In basketball, measures of individual player performance provide critical guidance for a broad spectrum of decisions related to training and game strategy. However, most studies on this topic focus on performance level measurement, neglecting other important factors, such as performance variability. Here we model shooting performance variability by using Markov switching models, assuming the existence of two alternating performance regimes related to the positive or negative synergies that specific combinations of players may create on the court. The main goal of this analysis is to investigate the relationships between each player's performance variability and team line-up composition by assuming shot-varying transition probabilities between regimes. Relationships between pairs of players are then visualized in a network graph, highlighting positive and negative interactions between teammates. On the basis of these interactions, we build a score for the line-ups, which we show correlates with the line-up's shooting performance. This confirms that interactions between teammates detected by the Markov switching model directly affect team performance, which is information that would be enormously useful to coaches when deciding which players should play together.
Markov switching modelling of shooting performance variability and teammate interactions in basketball
Sandri M.;Zuccolotto P.;Manisera M.
2020-01-01
Abstract
In basketball, measures of individual player performance provide critical guidance for a broad spectrum of decisions related to training and game strategy. However, most studies on this topic focus on performance level measurement, neglecting other important factors, such as performance variability. Here we model shooting performance variability by using Markov switching models, assuming the existence of two alternating performance regimes related to the positive or negative synergies that specific combinations of players may create on the court. The main goal of this analysis is to investigate the relationships between each player's performance variability and team line-up composition by assuming shot-varying transition probabilities between regimes. Relationships between pairs of players are then visualized in a network graph, highlighting positive and negative interactions between teammates. On the basis of these interactions, we build a score for the line-ups, which we show correlates with the line-up's shooting performance. This confirms that interactions between teammates detected by the Markov switching model directly affect team performance, which is information that would be enormously useful to coaches when deciding which players should play together.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
