# Football Match Scoring Method Based on Adaptive Neural Network Algorithm

Authors: Yun Jin
Journal: Security and Communication Networks
 (2022)

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## Abstract

To study in more detail the impact of various indicators on the scoring system of a football match, the author suggests a football game scoring method based on an adaptive neural network algorithm. Firstly, the application background of football match prediction, the research and application status of the adaptive neural network algorithm, and the related research of football match prediction are described; Secondly, the factors affecting the outcome of football games are analyzed, and the applicability of the adaptive neural network algorithm in football match prediction are summarized. Through the system requirement analysis, the overall design of the system and the design of the database are completed. From here, we see that there is a huge opportunity to use an adaptive neural network in an automatic scoring system after a football match, which can replace manual scoring and reduce the manual workload.


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## Body

## 1. Introduction

Football is the world’s first sport. There are a lot of people around the world watching football. As fans pay great attention to different football games, there are more and more ways to predict and analyze the results [1]. In today’s fast-paced world of information, traditional predictive methods no longer meet the predictive needs of football matches, and new predictions are increasingly being expressed by experts and scientists, expressed through an adaptive neural network capable of solving nonlinear problems. People generally use football as a circle to describe the unpredictable change of football match results [2]. The usual prediction method is that relevant experts combine the historical battle records of the two sides and the current state of the two teams. This prediction method relies too much on historical battle records, and the impact of the current state of the team on the results of the upcoming game cannot be described quantitatively [3, 4].As time goes on and the use of the Internet spreads, the football industry is booming, and it is an outdoor sport with the highest production value, greatest influence, and public attention in the world. The sport is far ahead of other sports. According to relevant statistics, football, known as the “17 largest economies” in the world, accounted for 43% of the total value of products in the sports sector, overtaking many developed countries and reaching $ 500 billion. But the region’s GDP is undisputedly the world’s number one sport, far surpassing other sports such as basketball, golf, baseball, and F1 racing. According to FIFA, as of July 2016, more than 200 million players and 1.5 million people from 1.5 million teams are involved in various football events around the world. Mountain. According to statistics, people involved in football and related activities make up about 3% of the world’s population. The outcome of a football match can be considered a combination of these characteristics [5]. Each feature has a clear relationship to the outcome. This relationship is not a simple linear relationship but a nonlinear one. A neural network is actually a nonlinear representation from input to output. The site's scoring algorithm adds weight to important game data, which is closely related to player's positive or negative comments. These game data will be completely recorded and divided into three categories: attack data, defense data, and pass data. Important data events will also be scored based on 6 points and given different weights [6, 7]. The data that have a direct impact on the score, such as goals, assists, and shots, naturally obtain a higher weight. Figure 1 shows a model of a football game based on an adaptive neural network.Figure 1
Model of a football game based on an adaptive neural network.


## 2. Literature Review

As artificial intelligence technology continues to evolve, many related theories and technological applications are gaining more and more attention. At present, it has been widely used in the predictive intelligent system. In developing the stock hypothesis model, Pan et al. used the breast algorithm to optimize the neural network to improve the predictive effect, accuracy, and predictive speed, and compared it to the neural network optimized by the genetic algorithm and the particle optimization algorithm [8]. Matayoshi et al. utilized the genetic algorithm optimization process that uses only the fitness function instead of gradients and other auxiliary information. Adaptive neural networks make it possible to better predict functional output after optimizing initial weight by the genetic algorithm [9]. Nguyen et al. used genetic algorithms to optimize the initial weight and threshold of adaptive neural networks. Improved adaptive neural network design significantly improves the speed of integration during training, thereby improving the accuracy of house price predictions [10]. Meng proposed an improvement on the traditional genetic algorithm for the premature convergence caused by the same generation crossover in most genetic algorithms [11]. Lv proposed a method of localizing the number based on the AB neural network model. First, the location of the vehicle number is determined using the field histogram method and the rectangular sliding window method, and then the number position is determined using the field mean method and the average jump method [12]. Zhang et al. established an adaptive neural network prediction model for food safety index and used genetic algorithms to optimize neural network weights and thresholds, develop food safety models related to reliability, realism, simplicity, and recognition, and applied research [13]. Fang et al. established an atmospheric pollutant prediction model using wavelet technology and the backpropagation neural network (w-BPNN) [14].


