![Visual representation of the example regarding the selection procedure of talented U12 players (N = 100,000). A = wrongfully rejected (false negatives); B = rightfully accepted; C = rightfully rejected; D = wrongfully accepted (false positives). B/(B + D) = sensitivity, whereas C/(C + A) = specificity
Adapted from Taylor and Russell [108], with permission](https://www.researchgate.net/publication/333581089/figure/fig1/AS:963436372570112@1606712539166/Visual-representation-of-the-example-regarding-the-selection-procedure-of-talented-U12_Q320.jpg)
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POSITION STATEMENT
Methodological Issues inSoccer Talent Identication Research
TomL.G.Bergkamp1 · A.SusanM.Niessen1· Ruud.J.R.denHartigh2· WouterG.P.Frencken3,4· RobR.Meijer1
Published online: 3 June 2019
© The Author(s) 2019
Abstract
Talent identification research in soccer comprises the prediction of elite soccer performance. While many studies in this field
have aimed to empirically relate performance characteristics to subsequent soccer success, a critical evaluation of the meth-
odology of these studies has mostly been absent in the literature. In this position paper, we discuss advantages and limitations
of the design, validity, and utility of current soccer talent identification research. Specifically, we draw on principles from
selection psychology that can contribute to best practices in the context of making selection decisions across domains. Based
on an extensive search of the soccer literature, we identify four methodological issues from this framework that are relevant
for talent identification research, i.e. (1) the operationalization of criterion variables (the performance to be predicted) as
performance levels; (2) the focus on isolated performance indicators as predictors of soccer performance; (3) the effects of
range restriction on the predictive validity of predictors used in talent identification; and (4) the effect of the base rate on the
utility of talent identification procedures. Based on these four issues, we highlight opportunities and challenges for future
soccer talent identification studies that may contribute to developing evidence-based selection procedures. We suggest for
future research to consider the use of individual soccer criterion measures, to adopt representative, high-fidelity predictors
of soccer performance, and to take restriction of range and the base rate into account.
* Tom L. G. Bergkamp
T.L.G.Bergkamp@rug.nl
1 Department ofPsychometrics andStatistics, Faculty
ofBehavioral andSocial Sciences, University ofGroningen,
Grote Kruisstraat 2/1, 9712TSGroningen, TheNetherlands
2 Department ofDevelopmental Psychology, Faculty
ofBehavioral andSocial Sciences, University ofGroningen,
Grote Kruisstraat 2/1, 9712TSGroningen, TheNetherlands
3 Center forHuman Movement Sciences, University
ofGroningen, University Medical Center Groningen,
Hanzeplein 1, 9713GZGroningen, TheNetherlands
4 Football Club Groningen, Groningen, TheNetherlands
Key Points
A broad selection of soccer talent identification stud-
ies are considered and their methodology, in terms of
design, validity, and utility, is evaluated.
Four major methodological limitations are identified and
discussed: the use of performance levels as the criterion;
the focus on components as predictors of soccer perfor-
mance; the influence of restriction of range on the gener-
alization of findings; and the impact on the base rate on
the utility of talent identification procedures.
To increase the robustness of its research practices, we
propose that future soccer talent identification studies
should adopt more individual soccer performance out-
comes, high-fidelity predictors, where possible correct
for range restriction, and take the base rate into account.
1 Introduction
Sports organizations invest substantial resources in the
search for players who have the potential to excel. These
identification programs are aimed at detecting talented play-
ers who demonstrate strong performance in sport-specific
abilities that are predictive of future career success [1–3].
Typically, these players are selected and recruited for spe-
cialized development programs that provide the appropriate
learning conditions, facilities, equipment, and staff to realize
the players’ potential [4, 5].
Historically, talent identification programs are associ-
ated with the subjective evaluation of players’ potential by
coaches and scouts, who base their criteria primarily on
personal taste, knowledge, and experience [6, 7]. However,
in the last few decades, there has been an increasing inter-
est in complementing these subjective assessments with
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1318 T.L.G.Bergkamp et al.
evidence-based talent identification procedures, in order to
increase the probability of selecting successful players. As
a result, talent research has seen the integration of multidi-
mensional and comprehensive models that detail prerequi-
sites and predictors of successful adult performance [1, 8, 9],
as well as a plethora of studies that have aimed to estimate
the empirical relationships between these predictors and per-
formance criteria in different sports.
Predicting future sports performance is inherently mul-
tifaceted and complex. Players’ developmental trajectories
are rarely linear because cognitive and motor skills are
intertwined and develop through dynamic interactions with
the individual athlete’s performance environment [10–14].
Several recently published systematic reviews have aimed to
summarize the empirical evidence for factors that may deter-
mine elite sports performance in general [15, 16], and in
specific domains such as soccer [17–19]. Results from these
studies indicate that various physical, technical, tactical, and
psychological factors contribute to determining individual
sport-specific success. However, due to the considerable
variation in study designs, findings across individual talent
identification studies are inconsistent and difficult to com-
pare [15, 18, 20, 21], and therefore there is no clear set of
variables that uniformly predict skill level [15, 22].
Still, a major aim in the field of sport sciences is to
apply best-practice talent identification methods, that is,
methods that allow for valid predictions of players’ future
performance. To date, various articles have been published
discussing scientific or ethical challenges that hinder the
possibilities of identifying talents [16, 22–24], such as the
definition of the concept of talent [24], the influence of
maturation on performance [7], and the difficulties of early
selection and early prediction of adult performance based
on knowledge of how (physical) performance characteristics
develop [2, 13, 25, 26]. Furthermore, several papers have
discussed methodological and design features of talent iden-
tification studies [18, 19, 22]. However, we observed that
reflections of methodological issues specifically relevant for
research on predictors and criteria used for selection pur-
poses are scarce in the talent identification literature. Criti-
cal reflections on these issues are important for providing
insight into how research results should be interpreted, and
to provide guidelines for researchers in employing best prac-
tices from a methodological point of view.
The aim of this position paper is to provide an over-
view of the talent identification literature and discuss some
methodological issues that we consider particularly relevant
in the context of selection. More specifically, we discuss
methodological considerations commonly addressed in
psychological research on selection (further referred to as
selection psychology) regarding determinants of predic-
tive validity, utility, and interpretability of assessment and
selection procedures. Selection psychology is concerned
with how to best select candidates for different achievement
domains [12, 27, 28]. It provides psychometric and statisti-
cal tools for measuring human traits, skills, abilities, and
performance, and defines theoretical principles that affect
the relationship between a (set of) predictor(s) and a cri-
terion. While research in selection psychology has mostly
focused on selecting candidates for jobs, its psychometric
and statistical considerations are relevant for a wide range
of performance and expertise contexts that involve selection,
including higher education [12, 29, 30] and sports [12, 31].
