DOI: 10.4018/IJBAN.290406
In e na ional Jou nal o Business Analy ics
Volume 9 • Issue 1
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p o ided he au ho o he o iginal wo k and o iginal publica ion sou ce a e p ope ly c edi ed.
*Co esponding Au ho
1
Business Analy ics in Spo
Talen Acquisi ion:
Me hods, Expe iences, and Open
Resea ch Oppo uni ies
Rocio de la To e, Public Uni e si y o Na a e, Spain
Lau a O. Cal e , Uni e si a Obe a de Ca alunya, Spain
Da id Lopez-Lopez, ESADE, Spain
Angel A. Juan, Uni e si a Obe a de Ca alunya, Spain
h ps://o cid.o g/0000-0003-1392-1776
Sa a Ha ami, Uni e si a Obe a de Ca alunya, Spain
ABSTRACT
Rec ui men o young alen ed playe s is a c i ical ac i i y o mos p o essional eams in di e en
spo s such as oo ball, socce , baske ball, baseball, cycling, e c. In he pas , he selec ion o he mos
p omising playe s was done jus by elying on he expe s’ opinions bu wi hou sys ema ic da a
suppo . Nowadays, he exis ence o la ge amoun s o da a and powe ul analy ical ools ha e aised
he in e es in making in o med decisions based on da a analysis and da a-d i en me hods. Hence, mos
p o essional clubs a e in eg a ing da a scien is s o suppo manage s wi h da a-in ensi e me hods and
echniques ha can iden i y he bes candida es and p edic hei u u e e olu ion. This pape e iews
exis ing wo k on he use o da a analy ics, a i icial in elligence, and machine lea ning me hods in
alen acquisi ion. A nume ical case s udy, based on eal-li e da a, is also included o illus a e some
o he po en ial applica ions o business analy ics in spo alen acquisi ion. In addi ion, esea ch
ends, challenges, and open lines a e also iden i ied and discussed.
KEywORdS
Business Analy ics, Machine Lea ning, Spo s, Talen Acquisi ion
1. INTROdUCTION
In he p esen day, inding and hi ing alen ed wo ke s has become one o he op p io i ies o many
businesses. Ine icien hi ing p ac ices ha e a nega i e epe cussion on any o ganiza ion, and migh
impose conside able loses, bo h in e ms o money and ime. As poin ed ou by Da enpo e al.
(2010), hose companies ha a e capable o a ac ing and e aining he bes alen ed people a e among
he mos compe i i e ones. Acco ding o Ha is e al. (2011), in a globalized and highly compe i i e
en i onmen mos o ganiza ions should s a using da a o measu e and imp o e he con ibu ion o
hei human esou ces (HR) o hei pe o mance.
In he spo s sec o , Bake e al. (2017), De Bossche and De Rycke (2017) and Hanlon e al.
(2014) s a e ha he e is an inc easing in e es in unde s anding he cos s and bene i s o ini ia i es
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o ea ly iden i ica ion o alen ed playe s, as well as in unleashing he ac o s ha in luence a hle es’
de elopmen . These au ho s also a i m ha , while adi ional s a is ical analysis was ocused on ma ch
a iables, such us goals sco ed o playe s’ posi ion on he ield, ecen ad ances in spo s analy ics a e
ocused on mo e complex issues like alen acquisi ion. As poin ed ou by Ge a d (2017), he 2011
ilm ‘Moneyball’1 highligh ed he possibili ies o analy ics as a compe i i e s a egy, pa icula ly o
small-ma ke eams wi h ela i ely limi ed esou ces. Following F ied and Mumcu (2016), many
coaches employ da a on habi s and pe o mance indica o s o assess he po en ial o hei playe s.
In ac , i is possible o use da a o: (i) e alua e playe s’ pe o mance; (ii) ank playe s (Pappala do
e al., 2019); (iii) es ima e he alue o playe s in ans e ma ke s (Kim e al., 2019); (i ) loca e he
bes posi ion ha a playe can occupy in he ield; o ( ) o ecas playe s’ goal sco ing pe o mance
in he nex season (Apos olou and Tjo jis, 2019). As illus a ed in Figu e 1, adap ed om 21s Club2,
clubs can use da a o analyze he impac o a new playe on he eam’s o e all pe o mance le el.
