Modelling team performance in soccer using tactical features derived from position tracking data

Decision-makers in soccer routinely assess the tactical behaviour of a team and its opponents both during and after the game to optimize performance. Currently, this assessment is typically driven by notational analysis and observation. Therefore, potential high-impact decisions are often made based on limited or even biased information. With the current study, we aimed to quantitatively assess tactical performance by abstracting a set of spatiotemporal features from the general offensive principles of play in soccer using position tracking data, and to train a machine learning classifier to predict match outcome based on these features computed over the full game as well as only parts of the game. Based on the results of these analyses, we describe a proof of concept of a decision support system for coaches and managers. In an analysis of 302 professional Dutch Eredivisie matches, we were able to train a Linear Discriminant Analysis model to predict match outcome with fair to good (74.1%) accuracy with features computed over the full match, and 67.9% accuracy with features computed over only 1/4th of the match. We therefore conclude that using only position tracking data, we can provide valuable feedback to coaches about how their team is executing the various principles of play, and how these principles are contributing to overall performance.

Statistical models to predict discharge overflow

In the paper, a comparison of prediction results concerning the annual number of discharges of stormwater from the drainage system due to stormwater overflows is depicted. The prediction has been computed by means of storm water management model (SWMM) and probabilistic models. Regarding the probabilistic modelling some simple statistical models such as logit, probit, Gompertz and linear discriminant analysis model have been applied, and as for the hydrodynamic modelling a generator of synthetic rainfall based on the Monte Carlo method has been used. The analyses conducted has shown that logit, probit and Gompertz models give outputs that are comparable with the results of hydrodynamic modelling and are concordant with observations. Whereas the annual number of stormwater discharge predicted by the linear discriminant analysis model is significantly lower than the number obtained by hydrodynamic modelling. The calculations made have confirmed the possibility of using statistical models as an alternative for developing labour-consuming and complex hydrodynamic models. The statistical models can be used successfully to predict the stormwater overflows operation provided that the measurements of rainfall in the catchment and of filling the overflow are available.