Based on the selection psychology framework, we discuss
four methodological topics that are relevant for talent identifi-
cation research in soccer.1 Furthermore, we offer suggestions
based on these topics that can improve the design of future
talent identification studies and can contribute to the develop-
ment of evidence-based talent identification practices. These
topics are (1) the operationalization of criterion variables (the
performance to be predicted); (2) the fidelity of the perfor-
mance indicators used as predictors; (3) the effects of range
restriction on the predictive validity of predictors used in
talent identification; and (4) the effect of the base rate on
the utility of talent identification procedures. Some of these
issues have been briefly touched upon previously in the con-
text of talent identification in sports [8, 22, 24, 32], but they
are rarely thoroughly addressed (for an exception on some
issues, see Ackerman [33]). Moreover, since these issues are
not explicitly and specifically accounted for, we consider an
in-depth evaluation valuable for advancing the field.
Because the aim of this article is to relate some specific
methodological principles that are relevant in research on
selection, and thereby for talent identification in soccer, we
do not discuss analytic and design-related issues that have
been discussed previously. Examples are the use of step-
wise model selection methods [34, 35], presenting explora-
tory results as confirmatory findings [36, 37], the absence
of cross-validation, issues related to multiple testing [38],
and the use of small sample sizes, which are issues that are
relevant across various scientific disciplines.
2 Methodological Issues
2.1 Operationalizing theCriterion
Talent identification in soccer involves the measurement of
skills and abilities [1, 2, 22] that are related to an indicator of
1 We chose to focus our discussion on the domain of soccer because
most published studies on talent identification are focused on this
sport, and talent identification procedures across sports are difficult to
compare [15, 20]. However, our discussion can also be translated to
other specific domains of open-skilled sports.
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1319
Methodological Issues in Soccer Talent Identification
soccer performance (the criterion). This criterion is ideally
measured in the future (predictive validity), but is sometimes
measured at the same time (concurrent validity). In our view,
the talent identification literature has largely neglected to pay
attention to the operationalization of criterion variables that
provide information about the differences between players in
terms of soccer performance after selection [39]. More spe-
cifically, an explicit measure of soccer performance is rarely
used as a criterion. Instead, the criterion used in most studies
is the selection decision itself, which is usually a categorical
variable indicating performance or skill level. Examples of
performance-level indicators that have been used in studies
are elite, sub-elite, and non-elite level [40–42]; professional,
semi-professional, or non-professional level [43–45]; first
team or reserves [46]; elite, club level, or dropouts [47, 48];
national or regional level [49–51]; selected and non-selected
players [52–55]; and nationally drafted or non-drafted play-
ers [56] (see Table1).
The operationalization of soccer performance as per-
formance level is appropriate if a talent researcher wants
to understand factors that distinguish players perceived as
talented from those perceived as ‘less talented’ [52, 57].
Furthermore, the use of performance level as a criterion
measure makes sense from a practical perspective because
measuring individual soccer performance objectively is dif-
ficult [58]. In contrast to individual sports such as track and
field and swimming, there is no definite measure of an indi-
vidual’s performance in an open-skilled sport such as soccer
[3]. Therefore, researchers may use performance level as a
practical instrument that is expected to represent an indi-
rect measure of the players’ general soccer performance as
assessed by coaches and scouts, who typically evaluate play-
ers over an extended time period and take multidisciplinary
performance factors into account [6, 59].
While using performance level as a criterion measure is
understandable from a pragmatic point of view, it also car-
ries some problems. First, this approach provides limited
information on the individual differences between players
[60, 61] on the actual outcome of interest, i.e. soccer perfor-
mance in 11-a-side games [9]. We believe that the ultimate
aim of soccer talent identification research is to predict indi-
vidual soccer performance as a function of performance in
talent identification procedures, not selection as a function
of performance in talent identification procedures [39, 62].
Thus, talent identification procedures should strive to predict
how players will perform, relative to others, but research
designs that adopt a performance-level criterion implicitly
assume that all players within a performance level perform
equally well. As a result of this operationalization, the pre-
dictive value of talent predictors is often investigated using
statistical analyses based on mean differences between the
selected and non-selected players (mostly through the use of
t-tests or [multivariate] analysis of variance; see Figueiredo
etal. [47], Lago-Penas etal. [63], and le Gall etal. [64]).
Although these statistical analyses can contribute to discov-
ering relevant predictors for talent identification research to
some extent, these designs cannot determine the value of
different combinations of performance factors in predicting
an outcome variable indicative of individual soccer ability
[22, 39, 43].
Second, determining factors that predict individual soc-
cer performance allows for successful selection of players
on the basis of those variables. However, the use of a selec-
tion decision as the criterion can hinder this aim because
the judgment of a player’s performance level might not
be an accurate representation of individual soccer perfor-
mance. This approach strongly depends on the validity of
the coach’s or scout’s judgment in distinguishing between
successful and ‘non-successful’ players. Yet, the validity of
these judgments is not well-established, and is often even
biased [12]. For example, judges are easily influenced by
factors unrelated to a player’s talent or performance, such
as the player’s skin color or reputation [65, 66]. In addi-
tion, the bias of judges to systematically select more mature
players or players born earlier in the year has been well-
reported in the talent identification literature [67, 68]. Thus,
it is not clear whether predictors of perceptions of successful
performance are also valid predictors of individual match
performance after selection [24].
There are only a few studies within the talent identifica-
tion literature that used individual soccer performance as an
outcome measure. Examples include structured ratings of in-
game performance [69–71], and metrics based on successful
and unsuccessful skill involvements during games [39, 72].
As we discuss in Sect.3.1, we believe that the validity and
reliability of such measures requires closer examination in
future research. Taken together, we argue that the criterion
measures that are currently used in most talent identifica-
tion studies are intuitive and straightforward, but have their
shortcomings and are insufficiently validated for studies
that aim to identify and understand what factors predict
individual soccer performance. In contrast, a reliable and
objective soccer-specific criterion measure is complicated
to operationalize, but allows for the measurement of indi-
vidual performance differences, so that the predictive value
of different measures can be determined more meaningfully.
2.2 Predictors ofSoccer Performance
The predictors that have been studied in soccer talent iden-
tification research are strongly influenced by the classifica-
tion scheme proposed by Williams and Reilly [1, 3], who
classified predictors of individual soccer performance into
four sport science disciplines: physical, physiological, psy-
chological, and sociological. Examples of predictors include
height, weight, and body composition (physical) [47, 53,
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1320 T.L.G.Bergkamp et al.