## 3. Research Methods

### 3.1. Adaptive Neural Network

The adaptive neural network refers primarily to the neural network of the human brain, which is a technical prototype of the artificial neural network. The human brain is the material base of a very flexible human mind. Mental activity is located in the cerebral cortex, containing about 1011 neurons. Each neive is connected to about 103 other neurons through synapses, forming a very complex structure dynamic network. The adaptive neural network mainly studies the structure, function, and functioning of the human neural network, and aims to study the laws of the human brain’s ability to think and function intelligently. The simplified sense of appropriate training in artificial neural networks is the technical rehabilitation and reproduction of adaptive neural networks. As a discipline, its main purpose is to create a practical artificial neural network model according to the principles of adaptive neural networking and practical application needs. The algorithm mimics some of the intelligent functions of the human brain and then technically implements it to solve practical problems. Thus, the adaptive neural network mainly studies the mechanisms of the mind, and the artificial neural network mainly studies the implementation of intelligent mechanisms, and the two complement each other. The adaptive neural network abstracts the neural networks of the human brain for information processing, creates a simple network model, and forms a connected network according to different communication methods [15, 16]. Over the past decade, the development and research of artificial neural networks have made steady progress, and many traditional tools and methods for recognizing, predicting, and calculating patterns have failed in biomedicine. The real problem is solved by good intelligence. Figure 2 shows a diagram of the computational model of the neurons.Figure 2
The calculation model of neurons.For a neuron, the information from the external or other neuronsi is Xi, their connection weight with the processing neuron is Wi, an internal offset for the processing unit, then the input value of the processing neuron is ∑i=1nWiXi, and the output value is y=f∑i=1nWiXi−θ.Adaptive neural networks is a multilayer conductive neural network prepared according to an error feedback algorithm [17]. The latent layer may contain many layers of nerve cells. Learning to adapt is a controlled learning process. The model continuously adjusts the parameters in the neural network by learning and correcting the redistribution error. The study of adaptive neural networks is divided into two stages [18]. Figure 3 shows specific flowchart of the adaptive neural network.Figure 3
Training flowchart of adaptive neural network.


### 3.2. Adaptive Neural Network Model for Football Scoring Method Prediction

#### 3.2.1. Design of Relevant Parameters of Adaptive Neural Network

The model for the reconstruction of the neural network is mainly the number of neural structures, the number of neurons on the entrance floor, the number of neurons on the inner layer and the number of neurons on the outer layer. It is like a function to activate. The structure of the network is determined as follows: the reconstruction network can contain a layer of input, layer of inner space, a layer of output, and a layer of outer space, or a few neurons. Increasing the number of layers in the ocean is possible to improve the visualization of nonlinear connections, but too many distant layers are able to adapt, which will affect the performance of the network. This broadcast selected a three-fold adjustment network. The three-fold neuron network confirms the mathematics theory that any nonlinear function can be combined in detail [19, 20].① The first step is to define the number of neurons in each layer. Position level: the number of neurons in the spinal column determines the number of problems that will solve. This document takes CSL as an object of research and predicts the results of the CSL game, and learns how to create an adaptive nerve network model to predict the results and to use it. To analyze the factors that affect the results of football matches, the input variables determined in this paper are the number of goals, shots, goals, passes, corners, overtaking, steals, and ball possession rates of both teams in the past in five games. In addition, it also includes 20 variables such as home and away, and competitive status, so the input layer has 20 neurons. Output layer: In the football match prediction model, the solution to the problem is to find the winning probability of a team. The output is the value in the interval [0, 1], i.e., the number of neurons in the output layer is 1. Hidden layer: The number of nerve cells in the underlying layer directly affects the performance of all adaptive neural networks. The improper number of hidden layer neurons will lead to overfitting in neural network training [21]. In general, there are three empirical formulas for determining the number of nerve cells, as shown in formulas (1)–(3):(1)m=n+2+υ,(2)m=log2m,(3)n=nl.The totalm values calculated by 1 are [5, 15] in the city, and the second equation is m equal to 4, and the third term is m equal to 4. If you combine the three epidemics, you can eventually adjust the number of fractions by 4.② Determination of activation function: different activation functions determine the mathematical model of each neuron, which makes the neural network adopt different information processing modes for various types of data [22, 23]. Information mode is one of the three elements of the overall performance of neural network. Selecting a good activation function can improve the efficiency and predictive accuracy of neural network operations. Aiming at the problem of the football game scoring method, this paper selects different activation functions for the three-layer adaptive neural network.Input layer: the training samples received by the input layer are discrete data without function conversion. Therefore, the input layer can use the general linear activation function as shown in the following equation:(4)fs=s.Hidden layer: the activation function selected in this article to improve the ability to regulate neural network communication is the sigmoid function in the latent layer. The sigmoid function is a common biological sigmoid function and is called the sigmoid growth curve. The formula is shown in the following equation:(5)Sx=11+e−x.The sigmoid function is a nonlinear function. The middle position has a greater influence on the signal gain, and the gain of two-way signals is relatively low. Therefore, it has a good effect on the feature space mapping of the signal. It wakes up the nonlinear combination of input signals through weighting and generates the corresponding nonlinear decision boundary. From a biological point of view, the central region is similar to the state of nerve cell excitation, and the two ends are similar to the state of nerve cell suppression. The sigmoid function lowers neuronal input variables in the interval [0, 1]. The advantage is that the data are not easily separated during the conversion.Output layer: tidden layer activation function, sigmoid function value range [0, 1], and the output result of football in this problem are a probability value in an [0, 1] interval, so it is necessary to map the input variables to the corresponding interval. The activation function used in the output layer of this article is the tanh function, as shown in the following equation:(6)fx=ex−e−xex+e−x.If the sigmoid input is between [−1, 1], the function value is sensitive to change. Once it is close to or exceeds this interval, it will lose sensitivity and saturation; this affects the accuracy of the neural network hypotheses. Tanh’s outputs and inputs can maintain a nonlinear monotonous ascending and descending relationship consistent with the BP network gradient solution. It has a high tolerance and limitability to injury, and gradually approaches 0 and 1, in accordance with the law of nerve saturation in the human brain. The result of predicting the outcome of a football match is the winning rate of the team, and the output tanh function between 0 and 1 is just right. Therefore, in this paper, we have chosen to use the sigmoid function in the latent layer and the tanh function in the output layer.The data and information are entered to select the last elements of 19 serious hazards. It includes the average value of goals, shots, goal shots, passes, corner shots, overtaking numbers, steals, and ball control rate in the first five rounds of the game, as well as the competitive status of the team in the first five rounds of the league. There are 19 input data in total (we set the initial value as 0, add 0.2 to win one game, add 0.1 to draw one game, and do not add to lose one game. Finally, the competitive state will be quantified within the range of [0, 1]), and the output data are the victory index of the team. Finally, the prediction results are obtained by comparing the victory indexes of both sides. Through the test, the best comparison method is shown in Table1; the home team’s victory index is x, and the away team’s victory index is y.Table 1
Competition scoring method.
Index comparisonPrediction resultsX > y && 1>x > 0.4Home team winsy > y && 1>y > 0.4Visiting team winsX > 0.4 && y < 0.4It ends in a draw


