Hey everyone! It’s been a long time since I asked for help to my bachelor project. After many months, the project has finally been written, defended, and graded. I could have put it up earlier, but there were a lot of other things I had to attend to first. I might also have forgotten it for a brief moment. :P
I decided to publish my project, however in a slightly different version than the one I handed in. The project itself ended up with top grade, however remember that the project was not graded from a Pokémon player’s perspective, so maybe I missed out something in the paper (seen from a players point of view), but the math should be correct.
The paper is quite extensive for an article and therefore I placed the abstract here and then for those of you, who would like to give it a closer look, you are welcome to download the PDF.
Much attention was given to the method of estimation and still there was a lot more there could be done. Even though the results are stated in numbers, we are still only able to conclude in which direction the effect of a factor would have and if it was weak or strong. This you can read a lot more about in the paper itself.
You can all skip chapter 1, since it would just be boring for most of you. I strongly believe that people here know the game; that chapter only contains introductory stuff.
Chapter 2 considers the data collection and would be nice to read. Going on to chapter 3, it would start to contain a lot of math and would be quite hard for many to read and understand. Don’t worry; the important stuff is contained in chapter 4-6, which certainly is worth reading.
An important notice. This paper was based on the previous season and might not seem worth anything for now, but remember that this model can just be transformed and used in an updated version for a new season and thereby tells us some interesting things.
The estimation method is still valid, but as I indicated in the paper, it can still be improved, which may be done in the future. I’m just glad that I could get the chance to work with it and analyse the game I really like to play.
I hope that you all will enjoy reading the paper. :)
The strategic card game known as Pokémon Trading Card is a dynamic game where a lot of different factors can influence the outcome of a game. This paper investigates what affects a Pokémon TCG player’s win-rate besides luck, with respect to the season 2010-2011. In order to do so a cross sectional dataset containing 84 individuals was collected along with 7 interviews.
Techniques from econometrics are applied in other to determine the effect of the different factors on a player’s win-rate. In a situation where the dependent variable is a coding of qualitative outcome, a probability model is applied to maintain the familiar type of regression. The dependent variable could then be linked to a list of factors, each of them with a different impact on the probability for a higher or lower win-rate.
The model chosen is the logit model due to its mathematical convenience and the fact that the posterior distribution is a continuous probability function which then holds with the theory of probability models. To take account for problems with heteroscedasticity a weighted least-squares logistic regression for grouped data, known as glogit, has been used to estimate the data.
Results revealed a positive effect of experience, playing decks containing a SP-engine and being a Pokémon professor. Ageing proves to have a negative effect on a player’s win-rate. There is no difference whether a player is from the USA or not. Also having family members playing, having a job or playing abroad during the season showed no significant influence on the win-rate.
View the rest: (Removed at the Request of the Author)