How Accurate Are Fishing Forecast Apps?

Three places the uncertainty comes from

The honest answer to how accurate these forecasts are is that nobody has measured it. But that does not leave us with nothing to say, because the sources of error can each be examined separately.

There are three. The weather forecast underneath has known and published error. The relationship between conditions and catch has been measured repeatedly and is small. And the fish-side mechanism — what makes one fish take a bait when another does not — has resisted prediction even under direct observation.

Stack those and you get a picture of what a bite score can be at its best. It is not nothing, and it is a long way from a prediction.

The forecast underneath the forecast

Every bite score inherits the error of the weather data it consumes, and that error is documented.

NOAA states the figures plainly for a general audience: a five-day forecast is accurate about 90% of the time, a seven-day forecast about 80%, and a ten-day or longer forecast is right about half the time. The reason given is simply that the atmosphere is changing all the time, so estimates get less reliable the further ahead you look.

Nothing downstream can improve on that. If a bite score for next Saturday depends on Saturday’s wind and pressure, and the wind and pressure forecast is 80% reliable, the score is working from a foundation that is already uncertain — before any judgement about how much wind matters is applied on top.

This is the practical case for checking a forecast again close to the day. It is not that the model changed its mind. It is that the weather forecast underneath became a shorter-range one.

Who is fishing matters more than the weather

This is the finding that most constrains what a weather-driven forecast can achieve, and it comes from an unusually good dataset.

A study on Escanaba Lake in Wisconsin used a complete-census creel covering 13 years, 2,005 walleye trips and 2,771 muskellunge trips. Its conclusion was that trip-specific angler variables tended to be the most highly weighted factors influencing trip success and catch rates for both species — and that guide status and bait type had the highest effect size relative to all other parameters examined.

Environmental variables were not absent. Solar radiation, time of day, lunar position and phase, and air temperature all registered for walleye. But every environmental effect was classed as very small, with Cohen’s d below 0.2.

One detail deserves separating out, because it contradicts the single most repeated claim in fishing content. The authors report that large changes in barometric pressure and precipitation were not highly weighted in any of the analyses. Pressure is a headline input in most bite forecasts, ours included, and on this evidence it is carrying less than its reputation suggests — which is also the conclusion of our barometric pressure article.

Follow the logic through. If whether you hired a guide and what bait you tied on outweigh every weather variable measured, then a forecast built purely on weather is working with the smaller half of the problem by construction.

Why more data would not fix it

Part of the limit is mechanistic rather than statistical, and that distinction matters because it cannot be solved by collecting more.

A whole-lake study of carp and tench combined acoustic telemetry with controlled experimental angling — meaning the researchers knew where the tagged fish were and when a bait was available to them. The finding was that encountering the bait is a necessary but insufficient condition for determining capture probability. Fish met the bait and then differed in whether they took it.

More striking is the null result attached: there was no support for any behavioural variable being a predictor of speed to capture or capture probability in either species. The researchers had continuous tracking data on individual fish and still could not say which would be caught.

If capture turns partly on individual variation that direct observation cannot predict, no amount of weather data collected at the surface will resolve it. This is a ceiling on precision, not a gap in the dataset.

What a real accuracy claim would require

Verification is a solved problem in meteorology, and the same methods would work here — which is precisely why the absence of any published score is worth noticing.

NOAA already scores its own precipitation probability forecasts with a Brier score, alongside threat scores, mean absolute error and anomaly correlations. The Brier score measures the mean squared probability error, running from 0 to 1 with 0 perfect. The Brier skill score then measures the improvement of a probabilistic forecast relative to a reference — usually climatology or persistence — which takes the base rate into account.

That last part is the one that would bite. A skill score compares your forecast against simply knowing the long-run average. NOAA’s own explanation of skill scoring puts it bluntly: a score of zero means the forecast did no better than would be expected by chance.

So here is the test, stated concretely enough that anyone could run it on us:

1. Log the forecast as a probability before the trip, not after.
2. Log the binary outcome — fish caught or not — with effort recorded separately, so the forecast is not credited for predicting when people chose to fish.
3. Compute a Brier score against those outcomes, then a Brier skill score against a climatological baseline for that species, month and water.

If the skill score is zero or below, the forecast is adding nothing beyond knowing what time of year it is.

One complication is worth flagging rather than hiding: catch rate is an imperfect measure of what is actually there. Fisheries science treats catch per unit effort as proportional to density only under assumptions that break when fishing concentrates on high-density areas or when gear improves — and a study of harvest data in a terrestrial species found the relationship between catch per unit effort and abundance varied with the population trajectory, with R² ranging from 0.29 to 0.73. So even a well-run test would need care about what it treats as truth.

We have not run this test on BiteScore. We are describing it in public because a forecast that cannot say how it would be proven wrong is not making a claim at all — it is making an impression.