Table 3. Multiple linear regression analysis for annual variation in competition performance (AVCP), number of races per year (races), and age.

Mean values of the last two years prior to the 2018EC were used for the regression model.

	Regression model					Regression coefficients
	Entries	R square	f square	F value	P value		Beta	T value	P value
All strokes and distances	589	0.22	0.28	F(3,585) = 54	P < 0.001	AVCP	-0.04	T = -1	P = 0.310
						Races	-0.19	T = -5	P < 0.001
						Age	-0.39	T = -10	P < 0.001
Butterfly	95	0.29	0.41	F(3,91) = 13	P < 0.001	AVCP	-0.01	T = 0	P = 0.891
						Races	-0.29	T = -3	P = 0.003
						Age	-0.37	T = -4	P < 0.001
Backstroke	116	0.46	0.85	F(3,112) = 31	P < 0.001	AVCP	0.00	T = 0	P = 0.952
						Races	-0.36	T = -5	P < 0.001
						Age	-0.49	T = -7	P < 0.001
Breaststroke	119	0.25	0.33	F(3,115) = 13	P < 0.001	AVCP	0.01	T = 0	P = 0.865
						Races	-0.29	T = -4	P < 0.001
						Age	-0.41	T = -5	P < 0.001
Freestyle	149	0.36	0.56	F(3,145) = 27	P < 0.001	AVCP	-0.10	T = -2	P = 0.134
						Races	-0.36	T = -5	P < 0.001
						Age	-0.41	T = -6	P < 0.001
Individual medley	26	0.43	0.75	F(3,22) = 6	P = 0.005	AVCP	-0.24	T = -1	P = 0.164
						Races	-0.19	T = -1	P = 0.278
						Age	-0.55	T = -3	P = 0.003
50m	52	0.43	0.75	F(3,48) = 12	P < 0.001	AVCP	-0.04	T = 0	P = 0.758
						Races	-0.40	T = -3	P = 0.002
						Age	-0.43	T = -4	P < 0.001
100m	50	0.34	0.52	F(3,46) = 8	P < 0.001	AVCP	-0.15	T = -1	P = 0.231
						Races	-0.36	T = -3	P = 0.005
						Age	-0.40	T = -3	P = 0.002
200m	47	0.35	0.54	F(3,43) = 8	P < 0.001	AVCP	-0.05	T = 0	P = 0.675
						Races	-0.29	T = -2	P = 0.027
						Age	-0.47	T = -4	P < 0.001
400m	30	0.49	0.96	F(3,26) = 8	P < 0.001	AVCP	-0.12	T = -1	P = 0.422
						Races	-0.38	T = -3	P = 0.014
						Age	-0.61	T = -4	P < 0.001
800m	18	0.34	0.52	F(3,14) = 2	P = 0.107	AVCP	-0.20	T = -1	P = 0.384
						Races	-0.12	T = -1	P = 0.590
						Age	-0.58	T = -3	P = 0.020
1500m	15	0.50	1.00	F(3,11) = 4	P = 0.049	AVCP	-0.10	T = 0	P = 0.662
						Races	-0.39	T = -2	P = 0.112
						Age	-0.62	T = -3	P = 0.016