How DeerLab Patterns Bucks

How DeerLab Patterns Bucks

Behind the scenes, DeerLab crunches data for millions of trail camera photos. For every photo, we extract the timestamp, which in turn lets us pull numerous weather data points from your local weather station, calculate the appropriate moon phase, etc. This data provides an immense amount of benefits and is very valuable when understanding activity patterns for specific bucks.

Unlike other trail camera apps out there that just give data for individual photos, DeerLab takes several factors into account. Why? Not doing so would have you sitting in the wrong stand and the wrong time.

Counting Individual Trail Cam Photos Skews Results

To highlight this issue, let’s use a basic scenario using two trail cameras.

Camera A is set on a trail, while Camera B is set over a food plot. Roscoe P. Coltrane, a six-year-old buck we are trying to pattern, is captured once on Camera A and thirty times while he’s chewing on some turnips in front of Camera B. All of this took place within a 15-minute time frame.

Over the course of the week, Roscoe is captured 5 more times on Camera A, located on the trail, but he never heads over to the food plot where Camera B is.

Patterning bucks with trail camera photos

If the method used is to count every photo, the stats would lean heavily (and incorrectly) on Camera B since all thirty photos of Roscoe took place during a 15-minute time frame. Camera A had only six photos of him, but he showed up there six different times compared to only once at the food plot.

On a side note, this also takes into account what camera settings you have. If you have one camera on hyper burst mode with another one on its regular settings, what cam would be the most popular? Counting all photos of the cam on hyper burst would win, but it doesn't mean that's the most active trail camera.

DeerLab Automatically Groups Photos

To correct this deficiency in providing accurate results, DeerLab automatically groups photos in 15-minute intervals. No matter how many photos a camera takes of a buck in 15 minutes, we now count that as “1” sighting. So in the scenario above, Camera A is the most active camera. With this, you can upload all your photos without worrying that one camera will be unevenly weighted compared to other cameras.

DeerLab still displays all your photos, but it also includes the number of sightings calculated, as seen in a profile below.

Grouping trail camera photos

How Sightings are Calculated for Profiles

As previously mentioned, we group photos every 15 minutes. If a buck is captured multiple times in a 15-minute period, he will be counted only once, even though we will still provide you with the total number of photos captured. Let’s say a buck comes to a food plot, and your camera captures 35 photos of him over a 47-minute time frame (from 7:00 am to 7:47 am). In this scenario, DeerLab will count a total of 4 sightings. One between 7:00 am and 7:15, one between 7:15 and 7:30, between 7:30 and 7:45, and one between 7:45 and 8 am.

Animal Profile Activity

No matter what reports you're looking at, all data points are based on one sighting per 15 minutes instead of individual photos. You’ll notice stats like the one below when you select an animal profile.

Profile of animal showing sightings vs the number of photos.

Final Thoughts

We believe automatically grouping photos by time frames sets DeerLab apart from everyone else and dramatically improves how stats are reported. Our goal has been to give actionable insights, and we’re really excited that this not only dramatically improves your chances of choosing the right stand and time frame but it lays the foundation for some exciting new features we are working on.

If you have any questions, please feel free to connect with our live chat. If we are out, we will make sure to get back as soon as possible.

Jon Livingston
Jon Livingston
Co-founder, DeerLab
Receive DeerLab's Hunting Strategy Newsletter
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form. Please try again.