywhere in the US:
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Anywhere in the US:
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Anywhere in the US:
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No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for umpires / referees / other sports officials, and we are not going to point you at the nearest one and call it a fit.

There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 19% of the work on this page is already inside what they can do.

Try The AI Authority free →

7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.

The AI Authority is a general community about working with AI, not a course for umpires / referees / other sports officials. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.

Noted, and thank you. We’ll email you if a Space for umpires / referees / other sports officials launches. Nothing else.

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No Space for umpires / referees / other sports officials yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for umpires / referees / other sports officials existed, with researched problems, courses and people in the same boat, would you want in?

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We use your email address for one thing: to tell you if a Space for umpires / referees / other sports officials launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Umpires, Referees, and Other Sports Officials?: Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Umpires, Referees, and Other Sports Officials (United States, SOC 27-2023), 19% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 19 out of 100 (range 16–24, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Umpires, Referees, and Other Sports Officials” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Compile scores and other athletic records” (69/100, high); “Research and study players and teams to anticipate issues that might arise in future engagements” (68/100, high); “Report to regulating organizations regarding sporting activities, complaints made, and actions taken or needed, such as fines or other disciplinary actions” (66/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Umpires, Referees, and Other Sports Officials” stay human?: About 81% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Direct participants to assigned areas, such as starting blocks or penalty areas” (0/100, minimal); “Start races and competitions” (0/100, minimal); “Inspect sporting equipment or examine participants to ensure compliance with event and safety regulations” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Umpires, Referees, and Other Sports Officials” do about AI?: Start from the ledger rather than the headline: 19% of this job's weighted core work is exposed, and roughly 81% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
How is the AI exposure score for Umpires, Referees, and Other Sports Officials calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 16 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
3 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

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Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.