actuarial summit 2015 – colegio actuarial mexicano – relation between football players´ market...
TRANSCRIPT
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
Relation between football players´ market value and national team performance during 2014 FIFA World Cup qualificationAutor: Martin FléglFecha: 12.8.2015
AVISO LEGALEste material ha sido elaborado y presentado bajo la responsabilidad exclusiva del autor. Los puntos de vista, opiniones y contenido de este material así como los derechos intelectuales son responsabilidad exclusiva del autor a título individual y no representan ninguna de las posturas oficiales de la Agrupación Actuarial CAM, A.C. quienes a través de su marca Colegio Actuarial Mexicano (CAM) y sus miembros no aceptan responsabilidad o pérdida causada por personas o entidades por el uso, actuación o enfoque derivados de la información de sus contenidos, comunicados, seminarios, programas publicaciones o actividades de carácter general, ya sea que dicha responsabilidad o pérdida haya sido causada por negligencia, omisión o alguna otra índole.
1
Actuarial Summit2015
México
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
Content
• Introduction• The initial study• Data Envelopment Analysis• Results of the initial study• Relation between market value and performance• New DEA model• Conclusion• References • About the author
2
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.orgPerformance analysis during the 2014 FIFA
World Cup qualification• This contribution is based on the article that has been
published in the Open Sports Science Journal [1]• The initial idea was to compare the difficulty leading to
successful qualification to the 2014 FIFA World Cup in Brazil• Which team had the taughest way to Brazil?• What differences could we observe within the qualification
zones?• For this purpose Data Envelopment Analysis (DEA) was used to
assess the performance of all qualified teams (31 teams)
3
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
2014 FIFA World Cup – sample
• Sample consisted of 31 national teams• Asian zone (AFC): Australia, Iran, Japan, and Korea Republic• African zone (CAF): Algeria, Cameroon, Côte d'Ivoire, Ghana, and
Nigeria• North, Central America and Caribbean zone (CONCACAF): Costa Rica,
Honduras, Mexico, and USA• South American zone (CONMEBOL): Argentina, Chile, Colombia,
Ecuador, and Uruguay• European zone (UEFA): Belgium, Bosnia-Herzegovina, Croatia,
England, France, Germany, Greece, Italy, Netherlands, Portugal, Russia, Spain, and Switzerland
• No team from the Oceania zone (OFC), because New Zealand lost the intercontinental play-off against Mexico
• Data referred to the period of the FIFA World Cup qualification from June 2011 to November 2013 depending qualification zone 4
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
2014 FIFA World Cup – data
1) For the analysis data from the official FIFA website was used:• The FIFA/Coca-Cola World Ranking [2]• Qualification statistics [3]
2) Data from official websites of each football confederation (UEFA, CONMEBOL, CONCACAF, AFC, CAF, OFC)
5
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
2014 FIFA World Cup – qualification zones
• Significant differences among qualification zones exist• Different teams´ qualities regarding the FIFA world ranking• CONMEBOL and UEFA zones are the most difficult ones• This fact was reflected by the number of qualified teams to Brazil
(CONMEBOL 4.5 spots and UEFA 13.o spots, while AFC 4.5 spots, CAF 5.0 spots, CONCACAF 3.5 spots, OFC 0.5 spot)
6
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.orgMeasuring efficiency and performance in
football• Football performance evaluation is a much discussed issue, as many
other business and industrial areas• Mainly the evaluations focus on the club level within national
leagues [4], [5], [6], [7]• Few studies focus on performance of football players [8] or
offensive/defensive aspects of a game [9]• Most of the studies use Data Envelopment Analysis for the
assessment
• What is performance?• Performance in the 2014 FIFA World Cup qualification is as a long-term
capability to transform effectively its inputs (players, played matches etc.) to outputs (gained points and score).
• Thus, a higher performance reaches a team, which uses fewer players and plays against more difficult opponents, and, on the other hand, gains either more points or reaches better goal difference.
7
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
Data Envelopment Analysis
• The Data envelopment analysis (DEA) enables to assess various Decision-Making Units (DMUs) with regard to their abilities to cover multiple inputs into multiple outputs [10]
• DMUs can be of various character, such as universities, hospitals, government institutions etc.
• Each DMU can have various amounts of m different inputs to produce s different outputs
• DEA model is either input or output oriented• If we suppose constant returns to scale (CRS), the so-called CCR
model can be used.
8
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
Data Envelopment Analysis
• The output-oriented CCR model is formulated as follows:
9
subjected to
• where xij is the amount of input i of DMUj, yrj is the amount of output r of DMUj, vi and µr are weights of inputs and outputs, and ε is a so-called non-Archimedian element.
