The Estimation of German Football League (Bundesliga) Team Ranking via Artificial Neural Network Model
This study was conducted to estimate the places of teams in league ranking by the analysis of the time intervals of the scored and conceded goals in football using Artificial Neural Network (ANN). In the study, the data of the minutes of the scored and conceded goals (0-15, 16-30, 31-45, 46-60, 61-7...
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| Language: | English |
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Selcuk University Press
2022-04-01
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| Series: | Türk Spor ve Egzersiz Dergisi |
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| Online Access: | https://dergipark.org.tr/tr/download/article-file/1750185 |
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| author | Faruk Kılıç Hasan Aka Serkan İbiş Zait Burak Aktuğ |
| author_facet | Faruk Kılıç Hasan Aka Serkan İbiş Zait Burak Aktuğ |
| author_sort | Faruk Kılıç |
| collection | DOAJ |
| description | This study was conducted to estimate the places of teams in league ranking by the analysis of the time intervals of the scored and conceded goals in football using Artificial Neural Network (ANN). In the study, the data of the minutes of the scored and conceded goals (0-15, 16-30, 31-45, 46-60, 61-75, 76-90) in total 918 matches played in 3 seasons (2015/2016, 2016/2017, 2017/2018) in German Soccer League (Bundesliga) were used. Total 12 input values (scored and conceded goals) and 1 output (league ranking) value was obtained. 4 different models were determined. 3 seasons league rankings were estimated by training the first 2 season data. All data were separated randomly for training and testing. League ranking was obtained by normalizing between the range of 0,1 – 0,9. Since the produced value in the range of 0 – 1, it was multiplied with 100 for a trained network and the league ranking was obtained. It was determined that the model developed according to our findings estimated the league ranking with above 99% accuracy for many teams (test data set) according to the minutes of the scored and conceded goals. The lowest mean square error (MSE) value was obtained as 0.00004. As a consequence, it was determined that the minutes of scored and conceded goals in soccer affect the league ranking of the teams. Obtained ANN prediction model can be a guide for coaches to determine the offensive and defensive organizations. |
| format | Article |
| id | doaj-art-68eb00723998460ab9b3f9bd88d97821 |
| institution | Kabale University |
| issn | 2147-5652 |
| language | English |
| publishDate | 2022-04-01 |
| publisher | Selcuk University Press |
| record_format | Article |
| series | Türk Spor ve Egzersiz Dergisi |
| spelling | doaj-art-68eb00723998460ab9b3f9bd88d978212025-01-02T22:45:57ZengSelcuk University PressTürk Spor ve Egzersiz Dergisi2147-56522022-04-012412229154The Estimation of German Football League (Bundesliga) Team Ranking via Artificial Neural Network ModelFaruk Kılıç0Hasan Aka1Serkan İbiş2Zait Burak Aktuğ3GAZİ ÜNİVERSİTESİNİĞDE ÖMER HALİSDEMİR ÜNİVERSİTESİNİĞDE ÜNİVERSİTESİ, NİĞDE BEDEN EĞİTİMİ VE SPOR YÜKSEKOKULUNİĞDE ÖMER HALİSDEMİR ÜNİVERSİTESİThis study was conducted to estimate the places of teams in league ranking by the analysis of the time intervals of the scored and conceded goals in football using Artificial Neural Network (ANN). In the study, the data of the minutes of the scored and conceded goals (0-15, 16-30, 31-45, 46-60, 61-75, 76-90) in total 918 matches played in 3 seasons (2015/2016, 2016/2017, 2017/2018) in German Soccer League (Bundesliga) were used. Total 12 input values (scored and conceded goals) and 1 output (league ranking) value was obtained. 4 different models were determined. 3 seasons league rankings were estimated by training the first 2 season data. All data were separated randomly for training and testing. League ranking was obtained by normalizing between the range of 0,1 – 0,9. Since the produced value in the range of 0 – 1, it was multiplied with 100 for a trained network and the league ranking was obtained. It was determined that the model developed according to our findings estimated the league ranking with above 99% accuracy for many teams (test data set) according to the minutes of the scored and conceded goals. The lowest mean square error (MSE) value was obtained as 0.00004. As a consequence, it was determined that the minutes of scored and conceded goals in soccer affect the league ranking of the teams. Obtained ANN prediction model can be a guide for coaches to determine the offensive and defensive organizations.https://dergipark.org.tr/tr/download/article-file/1750185artificial neural networkrankingpredictionsoccer league |
| spellingShingle | Faruk Kılıç Hasan Aka Serkan İbiş Zait Burak Aktuğ The Estimation of German Football League (Bundesliga) Team Ranking via Artificial Neural Network Model Türk Spor ve Egzersiz Dergisi artificial neural network ranking prediction soccer league |
| title | The Estimation of German Football League (Bundesliga) Team Ranking via Artificial Neural Network Model |
| title_full | The Estimation of German Football League (Bundesliga) Team Ranking via Artificial Neural Network Model |
| title_fullStr | The Estimation of German Football League (Bundesliga) Team Ranking via Artificial Neural Network Model |
| title_full_unstemmed | The Estimation of German Football League (Bundesliga) Team Ranking via Artificial Neural Network Model |
| title_short | The Estimation of German Football League (Bundesliga) Team Ranking via Artificial Neural Network Model |
| title_sort | estimation of german football league bundesliga team ranking via artificial neural network model |
| topic | artificial neural network ranking prediction soccer league |
| url | https://dergipark.org.tr/tr/download/article-file/1750185 |
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