Analyzing your spotting statistics: what your sightings reveal about types, airlines and airports

At some point almost every spotter reaches the moment: the logbook has a few hundred entries, and the question comes up of how many types, airlines and airports have actually piled up. If you want to analyze your spotting statistics, you quickly notice that the answer depends less on the calculation than on the rules before it. This guide shows you how to count types, airlines, airports and first sightings cleanly, which key figures really help you on your next tour, and where statistics typically fall apart.

Spotting statistics: the short answer

A spotting statistic is the analysis of your own sightings using fixed counting rules and a few key figures. It answers three questions: how complete is my collection, where is my time best spent, and what am I still missing? You already have the raw data for it if you note date, time, airport, spot, registration, type and airline for each sighting. How such a logbook is built is explained in the guide Documenting sightings with a logbook.

The crux is not the math – almost everything is simple counting and division. The crux is definitions. Is an A321neo with the long-range option a type of its own? Does an aircraft on a wet lease count for the airline on the fuselage or the one on the boarding pass? Is an airport “collected” if you only flew over it on approach? If you settle this once, you have statistics that stay comparable over the years.

Set counting rules before you calculate

Types: family, variant or ICAO code?

For aircraft types there are three sensible levels. The coarsest is the family – “A320 family” or “737.” The finest is the sub-variant with all differences in engine, weight or cabin layout. In between is the ICAO type designator, which ICAO assigns in its Document 8643 for aircraft types: two to four characters, such as A20N for the Airbus A320neo, B38M for the Boeing 737 MAX 8 or B77W for the Boeing 777-300ER.

For most spotters the ICAO code is the best counting unit. It distinguishes the variants you can usually recognize from the outside, without listing every certification variant individually. And it is written unambiguously – important as soon as you filter or sort. How the designators are built is explained in the article ICAO type designators explained.

Airlines: brand, operator or code?

With airlines the situation is trickier. On the apron you meet brands with several operations, regional carriers flying on behalf of big network airlines, and aircraft leased out together with their crews to other carriers. The most robust rule: count the carrier that operates the aircraft – recognizable by the ICAO airline code in the flight plan and usually by the paint scheme. If you also record which airline a flight was operated for, you lose no information.

For groups like the Lufthansa Group, a deliberate decision is worthwhile: Lufthansa, Lufthansa CityLine and Eurowings are separate carriers. Whether you count them separately or as a group is a matter of taste, but it should apply the same way across your entire collection. You’ll find more ideas on drawing the line under Collecting airlines.

Airports: visited or seen?

For airports there are three variants that often get mixed up: airports where you spotted, airports where you were a passenger, and airports you only know as the origin or destination of flights you saw. For a spotting statistic, the first group counts first and foremost. The second belongs more in a flight log, as described in the guide Documenting your own flights. As a notation, four-letter ICAO airport codes are a good option – EDDF for Frankfurt Airport, EDDM for Munich Airport – or the better-known three-letter IATA codes. All that matters is that you stick to one variant.

Level	Recommended counting unit	Typical stumbling block	Pragmatic rule
Type	ICAO type designator (e.g., A20N)	Family and variant mixed	One type = one ICAO code
Airline	operating carrier	Wet lease, group brands	Count the operator, note the customer
Registration	Registration like D-AIxx	Re-registration of the same airframe	Count registrations, use the MSN as a link
Airport	Place where you spotted	Passenger and spotting visits mixed	Separate columns for both roles

The six key figures for your spotting statistics

With fixed counting rules, the key figures become a matter of diligence. These six have proven themselves:

1. Total sightings: every single observation. The number looks impressive, but on its own says little.
2. Distinct registrations: how many different aircraft you have seen. That is the core of a registration collection.
3. First-sighting rate: new registrations divided by all sightings of a session. It shows whether a spot still brings you anything new.
4. Type coverage: types seen divided by the types on a clearly defined list, such as an airline’s fleet.
5. Airline distribution: what share of your sightings goes to the biggest airlines. At a hub, the home airline usually dominates.
6. Yield per airport or spot: first sightings per visit or per hour on site.