#### 3.2.2. Normalization Processing Method of Input Data

In the application of the adaptive neural network, we cannot directly take the obtained training data as the input of adaptive neural network. There are three common normalization functions. The linear function conversion is shown in the formula as follows:(7)y=x−MinValueMaxValue−MinValue.The transformation of the logarithmic function is shown in the equation as follows:(8)y=log10x.The transformation of the inverse cotangent function is shown in the equation as follows:(9)y=atanx∗2pi,where x is the initial value before transformation, y is the value after transformation, and maxValue and minValue, respectively, represent the maximum value and minimum value of the training template. In the responsive nervous central system network model, the independent variable is entered as greater than or equal to 0. Through comparison, the system uses the inverse cotangent function to normalize the data [24].


### 3.3. Application Test of Adaptive Neural Network Algorithm

As an example of specific competition, the first step in implementing an adaptive neural network algorithm using java to predict Jiangsu Suning’s victory rate is to obtain a sample data that can be read and processed using SQL statements, as shown in Table2.Table 2
Sample data to be processed.
Rounds56789Home and away1.00.01.00.01.0Team goals1.641.71.31.31.3Team shots11.6411.112.311.311.5Team shooting positive4.03.53.12.74.1Team passes436.64430.7450.1424.3403.3Team competitive state0.60.80.70.70.7Opponent’s goals1.140.31.10.71.0Opponent’s shots17.010.57.312.78.5Opponent firing positive number6.142.31.15.34.0Opponent passes371.64440.3318.1405.7315.3Score value1.00.41.00.40.4We need to learn five columns of data, and we are done with a data analysis at the ninth floor of the data. The second step is to find the model data that we found using the transformation of the predefined cotangent function, which is to find the model data that we learned, as shown in Table3.Table 3
Sample data to be trained.
Rounds56789Home and away1.0000.0001.0000.0001.000Team goals0.65840.66610.60410.60410.6041Team shots0.93480.93680.94150.93770.9385Team shooting positive0.86320.85260.84010.82610.8680Team passes0.98750.98750.98750.98740.9873Team competitive state0.60000.80000.70000.70000.7000Opponent’s goals0.56040.23110.54660.41850.4000Opponent’s shots0.85460.93520.91360.94280.9228Opponent firing positive number0.88800.80680.80610.90120.8632Opponent passes0.987250.98750.98710.98730.9870Score value1.0000.40001.00000.40000.4000


## 3.1. Adaptive Neural Network

The adaptive neural network refers primarily to the neural network of the human brain, which is a technical prototype of the artificial neural network. The human brain is the material base of a very flexible human mind. Mental activity is located in the cerebral cortex, containing about 1011 neurons. Each neive is connected to about 103 other neurons through synapses, forming a very complex structure dynamic network. The adaptive neural network mainly studies the structure, function, and functioning of the human neural network, and aims to study the laws of the human brain’s ability to think and function intelligently. The simplified sense of appropriate training in artificial neural networks is the technical rehabilitation and reproduction of adaptive neural networks. As a discipline, its main purpose is to create a practical artificial neural network model according to the principles of adaptive neural networking and practical application needs. The algorithm mimics some of the intelligent functions of the human brain and then technically implements it to solve practical problems. Thus, the adaptive neural network mainly studies the mechanisms of the mind, and the artificial neural network mainly studies the implementation of intelligent mechanisms, and the two complement each other. The adaptive neural network abstracts the neural networks of the human brain for information processing, creates a simple network model, and forms a connected network according to different communication methods [15, 16]. Over the past decade, the development and research of artificial neural networks have made steady progress, and many traditional tools and methods for recognizing, predicting, and calculating patterns have failed in biomedicine. The real problem is solved by good intelligence. Figure 2 shows a diagram of the computational model of the neurons.Figure 2
The calculation model of neurons.For a neuron, the information from the external or other neuronsi is Xi, their connection weight with the processing neuron is Wi, an internal offset for the processing unit, then the input value of the processing neuron is ∑i=1nWiXi, and the output value is y=f∑i=1nWiXi−θ.Adaptive neural networks is a multilayer conductive neural network prepared according to an error feedback algorithm [17]. The latent layer may contain many layers of nerve cells. Learning to adapt is a controlled learning process. The model continuously adjusts the parameters in the neural network by learning and correcting the redistribution error. The study of adaptive neural networks is divided into two stages [18]. Figure 3 shows specific flowchart of the adaptive neural network.Figure 3
Training flowchart of adaptive neural network.