The spo s indus y is being ans o med by da a analy ics in he ollowing dimensions: (i) a
clubs le el, e.g., socce clubs like Li e pool, Ba celona3,
A senal, Manches e Ci y, o Milan a e among he ones ha al eady use da a analysis o imp o e
pe o mance, analyze i als, p e en inju ies, op imize he managemen o he ans e ma ke , and
also he acquisi ion o new alen ; (ii) ega ding new en an s in da a managemen and analysis, new
pla o ms o da a analysis and managemen appea o p o ide se ices o clubs, such as Wiscou 4
and Scispo s5; (iii) as ega ds as new en an s in da a cap u e and gene a ion, la ge companies such
as In el ha e launched he c ea ion o wea able In e ne -o -Things de ices, which a e capable o
cap u ing playe s’ in o ma ion in eal ime6; (i ) wi h espec o ans o ma ion o spo s managemen
p o essionals, new o icial uni e si y deg ees dedica ed o spo s managemen ha e eme ged, mos
o hem wi h a special emphasis on da a science; and ( ) a ans o ma ion o spo s en husias s, who
ha e begun o consume complex da a, bo h o hei own digi iza ion p ocess as well as o hei
leisu e and un when using online be ing applica ions.
Figu e 1. Playe ecommenda ion based on da a analy ics (adap ed om 21s Club)
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This pape p esen s a comp ehensi e e iew o he s a e o he a ega ding da a-d i en
app oaches o alen acquisi ion in spo s. In addi ion, he pape discusses he mos common used
me hods o alen acquisi ion. Some o hese me hods belong o he ields o a i icial in elligence
(AI) and machine lea ning (ML), which a e also in oduced in he con ex o spo s analy ics. Finally,
he pape iden i ies and discusses ends, challenges, and open esea ch lines ela ed o his esea ch
a ea. The es o he pape is o ganized as ollows. Sec ion 2 b ing ou he concep o alen analy ics,
specially in he spo s sec o . Sec ion 3 p o ides a sho in oduc ion o he ields o AI and ML, hus
allowing he un amilia eade o ollow he ollowing sec ions. Sec ion 4 discusses di e en da a-
d i en analy ic app oaches. A e wa ds, Sec ions 5 and 6 e iew esea ch pape s on da a analy ics
in ec ui men and spo s alen acquisi ion, espec i ely. Sec ion 7 p o ides an o iginal case s udy
based on a eal-li e da ase o Eu opean socce playe s, whe e some o he po en ial o ML me hods
is illus a ed. T ends, challenges, and open esea ch lines a e discussed in Sec ion 8. Finally, Sec ion
9 summa izes he main con ibu ions o his pape .
2. TALENT ANALyTICS IN SPORTS
Talen analy ics (TA) ep esen s a g oundb eaking oppo uni y o many o ganiza ions in he spo s
sec o . TA is de ined by Bassi (2011) as “an e idence-based app oach o making be e decisions on
he people side o he business; i consis s o an a ay o ools and echnologies, anging om simple
epo ing o HR me ics all he way up o p edic i e modeling”. Du ing he las yea s, p o essional
and eli e spo eams a e gi ing a conside able a en ion o implemen business-o ien ed TA in hei
s a egy. Hence, o Da ids and A au´jo (2019) he main challenge is no he managemen o alen
among he playe s who al eady belong o a eam, bu he acquisi ion o young and alen ed playe s
o he u u e. When conside ing young people, e olu ion and o ecas ing models ha go beyond da a
on he cu en pe o mance le el should be buil as well (Webb e al., 2020; Pi e , 2019; Fo d and
Williams, 2017). As Williams and Reilly (2000) s a e, mul i-dimensional da a (including physical,
psychical, and sociological cha ac e is ics) has o be conside ed. Likewise, Da che a (2014) main ains
ha da a ob ained om social ne wo ks could be used as a p edic o o po en ial child en’s alen ,
while Ma in (2015) explo es he in luence o he child socio-economic s a us on i s pe o mance
and u u e e olu ion.
