Data mining techniques are very effective and useful for forecasting in many domains or fields. In this
research, prediction of Spanish la liga football match outcomes is carried out using various data mining techniques
(Multilayer Perception, Decision Tables, Random Forest, Reptree and Meta. Bagging) to determine the most accurate
among these techniques.
IRJET- Error Reduction in Data Prediction using Least Square Regression MethodIRJET Journal
ย
This document proposes a modification to the least squares regression method to reduce errors in data prediction. It divides the original data set into three parts, uses the first part to make predictions with least squares regression and fits those predictions to the second part of the data to minimize errors. It then validates the model on the third part of data and compares errors to the original least squares method. The proposed method shows reduced errors in prediction based on mean absolute error, mean relative error and root mean square error metrics in most test ranges of the validation data.
Publication - The feasibility of gaze tracking for โmind readingโ during searchA. LE
ย
We perform thousands of visual searches every day, for example, when selecting items in a grocery store or when looking for a specific icon in a computer display. During search, our attention and gaze are guided toward visual features similar to those in the search target. This guidance makes it possible to infer information about the target from a searcherโs eye movements. The availability of compelling inferential algorithms could initiate a new generation of smart, gaze-controlled interfaces that deduce from their usersโ eye movements the visual information for which they are looking. Here we address two fundamental questions: What are the most powerful algorithmic principles for this task, and how does their performance depend on the amount of available eye-movement data and the complexity of the target objects? While we choose a random-dot search paradigm for these analyses to eliminate contextual influences on search, the proposed techniques can be applied to the local feature vectors of any type of display. We present an algorithm that correctly infers the target pattern up to 66 times as often as a previously employed method and promises sufficient power and robustness for interface control. Moreover, the current data suggest a principal limitation of target inference that is crucial for interface design: If the target patterns exceed a certain spatial complexity level, only a subpattern tends to guide the observers' eye movements, which drastically impairs target inference.
A REVIEW ON PREDICTIVE ANALYTICS IN DATA MININGijccmsjournal
ย
The data mining its main process is to collect, extract and store the valuable information and now-a-days itโs
done by many enterprises actively. In advanced analytics, Predictive analytics is the one of the branch which is
mainly used to make predictions about future events which are unknown. Predictive analytics which uses
various techniques from machine learning, statistics, data mining, modeling, and artificial intelligence for
analyzing the current data and to make predictions about future. The two main objectives of predictive
analytics are Regression and Classification. It is composed of various analytical and statistical techniques used
for developing models which predicts the future occurrence, probabilities or events. Predictive analytics deals
with both continuous changes and discontinuous changes. It provides a predictive score for each individual
(healthcare patient, product SKU, customer, component, machine, or other organizational unit, etc.) to
determine, or influence the organizational processes which pertain across huge numbers of individuals, like in
fraud detection, manufacturing, credit risk assessment, marketing, and government operations including law
enforcement.
IRJET- Expert Independent Bayesian Data Fusion and Decision Making Model for ...IRJET Journal
ย
This document proposes an expert independent Bayesian data fusion and decision making model for multi-sensor systems smart control. The model uses a Naive Bayes classifier to predict the system state based only on prior and current sensor data. Simulations of a three sensor system (soil temperature, air temperature, and moisture) achieved an overall prediction accuracy of more than 96%. However, real-world implementation of the proposed algorithm is still needed.
This document compares the performance of various machine learning and classification algorithms, including neural networks, support vector machines, Naive Bayes, decision trees, and decision stumps. It analyzes these algorithms using a dataset of annual and monthly temperature data from India over 1901-2012. The analysis is conducted in RapidMiner and finds that neural networks and support vector machines can effectively model complex nonlinear relationships to predict temperature. Neural networks achieved reasonably accurate predictions of annual temperature compared to the original data values. The document concludes by comparing the performance of the different algorithms.
This document summarizes a study that uses a genetic algorithm to optimize imputing missing cost data for fans used in road tunnels in Sweden. The genetic algorithm is used to impute the missing cost data by optimizing the valid data period used. The results show highly correlated data (R-squared of 0.99) after imputing the missing values, indicating the genetic algorithm provides an effective way to optimize imputing and create complete data that can then be used for forecasting and life cycle cost analysis. The document also reviews other methods for data imputation and discusses experimental results comparing the proposed two-stage approach using K-means clustering and multilayer perceptron on several datasets.
IRJET- Error Reduction in Data Prediction using Least Square Regression MethodIRJET Journal
ย
This document proposes a modification to the least squares regression method to reduce errors in data prediction. It divides the original data set into three parts, uses the first part to make predictions with least squares regression and fits those predictions to the second part of the data to minimize errors. It then validates the model on the third part of data and compares errors to the original least squares method. The proposed method shows reduced errors in prediction based on mean absolute error, mean relative error and root mean square error metrics in most test ranges of the validation data.