## 3.2. Adaptive Neural Network Model for Football Scoring Method Prediction

### 3.2.1. Design of Relevant Parameters of Adaptive Neural Network

The model for the reconstruction of the neural network is mainly the number of neural structures, the number of neurons on the entrance floor, the number of neurons on the inner layer and the number of neurons on the outer layer. It is like a function to activate. The structure of the network is determined as follows: the reconstruction network can contain a layer of input, layer of inner space, a layer of output, and a layer of outer space, or a few neurons. Increasing the number of layers in the ocean is possible to improve the visualization of nonlinear connections, but too many distant layers are able to adapt, which will affect the performance of the network. This broadcast selected a three-fold adjustment network. The three-fold neuron network confirms the mathematics theory that any nonlinear function can be combined in detail [19, 20].① The first step is to define the number of neurons in each layer. Position level: the number of neurons in the spinal column determines the number of problems that will solve. This document takes CSL as an object of research and predicts the results of the CSL game, and learns how to create an adaptive nerve network model to predict the results and to use it. To analyze the factors that affect the results of football matches, the input variables determined in this paper are the number of goals, shots, goals, passes, corners, overtaking, steals, and ball possession rates of both teams in the past in five games. In addition, it also includes 20 variables such as home and away, and competitive status, so the input layer has 20 neurons. Output layer: In the football match prediction model, the solution to the problem is to find the winning probability of a team. The output is the value in the interval [0, 1], i.e., the number of neurons in the output layer is 1. Hidden layer: The number of nerve cells in the underlying layer directly affects the performance of all adaptive neural networks. The improper number of hidden layer neurons will lead to overfitting in neural network training [21]. In general, there are three empirical formulas for determining the number of nerve cells, as shown in formulas (1)–(3):(1)m=n+2+υ,(2)m=log2m,(3)n=nl.The totalm values calculated by 1 are [5, 15] in the city, and the second equation is m equal to 4, and the third term is m equal to 4. If you combine the three epidemics, you can eventually adjust the number of fractions by 4.② Determination of activation function: different activation functions determine the mathematical model of each neuron, which makes the neural network adopt different information processing modes for various types of data [22, 23]. Information mode is one of the three elements of the overall performance of neural network. Selecting a good activation function can improve the efficiency and predictive accuracy of neural network operations. Aiming at the problem of the football game scoring method, this paper selects different activation functions for the three-layer adaptive neural network.Input layer: the training samples received by the input layer are discrete data without function conversion. Therefore, the input layer can use the general linear activation function as shown in the following equation:(4)fs=s.Hidden layer: the activation function selected in this article to improve the ability to regulate neural network communication is the sigmoid function in the latent layer. The sigmoid function is a common biological sigmoid function and is called the sigmoid growth curve. The formula is shown in the following equation:(5)Sx=11+e−x.The sigmoid function is a nonlinear function. The middle position has a greater influence on the signal gain, and the gain of two-way signals is relatively low. Therefore, it has a good effect on the feature space mapping of the signal. It wakes up the nonlinear combination of input signals through weighting and generates the corresponding nonlinear decision boundary. From a biological point of view, the central region is similar to the state of nerve cell excitation, and the two ends are similar to the state of nerve cell suppression. The sigmoid function lowers neuronal input variables in the interval [0, 1]. The advantage is that the data are not easily separated during the conversion.Output layer: tidden layer activation function, sigmoid function value range [0, 1], and the output result of football in this problem are a probability value in an [0, 1] interval, so it is necessary to map the input variables to the corresponding interval. The activation function used in the output layer of this article is the tanh function, as shown in the following equation:(6)fx=ex−e−xex+e−x.If the sigmoid input is between [−1, 1], the function value is sensitive to change. Once it is close to or exceeds this interval, it will lose sensitivity and saturation; this affects the accuracy of the neural network hypotheses. Tanh’s outputs and inputs can maintain a nonlinear monotonous ascending and descending relationship consistent with the BP network gradient solution. It has a high tolerance and limitability to injury, and gradually approaches 0 and 1, in accordance with the law of nerve saturation in the human brain. The result of predicting the outcome of a football match is the winning rate of the team, and the output tanh function between 0 and 1 is just right. Therefore, in this paper, we have chosen to use the sigmoid function in the latent layer and the tanh function in the output layer.The data and information are entered to select the last elements of 19 serious hazards. It includes the average value of goals, shots, goal shots, passes, corner shots, overtaking numbers, steals, and ball control rate in the first five rounds of the game, as well as the competitive status of the team in the first five rounds of the league. There are 19 input data in total (we set the initial value as 0, add 0.2 to win one game, add 0.1 to draw one game, and do not add to lose one game. Finally, the competitive state will be quantified within the range of [0, 1]), and the output data are the victory index of the team. Finally, the prediction results are obtained by comparing the victory indexes of both sides. Through the test, the best comparison method is shown in Table1; the home team’s victory index is x, and the away team’s victory index is y.Table 1
Competition scoring method.
Index comparisonPrediction resultsX > y && 1>x > 0.4Home team winsy > y && 1>y > 0.4Visiting team winsX > 0.4 && y < 0.4It ends in a draw