Table 1 Design and methodological characteristics of soccer talent identification studies
Study Prognostic
period (follow-
up)
Age at assessment NCriterion Predictors Considers restriction of range
Reilly etal. 2000 [3] Cross-sectional U17 16
15
Elite
Sub-elite
Low-fidelity:
Height, weight, body composition
(physical—7 variables)
Speed, endurance, agility, strength
(physiological—10 variables)
Dribbling and shooting (soccer-
specific—2 variables)
Anxiety intention and direction,
anticipation, motivation (psycho-
logical—11 variables)
Partially—authors briefly consider
if findings will replicate in highly
selected players who are exposed to
more systematic training
Vaeyens etal. 2006 [73] Cross-sectional U13–U16 490aElite
Sub-elite
Non-elite
Low-fidelity:
Height, weight (physical—3 vari-
ables)
Speed, endurance, agility, strength
(physiological—10 variables)
Dribbling, shooting, passing,
juggling (soccer-specific—4 vari-
ables)
Yes—authors consider that differen-
tiating the ability of performance
indicators might be dependent on
competitive age class, and relate
findings to homogeneity of sample
due to preselection
Toering etal. 2009 [75] Cross-sectional U12–U18 159
285
Elite
Non-elite
Low-fidelity:
Self-regulation (psychological—6
variables)
No, but authors did control for effects
of age
Coelho e Silva etal. 2010
[84]
Cross-sectional U14 69
45
Elite
Local
Low-fidelity:
Maturity (3 variables)
Height, weight, body composition
(physical—3 variables)
Speed, endurance, agility, and power
(physiological—5 variables)
Dribbling, shooting, passing (4
variables)
Task and ego orientation (psycho-
logical—2 variables)
Other:
Soccer experience (1 variable)
No
Waldron and Worsfold 2010
[40]
Cross-sectional U14 69
32
Elite
Sub-elite
High-fidelity:
Attempted, successful and unsuc-
cessful skill involvements in a
match, such as passing, shooting,
tackling (18 variables)
No
Kavussanu etal. 2011 [42] Cross-sectional U13–U17 69
49
Elite
Non-elite
Low-fidelity:
Task and ego orientation, perceived
parental environment (psychologi-
cal—11 variables)
No
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1321
Methodological Issues in Soccer Talent Identification
Table 1 (continued)
Study Prognostic
period (follow-
up)
Age at assessment NCriterion Predictors Considers restriction of range
Waldron and Murphy 2013
[100]
Cross-sectional U15 15
16
Elite
Sub-elite
Low-fidelity:
Speed, strength, agility (physiologi-
cal—5 variables)
Dribbling (soccer-specific—2 vari-
ables)
High-fidelity:
Attempted, successful and unsuc-
cessful skill involvements in a
match, such as passing, shooting,
tackling (6 variables)
Physiological performance during
games, such as intensity move-
ments and distance covered (9
variables)
Other:
Heart rate and perceived exertion (2
variables)
No
Haugaasen etal. 2014 [44] Cross-sectional U14–U22 615
81
Non-professional
Professional
Other:
Engagement in soccer-specific activ-
ities (sociological—4 variables)
Partially—authors specifically exam-
ine participation in soccer-specific
activities in different age categories,
but do not relate their findings to the
homogeneity of the sample, due to
preselection
Verburgh etal. 2014 [77] Cross-sectional U9–U17 84
42
Highly-talented
Amateur
Low-fidelity:
Executive functions (psychologi-
cal—8 variables)
Partially—authors briefly state that
findings can only be considered
in the context of thesamples, but
authors do not examine the differen-
tiating ability of predictors per age
category, and did not control for age
Baláková etal. 2015 [79] Cross-sectional U14 91aTalented
Less-talented
Low-fidelity:
Cognitive functions (psychologi-
cal—16 variables)
No
Goto etal. 2015 [54] Cross-sectional U9–U10 14
20
Retained
Released
Low-fidelity:
Maturity (1 variable)
High-fidelity:
Physiological performance during
games, such as intensity move-
ments and distance covered (6
variables)
No
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1322 T.L.G.Bergkamp et al.
Table 1 (continued)
Study Prognostic
period (follow-
up)
Age at assessment NCriterion Predictors Considers restriction of range
Huijgen etal. 2015 [41] Cross-sectional U14–U18 47
41
Elite
Sub-elite
Low-fidelity:
Lower and higher cognitive func-
tions (psychological—6 variables)
No
Fenner etal. 2016 [69] Cross-sectional U10 16 Rating of technical performance in
SSGbLow-fidelity:
Speed, strength (physiological—3
variables)
High-fidelity:
Individual performance in SSGs,
time–motion characteristics (5
variables)
Yes—authors compare findings to
a similar study with older players,
and suggest that these findings did
not replicate due to the increased
homogeneity of technical skills in
the older players.
Bennett etal. 2017 [101] Cross-sectional U12–U16 36
37
High-level
Low-level
High-fidelity:
Attempted, successful and unsuc-
cessful skill involvements in a
match, such as passing, shooting,
dribbling (13 variables)
No
Den Hartigh etal. 2017 [55] Cross-sectional U11 49
39
Selected
Non-selected
Low-fidelity:
Game reading based on video
images (1 variable)
No
Rowat etal. 2017 [71] Cross-sectional U18 27 Technical performance in SSG
ratingbLow-fidelity:
Maturity (1 variable)
Speed, endurance (physiological—2
variables)
Dribbling, passing, shooting (soccer-
specific—4 variables)
No
Wilson etal. 2017 [39] Cross-sectional NA 32 Individual performance in 1-vs-1
and 11-a-side gamesbLow-fidelity:
Height, weight, body composition
(physical—7 variables, 2 latent
variables)
Speed, strength, balance (physi-
ological—7 variables, 3 latent
variables)
Dribbling, juggling, shooting, pass-
ing (soccer-specific—5 variables, 2
latent variables)
No
Gil etal. 2007 [107]< 1year U15–U18 126
68
Selected
Non-selected
Low-fidelity:
Height, weight, body composition
(physical—22 variables)
Speed, endurance, agility, power
(physiological—10 variables)
Partially—authors briefly consider
that technical, tactical and psy-
chological skills may have more
discriminative power for selected
players at later ages, when growth
differences are less important
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1323
Methodological Issues in Soccer Talent Identification
Table 1 (continued)
Study Prognostic
period (follow-
up)
Age at assessment NCriterion Predictors Considers restriction of range
Gravina etal. 2008 [46]< 1year U11–U14 44
22
First team
Reserves
Low-fidelity:
Height, weight, body composition
(physical—13 variables)
Speed, strength (physiological—10
variables)
Partially—authors very briefly relate
findings to extended population, but
do not discuss homogeneity of the
sample due to preselection
Huijgen etal. 2014 [52]< 1year U17–U19 76
47
Selected
Deselected
Low-fidelity:
Speed, endurance (physiological—4
variables)
Dribbling (soccer-specific—4 vari-
ables)
Tactical characteristic questionnaire
(4—variables)
Task and ego orientation, anxiety,
concentration, motivation (psycho-
logical—8 variables)
No, but authors did control for effects
of age
Lago-Penas etal. 2014 [63]< 1year U15/U17/U20 156aSelected
Non-selected
Low-fidelity:
Height, weight, body composition
(physical—6 variables)
Speed, endurance, strength (physi-
ological—3 variables)
No
Zuber and Conzelmann
2014 [70]
< 1year U13 140 Overall soccer performance ratingbLow-fidelity:
Achievement motive (psychologi-
cal—2 latent variables)
Speed, endurance, strength, agility
(physiological—4 variables, 1
latent variable)
Dribbling, juggling and ball control
(soccer-specific—3 variables, 1
latent variable)
Yes—authors relate findings to
homogeneity of the sample due to
preselection
Aquino etal. 2017 [57]< 1year U17 28
38
Selected
Non-selected
Low-fidelity:
Maturity (1 variable)
Height, body composition (physi-
cal—3 variables)
Speed, endurance, strength (physi-
ological—7 variables)
Shooting, ball control, dribbling, tac-
tical skills questionnaire (soccer-
specific—4 variables)
No
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1324 T.L.G.Bergkamp et al.