Publication - The feasibility of gaze tracking for โmind readingโ during searchA. LE
ย
We perform thousands of visual searches every day, for example, when selecting items in a grocery store or when looking for a specific icon in a computer display. During search, our attention and gaze are guided toward visual features similar to those in the search target. This guidance makes it possible to infer information about the target from a searcherโs eye movements. The availability of compelling inferential algorithms could initiate a new generation of smart, gaze-controlled interfaces that deduce from their usersโ eye movements the visual information for which they are looking. Here we address two fundamental questions: What are the most powerful algorithmic principles for this task, and how does their performance depend on the amount of available eye-movement data and the complexity of the target objects? While we choose a random-dot search paradigm for these analyses to eliminate contextual influences on search, the proposed techniques can be applied to the local feature vectors of any type of display. We present an algorithm that correctly infers the target pattern up to 66 times as often as a previously employed method and promises sufficient power and robustness for interface control. Moreover, the current data suggest a principal limitation of target inference that is crucial for interface design: If the target patterns exceed a certain spatial complexity level, only a subpattern tends to guide the observers' eye movements, which drastically impairs target inference.
A REVIEW ON PREDICTIVE ANALYTICS IN DATA MININGijccmsjournal
ย
The data mining its main process is to collect, extract and store the valuable information and now-a-days itโs
done by many enterprises actively. In advanced analytics, Predictive analytics is the one of the branch which is
mainly used to make predictions about future events which are unknown. Predictive analytics which uses
various techniques from machine learning, statistics, data mining, modeling, and artificial intelligence for
analyzing the current data and to make predictions about future. The two main objectives of predictive
analytics are Regression and Classification. It is composed of various analytical and statistical techniques used
for developing models which predicts the future occurrence, probabilities or events. Predictive analytics deals
with both continuous changes and discontinuous changes. It provides a predictive score for each individual
(healthcare patient, product SKU, customer, component, machine, or other organizational unit, etc.) to
determine, or influence the organizational processes which pertain across huge numbers of individuals, like in
fraud detection, manufacturing, credit risk assessment, marketing, and government operations including law
enforcement.
IRJET- Expert Independent Bayesian Data Fusion and Decision Making Model for ...IRJET Journal
ย
This document proposes an expert independent Bayesian data fusion and decision making model for multi-sensor systems smart control. The model uses a Naive Bayes classifier to predict the system state based only on prior and current sensor data. Simulations of a three sensor system (soil temperature, air temperature, and moisture) achieved an overall prediction accuracy of more than 96%. However, real-world implementation of the proposed algorithm is still needed.
This document compares the performance of various machine learning and classification algorithms, including neural networks, support vector machines, Naive Bayes, decision trees, and decision stumps. It analyzes these algorithms using a dataset of annual and monthly temperature data from India over 1901-2012. The analysis is conducted in RapidMiner and finds that neural networks and support vector machines can effectively model complex nonlinear relationships to predict temperature. Neural networks achieved reasonably accurate predictions of annual temperature compared to the original data values. The document concludes by comparing the performance of the different algorithms.
This document summarizes a study that uses a genetic algorithm to optimize imputing missing cost data for fans used in road tunnels in Sweden. The genetic algorithm is used to impute the missing cost data by optimizing the valid data period used. The results show highly correlated data (R-squared of 0.99) after imputing the missing values, indicating the genetic algorithm provides an effective way to optimize imputing and create complete data that can then be used for forecasting and life cycle cost analysis. The document also reviews other methods for data imputation and discusses experimental results comparing the proposed two-stage approach using K-means clustering and multilayer perceptron on several datasets.
This document presents a literature review on existing football match result prediction systems and their limitations. It then proposes developing an improved prediction system using knowledge discovery in databases (KDD) and data mining techniques. Specifically, it will gather 9 features that affect match results and use artificial neural networks (ANN) and logistic regression to predict outcomes, aiming for higher accuracy than existing systems. The literature review finds most similar systems achieved prediction accuracies between 55-95% but had limitations like insufficient features, complexity, or only predicting one team's results. The proposed system seeks to overcome these by including more relevant features through data mining tools.
This document describes a system to predict cricket match scores and outcomes using machine learning algorithms. It involves collecting historical match data, cleaning the data, splitting it into training and test sets, and using algorithms like Naive Bayes, SVM, K-Means, logistic regression, and decision trees. The proposed system architecture involves data collection, feature extraction, model training, and prediction. Performance is evaluated using metrics like accuracy, precision, and recall from a confusion matrix. The authors aim to use multiple algorithms and compare their relative performance to improve predictions.
This document describes a system to predict cricket scores and match winners in the Indian Premier League (IPL) using machine learning algorithms. The system has two parts: 1) predicting live scores using lasso regression based on parameters like overs bowled, runs scored etc, and 2) predicting the winning team using a random forest classifier based on parameters like toss winner and venue. The authors collected ball-by-ball data from Kaggle, performed data preprocessing, and divided the data into training and test sets. They developed a graphical user interface using Flask to allow users to input match details and get predictions. The system aims to improve fan engagement with the IPL by providing data-driven score and winner predictions.
This document discusses predicting the outcome of cricket matches and assisting coaches. It will use algorithms like Naive Bayes and ID3 to predict matches based on factors such as home advantage, toss result, and team combination. These predictions will determine betting odds. The system will also assist coaches by selecting the best team using player records and using algorithms like Gale-Shapley to determine the optimal batting order. The document reviews several research papers on related topics and summarizes previous work on analyzing cricket matches.
This document discusses using machine learning to analyze and predict results from matches in the Indian Premier League (IPL). Data from 2008-2020 is scraped and preprocessed. Models like random forest regression and logistic regression are used to predict first innings scores and the probability of the second team winning. Visualizations show team and player performances in different match situations. The models are deployed in a web app. The analysis provides insights to help teams and players improve strategies. Future work could incorporate more detailed match and player stats.
