One-rep max O'Conner calculator
Lifting 100 kg for 5 reps gives an O'Conner one-rep max of 112.5 kg. Enter the weight and the reps you completed to set training percentages with the linear formula your program or textbook names. Free in your browser.
- Free
- Runs in your browser
- No sign-up
- Your data stays on your device
Run — free
Worked example
- Weight
- 100
- Reps
- 5
- One rep max
- 112.5
- Precision
- 2
- Equation
- weight * (1 + 0.025 * reps)
This O'Conner one-rep max calculator estimates the heaviest load you could theoretically complete for a single clean repetition from a submaximal set you already finished. You enter the weight you lifted and how many successful reps you completed, and the engine applies the classic O'Conner closed form: one-rep max equals weight times one plus zero point zero two five times reps. The path is fully offline and deterministic—the same weight and rep count always produce the same rounded estimate, with no network calls, no sampling, and no date-dependent shortcuts.
What the O'Conner equation means and what you get
A one-repetition maximum is an estimate of the heaviest load an athlete can lift once with acceptable form for a given exercise. Direct testing of a true max can be fatiguing and is not always appropriate in early training blocks, so coaches often back-calculate 1RM from a submaximal set using a published prediction equation. The O'Conner formula is a linear model of the form one-rep max equals weight times one plus zero point zero two five times the number of completed repetitions.
It is simple enough for hand calculation yet specific enough that people search for an O'Conner calculator rather than a generic multi-formula panel. This page does not measure bar path or film your set; it applies that closed form to the two numbers you provide and returns structured fields you can cite or wire into software.
Where an O'Conner 1RM estimate fits in training, teaching, and software
Coaches and athletes often start load planning with a predicted ceiling before refining with heavy singles or competition attempts, and the O'Conner equation is a common linear choice when a program or textbook names that formula specifically. Teachers can pre-generate answer keys for modest sets without trusting opaque apps that silently change coefficients or drop decimals.
Developers call the same contract from scripts to seed deterministic test vectors for strength-training UIs, to normalize user-entered sets before percentage tables, or to satisfy programming-challenge statements that demand the O'Conner 1RM formula rather than a multi-model average. Contest and certification practice sometimes needs a fast, offline source of 1RM estimates so solutions can be checked without a loaded barbell in the room.
Product and education tools use the estimate when a learner types weight and reps and the UI must show a single textbook formula with a clear equation label.
What you can do with it
Strength program load planning
Estimate 1RM from a completed submaximal set with the O'Conner formula so coaches and athletes can set percentages without testing a true max every week.
Homework and strength education
Generate reproducible 1RM answers for textbooks, quizzes, and labs that teach the O'Conner one-repetition maximum equation.
FAQ
What is the O'Conner one-rep max formula?
1RM = weight × (1 + 0.025 × reps).
What inputs does the calculator require?
Optional precision (0–15, default 2) controls decimal places.
What happens if weight is zero or reps are out of range?
Non-positive weight and reps outside 1–12 are rejected and are not billed.
Is this the same as Epley or Brzycki?
No. This capability uses only the O'Conner linear model. Multi-formula Epley/Brzycki comparison lives on a separate one-rep max tool with its own SEO landing.
Why can I enter at most 12 reps?
The O'Conner formula is a linear model meant for low-to-moderate rep sets, not high-volume pump work. Above 12 reps the straight-line assumption stops being useful, so the calculator accepts whole numbers from 1 to 12.
Related tools
For developers — API access
Everything on this page is available programmatically. This section is for teams who want to wire it into their own systems; everyone else can just use the tool above.
API endpoint
Prefer to automate it? One authenticated POST creates the task; the result comes back by webhook or a signed link. The same capability also runs here on the web, by email and from Telegram — and soon from our app too.
