We present new upper and lower bounds on the number of learner mistakes in the 'transductive' online learning setting of Ben-David, Kushilevitz and Mansour (1997). This setting is similar to standard online learning, except that the adversary fixes a sequence of instances x_1,...,x_n to be labeled at the start of the game, and this sequence is known to the learner. Qualitatively, we prove a _trichotomy_, stating the the asymptotic minimal number of mistakes made by the learner as n grows can take only one of precisely three possible values: n, Θ(log (n)), or Θ(1). Furthermore, this behavior is determined by a combination of the VC dimension and the Littlestone dimension. Quantitatively, we show a variety of bounds relating the number of mistakes to well-known combinatorial dimensions. In particular, we improve the known lower bound on the constant in the Θ(1) case from Ω(√(log(d))) to Ω(log(d)) where d is the Littlestone dimension. Finally, we extend our results to cover multiclass classification and partial concept classes.