IPL Match Prediction System Using Machine Learning.pptxAJAman7
ย
The document describes a presentation on developing an IPL (Indian Premier League) match winning prediction system using machine learning. It includes sections on introduction, literature survey, research gaps, problem statement, proposed methodology, experimental results and analysis, conclusions, and future scope. The presentation aims to collect IPL match data and develop a model using machine learning techniques to predict the outcome and winning probability of upcoming matches based on input data like teams, scores, overs remaining, and wickets lost.
Comparative Analysis of Machine Learning Models for Cricket Score and Win Pre...IRJET Journal
ย
This document presents a comparative analysis of machine learning models for cricket score and win prediction using a case study of linear regression, random forest, neural network, and elastic net algorithms. The models take into consideration factors like the teams playing, location of the match, current runs/wickets, and runs/wickets in the last 5 overs to predict the score and winner. Random forest performed the best with the highest R2 value and lowest mean squared error and mean absolute error. The random forest algorithm is also used for the live win prediction system.
The document proposes an automated framework for generating Tamil summaries of cricket matches from statistical scorecard data. The framework performs data analytics on scorecards, determines interesting aspects of matches, extracts key events, and generates customized summaries in Tamil. It evaluates summaries based on their similarity to human-written ones. The implementation summarizes 90 cricket matches between various countries. Results found many hidden patterns and determined factors influencing match interestingness. Summaries were 70-85% similar to human ones, showing the framework can effectively analyze matches and automatically generate concise Tamil summaries.
Metulini, R., Manisera, M., Zuccolotto, P. (2017), Sensor Analytics in Basket...University of Salerno
ย
A new approach in team sports analysis consists in studying positioning and movements of players during the game in relation to team performance. State of the art tracking systems produce spatio-temporal traces of players that have facilitated a variety of research aimed to extract insights from trajectories. Several methods borrowed from machine learning, network and complex systems, geographic information system, computer vision and statistics have been proposed. After having reviewed the state of the art in those niches of literature aiming to extract useful information to analysts and experts in terms of relation between players' trajectories and team performance, this paper presents preliminary results from analysing trajectories data and sheds light on potential future research in this eld of study. In particular, using convex hulls, we find interesting regularities in players' movement patterns.
Social Networking Site Data Analytics Using Game Theory ModelIRJET Journal
ย
This document presents a study that uses game theory to analyze data from social networking sites Facebook and Instagram.
A questionnaire was used to collect data from 100 users on 7 shared characteristics of Facebook and Instagram: chat interface, live videos, private/public accounts, stories, likes/comments, groups, and security. Descriptive statistics and graphs showed trends in the data.
A 7x7 game theory model was created using intercept values from regression analysis of the data. The model was solved to find the optimal strategies for Facebook and Instagram and determine the value of the game. The results provide insights into how game theory can inform decision-making around social media strategies.
Familiarising Probabilistic Distance Clustering System of Evolving Awale PlayerGiselleginaGloria
ย
This study developed a new technique based on Probabilistic Distance Clustering (PDC) for evolving Awale player and to compare its performance with that of a technique based on approximation of minimum and maximum operators with generalized mean-value operator. The basic theory of pd-clustering is based on the assumption that the probability of an Euclidean point belonging to a cluster is inversely proportional to its distance from the cluster centroid. Treating game strategies as a vector space model, it is possible to extend pd-clustering technique to game playing by estimating the probability that a given strategy is in a certain cluster of game strategies. As a result, the strategy that has the highest probability and shortest distance to a cluster of alternative strategies is recommended for the player.
This document discusses predicting the outcomes of National Hockey League (NHL) games using machine learning models. It aims to improve upon the results of a previous study by the University of Ottawa that achieved 60% accuracy. The document uses the same dataset from the Ottawa study containing statistics from 517 NHL games. It builds machine learning models using decision trees, neural networks, and a proprietary software to predict game outcomes. The models are built using different combinations of the dataset's categorical and continuous variables. The best performing models achieve accuracies between 57-62%, showing an improvement over the previous study.
Basketball players performance analytic as experiential learning approachNurfadhlina Mohd Sharef
ย
To cite: Sharef, N.M., Mustapha, A., Azmi, M.N., Nordin, R., (2020), "Basketball Players Performance Analytic as Experiential Learning Approach in Teaching Undergraduate Data Science Course", International Conference on Advancement in Data Science, E-learning and Information Systems (ICADEIS 2020).
This document presents a literature review for a study comparing the build-up play in goals scored between successful and unsuccessful English Premier League teams. It discusses previous research that has analyzed various aspects of goal scoring notation, including frequency, time of goals scored, and build-up passing sequences. Specifically, it outlines a 1968 study by Reep and Benjamin that first analyzed the relationship between build-up passing sequences and goals scored. The literature review also notes that while previous studies have found passing sequences of three passes or less lead to about 80% of goals, more research is needed to better understand differences in build-up play between successful and unsuccessful teams.
Elg 5100 project report anurag & jayanshuAnurag Das
ย
The document describes a study that developed a size estimation model for board-based desktop games. The authors identified relevant parameters like number of rules, players, animation complexity, etc. They collected data from over 60 open source games to analyze the parameters and derive a linear regression model. The model was then assessed using various accuracy metrics and validated through cross-validation. A case study demonstrated how the model can be used to estimate the size of a new board game.