### 3.2.2. Normalization Processing Method of Input Data

In the application of the adaptive neural network, we cannot directly take the obtained training data as the input of adaptive neural network. There are three common normalization functions. The linear function conversion is shown in the formula as follows:(7)y=x−MinValueMaxValue−MinValue.The transformation of the logarithmic function is shown in the equation as follows:(8)y=log10x.The transformation of the inverse cotangent function is shown in the equation as follows:(9)y=atanx∗2pi,where x is the initial value before transformation, y is the value after transformation, and maxValue and minValue, respectively, represent the maximum value and minimum value of the training template. In the responsive nervous central system network model, the independent variable is entered as greater than or equal to 0. Through comparison, the system uses the inverse cotangent function to normalize the data [24].


## 3.2.1. Design of Relevant Parameters of Adaptive Neural Network

The model for the reconstruction of the neural network is mainly the number of neural structures, the number of neurons on the entrance floor, the number of neurons on the inner layer and the number of neurons on the outer layer. It is like a function to activate. The structure of the network is determined as follows: the reconstruction network can contain a layer of input, layer of inner space, a layer of output, and a layer of outer space, or a few neurons. Increasing the number of layers in the ocean is possible to improve the visualization of nonlinear connections, but too many distant layers are able to adapt, which will affect the performance of the network. This broadcast selected a three-fold adjustment network. The three-fold neuron network confirms the mathematics theory that any nonlinear function can be combined in detail [19, 20].① The first step is to define the number of neurons in each layer. Position level: the number of neurons in the spinal column determines the number of problems that will solve. This document takes CSL as an object of research and predicts the results of the CSL game, and learns how to create an adaptive nerve network model to predict the results and to use it. To analyze the factors that affect the results of football matches, the input variables determined in this paper are the number of goals, shots, goals, passes, corners, overtaking, steals, and ball possession rates of both teams in the past in five games. In addition, it also includes 20 variables such as home and away, and competitive status, so the input layer has 20 neurons. Output layer: In the football match prediction model, the solution to the problem is to find the winning probability of a team. The output is the value in the interval [0, 1], i.e., the number of neurons in the output layer is 1. Hidden layer: The number of nerve cells in the underlying layer directly affects the performance of all adaptive neural networks. The improper number of hidden layer neurons will lead to overfitting in neural network training [21]. In general, there are three empirical formulas for determining the number of nerve cells, as shown in formulas (1)–(3):(1)m=n+2+υ,(2)m=log2m,(3)n=nl.The totalm values calculated by 1 are [5, 15] in the city, and the second equation is m equal to 4, and the third term is m equal to 4. If you combine the three epidemics, you can eventually adjust the number of fractions by 4.② Determination of activation function: different activation functions determine the mathematical model of each neuron, which makes the neural network adopt different information processing modes for various types of data [22, 23]. Information mode is one of the three elements of the overall performance of neural network. Selecting a good activation function can improve the efficiency and predictive accuracy of neural network operations. Aiming at the problem of the football game scoring method, this paper selects different activation functions for the three-layer adaptive neural network.Input layer: the training samples received by the input layer are discrete data without function conversion. Therefore, the input layer can use the general linear activation function as shown in the following equation:(4)fs=s.Hidden layer: the activation function selected in this article to improve the ability to regulate neural network communication is the sigmoid function in the latent layer. The sigmoid function is a common biological sigmoid function and is called the sigmoid growth curve. The formula is shown in the following equation:(5)Sx=11+e−x.The sigmoid function is a nonlinear function. The middle position has a greater influence on the signal gain, and the gain of two-way signals is relatively low. Therefore, it has a good effect on the feature space mapping of the signal. It wakes up the nonlinear combination of input signals through weighting and generates the corresponding nonlinear decision boundary. From a biological point of view, the central region is similar to the state of nerve cell excitation, and the two ends are similar to the state of nerve cell suppression. The sigmoid function lowers neuronal input variables in the interval [0, 1]. The advantage is that the data are not easily separated during the conversion.Output layer: tidden layer activation function, sigmoid function value range [0, 1], and the output result of football in this problem are a probability value in an [0, 1] interval, so it is necessary to map the input variables to the corresponding interval. The activation function used in the output layer of this article is the tanh function, as shown in the following equation:(6)fx=ex−e−xex+e−x.If the sigmoid input is between [−1, 1], the function value is sensitive to change. Once it is close to or exceeds this interval, it will lose sensitivity and saturation; this affects the accuracy of the neural network hypotheses. Tanh’s outputs and inputs can maintain a nonlinear monotonous ascending and descending relationship consistent with the BP network gradient solution. It has a high tolerance and limitability to injury, and gradually approaches 0 and 1, in accordance with the law of nerve saturation in the human brain. The result of predicting the outcome of a football match is the winning rate of the team, and the output tanh function between 0 and 1 is just right. Therefore, in this paper, we have chosen to use the sigmoid function in the latent layer and the tanh function in the output layer.The data and information are entered to select the last elements of 19 serious hazards. It includes the average value of goals, shots, goal shots, passes, corner shots, overtaking numbers, steals, and ball control rate in the first five rounds of the game, as well as the competitive status of the team in the first five rounds of the league. There are 19 input data in total (we set the initial value as 0, add 0.2 to win one game, add 0.1 to draw one game, and do not add to lose one game. Finally, the competitive state will be quantified within the range of [0, 1]), and the output data are the victory index of the team. Finally, the prediction results are obtained by comparing the victory indexes of both sides. Through the test, the best comparison method is shown in Table1; the home team’s victory index is x, and the away team’s victory index is y.Table 1
Competition scoring method.
Index comparisonPrediction resultsX > y && 1>x > 0.4Home team winsy > y && 1>y > 0.4Visiting team winsX > 0.4 && y < 0.4It ends in a draw


