# Research on Athlete Training Effect Evaluation Based on Machine Learning Algorithm

Authors: Yan Zou; Chu Wang; Qianqian Jiao
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

In order to achieve a quantitative analysis of the training effects of athletes, this paper combines machine learning algorithms to evaluate and analyze athletes’ training effects and analyzes the evaluation algorithms based on machine learning interpretable models. Moreover, after analyzing a variety of algorithms, this paper selects an intelligent evaluation method suitable for this model and builds an intelligent evaluation system based on the current athletes’ training needs. In addition, this paper verifies the effect of the system with the support of intelligent algorithms and experiments. The experimental research results show that the athlete training effect evaluation system based on the machine learning algorithm proposed in this paper has good results, and it can be applied to subsequent athletes’ sports training evaluation.


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## Body

## 1. Introduction

Sports training and sports competition constitute the two pillars of competitive sports, and the two have obvious differences and are closely related. The primary goal of sports training is to completely develop players’ physical and psychological qualities, as well as to maximise the potential of all components of the human body, in order to enhance sports performance and win competitions. Sports competition may be used to not only give players with the chance to compete on the same field but also to measure the efficiency of sports training carried out by all parties involved in the tournament. Sports training and sports competitions promote and depend on each other. Although the level of sports training must be specifically expressed through the organization of sports competitions, high-level sports training is the basis of high-quality sports competitions. Sports training practice has proved that excellent sports performance must be through long term and hard training. This training process will determine whether the athlete’s athletic potential can be fully tapped, whether the coach’s training ideas and coaching art can be fully embodied, and whether the role of training management personnel, scientific and technological personnel and other auxiliary personnel can be fully utilized. The results of the joint efforts of all these people have continuously improved the quality of sports training. This can also be expressed by a concept that we are more familiar with, namely the scientification of sports training.The scientificization of sports training reflects a state of sports training level and reflects a dynamic development process of sports training practice activities. With the development of sports, especially in the modern era, competitive sports have become an important means for countries in the world to show their national strength and invigorate their national spirit in the era of peace. Practice has proved that the higher the level of competitive sports development, the more difficult it is to improve sports performance, and the factors that affect sports training are becoming more and more complex. Quality, physicality, function, psychology, technology, tactics, intellect, and many other social aspects are among the more than 150 influencing factors involved in increasing sports performance, according to some experts. Human morphology, genetics, anatomy, histology, physiology, biochemistry, nutrition, medicine, psychology, education, management, and information technology are all involved in studies on this issue. Science and a variety of other fields: sports workers and coaches in many countries strive to make full use of the most modern scientific and technological means to create the latest and most effective training methods to tap the potential of human movement. These extensive and profound scientific and technological practices make sports training scientific. The degree of chemistry continues to increase.This article combines machine learning algorithms to evaluate and analyze the effects of athletes’ training, builds an intelligent evaluation system based on current athletes’ training needs, and validates the effects of the system with the support of intelligent algorithms and experiments.


