The aim of this study was to explore the ranking model TrueSkill™ developed by Microsoft, applying it on various sports and constructing extensions to the model. Two different inference methods for TrueSkill was constructed using Gibbs sampling and message passing. Additionally, the sequential method using Gibbs sampling was successfully extended into a batch method, in order to eliminate game order dependency and creating a fairer, although computationally heavier, ranking system. All methods were further implemented with extensions for taking home team advantage, score difference and finally a combination of the two into consideration. The methods were applied on football (Premier League), ice hockey (NHL), and tennis (ATP Tour) and evaluated on the accuracy of their predictions before each game. On football, the extensions improved the prediction accuracy from 55.79% to 58.95% for the sequential methods, while the vanilla Gibbs batch method reached the accuracy of 57.37%. Altogether, the extensions improved the performance of the vanilla methods when applied on all data sets. The home team advantage performed better than the score difference on both football and ice hockey, while the combination of the two reached the highest accuracy. The Gibbs batch method had the highest prediction accuracy on the vanilla model for all sports. The results of this study imply that TrueSkill could be considered a useful ranking model for other sports as well, especially if tuned and implemented with extensions suitable for the particular sport.