How accurate is calorie counting?

Short answer: accurate enough. Calories as a unit have a small error, and it averages out. The bigger error is in how you count, and it only hurts when it’s systematic or random. Log the same food the same way every time, and your weight trend shows whether your numbers are right.

A common myth goes like this: calories lie, you can’t count them exactly, so there’s no point in counting at all. That’s nonsense, of course. Calories really aren’t perfectly accurate, and they have two main sources of error: the unit of measurement itself, and mistakes in counting.

Are calories accurate as a unit of measurement?

Are calories accurate as a unit of measurement? Not accurate enough for the International Bureau of Weights and Measures. Two apples of the same weight will always have slightly different calories and nutrients, simply because they grew in different orchards, or on different trees. But for weight loss (or gain), this difference doesn’t matter, because it’s small. And by the law of large numbers, the calories in all the apples you eat will tend toward the average value. That average has already been worked out and written into food trackers.

Counting mistakes: where the real error is

Counting mistakes are more interesting, and closer to the truth. You really can get counting quite wrong. That’s why it’s worth treating calories as your own unit of measurement, not as an exact lab number.

That’s also how Deficit works. It starts with an estimate in absolute numbers. Then, for the cycles that follow, it adjusts the estimate to your own numbers: what you logged, and what your weight and body fat really did.

On average, a person eats the same food, and I don’t mean the same meals every day. Luckily, the fitness meal plans of boiled chicken and rice from the 2010s are long behind us. It’s just that a usual diet is 30–50 different foods and their combinations. There’s a core of foods, and ~95% of it stays the same from day to day. The other 5% is for unusual food: restaurants, vacations and cooking experiments. But to get results (and even more so to keep them), you need to work with the core. If you count in an organized way and accurately enough, that’s enough to succeed. (The same core is why logging gets fast after the first few days.)

Let’s say the goal is to lose weight, and the calculated daily limit is 2,000 calories. Imagine someone counts wrong: they really eat 2,400 calories but log 2,000. That can happen if you count wrong systematically.

For example, 150 g (5.3 oz) of dry rice always gets logged as if it were cooked rice. Then instead of ~550 calories, the tracker shows ~200.

• Calories you ate
• Calories you logged
• Your limit: below it, you lose weight

What happens then? The person simply won’t lose weight, and the scale will show no downward trend (that’s why it’s so important to weigh yourself every day). Next, they cut their portions and their logged calories. Now they really eat 2,000 and log 1,600. They tell everyone how few calories they eat and how tiny their portions have become. But they start to lose weight.

• Calories you ate
• Calories you logged
• Your limit: below it, you lose weight

So why does accuracy still matter? Because once you start to log the rice correctly, you’ll see that about a quarter of your daily calories goes to rice. Then you can, for example, make the portion smaller and free up room for other food. Or you can love rice very much and change nothing, but at least you’ll know where your calorie budget goes.

Still, if the errors are too big, or the food is logged at random, nothing averages out. If every apple weighs 100 g, every portion of rice is “1 cup” and every piece of meat is “one breast”, your food tracker holds meaningless white noise instead of data. You can’t draw any conclusions from data logged at random like that, and the time you spent logging it is simply wasted.

• Calories you ate
• Calories you logged
• Your limit: below it, you lose weight (but not like this)

So up to a point, accuracy matters. Aim to log correctly, but don’t worry about absolute accuracy.

How to cut the noise in your food log

The main rule: find the right food once, and keep logging it.

Do you eat chicken breast? The tracker will offer you lots of entries for it. Pick one and only ever log that one. (Deficit does its best to show you only reliable entries, but the rule still holds.)

⌕ chicken breast

• Chicken breast, raw, skinless per 100 g (3.5 oz) Log only this one 120 cal
• Chicken breast, roasted, skinless per 100 g (3.5 oz) 165 cal
• Chicken breast, roasted, with skin per 100 g (3.5 oz) 197 cal
• Chicken breast, fried in batter per 100 g (3.5 oz) 260 cal

Do you get a coffee with milk at a coffee shop every morning? Better to pick, once, the cappuccino in the app that’s closest to the truth in size, and choose that exact one every time. Or add a custom entry for it, or take a photo of it once and reuse that entry. Do you order the same uncountable pad thai for delivery? Pick one of the pad thai options in the tracker and log it.

Do you often get a kebab from the place near your office? Find it in the tracker or take a photo of it, save it as a custom food, and reuse it.

This matters even more with AI. A common problem with AI trackers, and even with ChatGPT or Claude, is that they aren’t consistent: ask about the same meal twice, and you can get two different numbers. Deficit uses the foods you chose before as the baseline for its AI estimates, so the same meal gets the same number.