## 2. Related Work

In sports, such as football, basketball, and other team ball games, athletes must continue to pay attention to and track multiple sports objects on the sports field, such as paying attention to and tracking the position of the ball, the position of the teammate, and the position of the opponent. There are many similarities between the attention characteristics of athletes in sports scenes and the attention characteristics when completing multitarget tracking tasks, and both require the use of visual attention to track multiple sports objects. As a result, this paradigm is a useful tool for studying athletes’ continuous and dynamic visual attention features [1]. In multitarget tracking, researchers employ this paradigm to investigate the performance characteristics of athletes and nonathletes [2]. Athletes have superior tracking accuracy than nonathletes, according to studies [3]. According to studies, the stronger an athlete’s motor abilities are, the more visible their advantages in multitarget tracking tasks [4]. Literature [5] found that compared with ordinary college students, volleyball players have higher accuracy in multitarget tracking and faster reaction speed; literature [6] found that the multitarget tracking ability of expert athletes in sports events is better than that of novice athletes and athletes. Control group: the above research shows that the performance of multitarget tracking is affected by skill level, and the multitarget tracking ability of athletes is better than that of novice nonathletes. There are also studies that have not found differences in the performance of multitarget tracking between athletes and novices. For example, the literature [7] tested athletes with different skill levels, and the research results revealed that athletes of different skill levels did not perform significantly in completing multitarget tracking tasks. The difference: in addition, researchers set different attention loads, such as changing the number of targets, and exercise speed, to investigate whether athletes’ multitarget tracking performance under different attention loads has advantages [8]. The literature [9] changed the attention load by manipulating the number of nontargets. The results of the study found that as the attention load increased, the athletes responded to the target significantly faster than the control group. By manipulating the color of the target to change attention load, the study found that the athlete’s multitarget tracking performance is not affected by the color change of the target. Literature [10] found that when tracking 4 targets, the tracking performance of high-skilled athletes was significantly better than that of the low-skilled group and the control group. When tracking 2 targets, there was no significant difference in the accuracy of multitarget tracking among the three groups. Literature [11] also found that national referees can better complete the tracking task in different levels of referees. When tracking 5 targets, the tracking advantage is the most significant. Literature [12] found that the attention load was adjusted by changing the number of targets in the multitarget tracking task. Under the condition of different target numbers, there was no significant difference in the multitarget tracking performance of athletes and nonathletes; the attention load was adjusted by changing the movement speed. Under the conditions, compared with nonathletes, athletes perform better in multitarget tracking. It can be observed that the difference in tracking performance between athletes and nonathletes cannot yet be detected in multitarget tracking tasks with varied attention burdens. In ball sports, players’ field location has an impact on their multitarget tracking performance. Literature [13] investigates the link between multitarget tracking performance and field performance, whereas literature [14] examines high-skilled athlete performance. The connection between performance and the top limit of tracking speed has been investigated, and it has been discovered that the upper limit of tracking speed is linked to athletic performance. Athletes’ athletic performance may be improved by using multitarget tracking activities to train them. Literature [15] used 3D multitarget tracking activities to teach beginner level athletes, and then examined their sports performance before and after training, finding that the players’ scores increased considerably after training.The above research shows that the tracking performance of athletes is affected by attention load and position on the field, and the tracking performance of players is related to sports performance. However, in the multitarget tracking tasks with different attention loads, whether athletes have the advantage of multitarget tracking is not yet a consistent conclusion, and less attention has been paid to the EEG rhythm oscillation activity when athletes and nonathletes complete the multitarget tracking task.