INCREASED PREDICTION ACCURACY IN THE GAME OF CRICKETUSING MACHINE LEARNINGIJDKP
ย
Player selection is one the most important tasks for any sport and cricket is no exception. The performance
of the players depends on various factors such as the opposition team, the venue, his current form etc. The
team management, the coach and the captain select 11 players for each match from a squad of 15 to 20
players. They analyze different characteristics and the statistics of the players to select the best playing 11
for each match. Each batsman contributes by scoring maximum runs possible and each bowler contributes
by taking maximum wickets and conceding minimum runs. This paper attempts to predict the performance
of players as how many runs will each batsman score and how many wickets will each bowler take for both
the teams. Both the problems are targeted as classification problems where number of runs and number of
wickets are classified in different ranges. We used naรฏve bayes, random forest, multiclass SVM and decision
tree classifiers to generate the prediction models for both the problems. Random Forest classifier was
found to be the most accurate for both the problems.
Machine Learning Based Selection of Optimal Sports team based on the Players ...IRJET Journal
ย
This document presents a machine learning model to select an optimal starting 11 for the Indian cricket team based on players' past performance data. The model categorizes players' performances for batting, bowling, and all-rounder roles. It then uses a random forest classifier to predict players' future performances with 76% accuracy for batters, 67-69% for bowlers, and 95% for all-rounders. The model incorporates additional features like weather and number of matches played. It aims to select the best combination of players to compete under specific circumstances. The implementation uses a Flask API to train models in Python and predict selections for different player roles.
This document describes a study that investigated using a support vector machine (SVM) to develop a football match result prediction system. The SVM model was trained on 16 datasets from the 2014-2015 English Premier League season and tested on 15 additional matches. The SVM used a Gaussian combination kernel and various parameters were optimized. The prediction accuracy of the SVM model was 53.3%, which is relatively low. The study concludes that an SVM may not be well-suited for football match prediction based on the feature sets used, and that other machine learning techniques like artificial neural networks may perform better.
Support Vector MachineโBased Prediction System for a Football Match Resultiosrjce
ย
This document describes a study that used a support vector machine (SVM) to develop a football match result prediction system. The SVM model was trained on 16 datasets from the 2014-2015 English Premier League season and tested on 15 additional matches. The SVM used a Gaussian combination kernel and was optimized with various parameters. The system achieved a prediction accuracy of 53.3%, which the study concluded was a relatively low performance. The document discusses related work on prediction systems and provides details on SVM algorithm implementation and parameters used in an effort to predict football match results.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
ย
(๐๐๐ ๐๐๐) (๐๐๐ฌ๐ฌ๐จ๐ง ๐)-๐๐ซ๐๐ฅ๐ข๐ฆ๐ฌ
๐๐ข๐ฌ๐๐ฎ๐ฌ๐ฌ ๐ญ๐ก๐ ๐๐๐ ๐๐ฎ๐ซ๐ซ๐ข๐๐ฎ๐ฅ๐ฎ๐ฆ ๐ข๐ง ๐ญ๐ก๐ ๐๐ก๐ข๐ฅ๐ข๐ฉ๐ฉ๐ข๐ง๐๐ฌ:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
๐๐ฑ๐ฉ๐ฅ๐๐ข๐ง ๐ญ๐ก๐ ๐๐๐ญ๐ฎ๐ซ๐ ๐๐ง๐ ๐๐๐จ๐ฉ๐ ๐จ๐ ๐๐ง ๐๐ง๐ญ๐ซ๐๐ฉ๐ซ๐๐ง๐๐ฎ๐ซ:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
This document presents a literature review on existing football match result prediction systems and their limitations. It then proposes developing an improved prediction system using knowledge discovery in databases (KDD) and data mining techniques. Specifically, it will gather 9 features that affect match results and use artificial neural networks (ANN) and logistic regression to predict outcomes, aiming for higher accuracy than existing systems. The literature review finds most similar systems achieved prediction accuracies between 55-95% but had limitations like insufficient features, complexity, or only predicting one team's results. The proposed system seeks to overcome these by including more relevant features through data mining tools.
This document describes a system to predict cricket match scores and outcomes using machine learning algorithms. It involves collecting historical match data, cleaning the data, splitting it into training and test sets, and using algorithms like Naive Bayes, SVM, K-Means, logistic regression, and decision trees. The proposed system architecture involves data collection, feature extraction, model training, and prediction. Performance is evaluated using metrics like accuracy, precision, and recall from a confusion matrix. The authors aim to use multiple algorithms and compare their relative performance to improve predictions.
This document describes a system to predict cricket scores and match winners in the Indian Premier League (IPL) using machine learning algorithms. The system has two parts: 1) predicting live scores using lasso regression based on parameters like overs bowled, runs scored etc, and 2) predicting the winning team using a random forest classifier based on parameters like toss winner and venue. The authors collected ball-by-ball data from Kaggle, performed data preprocessing, and divided the data into training and test sets. They developed a graphical user interface using Flask to allow users to input match details and get predictions. The system aims to improve fan engagement with the IPL by providing data-driven score and winner predictions.
This document discusses predicting the outcome of cricket matches and assisting coaches. It will use algorithms like Naive Bayes and ID3 to predict matches based on factors such as home advantage, toss result, and team combination. These predictions will determine betting odds. The system will also assist coaches by selecting the best team using player records and using algorithms like Gale-Shapley to determine the optimal batting order. The document reviews several research papers on related topics and summarizes previous work on analyzing cricket matches.
