We exploit the feasibility of predictive modeling combined with the support given by a suitably defined IoT Cloud Infrastructure in the attempt of assessing and reporting relative performances for user-specific settings during a bike trial. The matter is addressed by introducing a suitable dynamical system whose state variables are the so-called origin-destination (OD) flow deviations obtained from prior estimates based on historical data recorded by means of mobile sensors directly installed in each bike through a fast real-time processing of big traffic data. We then use the Kalman filter theory in order to dynamically update an assignment matrix in such a context and gain information about usual routes and distances. This leads us to a dynamical ranking system for the users of the bike trial community making the award procedure more transparent.