Sports Data Mining

2010 ◽  
Author(s):  
Robert P. Schumaker ◽  
Osama K. Solieman ◽  
Hsinchun Chen
Keyword(s):  
Data Mining ◽  
Sports Data

Sports Data Mining: The Field

2010 ◽  
pp. 1-13 ◽  
Author(s):  
Robert P. Schumaker ◽  
Osama K. Solieman ◽  
Hsinchun Chen
Keyword(s):  
Data Mining ◽  
Sports Data

Sports Data Mining Methodology

2010 ◽  
pp. 15-21 ◽  
Author(s):  
Robert P. Schumaker ◽  
Osama K. Solieman ◽  
Hsinchun Chen
Keyword(s):  
Data Mining ◽  
Sports Data

scholarly journals Sports Data Mining: Predicting Results for the College Football Games

2014 ◽  
Vol 35 ◽  
pp. 710-719 ◽  
Author(s):  
Carson K. Leung ◽  
Kyle W. Joseph
Keyword(s):  
Data Mining ◽  
College Football ◽  
Sports Data

scholarly journals Perbandingan Simple Logistic Classifier dengan Support Vector Machine dalam Memprediksi Kemenangan Atlet

2017 ◽  
Vol 3 (2) ◽  
pp. 87 ◽  
Author(s):  
Ednawati Rainarli ◽  
Arif Romadhan
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Selection Process ◽  
Support Vector ◽  
Good Prospect ◽  
Features Selection ◽  
Processing Times ◽  
Simple Logistic ◽  
Sports Data

Abstrak— Prediksi kemenangan atlet adalah hal yang harus dilakukan oleh pelatih ketika memutuskan pemain  yang akan diturunkan dalam suatu pertandingan. Banyaknya faktor-faktor yang mempengaruhi kemenangan atlet membuat keputusan tersebut tidak mudah untuk ditentukan. Dalam penelitian ini akan dilakukan perbandingan dari penggunaan metode Simple Logistic Classifier (SLC) dengan Support Vector Machine (SVM)  dalam memprediksi kemenangan atlet berdasarkan data kesehatan dan data latihan fisik. Data yang digunakan diambil dari 28 cabang olahraga perorangan. Rata-rata akurasi SLC dan SVM masing-masing diperoleh sebesar 80% dan 88%, sedangkan rata-rata kecepatan pemrosesan metode SLC dan SVM adalah 1,6 detik dan 0,2 detik.  Hal ini menunjukkan bahwa penggunaan metode SVM lebih unggul daripada SLC, baik dari segi kecepatan maupun dari nilai akurasi yang dihasilkan. Selain pengujian akurasi, dilakukan pula pengujian terhadap 24 fitur yang digunakan dalam proses klasifikasi.  Hasilnya diketahui bahwa pengurangan fitur melalui tahap seleksi mengakibatkan penurunan nilai akurasi. Berdasarkan hal tersebut disimpulkan bahwa semua fitur yang digunakan dalam penelitian ini adalah fitur yang berpengaruh dalam penentuan prediksi kemenangan atlet. Kata Kunci— Prediksi, Simple Logistic Classifier, Sports Data Mining, Support Vector MachineAbstract— A coach must be able to select which athlete has a good prospect of winning a game.  There are a lot of aspects which influence the athlete in winning a game, so it's not easy by coach to decide it.This research would compare Simple Logistic Classifier (SLC) and Support Vector Machine (SVM) usage applied to predict winning game of athlete based on health and physical condition record.  The data get from 28 sports. The accuracy of SLC and SVM are 80% and 88% meanwhile processing times of SLC and SVM method are 1.6 seconds dan 0.2 seconds.The result shows the SVM usage superior to the SLC both of speed process and the value of accuracy.  There were also testing of 24 features used in the classifications process. Based on the test,  features selection process can cause decreasing the accuracy value. This result concludes that all features used in this research influence the determination of a victory athletes prediction. Keywords— Prediction, Simple Logistic Classifier, Sports Data Mining, Support Vector Machine

scholarly journals Sports Policy and Training Decision Support Method Based on Wireless Sensor Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhi Li
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Real Time ◽  
Average Error ◽  
Scientific Management ◽  
Sports Training ◽  
Scientific Decision ◽  
Decision Support Method ◽  
Sports Data

