Sroi-based sport school performance efficiency rating analysis

Dr. Sc. Econ., Professor A.M. Makarov1
PhD, Associate Professor A.V. Litvin1
PhD A.N. Lashkarev1
Master's student D.P. Arkalov1
1Udmurt State University, Izhevsk

Keywords: social return on sport investments, SROI concept, sport school performance rating.

Background. The national government gives a high priority to the social service sector (including the mass sport sector) development initiatives, with the sport investment activity supported by the full range of the relevant governmental (federal, regional and local) agencies and the growing public-private partnerships (PPP); and with the social investment design and management issues addressed in a wide variety of legal and regulatory provisions and target developmental programs. Efficiency of the social investment projects needs to be secured by the relevant progress tests and analytical toolkit that, in its turn, requires a sound test, data processing and analytical methodology for success [2].

It is common knowledge that modern mass sports are highly beneficial for the national communities due to their physical, mental and emotional conditioning effects; benefits for individual adaptability, including those for the migrant/ ethnic population groups; positive effects on the hate crime statistics; savings in spending on the state-supported health services etc. [6, 1].

One of the key reporting problems for the state-supported sport institutions is that only a relatively small part of the social benefits they generate may be quantified and monetized. It was in the late 1990s that the Social Return on Investment (SROI) tool was offered for the project cost-benefit analysis [5, 6]. Generally the SROI analyses factor in the economic, environmental and social costs and benefits of the sport projects presentable in monetary terms. A special emphasis is being made today on the initiatives to improve the SROI rates, derivatives and toolkits in application to specific sport facilities, sport disciplines, competitive events etc., with the relevant issues comprehensively addressed by the professional sport school management training curricula [3].

Objective of the study was to assess benefits of the SROI-based sport school performance rating analysis.

Methods and structure of the study. We mined the 2017 reports of the Izhevsk sport schools for the study purposes to summarize and analyze them in Table 1 hereunder, with the sampled schools coded by numbers. The school operation and maintenance (O&M) costs, trainees’ numbers and the competitive success statistics were mined in the Sport School Reports (Form 5PE) and the data available on the official municipal website bus.gov.ru; and the academic progress groups (with the excellent and good scores) were obtained from the sport school management reports.

For the SROI analysis of the school performance statistics, we computed the school costs and wins (first places in competitions) per capita (trainee), followed by the win-to-cost (‘win cost’) calculations. Of special interest for the SROI analysis is the correlation between the school competitive success rate (wins per capita) and school progress, i.e. the excellent and good rated school populations.

Table 1. Performance statistics reported by the Izhevsk sport schools

School code

School operation and maintenance cost, RUR’000

Trainees

Cost per capita, RUR’000

Share of the excellent and good trainees

Wins in the national sport events per capita

Wins in the national sport events

1

17 936,20

984

18,23

0,58

0,021

21

2

14 655,70

555

26,41

0,63

0,000

0

3

20 717,80

753

27,51

0,63

0,008

6

4

19 250,30

949

20,28

0,21

0,005

5

5

11 130,80

705

15,79

0,48

0,000

0

6

10 181,80

732

13,91

0,70

0,003

2

7

33 699,00

826

40,80

0,40

0,011

9

8

12 267,90

312

39,32

0,53

0,054

17

9

10 701,00

259

41,32

0,41

0,000

0

10

7349,40

325

22,61

0,52

0,006

2

11

8920,90

531

16,80

0,58

0,064

34

12

10 395,90

355

29,28

0,59

0,006

2

13

5170,30

97

53,30

0,49

0,000

0

14

36 866,20

1156

31,89

0,44

0,007

8

15

10 329,60

407

25,38

0,76

0,000

0

16

3469,10

43

80,68

0,05

0,023

1

17

24 188,70

1590

15,21

0,89

0,000

0

18

16 177,50

621

26,05

0,54

0,031

19

19

19 083,50

492

38,79

0,82

0,026

13

20

8817,10

552

15,97

0,90

0,013

7

 

The reporting data were processed by the mean values computation and data processing, grouping and correlation analyzing Data Mining toolkit. [1, 4]. The toolkit requires no prior hypotheses, and this is the reason why it is popular as the ‘surveillance analytical’ instrument.

Study findings and discussion. The sampled sport schools were classified into two groups by their costs per capita (see Table 2), with the median value estimated at RUR26.23 thousand; and with the mean arithmetic costs estimated at RUR19.02 and RUR40.03 thousand for Groups 1 and 2, respectively. The cost-per-capita data show almost a two-times gap between the school groups that may be explained by their sport specialization on the one hand and the cost management efficiency on the other hand.