First sightings: the most exciting number

First sightings are the salt of statistics. Count them separately on three levels: new registration, new type, new airline. A day with five new registrations but no new type is a different day from one with a single sighting that brings a new type and a new airline at once. How rare a find actually is can be put in context with the guide How rare is my sighting?

If you plot a year’s first sightings month by month, a typical curve emerges: at the beginning it rises steeply because almost everything is new, later it flattens. That is not a sign of waning motivation, but a sign that your regular spots are exhausted. That is exactly when new airports, other times of day or targeted hunts for individual aircraft pay off.

Calculate your first-sighting rate yourself

Coverage: numbers need a denominator

“I’ve seen 37 types” is a nice number, but not progress – it lacks a reference. Only with a denominator does it become a key figure: all the types of an airline, all registrations of a particular fleet, all airlines that regularly serve an airport. Divide the entries you’ve seen by the size of that list, and you have a coverage rate with a real endpoint.

Where do you get the denominator? For fleets, fleet databases are an option; the article Fleet databases at a glance gives an overview. How to tick off an airline’s fleet registration by registration is shown in the guide Completing entire fleets. For types overall, your own target list is a good idea, as described under Collecting all aircraft types.

Finding patterns: when and where your time pays off

Once there is enough data, it tells you more than just totals. Three analyses bring particularly much:

• Time of day: Sort your first sightings by time windows. At many airports long-haul aircraft arrive in waves, freight and special flights often at off-peak times. Your own data shows which windows really deliver at your spots.
• Runway direction in use: If you also note the active landing direction, you later see which spot works with which direction. That helps enormously when choosing between two locations, such as at the Munich Airport visitors’ hill.
• Season: The summer and winter schedules bring different airlines and types. Leisure airlines dominate in summer, some long-haul routes run only seasonally. A year-over-year comparison shows when an airport is especially worthwhile for you.

You can often close gaps in your own logbook after the fact: with flight tracker history and ADS-B data you add times and flight numbers to sightings where you only noted the registration. How that works is explained in Matching sightings with ADS-B.

Typical mistakes that distort your statistics

• Inconsistent spellings. “A321neo,” “A321N” and “A21N” are three different types to a spreadsheet. Commit to one form and correct old entries once.
• Overlooking re-registrations. An aircraft that moves from one airline to the next often gets a new registration. For a registration collection that is a first sighting, for an airframe collection it isn’t. The serial number links both entries; background is provided by Re-registrations and MSN, c/n and line number.
• Duplicate entries. If you add the same aircraft from a photo and from a notepad, you count it twice. Same registration, same day and same spot are a clear signal to check.
• Counting other people’s sightings. Reports from groups and forums belong at most in a field of their own, otherwise your statistics count aircraft you never saw.
• Leaving out zero-result sessions. Visits without a first sighting belong in the logbook too. If you only record good days, you consider every spot more productive than it is.
• Counting special liveries twice. A special livery is neither a new type nor a new aircraft. Better to keep it as a separate list – the guide Collecting and documenting special liveries offers suggestions.

From statistics to your next target

The real value of statistics is the gap list. It comes together in a few steps:

From this you can derive goals that motivate instead of stress: “All types of the home airline this year” has an endpoint, “as many sightings as possible” does not. The article Spotting goals and challenges collects more ideas. And if you also count your spotting locations, the guide Documenting visited airports and spots helps.

Paper, spreadsheet or app?

Mathematically, a spreadsheet is enough: count functions, filters and pivot tables cover all the key figures described here. You’ll find a ready-made structure in the guide Spotting logbook in Excel or Google Sheets. The bigger the collection gets, though, the more important consistent drop-down lists for types and airlines, automatic timestamps and the assignment to airport and spot become. Which variant suits you is compared in the article Notebook, spreadsheet or app.

Conclusion

Analyzing your own spotting statistics doesn’t start with formulas, but with decisions: what is a type, an airline, an airport to you? With fixed counting rules, six key figures become a tool, above all the first-sighting rate and the coverage of a clearly defined list. They show you when a spot is exhausted, which time windows deliver and what you’re still missing. In the end, the biggest gain is a short, concrete gap list for your next tour.