O he au ho s (Kand a´ˇc e al., 2019; Picke ing e al., 2019; Loland, 2015; Webbo n e al.,
2015; Cˆo ´e, 1999) discuss abou whe he alen is an inna e skill o no , and i da a analysis should
be o ien ed owa ds inna e capaci ies and genes a he han o pe o mance. Gi en he numbe o
ac o s ha may a ec decisions ela ed o alen acquisi ion, au ho s such as Vaeyens e al. (2008),
Ge a d (2017), and Be gkamp e al. (2019) conclude ha alen acquisi ion is a mul i-dimensional
challenge, and one ha is no only based on he skills o indi idual playe s bu also on he whole
eam, i.e., he cu en eam con igu a ion has o be conside ed as well. In his con ex , Ge a d (2017)
p opose he use o simula ion models as mo e e ec i e ools han expe judgmen , especially in a
mul i-dimensional en i onmen like he one being conside ed.
Using da a analy ics, Gandelman (2009) was able o show ha ou side oppo uni ies we e highe
o socce playe s wi h a supe io socioeconomic backg ound and a be e educa ion. He also ound
e idence o acial dis- c imina ion in he U uguayan socce ma ke , whe e ob aining a p o essional
con ac was easie o whi e playe s. Simila ly, Be i and B ook (2010) used da a analy ics o iden i y
some e iciencies ega ding he e alua ion o playe s’ pe o mance in he Na ional Hockey League.
Employing Bayesian analysis combined wi h Ma ko Chain Mon e Ca lo es ima ion, Rimle e al.
(2010) s udied he echnical e iciency o a baske ball eam. They we e no able o ind signi ican
di e ences, in echnical e iciency le els, ac oss eams playing in he same ca ego y. B yson e al.
(2013) in es iga ed how he sala y o a socce playe migh depend upon his abili y o play wi h bo h
ee . A e analyzing da a om he i e main Eu opean leagues, hey concluded ha he a o emen ioned
playe s end o ecei e a no iceable sala y p emium.
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3. ARTIFICIAL INTELLIGENCE ANd MACHINE LEARNING
The popula i y o AI and ML me hods has been cons an ly inc easing du ing he las decade (Joshi,
2019). The disciplines in which hey ha e been o igina ed a e nume ous, including: Compu e Science,
Ma hema ics, Elec ical Enginee ing, S a is ics, Signal P ocessing, e c. These ields ind applica ions in
a wide ange o indus ies, such as image p ocessing, na u al language p ocessing, and online shopping.
Figu es 2 and 3 shows he a ie y o AI- ela ed and ML- ela ed esea ch disciplines, and he impac
gene a ed measu ed in e ms o he numbe o indexed publica ions in he Web o Science (WoS).
Figu e 2. WoS-indexed con ibu ions in A i icial In elligence acco ding o i s esea ch a ea
Figu e 3. WoS-indexed con ibu ions in Machine Lea ning acco ding o i s esea ch a ea
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Acco ding o B oussa d e al. (2019), AI e e s o machines capable o pe - o ming one o mo e
asks associa ed wi h he human na u e, o ins ance: lea n and p ocess human language, execu e
mechanical asks ha equi e complex maneu e ing, sol ing compu e -based complex p oblems ha
may in ol e la ge amoun da a in a e y sho ime lapse. O he s, like Li and Du (2017), de ine he
e m as “ a ie y o human in elligen beha io s, such as pe cep ion, memo y, emo ion, judgmen ,
o easoning, ha can be ealized a i icially by a machine, a sys em, o a ne wo k”. Mos cu en
applica ions a e ocused on he ield o neu al ne wo ks. Fo ins ance, deep neu al ne wo ks a e used
o speech ecogni ion (T e e , 1997), image classi ica ion (Deepa and De i, 2011), o p edic ion o
wo ds in a ex (Ba aglia e al., 2016).
ML as subse o AI e e s o a compu e p og am ha can lea n how o p oduce a ce ain beha io ,
which was no explici ly p og ammed in i (Ko sian is e al., 2007). Indeed, i can be capable o
showing beha io s om which he p og amme may be comple ely unawa e o .
As in human beha io , many aspec s o lea ning and in elligence in AI a e closely ela ed o he
ep esen a ion o unce ain y. The e o e, p obabilis ic app oaches a e undamen al (Ghah amani,
2015).
P obabilis ic me hods y o assign an unce ain y measu e o he un- known a iables, as well
as a ce ain p obabili y o known a iables. Hence, he goal is o ind he unknown alues using
p obabilis ic models. These models a e classi ied in o wo main ypes, so called gene a i e and
disc imina i e: disc imina i e models y o o ecas he changes in he ou pu jus conside ing he
changes occu ed in he inpu , while gene a i e models a e he ones in which he changes in he ou pu
can be explained as a consequence o changes in he inpu as well as changes in he s a e (Joshi, 2019).
