Traditional and synergized competitive performance models for adaptive combat sports

ˑ: 

PhD, Professor V.A. Vishnevskiy
Surgut State University, Surgut

Keywords: adaptive combat sports, athletic training, competitive performance, generalized data array, chaos-and-synergy theory.

Background. Growing competitiveness of the global sports urges the relevant sport agencies to give a special priority to integrated performance tests with the tests simulating the best competitive performance standards as close as possible, with due theoretical and practical support for the training systems [1, 4, 5]. The upcoming problems need to be addressed by interdisciplinary prudent practical research methods. We have comprehensively grounded and analyzed benefits of our new systemic synergizing approach to the individual training trajectories in our prior study report [2].
Objective of the study was to analyze different aspects of pre-season trainings in modern adaptive combat sports based on generalized data analyses and the chaos-and-synergy theory.
Methods and structure of the study. The study included analyses of the top-ranking (national and international) competitive bouts in modern adaptive taekwondo and judo, with a special priority to the physical, technical, tactical and mental fitness aspects versus the ideal performance standards and based on the chaos-and-synergy theory. We used the V.M. Yeskov’s method [3] to compute the following: quasi-attractor volume in the m-dimensional phase space of states; its statistical and geometric centers; and quasi-attractor asymmetry. The key parameters (main variables) of the system were computed by comparing two data clusters (for two competitive bouts) with sequential elimination of the variables plus comparisons of the changes in the attractor parameters. The analyzing method was considered in more detail in our prior study report [2].
Study findings and discussion. Subject to our first analysis were the effectively mobilized competitive skills in the adaptive taekwondo bouts at the 2016/ 2017 European Championships: see Table 1.

Table 1. National and international adaptive taekwondo bouts at the 2016/ 2017 European Championships: trained competitive skills mobilization factors

Athlete’s initials, nation

Trained skills mobilization factor

Difference

2016

2017

IL, Russia

1501,79

1881,07

379,28

AA, Russia

1589,35

1714,09

124,74

FV, Russia

1703,13

1830,70

127,57

II, Russia

1475,08

1417,05

-58,03

IM, Russia

1474,58

1851,18

376,6

SN, Serbia

1729,88

2115,39

385,51

MI, Croatia

1385,03

1637,14

252,11

SS, Iran

1685,02

1867,23

182,21

KV, Russia

1599,67

2082,33

482,66

 

Our analysis demonstrated that the all-round competitive fitness (integrating every fitness aspect) including the trained skills mobilization factors) has grown up in 2017 to 2016 in most of the sampled athletes. Comparative analysis of the generalized data arrays showed the above growth being secured by progress in scoring actions (56.76%), competitive performance (56.17%), improved technical actions (22.97%); attack success rate (16.52%); total technical fitness (15.01%); accuracy (14.62%); physical fitness in every aspect (14.19%); emotional control skills (13.99%); tactical versatility (13.95%); competitive determination (12.46%); and the improved leadership qualities (10.29%).
Our data analysis governed by the chaos-and-synergy theory (see Table 2) showed that the growth in the technical skills mobilization rate in 2016 through 2017 was associated with the growth of the quasi-attractor volume, asymmetry and chaotic processes in the system.

Table 2. Quasi-attractor parameters versus the trained skills mobilization aspects in the national and international adaptive taekwondo bouts at the 2016/ 2017 European Championships

Parameters

2016

2017

Quasi-attractor volume (General V value)

2,06793348E0046

1,31071592E0048

Quasi-attractor asymmetry (General asymmetry value)

217,3532

234,0698

Chaotic process center (Ch)

95,2613

Geometric center (Cg)

88,4771

 

The training system aspects that may be qualified group order parameters of special influence on the inter-attractor distances (i.e. on the trained skills mobilization factor and shares) are the following: competitive success rate; competitive performance variations; scoring technical actions; and the scoring attacks.
Our comparisons of the competitive performance aspects of the national versus international competitors showed leadership of the latter (1676.67 versus 1736.62 points, respectively). The national athletes were found to lag behind mostly in the complex-coordination-intensive (-23.21%) and strength-intensive (-20.55%) skills; scoring technical actions (-19.63%); tactical versatility (-8.84%); and special coordination (-8.46%). The national athletes were tested, however, with modest advantages in the overall technical fitness (9.94%); emotional control skills (7.74%); tactical control skills (7.26%); technical versatility (7.15%); accuracy (6.89%); and the technical efficiency (6.64%).
Analyzed in Table 3 hereunder are the trained skills mobilization factors versus the weight classes in Russian Adaptive Judo Championship; with the data analysis underlining the following two aspects. First, the average trained skills mobilization factor was found to normally fall with the weight growth. And second, the highest trained skills mobilization factors are normally scored by the final bouts.

