Back in August of 2010, I posted about a variety of gaming terms based on Google Trend data. While its not quite 4 years later (closer to 3.5), let’s see what those terms are doing today.
These are the same terms, with the addition of some new terms (noted below), which I suspected would interesting. Nothing has really changed over the course of a few years. Pretty much anything gaming related is sloping down. Of course, this is trend data based on search terms and there are many, many alternate ways people find materials today; even more than there were four years ago with the explosion of social media.
I also added a few terms based on systems that I hear about on a regular basis — Labyrinth Lord, Pathfinder, and Dungeon Crawl Classics. Virtual Table Top was a new entry as well, since it has been popular the last couple of years. (I tried LOTFP but no trend data was available). There are many others but I chose that small set. Pathfinder and Labyrinth Lord bucked the down and to the right trend. PF is gaining audience based on search popularity and LL is holding near flat. DCC appears to have the same general trend as gaming in general. VTT has a curious cliff at the end of the chart. People appear to be searching for a specific client rather the generic term — namely Roll20. The Roll20 trend line is awesomely upwards.
The Original Terms
Overall, the original terms are pretty much what one would expect. Almost everything gaming is down and to the right. Porn is holding strong but seems to have hit a wall of late. Farmville had its moment in the spotlight but lost its luster quickly. It will be interesting to track the downfall along with the “D&D” and “Dungeons and Dragons” terms. I suspect it will crater at some point in the future; a fate I do not foresee with the D&D brand. Cat videos are still amazingly popular. I have no idea why.
I am a bit sad that the donkey videos are flat-lining. Cats are more popular than ever but donkeys just don’t get enough love. Perhaps they are too closely associated with jack asses for the modern politically correct aesthetic.
Well, I was. Decided it was time to whip up a new generator. I’ve been meaning to do one for boats and ships. The original intention was to do something chaotic. Instead, I ended up with around 85000 unique ship names from the mostly modern age. The names range from small boats, yachts, and ships. Some names are older, most are quite modern. The generator is available here.
Someday in the future I might do what I originally intended. Until then, time to slack once again.
Wow! The Namespace: Wild West Names volume is now a Copper best seller on DriveThru. Woot! Honestly, I never expected to hit any level of sales metrics on the Namespace volumes. The western names is far more comprehensive than most western name supplements. Still, its a Pay-What-You-Want product. I believe that is a testament to contents.
Thanks to everyone who has purchased the product beyond a mere download. Your purchases are much appreciated. For those of you have purchased, I’d love a few more reviews if you have the time.
RPG Kickstarter projects are such a mixed bag. Generally, those with products at or nearly complete deliver a few months late. Frankly, even the best organized projects are delayed by weeks or a few months. Rarely one actually shows up on time. Welcome to Mortiston, USA: An All American Zombie Apocalypse by Scrying Eye Games is not one of the rare or uncommon ones. The delivery is now 15 months beyond the original delivery date of June 2012. To be fair, I did get the PDF of the supplement around the due date even though I already HAD a copy of the PDF.
What I haven’t seen is the softcover, the hardcover, or the miscellaneous perk items (Extended Electronic Package supplements, minis, postcards, or additional map sets). I received absolutely nothing beyond what I could have gotten without backing the kickstarter project. Backing this particular Kickstarter project was foolish. In hindsight, the amount raised was never going to cover the costs of the promised materials.
Where it all went wrong based on sporadic updates… (in no particular order)
- From the start
- Printer problems
- More printer problems… but oh yeah order a softcopy off our website
- Cover problems with the printer
- Woops, burned all that cash going to conventions
- Oh shit, its over budget.
- Hang on, we are working on another solution (meanwhile we’ll issue a different product release on our website)
- Personal Illness
- Holy shit, businesses have to PAY TAXES?!??
I grew tired of re-reading the various updates. When you are digging a year into your email archives to figure out what’s happening, you know the project is going nowhere fast. Taking an overall look, it is pretty obvious that the common mistakes were made by a small publisher — a) underestimate costs, b) over promise on non-core rewards, c) fail to manage the core deliveries and likely d) burn through raised funding on other affairs not related to the project.
