Football game scores from former seasons, cost of the team, league points of a team, and bet rates from websites were collected and manually entered into database. After filtering and processing the data and also by normalizing data, we applied decision tree approach, which helps us to decide whether a team scores or not in an upcoming game. Then, we designed and developed a user-friendly graphical interface, which makes able users to select the teams from Turkish Superlig and makes able users to see the estimation by clicking the button. II. LITERATURE SURVEY In this literature survey, we made a research on the former studies about football match prediction and similar statistical studies. In our internet age, betting companies became widespread with the effect of the internet and so many people use internet to bet on different games from football to basketball and even to ice-hockey. However, most people do wild guesses on these games and they don’t think properly over the possibilities, so they lose money and decrease their chance of profit unfortunately. Therefore, as Gomes, Portela, and Santos (2015) aimed to support users to increase their profits on bets related to football matches, and give them advice which bet they should choose, we are also, in this study, mainly aiming to help people who bet and lose money on games unfortunately because of limited information, we want to give them better choices to minimize their losses and increase their profits. Moreover, in another similar study, stanford graduates Cheng, Dade, Lipman, and Mills (2013) researched to predict the NBA game outcomes more accurately than the experts who decide on the betting lines, this way they tried to help people with better prediction than professional analysts and basketball experts. In the part of data collection, we can say that taking the data of the previous years from websites is a popular and important way of data collection. As we collect the data from , the other researchers in this topic mostly took their data from sports websites like Liu and Lai (2010) received the data from which is a american college football statistics repository. Not only for american football but also for football (soccer) and basketball, previous researchers almost always looked to sports websites for match
Validation None Maximum Tree Depth 3 Minimum Cases in Parent Node 30 Minimum Cases in Child Node 10 Results Independent Variables Included Homegoalscoredavegame, Team2Pointsplayedgamesintheseason, Awaygoalsconcededavegame, Awaygoalsscoredavegame, BetX, Bet2 Number of Nodes 23 Number of Terminal Nodes 16 Depth 3 Risk Estimate Std. Error .213 .011 Growing Method: CHAID Dependent Variable: Team1Scoredbinary Classification Observed Predicted .0 1.0 Percent Correct .0 103 208 33.1% 1.0 71 930 92.9% Overall Percentage 13.3% 86.7% 78.7% Growing Method: CHAID Dependent Variable: Team1Scoredbinary Here, the decision tree first divided the first node “Team1scoredornot” into 4 nodes that are based on “Homegoalscoredavegame” that is how many goals home team scored per game on average.
seasons. We developed classifying decision tree model on IBM SPSS and used it in our website as a background. Meeting Place: Hisar Campus Meeting Time: Fridays, 13:00 – Wednesdays, 14:00 Coordinator: Yerzhan Berdimbet Task Allocation: Development: Neslişah Kocademir, Sezgin Değirmenci, Yakup Can Karadeniz, Yerzhan Berdimbet Design: Yakup Can Karadeniz Creating algorithms: Yerzhan Berdimbet Database: Sezgin Değirmenci, Yakup Can Karadeniz, Neslişah Kocademir, Yerzhan Berdimbet Documentation: Neslişah Kocademir Data Collection: Sezgin Değirmenci
REFERENCES ------------Journal paper-------------------- Gomes, J. Portela, F. and Santos, M. F. (2016) “Real-Time Data Mining Models to Predict Football 2-Way Result.” Jurnal Teknologi, Penerbit UTM Press. (accepted for publication) Haghighat. M. Rastegari, H. and Nourafza, N. (2013) “A Review of Data Mining Techniques for Result Prediction in Sports”, ACSIJ Advances in Computer Science: an International Journal, Vol. 2, Issue 5, No.6, pp.1-6. Joseph, A. Fenton, N.E. and Neil, M. (2006) “Predicting Football Results Using Bayesian Nets and Other Machine Learning Techniques”, Knowledge Based Systems, vol.19, no.7, pp.544-553. Pischedda, G. (2014) “Predicting NHL Match Outcomes with ML Models”, International Journal of Computer Applications, vol.101, no.9, pp.15-22. ----------Conference Proceedings--------- Buursma, D. (2011) “Predicting Sports Events from Past Results”, 14th Twente Student Conference on IT, Enschede, Netherlands Langseth, H. (2013) “Beating the Bookie: A Look at Statistical Models for Prediction of Football Matches”, Presented at the 12th Scandinavian AI conference, Aalborg, Denmark -----------Web sources----------------------- Cheng, B. Dade, K. Lipman, M. & Mills, C. (2013). “Predicting the betting line in NBA games”. Retrieved from Stanford University, Program on Computer Science Web Site: Liu, B. and Lai, P. (2010). “Beating the NCAA Football Point Spread”. Retrieved from Stanford University, Program on Computer Science Web Site: