# Special Physical Characteristic and Training Strategy of Badminton Based on Machine Learning Algorithm

Authors: Xiangwei Guan; Yang Li; Yinhui Bai
Journal: International Transactions on Electrical Energy Systems
 (2022)

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## Abstract

Machine learning algorithms involve many fields such as statistics and probability theory. It is mainly aimed at computer simulation or execution of human learning behaviors to acquire new knowledge and skills. And the existing knowledge is to be integrated and then continue to be improved. This study aims to introduce a machine learning algorithm to establish a corresponding training effect evaluation model, to use the model to research and analyze the special physical fitness training strategy of badminton, and to discuss the appropriate badminton physical fitness training method. At the same time, the Markov model is used to compare the effect of the machine learning algorithm model. The experimental results show that the combination of four kinds of training of phosphate energy supply capacity, speed, sensitivity, and core strength can improve the training performance of badminton players by 10 points and the training efficiency by 4%. Therefore, it is necessary to comprehensively and reasonably arrange the four physical training methods to achieve better training effects. The average evaluation and prediction accuracies of the machine learning algorithm model were 86% and 87%, respectively, and the computing time was 26.7 ms and 13.6 ms. The average evaluation and prediction accuracies of the Markov model were 79% and 82%, respectively, and the computing time was 19.5 ms and 11.8 ms. It can be seen that the model based on the machine learning algorithm is more accurate than the Markov model.


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## Body

## 1. Introduction

With the continuous improvement of the pace of life, people are more and more reluctant to do physical exercise, especially in urban residents, the reduction of physical activity has brought a great impact on the body. In this case, many people choose to exercise in order to enhance their physical fitness and health. As a popular sport, badminton has obvious sports effects and does not have strict venue requirements. Therefore, badminton is very popular among the masses. Many people have started professional training to improve their badminton skills, and special physical training is an important part of improving badminton skills and physical fitness. In the special physical training of badminton, it is necessary to carry out targeted physical exercise according to its characteristics in order to better improve the level of badminton. Therefore, it is necessary to study and explore reasonable and scientifically effective badminton physical training strategies.For the special physical characteristics and training strategies of badminton, many scholars have made relevant research and given a series of suggestions. In order to understand the effect of shadow training on the sports health components of school badminton players, Nirendan selected thirty school-level badminton players and divided them into two groups for experimental investigation. Experiments have shown that the findings of the study were consistent with findings conducted by disparate experts in the field, and that shadow training significantly improved agility and reaction time in school-level badminton players [1]. Minkai compared reentry runs to reactive priming exercises to determine the relative effectiveness of these exercises in improving agility on the badminton court. The results showed that both training methods shortened the average running time. However, when agility was assessed without expectations, only reactive initiation training additionally reduced initiation time and its proportion in those time-consuming positions [2]. On the basis of the corresponding training of badminton skills and psychological quality, Huanpin studied the training process and significance of badminton abduction training. And by combining the function of heart rate variation, the application of abduction training in badminton was explored. The conclusion was that badminton players could bid farewell to blind training and enter scientific and efficient training after training [3]. To study the effect of yoga resistance training on selected badminton players’ kinematic variables, Nirendan selected forty school-level badminton players as subjects for an experimental investigation. Experimental results showed that the effect of resistance training significantly improved wrist flexion and extension in badminton players [4]. Many scholars have proposed different badminton training methods, but there are still some deficiencies in these studies. To this end, this study introduces machine learning algorithms to analyze the physical training strategies of badminton.Machine learning algorithms are involved in various fields today, and many scholars have made a series of corresponding researches based on machine learning algorithms in various fields. To measure the importance of key predictor variables or features based on statistical analysis, Oduro et al. identified model-based machine learning methods based on performance evaluations for predicting campaign success and compared the different machine learning algorithms selected. Research has shown that in machine learning models for predictive analytics, Bayesian semiparametric methods are useful for capturing information about the underlying distribution of more complex data using an infinite number of parameters [5]. Meghana et al. proposed an architecture that enables secure data storage, computation, and sharing in a large-scale Internet environment, which aimed to enable a safer cyberspace through real big data, thereby augmenting artificial intelligence with a multitude of data sources. In addition, typical usage scenarios for the architectural model and possible alternative deployments were discussed. And its effectiveness was analyzed in terms of network security and economic income [6]. Okumu et al. used machine learning methods to predict the power of main and auxiliary engines used in the numerical calculation of ship emissions, which provided data for researchers engaged in emissions calculations. Studies have shown that the gradient lifter regression algorithm provides a more accurate solution than other algorithms used in the study to estimate the main and auxiliary engine power of ships [7]. Liu et al. used four representative machine learning methods to construct the Xulong landslide susceptibility map gully. And by comparing the models, the model with the best performance was selected. The experimental results show that the linear improvement of the optimal SVM model has better performance and the ability to overcome factor defects [8].In order to have a more detailed understanding of the effect of badminton physical fitness training, this study builds a badminton physical fitness training effect evaluation and prediction model based on machine learning algorithms. The effects of four badminton fitness training methods are evaluated and predicted, and the Markov model is used to evaluate the training effect and compare with the machine learning algorithm. The experimental data show that among the four training methods, the training efficiency of the phosphagen energy supply capacity is the highest, reaching 90%. Compared with each training carried out separately, the training effect of the combination of the four training methods is better, and the average training efficiency is increased by 4%. When evaluating the training effect, the average prediction accuracy of the machine learning algorithm evaluation is 86% and 87%, respectively, and the Markov model is 79% and 82% respectively. Therefore, it can be seen that, compared with the Markov model, the machine algorithm model is more accurate in evaluating and predicting the training effect of badminton.