This document discusses using machine learning to analyze and predict results from matches in the Indian Premier League (IPL). Data from 2008-2020 is scraped and preprocessed. Models like random forest regression and logistic regression are used to predict first innings scores and the probability of the second team winning. Visualizations show team and player performances in different match situations. The models are deployed in a web app. The analysis provides insights to help teams and players improve strategies. Future work could incorporate more detailed match and player stats.
IPL Match Prediction System Using Machine Learning.pptxAJAman7
ย
The document describes a presentation on developing an IPL (Indian Premier League) match winning prediction system using machine learning. It includes sections on introduction, literature survey, research gaps, problem statement, proposed methodology, experimental results and analysis, conclusions, and future scope. The presentation aims to collect IPL match data and develop a model using machine learning techniques to predict the outcome and winning probability of upcoming matches based on input data like teams, scores, overs remaining, and wickets lost.
Comparative Analysis of Machine Learning Models for Cricket Score and Win Pre...IRJET Journal
ย
This document presents a comparative analysis of machine learning models for cricket score and win prediction using a case study of linear regression, random forest, neural network, and elastic net algorithms. The models take into consideration factors like the teams playing, location of the match, current runs/wickets, and runs/wickets in the last 5 overs to predict the score and winner. Random forest performed the best with the highest R2 value and lowest mean squared error and mean absolute error. The random forest algorithm is also used for the live win prediction system.
The document proposes an automated framework for generating Tamil summaries of cricket matches from statistical scorecard data. The framework performs data analytics on scorecards, determines interesting aspects of matches, extracts key events, and generates customized summaries in Tamil. It evaluates summaries based on their similarity to human-written ones. The implementation summarizes 90 cricket matches between various countries. Results found many hidden patterns and determined factors influencing match interestingness. Summaries were 70-85% similar to human ones, showing the framework can effectively analyze matches and automatically generate concise Tamil summaries.
Metulini, R., Manisera, M., Zuccolotto, P. (2017), Sensor Analytics in Basket...University of Salerno
ย
A new approach in team sports analysis consists in studying positioning and movements of players during the game in relation to team performance. State of the art tracking systems produce spatio-temporal traces of players that have facilitated a variety of research aimed to extract insights from trajectories. Several methods borrowed from machine learning, network and complex systems, geographic information system, computer vision and statistics have been proposed. After having reviewed the state of the art in those niches of literature aiming to extract useful information to analysts and experts in terms of relation between players' trajectories and team performance, this paper presents preliminary results from analysing trajectories data and sheds light on potential future research in this eld of study. In particular, using convex hulls, we find interesting regularities in players' movement patterns.
Social Networking Site Data Analytics Using Game Theory ModelIRJET Journal
ย
This document presents a study that uses game theory to analyze data from social networking sites Facebook and Instagram.
A questionnaire was used to collect data from 100 users on 7 shared characteristics of Facebook and Instagram: chat interface, live videos, private/public accounts, stories, likes/comments, groups, and security. Descriptive statistics and graphs showed trends in the data.
A 7x7 game theory model was created using intercept values from regression analysis of the data. The model was solved to find the optimal strategies for Facebook and Instagram and determine the value of the game. The results provide insights into how game theory can inform decision-making around social media strategies.
Familiarising Probabilistic Distance Clustering System of Evolving Awale PlayerGiselleginaGloria
ย
This study developed a new technique based on Probabilistic Distance Clustering (PDC) for evolving Awale player and to compare its performance with that of a technique based on approximation of minimum and maximum operators with generalized mean-value operator. The basic theory of pd-clustering is based on the assumption that the probability of an Euclidean point belonging to a cluster is inversely proportional to its distance from the cluster centroid. Treating game strategies as a vector space model, it is possible to extend pd-clustering technique to game playing by estimating the probability that a given strategy is in a certain cluster of game strategies. As a result, the strategy that has the highest probability and shortest distance to a cluster of alternative strategies is recommended for the player.
This document discusses predicting the outcomes of National Hockey League (NHL) games using machine learning models. It aims to improve upon the results of a previous study by the University of Ottawa that achieved 60% accuracy. The document uses the same dataset from the Ottawa study containing statistics from 517 NHL games. It builds machine learning models using decision trees, neural networks, and a proprietary software to predict game outcomes. The models are built using different combinations of the dataset's categorical and continuous variables. The best performing models achieve accuracies between 57-62%, showing an improvement over the previous study.
Basketball players performance analytic as experiential learning approachNurfadhlina Mohd Sharef
ย
To cite: Sharef, N.M., Mustapha, A., Azmi, M.N., Nordin, R., (2020), "Basketball Players Performance Analytic as Experiential Learning Approach in Teaching Undergraduate Data Science Course", International Conference on Advancement in Data Science, E-learning and Information Systems (ICADEIS 2020).
This document presents a literature review for a study comparing the build-up play in goals scored between successful and unsuccessful English Premier League teams. It discusses previous research that has analyzed various aspects of goal scoring notation, including frequency, time of goals scored, and build-up passing sequences. Specifically, it outlines a 1968 study by Reep and Benjamin that first analyzed the relationship between build-up passing sequences and goals scored. The literature review also notes that while previous studies have found passing sequences of three passes or less lead to about 80% of goals, more research is needed to better understand differences in build-up play between successful and unsuccessful teams.