I do not expect to see anything beyond the original PDF. At a tad less than $3500 worth of funding, getting a softcover out the door was speculative. A hard cover a bit beyond speculative. The stretch goals were absurdly beyond reach for the funding. I back projects for the core gaming material not the extra oddities.
Three bad conventions trashed our budget, both for the business and personally. In three weeks we’ll be doing the taxes for the year. We’re using our own money to get the printing done and shipped out.
It might be time to punt. Use your own money to refund what you cannot deliver. In fact, it is probably cheaper to issue a refund than it is to continue down the path of futility.
We aren’t planning on doing any more Kickstarter’s. I’ll admit it makes me feel better to know larger companies like Steve Jackson Games and Reaper can have such major problems, too. The Kickstarter process just takes away too much time from everything else we should be doing.
Uhm, say what? It’s hard to issue an update every few weeks about some delay or another? What exactly is your billable time worth? If it is so valuable, I’d suggest going back to that, earning some cash, and refunding the funding population across the board. As for not doing anymore kickstarter projects, I commend you for not double dipping into the well. No matter if it was incompetence, ignorance or extremely bad luck, you have proven you cannot deliver on a project. I’d gladly blackball any new project you launch.
As promised, everyone will get everything. When we hit that “SUBMIT” button, we agreed to do that, and we will. Very late, but we’re still going to get everything out. Since we got burned for the first batch of Kickstarter edition books, the costs have gone up and so has the shipping. International shipping has almost doubled for some areas. The costs of the special edition books and shipping now totals to just over 300% of the initial budget.
Shipping ALWAYS goes up. I doubt it went up 300% in a year. You planned poorly. As for getting burned by a printer, it happens. Most businesses vet partners before engaging in business with them. There are multitudes of honorable printers in the world. You chose poorly.
You should just acknowledge failure on the project and return the money. Money you likely already spent but if, as you stated, you can spend out of pocket, it will be far cheaper than trying to deliver a product. Most businesses make poor choices on one occasion or another. Businesses that stay in business do not continue to throw good money after bad. Especially when they have spent other peoples money.
Either way, Scrying Eye Games is dead to me.
I rolled out a new edition of the City Name Generator with a dozen new countries included in the database. The new countries are: Sri Lanka, Sudan, Syria, Tanzania, Tunisia, Uganda, Ukraine, Uzbekistan, the West Bank, Yemen, Zambia, and Zimbabwe. A fair number of the new entries are not in English. Instead, I continue to increase the number of city names spelled in the native tongue.
The most common request for the City Name Generator is for Country X. If I am missing a country you’d like to see included, send me a comment and I’ll see what I can do. If the data I have available doesn’t include at least a thousand locations, I’m not likely to include it. Find me an awesome data source for that country and I’ll do my best to make it happen.
The majority of my data sets are from a public United States entity. Those data sources are biased toward Americanized names for many locales. Which leads to the second complaint — these names are not part of my country. Often the names are small villages and towns. I think the smaller names are the most interesting given than most people can easily look at a large scale map and find the bigger cities. These complaints are often accurate — the americanized name variant is unlikely to match native names and are apt to cross across the bounds of modern countries into other neighboring countries. Ignore what doesn’t fit or let me know why it is in error.
The city and town name generator is one of the best available today for actual town and village names around the world. I strive to make it the best.
I’m a full fledged old school gamer so I haven’t used much of the newer mechanisms for encounter balancing. Still, the underlying math attempts to approximate an a metric for dominance levels of creature groups in a constrained environment.
The Challenge Rating (CR)
This is fuzzy math to determine how one creature may measure up against either another average party level and/or another creature. I stumbled on Kaww’s post describing Vorpal Tribble’s approach for determining CR of a creature on the fly.
#1. Divide creature’s average HP by 4.5 to 6.5.
4.5 for 5 HD or lower, 5 for 6-10 HD, 5.5 for 11-15 HD, 6 for 16-20 HD., 6.5 for 20-25 HD.