Table 1 (continued)
Study Prognostic
period (follow-
up)
Age at assessment NCriterion Predictors Considers restriction of range
Gil etal. 2014 [53] 1year U10–U11 21
43
Selected
Non-selected
Low-fidelity:
Maturity (3 variables)
Height, weight, body composition
(physical—9 variables)
Speed, endurance, strength (physi-
ological—7 variables)
Other:
Soccer experience (1 variable)
No
Vestberg etal. 2012 [78]< 2years Adult 29
28
High division
Low division
Goals scored and assistsb
Low-fidelity:
Executive functions (psychologi-
cal—3)
Yes—authors also have results for
non-soccer players, and are therefore
able to compare results with the
general population
Vestberg etal. 2017 [80]< 2years U13–U20 30 Goals scored and assistsbLow-fidelity:
Executive functions (psychologi-
cal—4 variables)
Yes—authors also have results for
non-soccer players, and are therefore
able to compare results with the
general population
Figueiredo etal. 2009 [47] 2years U12–U15 36
90
33
Drop-out
Club
Elite
Low-fidelity:
Height, weight, body composition
(physical—6 variables)
Speed, endurance, agility, and power
(physiological—6 variables)
Dribbling, shooting, passing (soccer-
specific—4 variables)
Task and ego orientation (psycho-
logical—2 variables)
Other:
Soccer experience (1 variable)
Rating of player’s potential (1—vari-
able)
No
Deprez etal. 2015 [48] 2years U10–U17 633
231
29
29
Club
Drop-out
Contract
No contract
Total minutes played in first teamb
Low-fidelity:
Maturity (2 variables)
Height, weight, body composition
(physical—3 variables)
Speed, power, endurance, motor
coordination (physiological—8
variables)
Dribbling (soccer-specific—2 vari-
ables)
Yes—authors examine the discrimi-
natory power of variables per age
group and discuss these results in
relation to the homogeneity of each
age group, in terms of physical abili-
ties. They also briefly relate their
findings to the extended, unselected
population
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1325
Methodological Issues in Soccer Talent Identification
Table 1 (continued)
Study Prognostic
period (follow-
up)
Age at assessment NCriterion Predictors Considers restriction of range
Zuber etal. 2015 [50] 2years U13 10
82
National team
Elite—not selected
Low-fidelity:
Achievement motivation, achieve-
ment goal orientation, self-
determination (psychological—5
variables)
Yes—authors investigate distinct
clusters formed of the different vari-
ables, for each age category. They
also briefly consider homogeneity of
the sample on examined variables
Zuber etal. 2016 [49] 3years U12 12
39
68
National
Regional
No talent card
Low-fidelity:
Maturity (1 variable)
Net hope (psychological—2 vari-
ables)
Speed, endurance, strength (physi-
ological—3 variables)
Dribbling, passing, juggling (soccer-
specific—3 variables)
Yes—authors investigate distinct
clusters formed of the different vari-
ables, for each age category. They
also note that results should only be
considered in the context of their
homogenous sample, and cannot
directly be translated to the general
population
Zibung etal. 2016 [51] 3years U13 10
30
64
National talent card
Regional talent card
No talent card
Low-fidelity:
Speed, endurance, agility (physi-
ological—3 variables)
Dribbling, passing, juggling (soccer-
specific—3 variables)
Yes—authors briefly discuss the
decrease of variance in performance
over time, as a result of increasing
homogeneity of the sample due to
preselection
Huijgen etal. 2013 [82] 1–3years U12–U19 269
50
Selected
De-selected
Low-fidelity:
Passing: Loughborough Soccer
Passing Test (soccer-specific—2
variables)
Partially—authors take the develop-
ment of skills into account and relate
the results to different age catego-
ries, but only very briefly consider
homogeneity of the sample due to
preselection
Höner and Feichtinger 2016
[21]
4years U12 308
2369
Youth academy
No youth academy
Low-fidelity:
Achievement motive, ego orienta-
tion, sport orientation, volition,
self-concept, self-efficacy, anxiety
(psychological—17 variables)
Yes—authors relate their findings to
the homogeneity of the sample due
to preselection
Kannekens etal. 2011 [83] 3—5years U17—U19 52
53
Professional
Amateur
Low-fidelity:
Tactical skills questionnaire (soccer-
specific—4 variables)
Other:
Soccer experience, practice per
week, non-specific sport practice
No
Gonaus and Müller 2012
[56]
1–6years U14–U17 821
3912
Drafted
Non-drafted
Low-fidelity:
Speed, endurance, strength, agility
(physiological—12 variables)
Yes—authors consider the homoge-
neity of the sample and relate the
discriminating power of variables to
a specific age group
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1326 T.L.G.Bergkamp et al.