Elg 5100 project report anurag & jayanshuAnurag Das
ย
The document describes a study that developed a size estimation model for board-based desktop games. The authors identified relevant parameters like number of rules, players, animation complexity, etc. They collected data from over 60 open source games to analyze the parameters and derive a linear regression model. The model was then assessed using various accuracy metrics and validated through cross-validation. A case study demonstrated how the model can be used to estimate the size of a new board game.
INCREASED PREDICTION ACCURACY IN THE GAME OF CRICKETUSING MACHINE LEARNINGIJDKP
ย
Player selection is one the most important tasks for any sport and cricket is no exception. The performance
of the players depends on various factors such as the opposition team, the venue, his current form etc. The
team management, the coach and the captain select 11 players for each match from a squad of 15 to 20
players. They analyze different characteristics and the statistics of the players to select the best playing 11
for each match. Each batsman contributes by scoring maximum runs possible and each bowler contributes
by taking maximum wickets and conceding minimum runs. This paper attempts to predict the performance
of players as how many runs will each batsman score and how many wickets will each bowler take for both
the teams. Both the problems are targeted as classification problems where number of runs and number of
wickets are classified in different ranges. We used naรฏve bayes, random forest, multiclass SVM and decision
tree classifiers to generate the prediction models for both the problems. Random Forest classifier was
found to be the most accurate for both the problems.
Machine Learning Based Selection of Optimal Sports team based on the Players ...IRJET Journal
ย
This document presents a machine learning model to select an optimal starting 11 for the Indian cricket team based on players' past performance data. The model categorizes players' performances for batting, bowling, and all-rounder roles. It then uses a random forest classifier to predict players' future performances with 76% accuracy for batters, 67-69% for bowlers, and 95% for all-rounders. The model incorporates additional features like weather and number of matches played. It aims to select the best combination of players to compete under specific circumstances. The implementation uses a Flask API to train models in Python and predict selections for different player roles.
This document describes a study that investigated using a support vector machine (SVM) to develop a football match result prediction system. The SVM model was trained on 16 datasets from the 2014-2015 English Premier League season and tested on 15 additional matches. The SVM used a Gaussian combination kernel and various parameters were optimized. The prediction accuracy of the SVM model was 53.3%, which is relatively low. The study concludes that an SVM may not be well-suited for football match prediction based on the feature sets used, and that other machine learning techniques like artificial neural networks may perform better.
Support Vector MachineโBased Prediction System for a Football Match Resultiosrjce
ย
This document describes a study that used a support vector machine (SVM) to develop a football match result prediction system. The SVM model was trained on 16 datasets from the 2014-2015 English Premier League season and tested on 15 additional matches. The SVM used a Gaussian combination kernel and was optimized with various parameters. The system achieved a prediction accuracy of 53.3%, which the study concluded was a relatively low performance. The document discusses related work on prediction systems and provides details on SVM algorithm implementation and parameters used in an effort to predict football match results.
Similar to Predicting Football Match Results with Data Mining Techniques (20)
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
ย
(๐๐๐ ๐๐๐) (๐๐๐ฌ๐ฌ๐จ๐ง ๐)-๐๐ซ๐๐ฅ๐ข๐ฆ๐ฌ
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-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
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Predicting Football Match Results with Data Mining Techniques
1. Predicting Football Match Results with Data
Mining Techniques
O. I. Aladesote, O. Agbelusi & M. Ganiyu
Abstract- Data mining techniques are very effective and useful for forecasting in many domains or fields. In this
research, prediction of Spanish la liga football match outcomes is carried out using various data mining techniques
(Multilayer Perception, Decision Tables, Random Forest, Reptree and Meta. Bagging) to determine the most accurate
among these techniques. The experimental results is done with Weka 3.9, shows that all the techniques performed well in
terms of accuracy but multilayer Perception was the most successful with an average accuracy of 100%..
I. INTRODUCTION
Football is a fast growing sport that is taking over as one the most viewed and richest sport therefore the drive to be
more than just a spectator has led to this research of being able to predict the final outcome of any match and
simultaneously making sport betting easier. One of the reasons for football being the most popular sport in the
planet is its unpredictability.
Every day, fans around the world argue over which team is going to win the next game or the next competition.
Many of these fans also put their money where their mouths are, by betting large sums on their predictions. Due to
the large amount of factors that can affect the result of a football match, it is incredibly difficult to correctly predict
its probabilities. With the increasing growth of the amount of money invested in sports betting markets, it is
important to verify how far data mining techniques can bring value to this area [9].
To solve this problem we propose building data-driven solutions designed through a data mining process. Data
mining is an aspect of computing that is used for extraction of hidden information and to automate the detection
of relevant patterns in a database. The data mining process allows us to build models that can give us predictions
according to the data that is fed into the system. The study is aimed at using data mining techniques for the
prediction of football match result. Every sport has particular rules, number of players, different styles, that is, a set
of different features. For a beginner, carrying out predictive model from the scratch with considerable dataset could
be somehow challenging. Finally, every individual especially football fans would be able to predict match result
based on identified factor at the end of this research.
We summarized the contributions of this paper as follows:
โข Forecasting of la liga football match outcome using data of five previous seasons
โข Comparative analysis to determine the most accurate technique.
The remainder of the paper is organized as follows: section 2 presents the literature review. In Section 3, the method
used to generate the results is presented. The experimental results for each data mining technique is presented and
discussed in section 4. Comparative analysis is done in section 5 and finally, conclusion and future work are
presented in section 6.