## 3. Sports Training Evaluation Algorithm Based on Machine Learning

In machine learning models, there are many models that are not interpretable. For example, a branch of machine learning-deep learning, deep learning is an algorithm that uses an artificial neural network as the architecture to characterize and learn data, such as Deep Neural Networks (DNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). However, there are many algorithms that can be explained, and the interpretable model used in this paper will be introduced.The output of the Linear Regression (LR) model is determined by the weighted sum of the input features. Linear models can be used for the contribution of certain featuresX to the goal Y. For the i sample containing k features, the output of the trained model and the features in the sample can be written as follows [16]:(1)yi=β0+β1x1i+β2x2i+⋯+βkxki+εi.We use a matrix to express the above formula as(2)Y=Xβ+ε.Among them, there are(3)X=1x11…xk11x12…xk2⋮…⋱⋮1x1n…xkn,β=β0,β1,…,βkT,ε=ε1,ε2,…,εnT,Y=y1,y2,…,ynT.The output of an instance is the weighted sum of itsk features. Among them, the first term β0 is called bias. However, the parameter βj1≤j≤k represents the feature coefficient learned after training, and it can also be regarded as the importance of this feature. The parameter xj1≤j≤k is the value of the jth feature of the sample. ε1,ε2,…,εn represents the error, which obeys the normal distribution, that is, the difference between the output result and the real result [17].Linear regression usually uses the least squares method to minimize the characteristic coefficients. For data containingn samples, the true result of the ith sample is represented by yi, and xji is the jth feature of the ith sample. Then, it only needs to minimize the squared difference between the real result and the predicted result to get the best weight, as shown below:(4)β^=argminβ0,…,βk︸∑i=1nyi−β0+∑j=1kβjxji2.The linear regression model is one of the simplest interpretable models. For a new sample, the trained model can know that thejth feature of the new sample is the contribution made by the output result, that is, the value of βjxj, and the contribution rate of each feature is [18]:(5)∂y∂x=∂y∂x1∂y∂x2⋯∂y∂xk=β1β2⋯βk.This vector depicts the gradient direction of the contribution change and represents the contribution rate of all characteristics.As demonstrated in Figure1, the linear regression model is effective at detecting linear relationships in data, and the training procedure is straightforward and quick. It is simple to update the model as the data grows. However, since it is linear, linear regression is not ideal for non-linear data, and the model trained using linear regression is incapable of understanding the interaction between two or more characteristics.Figure 1
Unary linear regression.Next, the Generalized Additive Model (GAM) is introduced. The core of GAM is the sum of characteristic effects. For each feature, its effects can be expressed by different functions. For a sample withk features, the expression of GAM is as follows:(6)gEYy|x=β0+∑i=1kfixi.Among them,g represents the connection function.For GAM, due to the introduction of the non-linear functionfi1≤i≤k, it may make the prediction more accurate. One disadvantage of GAM is that it does not take into account the interaction between features.One of the most popular algorithms is ensemble learning (Ensemble Learning, EL). The ensemble learning model combines multiple weak classifiers into a strong learning classifier. Although new ensemble learning algorithms are constantly being developed, most of these algorithms are improved by Bagging, Boosting, and Stacking. The Bagging algorithm uses self-service sampling to select the training set from the original data. Because each sample is selected with equal probability, it does not focus on a specific sample of the training set. Random Forest (RF) is an improved version of the Bagging algorithm. Boosting algorithm is an integrated meta-algorithm, mainly used to reduce bias and variance of supervised learning to transform weak learners into strong learners. This article introduces two Boosting algorithms, GBDT and XGBoost.The base learner of RF is a decision tree. Each base learner in RF judges and classifies new samples when performing classification tasks. As many base learners will get as many classification results. Which one of the classification results of the decision tree has the most classification, then the random forest will regard this result as the final result.Random forest is based on P decision treeshX,θp,p=1,2,…,P as the basic classifier. After training, the base classifier is used to obtain a combined classifier. Among them, θp,p=1,2,…,P is a sequence of random variables. When inputting new data, the output of RF is a simple voting ruling based on the results of each decision tree. Bagging thought and characteristic subspace thought are the two major randomization thoughts of RF.Gradient Boosting Decision Tree (GBDT) is part of the Boosting algorithm family. Its base classifier uses classification and regression tree (Classification and Regression Tree, CART). GBDT uses the forward distribution algorithm for iteration, and its weak learner can only use the CART model.The GBDT model can be seen as an additive model consisting ofM base models, as shown below [19]:(7)y^l=∑m=1Mfmxi,fm∈F.Among them,F is the function space composed of all trees. For N data, the loss function can be written as(8)Loss=∑i=1Nlyi,y^l.It may be considered that the strong learner obtained after the previous round of iteration isft−1x, and its loss function is Ly,ft−1x, then the goal of this round of iteration is to find a weak learner htx that minimizes the loss function of this round, as shown below:(9)Ly,ftx=Ly,ft−1x+htx.The fundamental concept of GBDT has already been discussed. When GBDT was initially suggested, however, no one knew how to quantify this round of