Table 1 (continued)
Study Prognostic
period (follow-
up)
Age at assessment NCriterion Predictors Considers restriction of range
le Gall etal. 2010 [64] 4–6years U14–U16 48
167
235
International
Professional
Amateur
Low-fidelity:
Maturity (3 variables)
Height, weight, body composition
(physical—3 variables)
Speed, endurance, agility, and power
(physiological—14 variables)
Partially—authors examine the dis-
criminative power of performance
characteristics per age group, but
only very briefly consider how
homogeneity of their sample due to
preselection may affect findings
Höner and Votteler 2016
[43]
4–7years U12 195
731
1025
20,892
National
Regional
Academy
Not selected
Low-fidelity:
Sprinting, agility (physiological—2
variables)
Dribbling, ball control, shooting
(soccer-specific—3 variables)
Yes—authors mention restriction of
range, relate findings to homogene-
ity of the sample due to preselection,
and consider that discriminatory
power may vary according to age
group and homogeneity of the
sample
Höner etal. 2017 [45] 8–10years U12 89
913
13,176
Professional
Semi-professional
Non-professional
Low-fidelity:
Relative age (1 variable)
Height, weight (physical—2 vari-
ables)
Speed, agility (physiological—2
variables)
Dribbling, shooting, ball control
(soccer-specific—3 variables)
Partially—authors briefly consider
how predictive value may differ for
different age categories, but do not
discuss homogeneity of their sample
due to preselection
Van Yperen 2009 [76] 15years U15–U18 18
47
Successful
Unsuccessful
Low-fidelity:
Goal commitment, coping, social
support (psychological—3 vari-
ables)
Other:
Assessment of initial performance
by coaches (1 variable)
No, but the author did control for
initial performance level
Martinez-Santos etal. 2016
[74]
2–18years Adult 74
161
First/second division
Semi-professional
Low-fidelity:
Speed, strength (physiological—3
variables)
No
Electronic databases (MEDLINE, SPORTDiscus, Google Scholar) were searched between 2000 and 2018 for empirical studies on talent identification, using the following combination of terms:
talent identification OR selection OR prediction and performance and soccer OR football. Additionally, snowballing was used to identify other relevant studies. Studies were included if they
met the following criteria: (1) focused on soccer or association football; (2) aimed to relate empirically multidimensional abilities and skills (e.g. physical, physiological, psychological, techni-
cal, tactical) or assessment methods to soccer performance or skill level; and (3) were peer-reviewed journal articles written in English. To restrict our sample, we excluded studies that focused
predominantly on other types of football (e.g. futsal, American Football, Australian Rules football), and goalkeepers. Moreover, we excluded studies that mainly focused on the effects of relative
age, maturity and genetic disposition. Although these topics are highly relevant for understanding talent development, we believe they warrant their own discussion and are therefore not within
the scope of this paper. Finally, both cross-sectional and longitudinal studies were included. Although the empirical value of cross-sectional studies is limited compared with those with longitu-
dinal designs, the methodological topics that are addressed in this paper also apply to those studies
U Under, i.e. U18 means under the age of 18years, SSG small-sided game, NA not available
a The exact number of players per performance level could not be retrieved
b An individual soccer criterion measure, instead of performance or skill level
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1327
Methodological Issues in Soccer Talent Identification
73]; speed, strength and endurance (physiological) [43, 52,
56, 74]; self-regulation, motivation, task and ego orienta-
tion, and cognitive functions (psychological) [3, 21, 50, 52,
75–80]; and hours of practice and perceived social support
(sociological) [44, 76]. Other predictors that are derived
from this classification scheme are technical skills, such as
dribbling and passing technique, and self-assessed tactical
skills [3, 45, 48, 81–84] (see Table1).
Given the multifaceted nature of soccer performance, it
makes sense to investigate the extent to which these vari-
ables combined predict success and individual performance.
Different studies have demonstrated that some of these
skills and abilities are able to discriminate between players
of varying performance levels [15–18]. More importantly,
the major advantage of this approach in talent identification
procedures is that skills and abilities, such as intermittent
endurance capacity, dribbling technique, and passing ability,
are relatively straightforward to measure in a standardized
and reliable way [85–87].
Although many studies have examined the predictive
relevance of these variables in soccer, the reported effect
sizes are generally small to moderate [18, 43, 45, 56]. An
explanation from selection psychology for the limited pre-
dictive validities in soccer talent identification research
may be related to the ‘fidelity’ of the predictors, that is, the
extent to which the performance task mimics the criterion
behavior in content and context. On one side of the fidelity
continuum are low fidelity predictors, which have relatively
little overlap with the criterion in terms of the behavior the
player should show and the context in which the player must
perform [31, 88]. These low fidelity predictors measure dis-
tinct, general performance components that are thought to
be related to the criterion behavior. Such low fidelity pre-
dictors are referred to as ‘signs’ in the selection psychology
literature [89]. Thus, most of the predictors classified by
Williams and Reilly [1], such as height, speed, and motiva-
tion, can be characterized as signs because they measure
distinct characteristics and lack fidelity to the criterion of
soccer performance in terms of the task and or the context
in which they are assessed [31].
The selection psychology literature shows that the predic-
tive validity of assessment procedures often improves when
the degree of fidelity increases, that is, when the predictor
becomes more similar to the criterion in terms of behavior,
task, and contextual constraints [8, 12, 90]. The underly-
ing rationale is the notion of behavioral consistency: ‘the
best predictor of future behavior is similar past or current
behavior’ [89, 91–93]. Tests that assess soccer-specific tech-
nical skills, such as dribbling and passing technique, possess
higher fidelity to the criterion of soccer performance than
variables such as height, speed, and motivation. Accordingly,
there is evidence that these predictors have better prognos-
tic relevance [45, 82], and discriminate more consistently
between skill groups than the latter group of variables [19,
39, 45]. Still, these tests measure distinct skills, and do not
incorporate many of the necessary contextual constraints
of in-game soccer performance, such as the task of scoring
goals and the presence of moving opponents. In other words,
such tests may still not mimic the criterion of interest, which
is in-game soccer performance, to a large enough extent
[60]. For example, the Loughborough Soccer Passing Test,
a test frequently used to assess the passing ability of soccer
players [82, 85], was recently found to be a poor predictor
of passing performance during a match [94].
An important avenue therefore is to develop predictors
that further minimize the ‘inferential leap’ from the predic-
tor to the criterion, and thus possess even higher fidelity.
One approach to establish such predictors in soccer is to take
a ‘sample’ of the criterion performance in a highly repre-
sentative context [31, 88], for example, in small-sided games
(SSGs). SSGs are games played on reduced pitch areas and
with fewer players (e.g. 4 vs. 4, or 7 vs. 7) than in an official
match. Individual performance in SSGs can be considered
a sample-based predictor because it is obtained based on
behavior, task, and contextual constraints similar to those
present in the criterion performance.