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2. II. LITERATURE REVIEW
Data mining is an important tool in event prediction. The literature selected and discussed in this section are those
that are more related and relevant to the result of football match prediction.
A match results prediction system is proposed using four data mining techniques. The author used basketball results
of four seasons (from 2005/2006 to 2009/2010) as training data and in order to assess or appraise the models, the
result of 2010/2011 season was used as test data. The result shows that the models performed with comparable
classification accuracy rate, with 67.8% as the highest [4].
The authors proposed the use of ANN and logistic regression techniques to forecast the outcome of 2014-2015
English premier league results to strengthen the complexity and inaccurate prediction results produced by statistical
approaches. The records of nine significant features are randomly selected from the records. The experimental result
of the model shows that logistic regression perform better than ANN and that the techniques show higher prediction
accuracy [17].
[13] carried out a preliminary investigation to forecast result of National Football League (NFL) using artificial
neural network (ANN). Five variables randomly extracted from first eight rounds of the competition was used for
the prediction. Teams were classified to be either strong or weak using cluster related methods.
The paper proposes data mining techniques to strengthen the limitations introduced by numeric prediction approach.
Eight years of data was used. To evaluate the performance of these techniques, both classification and regression
models were used. The experimental results clearly show that the accuracy rate of classification model outweigh
regression model [15]
The researchers carried out a performance evaluation using three classification models (naive Bayes, artificial neural
networks (ANNs) and decision trees) [16]. The models was built using different variables of NBA matches. The
experimental result shows that the accuracy of the proposed model is very reliable and that defensive fence is the
most significant variable among others. Three other variables were also chosen to be the significant.
The researcher adopted three data mining approaches to propose models for game outcome using historical data. The
purpose for this is to counter the idea of eligibility in ranking winning game based on experience. At the end of the
modeling process, all the three models were capable of forecasting the winner of the game and decision tree
produces the highest accuracy [5].
A reliable tennis match outcome prediction model is proposed with numerous factors that are systematically
prioritized to determine the match accuracy. The result shows that the proposed model with combine data and
judgement has 85.1% accuracy outcome of a match [7].
Machine learning method was adopted to forecast the result of future soccer matches based on dataset from past
matches. In this research, two important ideals were discovered as a result of some challenges encountered during
the modeling process of 2017 soccer match result. These two ideals brought about new feature engineering
methods (Recency and rating extraction) for match result forecasting. The author concluded that good forecasting
should be based on the knowledge of machine learning [3].
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3. The authors developed predictive models to forecast the outcome of football match for 2008/2009 and 2015/2016
seasons. Techniques like artificial neural network (ANN), Random Forest (RF) and Support Vector Machine
(SVM) were used to develop models. Comparative analysis was made and the result shows that they are capable of
carrying out prediction correctly as compare with the result from the experience of football match analyst [8].
This paper proposes machine learning methods to determine the result of NBA match. The forecasting
process was based on the historical data, performance evaluation was done among the models developed and the
result shows that defensive rebounds features was an important features demonstrated by all the
methods for optimal prediction of the game result. Further research will be carried out using model like function
based techniques and deep learning [16].
III. METHODOLOGY
This section describes the dataset, classification techniques and performance analysis. The experiment is done using
Weka 3.9.2 on five algorithms: Multilayer Perception, Decision Tables, Random Forest, Reptree and Meta. Bagging.
In Weka, 10% cross-validation fold is adopted as classifier evaluation option.
A. Dataset
The dataset used for the implementation was the Spanish La Liga League of 2014/2015 to 2018/2019 seasons [18].
The league consists of twenty teams played both home and away matches, equaled to 380 matches per season and
1900 matches for these five seasons. The data consists of 61 features, in which 22 consists various statistical data
such as full and halt time result, home and away team shot, etc. while the remaining 39 consist of football betting
details. Out of the 22 features of the dataset, 10 features were randomly selected as predictors while full time results
(Home Win, Away Win and Lose) as the target.
B. Performance Analysis
The performance of these classification algorithms was measured based on the accuracy. Accuracy shows the rate at
which the classifier meets the correct target class, that is, it determines the instances of data correctly classified [2].
Accuracy = (1)
The total number of correctly predicted Home Win, Away Win and Lose match results is equivalent to the total
number of correctly predicted match results.
IV. RESULT AND DISCUSSION
The results of the experiment carried out on the five classification techniques would be presented and analysed
based on the percentage of accuracy of each technique. 10-foldcross validation techniques was adopted because of
small size of the data.
A. Multilayer Perception
Multilayer Perception is a type of neural network or artificial neural networks, which has appeared to be very a
valuable alternatives to old statistical techniques and does not create previous assumptions of data distribution [6].
Multilayer Perception is applied to the La Liga datasets using Weka 3.9.2. The percentage accuracy for the seasons
is 100% as depicted in Table 1 below and the result of Multilayer Perception for 2018/2019 Season in Figure 1.
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4. TABLE I
PERCENTAGE ACCURACY OF THE MULTILAYER PERCEPTION FOR FIVE SEASONS
Season Accuracy (%)
2018/2019 Season 100
2017/2018 Season 100
2016/2017 Season 100
2015/2016 Season 100
2014/2015 Season 100
FIGURE 1: DETAILED OUTPUT OF MULTILAYER PERCEPTION OF 2018/2019 SEASON
B. Decision Tables
Decision table is a type of rules that indicates actions to be taken when certain conditions are meant [12]. The dataset
are imported into Weka 3.9.2 and the data are run sing Decision Tables technique. The percentage accuracy for
2018/2019 season is 97.38%, 91.58% for 2017/2018 season, 94.74% for 2016/2017 season, 98.95% for 2015/2016
season and 96.84% for 2014/2015 season. The percentage for the seasons using Decision Tables is presented in
Table 2.