Cu en esea ch ega ding he on ie o p obabilis ic ML app oaches (bo h disc imina i e
as well as gene a i e) is mainly ocused on: (i) p obabilis ic p og amming (as a gene al amewo k
o exp essing p obabilis ic models as compu e p og ams); (ii) Bayesian op imiza ion ( o globally
op imizing unknown unc ions); (iii) hie a chical modeling o lea ning many ela ed models; and (i )
p obabilis ic da a comp ession (Ghah amani, 2015). ML app oaches can be di ided in o supe ised
and unsupe ised lea ning.
The o me a e in ol ed in many applica ions and deal wi h p oblems ela ed o lea ning wi h
guidance. In o he wo ds, he aining da a in supe ised lea ning me hods needs labeled samples.
Thus, o ins ance, samples wi h class labels a e equi ed in a classi ica ion p oblem. Hence, he
ma hema ical model lea ns i s pa ame e s om labeled samples wi h he main goal o making
p edic ions on samples ha he model has no seen be o e. Then, he classi ie is used o assigning
class labels o he es ing ins ances in which he alues o he p edic o ea u es a e known, bu he
alue o he class label emains unknown. Since he supe ised classi ica ion is one o he asks
equen ly de eloped by ‘in elligen sys ems’, i seems logical ha a g ea numbe o echniques
a e based on AI and s a is ics. Meanwhile, unsupe ised lea ning deals wi h p oblems ha in ol e
da a wi hou labels. In his case, he machine ecei es inpu s bu ob ains nei he ou pu s no ewa ds
om i s en i onmen (Ghah amani, 2003). Unsupe ised app oaches y o ind ends and some
kind o s uc u e in he aining da a. Tha is, hese app oaches y o unde s and he o igin o he
da a i sel and o build ep esen a ions o he inpu s ha can be used o decision-making, e icien ly
communica ing he inpu s o ano he machine o p edic ing u u e inpu s. Clus e ing is a ypical
example o unsupe ised lea ning. In unsupe ised lea ning, he majo i y o he wo k can be conside ed
as a lea ning p ocess o a p obabilis ic model. When he scena io is no able o gi e he machine any
supe ision o ewa d, he machine can design a model ha ep esen s he p obabili y dis ibu ion o
a new inpu . This is achie ed by jus conside ing a p e ious use ul inpu (e.g., s ock p ices o wea he
condi ions). P obabilis ic models ha can be used in unsupe ised lea ning a e, among o he s: ac o
analysis, independen componen s analysis, p incipal componen s analysis, o Gaussians models.
The e a e si ua ions whe e he supe ised me hods a e no he bes op ion. The i s and mos
impo an is he high cos o labeling. Mo eo e , ha ing all he aining da a ully labeled can be
p ac ically impossible. In hese cases, i is common o s a wi h supe ised me hods –using a small

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se o labeled da a–, and hen imp o e he model in an unsupe ised way–i.e., using a la ge se o
unlabeled da a.
AI and ML echniques a e p esen in almos all sec o s, including spo s. In ac , mos a iables
ha can be quan i ied can also be p edic ed using AI and ML. The spo sec o is ull o quan i iable
elemen s, which makes i ideal o he use o hese echniques. Fo example, ec ui men o playe s
is one o he a eas in spo s whe e AI and ML a e inc easingly employed (Cha an, 2019; He old e
al., 2019; Musa e al., 2019; C´wiklinski e al., 2021).