An important conclusion from the selection psychol-
ogy literature is that sample-based assessments can be very
good predictors of future performance [95–98], especially
in homogeneous samples and for multidimensional outcome
measures [99]. Because soccer talent identification research
is often based on homogenous samples (e.g. players who
are already in a talent program), and soccer performance is
multidimensional [1], a samples approach to prediction is
expected to result in greater predictive value [12]. Accord-
ingly, several recent studies have related performance or skill
level to predictors that we would characterize as sample-
based, such as attempted and completed skill involvements
(i.e. event data) within SSGs or regular games [40, 100,
101]. These sample-based predictors were relatively suc-
cessful in distinguishing between groups of elite and sub-
elite or non-elite players, and these results demonstrate how
high-fidelity methods may be useful as alternatives to iso-
lated components in predicting soccer performance [40, 100,
101]. However, similar to individual soccer performance cri-
terion measures, the reliability of individual performance
assessed through SSGs needs to be addressed in future stud-
ies (see Sect.3.2).
Finally, the suggestion of samples as predictors of perfor-
mance is also directly in accordance with theoretical devel-
opments in the field of motor learning and talent develop-
ment regarding the use of representative designs for learning
and assessment purposes [12, 102–104]. Several authors
have already suggested that talent identification procedures
should include more representative measures [8, 9, 15, 22].
In using samples as predictors of soccer performance, the
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1328 T.L.G.Bergkamp et al.
interaction between different performance components is
embedded in behavior that is representative of the criterion
performance, thereby closing the gap between predictor and
criterion.
In conclusion, soccer talent identification research has
generally focused on low- or moderate-fidelity predictors
of soccer performance, which has not only resulted in some
interesting findings but also in an inconsistent body of evi-
dence that does not provide clear guidelines for stakeholders
in practice. The selection psychology literature suggests that
high-fidelity measures may enhance the predictive value of
talent identification procedures, but such methods are not
often applied in the soccer talent identification literature yet.
2.3 Restriction ofRange
Talent identification studies often compare samples that are
already highly restricted in terms of talent or skill, such as
elite versus sub-elite athletes. In such cases, empirical rela-
tionships between performance indicators used as predictors
and the criterion performance often deviate from relation-
ships in the population [33]. This is a problem when, due
to selection, a relatively homogenous sample that is not
representative of the population of interest (containing all
candidates, selected and not selected) is used to establish
predictor–criterion relations [24]. As a result, predictor–cri-
terion relationships obtained from such samples are usually
underestimated because of ‘restriction in range’ [105].
To illustrate the effect of range restriction, we consider
the study by le Gall etal. [64]. They examined anthropomet-
ric and physical characteristics of highly trained U14–U16
soccer players in a national academy, who, upon leaving
the academy, achieved either international or professional
status, or remained amateurs. The authors investigated the
mean differences for 17 dependent variables, ranging from
height, weight, and maturity measurements, to sprint and
endurance performance and lower body explosiveness.
Although statistically significant mean differences were
found for some variables, there were no large differences
between the groups on most performance indicators within
age categories. For instance, in the U16 category, maximal
anaerobic power and height distinguished between future
internationals and amateurs with moderate effect sizes, but
there was no strong evidence for vertical jump, 10-, 20-, 30-,
and 40-meter sprint, and lower body explosiveness distin-
guishing between any combination of international, profes-
sional, and amateur players.
Based on these findings, the conclusion may be that these
variables are not very useful for differentiating future career
success in elite-level U16 players. However, it would be false
to conclude that these characteristics are not important for
attaining soccer-specific success in general [33]. It is likely
that the sample of academy players were exposed to the
same training routine, had similar practice histories, and
were (directly or indirectly) preselected on at least some of
the variables in this study. This preselection in an homog-
enous group of athletes in terms of physical performance
results in a reduction in variance in the predictors and in the
criterion. If the same predictors were studied in a more het-
erogeneous group of soccer players, larger effect sizes would
likely have been found for at least some of these predictors
[1, 33] (e.g. Franks etal. [106]).
Although the issue described above sounds straightfor-
ward, the effects of range restriction are often not explicitly
taken into account in talent identification research. Range
restriction is generally an issue when the aim of a study is to
generalize results obtained from a specific selected group of
elite players to a more general group, which is often the case
when we study relationships between performance criterion
variables and predictors. Aside from general issues such as
insufficient power, careful consideration of the homoge-
neity of the participant group, in terms of the predictors
the study examines, is also required to accurately interpret
why certain relationships were or were not found. This is
important because the ability of predictors to differentiate
between players also depends on the degree of restriction
in the sample. For example, some evidence suggests that a
physiological sign such as sprinting ability is more suitable
for differentiating between performance levels for relatively
younger (e.g. U14–U16) than for older (e.g. U17–U19)
skilled players [48, 73, 107], probably because the former
group is more physically diverse, less exposed to systematic
training, and not as strongly preselected on this variable.
Some talent identification researchers relate their findings to
the homogeneity of the sample and acknowledge that the dis-
criminating or predictive value likely changes with the com-
petitive level [48, 56, 73]. However, findings to date have
been too inconsistent across studies to accurately determine
what is important for any specific age group or skill level.
Thus, restriction of range is common in talent identifica-
tion research, but is rarely considered explicitly when the
generalizability of predictive validities is discussed (see
Table1).
2.4 The Base Rate andtheUtility ofTalent
Identication Programs
Successful talent identification procedures strive to select
individuals who will attain excellent performance, and reject
individuals who will not [22]. The focus of talent identifica-
tion research is on the predictive value of different perfor-
mance indicators; however, the practical usefulness or utility
of these predictors, in terms of correctly identified players,
is often not considered when evaluating the effectiveness of
talent identification programs [32, 33].
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1329
Methodological Issues in Soccer Talent Identification
The utility of selection procedures is greatly affected by
contextual factors, especially the base rate and the selec-
tion ratio. The base rate is the proportion of individuals in
the population of interest who are able to reach satisfactory
criterion performance, that is, the proportion of individu-
als performing successfully if there is no selection [108].
Thus, the base rate is the prior probability of success for any
given candidate [109]. Naturally, the base rate depends on
the population of interest (i.e. the candidate pool) and on the
criterion of interest. For example, several prospective cohort
studies aimed to predict elite adult or late adolescent soccer
success on the basis of performance indicators in groups of
early adolescent players who were selected from large popu-
lations [43, 45]. This context is characterized by a very low
base rate because very few young players have the ability
to attain the elite adult level [110]. The base rate is higher
when we consider, for example, strongly preselected older
players in an elite youth academy, and when our criterion is
operationalized as progressing to next year’s age class in the
academy [52, 57, 107].
