PERFORMANCE OF CZECH FOOTBALL CLUBS: MALMQUIST INDEX APPROACH

102 102 ACC JOURNAL 2021, Volume 27, Issue 2 DOI: 10.15240/ ul/004/2021-2-009 PERFORMANCE OF CZECH FOOTBALL CLUBS: MALMQUIST INDEX APPROACH Michal Tomíček1; Na alie Pelloneo á2 Technical Uni e si y o Libe ec, Facul y o Economics, Depa men o Business Adminis a ion and Managemen , S uden ská 1402/2, 461 17 Libe ec, Czech Republic e-mail: 1[email p o ec ed]; 2na alie.pellone[email p o ec ed]z Abs ac Spo has become an impo an pa o ou li es in he mode n imes and spo ing si es con ibu e signi ican ly o he image and ex u e o mode n ci ies. Rega ding he popula i y o spo , and oo ball in pa icula , i has become an impo an mode n place whe e speci ic ypes o economic and social in e ac ion ake place. The aim o his a icle is o p opose a me hod o e alua ing he pe o mance o oo ball clubs based on DEA and Malmquis index. P o essional Czech oo ball clubs playing in he Czech oo ball compe i ion Fo una:Liga we e selec ed o empi ical analysis. To analyze he ela i e e iciency o oo ball clubs, BCC and CCR models we e employed. The s udy was conduc ed on a sample o 20 clubs h ough 2 inpu s and 1 ou pu collec ed du ing he 2015/16 – 2019/20 seasons. Fo some clubs he alues o he Malmquis index we e calcula ed. Wi h help o MI i was possible o quan i y he o al p oduc i i y change ac o and o decompose i o echnological change and echnical e iciency change. The esul s show ha Czech oo ball clubs achie ed a ela i ely high le el o e iciency in he pe iod moni o ed and ha adi ional clubs achie ed he highes e iciency sco e. These esul s could help club manage s imp o e he pe o mance o hei eams. Keywo ds DEA; Spo s s a is ics; Foo ball; Czech p o essional oo ball clubs; Malmquis index. In oduc ion P o essional spo s compe i ions a ac millions o spec a o s wo ldwide. Spo s ac i i ies hen expand u he and each o he c i ical economic sec o s, such as hospi ali y, he media, o ou ism. One o he wo ld’s mos impo an spo s compe i ions is mainly oo ball compe i ions, which a e ex emely impo an o hei economic and social ac i i y. In he Czech Republic, p o essional oo ball clubs a e ep esen ed by a p o essional oo ball league called Fo una:Liga. 16 eams pa icipa e in he Czech highes oo ball compe i ion Fo una:Liga. His o ically, he mos success ul club has been P ague AC Spa a wi h 36 i les, ollowed by SK Sla ia P ague wi h 20 i les. In he las i e seasons, he Czech leagues ha e been domina ed by he FC Vik o ia Plzeň and SK Sla ia P aha eams [1]. Only h ee eams ha e pa icipa ed in all 27 yea s o he independen Czech league (since 1993): AC Spa a P aha, SK Sla ia P aha and FC Slo an Libe ec [1]. Al hough oo ball clubs ope a e wi hin he same legal amewo k as o he companies, hey a e a pa icula ype o business due o being mainly condi ioned by spo ing ac i i ies. The ques ion, he e o e, a ises as o how o measu e he pe o mance o oo ball clubs. Some s udies mainly show a posi i e ela ionship be ween spo s and inancial pe o mance. In his con ex , he e is a need o ecommend oo ball manage s’ p ac ices ha could help clubs achie e bo h spo s and business achie emen s. In his a icle, a me hod o e alua ing he 103 103 pe o mance o oo ball clubs based on da a en elopmen analysis and Malmquis index will be p oposed. 