Win-stay lose-shift strategy in formation changes in football

EPJ Data Science

Table 3 Results of the GLMM analysis when all the independent variables were considered

Data set	Variable	Coefficient	SE	p -value
J-League	\(\mathrm{Win}_{t-1}\)	−0.299	0.124	0.016
	\(\mathrm{Win}_{t-2}\)	−0.204	0.116	0.079
	\(\mathrm{Loss}_{t-1}\)	0.387	0.120	0.001
	\(\mathrm{Loss}_{t-2}\)	0.146	0.126	0.248
	\(\mathrm{Win}_{t-1} \times \mathrm{Win}_{t-2}\)	0.005	0.182	0.979
	\(\mathrm{Loss}_{t-1} \times\mathrm{Loss}_{t-2}\)	0.023	0.164	0.888
	Home	−0.062	0.072	0.392
	Strength	−0.192	0.202	0.343
Bundesliga	\(\mathrm{Win}_{t-1}\)	−0.207	0.040	<0.001
	\(\mathrm{Win}_{t-2}\)	−0.117	0.039	0.003
	\(\mathrm{Loss}_{t-1}\)	0.136	0.039	<0.001
	\(\mathrm{Loss}_{t-2}\)	0.007	0.040	0.867
	\(\mathrm{Win}_{t-1} \times\mathrm{Win}_{t-2}\)	0.025	0.059	0.676
	\(\mathrm{Loss}_{t-1} \times\mathrm{ Loss}_{t-2}\)	0.108	0.060	0.072
	Home	−0.118	0.027	<0.001
	Strength	−0.530	0.082	<0.001

\(\mathrm{Win}_{t-i}\) (i = 1,2) is the binary variable representing whether or not the team has won the (t − i)th match (0: no win, 1: win). Likewise for \(\mathrm{Loss}_{t-i}\) (i = 1,2) (0: no loss, 1: loss). Home is equal to 0 for an away game and 1 for a home game. Strength is equal to the fraction of matches that the team has won in a season. SE: standard error.