The selection ratio is defined as the proportion of play-
ers in the population of interest that is selected [108]. The
selection ratio and the base rate are easily confounded in
the soccer talent identification literature because the selec-
tion decision is often used as the criterion measure in this
research field, as discussed in Sect.2.1. Yet, they are essen-
tially different and need to be defined separately in order to
estimate the utility of a predictor.
The base rate, the selection ratio, and an unrestricted cor-
relation coefficient between the predictor and the criterion
can be used in utility models to estimate the gain in criterion
performance as a result of using a particular predictor [30,
33]. There are several utility models, mostly developed in
the context of personnel selection [108, 111–113]. As an
example, we provide a description of the simplest model,
the Taylor and Russell model [108].
In the Taylor and Russell model, a continuous criterion
variable is dichotomized into a ‘successful’ and ‘unsuccessful’
group, based on a certain cut-off value used to define success-
ful performance. Subsequently, utility is defined as the propor-
tional increase in successful soccer players among those who
are selected (the success ratio), resulting from using a spe-
cific selection procedure, compared with having no selection
procedure (the base rate), or compared with the success ratio
that would result from using a different selection procedure.
In selection decisions, four groups can thus be distinguished:
selected athletes who are successful (true positives), selected
athletes who are unsuccessful (false positives), unselected
athletes who would have been successful (false negatives),
and unselected athletes who would not have been successful
(true negatives). Accordingly, the proportion of true positives
among all selected candidates corresponds to the sensitivity of
a selection procedure, whereas the proportion of true negatives
among all unselected candidates corresponds to the specificity.
These terms are often used in medical research. Figure1 visu-
ally represents these areas. In general, procedures with a high
predictive validity, applied in contexts with a low selection
ratio and a base rate that yields balanced groups of ‘suitable’
and ‘unsuitable’ players (approximately 0.50), yield the highest
utilities. In addition, even when an assessment procedure has
high predictive validity, utility will be relatively low when the
selection ratio is high, and/or when the base rate is either very
high or very low [108, 109].
Consider the following example. Assume that approxi-
mately 5000 U12 competence center players are selected
annually from a total of 100,000 amateur club players (e.g.
Höner and Votteler [43]), resulting in a selection ratio of
5%. Furthermore, they are selected based on a procedure
that shows an unrestricted correlation of r = 0.4 with elite
adult soccer performance. Note that r = 0.4 suggests rela-
tively high predictive validity, especially considering the
complexity in predicting a performance outcome of young
players several years in the future from the time of testing
[33]. In addition, only 1% of the population of U12 players
(i.e. 1000 players) has the ability to obtain excellent elite
adult soccer performance (the base rate). With this informa-
tion, the success ratio resulting from the talent identification
procedure can be computed (e.g. by using an online Theo-
retical Expectancy Calculator [114]).
The results based on this example are shown in Fig.1.
We obtained a success ratio of 5.3%, which means that only
5.3% (265/5000) of the selected players will be successful
in achieving elite adult soccer performance. This may seem
like a modest result; however, compared with the base rate
of 1%, this may be a substantial increase. Moreover, 73.5%
(735/1000) of all ‘suitable’ players among the population
of U12 players are not selected. Conversely, of the 99,000
players who do not have the ability to be successful, approxi-
mately 95% (94,265/99,000) are not selected.
This example demonstrates how the base rate and the
selection ratio can influence expectations regarding the
utility of talent identification procedures for performance
predictions [32]. To date, the talent identification literature
has not generally taken this into account. We were able to
identify one study within the talent identification literature
that considered utility [43], whereas the effect of the base
rate on the usefulness of the examined predictors was not
discussed in the other studies in Table1.
3 Discussion andSuggestions forFuture
Research
The aim of this position paper was to evaluate the meth-
odology in the soccer talent identification literature based
on common principles from selection psychology that are
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1330 T.L.G.Bergkamp et al.
relevant for talent identification research. We are aware that
talent identification, in particular at younger ages, is very
difficult [10, 32], yet we also believe that selection in gen-
eral can provide players with realistic opportunities for suc-
cessful development, and is often necessary from a practical
point of view [115]. An important challenge therefore is to
develop best-practice selection methods with clearly estab-
lished predictive validity and reliability. The realization of
a coherent body of knowledge regarding the prediction of
soccer performance should ultimately provide guidelines for
stakeholders and practitioners in talent identification. Con-
sidering the four topics discussed in this paper, we suggest
that future talent identification studies in soccer consider the
following points in order to help advance research practices
and increase their practical and scientific impact.
3.1 Develop Criterion Measures ofIndividual Soccer
Performance
First, we suggest that future studies pay more attention to the
criterion variables used in talent identification research, and
develop individual soccer performance measures. More spe-
cifically, future studies may develop criterion measures that
are not essentially selection decisions, and that can describe
individual differences within selected groups of players to
investigate what characteristics are related to which kind of
soccer performance.
It should be emphasized that the development of such
methods is a complicated task because of the dynamic nature
of soccer. Elite individual soccer performance emerges
through the complex interactions between the person and
environmental constraints [60, 103]. As of yet, there is
simply no single, objective measure of soccer performance
available that can capture these complex interactions. Indi-
vidual performance is dependent on the abilities of both
teammates and opponents, which makes valid and reliable
measurements very challenging [116]. The comparison of
individuals’ soccer performance is complicated even further
when we consider that different positions require different
tasks and skills [58].
Despite the challenges, we believe that efforts to devise
meaningful criterion measures are necessary to clearly
establish predictor–criterion relationships. The literature is
limited in providing measures that can describe individual
performance differences, keep the person–task–environ-
ment relation intact, and account for the complex interac-
tions between teammates and opponents [117]. Yet, there
are several ways to obtain individual soccer performance
measures that may provide a useful step in the right direc-
tion. For example, notation data on the frequency and quality
Fig. 1 Visual representation
of the example regarding the
selection procedure of talented
U12 players (N = 100,000).
A = wrongfully rejected
(false negatives); B = right-
fully accepted; C = rightfully
rejected; D = wrongfully
accepted (false positives). B/
(B + D) = sensitivity, whereas
C/(C + A) = specificity Adapted
from Taylor and Russell [108],
with permission
Not selected
N = 95,000
Selected
N = 5000
Unsuccessful
N = 99,000
Adult performance
U12 performance
Successful
N = 1000
D
N = 4735
B
N = 265
A
N = 735
C
N = 94,265
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1331
Methodological Issues in Soccer Talent Identification
of match events (e.g. Waldron and Worsfold [40], van Maar-
seveen etal. [118]) may be weighted and combined to assess
performance per position. The weights of the events that are
relevant for different positions can be determined by experts,
such as coaches or scouts, or through machine-learning
approaches when large amounts of data are available [72].