## 2. Physical Characteristics and Training Methods of Badminton

### 2.1. Special Physical Characteristics of Badminton Training

#### 2.1.1. Energy Supply Characteristics of the Original Phosphate Energy Supply System

The badminton game consists of alternating short-term high-intensity and short-term intervals. The general exercise density is between 27% and 46%, and the composition time period of this density is around 100, of which 80% are 1 to 10 seconds, and about 20% are more than 10 seconds. Based on this feature, badminton also has its corresponding energy metabolism. Intense exercise in a short period of time, the phosphate source energy supply system ATP-CP provides muscle vitality by breaking down energy and restores before ATP-CP is depleted [9]. Therefore, in the special physical training of badminton, it is necessary to focus on improving the attack power and aerobic metabolism of ATP-CP.


#### 2.1.2. Nonperiodic Characteristics

The nonperiodic characteristics of badminton determine that its special quality is composed of various sports qualities [10]. In badminton, it is regular to just look at a person’s movement and play style, but due to the instability of the direction, distance, and force of the ball and other characteristics, it leads to the uncertainty of the landing point. Therefore, there is no set rule in the technical movements of badminton.The characteristics of badminton include variable speed and aperiodicity, which determines that the player’s ball control ability must be very comprehensive and all-round. In the game, the spatial position of the athlete is changing. It is necessary to accurately judge the direction of the opponent’s incoming ball. By changing direction, changing speed, quickly stretching the body, and using actions such as jumping and pedaling, hit a strong ball to the opponent. The nonperiodic nature of badminton makes players have to have very strong strength, reaction, and speed.


#### 2.1.3. Features of Infinite Time

The badminton match adopts a best-of-three system. Whoever gets the specified result first is the winner. There is no time limit. Especially in international-level competitions, because the badminton level of both sides is equal, the competition is often deadlocked, and there may even be hundreds of beats in one round. In such a competition, both people’s physical fitness is greatly consumed, so they must have good physical fitness and endurance. Badminton players must have certain exercise endurance, such as hitting the ball, jumping, and nonstop exercise, all of which consume a lot of physical strength. Different from the cycle endurance quality required by long-distance runners, this quality is a special speed endurance quality formed by the influence of badminton sports characteristics. It requires that the intensity must be constantly changed while the speed is effectively controlled.


#### 2.1.4. Ever-Changing Characteristics

The flying speed of badminton is very high, which can reach 200 kilometers per hour, which requires a high response ability of athletes [11]. In the anti-control and control of the game, the mastery of the initiative mainly depends on the player’s switching time between different states and whether the judgment of the incoming ball can be accurate and fast. Therefore, the athlete’s batting, moving and other coping actions are the premise of performing any technique and tactics, and it is necessary to make correct judgments and counterattacks in an instant. In terms of tactics, athletes must quickly adjust their tactics and formulate the most reasonable coping strategies according to the position and characteristics of the opponent, so as to make it easier to win badminton.


### 2.2. Special Training Strategies for Badminton

#### 2.2.1. Training Methods for the Ability of Phosphate to Supply Energy

Phosphate is the most important source of energy in badminton. Besides, glycolysis is also an important energy source [12]. Both types of energy are for building endurance and speed. Therefore, in daily training, it must pay attention to the training of endurance and speed. In order to ensure that players can cope with these technical movements during long-term badminton training, they have to carry out load exercise through multiple training methods with low weight, so that their blood lactic acid concentration reaches 14 mmol/L. Through long-term training, their body gradually adapts to this value and maintains it at this level, so that their endurance can be greatly improved.


#### 2.2.2. Speed Strength Training Methods

The key and difficulty of badminton is to improve the speed. There are many training methods to improve the speed of the athletes, such as changing the pace, rushing to stop, and so on. These are important means to enhance speed ability. At the same time, athletes can perform high-intensity exercise within 60 minutes to 90 minutes by increasing the number of movements and prolonging the movement time [13]. This requires them to have a higher oxygen metabolism. Athletes should adjust their physical fitness according to the competition time structure, so that the intensity and quality of training are higher than the competition requirements. Through endurance and speed training to a certain limit, the aerobic recovery can reach a certain level.


#### 2.2.3. Sensitivity Training Method

Different sports have similarities when it comes to exercising sensitivity. In addition to these commonalities, badminton also has many particularities. Therefore, in order to improve the sensitivity of badminton players, it is necessary to adopt a targeted program for exercise. General sports events are formulated from the nature, structure, means, and methods of the event, as well as the athlete’s own situation [14]. Badminton and tennis have similarities, so they can refer to each other when making plans. For sensitivity, practice catching the ball, step movement training, changing the method speed, etc., should be repeated more than 5 times, and the number of times of each exercise should be more than 20 times. As the number of training increases, it is necessary to appropriately increase the number of single training sessions to ensure that the athlete’s muscles are always sensitive to external stimuli.


#### 2.2.4. Training Methods for Core Strength

Core competencies are something that is involved in any sport. Core competence not only affects the current situation of the athlete, but also affects the overall physical quality and future development of the athlete [15]. Core strength tests the stability, athletic ability, posture, and technique of the body, as well as the overall strength of the body, which plays a key role in the coordinated development of upper and lower limbs. The waist, abdomen, pelvis, and hip joints are all in the center of the body. The muscles in these areas are weaker than the limbs, but they also play a role in stabilization and connection. Therefore, it is necessary to strengthen the muscles of the back, abdomen, waist, and other parts in order to break through the limits of the body. Exercises such as sit-ups, one-handed push-ups, and bridges require some other equipment, such as strength equipment training, in addition to body movements. Exercises such as sit-ups, one-handed push-ups, bridges, and so on require some other equipment, such as strength equipment training, in addition to body movements.


### 2.3. Evaluation and Prediction of Training Effect Based on Machine Learning Algorithm

#### 2.3.1. Concept of Machine Learning Algorithm

Machine learning is about multidisciplinary research, including probability theory, statistics, approximation theory, and other disciplines. The algorithm studies how computers imitate or implement human learning behaviors to acquire new knowledge and skills and to reorganize existing knowledge so that it can continue to improve [16]. Machine learning algorithms are the key to artificial intelligence and the basis for making computers intelligent. They are widely used in artificial intelligence, mainly using induction and synthesis instead of reasoning. There are many kinds of machine learning algorithms, such as support vector machines, maximum entropy, random forests, and artificial neural networks, among which BP neural network is one of the classic common algorithms, and its network structure is shown in Figure 1.Figure 1
Network structure.