Call it from your stack
curl -X POST https://api.kit.forhosting.com/health/one-rep-max-oconner \
-H "Authorization: Bearer $KIT_KEY" \
-H "Content-Type: application/json" \
-d '{"weight":100,"reps":5}'const res = await fetch("https://api.kit.forhosting.com/health/one-rep-max-oconner", {
method: "POST",
headers: {
"Authorization": `Bearer ${process.env.KIT_KEY}`,
"Content-Type": "application/json"
},
body: JSON.stringify({
"weight": 100,
"reps": 5
})
});
const { task_id } = await res.json();import os, requests
res = requests.post(
"https://api.kit.forhosting.com/health/one-rep-max-oconner",
headers={"Authorization": f"Bearer {os.environ['KIT_KEY']}"},
json={
"weight": 100,
"reps": 5
},
)
task_id = res.json()["task_id"]<?php
$res = file_get_contents("https://api.kit.forhosting.com/health/one-rep-max-oconner", false, stream_context_create([
"http" => [
"method" => "POST",
"header" => "Authorization: Bearer " . getenv("KIT_KEY") . "\r\nContent-Type: application/json",
"content" => '{"weight":100,"reps":5}',
],
]));
$task = json_decode($res, true);body := bytes.NewBufferString(`{"weight":100,"reps":5}`)
req, _ := http.NewRequest("POST", "https://api.kit.forhosting.com/health/one-rep-max-oconner", body)
req.Header.Set("Authorization", "Bearer "+os.Getenv("KIT_KEY"))
req.Header.Set("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)Example request
{
"weight": 100,
"reps": 5
}Example response
{
"task_id": "tsk_a1b2c3d4e5f6a1b2c3d4e5f6",
"type": "health.one_rep_max_oconner",
"status": "queued",
"_links": {
"result": "/tasks/tsk_…/result"
}
}The API is asynchronous: the call returns a task_id immediately and the result arrives by webhook. Polling is capped at 1 req/s per task.
Pricing
Published price — no tokens, no invented credits. A failed task is never charged.
Errors
| HTTP | Code | Meaning |
|---|---|---|
401 | unauthorized | Missing or invalid API key. |
402 | insufficient_balance | Your balance doesn't cover the task price. |
404 | unknown_type | That task type doesn't exist. |
429 | rate_limited | Too many requests. Use the webhook instead of polling. |
Technical notes
What the O'Conner 1RM equation means and what the response contains
For each successful request you get the validated weight and reps, one_rep_max from the O'Conner product, method tagged as oconner, the precision used for rounding, plus formula and equation string fields that document the model explicitly. There is no wearable sync path and no random noise: the engine either builds a finite estimate or rejects the input with invalid_input. Rejected cases include missing weight or reps, non-finite values, non-positive weight, and rep counts outside the supported integer range of one through twelve, because the submaximal linear model is intended for low-to-moderate rep sets rather than high-volume pump work.
How weight and reps are read and how the path stays reproducible
Optional leading plus signs and scientific notation on numeric strings are allowed when they parse as finite decimals; embedded spaces, hex prefixes, and non-numeric tokens are rejected with a clear message so a bad cell does not become a wrong training max. The preferred fields are weight for the load lifted and reps for the number of successful repetitions, but aliases load, w, and mass for weight, and repetitions, r, and n for reps, are accepted so spreadsheets, query strings, and typed forms can reuse the same contract. Weight must be a strictly positive finite number in whatever mass unit you want reflected in the 1RM estimate—kilograms or pounds—because the formula is unit-homogeneous: if you enter pounds, one_rep_max is in pounds. Reps must be an integer from one to twelve inclusive; fractional reps, zero, negatives, and counts above twelve fail with invalid_input so clients do not silently apply a linear model outside its intended submaximal band. An optional precision field, with aliases decimals and dp, chooses how many decimal places to expose for weight and one_rep_max, defaulting to two and capped at fifteen so JSON goldens stay stable. Pair the free browser run on this page with the priced API when you need the identical mathematical result inside an automated pipeline without re-implementing the O'Conner regression yourself.
Where an O'Conner 1RM estimate fits in training, teaching, and software
On this page the work runs entirely in your browser; through the API each successful lookup costs $0.002, failed validation is not charged, and nothing is stored after the response returns. That split—free interactive exploration and a priced, identical API—matches the rest of the KIT tier-A catalog and lets you move from a classroom demo to an automated pipeline without changing the mathematical result. Pair it with other health and training endpoints when you are building a small lab of free, deterministic fitness helpers, and always treat population prediction formulas as estimates rather than a medical diagnosis or a substitute for supervised strength testing and good form.
Integration use cases
Apps and automated test fixtures
Call the same deterministic contract from scripts and CI to pin one_rep_max for known weight and rep inputs without re-coding the coefficient.
Technical questions
What does the O'Conner 1RM calculator cost?
The browser tool is free. API requests use prepaid KIT balance at $0.002 per successful call; invalid input is not billed.