1 Li e a u e Re iew In he las ew yea s, a conside able numbe o pape s ha e been published ha de e mine he economic and manage ial e iciency by da a en elopmen analysis in a ious spo s. Bha , Sul ana and Da [2] p esen an ex ensi e s udy on he applica ion o a ious models o DEA in baseball, baske ball, c icke , cycling, oo ball, gol , handball, and ennis. I is ound ha DEA iden i ied he sou ces o ine iciency and p o ided possible di ec ions o imp o emen . A summa y o selec ed au ho s who apply he DEA me hod in hei esea ch ollows. Espi ia- Escue and Ga cia-Ceb ian [3] apply he DEA me hod and Malmquis indices o he e alua ion o he oo ball eams ha ha e pa icipa ed in he UEFA Champions League. B osed Láza o, Espi ia-Escue and Ga cia-Ceb ian [4] e alua e he pe o mance o Spanish i s -di ision baske ball eams, in e ms o e iciency. The second aim is o examine he o al ac o p oduc i i y e olu ion ha ing in o ma ion om se e al yea s. In hei a icle, Ga cia- Ceb ian and Espi ia-Escue [5] analyze p oduc i i y le els and i s componen s o eams ha pa icipa ed in he UEFA Champions League be ween 2003 and 2012. A s udy by Kang [6] measu ed he ela i e e iciency and p oduc i i y change o Ko ean p o essional spo s eams using he DEA model and he Malmquis Index o 2006-2009. Ja din [7] e alua e he e iciency o F ench oo ball clubs om 2004 o 2007 using da a en elopmen analysis. Then, in his esea ch, s udies he dynamics o clubs’ pe o mance using he Malmquis index. Ba os and Dou is [8] es ima e changes in p oduc i i y using da a en elopmen analysis applied o a ep esen a i e sample o oo ball clubs ope a ing in he wo Eu opean coun ies: Po ugal and G eece. Ba os and Dou is ank he oo ball clubs acco ding o hei change in p oduc i i y be ween he 1999/2000 and 2002/2003 seasons, concluding ha some clubs expe ienced p oduc i i y g ow h while o he s expe ienced a dec ease in p oduc i i y. Ba os, Assa and Sá-Ea p [9] p esen a wo-s age boo s apped DEA model o analyze he echnical e iciency o B azilian p emie league oo ball clubs o he pe iod 2006–2007. 2 Me hodology The da a used o he pu poses o he esea ch come om he o icial da abases o he Czech Fo una:Liga and a e supplemen ed by p i a e da abases o companies om he oo ball en i onmen . The esea ched pe iod includes he seasons om 2015/16 o 2019/20. The esea ch is di ided in o he ollowing i e s eps. S ep 1: De ining he esea ch sample and compiling a lis o oo ball clubs o be e alua ed. The esea ch included a g oup o 20 oo ball clubs playing in he highes Czech oo ball league called Fo una:Liga. In o ma ion abou hese clubs was subsequen ly ob ained o he en i e selec ed pe iod. The co e is da a om InS a [10], which analyzes he pe o mance o a hle es and spo s eams. They a e supplemen ed by he da abase o he T ans e ma k .com se e [11] and he da abases o he Czech Fo una:Liga [1]. S ep 2: De e mina ion o ou pu and inpu a iables. Due o he numbe o DMUs (16 clubs in one season), wo inpu and one ou pu a iable we e included in he DEA model. Fo he model wi h 16 oo ball clubs (DMUs) and h ee a iables included in he e iciency model, he model has su icien disc iminan powe . The i s inpu a iable is he numbe o playe s (he eina e I1). The second inpu a iable is o al squad ma ke alue (he eina e I2). The numbe o poin s achie ed in he season was chosen as he ou pu a iable (he eina e O1). 