4. TyPES OF ANALySIS IN TALENT ANALyTICS
Da a analy ic app oaches, which a e ypically based on ML and s a is ics me hods, can b ing insigh s
ha a e c i ical o imp o ing ope a ional and business ou comes o many o ganiza ions. These
app oaches play a ole as powe ul ools in he seeking and hi ing young alen . Talen analy ics is
a sys ema ic p ocess ha applies s a is ics, echnology, and expe ise o la ge se s o people da a o
disco e he meaning ul pa e ns ha allow o sup- po ing decision-making in ec ui men . Th ee
common ypes o analy ics
–desc ip i e, p edic i e, and p esc ip i e– a e used in TA, people and human esou ce analy ics
amewo ks o measu e e iciency, e ec i eness, quali y o ec ui men , and impac (Necula and
S imbei, 2019). F om desc ip i e o p esc ip i e analysis, no only he model inc eases in he
complexi y o he da a being used, bu also he analysis p og essi ely ge s mo e sophis ica ed. Each
o he a o emen ioned ypes a e desc ibed nex :
Desc ip i e Analy ics: epo ing / isualiza ion is he i s s ep o ca ying ou s a is ical analyses,
and i is used o desc ibe he basic ea u es o he da a ia applying o he collec ed da a h ough
he s uc u ed ques ionnai e (Ma ybe h e al., 2019); i plays a ele an ole in p o iding a iew
in o ac i i y –such as equisi ion olume, alen pool size, sou ce o hi es, e c.–, as well as o
e eal he le els o ac i i y and e iciency in each candida e gene a ion.
P edic i e Analy ics: uses da a o ind pa e ns and employs hem o p edic he u u e; i pe mi s
o iden i y s a is ical ela ionships be ween a se o ac i i ies and he expec ed ou comes, ha
will help o:
(i) o ecas wha will happen in he u u e o o explain he ob ained ou come (like a candida e’s likely
cul u al i , le el o pe o mance, and e en ion); o (ii) no ice po en ial alen sho ages o skills
gaps, and ma ke a ailabili y (wo k o ce planning); also, p edic i e analy ics is mainly ela ed
o selec ion o ejec ion o candida e, accep ance o p o ided o e by selec ed candida es and
oo cause analysis o o e decline (S i as a a e al., 2015); p edic i e analysis inds answe s
o ques ions such as ‘wha will he u u e look like?’, ‘wha ac ics mos in luence business
ou comes?’, e c.; he esul o his p edic i e analysis help sho en he en i e ec ui ing p ocess
while imp o ing he hi ing p ocess.
P esc ip i e Analy ics: goes a s ep u he in he u u e and a emp s o p o ide and sugges be e
decisions using da a echniques such as decision modeling, ML, heu is ic, simula ion, neu al
ne wo ks; hese decisions a e based on he esul s p o ided by p edic i e analysis; i ies
o e alua e he e ec and impac o he p o ided decisions in o de o modi y hem be o e
implemen a ion; i usually esul s in ules and ecommenda ion o nex s eps (A a an and
A a an, 2018); o example alen acquisi ion eam ecei es he hi e o no hi e sugges ions o
s a egy ecommenda ion om p edic i e analysis.
A common da a analy ics amewo k used in TA is shown in Figu e 4. The mos common da a-d i en
me hodologies used in TA may classi ied in
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eg ession, classi ica ion, clus e ing, associa ion ule mining, and anomaly de ec ion. The eade
in e es ed in a mo e comple e in oduc ion o TA is e e ed o Da enpo e al. (2010), which illus a es
uses and desc ibes he undamen als o build a capaci y in his domain, i.e.: access o high-quali y da a,
en e p ise o ien a ion, analy ical leade ship, and s a egic a ge s. In his con ex , Nocke and Sena
(2019) discusses he ad an ages and cos s induced (in e ms o da a go e nance and e hics) by using
TA wi hin an o ganiza ion. The au ho s p esen a numbe o case s udies o analyze he posi i e e ec
o TA usage in o ganiza ional decision-making p ocesses and de e mine he key channels h ough
which he TA adop ion imp o e HR managemen and, subsequen ly, he whole o ganiza ion unc ion.
5. dATA ANALyTICS IN RECRUITMENT
A mo e da a-d i en cul u e is becoming inc easingly popula among companies and go e nmen s.
HR cons i u es an example o a business’ depa men ha has d ama ically changed du ing he
las decades due o he use o da a analy ics me hodologies and echnologies. Indeed, companies
a e inc easingly adop ing sophis ica ed me hods o s udy employee’s da a in o de o imp o e he
decision-making p ocess, so hey can s eng h hei compe i i e ad an age (Da enpo e al., 2010).
Acco ding o Rana e al. (2019), TA shows he po en ial wi hin he decisions ega ding hi ing,
aining, imp o ing p oduc i i y, and e aining alen , all o hem wi h he main pu pose o make a
company mo e compe i i e.