#### 2.3.2. Machine Learning Algorithm Indicators

(1) Precision and Recall. Precision is a measure of the ratio of correctly classified positive samples to the total number of positive samples classified as positive [17]. Recall is a measure of the ratio of correctly classified positive samples to the total number of positive samples. Assuming that the correctly classified set is I and the wrongly classified set is O, then(1)PrecisionI,O=I∩OI,RecallI,O=I∩OI.(2) Accuracy Rate and Coverage Rate. Coverage rate refers to the ratio of the number of samples classified to the total number. Accuracy rate refers to the ratio of the number of correctly classified samples to the total number [18]. When calculating the accuracy of the machine learning algorithm, it is usually assumed that xn is the nth sample prediction category and Xn is the real sample category, then the accuracy calculation formula is(2)accuracy=1i∑n=1ixn=Xn.(3) ROC Curve and AUC. ROC curve is commonly used in binary classification problems, which is a description of the ratio of correctly classified positive samples to the total number of positive samples. In the ROC curve, the better the classifier is, the larger the area under the curve will be. The size of this area is the value of AUC.(4) Confidence and Support. Support is a measure of the frequency of IO co-occurrences. The higher the frequency, the higher the degree of correlation between IOs, and vice versa. Confidence is a measure of whether IOs occur at the same time or the probability of simultaneous occurrence. The lower the confidence level, it means that whether the IOs appear to have no great correlation with each other, and vice versa, there is a large correlation. The expression is(3)SupportI⟶O=PI∪O,ConfidenceI⟶O=PR|I.(5) F1-Score. F1-Score is the harmonic average of the recall rate and the precision rate to make it closer to the smaller value of the two. Its expression is(4)F1=2precision. recallprecision+recall.(6) Mean Square Error Value and Root Mean Square Error. The mean squared error value refers to the expectation of the squared difference between the true value of the parameter and the estimated value of the parameter. The root mean square error is the arithmetic square root of the mean square error value and is expressed as(5)RMSE=1I∑n=1Iobserved−predicted2.(7) Mean Absolute Error. Mean absolute error is literally the average of the absolute errors. The mean absolute error can reflect the error situation of the predicted value very well, and its expression is(6)MAE=1I∑n=1Ign−Xn.


#### 2.3.3. Establishment of Badminton Training Effect Model

This study introduces chaos theory when building a badminton training effect model based on machine learning algorithms. The specific steps are as follows:The performance of the first badminton players over a certain period of time was collected, and certain incorrect or invalid data were excluded.Then, the C–C and Cao algorithms were used to calculate the optimalτ and m, and the values of τ and m were used to process the training performance data of badminton players.Then, by analyzing the performance of badminton training players, the initial weight of the neural network was constructed.Then, the PSO algorithm was used to optimize the initial weight of the neural network, and the position of the particle was updated by the following formula:(7)vndn+1=αvndn+a1randpbest−xndn+a2randgbest−xndn,xndn+1=xndn+vndn+1.In the formulas:n = 1, 2, ..., j; a1, a2—velocity factor; vnd (n), vnd (n+1)—particle velocity at this moment and the next moment; α—inertial weight; rand—random function.After the initial weights were obtained, the training effect evaluation and prediction model of the athlete can be established.Finally, by predicting the performance of other players, if the predicted results were credible [19], the model can be used. On the contrary, the prediction model was reconstructed according to the prediction result until the expected results were achieved.According to the above steps, a badminton exercise training effect evaluation and prediction model based on the machine learning algorithm can be established, as shown in Figure2.Figure 2
Prediction model of badminton player training effect evaluation.


#### 2.3.4. Markov-Based Training Effect Evaluation Prediction Model

In order to explore the evaluation and prediction effect of the machine learning algorithm model, this study also uses the Markov model to evaluate and predict the training effect, so as to compare the effect. The following is an analysis of the Markov model to evaluate the effect of badminton training.The training effect evaluation model of badminton players is constructed, and the evaluation parameters of the model are analyzed and processed, and the fuzzy scheduling function obtained is(8)Pmr+1=α1+Pmr+α2Pqrα1+α2,mbestr+1=1i∑m=1iPmr,Ymr+1=Pmr+1±β·mbestr+1−Ymr·ln1umr+1.In the formulas, Ym (r is the fuzzy rule set for evaluating the physical training effect of badminton players after the rth iteration.A fuzzy subspace scheduling model for evaluating the physical training effect of badminton players is established, and its output stability is controlled [20]. The given fuzzy scheduling function is(9)Wv=M1∑n=1i·jHn−Sn+WhM2∑n=1i·jSn−Hn+M3∑n=1i·jVn−Hn.In the formula, Wh is the asociation rule distribution function of badminton players’ physical training effect evaluation.In the dataset of badminton player training effect evaluation,i samples are included, samples are xn, n = 1, 2, ..., i. Combined with the Markov model, the quantitative relationship of the physical training effect evaluation of badminton players is obtained:(10)hr=∑nαnremγnrδr−nTs.By analyzing the mechanical characteristics of badminton, the explosive force output equation of badminton players is obtained:(11)ddr∂L∂qn−∂L∂qn=Rn.To sum up, a Markov-based training effect evaluation and prediction model can be established to effectively improve the training effect of badminton players and improve the evaluation and prediction accuracy of the model. The flow chart of the model can be seen in Figure3.Figure 3
Markov model physical fitness training evaluation and prediction process.