• DMU is 100% efficient if q=1, i.e. there is no other DMU that produces more outputs with the same combination of inputs. On the other hand, DMU is inefficient if q≥1.
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
Data Envelopment Analysis
• If the model supposes variable return to scales (VRS), the so-called BCC model can be used [11].
• The output-oriented BCC model is then formulated similarly as the CCR model, but requires constraint .
• This added constraint introduces an additional variable µ0 into the model.
• This extra variable enables to express variable returns to scale [10].
10
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
DEA model structure
• Structure of the model was chosen with regard to an effort to minimize differences among all qualification zones
• Inputs of the model expressed the whole qualification process of each team:• First input PLAYERS refers to a number of players, which each
national team used in the qualification matches• Second input PLRTIME expresses a ratio of minutes played per each
player of the national team during the qualification• Third input MATCHES refers to a number of played matches during
the qualification• Fourth input GPRRANK expresses the difficulty of each qualification
group• GRPRANK includes FIFA ranking from June 2011 (when the qualification
began), from June 2012 (the middle of the qualification), and from November 2013 (when the qualification finished). So, GRPRANK includes weighted average of all opponent teams 11
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
DEA model structure
• Outputs of the model:• First output PTS describes how many points each national team
gained during the qualification• Due to the differences in each qualification zones, this output expresses a
percentage of possible gained points• Second output SCORE reflects the difference between goals for
(number of goals that a national team scored) and goals against (number of goals opposite teams scored)• The goals difference is calculated per a match
12
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
DEA model – discrimination ability
• For higher discrimination ability of the DEA model a set of constraints (AR – assurance region constraints) were introduced according to [10]:
13
where Lij is the lower bound assigned to the ratio of variables i and j, Uij is the upper bound assigned to the ratio of variables i and j, vi is the weight for the variable i, vj and is the weight for the variable j
0.5 ≤ (PLAYERS/MATCHES) ≤ 20.5 ≤ (GRPRANK/PLRTIME) ≤ 20.5 ≤ (PTS/SCORE) ≤ 2
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
Initial study - Results
• The output-oriented DEA model with constant returns to scale (CCR model), and variable returns to scale (BCC model) were used.
• Output-orientation was used due to the main objective of the paper, i.e. to calculate the performance of the national football teams in the 2014 FIFA World Cup qualification according to their outputs
• According to the CCR DEA model, only four national teams reached the performance of 100%: Belgium, Colombia, Germany and Uruguay (representing only 12.9% of all qualified teams)
• Bosnia-Herzegovina (95.271%) and Ecuador (98.634%) are both close to the performance of 100%.
• On the other hand, the lowest CCR performance scores reached Korea Republic (35.395%), Croatia (40.557%), Cameroon (42.791%), Australia (43.902%) and Honduras (45.504%).
• The average CCR performance during the 2014 FIFA World Cup qualification was 67.242%. 14
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
Initial study - Results
• Regarding the BCC DEA model, two more national teams reached the performance of 100% (Ecuador and Netherlands), representing now 19.34% of all national teams.
15
• The presented model included only game factors• However, we should also consider other factors, such as quality of
players or prestigious of each national league• Quality of players can be measured as their market value
• Is there a relation between players´ market value (national team value) and national team performance?
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.orgRelation between national team value and
performance• To express national team value, I used the official statistics from
Transfermarket.com• This site constantly observes and assesses the players´ market values• It is the most used source about market value available for public• Primarily, the site focused only on the European market• Nowadays, the site assesses market values in almost all countries
(data unavailability in particular cases, mainly for countries with a very small market value).
• In total, the market value analysis consists of 713 football players from all qualified teams to the 2014 FIFA World Cup• Similarly as to GRPRANK, market value is linked to June 2011 (when
the qualification began), June 2012 (the middle of the qualification), and from November 2013 (when the qualification finished)
• As the market value is not published on a monthly basis (irregularities), market value is with regard to the closest date to the above mentioned stick dates.