Ga ne , Inc.7, poin s ou ha he olume o da a and me ics a ailable o HR has inc eased
exponen ially, while 70% o companies expec o in- c ease he esou ces hey dedica e o TA in he
coming yea s. E en so, only 21% o he HR leade s belie e ha hei o ganiza ions a e e ec i e a using
alen da a o in o m business decisions. Dey and De (2015) poin s ou i e key a eas whe e p edic i e
analy ics can c ea e alue in HR: (i) employee p o iling and segmen a ion, employee a i ion, and
loyal y analysis; (ii) o e- cas ing o HR capaci y and ec ui men needs; (iii) app op ia e ec ui men
p o ile selec ion; (i ) employee sen imen analysis; and ( ) employee aud isk managemen . Figu e
Figu e 4. Da a analy ics amewo k using in Talen Analy ics
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5 lis s he main TA applica ions, me hodologies, and echnologies. I is based on Kau and Fink
(2017), which o e s a e iew o key app oaches, compe encies and ools, building on 22 in e iews
wi h academics, consul an s and p ac i ione s a 16 co po a ions, as well as on o he TA expe s.
5.1. Rec ui men and Talen Acquisi ion
Acco ding o Wikipedia, ec ui men may be de ined as “ he p ocess o a ac ing, sho lis ing,
selec ing, and appoin ing sui able candida es o jobs wi hin an o ganiza ion, and is a key unc ion o
human esou ce managemen .” Ano he in e es ing de ini ion is p o ided by B eaugh (2008), which
de ines ex e nal ec ui men as “an employe ’s ac ions ha a e in ended o:
(i) b ing a job opening o he a en ion o po en ial job candida es who do no cu en ly wo k o he
o ganiza ion; (ii) in luence whe he hese indi iduals apply o he opening; (iii) a ec whe he
hey main ain in e es in he posi ion un il a job o e is ex ended; and (i ) in luence whe he a
job o e is accep ed”. Rec ui men plays an essen ial ole in de e mining he e ec i eness o
o ganiza ions, and i is composed o se e al sub-p ocesses: (i) job analysis, which consis s in
documen ing he knowledge, skills, abili ies, and o he cha ac e is ics (KSAOs) equi ed o a
job; (ii) sou cing, which is he p ocess o a ac ing o iden i ying candida es; and inally (iii)
sc eening and selec ion. O ganiza ions apply ec ui men s a egies o iden i y hi ing acancy,
es ablish imelines, and de ine goals h oughou he ec ui men p ocess. Each o ganiza ion
designs i s own s a egies o ec ui men , bu he e a e equen app oaches such as using social
ne wo ks o ex e nal ec ui men ad e isemen o employing s anda d psychological es s,
g oup discussion and a numbe o in e iews o assess a a ie y o KSAOs. Thus, he ec ui men
p ocess is complex and equi es big amoun s o e o and in es men .
Bha acha yya (2015) de ines alen acquisi ion as a s a egic app oach aiming o iden i y, a ac ,
and b ing onboa d op alen o mee dynamic business needs. Acco ding o his au ho , ec ui ing
is mo e ac ical and ocuses mos ly on immedia e hi ing needs, i.e., a p ocess o illing he open
posi ions. Figu e 6 allows us o check he g owing popula i y o his esea ch ield. Mo e speci ically,
i shows he e olu ion, om 2000 o 2019, in he numbe o wo ks epo ed by he Web o Science
when sea ching o : (i) “Talen acquisi ion” o “Talen ec ui men ” (which some imes a e used as
synonyms in he li e a u e) in “Social Science” (in o ange); and (ii) he same bu in “spo s” (in blue).
Clea ly, he e has been a posi i e and sus ained end du ing he las 10 o 15 yea s.
5.2. S udies on da a-d i en Rec ui men
He e, we desc ibe a ew ecen and ep esen a i e wo ks on ec ui men using da a-in ensi e
me hodologies and echniques. Fo ins ance, Mohapa a and Sahu (2017) p esen s a case s udy o
highligh he misconcep ions in capaci y planning me hods (i.e. hi ing p ocess) and in he de ec ion
o bo le- necks in he hi ing pipeline by using ec ui men unnel echnique and some
me ics o check e iciency o he a o emen ioned hi ing p ocess. Mo eo e , he au ho s depic
app op ia e sou ces o hi ing based on he pe o mance o candida es hi ed om hose sou ces.
