Quantifying and modelling the game speed outputs of English Championship soccer players

Figures & data

Figure 1. Game-speed Power-Law model fitted to all observations. Data reported as mean ± standard deviation.

Figure 2. Game speed Power-Law models fitted to the Home only & Away only game observations. Data reported as mean ± standard deviation.

Table 1. MANOVA: Pillai Test

Cases	df	Approx. F	Trace Pillai	Num df	Den df	p
(Intercept)	1	1675.016	0.993	10	121.000	< .001
Game	1	1.378	0.102	10	121.000	0.199
Residuals	130

Table 2. RMANOVA Between Subjects Effects

Cases	Sum of Squares	df	Mean Square	F	p	ω2
Player Position	66,252.609	4	16,563.152	15.380	< .001	0.093
Residuals	119,540.043	111	1076.937

Table 3. Post Hoc Comparisons – Player Position

				95% CI for Mean Difference
		Mean Difference		Lower	Upper	SE	t	Cohen’s d	p
CB	CF	−12.120		−22.090	−2.150	3.481	−3.482	−0.323	0.007	**
	CM	−18.497		−27.784	−9.210	3.242	−5.705	−0.530	< .001	***
	FB	−11.357		−19.996	−2.717	3.016	−3.765	−0.350	0.003	**
	WM	1.041		−7.539	9.622	2.996	0.348	0.032	1.000
CF	CM	−6.377		−16.241	3.487	3.444	−1.852	−0.172	0.667
	FB	0.763		−8.493	10.020	3.232	0.236	0.022	1.000
	WM	13.162		3.960	22.363	3.213	4.097	0.380	< .001	***
CM	FB	7.140		−1.376	15.657	2.974	2.401	0.223	0.180
	WM	19.539		11.082	27.996	2.953	6.617	0.614	< .001	***
FB	WM	12.398		4.658	20.139	2.702	4.588	0.426	< .001	***

Notes: *p < .05, **p < .01, ***p < .001

Figure 3. Mean ± 95% CI game-speed output per player positional group over 1–10 minute moving average periods.