## 3.2.2. Normalization Processing Method of Input Data

In the application of the adaptive neural network, we cannot directly take the obtained training data as the input of adaptive neural network. There are three common normalization functions. The linear function conversion is shown in the formula as follows:(7)y=x−MinValueMaxValue−MinValue.The transformation of the logarithmic function is shown in the equation as follows:(8)y=log10x.The transformation of the inverse cotangent function is shown in the equation as follows:(9)y=atanx∗2pi,where x is the initial value before transformation, y is the value after transformation, and maxValue and minValue, respectively, represent the maximum value and minimum value of the training template. In the responsive nervous central system network model, the independent variable is entered as greater than or equal to 0. Through comparison, the system uses the inverse cotangent function to normalize the data [24].


## 3.3. Application Test of Adaptive Neural Network Algorithm

As an example of specific competition, the first step in implementing an adaptive neural network algorithm using java to predict Jiangsu Suning’s victory rate is to obtain a sample data that can be read and processed using SQL statements, as shown in Table2.Table 2
Sample data to be processed.
Rounds56789Home and away1.00.01.00.01.0Team goals1.641.71.31.31.3Team shots11.6411.112.311.311.5Team shooting positive4.03.53.12.74.1Team passes436.64430.7450.1424.3403.3Team competitive state0.60.80.70.70.7Opponent’s goals1.140.31.10.71.0Opponent’s shots17.010.57.312.78.5Opponent firing positive number6.142.31.15.34.0Opponent passes371.64440.3318.1405.7315.3Score value1.00.41.00.40.4We need to learn five columns of data, and we are done with a data analysis at the ninth floor of the data. The second step is to find the model data that we found using the transformation of the predefined cotangent function, which is to find the model data that we learned, as shown in Table3.Table 3
Sample data to be trained.
Rounds56789Home and away1.0000.0001.0000.0001.000Team goals0.65840.66610.60410.60410.6041Team shots0.93480.93680.94150.93770.9385Team shooting positive0.86320.85260.84010.82610.8680Team passes0.98750.98750.98750.98740.9873Team competitive state0.60000.80000.70000.70000.7000Opponent’s goals0.56040.23110.54660.41850.4000Opponent’s shots0.85460.93520.91360.94280.9228Opponent firing positive number0.88800.80680.80610.90120.8632Opponent passes0.987250.98750.98710.98730.9870Score value1.0000.40001.00000.40000.4000


## 4. Result Discussion

### 4.1. Analysis and Design of Football Scoring Method System

#### 4.1.1. Demand Analysis

Requirement analysis is an important basis for system design and development. Therefore, we need to use some more effective methods to strictly review and verify the requirements analysis of the system. The basic task of requirement analysis is to solve the problem of what functions the system to be designed needs to have. The system realizes the following functions for users [25]. Figure 4 shows the user case diagram of the football game scoring method system.(1)
Scoreboard query. The user can query the scoreboard after the selected round according to the demand(2)
Competition information management. Users can add, delete, modify, and check the competition schedule and detailed data(3)
Prediction of the outcome of the game. Users can query the outcome prediction of the recent competition that has not been carried out and can also adjust the relevant parameters of the neural networkFigure 4
User case diagram of football game scoring method system.


#### 4.1.2. Overall System Design

Systems analysis is the process of identifying and sorting out the needs of the user and creating an accurate model. System design is the process of transforming the requirements obtained in the analysis stage into an abstract system implementation scheme that meets the requirements of cost and quality. It is the process of gradually expanding a model from object-oriented analysis to object-oriented design. In other words, object-oriented design is the process of establishing a solution domain model from an object-oriented point of view. In addition to following the traditional basic principle guiding software design, object-oriented design should also have its own characteristics. They are the principles of modularity, abstraction, information hiding, low coupling, strong cohesion, and reusability. There are several key points in system design, including system design, function module design, and database design. As shown in Figure5, according to the demand analysis, the football game victory and defeat prediction system is divided into three modules: scoreboard information query module, game information management module, and game victory and defeat prediction module. The competition information module includes three subfunction modules of adding, modifying, and deleting competition information. The scoreboard query module includes the real-time query function of the scoreboard, and the competition victory and defeat prediction module includes the network parameter adjustment function and prediction function.Figure 5
Overall design of football match victory and defeat prediction system.