Furthermore, positional data (e.g. Frencken etal. [119],
Memmert etal. [120]) may be used to quantify spatial-tem-
poral patterns of play, which may be related to individual
in-game success. Both these tools can be used to construct
composite measures of ‘general’ soccer performance [72],
or to measure a specific aspect of performance, such as pass-
ing [121], when the emphasis is on assessing the tasks of a
specific player position [31]. Finally, simpler measures such
as structured expert ratings are efficient tools for quantita-
tively evaluating individual performance [122], but it should
be kept in mind that these also introduce more subjectivity,
which can lead to biases and low interrater reliability [123].
Most importantly, studies are warranted that evaluate the
validity and reliability of criterion measures, before they
are implemented in predictive talent identification research.
3.2 Close theGap betweenPredictor andCriterion
Variables
Second, we suggest that future studies explore the use of
predictors that are more in line with the criterion. Specifi-
cally, talent identification research may broaden its current
focus on low-fidelity signs as predictors to include high-
fidelity samples as predictors of performance. With respect
to the notion of behavioral consistency, several recent studies
have demonstrated that prior competitive success in differ-
ent sports is a relatively good predictor of short-term (i.e.
1–2years) success [10, 124–126]. However, studies on soc-
cer generally based individual performance on the highest
(inter)national level of competition reached, which is less
relevant forsoccer talent identification procedures, and also
suffers from limitations regarding the categorization of play-
ers. Therefore, it will be interesting to see whether samples
of past soccer performance as predictors yield higher pre-
dictive validities of future individual soccer performance,
compared with signs.
Match event data, positional data, and structured ratings
can also be used to develop predictors by quantifying per-
formance in sample-based assessment procedures, such as
SSGs or 11-a-side games. However, it is important to note
that similar to using an individual soccer criterion measure,
measurements based on sample-based predictors may pose
challenges related to the complex nature of soccer perfor-
mance, including the dependence of individual performance
on teammates and opponents, comparing different positions
and competitions, and biases related to judgment. The reli-
ability of such measurements needs to be investigated in
future studies to develop optimally valid measures. Accord-
ingly, recent efforts have been made to develop reliable
structured rating forms to measure performance in SSGs
[118, 127]. As mentioned by other researchers [1, 8, 22,
128], performance should preferably be assessed longitu-
dinally over a series of games in order to obtain reliable
assessments of individual soccer performance based on
these samples. In addition, when a researcher aims to investi-
gate match performance for a given group of players, and has
control over the organization of the games, the performance
level of opponents and teammates can be controlled for by
reorganizing players into different teams after each (small-
sided) game, as was done by Fenner etal. [69].
3.3 Consider Restriction ofRange
Third, future studies should take into account the potential
effect of range restriction on their conclusions by carefully
considering the homogeneity of their study participants in
terms of physical, physiological, and other soccer-related
characteristics. Subsequently, researchers should clearly
state the population to which findings may be generalized.
In strongly restricted samples, the absence of observed
predictor–criterion relationships does not necessarily
imply that a predictor is not positively related to attain-
ing elite performance in the general population, or to the
initial performance level prior to the selection decision.
In addition, which predictors are useful for differentiating
between players probably depends on the level of exper-
tise, and hence the degree of preselection, in the popula-
tion of interest. Future research could pay close attention
to which predictors work in which specific populations.
It should be noted that correcting for the effects of range
restriction has been challenging in talent identification
research. Range restriction is an issue that occurs in most
selection contexts, including personnel and educational
selection. In a typical selection study, the entire candi-
date pool would be assessed on the predictor variables,
but criterion performance data are only available for the
candidates who were selected. The resulting underesti-
mated predictor–criterion relationship can be corrected
using several available formulas [105, 129], which yield
estimates of the predictor–criterion relationship in the
unrestricted population of interest [105, 130]. These cor-
rections are often applied in the selection psychology
literature [131]. However, they have not been used in a
talent identification context, which is most likely due to
the design of most talent identification studies; because
performance level or a selection decision functions as
the criterion, range restriction does not occur within the
sample(s) under study. Accordingly, when the design of
future studies includes soccer criterion measures that can
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1332 T.L.G.Bergkamp et al.
differentiate between individual players’ performance after
selection, range-restricted relationships can be accounted
and corrected for using correction formulas that take the
variance in the candidate pool into account [105, 130].
3.4 Identify theUtility ofPredictors
Finally, we suggest that future studies discuss the potential
utility of predictors more often, and consider realistic esti-
mates of contextual factors such as the base rate and the
selection ratio. For instance, future studies may investigate
how novel predictors compare with current selection deci-
sions made by coaches and scouts, in terms of incremental
validity and utility. We acknowledge that it is difficult to
obtain estimates of the base rate based on empirical data.
However, an educated guess about a range of plausible val-
ues of the base rate [132] can be obtained based on inter-
actions with experts, such as by asking several coaches or
scouts to estimate the proportion of players who they think
have the potential to obtain excellence. That range of plau-
sible values can be used in utility models. Since this base
rate is generally very low in talent identification contexts
[33, 43], and arguably often lower than the selection ratio,
not all selected players can become successful, regard-
less of the predictor’s validity. Therefore, we believe that
utility estimates will help to create realistic expectations
for researchers and stakeholders about talent identification
procedures.
4 Conclusion
In the current position paper we discussed several meth-
odological issues common in the soccer talent identification
literature, and provided suggestions to improve the meth-
odological quality and robustness of research practices in
future talent identification studies. We hope that the gen-
eral principles discussed here will also transfer to practical
selection contexts, and we believe that researchers have an
important responsibility to communicate the reliability and
validity of talent identification procedures to the sports field
[133]. Thinking critically about the methodology and design
of studies in sports opens the door for innovative research
that advances this exciting field, and hopefully leads to a
more coherent scientific and practical framework for talent
identification.
Compliance with Ethical Standards
Funding No sources of funding were used to assist in the preparation
of this review.
Conflict of interest Tom L.G. Bergkamp, A. Susan M. Niessen, Ruud
J.R. den Hartigh, Wouter G.P. Frencken and Rob R. Meijer declare that
they have no conflicts of interest relevant to the content of this review.
Open Access This article is distributed under the terms of the Crea-
tive Commons Attribution 4.0 International License (http://creat iveco
mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribu-
tion, and reproduction in any medium, provided you give appropriate
credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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