Check back later today, or tomorrow, for updates on the day’s events.

Woohoo!  Hunting season is here.

I had been looking for some sort of software that would track this data, and allow me to analyze it in some form.  I know there are a few pieces of software out there that do this, but the price was a little steep, and with the newborn on the way, I just didn’t want to part with that kind of cash.

My cousin Matthew and I were talking the other night about this topic, and he pointed me in a great direction.  After doing numerous Google searches he found a product call Deer Days.

Deer Days is a software package that does all of the things I want to do:  It allows you to input hunting data, analyze that data, and even has a Trend Analyzer that allows you to see certain trends in your data, and allow you to use them to your advantage.  With this Trend Analyzer you can look at the data that is available to you for today’s hunt, i.e., wind, barometer, weather, temperature, and use that data to best decide where to hunt for the day’s conditions.

I know this all seems a little anal, but if you read stories from a lot of successful hunters, plenty of them keep some sort of notebook about their previous hunts and use that information to best decide where to hunt next.  It only makes sense, and I can totally understand the benefit of being able to analyze this data.

I just started using this software, so I don’t want to say one way or another on how well it works, but I definitely think-from what I’ve seen so far-that it has some great potential to benefit all the hunters out there.  Plus, the basic package of the software is free.  Then, if you want to use of the other features, you only have to pay $20 to use the software to its full potential.

For now, I’m going to use the free software until I get a decent amount of information into the database.  Then, I am going to invest in the other features, and see just how well this software does work.  I think it has great potential, and I’m excited to get started in gathering all the data.

Because gathering data, means you have to be hunting.  What could be better than that?

An interesting piece of software, and I promise to keep everyone informed on how well it works out for me.  The potential is there, so I will keep everyone informed.

Many hunters keep a log of their hunts, and I know that Arthur is one of them. He is always posting when he hunts along with many of the details of the day such as what the temperature was and the number of deer he saw from which stand.I think keeping track of our hunts is a great idea, and I have kept a journal on and off over the years because there is so much more information in the details of a log book besides just precious outdoor memories. A log’s details will often indicate how you can hunt your property better as well as how to be more successful in less time. And while I have not been consistent with my log book entries lately, I thought I would show what all can be pulled from such a record. (Hopefully it will also remind me of the value of consistently logging my hunts so that I get back to it).    

Four years ago, I recorded every detail of every hunt that my friend Will and I had on our two leases totaling 310 acres in the lowcountry of South Carolina. Between us, we hunted 97 times totaling 284 hours, and we saw 149 deer. This averaged out to 3 hours per hunt with 1.54 deer seen per hunt and 0.52 deer seen per hour. We also killed 12 deer over those 97 hunts with 10 being bucks and 2 being does. This worked out to 8 hunts per deer killed, 9.7 hunts per buck killed and 48.5 hunts per doe killed. The biggest buck was my 4.5 year old 8 point that weighed in at 165 pounds while the smallest was a yearling spike that weighed 110 pounds. The farthest shot made on a deer was 250 yards and the shortest was 30 yards. Will and I also each missed one deer.

We saw 84 deer over 20 hunts during a first quarter moon, 15 deer over six hunts during a full moon, 14 deer over three hunts during a last quarter moon, and 36 deer over 13 hunts during the new moon. That means that we only saw deer on 42 of our 97 hunts, so 57% of the time we saw NOTHING. But what else does this tell us about hunting different moons? Well, we averaged seeing about 4 deer per hunt on the first quarter, about 2 deer per hunt on a full moon, 3 deer per sit on a last quarter hunt and almost 3 deer per sit during the new moon. This shows us that hunting during the first quarter moon should produce more sightings than during any of the other three phases. It also helps us to see that while not a total loss, hunting during a full moon should produce 50% less deer sightings than hunting during the first quarter and about 25% less deer sightings if we hunted a new moon or last quarter. Therefore if we are able to choose when to hunt and all other things are the same (hunting pressure, weather, food, etc), we would be better off hunting during the first quarter.

As far as which stands were better, we would have to break down how many deer we saw from each of our 10 stands to figure that out:

• * 27 deer from 4 hunts at the big field back stand = 6.75 deer per sit
• *  7 deer from 3 hunts at the honey hole stand = 2.33 deer per sit
• * 12 deer from 5 hunts at the first barrel stand = 2.4 deer per sit
• *  10 deer from 5 hunts the second barrel stand = 2 deer per sit
• *  1 deer from 1 hunt at the old tripod stand = 1 deer per sit
• *  10 deer from 7 hunts at the middle stand = 1.42 deer per sit
• *  49 deer from 9 hunts at the soybean field front stand = 5.44 deer per sit
• *  16 deer from 5 hunts at the Snooks stand = 3.2 deer per sit
• *  10 deer from 2 hunts at the wood pile stand = 5 deer per sit
• * 1 deer from 1 hunt at the new tripod stand = 1 deer per sit

This data tells me that the big field back stand and the wood pile should allow us to see the most deer per hunt while the least deer per hunt from the new tripod, old tripod and the middle stand. As for what the barometric pressure told us about our time spent hunting, it said that a steady pressure was the best. I say this because we only saw deer during 8 sits with a falling barometer and no deer during sits on a rising barometer. However we saw deer on 34 sits while it was steady. So, according to the data, we should be hunting the big field’s back stand or the wood pile during a first quarter moon with a steady barometer to see the most deer and have the best chance at killing one! Great, now let’s go put one in the bed of the truck…

Unfortunately this is where an incomplete log can hurt you and show that sometimes simple numbers will not tell the whole story. Smart hunters will then increase the amount of information they record as well as use their own experience and knowledge to add to the culled data’s usefulness. For instance, the deer-seen-per-sit figures should mean that I am wasting my time hunting the middle stand compared to the big field’s back stand (6.75 deer per sit). However that is until you realize that our biggest buck of the season came from the middle stand, the one that only produced 1.42 deer sightings per sit. The numbers also don’t tell you that the big field back stand produced all of its deer sightings early in the year when the planted soybeans were the deer’s preferred food, meaning a hunter will be wasting a lot of time later in the year once the deer abandon the beans for acorns and corn. And frankly the percentages of deer seen per sit and on which moon could be better since my program did not include all of the hunts where no deer were spotted. Nor did my program compute how many deer were seen during the different barometric pressures, just that deer were either seen or not seen. And frankly the numbers of hunting days invested to kill a deer do not tell the whole story since we passed on many deer, bucks and does included.

However while our numbers could be better, I truly hope that this look at one year from my log book encourages everyone to keep or start one. I know that I am looking forward to having another season’s worth of opportunities to track my hunting to see how much more information I can pull from the numbers.

A sincere thanks to Arthur for allowing me to write a guest post!