Associate Professor, PhD A.A. Kabanov2
Associate Professor, Dr.Hab. V.M. Bashkin1
1St. Petersburg State University of Aerospace Instrumentation, St. Petersburg
2St. Petersburg Polytechnic University n.a. Peter the Great, St. Petersburg
Keywords: diagnosing forecast algorithm, educational diagnostics, sport, management technology, training process
In the situation of the growing priority being given both to mass sport and elite sports, it is the issues of scientific provisions for the training process improvement and management technology that come to the forefront of the sport theory and practice. The higher are the training process intensities and workloads – that have come very close to the natural biological limits – and the closer are the general athletic fitness rates and the leading athletes’ mastery levels, the more sophisticated athletic training and excelling process management methods are needed for the modern training system optimization purposes .
Objective of the study was to give theoretical provisions for the sport educational diagnostics in modern training process.
Study results and discussion. We would define an athlete’s condition diagnostics as the research and methodological activity performed on a permanent basis and viewed as the educator’s creative process. Diagnosing analysis is interpreted herein as the focused permanent cognitive activity duly designed and scientifically substantiated [4, 2]. In the course of our theoretical study, we identified the following three types of the diagnostic process missions that jointly determine its nature:
First type missions are designed to rate the athlete’s current fitness level and include the educational control tasks and the athlete’s current state and fitness assessment tasks performed on a real-time basis;
Second type missions imply the expert ratings of the athlete’s fitness levels at certain time points including the historic ones; and
Third type missions are intended to forecast future athletic fitness rates as of the certain date in future.
The diagnosing process will be designed to verify whether or not the actual athletic fitness rates correspond to the forecast ones. Since a competitive sport accomplishment may be interpreted as the complex multi-component phenomenon, one of the key missions of the diagnosing process is to reasonably cut down the variety of applicable performance indicators and to find the most diagnostically informative ones – i.e. the ones that provide (with the test process being as limited as possible) the best and broadest possible information for a grounded conclusion to be made. The set of the most informative rates will be optimized so as to be most representative of the real situation and highly expressive in terms of the rating process results. It should be noted in this context that a real athletic fitness level as of specific moment basically depends both on the preceding process conditions and the set of training actions applied for the period under analysis .
The study has demonstrated that certain realignments of the athletic training process components (in terms of both their weights and interactions within the system) naturally take place in different training periods. Knowing that, the analyst will be guided – when making the athletic fitness ratings for every specific time period – by the period-specific set of the process modeling and diagnosing characteristics viewed as the key integrated criteria of the specific athletic fitness. Moreover, the rating will be made with a standard minimum set of informative indices being applied to rate every key aspect of the athletic fitness. The set minimizing efforts will be geared to purposefully select the reasonably limited best indicators sufficient – even when the input information is limited enough – for fair reflection of the subject athletic fitness parameters. Applied for these purposes may be the relevant mathematical data condensing methods based on the statistical/ logical analysis optimization concept .
We have developed a universal design algorithm to develop a model diagnosing system, the algorithm being adaptable to specific tasks with no detriment for the algorithmic benefits. It implies that the analytical tasks will be solved by methods giving the means to put together a primary description of the phenomenon subject to analysis. The method makes it possible to find solutions for a variety of educational missions including the athlete’s condition and fitness rating data collection, with the data being comprehensive and objective; and finding correlations of the relevant indices and their integration degrees.
The diagnosing forecast algorithm is based on certain most efficient management process parameters being selected and used to: achieve the expected fitness conditions and levels; and assess the aspects and degrees of influence of the selected parameters on the expected result of the athlete’s conditioning process. The method facilitates a few educational missions being fulfilled, including the following: athletes’ condition and fitness rating as of the certain time period in future; and the athlete’s physical performance forecast for a future period.
Sport educational diagnostics may be interpreted as the educational decision-making process management method designed to: rate the athlete’s condition and fitness level; identify the cause-and-effect relations in the “objective-means-result” chain; and offer the necessary corrective actions to optimize the athlete’s physical performance management process. It terms of the specific solvable tasks, the integrated sport educational diagnostic method is designed to:
- Rate the athlete’s current fitness, i.e. the athlete’s condition as of the current date (fitness control tasks);
- Rate the athlete’s fitness as of the certain date in the past (retro-genesis related tasks); and
- Forecast the athletes’ condition and fitness rate that may or must be achieved by the athlete as of the certain date in future (forecasting tasks).
The optimal integrated data collection system providing full information on the athletes’ condition and fitness rates based on the proved and most informative competitive performance indices; the fitness diagnosing and modeling integrated systems; due targeting of the data flow in the process; and the efficient benchmarking analysis of the past and future data arrays (on a qualitative and quantitative bases) – make it possible to optimize the educational diagnosis of the athlete’s fitness rate and improve the diagnosing process objectivity and dependability. These benefits give grounds to consider the sport educational diagnostics a new methodological avenue providing a special integrated toolkit for the modern athletic fitness management system.
The sport educational diagnostic system may be harmonically integrated in a regular athletic training system as an efficient method that combines the process control and the result modeling and forecasting functions. It is the logically grounded process correction tools driven by the diagnostic data flow that allow the method be rated among the key tools of a modern training process management system.
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