Rhythmic information plays an important role in Music Information Retrieval. Example applications include automatically annotating large databases by genre, meter, ballroom dance style or tempo, fully automated D.J.-ing, and audio segmentation for further retrieval tasks such as automatic chord labeling. In this article, we therefore provide an introductory overview over basic and current principles of tempo detection. Subsequently, we show how to improve on these by inclusion of ballroom dance style recognition. We introduce a feature set of 82 rhythmic features for rhythm analysis on real audio. With this set, data-driven identification of the meter and ballroom dance style, employing support vector machines, is carried out in a first step. Next, this information is used to more robustly detect tempo. We evaluate the suggested method on a large public database containing 1.8 k titles of standard and Latin ballroom dance music. Following extensive test runs, a clear boost in performance can be reported.