#2. Add 1 for each five points above 10 its AC is, subtracting 1 for every 5 below.
#3. Add 1 for each special attack (+2 to +5 or more if its got a decent number of spells in its spell-like abilities).
#4. Add 1 for each quality unless you deem it worthy of more. Add 1 for each resistance and 10 points of DR it has, and 2 for each immunity. Subtract 1 for each vulnerability.
#5. Add 1 for every two bonus feats it has.
#6. Divide total by 3. This should be its rough CR
I have a couple of issues with this. The first (#1) is the segmentation of the average HP calculation. While dividing by HD might be more elegant, simplicity is key so I will just use the average in the middle of 5.5.
Most of the games I play use descending not ascending AC so just invert #2. #3 is judgement call so to automate it, I’ll just use the raw number of special attacks. #4 will just use the number of special abilities. Designating one ability over another is complicated so roughly speaking they will just all be the same. My systems of choice have no feats so it will just be ignored. Others may choose to include it.
The simplified calculation is:
CR = (HP / 5.5) + 1 * (#Special Attacks) + 1 * (#Special Abilities)
This ignores AC for now. I will need to source a table that gives some indicator of descending to ascending armor class comparisons. Given the descending nature, I think one bonus CR per 3 points of AC below 10 (9) would work reasonably at first glance. So we could add:
CR += abs(10 – AC) % 3
Then divide it all by 3 per the original calculation.
CR /= 3
Apply this on a per creature basis then average for the group of creatures. This will give a baseline CR for the group. So now there is a rough CR for the creature(s) as a group. Couple that with the number appearing and you can begin to resolve the CR relationship.
CR is Non-Linear
Based on the explanations I’ve read, CR is a non-linear relationship. There is little explanation that determines how 1n CR = 2 CR(n-2) or 1n CR = 4 CR (n-4). There is probably an obvious calculation this non math guy is missing.
My buddy, Keith, came to rescue with a simple approximation
#Appearing * (CR^2) = Dominance Level (DL)
Dominance Level is similar to encounter level in some contexts. Both break down at the extreme of disparate levels. As a rough approximation, it allows for large numbers of lower level creatures to exist within the space of a far more dominant monster.
It is not a perfect metric as Keith explains:
For the moment, though, I would suggest when considering dominance use an exponential-based relationship. The square of the HD would be dead easy, but in D&D 3.x the CR is perhaps a better measure, and one creature of CR n is roughly equated to four creatures of CR n-4 (and two creatures of CR n-2).
By this math 8 CR 3 creatures might be considered roughly equivalent Dominance to a single CR 9 creature (and nowhere near the dominance of a CR 24 creature, 8*3=24 notwithstanding).
Squaring and adding, on the other hand, would give 8*3*3 = 72, while a single CR 9 creature would be 1*9*9 = 81… not so far off, really. For even lower-level creatures you might see 9 CR 1 = 9*1*1 = 9 vs. a single CR 3 = 1*3*3 = 9.
I’m not sure which works better. Nine CR 1 creatures against a single CR 7 creature can be a kind of even fight in the right circumstances (focused fire has some happy effects), but it’ll be touch and go and the CR 1 guys can expect a lot of casualties, possibly TPK. This suggests to me that a lower-CR creature could effectively hold dominance (the single CR 3 creature might be enough to balance them ‘socially’ — equal Dominance score). They could probably beat him if it came to a fight, but would it be worth the trouble and the possibly (likely) casualties?
DL is not the end-all-be-all metric. It is one of many drivers within a simulated social network driven dungeon. The overall relationships including friendship will determine if one group tries to overtake another. On one hand, if you used it without consequence, the dominant groups would overrun the dungeon. However, those same groups are aligned with other groups or perhaps co-exist so they have no desire to overrun less powerful creatures wholesale.