## 2.1. Special Physical Characteristics of Badminton Training

### 2.1.1. Energy Supply Characteristics of the Original Phosphate Energy Supply System

The badminton game consists of alternating short-term high-intensity and short-term intervals. The general exercise density is between 27% and 46%, and the composition time period of this density is around 100, of which 80% are 1 to 10 seconds, and about 20% are more than 10 seconds. Based on this feature, badminton also has its corresponding energy metabolism. Intense exercise in a short period of time, the phosphate source energy supply system ATP-CP provides muscle vitality by breaking down energy and restores before ATP-CP is depleted [9]. Therefore, in the special physical training of badminton, it is necessary to focus on improving the attack power and aerobic metabolism of ATP-CP.


### 2.1.2. Nonperiodic Characteristics

The nonperiodic characteristics of badminton determine that its special quality is composed of various sports qualities [10]. In badminton, it is regular to just look at a person’s movement and play style, but due to the instability of the direction, distance, and force of the ball and other characteristics, it leads to the uncertainty of the landing point. Therefore, there is no set rule in the technical movements of badminton.The characteristics of badminton include variable speed and aperiodicity, which determines that the player’s ball control ability must be very comprehensive and all-round. In the game, the spatial position of the athlete is changing. It is necessary to accurately judge the direction of the opponent’s incoming ball. By changing direction, changing speed, quickly stretching the body, and using actions such as jumping and pedaling, hit a strong ball to the opponent. The nonperiodic nature of badminton makes players have to have very strong strength, reaction, and speed.


### 2.1.3. Features of Infinite Time

The badminton match adopts a best-of-three system. Whoever gets the specified result first is the winner. There is no time limit. Especially in international-level competitions, because the badminton level of both sides is equal, the competition is often deadlocked, and there may even be hundreds of beats in one round. In such a competition, both people’s physical fitness is greatly consumed, so they must have good physical fitness and endurance. Badminton players must have certain exercise endurance, such as hitting the ball, jumping, and nonstop exercise, all of which consume a lot of physical strength. Different from the cycle endurance quality required by long-distance runners, this quality is a special speed endurance quality formed by the influence of badminton sports characteristics. It requires that the intensity must be constantly changed while the speed is effectively controlled.


### 2.1.4. Ever-Changing Characteristics

The flying speed of badminton is very high, which can reach 200 kilometers per hour, which requires a high response ability of athletes [11]. In the anti-control and control of the game, the mastery of the initiative mainly depends on the player’s switching time between different states and whether the judgment of the incoming ball can be accurate and fast. Therefore, the athlete’s batting, moving and other coping actions are the premise of performing any technique and tactics, and it is necessary to make correct judgments and counterattacks in an instant. In terms of tactics, athletes must quickly adjust their tactics and formulate the most reasonable coping strategies according to the position and characteristics of the opponent, so as to make it easier to win badminton.


## 2.1.1. Energy Supply Characteristics of the Original Phosphate Energy Supply System

The badminton game consists of alternating short-term high-intensity and short-term intervals. The general exercise density is between 27% and 46%, and the composition time period of this density is around 100, of which 80% are 1 to 10 seconds, and about 20% are more than 10 seconds. Based on this feature, badminton also has its corresponding energy metabolism. Intense exercise in a short period of time, the phosphate source energy supply system ATP-CP provides muscle vitality by breaking down energy and restores before ATP-CP is depleted [9]. Therefore, in the special physical training of badminton, it is necessary to focus on improving the attack power and aerobic metabolism of ATP-CP.


## 2.1.2. Nonperiodic Characteristics

The nonperiodic characteristics of badminton determine that its special quality is composed of various sports qualities [10]. In badminton, it is regular to just look at a person’s movement and play style, but due to the instability of the direction, distance, and force of the ball and other characteristics, it leads to the uncertainty of the landing point. Therefore, there is no set rule in the technical movements of badminton.The characteristics of badminton include variable speed and aperiodicity, which determines that the player’s ball control ability must be very comprehensive and all-round. In the game, the spatial position of the athlete is changing. It is necessary to accurately judge the direction of the opponent’s incoming ball. By changing direction, changing speed, quickly stretching the body, and using actions such as jumping and pedaling, hit a strong ball to the opponent. The nonperiodic nature of badminton makes players have to have very strong strength, reaction, and speed.


## 2.1.3. Features of Infinite Time

The badminton match adopts a best-of-three system. Whoever gets the specified result first is the winner. There is no time limit. Especially in international-level competitions, because the badminton level of both sides is equal, the competition is often deadlocked, and there may even be hundreds of beats in one round. In such a competition, both people’s physical fitness is greatly consumed, so they must have good physical fitness and endurance. Badminton players must have certain exercise endurance, such as hitting the ball, jumping, and nonstop exercise, all of which consume a lot of physical strength. Different from the cycle endurance quality required by long-distance runners, this quality is a special speed endurance quality formed by the influence of badminton sports characteristics. It requires that the intensity must be constantly changed while the speed is effectively controlled.


## 2.1.4. Ever-Changing Characteristics

The flying speed of badminton is very high, which can reach 200 kilometers per hour, which requires a high response ability of athletes [11]. In the anti-control and control of the game, the mastery of the initiative mainly depends on the player’s switching time between different states and whether the judgment of the incoming ball can be accurate and fast. Therefore, the athlete’s batting, moving and other coping actions are the premise of performing any technique and tactics, and it is necessary to make correct judgments and counterattacks in an instant. In terms of tactics, athletes must quickly adjust their tactics and formulate the most reasonable coping strategies according to the position and characteristics of the opponent, so as to make it easier to win badminton.


## 2.2. Special Training Strategies for Badminton

### 2.2.1. Training Methods for the Ability of Phosphate to Supply Energy

Phosphate is the most important source of energy in badminton. Besides, glycolysis is also an important energy source [12]. Both types of energy are for building endurance and speed. Therefore, in daily training, it must pay attention to the training of endurance and speed. In order to ensure that players can cope with these technical movements during long-term badminton training, they have to carry out load exercise through multiple training methods with low weight, so that their blood lactic acid concentration reaches 14 mmol/L. Through long-term training, their body gradually adapts to this value and maintains it at this level, so that their endurance can be greatly improved.