104 104 S ep 3: Selec ion and cons uc ion o DEA models. The cen al pa o his is he esea ch o pe o mance o Czech p o essional oo ball clubs playing he highes compe i ion Fo una:Liga, h ough he me hod o da a en elopmen analysis and Malmquis index. Da a en elopmen analysis (he eina e DEA) is used as a specialized modelling ool o e alua ing he e iciency o a g oup o compa able decision- making uni s (i.e., oo ball clubs). In gene al, DEA models a e based on he inpu s and ou pu s o he e alua ed uni s. Fu he , he e iciency wi h which he oo ball club can ans o m i s inpu s in o ou pu s is compa ed, i.e. he ex en o he ou pu s he oo ball club can achie e wi h he numbe o a ailable inpu s [12]. Gene ally, DEA models can be di ided acco ding o he model’s o ien a ion in o inpu - o ien ed, ou pu -o ien ed, and non-o ien ed models. Fu he so ing o DEA models can be done based on he na u e o he p oduc ion p ocess. In his case, a dis inc ion can be made be ween models based on he assump ion o cons an e u ns o scale (CCR model), and models based on he assump ion o a iable e u ns o scale (BCC model) [13]. In he p esen ed a icle, he CCR-I and BCC-I models wi h inpu o ien a ion we e applied o he ob ained da a. In he basic inpu -o ien ed CCR-I model wi h he assump ion o cons an e u ns o scale, he objec i e unc ion is maximized unde es ic i e condi ions (1) [12]. In he case o conside ing a iable e u ns om scale (BCC-I model), i is su icien o ex end he p e ious model by he condi ion o con exi y [14]. The CCR-I and BCC-I models di e only by alid con exi y condi ion (2). 𝐸0=𝑚𝑖𝑛.𝜃−𝜀(∑ 𝑠𝑖− 𝑚 𝑖=1 +∑𝑠𝑟+ 𝑠𝑟=1 ) 𝑠.𝑡.∑𝜆𝑗𝑋𝑖𝑗+𝑠𝑖−=𝜃𝑋𝑖0,𝑖=1,…,𝑚 𝑛 𝑗=1 ∑𝜆𝑗𝑌𝑟𝑗−𝑠𝑖+=𝑌𝑟0,𝑟=1,…,𝑠 𝑛 𝑗=1 𝜆𝑗,𝑠𝑖−,𝑠𝑖+≥0,𝑗=1,…,𝑛,𝑖=1,…,𝑚,𝑟=1,…,𝑠. 𝜃 un es ic ed in sign. (1) ∑𝜆𝑗=1 𝑛 𝑗=1 (2) In his model, λj a e he weigh s o all DMUs, s-i and s+ a e slack a iables, ε > 0 is an in ini esimal cons an de ined o be smalle han any posi i e eal numbe and θ is he e iciency sco e ha exp esses he educ ion a e o inpu s in o de o his uni o each he e icien on ie . S ep 4: Technical e iciency sco e calcula ion. Technical e iciency and scale e iciency sco es we e de e mined o each oo ball club. All necessa y calcula ions we e pe o med using OSDEA-GUI so wa e. The echnical e iciency sco e de e mined using he CCR-I model is called he o e all echnical e iciency (he eina e OTE). In con as , he sco e de e mined using he BCC-I model is called pu e echnical e iciency (he eina e PTE). The o e all echnical e iciency sco e can, he e o e, be di ided in o he pu e echnical e iciency sco e and he scale e iciency sco e (he eina e SE), see ela ion (3). The scale e iciency measu es he deg ee o which a uni can imp o e i s e iciency by changing i s size [15]. 