Random thoughts come with random generators. Early in the week, I was cleaning up a bunch of files from prior programs at work and a good part of it was relationship graph code that was targeted at a program which ended prematurely. Arriving at home, I decided to do a functional check of various random generators. One of those was the monster stocker. As I was testing and looking at the raw output, it struck me that rather than having to manually figure out how the monsters fit in a dungeon it would be cool to build up a relationship model. Keith over at In My Campaign has written about Node Based Megadungeon Design. He also happens to be on G+ so I tossed the idea at him last night. His input is intermixed with my own thoughts and I’ve tried to designate his insight appropriately.
Generating random affiliations is a complicated problem. Ad hoc language is easy to describe verbally — the bugbears in areas #2, #7, and #9 have subjugated the kobolds in #3, #5 and #6. Coding that is significantly more difficult. However, if you simplify the relationship into a few definable terms, one can begin to construct the nature of the affiliations between groups. From there, you begin to place those creatures in actual locations in the dungeon. For now, I am going to ignore physical space and just consider building up a rational model of the creatures within a dungeon.
Step 1 – Plausible Population
Assuming a map is available and general metric of the power rank of the creatures, it is simple enough to generate random population of plausible creatures. For simplicity, I will start with a single level dungeon with a single entrance into the world. Say 30 rooms with about 2/3rds of them populated with one creature or another. Just generating 20 creatures is unlikely to be an interesting population. Instead, pick out your random table of choice and do 3-6 times that number of creatures. Massive list, right?
Make it more interesting. Add in a fractional percentage of higher level creatures. Say we did 3X the number of rooms to start. Add in 1/2X creatures from the next level of power. 1/4 from the 2+ the starting point and perhaps 1/8th from 3+. So we end up with a starting population of 90 plausible rank 1 creatures, 15 rank 2, 7 rank 3, and 3 rank 4+. All told, there are 115 possible creatures for a mere 30 rooms of which only 20 will be actively occupied.
Step 2 – Density
Now we have a major pile of creatures that need to be pruned downwards. Nothing gets culled at this stage. Instead, the target population is evaluated for occurrence of the same beasts. This should not be a simple count of Creature Y showed up Z times. Evaluation should consider the number of creatures that could plausibly appear for each room / encounter. So if kobolds show up 3 times in the initial population but 12 of them can occur each time, the kobold density metric is 3*12=36 rather than 3. Thirty six creatures vs a singular purple worm that showed up from one of the upper level tables is significantly different.
Sum up the total creatures plausible then calculate possible density (total # / total possible). The value is between 0 and 1. Honestly, its slightly above 0 and 1 should hopefully never show up in a rational random table.
Step 3 – Segmentation
Next up, divide the population into two groups — actors and outliers. The actors are the top 10% (or whatever cutoff you choose) of the density metric. The rest are not dominant players in the dungeon but may be present. The group of creatures with the highest density is the starting target of the “lair” monster. Assign every creature an appropriate boolean actor flag.
Step 4 – Starting Relationships
Initial seeding of group dynamics can take many forms. Keith suggested a seeding metric based on alignment, Friendship. I considered one based on hit dice, Dominance. For alignment, depending on your preference, you either have 3 or 9 different models. On the ease of use stand point, use Law/Neutral/Chaos. Those with the same alignment get a 1 for friendship, 0 for one step removed and a -1 for opposing views. Granularity for the 9 could be added but I’m keeping the idea as simple as possible.
For Dominance, just look at HD vs HD at a group level. Nine 1-HD creatures can be treated at the 9 HD metric vs something with 3 HD. This is a rough metric; certainly not useful for with something with special abilities and/or other powers. I think a better metric might be using XP per creature as the relationship value.
Both Friendship and and Dominance are a 1-to-1 relationship. Every creature has a friendship and dominance model to every other one.
I’m out of time for the evening. This is a rough, raw look at how I’d begin to assemble a population in code. The outliers of the population need more significant relationships to the actors. Then cycle the “life” of the dungeon a few times to erode/enhance friendship and dominance. Sprinkle in a few more relationship metrics. Rinse, repeat, or deviate. I cannot predict where the idea is going to flow.