#### 4.1.3. System Function Description

The addition, deletion, modification, and query of information is an indispensable part of the information system. The system develops these four functions to maintain football match related information and provide effective data support for the following prediction. The scoreboard information management module is mainly the scoreboard query function, and users can query the scoreboard after relevant League rounds in real time. After receiving the user’s score help query request, the server initializes the scoreboard information, then queries this round and previous game information according to the League Round selected by the user, modifies the scoreboard, then ranks according to the ranking rules of China Football Super League, and finally feeds back the results to the user. Figure6 shows the specific flowchart of scoreboard query function.Figure 6
Flowchart of scoreboard query function.The adaptive neural network parameter data table consists of three attribute fields, including learning rate, error accuracy, and maximum learning number. And the relevant attribute types are shown in Table4. The data type of learning rate and error accuracy is double, the data type of maximum learning times is int (4), and there is no primary key set in the network parameter table.Table 4
Data sheet of network parameters.
Field nameField codeData typeLearning rateLearningrateDOUBLEError accuracyGlobalerrorDOUBLEMaximum learning timesMaxstudytimesINT(4)


### 4.2. Case Analysis

Using the adaptive neural network algorithm and other algorithms, the postgame score of football game is analyzed based on random input variables. Two different algorithms are improved to find a more suitable prediction algorithm for the postgame score of football game, as shown in Figures7 and 8.Figure 7
Different algorithms and media ratings.Figure 8
Error of different algorithms.Human scoring is subjective. Machine scoring integrates the influence of big data and is relatively objective. However, it cannot be denied that sometimes the scores given by machines are not very correct, but in 90% of the cases, the data given by machines are relatively correct because it excludes people’s subjective views and is more objective. This time, the adaptive neural network algorithm is used for scoring. Among the 30 data, only 3 data errors are large (the difference between absolute values exceeds 0.5), nearly half of the data errors are less than 0.1, and only 4 data variances exceed the recognized scoring acceptable error. The adaptive neural network has a huge potential in the automatic scoring system after a football game, which can replace manual scoring and reduce manual load.


## 4.1. Analysis and Design of Football Scoring Method System

### 4.1.1. Demand Analysis

Requirement analysis is an important basis for system design and development. Therefore, we need to use some more effective methods to strictly review and verify the requirements analysis of the system. The basic task of requirement analysis is to solve the problem of what functions the system to be designed needs to have. The system realizes the following functions for users [25]. Figure 4 shows the user case diagram of the football game scoring method system.(1)
Scoreboard query. The user can query the scoreboard after the selected round according to the demand(2)
Competition information management. Users can add, delete, modify, and check the competition schedule and detailed data(3)
Prediction of the outcome of the game. Users can query the outcome prediction of the recent competition that has not been carried out and can also adjust the relevant parameters of the neural networkFigure 4
User case diagram of football game scoring method system.


### 4.1.2. Overall System Design

Systems analysis is the process of identifying and sorting out the needs of the user and creating an accurate model. System design is the process of transforming the requirements obtained in the analysis stage into an abstract system implementation scheme that meets the requirements of cost and quality. It is the process of gradually expanding a model from object-oriented analysis to object-oriented design. In other words, object-oriented design is the process of establishing a solution domain model from an object-oriented point of view. In addition to following the traditional basic principle guiding software design, object-oriented design should also have its own characteristics. They are the principles of modularity, abstraction, information hiding, low coupling, strong cohesion, and reusability. There are several key points in system design, including system design, function module design, and database design. As shown in Figure5, according to the demand analysis, the football game victory and defeat prediction system is divided into three modules: scoreboard information query module, game information management module, and game victory and defeat prediction module. The competition information module includes three subfunction modules of adding, modifying, and deleting competition information. The scoreboard query module includes the real-time query function of the scoreboard, and the competition victory and defeat prediction module includes the network parameter adjustment function and prediction function.Figure 5
Overall design of football match victory and defeat prediction system.