16
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.orgRelation between national team value and
performance
17• Argentina depends on Messi and Portugal on Ronaldo• The highest market value have teams form TOP 5 leagues in
Europe (Spain, England, Germany, France, Italy)
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.orgRelation between national team value and
performance
18• The market value depends on the European market
• That is the reason for 13.0 spots (42%) for UEFA members in World Cups
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.orgRelation between national team value and
performance• European market value is 1.5 time bigger than South American
zone (CONMEBOL) and 3 times bigger than African zone (CAF)
19
• However, team market value does not correspond with its performance (r=39.94%), i.e. moderate strength
• Similar moderate strength (r=40.03%) can be observed between GRPRANK and performance biggest input influence
• As a result, the performance relates to combination of several indicators (SCORE has the biggest output influence as r=48.81%)
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.orgRelation between national team value and
performance• To reflect this observations, a modified DEA model is calculated
• The input/output structure is the same, except the addition of market value input (so, 5 inputs and 2 outputs)
• Expectation: growth of performances in case of teams with lower market value
20
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.orgRelation between national team value and
performance
21• Including market value into the DEA model influences teams from CONCACAF, ACF, and partly CAF zones (i.e. teams with lower market value)• Average BCC performance increased from 70.97% to 82.94%
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
Conclusion
• There is no relation between teams´ performance and their market value
• The relation is only moderate (r=39.94%)• Including market value indicator into DEA model results in:
1) Growth of the average performance by 11.97%2) Performance growth for CONCACAF, ACF, and CAF teams
• Different structure can led to different results• We can take presented results as a preliminary study for future
research
22
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
References
[1] Flégl, M. 2014. Performance Analysis During the 2014 FIFA World Cup Qualification, The Open Sports Sciences Journal, vol. 7, pp. 183-197. http://dx.doi.org/10.2174/1875399X01407010183[2] FIFA. FIFA/Coca-Cola World Ranking Procedure. Fédération Internationale de Football Association (FIFA). 2014. [cited 2014 April 10]. Available from: http://www.fifa.com/worldranking/rankingtable/index.html?intcmp=fifacom_hp_module_associations[3] FIFA. 2014 FIFA World Cup Brazil - Regulations. Fédération Internationale de Football Association (FIFA). 2011. [cited 2013 December 4]. Available from: http://www.fifa.com/mm/document/tournament/competition/01/47/38/17/regulationsfwcbrazil2014_en.pdf[4] Carmichael F, Thomas D, Ward D. Team performance: the case of English premiership football. Managerial and Decision Economics 2000; 21 (1): 31-45. http://dx.doi.org/10.1002/1099-1468(200001/02)21:1<31::AID-MDE963>3.3.CO;2-H[5] Barros CP, Garcia-del-Barrio P. Efficiency measurement of the English football Premier League with a random frontier model. Economic Modelling 2008; 25: 994-1002. http://dx.doi.org/10.1016/j.econmod.2008.01.004[6] García-Sánchez IM. Efficiency and effectiveness of Spanish footbal teams: a three-stage-DEA approach. Central European Journal of Operations Research 2007; 15 (1): 21-45. http://dx.doi.org/10.1007/s10100-006-0017-4[7] Haas DJ. Technical efficiency in the Major League Soccer. Journal of Sports Economics 2003; 4 (3): 203-215. http://dx.doi.org/10.1177/1527002503252144
23
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
References
[8] Tiedemann T, Francksen T, Latacz-Lohmann U. Assessing the performance of German Bundesliga football players: a non-parametric metafrontier approach. Central European Journal of Operations Research 2011; 19 (4): 571-587. http://dx.doi.org/10.1007/s10100-010-0146-7[9] Boscá JE, Liern V, Martínez A, Sala R. Increasing offensive or defensive efficiency? An analysis of Italian and Spanish football. 2009; 37: 63-78. http://dx.doi.org/10.1016/j.omega.2006.08.002[10] Cooper WW, Seiford LM, Zhu J. Handbook on Data Envelopment Analysis. International Series in Operations Research & Management Science; 164. Springer, 2nd edition 2011. http://dx.doi.org/10.1007/978-1-4419-6151-8[11] Banker R, Charnes A, Cooper WW. Some models for estimating technical and scale inneficiencies in data envelopment analysis. Management Science 1984; 30: 1078-1092.
24
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
25
Questions???
Actu
aria
l Sum
mit
2015
– C
oleg
io A
ctua
rial M
exic
ano
– w
ww
.col
egio
actu
aria
l.org
About the author
• Martin Flégl obtained his Ph.D. degree at Department of Systems Engineering, Czech University of Life Sciences Prague, Czech Republic. He is currently a lecturer at Business School, University La Salle. During his studies, he also spent 2 semesters at Catholic University of Lyon, France (ESDES – Commerce and Management) and one semester at University of Plymouth, United Kingdom.
• His main research interests include decision-making models and efficiency modeling within the area of higher education, sport and economy.
• He has been teaching subjects such as Decision-making models, Methods of Operational Research and Management Science and Mathematics.
• Last but not least, he is Executive editor of the Journal on Efficiency and Responsibility in Education and Science and Editor of international conference on Efficiency and Responsibility in Education.
26Contact:[email protected]