𝑆𝐸=𝑂𝑇𝐸 𝑃𝑇𝐸 (3) 105 105 S ep 5: Calcula ion o he Malmquis Index. Basic DEA models do no ake in o accoun ends o changes in he e iciency o clubs’ ac i i ies o e ime. This de iciency is elimina ed using he Malmquis Index (he eina e MI), which e alua es changes in e iciency o e ime. The MI can be di ided in o wo pa s [16]. The i s componen measu es echnical e iciency changes (E) and he second componen measu es echnological changes (T) be ween pe iods and + 1. In his a icle, inpu -o ien ed MI was used, which can be exp essed by equa ion (4). Whe e x a e inpu s in pe iod , y a e ou pu s in pe iod , x +1 a e inpu s in pe iod + 1, y +1 a e ou pu s in pe iod + 1. Eq is he change in he ela i e e iciency o uni q wi h espec o o he uni s be ween pe iods and + 1, Tq is he change in p oduc ion possibili y on ie as a esul o echnology de elopmen be ween pe iods and + 1, o in o he wo ds echnological change. In his equa ion, he MI is calcula ed as he p oduc o Eq and Tq, and i p o ides se e al ad an ages o e o he indices [16]. Componen s Eq and Tq a e gi en by equa ions (5) and (6). 𝑀𝐼𝑞(𝑥𝑡+1,𝑦𝑡+1,𝑥𝑡,𝑦𝑡)=𝐸𝑞𝑇𝑞 (4) 𝐸𝑞𝐷𝑞 𝑡+1(𝑥𝑡+1,𝑦𝑡+1) 𝐷𝑞 𝑡(𝑥𝑡,𝑦𝑡) (5) 𝑇𝑞=√ 𝐷𝑞 𝑡(𝑥𝑡+1,𝑦𝑡+1)𝐷𝑞 𝑡(𝑥𝑡,𝑦𝑡) 𝐷𝑞 𝑡+1(𝑥𝑡+1,𝑦𝑡+1)𝐷𝑞 𝑡+1(𝑥𝑡,𝑦𝑡) (6) Fo selec ed clubs and each pe iod, he alues o he dis ance unc ions and o each componen o he MI we e de e mined in he MaxDEA 7 Ul a so wa e en i onmen . Finally, he alue o he Malmquis Index was calcula ed using equa ion (4). A alue o MIq > 1 indica es an inc ease in p oduc i i y; MIq = 1 means he e has been no p oduc i i y change; and MIq < 1 means a dec ease in p oduc i i y [17]. 3 Resul s This pa o he a icle is de o ed o empi ical esea ch, whe e he nonpa ame ic DEA me hodology was used. Table 1 shows he esul ing e iciency sco es o CCR-I model o all clubs examined om all seasons o he Fo una:Liga. The las ow o Table 1 shows he a e age alues o OTE sco e. The CCR-I model esul ed in he iden i ica ion o up o ou e icien clubs in a single season. This means ha he size o hese clubs is op imal and a he same ime, he clubs a e able o ans o m he gi en inpu s in o ou pu s e icien ly. 106 106 Tab. 1: OTE sco e o Czech clubs playing Fo una:Liga Club OTE A e age 2015/16 2016/17 2017/18 2018/19 2019/20 1. FC Slo ácko 0.85 0.89 0.87 0.72 0.85 0.84 1. FK Příb am 0.52 0.71 - 0.74 0.50 - AC Spa a P aha 0.69 0.63 0.55 0.66 0.62 0.63 Bohemians P aha 1905 0.64 0.76 0.85 0.82 0.76 0.77 FC Baník Os a a 0.23 - 0.71 1.00 1.00 - FC Fas a Zlín 0.69 0.99 0.58 0.85 0.54 0.73 FC H adec K álo é - 0.96 - - - - FC Slo an Libe ec 0.85 0.60 0.63 0.71 0.72 0.70 FC Vik o ia Plzeň 1.00 0.78 1.00 1.00 1.00 0.96 FC Vysočina Jihla a 0.65 0.89 0.70 - - - FC Zb ojo ka B no 1.00 0.87 0.55 - - - FK Dukla P aha 0.66 0.83 0.79 0.48 - - FK Jablonec 0.70 0.85 0.81 0.83 0.86 0.81 FK Mladá Bolesla 0.87 0.84 0.46 0.54 0.61 0.66 FK Teplice 0.51 1.00 0.60 0.63 0.67 0.68 MFK Ka iná - 0.89 0.80 0.56 0.44 - SFC Opa a - - - 1.00 0.65 - SK Dynamo Č. Budějo ice - - - - 1.00 - SK Sigma Olomouc 0.53 - 1.00 0.68 0.73 SK Sla ia P aha 0.97 1.00 0.70 1.00 0.90 0.91 Mean 0.71 0.84 0.73 0.76 0.74 Sou ce: Own Table 2 shows he esul ing e iciency sco es o BCC-I model o all clubs examined om all seasons o he Fo una:Liga. The las ow o Table 2 shows he a