Table 3. Trained skills mobilization factors versus the weight classes at the Russian Adaptive Judo Championship

Weight class, kg

Trained skills mobilization rates of the winners

Trained skills mobilization rates of the losers

First bout

Second bout

Third bout

First bout

Second bout

Third bout

66

1323,5

1345,5

1466,5

1169

1224

1302

81

1230,8

1227,3

1230,6

1076

929

999

100

1060,8

1145

1201

977

1043

939

 

Having analyzed the competitive performances, we found the losers being second in the trained skills mobilization factors in every bout. Advantages of the winners were widely different albeit dominated by the trained skills mobilization factors (178.1%); win tactics (22.9%); defense success rate (12.5%); competitive determination (11.2%); emotional control skills (10,6%); and the competitive stress tolerance (10.5%).
Our data analysis governed by the chaos-and-synergy theory (see Table 4) showed the same regularities as in case of the trained skills mobilization factors. The analysis found high positive correlations of the trained skills mobilization factor with the quasi-attractor asymmetry (r=0.979, p<0.01, n=18); and mean inverse correlation with the body mass (r=-0.663, p<0.01, n=18). The similar correlation was found for the quasi-attractor asymmetry and body mass (r=-0.521, p<0.05, n=18).

Table 4. Quasi-attractor parameters versus the weight classes at the Russian Adaptive Judo Championship

Weight class, kg

Quasi-attractor asymmetry (general asymmetry value) of the winners

Quasi-attractor asymmetry (general asymmetry value) of the losers

First bout

Second bout

Third bout

First bout

Second bout

Third bout

66

293,82

296,72

323,21

262,83

273,27

293,79

81

286,36

282,33

285,22

245,44

212,18

231,89

100

258,80

269,18

280,26

237,96

250,08

222,56

 

Effects of the key variables were found rather individual for the sample. Thus the variables that may be qualified as order parameters of influence on the inter-attractor distances for AR (66 kg weight class) were ranked as follows by importance: special strength rate; physical stress tolerance rate; preparatory tactical actions; situation-specific tactical actions; and the overall tactical fitness. In case of KR (81 kg weight class), the attractor parameters were dominated by the physical training system efficiency; competitive performance versatility; and the scoring technical actions. And in case of KA (100 kg weight class), variations in the attractor trajectory were driven by the tactical control skills; situation-specific technical actions; and the overall technical and tactical fitness rates.
Conclusion. Analyses of the generalized data arrays to rate the trained skills mobilization in the top-ranking competitions and the analyses governed by the chaos-and-synergy theory may be viewed as complementary. The analytical method proposed herein may be recommended for the training system design and management purposes in the modern adaptive combat sports for competitive success.

References

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  5. Simakov A.M., Pavlenko A.V., Simakova E.A. , Tajmazov V.A.  [ed.] K voprosu o nauchno-metodicheskom soprovozhdenii regionalnykh sbornykh komand v olimpiyskom tkhekvondo [Scientific and methodological support of regional teams in Olympic taekwondo]. ‘Sport, Chelovek, Zdorovye' ['Sport, Man, Health']. Proc. 8th International Congress, Oct.12-14 2017.  St. Petersburg: SPbU publ., 2017.

Corresponding author: sakurasurgut@mail.ru

Abstract
The study provides experimental data to substantiate selection and assessment of the competitive performance models for adaptive combat sports based on a generalized data analysis and chaos-and-synergy theory. It was found that the numbers of actual combat skills actually employed in competitive bouts are in an inverse correlation with the body mass, with the actual competitive records confirming the finding. A competitive success depends on how efficient is the fighter in mobilizing every trained skill and versatility in pursuing the own tactics (fight plan), in defense and attacks, and how mentally and emotionally balanced and tolerant the athlete is to the stresses. Synergy analysis showed that the higher is the trained skills mobilization factor the higher is the quasi-attractor asymmetry with the growing chaos in the system. The trained skills mobilization factor was found positively correlated both with the quasi-attractor asymmetry (r=0.979, p<0.01, n=18) and inversely correlated with the body mass (r=-0.663, p<0.01, n=18). The analysis also found the following variables of the highest effect on the degree of order and trained skills mobilization factor: success rate; competitive determination; scoring technical actions; and the attack success rate.