Perfect World Entertainment released the latest trailer for Blacklight: Retribution, the upcoming F2P FPS from Zombie Studios. The short trailer, seen above, breaks down the beta process by everyone’s favorite subject, math. Even if number munching isn’t your cup of tea, it’s pretty astounding how much action the beta process has seen in a few short weeks.
14 million minutes played
280 million shots fired
13 million agents K.I.A
494 million combat points spent
04 03 12
Zombie should have taken it a step further. It’s even more interesting is when you analyze these numbers against each other.
26.64 years of human productivity gone. Conversely, 26.64 years of hand-eye coordination training for post-apocalyptic survivors.
21.54 shots per kill. Marines we are not.
0.33 shots per second. Trigger happy, we are.
1.76 combat points earned per shot or…
35.29 combat points per minute player.
April 3, 2012 – I already took vacation.
Simple take away; shoot more for more CP!
Blacklight: Retribution will be launching on April 3, 2012.
One of the design goals of StarCraft II‘s multiplayer is to match players with opponents such that they win 50% of their games on average. The system does this by matching players very close in MMR (matchmaking rating); the closer the MMRs, the better. Players identical in skill level should often have close and exciting games, a recipe for fun! The details of the search algorithm are not known, but it seems that it begins by searching for players in a a very small range around your MMR and slowly expands it (perhaps the “expanding search?”) (and probably in a logarithmic range as opposed to linear) as time searching progresses. Once players are found, they are put into a game.
MMR itself is never displayed and as such the details of its calculation are very hard to gleam. Assuming its similar to Elo, the system will increase a player’s MMR by more if they defeat a relatively more skilled opponent and decrease it by less if they lose to a more skilled opponent. The Elo-style math is a bit messy, but essentially the algorithm calculates a percentage chance that each player will win based on the players’ MMRs. The chance scales logarithmically with their differences; a 400 point rating difference might means A has a ten times greater chance of winning than B, eg, about a 90% chance to win. If a player wins, the system increases their MMR by the chance that they would lose times some system-wide constant, K, and does the opposite for their opponent. For example, if K is set to 12 and player A has a 90% chance of winning against B, and A does indeed win, his rating would increase by only .1*12=1.2, while B’s rating would decrease by the same. If B won, his rating would increase by .9*12=10.8 and A‘s would decrease by the same amount. The system does not punish B very much for having to play A, but rewards him significantly if he does well. Continue Reading
Starcraft 2 has one of the most robust systems for ranking players of all skill levels and giving them fair games and a sense of progression. Blizzard doesn’t want to reveal too much detail about the way the rankings work as to prevent gaming of the system, and as such, a lot of speculation has caused a lot of misunderstanding among the player base. Still, we know that the ladder system is based around a few core principles and we have a lot of information that can be pieced together, so let’s examine the inner workings of the ladder system.
First, let’s tackle something a lot of people don’t know: the only thing that affects your ranking is winning or losing. Any good rating system for a competitive game must operate in this way. If it was based on how long the match takes, actions-per-minute (APM), or other factors, players could easily inflate those numbers to artificially increase their rating. Like ELO, the chess rating system on which Blizzard’s rating systems are based, each player has an associated number representing their skill level. In addition, there is an important second statistic called volatility that represents the algorithm’s confidence that your matchmaking rating (MMR) is accurate. This innovation allows players to be ranked very quickly and jump right into a range close to their skill. In other systems, like Warcraft III and even WoW’s arena system (to a degree), players must start at the bottom and work their way upward. The Starcraft 2 system prevents “n00b stomping” by creating new accounts and other abuses.
If you’ve tried the Starcraft 2 multiplayer, you are familiar with the process of acquiring a ranking. It asks you if you first want to play some practice games, which are played on a slower speed with “no rush” rules enforced. It is unclear if this data is incorporated into your early MMR, but I suspect that it is, as there is no drawback to starting players with an accurate rating. You then play 5 “placement” matches, where it chooses opponents from across the spectrum to try to get a general idea of your MMR. Keep in mind that it is only looking at win/loss data, and there are a lot of variables that go into deciding victory in a Starcraft 2 game. As such, with just five data points, the MMR it approximates for you is going to have a very high volatility.
Massively multiplayer games have a lot of opportunity for self-expression. You can customize your character’s race, their role in group play, and what you do with them day-to-day. There is massive potential for sociology and social psychology if you can somehow aggregate this kind of data. Second Life has been the poster-child for academic studies, but it really just doesn’t have the user base of World of Warcraft. Blizzard doesn’t release any of these type of statistics, but there are some services and people who are attempting to gather and analyze this type of data.
Warcraft Realms is one of the premier sites for contributing and retrieving population data and statistics. Players install an addon, then upload their data to the site to be integrated into the database. It’s unclear how accurate the data is, but it seems to be in-line with my experiences, such as the Alliance-Horde ratio on my server, class ratios, etc.
Gay-nerds.com recently posted a quick analysis of global statistics versus the statistics of Taint, the largest guild of The Spreading Taint family of guilds. Taint is a social LGBT-friendly (lesbian, gay, bisexual, and transgender) guild with open invitations; all LGBT-friendly people are welcome to join. Taint alone has 5,000 characters, and 2,493 were recorded on Warcraft Realms at the time of their study. This is a very large data set, making it excellent for analysis; Taint is largely accepted as the second largest World of Warcraft guild. Additionally, most of the members of Taint are LGBT, a significant difference from the overall player population. What differences did they find? Click-through for more. Continue Reading
Game theory is a field of mathematics that “attempts to mathematically capture behavior in strategic situations, in which an individual’s success in making choices depends on the choices of others” (Wikipedia). It’s a relatively simple model for making optimal decisions and predicting the behavior of others. A lot of times while playing, I often wonder why players behave the way they do and whether they make the best decisions. For example, I hear all the time in Arathi Basin that if we currently control 3 nodes, we should just defend them, as we only need 3 to win. However, in Warsong, once we cap the flag once, I don’t hear any suggestions that we should just defend until time runs out. These seem like very similar strategies, and I’m curious if they are truly similar and good strategies. As such, I’ve decided to explore how game theory can be applied to World of Warcraft, beginning with battlegrounds.
First, lets get some game theory basics out of the way. In game theory, the principle object we are dealing with is games. The “game” we are talking about here is not World of Warcraft, but rather a meta-game, such as deciding which node to attack in Eye of the Storm. Anything that we consider a “game” needs three components:
“Players“, which is an decision-making entity. It could be all 80 players in a full Alterac Valley, or it could be the 2 teams in Warsong Gulch.
“Strategies“, which are choices for each player to make. Each player must have the ability to make decisions, even if they are bad decisions. Decisions could be sending 11 players to the flag in Eye of the Storm if our player is a team, or walking into the enemy’s flag room is our player is a single person.
“Payoffs“, or the ultimate rewards (or repercussions) for your strategies subject to all other player’s strategies. For example, heading to the center of Eye of the Storm and attempting to capture the flag could have several payoffs: earning a flag capture worth perhaps 100 points if enough players decide to help you, having no net effect if enough players from each faction are in the center, or wasting your time and possibly losing a node if the enemy attacks your bases.
That last description is a little vague, so lets jump into how we actually analyse something and get into examples. Continue Reading