losses. Furthermore, the loss function’s negative gradient is commonly used to fit the approximate value of the current round of loss before proceeding to the next step of calculation. The loss function of theith sample in round t has a negative gradient that may be written as(10)rt,i=−∂Lyifxi∂fxifx=ft−1x.Then, it only needs to usexi,rt,ii=1,2,…,m to fit the tth CART regression tree, and its corresponding leaf node area is set to Rt,j,j=1,2,…,J, where J is the number of leaf nodes.For the samples in each leaf node, we minimize the loss function to get the best output of the fitted leaf node, as shown below:(11)ct,j=argmin︸c∑xi∈Rt,jLyi,ft−1xi+c.Then, the current round of decision tree fitting functionhtx=∑j=1Jct,jIx∈Rt,j is obtained, so the final prelearner obtained in this round is(12)ftx=ft−1x+∑j=1Jct,jIx∈Rt,j.The full name of XGBoost is eXtreme Gradient Boosting, which is the ultimate gradient boosting algorithm. Nowadays, XGBoost is widely used in many machine learning competitions such as Kaggle, and it has achieved good results. Unlike traditional GBDT, XGBoost can use CART or linear classifiers as base classifiers. The loss function of XGBoost is based on the loss functionLy,ft−1x+htx of GBDT, adding a regularization term, as shown below [20]:(13)Ωht=γJ+λ2∑j=1Jwtj2.Among them,J is the number of leaf nodes and wt,j has the same meaning as the parameter ct,j mentioned in the previous section. Therefore, the loss function of XGBoost is(14)Lt=Ly,ft−1xi+htxi+γJ+λ2∑j=1Jwtj2.In order to obtain the optimal solutionwtj for the J node of the tth decision tree and all the regions of each leaf node, the minimum calculation of formula (14) is first performed. XGBoost first performs the second-order Taylor expansion of formula (14) and then calculates it.Although it is the same as GBDT, XGBoost is also based on the Boosting algorithm model. However, XGBoost has greatly improved scalability and training speed. XGBoost is different from GBDT in three main points: different derivative information, different classifiers, and regular terms.We assume that in the trained modelf, the model f uses k features, x represents the vector of feature values of the instance to be explained, and the set S is a subset of k features. The Shapley value of each feature will contribute to the output of the model. We weighted summation on all possible combinations of eigenvalues, and we have the following formula:(15)ϕjval=∑S⊆x1,…,xk\xjSp−S−1!k!valS∪xj−valS.This article marginalizes the features that do not belong to the setS to obtain valxS, which is a prediction of the feature value in the set S, as shown below:(16)valxS=∫fx1,…xkdPx∉S−EXfX.For example, our model has 4 features, and theith feature is denoted as xi. The following evaluates the val values of the 2nd and 4th features:(17)valxS=valxx2,x4=∫R∫RfX1,x2,X3,x4dPX2X4−EXfX.The sum of all the feature contributions of a specific instance in the linear model isfx−EfX and (17) is very similar to this. The Shapley value contains the following 4 characteristics [21]:(1)
The benefit is:∑j=1kϕj=fx−EXfX, this formula indicates that the Shapley value of a sample is the sum of the contributions of all its characteristics.(2)
The symmetry is: if theith feature and the jth feature have the same contribution as the set S composed of other features, then the contributions of these two features to the Shapley value are equal.That is, forS⊆x1,…,xk/xi,xj, if valS∪xi=valS∪xj, then ϕi=ϕj.(3)
This virtuality is: for any setS, no matter if S contains or does not contain feature xi, it will not change the Shapley value, then the contribution value of feature xi is 0.That is, forS⊆x1,…,xk, if valS∪xi=valS, then ϕi=0.(4)
The additivity is: for a game with a combination, consisting of val andval+, then its Shapley value is ϕj+ϕj+.The human brain may be thought of as a multilayered structure. The human brain’s primary function is divided into two organising principles. When coping with issues, the human brain depends on the synchronisation of many brain areas. The interplay of several brain areas of the human brain is investigated in this article using the complex network approach. Furthermore, researching the human brain’ complex system is a way of brain science research.Many concepts in complex network analysis come from graph theory (GT). A complex network can be abstracted as a graphG=N,E composed of two sets N and E. Among them, the set NG=1,2,…,n represents the set of nodes (node), n is the number of nodes in the network, and the size of n reflects the scale of the network. The set EG=u,v|u,v∈NG represents the edge, and the size of E e represents the total number of edges between network nodes.An adjacency matrixA can express the connection relationship between the nodes of the entire network. If only considering whether two nodes are directly connected, it can be represented by an unweighted graph. If node i and node j are connected, then the adjacency element corresponding to the adjacency matrix A is aij=1, otherwise, aij=0. However, the power graph not only considers whether there is a connection between network nodes but also considers the strength of its connection. The strength of the connection relationship between nodes i and j can be represented by aij=wij, where wij is the connection weight of the edge. In addition, the graph can be divided into a directed graph and an undirected graph. If u,v=v,u, then the graph G is an undirected graph, otherwise, it is a directed graph.After constructing the functional network, the topology of the network can be analyzed to varying degrees, and some useful information can be obtained. From the statistical measurement of the network, the topology of the network can be better analyzed. The basic statistical characteristics include degree distribution, clustering coefficient and average path length. The following will analyze the main characteristics of the complex network from the statistical characteristics of the network.