### 4.1.3. System Function Description

The addition, deletion, modification, and query of information is an indispensable part of the information system. The system develops these four functions to maintain football match related information and provide effective data support for the following prediction. The scoreboard information management module is mainly the scoreboard query function, and users can query the scoreboard after relevant League rounds in real time. After receiving the user’s score help query request, the server initializes the scoreboard information, then queries this round and previous game information according to the League Round selected by the user, modifies the scoreboard, then ranks according to the ranking rules of China Football Super League, and finally feeds back the results to the user. Figure6 shows the specific flowchart of scoreboard query function.Figure 6
Flowchart of scoreboard query function.The adaptive neural network parameter data table consists of three attribute fields, including learning rate, error accuracy, and maximum learning number. And the relevant attribute types are shown in Table4. The data type of learning rate and error accuracy is double, the data type of maximum learning times is int (4), and there is no primary key set in the network parameter table.Table 4
Data sheet of network parameters.
Field nameField codeData typeLearning rateLearningrateDOUBLEError accuracyGlobalerrorDOUBLEMaximum learning timesMaxstudytimesINT(4)


## 4.1.1. Demand Analysis

Requirement analysis is an important basis for system design and development. Therefore, we need to use some more effective methods to strictly review and verify the requirements analysis of the system. The basic task of requirement analysis is to solve the problem of what functions the system to be designed needs to have. The system realizes the following functions for users [25]. Figure 4 shows the user case diagram of the football game scoring method system.(1)
Scoreboard query. The user can query the scoreboard after the selected round according to the demand(2)
Competition information management. Users can add, delete, modify, and check the competition schedule and detailed data(3)
Prediction of the outcome of the game. Users can query the outcome prediction of the recent competition that has not been carried out and can also adjust the relevant parameters of the neural networkFigure 4
User case diagram of football game scoring method system.


## 4.1.2. Overall System Design

Systems analysis is the process of identifying and sorting out the needs of the user and creating an accurate model. System design is the process of transforming the requirements obtained in the analysis stage into an abstract system implementation scheme that meets the requirements of cost and quality. It is the process of gradually expanding a model from object-oriented analysis to object-oriented design. In other words, object-oriented design is the process of establishing a solution domain model from an object-oriented point of view. In addition to following the traditional basic principle guiding software design, object-oriented design should also have its own characteristics. They are the principles of modularity, abstraction, information hiding, low coupling, strong cohesion, and reusability. There are several key points in system design, including system design, function module design, and database design. As shown in Figure5, according to the demand analysis, the football game victory and defeat prediction system is divided into three modules: scoreboard information query module, game information management module, and game victory and defeat prediction module. The competition information module includes three subfunction modules of adding, modifying, and deleting competition information. The scoreboard query module includes the real-time query function of the scoreboard, and the competition victory and defeat prediction module includes the network parameter adjustment function and prediction function.Figure 5
Overall design of football match victory and defeat prediction system.


## 4.1.3. System Function Description

The addition, deletion, modification, and query of information is an indispensable part of the information system. The system develops these four functions to maintain football match related information and provide effective data support for the following prediction. The scoreboard information management module is mainly the scoreboard query function, and users can query the scoreboard after relevant League rounds in real time. After receiving the user’s score help query request, the server initializes the scoreboard information, then queries this round and previous game information according to the League Round selected by the user, modifies the scoreboard, then ranks according to the ranking rules of China Football Super League, and finally feeds back the results to the user. Figure6 shows the specific flowchart of scoreboard query function.Figure 6
Flowchart of scoreboard query function.The adaptive neural network parameter data table consists of three attribute fields, including learning rate, error accuracy, and maximum learning number. And the relevant attribute types are shown in Table4. The data type of learning rate and error accuracy is double, the data type of maximum learning times is int (4), and there is no primary key set in the network parameter table.Table 4
Data sheet of network parameters.
Field nameField codeData typeLearning rateLearningrateDOUBLEError accuracyGlobalerrorDOUBLEMaximum learning timesMaxstudytimesINT(4)


## 4.2. Case Analysis

Using the adaptive neural network algorithm and other algorithms, the postgame score of football game is analyzed based on random input variables. Two different algorithms are improved to find a more suitable prediction algorithm for the postgame score of football game, as shown in Figures7 and 8.Figure 7
Different algorithms and media ratings.Figure 8
Error of different algorithms.Human scoring is subjective. Machine scoring integrates the influence of big data and is relatively objective. However, it cannot be denied that sometimes the scores given by machines are not very correct, but in 90% of the cases, the data given by machines are relatively correct because it excludes people’s subjective views and is more objective. This time, the adaptive neural network algorithm is used for scoring. Among the 30 data, only 3 data errors are large (the difference between absolute values exceeds 0.5), nearly half of the data errors are less than 0.1, and only 4 data variances exceed the recognized scoring acceptable error. The adaptive neural network has a huge potential in the automatic scoring system after a football game, which can replace manual scoring and reduce manual load.


## 5. Conclusion

According to the review data information of multiple games, we select a network optimization algorithm for the adaptive nervous system and study the loss of points after the game of the basic parameters of each basketball game in a special application environment, because adaptive neural system network analysis and estimation have been widely used. Therefore, this article chooses this method to analyze and predict the score after the basketball game. The adaptive neural network optimization algorithm selected in this article has been slightly improved based on the standard neural network optimization algorithm, and iterative calculations are carried out based on the application of three layers (two layers). The results show that through the analysis of the basketball game comment data information, the three basic parameters of shooting frequency, positive number, passing and receiving rate, etc., have a strong correlation with the score after the game, and can be used to create the score after the game. In the estimation of the uncertainty index of the calculation of the relevant function formula, solid model adaptive neural network interpolation methods can be improved by selecting an adaptive neural network optimization algorithm.



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*Source: 9502218-2022-04-18.xml*