### 2.2.2. Speed Strength Training Methods

The key and difficulty of badminton is to improve the speed. There are many training methods to improve the speed of the athletes, such as changing the pace, rushing to stop, and so on. These are important means to enhance speed ability. At the same time, athletes can perform high-intensity exercise within 60 minutes to 90 minutes by increasing the number of movements and prolonging the movement time [13]. This requires them to have a higher oxygen metabolism. Athletes should adjust their physical fitness according to the competition time structure, so that the intensity and quality of training are higher than the competition requirements. Through endurance and speed training to a certain limit, the aerobic recovery can reach a certain level.


### 2.2.3. Sensitivity Training Method

Different sports have similarities when it comes to exercising sensitivity. In addition to these commonalities, badminton also has many particularities. Therefore, in order to improve the sensitivity of badminton players, it is necessary to adopt a targeted program for exercise. General sports events are formulated from the nature, structure, means, and methods of the event, as well as the athlete’s own situation [14]. Badminton and tennis have similarities, so they can refer to each other when making plans. For sensitivity, practice catching the ball, step movement training, changing the method speed, etc., should be repeated more than 5 times, and the number of times of each exercise should be more than 20 times. As the number of training increases, it is necessary to appropriately increase the number of single training sessions to ensure that the athlete’s muscles are always sensitive to external stimuli.


### 2.2.4. Training Methods for Core Strength

Core competencies are something that is involved in any sport. Core competence not only affects the current situation of the athlete, but also affects the overall physical quality and future development of the athlete [15]. Core strength tests the stability, athletic ability, posture, and technique of the body, as well as the overall strength of the body, which plays a key role in the coordinated development of upper and lower limbs. The waist, abdomen, pelvis, and hip joints are all in the center of the body. The muscles in these areas are weaker than the limbs, but they also play a role in stabilization and connection. Therefore, it is necessary to strengthen the muscles of the back, abdomen, waist, and other parts in order to break through the limits of the body. Exercises such as sit-ups, one-handed push-ups, and bridges require some other equipment, such as strength equipment training, in addition to body movements. Exercises such as sit-ups, one-handed push-ups, bridges, and so on require some other equipment, such as strength equipment training, in addition to body movements.


## 2.2.1. Training Methods for the Ability of Phosphate to Supply Energy

Phosphate is the most important source of energy in badminton. Besides, glycolysis is also an important energy source [12]. Both types of energy are for building endurance and speed. Therefore, in daily training, it must pay attention to the training of endurance and speed. In order to ensure that players can cope with these technical movements during long-term badminton training, they have to carry out load exercise through multiple training methods with low weight, so that their blood lactic acid concentration reaches 14 mmol/L. Through long-term training, their body gradually adapts to this value and maintains it at this level, so that their endurance can be greatly improved.


## 2.2.2. Speed Strength Training Methods

The key and difficulty of badminton is to improve the speed. There are many training methods to improve the speed of the athletes, such as changing the pace, rushing to stop, and so on. These are important means to enhance speed ability. At the same time, athletes can perform high-intensity exercise within 60 minutes to 90 minutes by increasing the number of movements and prolonging the movement time [13]. This requires them to have a higher oxygen metabolism. Athletes should adjust their physical fitness according to the competition time structure, so that the intensity and quality of training are higher than the competition requirements. Through endurance and speed training to a certain limit, the aerobic recovery can reach a certain level.


## 2.2.3. Sensitivity Training Method

Different sports have similarities when it comes to exercising sensitivity. In addition to these commonalities, badminton also has many particularities. Therefore, in order to improve the sensitivity of badminton players, it is necessary to adopt a targeted program for exercise. General sports events are formulated from the nature, structure, means, and methods of the event, as well as the athlete’s own situation [14]. Badminton and tennis have similarities, so they can refer to each other when making plans. For sensitivity, practice catching the ball, step movement training, changing the method speed, etc., should be repeated more than 5 times, and the number of times of each exercise should be more than 20 times. As the number of training increases, it is necessary to appropriately increase the number of single training sessions to ensure that the athlete’s muscles are always sensitive to external stimuli.


## 2.2.4. Training Methods for Core Strength

Core competencies are something that is involved in any sport. Core competence not only affects the current situation of the athlete, but also affects the overall physical quality and future development of the athlete [15]. Core strength tests the stability, athletic ability, posture, and technique of the body, as well as the overall strength of the body, which plays a key role in the coordinated development of upper and lower limbs. The waist, abdomen, pelvis, and hip joints are all in the center of the body. The muscles in these areas are weaker than the limbs, but they also play a role in stabilization and connection. Therefore, it is necessary to strengthen the muscles of the back, abdomen, waist, and other parts in order to break through the limits of the body. Exercises such as sit-ups, one-handed push-ups, and bridges require some other equipment, such as strength equipment training, in addition to body movements. Exercises such as sit-ups, one-handed push-ups, bridges, and so on require some other equipment, such as strength equipment training, in addition to body movements.


## 2.3. Evaluation and Prediction of Training Effect Based on Machine Learning Algorithm

### 2.3.1. Concept of Machine Learning Algorithm

Machine learning is about multidisciplinary research, including probability theory, statistics, approximation theory, and other disciplines. The algorithm studies how computers imitate or implement human learning behaviors to acquire new knowledge and skills and to reorganize existing knowledge so that it can continue to improve [16]. Machine learning algorithms are the key to artificial intelligence and the basis for making computers intelligent. They are widely used in artificial intelligence, mainly using induction and synthesis instead of reasoning. There are many kinds of machine learning algorithms, such as support vector machines, maximum entropy, random forests, and artificial neural networks, among which BP neural network is one of the classic common algorithms, and its network structure is shown in Figure 1.Figure 1
Network structure.