### 3.1. Network Node Degree and Degree Distribution

The degree of a node in the network (Degree) is expressed as the number of edges directly connected to the node. For an undirected unweighted graph, its adjacency matrix isA, and the elements in A are represented by aij (i, J represent nodes in the network, and i≠j), then the degree dNi of node i is(18)dNi=∑j∈Naij.The degree reflects the evolution characteristics of the network, and the degree value of a node is directly proportional to the importance of the node in the network. If the nodes are selected in the network with equal probability, the probability that the selected node has exactlyk connected edges is represented by Pk, and then Pk represents the degree distribution of the network nodes. In actual analysis, the ratio of nodes with degree k to the total nodes can be approximated as(19)Pk=mn.Among them,m represents the number of nodes with degree k and n represents the total number of nodes. Degree distribution is a basic topological attribute of the network.


### 3.2. Network Density and Clustering Coefficient

The network density indicates the closeness of the connections between different nodes in the network, and it is a statistical characteristic that measures the integrity of the network. In an undirected network, a network withn nodes contains at most nn−1/2 edges. We assume that the number of edges that actually exist is e, then the network density can be expressed as(20)dG=2enn−1.The range of network density is0≤dG≤1. When the network is fully connected, dG=1. When there is no edge relationship in the network, dG=0.The clustering coefficient of the network indicates the possibility of interconnection between two different connected nodes of a certain nodei. In an undirected network, if node i has ki connected nodes, then there are at most kiki−1/2 edges between these ki nodes. The clustering coefficient Ci indicates that the ratio of the true number of connected edges ei to the most possible edges of the ki nodes is(21)Ci=2eikiki−1.The clustering coefficient of a complex network is defined as the average of the clustering coefficients of all nodes, as shown below:(22)C=1n∑i=1nCi.Among them,n is the number of nodes in the network. Because 0≤Ci≤1, the clustering coefficient of the network satisfies: 0≤C≤1. When C = 1, the network is fully connected. The network density reflects the overall statistical characteristics of the network, while the clustering coefficient reflects the local connection characteristics of the network.


### 3.3. Average Path Length

The connection of two nodesi and j means that from node i to node j along some edges in the network, there are generally multiple paths from node i to node j. The path from node i to node j that passes the fewest edges is called the shortest path between these two nodes. The shortest path represents the distance dij between node i and node j. If it is impossible to reach node j from node i via the network, then it means that node i and node j are not connected, and the distance is specified as dij=∞. At this time, the efficiency of information transfer between the two nodes is zero. If node i and node j are the same node, then dij=0. We call the maximum distance between any two nodes in the network the diameter of the network, which is denoted as D, that is:(23)D=maxdij.The average path length of a complex networkL is the average of the distance between all nodes, that is(24)L=1nn−1∑i≠jdij.Among them,n is the number of nodes in the network. The average path length can measure the performance of network information transmission, and the average path length is also called the characteristic path length (CPL).


## 3.1. Network Node Degree and Degree Distribution

The degree of a node in the network (Degree) is expressed as the number of edges directly connected to the node. For an undirected unweighted graph, its adjacency matrix isA, and the elements in A are represented by aij (i, J represent nodes in the network, and i≠j), then the degree dNi of node i is(18)dNi=∑j∈Naij.The degree reflects the evolution characteristics of the network, and the degree value of a node is directly proportional to the importance of the node in the network. If the nodes are selected in the network with equal probability, the probability that the selected node has exactlyk connected edges is represented by Pk, and then Pk represents the degree distribution of the network nodes. In actual analysis, the ratio of nodes with degree k to the total nodes can be approximated as(19)Pk=mn.Among them,m represents the number of nodes with degree k and n represents the total number of nodes. Degree distribution is a basic topological attribute of the network.


## 3.2. Network Density and Clustering Coefficient

The network density indicates the closeness of the connections between different nodes in the network, and it is a statistical characteristic that measures the integrity of the network. In an undirected network, a network withn nodes contains at most nn−1/2 edges. We assume that the number of edges that actually exist is e, then the network density can be expressed as(20)dG=2enn−1.The range of network density is0≤dG≤1. When the network is fully connected, dG=1. When there is no edge relationship in the network, dG=0.The clustering coefficient of the network indicates the possibility of interconnection between two different connected nodes of a certain nodei. In an undirected network, if node i has ki connected nodes, then there are at most kiki−1/2 edges between these ki nodes. The clustering coefficient Ci indicates that the ratio of the true number of connected edges ei to the most possible edges of the ki nodes is(21)Ci=2eikiki−1.The clustering coefficient of a complex network is defined as the average of the clustering coefficients of all nodes, as shown below:(22)C=1n∑i=1nCi.Among them,n is the number of nodes in the network. Because 0≤Ci≤1, the clustering coefficient of the network satisfies: 0≤C≤1. When C = 1, the network is fully connected. The network density reflects the overall statistical characteristics of the network, while the clustering coefficient reflects the local connection characteristics of the network.


## 3.3. Average Path Length

The connection of two nodesi and j means that from node i to node j along some edges in the network, there are generally multiple paths from node i to node j. The path from node i to node j that passes the fewest edges is called the shortest path between these two nodes. The shortest path represents the distance dij between node i and node j. If it is impossible to reach node j from node i via the network, then it means that node i and node j are not connected, and the distance is specified as dij=∞. At this time, the efficiency of information transfer between the two nodes is zero. If node i and node j are the same node, then dij=0. We call the maximum distance between any two nodes in the network the diameter of the network, which is denoted as D, that is:(23)D=maxdij.The average path length of a complex networkL is the average of the distance between all nodes, that is(24)L=1nn−1∑i≠jdij.Among them,n is the number of nodes in the network. The average path length can measure the performance of network information transmission, and the average path length is also called the characteristic path length (CPL).


