Being able to predict the outcome of a game is useful in many aspects. Such as,to aid designers in the process of understanding how the game is played by theplayers, as well as how to be able to balance the elements within the game aretwo of those aspects. If one could predict the outcome of games with certaintythe design process could possibly be evolved into more of an experiment basedapproach where one can observe cause and effect to some degree. It has previouslybeen shown that it is possible to predict outcomes of games to varying degrees ofsuccess. However, there is a lack of research which compares and evaluates severaldifferent models on the same domain with common aims. To narrow this identifiedgap an experiment is conducted to compare and analyze seven different classifierswithin the same domain. The classifiers are then ranked on accuracy against eachother with help of appropriate statistical methods. The classifiers compete onthe task of predicting which team will win or lose in a match of the game BloodBowl 2. For nuance three different datasets are made for the models to be trainedon. While the results vary between the models of the various datasets the general consensus has an identifiable pattern of rejections. The results also indicatea strong accuracy for Support Vector Machine and Logistic Regression across allthe datasets.

<p>AMR as a cause of graft rejection has been long recognized and the presence of pre formed antibodies against donor HLA is a risk factor for increased graft rejection. FlowXM is the current clinical gold standard for detecting harmful DSA in the recipients and a positive FlowXM is considered a strong contraindication to transplantation. However, newer techniques such as SAB provide with a highly sensitive and specific method for DSA detection that is unattainable by FlowXM. But due to the intrinsic limitations associated with SAB assays, the clinical relevance of DSA detected on SAB has been highly disputable. Therefore, the overall aim of this study was to investigate the utility of SAB in predicting harmful DSA levels, by establishing a fluorescence range on SAB that correlated to positive FlowXM. This was done by retrospectively testing the highest serum dilutions on FlowPRA SAB that produced positive B or T cell FlowXM from 15 variably sensitized patients. Thus, a very narrow MFI range on SAB was established, for B and T cells separately, that correlated to positive FlowXM. On B cells this correlate ranged from 2780-7772 MFI (Mean MFI =5641), whereas T cell range was 1089-6731 (Mean MFI= 3226). In order to test these ranges for prediction of positive FlowXM, B and T cell FlowXM tests were carried out using various serum/cell combinations. DSA MFI of >3000 on SAB resulted in a significantly higher T cell positive FlowXM (p</p> / Master of Science (MSc)