### 2.3.2. Machine Learning Algorithm Indicators

(1) Precision and Recall. Precision is a measure of the ratio of correctly classified positive samples to the total number of positive samples classified as positive [17]. Recall is a measure of the ratio of correctly classified positive samples to the total number of positive samples. Assuming that the correctly classified set is I and the wrongly classified set is O, then(1)PrecisionI,O=I∩OI,RecallI,O=I∩OI.(2) Accuracy Rate and Coverage Rate. Coverage rate refers to the ratio of the number of samples classified to the total number. Accuracy rate refers to the ratio of the number of correctly classified samples to the total number [18]. When calculating the accuracy of the machine learning algorithm, it is usually assumed that xn is the nth sample prediction category and Xn is the real sample category, then the accuracy calculation formula is(2)accuracy=1i∑n=1ixn=Xn.(3) ROC Curve and AUC. ROC curve is commonly used in binary classification problems, which is a description of the ratio of correctly classified positive samples to the total number of positive samples. In the ROC curve, the better the classifier is, the larger the area under the curve will be. The size of this area is the value of AUC.(4) Confidence and Support. Support is a measure of the frequency of IO co-occurrences. The higher the frequency, the higher the degree of correlation between IOs, and vice versa. Confidence is a measure of whether IOs occur at the same time or the probability of simultaneous occurrence. The lower the confidence level, it means that whether the IOs appear to have no great correlation with each other, and vice versa, there is a large correlation. The expression is(3)SupportI⟶O=PI∪O,ConfidenceI⟶O=PR|I.(5) F1-Score. F1-Score is the harmonic average of the recall rate and the precision rate to make it closer to the smaller value of the two. Its expression is(4)F1=2precision. recallprecision+recall.(6) Mean Square Error Value and Root Mean Square Error. The mean squared error value refers to the expectation of the squared difference between the true value of the parameter and the estimated value of the parameter. The root mean square error is the arithmetic square root of the mean square error value and is expressed as(5)RMSE=1I∑n=1Iobserved−predicted2.(7) Mean Absolute Error. Mean absolute error is literally the average of the absolute errors. The mean absolute error can reflect the error situation of the predicted value very well, and its expression is(6)MAE=1I∑n=1Ign−Xn.


### 2.3.3. Establishment of Badminton Training Effect Model

This study introduces chaos theory when building a badminton training effect model based on machine learning algorithms. The specific steps are as follows:The performance of the first badminton players over a certain period of time was collected, and certain incorrect or invalid data were excluded.Then, the C–C and Cao algorithms were used to calculate the optimalτ and m, and the values of τ and m were used to process the training performance data of badminton players.Then, by analyzing the performance of badminton training players, the initial weight of the neural network was constructed.Then, the PSO algorithm was used to optimize the initial weight of the neural network, and the position of the particle was updated by the following formula:(7)vndn+1=αvndn+a1randpbest−xndn+a2randgbest−xndn,xndn+1=xndn+vndn+1.In the formulas:n = 1, 2, ..., j; a1, a2—velocity factor; vnd (n), vnd (n+1)—particle velocity at this moment and the next moment; α—inertial weight; rand—random function.After the initial weights were obtained, the training effect evaluation and prediction model of the athlete can be established.Finally, by predicting the performance of other players, if the predicted results were credible [19], the model can be used. On the contrary, the prediction model was reconstructed according to the prediction result until the expected results were achieved.According to the above steps, a badminton exercise training effect evaluation and prediction model based on the machine learning algorithm can be established, as shown in Figure2.Figure 2
Prediction model of badminton player training effect evaluation.


### 2.3.4. Markov-Based Training Effect Evaluation Prediction Model

In order to explore the evaluation and prediction effect of the machine learning algorithm model, this study also uses the Markov model to evaluate and predict the training effect, so as to compare the effect. The following is an analysis of the Markov model to evaluate the effect of badminton training.The training effect evaluation model of badminton players is constructed, and the evaluation parameters of the model are analyzed and processed, and the fuzzy scheduling function obtained is(8)Pmr+1=α1+Pmr+α2Pqrα1+α2,mbestr+1=1i∑m=1iPmr,Ymr+1=Pmr+1±β·mbestr+1−Ymr·ln1umr+1.In the formulas, Ym (r is the fuzzy rule set for evaluating the physical training effect of badminton players after the rth iteration.A fuzzy subspace scheduling model for evaluating the physical training effect of badminton players is established, and its output stability is controlled [20]. The given fuzzy scheduling function is(9)Wv=M1∑n=1i·jHn−Sn+WhM2∑n=1i·jSn−Hn+M3∑n=1i·jVn−Hn.In the formula, Wh is the asociation rule distribution function of badminton players’ physical training effect evaluation.In the dataset of badminton player training effect evaluation,i samples are included, samples are xn, n = 1, 2, ..., i. Combined with the Markov model, the quantitative relationship of the physical training effect evaluation of badminton players is obtained:(10)hr=∑nαnremγnrδr−nTs.By analyzing the mechanical characteristics of badminton, the explosive force output equation of badminton players is obtained:(11)ddr∂L∂qn−∂L∂qn=Rn.To sum up, a Markov-based training effect evaluation and prediction model can be established to effectively improve the training effect of badminton players and improve the evaluation and prediction accuracy of the model. The flow chart of the model can be seen in Figure3.Figure 3
Markov model physical fitness training evaluation and prediction process.


## 2.3.1. Concept of Machine Learning Algorithm

Machine learning is about multidisciplinary research, including probability theory, statistics, approximation theory, and other disciplines. The algorithm studies how computers imitate or implement human learning behaviors to acquire new knowledge and skills and to reorganize existing knowledge so that it can continue to improve [16]. Machine learning algorithms are the key to artificial intelligence and the basis for making computers intelligent. They are widely used in artificial intelligence, mainly using induction and synthesis instead of reasoning. There are many kinds of machine learning algorithms, such as support vector machines, maximum entropy, random forests, and artificial neural networks, among which BP neural network is one of the classic common algorithms, and its network structure is shown in Figure 1.Figure 1
Network structure.


