Analysis And Research Of Badminton Athletes Training Mode Optimization Based On Data Mining Techniques

Badminton is the key of our country gold projects, leads in the world rankings, occupies a very powerful position. With the implement of the new rules and the rising power of competitors, badminton players are put forward higher requirements. They should not only have excellent physical quality and psychological quality, but also have flexible technical and tactical qualities. The daily training of athletes is the primary path to improve the technical level and sports quality. The scientific, rational and targeted training process that coaches organize for athletes is particularly important.At present, in the badminton training, we base the experience of coaches and personal feeling of athletes on tactics training plan, there is no scientific data to refer to. To sum up, the factors of limiting athletes performance improving mainly include the following:firstly, the collection of badminton athletes’training and competition data is not timely, incomplete and inaccurate, and it is hard to effectively integrate these data. Secondly, the data analysis is not in-depth and systematic, and fails to draw useful rule for practical application. Thirdly, from the perspective of the training mode optimization, the targeted and personalized training program from the perspective of technique and tactics is less, and lacks the support of huge data.To solve above problems, this paper explores thecollection method for badminton players’training and competition data, designs and realizes a badminton training quality monitoring and spot tactics statistics system. The system gathers the athletes’daily training and competition data and establishes a unified comprehensive database. That solves the problem that the data acquisition is incomplete and not timely. On this basis, this paper improves the traditional Apriori algorithm of association rules, uses the improved association rule algorithm to make multidimensional data mining on these data, has carried out the simulation results, finds out athletes’weakness and advantages, makes the useful conclusion. That solves the problem that the data analysis is not in-depth. On the basis of the mining results, the paper applies the results to the badminton training, develops individualized training plan from the perspective of techniques and tactics, solves the problem that the individual training guidance is lack of data support. That provides new dieas for the optimization of badminton players training mode.The main contribution of the this paper are:firstly, data mining techniques are used to get the connection between badminton category and the goal, and applies the conclusion to the badminton training. It is the first time to put forward the individualized training plan with data support from the perspective of techniques and tactics. Secondly, the traditional Apriori association rules algorithm is improved, and the improved algorithm is applied to the badminton data for the first time, the simulation experiment is carried out. Thirdly, the paper sets up a comprehensive badminton database, stores a lot of badminton athletes’training and competition data, becomes a part of the national badminton team information database.