Cricket Win Predictor using Machine Learning
Every company firm is implementing the latest technologies to grow their business as the discipline of Data Sciences evolves. There is rivalry in the market for better management, higher evaluation quality, and better services. The only way to achieve all of these traits is to undertake data analysis with greater precision and purity. Machine learning is a new field that uses existing data to predict future outcomes and then make better decisions based on those predictions. Cricket is a well-known sport that is played and watched in 104 nations across the world. Many of these cricket fans want their team to do well and come out on top. To make sure their team’s win, team should build on their strengths and team performances. The performance of batsmen, team strengths, venue and weather conditions, and other factors all play a role in predicting the winner of a cricket match. Various variables were examined in this study in order to determine the game’s match winner. This project is about predicting the winner of a match before it is done. Machine learning models are trained on the selected features to predict the winner of the cricket match. On the test and training datasets, the Random Forest machine learning technique was used to generate the model. Cricket boards will profit from the prediction model in terms of analysing team strength and cricket analysis. This model will be a blessing in disguise for gambling applications and match reporting media.
PROJECT OUTPUT VIDEO:
- According to Ahmed & Nazir they implemented different statistical approaches for formation of datasets and tried various classification techniques to predict the winner of One Day Cricket (50 over) match. He has predicted the winner with 80 % accuracy.
- Shah predicted One Day International match results by using data of ICC match ratings, ICC ranking points for batsmen and bowlers, home factor, ICC rating differences and ground effects on the match. They implemented Logistic Regression on this data and achieved accuracy in predicting the results of matches 74.9% and in 81% matches they predicted the winner team correctly.
- Jhanwar predicted 71% accuracy in predicting winner of the One Day International cricket match. He used binary classification models such as Logistic Regression, KNN, Random Forest and Decision trees. Cross validation procedure was not carried out. Jhawar have done research on predicting the winner of the match at end of the over, player’s performance recent and past performance and other statistics’ which are necessary for predicting the winner of the match has been used
DISADVANTAGES OF EXISTING SYSTEM:
- With the fast-growing advancement of cricket, it turned into an exceptionally intriguing topic for all sports analysts. However, there are still conflicting and convoluted informational indexes.
- Despite extensive research, they could not leap forward and precisely anticipate the winner of the match.
- No method could reach the level of accuracy and scalability in terms of big data.
- Cricket is one of the most liked, played, encouraged, and exciting sports in today’s time that requires a proper advancement with machine learning and artificial intelligence (AI) to attain more accuracy. With the increasing number of matches with time, the data related to cricket matches and the individual player are increasing rapidly. Moreover, the need of using machine learning the opportunities of utilizing this big data effectively in many beneficial ways are also increasing, such as the selection process of players in the team, predicting the winner of the match, and many more future predictions. We applied the machine learning Random Forest model to predict the Winning team using Java.
- Using Computer Intelligence to analyze and model the game of Cricket is a promising research area. The increased popularity and financial benefits have made Cricket an interesting sport to be subjected to statistical analysis and machine learning. The dynamic nature of Cricket, complex rules governing Cricket makes the task a challenging one.
ADVANTAGES OF PROPOSED SYSTEM:
- Predict the Winning Team before the match ends.
- Effective prediction technique.
- Predict the win percentage.
- Useful for bidding apps.
- The prediction of winner produced through this project required a lot of domain information and expertise for observations and their relations to the winning team.
- System : Pentium i3 Processor
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB
- Operating system : Windows 10.
- Coding Language :
- Tool : Netbeans 8.2
- Database : MYSQL