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5. TABLE II
PERCENTAGE ACCURACY OF THE DECISION TABLES FOR FIVE SEASONS
Season Accuracy (%)
2018/2019 Season 97.38
2017/2018 Season 91.58
2016/2017 Season 94.74
2015/2016 Season 98.95
2014/2015 Season 96.84
FIGURE 2: DETAILED OUTPUT OF DECISION TABLE OF 2017/2018 SEASON
C. Random Forest
Random Forest is a statistical learning mode, which is a tree-based ensemble with each node relying on group of
random variables. It performs well with small or medium dataset and can perform better than latest algorithms [1],
[11]. The dataset are imported into Weka 3.9.2 and the data are run sing Random Forest technique. The percentage
accuracy for 2018/2019 season is 98.42%, 98.95% for 2017/2018 season, 98.16% for 2016/2017 season, 97.63% for
2015/2016 season and 99.47% for 2014/2015 season. The percentage accuracy for the seasons using Random Forest
is presented in Table 3 below.
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6. TABLE III
PERCENTAGE ACCURACY OF THE RANDOM FOREST FOR FIVE SEASONS
Season Accuracy (%)
2018/2019 Season 98.42
2017/2018 Season 98.95
2016/2017 Season 98.16
2015/2016 Season 97.63
2014/2015 Season 99.47
Figure 3: Detailed output of Random Forest of 2016/2017 Season
D RepTree
Reduced Error Pruning Tree (Reptree) is a fast decision tree learning, which uses regression tree logic to either build
a decision using information gain as splitting principle or reduces the variance [10]. The dataset of La Liga football
League of 2014/2015 season to 2018/2019 season are implemented into Weka 3.9.2 for the prediction. The
percentage accuracy for 2018/2019 season is 98.68%, 98.68% for 2017/2018 season, 98.42% for 2016/2017 season,
97.89% for 2015/2016 season and 98.95% for 2014/2015 season. The percentage accuracy for the seasons is
presented in Table 4.
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7. TABLE IV
PERCENTAGE ACCURACY OF THE REPTREE FOR FIVE SEASONS
Season Accuracy (%)
2018/2019 Season 98.68
2017/2018 Season 98.16
2016/2017 Season 98.42
2015/2016 Season 97.89
2014/2015 Season 98.95
FIGURE 4: DETAILED OUTPUT OF REPTREE OF 2015/2016 SEASON
E Meta Bagging
Meta Bagging is a machine learning ensemble algorithm developed to enhance the accuracy of statistical
classification and regression of any machine learning based algorithms [14]. The dataset of La Liga football League
of 2014/2015 season to 2018/2019 season are implemented into Weka 3.9.2 for the prediction. The percentage
accuracy for 2018/2019 season is 99.74%, 98.42% for 2017/2018 season, 98.16% for 2016/2017 season, 98.42% for
2015/2016 season and 99.47% for 2014/2015 season. The percentage accuracy for the seasons is presented in Table
5.
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8. TABLE V
PERCENTAGE ACCURACY OF THE META BAGGING FOR FIVE SEASONS
Season Accuracy (%)
2018/2019 Season 99.74
2017/2018 Season 98.42
2016/2017 Season 98.16
2015/2016 Season 98.42
2014/2015 Season 99.47
Figure 5: Detailed output of Meta Bagging of 2014/2015 Season
V. COMPARATIVE ANALYSIS
The comparative analysis of the result shows that Multilayer Perception has the overall best average percentage
accuracy with 100%, Meta Bagging with an average accuracy of 98.84% for the seasons, Random Forest has an
average percentage accuracy of 98.53%, Reptree has an average accuracy of 98.42 while Decision Tables has the
least average accuracy of 95.90% as presented in Table 6 and Figure 6 below
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9. TABLE VI
COMPARISON OF AVERAGE PERCENTAGE ACCURACY
Accuracy Multilayer
Perception
Decision Tables Random Forest Reptree Meta Bagging
2018/2019 Season 100% 97.38% 98.42% 98.68% 99.74%
2017/2018 Season 100% 91.58% 98.95% 98.16% 98.42%
2016/2017 Season 100% 94.74% 98.16% 98.42% 98.16%
2015/2016 Season 100% 98.95% 97.63% 97.89% 98.42%
2014/2015 Season 100% 96.84% 99.47% 98.95% 99.47%
Average Accuracy 100% 95.90% 98.53% 98.42% 98.84%
FIGURE 6: GRAPHICAL REPRESENTATION OF AVERAGE ACCURACY
VI. CONCLUSION AND FUTURE WORK
This work compared five data mining algorithms on Spanish la liga football match outcome. The experimental
results revealed Multilayer Perception has the most successful result, which makes it the best data mining technique
to predict la liga football match outcome with 100% accuracy as against Decision Tables with 95.90% accuracy,
Random Forest with 98.53%, Reptree with 98.42% and Meta Bagging with 98.84% accuracy. However, all data
mining techniques can also be applied in future work, consideration rating of each team as part of the variables.
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