## 2.3.2. Machine Learning Algorithm Indicators

(1) Precision and Recall. Precision is a measure of the ratio of correctly classified positive samples to the total number of positive samples classified as positive [17]. Recall is a measure of the ratio of correctly classified positive samples to the total number of positive samples. Assuming that the correctly classified set is I and the wrongly classified set is O, then(1)PrecisionI,O=I∩OI,RecallI,O=I∩OI.(2) Accuracy Rate and Coverage Rate. Coverage rate refers to the ratio of the number of samples classified to the total number. Accuracy rate refers to the ratio of the number of correctly classified samples to the total number [18]. When calculating the accuracy of the machine learning algorithm, it is usually assumed that xn is the nth sample prediction category and Xn is the real sample category, then the accuracy calculation formula is(2)accuracy=1i∑n=1ixn=Xn.(3) ROC Curve and AUC. ROC curve is commonly used in binary classification problems, which is a description of the ratio of correctly classified positive samples to the total number of positive samples. In the ROC curve, the better the classifier is, the larger the area under the curve will be. The size of this area is the value of AUC.(4) Confidence and Support. Support is a measure of the frequency of IO co-occurrences. The higher the frequency, the higher the degree of correlation between IOs, and vice versa. Confidence is a measure of whether IOs occur at the same time or the probability of simultaneous occurrence. The lower the confidence level, it means that whether the IOs appear to have no great correlation with each other, and vice versa, there is a large correlation. The expression is(3)SupportI⟶O=PI∪O,ConfidenceI⟶O=PR|I.(5) F1-Score. F1-Score is the harmonic average of the recall rate and the precision rate to make it closer to the smaller value of the two. Its expression is(4)F1=2precision. recallprecision+recall.(6) Mean Square Error Value and Root Mean Square Error. The mean squared error value refers to the expectation of the squared difference between the true value of the parameter and the estimated value of the parameter. The root mean square error is the arithmetic square root of the mean square error value and is expressed as(5)RMSE=1I∑n=1Iobserved−predicted2.(7) Mean Absolute Error. Mean absolute error is literally the average of the absolute errors. The mean absolute error can reflect the error situation of the predicted value very well, and its expression is(6)MAE=1I∑n=1Ign−Xn.


## 2.3.3. Establishment of Badminton Training Effect Model

This study introduces chaos theory when building a badminton training effect model based on machine learning algorithms. The specific steps are as follows:The performance of the first badminton players over a certain period of time was collected, and certain incorrect or invalid data were excluded.Then, the C–C and Cao algorithms were used to calculate the optimalτ and m, and the values of τ and m were used to process the training performance data of badminton players.Then, by analyzing the performance of badminton training players, the initial weight of the neural network was constructed.Then, the PSO algorithm was used to optimize the initial weight of the neural network, and the position of the particle was updated by the following formula:(7)vndn+1=αvndn+a1randpbest−xndn+a2randgbest−xndn,xndn+1=xndn+vndn+1.In the formulas:n = 1, 2, ..., j; a1, a2—velocity factor; vnd (n), vnd (n+1)—particle velocity at this moment and the next moment; α—inertial weight; rand—random function.After the initial weights were obtained, the training effect evaluation and prediction model of the athlete can be established.Finally, by predicting the performance of other players, if the predicted results were credible [19], the model can be used. On the contrary, the prediction model was reconstructed according to the prediction result until the expected results were achieved.According to the above steps, a badminton exercise training effect evaluation and prediction model based on the machine learning algorithm can be established, as shown in Figure2.Figure 2
Prediction model of badminton player training effect evaluation.


## 2.3.4. Markov-Based Training Effect Evaluation Prediction Model

In order to explore the evaluation and prediction effect of the machine learning algorithm model, this study also uses the Markov model to evaluate and predict the training effect, so as to compare the effect. The following is an analysis of the Markov model to evaluate the effect of badminton training.The training effect evaluation model of badminton players is constructed, and the evaluation parameters of the model are analyzed and processed, and the fuzzy scheduling function obtained is(8)Pmr+1=α1+Pmr+α2Pqrα1+α2,mbestr+1=1i∑m=1iPmr,Ymr+1=Pmr+1±β·mbestr+1−Ymr·ln1umr+1.In the formulas, Ym (r is the fuzzy rule set for evaluating the physical training effect of badminton players after the rth iteration.A fuzzy subspace scheduling model for evaluating the physical training effect of badminton players is established, and its output stability is controlled [20]. The given fuzzy scheduling function is(9)Wv=M1∑n=1i·jHn−Sn+WhM2∑n=1i·jSn−Hn+M3∑n=1i·jVn−Hn.In the formula, Wh is the asociation rule distribution function of badminton players’ physical training effect evaluation.In the dataset of badminton player training effect evaluation,i samples are included, samples are xn, n = 1, 2, ..., i. Combined with the Markov model, the quantitative relationship of the physical training effect evaluation of badminton players is obtained:(10)hr=∑nαnremγnrδr−nTs.By analyzing the mechanical characteristics of badminton, the explosive force output equation of badminton players is obtained:(11)ddr∂L∂qn−∂L∂qn=Rn.To sum up, a Markov-based training effect evaluation and prediction model can be established to effectively improve the training effect of badminton players and improve the evaluation and prediction accuracy of the model. The flow chart of the model can be seen in Figure3.Figure 3
Markov model physical fitness training evaluation and prediction process.