e age alues o PTE sco e. The BCC-I model esul ed in he iden i ica ion o up o six e icien clubs in a single season. This means ha he clubs a e able o ans o m he gi en inpu s in o ou pu s e icien ly. 107 107 Tab. 2: PTE sco e o Czech clubs playing Fo una:Liga Club PTE A e age 2015/16 2016/17 2017/18 2018/19 2019/20 1. FC Slo ácko 0.99 0.92 1.00 0.80 0.88 0.92 1. FK Příb am 0.85 0.90 - 0.81 0.84 - AC Spa a P aha 0.74 0.73 0.66 0.67 0.82 0.72 Bohemians P aha 1905 0.80 0.88 1.00 0.84 0.79 0.86 FC Baník Os a a 0.76 - 0.81 1.00 1.00 - FC Fas a Zlín 1.00 1.00 0.84 0.86 0.84 0.91 FC H adec K álo é - 1.00 - - - - FC Slo an Libe ec 0.88 0.70 0.75 0.77 0.76 0.77 FC Vik o ia Plzeň 1.00 0.83 1.00 1.00 1.00 0.97 FC Vysočina Jihla a 0.97 0.93 0.83 - - - FC Zb ojo ka B no 1.00 0.91 0.81 - - - FK Dukla P aha 0.86 0.84 0.91 0.79 - - FK Jablonec 0.86 0.97 0.81 0.83 1.00 0.89 FK Mladá Bolesla 0.95 0.87 0.64 0.59 0.74 0.76 FK Teplice 0.75 1.00 0.78 0.76 0.92 0.84 MFK Ka iná - 0.91 0.95 0.65 0.69 - SFC Opa a - - - 1.00 1.00 - SK Dynamo Č. Budějo ice - - - - 1.00 - SK Sigma Olomouc 0.87 - 1.00 0.77 0.89 - SK Sla ia P aha 1.00 1.00 0.76 1.00 1.00 0.95 Mean 0.89 0.90 0.85 0.82 0.89 Sou ce: Own Table 2 also shows ha all clubs ha we e classi ied as e icien acco ding o he CCR-I model a e also classi ied as e icien acco ding o he BCC-I model. I is appa en ha he PTE sco e in he BCC-I model is highe han he OTE sco e in he CCR-I model, as he CCR- I model conside s he scale e iciency (SE), which educes he OTE alue. Fo clubs ha a e classi ied as ine icien acco ding o he CCR-I model ye as e icien acco ding o he BCC-I model, i can be concluded ha hei echnical ine iciency is caused by scale ine iciency. The e o e, i can be s a ed ha he size o hese clubs is no op imal and an inapp op ia e spo s ac ic was chosen. Scale e iciency and scale ine iciency is shown in Table 3. 108 108 Tab. 3: Scale e iciency o Czech clubs playing Fo una:Liga Club SE A e age 2015/16 2016/17 2017/18 2018/19 2019/20 1. FC Slo ácko 0.86 0.97 0.87 0.89 0.97 0.91 1. FK Příb am 0.60 0.78 - 0.91 0.59 - AC Spa a P aha 0.93 0.86 0.84 0.98 0.76 0.87 Bohemians P aha 1905 0.80 0.86 0.85 0.98 0.96 0.89 FC Baník Os a a 0.30 - 0.87 1.00 1.00 - FC Fas a Zlín 0.69 0.99 0.69 0.99 0.64 0.80 FC H adec K álo é - 0.96 - - - - FC Slo an Libe ec 0.96 0.85 0.83 0.92 0.95 0.90 FC Vik o ia Plzeň 1.00 0,94 1.00 1.00 1.00 0.99 FC Vysočina Jihla a 0.67 0.96 0.85 - - - FC Zb ojo ka B no 1.00 0.95 0.68 - - - FK Dukla P aha 0.76 0.99 0.86 0.61 - - FK Jablonec 0.81 0.88 1.00 1.00 0.86 0.91 FK Mladá Bolesla 0.91 0,96 0.72 0.91 0.82 0.86 FK Teplice 0.68 1.00 0.76 0.83 0.72 0.80 MFK Ka iná - 0.98 0.83 0.86 0.65 - SFC Opa a - - - 1.00 0.65 - SK Dynamo Č. Budějo ice - - - - 1.00 - SK Sigma Olomouc 0.61 - 1.00 0.89 0.82 - SK Sla ia P aha 0.97 1.00 0.92 1.00 0.90 0.96 Mean 0.78 0.93 0.85 0.92 0.83 Sou ce: Own To es ablish he p oduc i i y le els a ained by Fo una:Liga clubs, an addi ional s udy using Malmquis index was ca ied ou o imp o e unde s anding o empi ical implica ions o p oduc i i y measu es in p o essional oo ball. The sample was educed o he 10 clubs ha pa icipa ed in he Fo una:Liga in all i e seasons. The change in p oduc i i y and i s wo componen s we e calcula ed o he clubs ha pa icipa ed in he Fo una:Liga in he seasons 2015/2016 and 2019/2020, as i hey had been consecu i e yea s, wi h he objec i e o app oxima ing he e olu ion o hei p oduc i i y o e he en i e pe iod analyzed in his a icle. The esul s a e shown in Table 4. 