Maturity-based correction mechanism for talent identification: When is it needed, does it work, and does it help to better predict who will make it to the pros?

Table 3. Four-step correction mechanism and example (three fictitious players with same raw test score but different maturity status).

Procedure	Example
Initialization					Fictitious player 1: Late maturer	Fictitious player 2: Average maturer	Fictitious player 3: Early maturer
	Variable 1	Indicator of maturity status (MS)	Use MS of all the players	MS = %RAH	MS = 85%	MS = 95%	MS = 100%
	Variable 2	Raw test score (RS)	Use RS of all the players	RS = 40-meter sprint	RS = 6.20 sec	RS = 6.20 sec	RS = 6.20 sec
			Calculate MS_Mean (sample mean)	MS_Mean = 95.4%
Correction mechanism	Step 1	Compute a simple linear regression with MS as independent variable and RS as dependent variable and save the regression coefficients	b₀	b₀ = 13.74
	Step 1		b₁	b₁ = –0.08
	Step 2	Compute the expected raw score (ERS) of each player	ERS = b₀ + b₁ · MS_Player	ERS = 13.74–0.08 · MS_Player	ERS = 13.74 – 0.08 · 85 = 6.74 sec	ERS = 13.74 – 0.08 · 95 = 5.91 sec	ERS = 13.74 – 0.08 · 100 = 5.50 sec
	Step 2	Compute the expected score of the average maturing player (ESA)	ESA = b₀ + b₁ · MS_Mean	ESA = 13.74–0.08 · 95.40 = 5.88 sec
	Step 3	Compute a correction factor (CF) for each player	CF = RS / ERS		CF = 6.20 / 6.74 = 0.92	CF = 6.20 / 5.91 = 1.05	CF = 6.20 / 5.50 = 1.13
	Step 4	Compute the corrected score (CS) for each player	CS = ESA · CF		CS = 5.88 · 0.9 = 5.41 sec	CS = 5.88 · 1.05 = 6.17 sec	CS = 5.88 · 1.13 = 6.63 sec

MS = maturity status; RS = raw score; ERS = expected raw score; ESA = expected score of the average maturing player; CF = correction factor; CS = corrected score; %RAH = attained percentage of the real adult height.