## 4. Research on Athlete Training Effect Evaluation Based on Machine Learning Algorithm

This research offers a machine learning regression assessment technique based on the demands of athletes’ training impact evaluation. This article recognises activities using the athlete’s movement feature recognition system. This research provides a cloud and fog collaborative distributed augmented reality computing system, as depicted in Figure2, based on the assessment of standard action comparison. The submodules are deployed in terminals, fog computing nodes, and cloud computing centres to construct a “end-fog-cloud” distributed AR collaborative computing architecture based on the variations in computing resources and latency bandwidth needs of each module.Figure 2
Interactive diagram of distributed augmented reality module.Figure3 shows the overall data flow processing flow chart of the platform. The user uploads the identification map and the screenshot of the 3D model to the server, and the server first searches the database for the corresponding mark based on the name of the identification map. If there is a marked patt template directly obtained, and the corresponding AR application template is obtained in the database through the marked template and the screenshot of the 3D model.Figure 3
The overall data flow processing flow chart of the platform.While a moving target is watched by numerous cameras in a range with an overlapping field of view, it is important to evaluate whether the moving target is the same moving target when passing through the different cameras. When there is an interfering target, it is important to complete the target handover under multicamera monitoring and establish if the interfering target has to be evaluated. As a result, in the multicamera moving target tracking procedure, there are primarily two scenarios. Figure4 depicts the multicamera moving target tracking procedure.Figure 4
Schematic diagram of multiview moving target tracking.The flow chart of the method for generating the boundary line of the field of view based on projection invariant is shown in Figure5.Figure 5
The dividing line of the field of view based on projection invariants.This paper introduces regional suggestions into the target tracking framework and proposes a new target tracking algorithm to deal with target tracking in uncertain motion scenes. The basic frame diagram of uncertain moving target tracking based on area recommendation is shown in Figure6.Figure 6
Basic frame diagram of uncertain moving target tracking based on area recommendation.Figure7 shows the process of athlete training feature recognition. Among them, Figure 7(a) is the original image of the athlete training, Figure 7(b) is the athlete target automatically selected by the system, Figure 7(c) is the background elimination process, and Figure 7(d) is the final feature recognition result.Figure 7
Athlete training feature recognition process. (a) Original image, (b) selected target, (c) digital background elimination, (d) athlete feature recognition result.
(a)(b)(c)(d)Through the evaluation and analysis of the athlete training effect evaluation model based on the machine learning algorithm proposed in this paper, the evaluation results shown in Table1 and Figure 8 are obtained.Table 1
Athlete training effect evaluation based on machine learning algorithm.
NumTraining evaluation192.41282.92384.85490.87582.68687.09781.91884.48984.701082.851190.251287.791389.441491.411590.041683.151789.441882.991981.732083.462188.642289.122390.592489.732589.452692.212782.322885.442988.173082.873186.933283.973387.763483.273581.033691.513788.963885.143989.434083.714189.174283.684383.044491.824591.464691.704783.834892.954984.555082.535182.775292.605390.805482.945589.315689.825787.655882.135990.116085.386181.666288.536391.26Figure 8
Statistical diagram of the training effect of athletes based on machine learning algorithms.Through the above research and analysis, it can be seen that the athlete training effect evaluation system based on the machine learning algorithm proposed in this paper has good results, so it can be applied to subsequent athletes’ sports training evaluation.


## 5. Conclusion

With the rapid development of modern science and technology, high-level scientific and technological achievements have been continuously applied in sports practice. At this time, people are no longer satisfied with training only according to the experience passed down from master to disciple. Instead, they have to explore new theories, new ideas, new science and technology, new instruments and equipment, new methods and methods, to explore the faster improvement of sports competition. This is the general trend of scientific sports training, which is in the ascendant worldwide. It is this trend and practical activities that have made the world’s competitive sports technology level rapidly improved, and some sports performances have reached an eye-popping level. This article combines machine learning algorithms to evaluate and analyze athletes’ training effects and builds an intelligent evaluation system based on current athletes’ training needs. The experimental research results show that the athlete training effect evaluation system based on the machine learning algorithm proposed in this paper has good results, so it can be applied to subsequent athletes’ sports training evaluation.



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