109 109 Tab. 4: Malmquis index o he clubs pa icipa ing in he Fo una:Liga in he pe iod analyzed 2015/2016 – 2019/2020 E iciency change Technological change Malmquis index AC Spa a P aha 1.028 0.941 0.968 Bohemians P aha 1905 1.030 0.991 1.021 FC Fas a Zlin 0.983 0.930 0.914 FC Slo acko 1.000 0.951 0.951 FC Slo an Libe ec 0.971 0.932 0.905 FC Vik o ia Plzen 1.000 0.904 0.904 FK Jablonec 1.038 0.964 1.000 FK Mlada Bolesla 0.955 0.910 0.869 FK Teplice 1.073 0.926 0.993 SK Sla ia P aha 1.000 0.911 0.911 A e age 1.008 0.936 0.944 Sou ce: Own On a e age, he esul s show a dec ease in p oduc i i y, an inc ease in e iciency and echnological eg ession. I he esul s a e in e p e ed indi idually o each eam, only Bohemians P aha 1905 inc eases i s p oduc i i y, due o an inc ease in e iciency ha compensa es o i s echnological eg ession. On he o he hand, FK Jablonec expe iences no change in p oduc i i y, inc ease in e iciency and show echnological eg ession. The es o he eams show a decline in p oduc i i y, some o hem due o a d op in bo h e iciency and echnological eg ession (FK Mlada Bolesla , FC Slo an Libe ec, and FC Fas a Zlin). Analyzing he in o ma ion in Table 4, one could conclude ha o e he pe iod s udied he e is a widesp ead decline in p oduc i i y, which is also e iden in i s wo componen s, bu p ima ily in a echnical eg ession. Conclusion Gi en oo ball clubs’ cu en economic and inancial si ua ion, he e is an inc easing need o know how e icien ly a club uses i s esou ces. In addi ion, his analysis is also c ucial o e alua ing he spo s pe o mance o clubs. Among he a ious ools ha a e widely used o measu e e iciency, he da a en elopmen analysis and Malmquis index we e chosen in his a icle. In his a icle, he DEA was applied o he bes clubs in he Czech Republic, which pa icipa e in he highes oo ball compe i ion Fo una:Liga. The e we e 20 di e en clubs ha played Fo una:Liga in i e seasons (2015/16 o 2019/20). The esea ch p o ided se e al conclusions. The i s conclusion: om he analyzed pe iod om he poin o iew o he OTE sco e, he winne o Fo una:Liga was always desc ibed as e icien wi h he excep ion o he 2019/20 season and he club wi h he lowes OTE sco e o Fo una:Liga mos ly le he league a he end o he season. The second conclusion can be d awn om he la ge numbe o seasons included in he esea ch. As can be seen, in he i e seasons analyzed, no club was able o main ain e iciency h oughou he pe iod unde e iew. I is impo an o no e ha he clubs and esou ces used change om season o season, as do he opponen ’s eams. The e o e, when an e icien club uses he same esou ces in he same way in pas seasons, i is no enough o be e icien in he coming seasons. 