In recent years there has been a big breakout in sport modelling due to the improvement of machine learning. There are more than 400.000 play-by-play data for one season, which is appropriate for modelling of basketball match with neural networks. Basketball match is modelled with two steps. First is the forecast of the next event, second is the forecast of time between two events. As input the algorithm receives all previous events, the match context and
characteristics of the teams and returns the probability distribution for the next event. The latter is then sampled and returns the next event. Then we build a chain of events and we get every event of the match with which we can calculate how many times each event is repeated. Time between two events is modelled with homogeneous model. It samples the next time based on the current time. In our experiments the best model was the one that used rating SRS and four factors as input for team's characteristics. We checked the logic of the vector embedding with algorithm TSNE. We found out that the algorithm finds some knowledge for teams because it finds at least one connection with number of points or some other attribute. Based on previous research, the betting shop is the best for forecasting basketball victories.
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