Spotting Statistics: What Your Own Sightings Reveal About You

Updated: August 2026 – Spotting statistics are more than a number under your profile: they show you which spot is worth your time, which classes you’re still missing, and what time of day you’re actually most successful. This guide explains the five metrics that really matter – and how to turn them into goals you can actually reach.

What’s the point of spotting statistics anyway?

Spotting statistics are the analysis of your own sightings using fixed metrics – and their purpose isn’t showing off, it’s deciding. They answer three practical questions: where is your time best spent, what are you still missing, and when are you actually most successful?

The appeal lies in the fact that these answers regularly contradict gut feeling. Almost every spotter overrates their favourite spot and underrates time windows that seem unremarkable. The precondition, though, is a clean data basis – what that looks like is described in the guide to keeping a sightings logbook with a fixed field structure.

The five metrics that really count

All of the following figures can be calculated from the standard fields date, time, location and vehicle. So you don’t need any extra data, just consistent recording.

Metric	How to calculate it	What it tells you	Common misreading
Hit rate	Sightings ÷ hours on site	How productive a location is per unit of time	Counting the journey there and unfairly comparing spots
Coverage	Vehicles seen ÷ vehicles possible	How complete your collection is	Meaningless without a defined denominator
Repeat rate	Known ÷ all sightings	Whether a spot is exhausted for you	Overrating short-term fluctuations
Time-of-day pattern	Sightings per hourly window	When you realistically see the most	Reading a single outlier day as the rule
Per-spot yield	Sightings per location	Which places pay off long-term	Judging new spots after a single visit

Hit rate: the most honest figure

The hit rate is the only metric that puts effort and return in relation to each other. It explains why a location with a hundred sightings in a day can be worse than one with thirty: if the first one cost eight hours and the second one cost one. Count only the time actually spent on site, or the figure will systematically favour nearby spots.

Coverage: the only true measure of completeness

Coverage needs a denominator – a list of what’s possible in the first place. That can be your local operator’s fleet, the classes on a line, or every type in a vehicle family. Without that denominator, any figure is arbitrary. With it, you immediately get a to-do list: everything that’s missing.

Repeat rate: the signal to move on

This metric measures the share of vehicles you already know. At the start it’s low, because everything is new. Once it rises consistently above roughly 80 percent, that simply means: this spot is now showing you what you already know. Then a new location gets you further than more time at the old one. The overview of the best stations for trainspotting in Germany sorts out which stations suit this.

Work out your own coverage

From a number to a better trip

Metrics are only worth something if they change your next decision. This mapping has proven itself in practice:

• Low hit rate, low repeat rate: the spot is quiet, but productive for new finds. Ideal for targeted sessions with a clear wanted vehicle.
• High hit rate, high repeat rate: a good place to practise photo technique, but no longer adding to the collection.
• High hit rate, low repeat rate: the ideal case. Test this location across different time windows.
• A weak time-of-day pattern outside peak hours: shift sessions rather than staying longer – duration doesn’t replace the right time window.
• Individual spots with no return visits: check whether light, access or sightlines were the reason before you write the location off.

Factoring live data into your planning shifts the hit rate noticeably upward, because less time is wasted waiting. The guide to live sightings and real-time sources shows how to read such sources correctly, complemented by the practical section in the overview of trainspotter live and train radar.

The analysis rhythm: how often, how deep

Statistics become annoying if you look at them too often, and useless if you look at them too rarely. In practice a three-tier rhythm has proven itself, costing just a few minutes a month.

After every session you record just two figures: time on site and number of sightings. Nothing more. These two values are the basis for the hit rate and can never be reconstructed later, because nobody remembers weeks on how long they really stood at the spot. That’s exactly why the “duration” field is the most commonly forgotten yet most valuable addition to a logbook.

Once a month you run three analyses: hit rate per location, the share of new vehicles, and the distribution of your sightings across the hours of the day. This level answers the question of whether your current routine still holds up. A typical finding after a few months: a spot you visit out of habit has delivered almost nothing but known vehicles for weeks, while a rarely visited location brings an above-average amount that’s new.

Once a quarter you look at coverage and adjust your goals. It’s also worth looking at what you never see at all: vehicle types that run in your region but never cross your path are usually tied to time windows or line sections you don’t currently cover. These gaps are the real payoff of statistics – they produce the most concrete goals for the coming weeks.

A note on the honesty of the data: unsuccessful sessions absolutely belong in the logbook. Anyone who only records days with sightings produces a statistic in which every spot looks great. Only the blank sessions show which locations actually deliver reliably and which just stayed in fond memory. The same goes for weather and day of the week: two extra fields you fill in in two seconds later explain a substantial share of the variation.

Setting goals that actually move you forward

A goal works when you can say at any moment how far along you are. That’s exactly where most spotting resolutions fail: “photograph more” or “lots of sightings” have no end point.

This is exactly where the playful side comes in: points and badges are ready-made analyses of the same fields and give you a progress indicator without you having to do any maths yourself. The guides to the trainspotter game with points and badges, the public transport spotter game and the tram spotter game describe how the principle is built.

What to watch for in your analysis

• Too small a sample. Under four weeks, every pattern is chance. A single strong afternoon proves nothing about a time window.
• Missing denominator. Coverage with no defined target set isn’t a metric, it’s just a number.
• The journey counted in the hit rate. Counting travel time systematically disadvantages every more distant spot.
• Counting other people’s reports. Sightings taken over from groups and forums need to be recorded separately, or you’re measuring someone else’s success.
• Recording only successes. Sessions with no sighting must go in the logbook too, or every spot looks better than it is.
• Ignoring cost. Anyone travelling long distances should factor in the effort involved. The guide what does trainspotting as a hobby cost? gives some perspective.
• Using statistics against people. Numbers are a tool, not a ranking system – in the trainspotting community, reliability counts for more than any counter.

Conclusion

Spotting statistics turn a collection into a decision-making aid. Five metrics are entirely enough for that, and all five come from fields you’re recording anyway. The real payoff isn’t in the numbers themselves, but in the corrections they trigger: a different time window, a different location, a shorter gap list than you thought. And if a metric starts taking the fun out of it for you, that’s a good reason to ignore it – not to optimise it. The overview of the trainspotter app with a sightings feature shows how to back all of this up technically.