Table 2. Sampled sport schools grouped by their O&M costs per capita

 

Group 1

Group 2

School codes

6; 17; 5; 20; 11; 1; 4; 10; 15; 18

2; 3; 12; 19; 8; 7; 9; 13; 16

Average group cost per capita, RUR’000

19.02

40.93

Furthermore, we calculated the schools’ win cost ratios (see Table 3), with the sample median value estimated at 0.26; and found the eight-fold gap between the school groups – indicative of the unusual difference of the group win costs. Reasons for this gap cannot be immediately explained without a special analysis. It may be only pertinent to mention in this context that Group 1 is dominated by the team sport disciplines.

Table 3. Sampled sport schools grouped by the win cost, in the won 1st places per RUR million

 

Group 1

Group 2

School codes

17; 5; 15; 2; 9; 13; 12; 6; 14; 4

7; 10; 16; 3; 19; 20; 1; 18; 8; 11

Average group win per RUR’000,000

0.11

0.87

The above data were consolidated in Table 4 to further group the schools into the following 4 categories: high efficiency schools by at least two performance efficiency rates; low efficiency schools by at least two performance efficiency rates; and the two interim categories. The high-efficiency category (20; 11; 1; 10; 18 in Table 4) includes mostly the sport schools specialized in martial arts, track and field sports and other individual sport disciplines. It should be mentioned that the high consolidated efficiency rate may be explained both by the natural reasons (e.g. the sport specifics) and the sport institution management efficiency. For the purposes of the SROI analysis, however, a special attention shall be given to the schools and sport disciplines that secure the highest return on investments. 

Table 4. Sampled sport schools grouped by the two social return on investment rates

SROI rates

Low costs per capita

High costs per capita

Low average group win per RUR’000,000

6; 17; 5; 4; 15

2; 12; 14; 9; 13

High average group win per RUR’000,000

20; 11; 1; 10; 18

3; 19; 8; 7; 16

 

To further visualize the above analytical data, we applied a multidimensional scaling tool to the data given in Table 1. The scaling makes it possible to locate the schools on the list so their locations correspond to the six performance efficiency rating factors. As provided by the Diagram 1 hereunder, a school position on X axis reflects the numbers of trainees and total finance; and on the Y axis – indicates their win costs per capita.

 

Diagram 1. Scaled averaged school performance efficiency data

 

The Diagram gives the means for a further deeper analysis to identify the factors of influence on the school locations versus the school performance efficiency rates. 

Conclusion. The study data and analyses showed significant differences in the SROI-based sport schools performance rates including the numbers of trainees and the win cost rates, with the sampled school groups found to differ two times on the costs per capita scale and eight times on the win cost scale. From the SROI standpoint, we see a great potential for the sport school performance improvements both by the sport-specific financial support and the school management efficiency improvement initiatives. Based on the study data and analyses, we offered a sport schools classification model with a special priority to the social return on investments, and classified the study sample into four school categories by the SROI rates. The factors of influence on the school performance categories and the relevant management system improvements need to be explored by further special studies. Our study and analyses support the prior hypothesis of the competitive and education progress being correlated in sport schools and largely determined by the school management efficiency, with the correlation estimated at -0.07 by the study. It is not improbable, however, that the performance efficiency is also sport-discipline specific and depends on the determination and gifts of the school instructors and trainees. 

References

  1. Dubrov A.M., Mkhitaryan B.C., Troshin L.I. Mnogomernye statisticheskie metody [Multidimensional statistical methods]. Moscow: Finansy i statistika, 2003, 352 p.
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  5. Emerson J., Wachowicz J., Chun S. Social Return on Investment: Exploring Aspects of Value Creation in the Nonprofit Sector. San Francisco: Roberts Enterprise Development Fund, 2009.
  6. Litvin A.V., Arkalov D.P., Anoshin A.V. Sotsialny vozvrat investitsiy v sport (SROI): modifitsirovannaya model [Social Return on Investment in Sport (SROI): Modified Model]. Global issues and new ideas in sport management: book of abstracts of the 2nd world association for sport management world conference, 20th-23rd June, 2017 Kaunas, Lithuania / eds.: I. Valantine, B. Pitts, J. J. Zhang. Kaunas : Lithuanian Sports University, 2017, pp. 102-103.

Corresponding author: aleksandrm.makarov@yandex.ru

Abstract

The study analyzes benefits of the SROI (Social Return on Investment) based sport school performance rating analysis, with the data for the analyses mined in the 2017 reports of the  Izhevsk sport schools including their statistical Sport School Reports (Form 5FK) and the data available on the official municipal website bus.gov.ru. The reporting data were processed by the mean values computation and data processing, grouping and correlation analyzing Data Mining toolkit. The analyses showed significant differences in the SROI-based sport schools performance rates including the numbers of trainees and the competitive success rates per every invested RUR. The SROI-based analyses showed a great potential of the sport schools in the sport financing and performance improvement aspects. Based on the study data and analyses, we offered a sport schools classification model with a special priority to the social return on investments, and classified the study sample into four sport school categories by the SROI derivative rates.