In recent years, the awareness of sports departments at all levels of society to promote sports through science and has been increasing, and the scientific decision-making and management of sports have been improved to a great extent. With the application of scientific decision-making combined with a real-time sports data monitoring network, the opponent’s advance information can be effectively observed during the game and reasonable decisions can be made to deal with the opponent’s offense. Therefore, high-level athletes appear to be more relaxed and calm in the game. It first requires the application of advanced information collection methods to obtain sports data quickly, in real time and at low cost, and extract information about athletes’ scientific management decision-making from massive data and then make scientific management decisions for sports training. The modern sports method is highly open, and big data mining also profoundly affects the relevant decision-making of sports training. How to design appropriate decision support tools to grasp the key points of the problem in sports information data and make reasonable and correct decisions is a problem that is closely watched by macro decision-makers and coaches at all levels. This article mainly introduces the training decision support method derived from data mining and intends to provide some technical directions for making scientific decisions in sports training. This paper proposes related algorithms of a training decision support method derived from data mining, including training effectiveness prediction model and decision tree algorithm, for the design of the training decision support method derived from data mining. Experimental data shows that the average error between the prediction of the effectiveness of the training method and the actual situation of the training decision support method in this paper is 0.913%, which is helpful for the management or coach to make decisions.

scholarly journals Computational Intelligence in Sports: A Systematic Literature Review

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Robson P. Bonidia ◽  
Luiz A. L. Rodrigues ◽  
Anderson P. Avila-Santos ◽  
Danilo S. Sanches ◽  
Jacques D. Brancher
Keyword(s):  
Data Mining ◽  
Computational Intelligence ◽  
Scientific Community ◽  
Information Industry ◽  
Useful Knowledge ◽  
Sports Organizations ◽  
Use Of Data ◽  
Slight Growth ◽  
Effective Use ◽  
Sports Data

Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large amount of data and the imminent need to turn it into useful knowledge. However, the effective use of data in some areas is still under development, as is the case in sports, which in recent years, has presented a slight growth; consequently, many sports organizations have begun to see that there is a wealth of unexplored knowledge in the data extracted by them. Therefore, this article presents a systematic review of sports data mining. Regarding years 2010 to 2018, 31 types of research were found in this topic. Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic.

scholarly journals Data Mining in Elite Beach Volleyball – Detecting Tactical Patterns Using Market Basket Analysis

2019 ◽  
Vol 18 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Sebastian Wenninger ◽  
Daniel Link ◽  
Martin Lames
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Rule Mining ◽  
Aggregated Data ◽  
Data Mining Approach ◽  
Sequential Rule ◽  
Sequential Association Rule Mining ◽  
High Level ◽  
Sports Coaches ◽  
Sports Data

Abstract Sports coaches today have access to a growing amount of information that describes the performance of their players. Methods such as data mining have become increasingly useful tools to deal with the analytical demands of these high volumes of data. In this paper, we present a sports data mining approach using a combination of sequential association rule mining and clustering to extract useful information from a database of more than 400 high level beach volleyball games gathered at FIVB events in the years from 2013 to 2016 for both men and women. We regard each rally as a sequence of transactions including the tactical behaviours of the players. Use cases of our approach are shown by its application on the aggregated data for both genders and by analyzing the sequential patterns of a single player. Results indicate that sequential rule mining in conjunction with clustering can be a useful tool to reveal interesting patterns in beach volleyball performance data.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
Sign in / Sign up

Export Citation Format

Share Document