110 110 As a hi d conclusion, a ious sou ces o ine iciency we e iden i ied. The i s sou ce o ine iciency is obse ed in pu e echnical e iciency (PTE) and is ela ed o he was e o inpu s. To achie e he same ou pu , a lowe alue o inpu s (i.e., a lowe o al squad ma ke alue o a lowe numbe o playe s) should su ice. The second sou ce o ine iciency can be obse ed by calcula ing scale e iciency and is associa ed wi h he selec ion o inapp op ia e spo s ac ics. In his case, o cou se, he eam’s head coach is he mos in ol ed. The p oblem is no jus how hey use hei spo s esou ces. These clubs should seek o de elop a medium- and long- e m s a egy o de elop new and di e en ac ics wi h he esou ces hey ha e o could ha e in he u u e. The pu chase o playe s should also ake place in he con ex o he de elopmen o hese new spo s ac ics. The e a e also clubs ha su e om bo h sou ces o ine iciency. In his case, clubs should educe esou ces and, in e ms o size, ind ou how e ec i e clubs a e de eloping spo s ac ics. In his a icle, changes in p oduc i i y in oo ball clubs playing in Fo una:Liga om 2015/2016 o 2019/2020 ha e been calcula ed by means o he Malmquis index. The esul s ob ained in his a icle show no clea inc ease in p oduc i i y du ing he pe iod s udied, bo h on he a e age and indi idually. In conclusion, i does no appea ha he o ganize o his ou namen has been able o inc ease he p oduc i i y o he eams ha has pa icipa ed in i du ing he pe iod analyzed. Consequen ly, he o e all ecommenda ion o he oo ball clubs analyzed in his s udy would be o imp o e hei p oduc i i y. B eaking down he Malmquis index in o e iciency change and echnical change and seeing ha nei he o he wo show a clea inc ease. The e o e, he ecommenda ion would be o design ules o he ou namen ha would imp o e he p oduc i i y o he pa icipa ing eams by linking spo s success o he inc ease in p oduc i i y. Li e a u e [1] FORTUNA:LIGA: S a is iky. [online]. 2021. [accessed 2021-02-15]. A ailable om WWW: h ps://www. o unaliga.cz/s a is iky?uni =1&-pa ame e =1 [2] BHAT, Z. U. H.; SULTANA, D.; DAR, Q. F.: A comp ehensi e e iew o da a en elopmen analysis (DEA) in spo s. Jou nal o Spo s Economics & Managemen . 2019, Vol. 9, Issue 2, pp. 82–109. ISSN: 2340-7425. [3] ESPITIA-ESCUER, M.; GARCÍA-CEBRIÁN, L. I.: P oduc i i y and Compe i i eness: The Case o Foo ball Temas Playing in he UEFA Champions League. A hens Jou nal o Spo s. DOI: 10.30958/ajspo.3-1-3 [4] LÁZARO, M. B.; ESPITIA-ESCUER, M.; GARCÍA-CEBRIÁN, L. I.: P oduc i i y in p o essional Spanish baske ball. Spo , Business and Managemen . DOI: 10.1108/SBM- 07-2013-0024 [5] GARCÍA-CEBRIÁN, L., I.; ESPITIA-ESCUER, M.: Technical p og ess and e iciency changes in oo ball eams pa icipa ing in he UEFA Champions League. Fó um Emp esa ial. 2015, Vol 20, Issue 1, pp. 1–27. ISSN: 2475-8752 [6] KANG, H.-J.: P oduc i i y Change and Rela i e E iciency o P o essional Spo Teams. The Jou nal o he Ko ea Con en s Associa ion. 2010, Vol. 10, Issue 10, pp. 456–463. eISSN: 2508-6723. DOI: 10.5392/JKCA.10.10.456 [7] JARDIN, M.: E iciency o F ench oo ball clubs and i s dynamics. [online]. Munich: Pe sonal RePEc A chi e. 2009. [accessed 2021-10-01]. A ailable om WWW: h ps://mp a.ub.uni-muenchen.de/19828/1/MPRA_pape _19828.pd