## 3. Training Effect Evaluation and Prediction Experiment

In order to verify the application performance of the model designed in this study for the evaluation and prediction of the physical training effect of badminton players, this study conducts an experimental investigation on 100 badminton players from 5 sports schools. A simulation test experiment was carried out on them, and a mechanical sensor was used to statistically analyze the daily training of these badminton players. The results are shown in Table1.Table 1
Statistical analysis values for the evaluation of athletes’ physical training effect.
VariableMeanStandard valueMinimumStatistical meanExcercise time0.2570.6760.1345.684Physical fitness0.3550.5340.3582.336Daily exercise0.5460.4470.4552.487Training intensity0.4440.4660.3444.276Correlation coefficient0.4150.5360.4143.555These data were input into both models as initial data to allow the models to perform the initial analysis. Then, the selected subjects were divided into four groups to carry out four physical trainings, namely, the training of phosphate pro-energy supply capacity, the training of speed strength, the training of sensitivity, and the training of core strength. According to the training data of each badminton player, the machine learning algorithm model (hereinafter referred to as MLA) and the Markov model are used to evaluate the training effects of the four kinds of training, and the training results are given by descriptive analysis, as shown in Figure4.Figure 4
Training effect of badminton players.As can be seen from Figure4, the training results of the four training methods are generally around 80 points, and they have achieved good training results. However, in terms of training efficiency, the training efficiency of phosphate energy supply capacity is much higher than the other three. Based on the evaluation of the machine learning algorithm model, the training efficiency reached 90%. This is because phosphate is the most important energy source in badminton. The improvement of the energy supply capacity of the original phosphate can significantly increase the speed, endurance, strength, and other abilities, so the benefit of improving the energy supply capacity of the original phosphate is greater.Table2 shows the training performance of the badminton players under various trainings actually recorded, and the average summation of the three training results is used as the actual training effect of the badminton players. The average effects of the four training methods are: 88 points for phosphate pro-energy training, 77.7 points for speed strength training, 73.7 points for sensitivity training, and 79 points for core strength training. Taking these data as actual training scores and comparing them with the scores given by the model, Figure 5 can be obtained.Table 2
Actual training results.
Training timesProphosphateSpeed forceSensitivityCore power186787782291746976387817579Figure 5
Comparison of model evaluation and actual situation.It can be clearly seen that the score output by the badminton training effect evaluation model based on the machine learning algorithm in Figure5 is closer to the real situation. The performance evaluation obtained by the Markov model is quite different from the actual training performance, the maximum difference value reaches 13.3. In contrast, the evaluation results of the badminton training effect evaluation model based on the machine learning algorithm are higher and closer to the real situation.Through the training of the network model constructed in this study, the data are learned. After the training is completed, the predicted value of the badminton player’s training effect can be obtained. Here, the selected 200 badminton sports students are unified to predict the training effect, divided into four groups, and each group has four training methods to complete a training cycle. The model prediction results are shown in Figure6.Figure 6
Predicted value of badminton training effect.It can be seen from Figure6 that the combination of the four training methods can greatly improve the training performance and training efficiency for badminton players. The prediction values of badminton training effect models based on machine learning algorithms generally exceed the Markov model. Among them, the highest predicted training score reaches 95 points, and the lowest predicted training score is 79 points. The highest prediction training efficiency is 86%, and the lowest prediction training efficiency is also 79%. The data show that the combined use of the four training methods has a more obvious effect on badminton players than single training. Therefore, in the daily physical training of badminton players, the time should be reasonably arranged, and the four training methods should be arranged in the training arrangements of badminton players.It is also compared with the actual recorded training results to test the prediction effect of the model. The comparison results are shown in Figure7.Figure 7
Comparison of model predicted value and actual value.As can be seen in Figure7, the badminton training effect model based on the machine learning algorithm is more accurate than the prediction effect of the Markov model. The prediction error of the training score of the machine learning algorithm model is within 2 points, while the maximum error of the prediction of the training score of the Markov model is 10 points. Among the four sets of experimental data, whether it is a machine learning algorithm model or a Markov model, the maximum prediction error is in the second group. It shows that there may be errors in the results of the second group of investigations, and it may be that the training effect data of badminton players after training is wrongly recorded. Therefore, the second group of wrong experimental data cannot be used as a reference.When the above evaluation and prediction are made through the network model established in this study, the overall operation data of the two network models are recorded, and the specific data are shown in Table3.Table 3
Comparison of the effects of the two models.
Average time spent on system evaluation (ms)Average evaluation accuracy (%)The system predicts the average time (ms)Average prediction accuracy (%)Machine learning algorithm model26.78613.687Markov model19.57911.882From the data comparison in Table3, it can be seen that the badminton training effect evaluation and prediction model based on the machine learning algorithm has higher training effect evaluation and prediction accuracy, with an average of 86% and 87%, respectively. The Markov model has a relatively low accuracy rate, with an evaluation accuracy rate of 79% and a prediction accuracy rate of 82%. However, in terms of model operation time, the Markov model is faster, with an average evaluation and prediction time of 19.5 ms and 11.8 ms, respectively. The machine learning algorithm takes 26.7 ms and 13.6 ms, which is slower than the former.


## 4. Conclusion

In order to explore the physical training strategy of badminton, this study randomly selects 100 badminton sports students as the experimental investigation object. Four sets of training methods are formulated according to the characteristics of badminton. And the machine learning algorithm model and Markov model are used to evaluate and predict. The experimental data show that:(1)
Among the four badminton physical fitness training methods, the training effect of phosphate source energy supply ability training is more obvious and effective, and the training effect of the rational arrangement of training is better when the four methods are combined.(2)
Compared with the Markov model, the evaluation and prediction model of badminton fitness training effect based on a machine learning algorithm has higher accuracy of evaluation and prediction, but the calculation time of the model is relatively longer. Therefore, machine learning algorithms can be used to accurately and effectively evaluate and predict the effect of badminton physical training.There are still some problems in this experiment. For example, the gap between the physical fitness of the athletes before training is not considered, which leads to different results in the preliminary test before training. Therefore, the relative progress can be considered as the survey value in future research, which is worthy of further research and improvement.



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*Source: 1345118-2022-09-06.xml*