They iden i y success ul p o iles in he company h ough compu ing co ela ions be ween selec ion
pa ame e s and pe o mance sco es. Finally, hey p o ide a ec ui men s a egy and u u e oad map
o he case s udy. Kau and Fink (2017) in es iga es wha is e- qui ed o se up and un an e ec i e
TA unc ion and he s uc u es, sys em and skills ha enable i . The au ho s discuss he answe o hese
ques ions h ough he collec ion and analysis o da a om 22 in e iews wi h academics, consul an s,
and di e en indus ies. The analysis shows ha da a in as uc u e and epo ing, ad anced analy ics,
and o ganiza ional esea ch a e h ee componen s o a ma u e TA unc ion.
Aza e al. (2013) p o ides a decision-making ool o help manage s du ing he ec ui men
p ocess. Au ho s claim ha he ool, h ough using da a mining echniques, is able o disco e pa e ns
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Figu e 5. Scheme o alen analy ics. Sou ce: based on Kau and Fink (2017)
Figu e 6. E olu ion o he numbe o ela ed wo ks om 2000. Da a sou ce: Web o science. Dashed lines ep esen he endencies
calcula ed as mo ing a e ages.
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Rocio de la To e is an Assis an P o esso in he Depa men o Business Managemen a he Public Uni e si y
o Na a e (Spain). She is also a esea che om INARBE-Ins i u e o Ad anced Resea ch in Business and
Economics. She holds a Ph.D. and a Bachelo ’s Deg ee in Indus ial Enginee ing om he Uni e si a Poli ecnica
de Ca alunya. He majo esea ch a eas a e ma hema ical p og amming o s a egic planning decisions in
knowledge-in ensi e o ganiza ions (KIOs) and supply chain design.
Lau a O. Cal e is a Lec u e o S a is ics in he Compu e Science Dep . a he Uni e si a Obe a de Ca alunya
(UOC) and Lec u e o Ma hema ics & P ojec Managemen a he Escola Uni e si à ia Salesiana de Sa ià (EUSS).
She holds a M.S. in Applied S a is ics and Ope a ions Resea ch comple ed a Uni e si a Poli ècnica de Ca alunya
(UPC) & Uni e si a de Ba celona (UB) and a Ph.D. in Ne wo k and In o ma ion Technologies comple ed a he
UOC. She is a membe o he ICSO@IN3 esea ch g oup. He main lines o esea ch a e: - Design o op imiza ion
algo i hms elying on he use o me aheu is ics, machine lea ning and/o simula ion applied o sus ainable logis ics
& compu ing - Applied s a is ics & economics: applica ions in heal h, disas e managemen , & lea ning.
Da id Lopez-Lopez is an academic collabo a o a ESADE. He holds a PhD in digi al ans o ma ion, and a join
MBA om ESADE (Spain) and he Uni e si y o Duke (USA). He is also managing pa ne in Fhios, ha employs
mo e han 180 consul an s.
Angel A. Juan is a Full P o esso o Ope a ions Resea ch & Indus ial Enginee ing in he Compu e Science Dep .
a he Uni e si a Obe a de Ca alunya (Ba celona, Spain). He is also he Di ec o o he ICSO esea ch g oup
a he In e ne In e disciplina y Ins i u e and Lec u e a he Eunce Business School. D . Juan holds a Ph.D. in
Indus ial Enginee ing and an M.Sc. in Ma hema ics. He comple ed a p edoc o al in e nship a Ha a d Uni e si y
and pos doc o al in e nships a Massachuse s Ins i u e o Technology and Geo gia Ins i u e o Technology. His
main esea ch in e es s include applica ions o simula ion, me aheu is ics, and machine lea ning me hods in
di e en ields, including: logis ics & anspo a ion, inance, and sma ci ies. He has published o e 110 a icles
in JCR-indexed jou nals and o e 250 pape s indexed in Scopus.
ENdNOTES
1 h ps://en.wikipedia.o g/wiki/Moneyball_( ilm)
2 www.21s club.com
3 h ps://ba cainno a ionhub.com/es/e en /ba ca-spo s-analy ics-summi -2019/
4 h ps://wyscou .com
5 h ps://www.scispo s.com
6 www.in el.co.uk/con en /www/uk/en/i -managemen /cloud-analy ic-hub/da a-powe ed- oo ball.h ml
7 h ps://www.ga ne .com/en/human- esou ces/insigh s/ alen -analy ics