Predicting the winner of C.Y. award

1. Predicting the winner of C.Y. award 指導教授：黃三益博士 組員： 尹川 陳隆賢 陳偉聖

2. Introduction • Baseball sport in Taiwan • CPBL (Chinese Professional Baseball League) • MLB (Major League Baseball) • Baseball sport in USA • Cy Young Award since 1956 • Baseball Writers Association of America • Weighted scores • Each league has one winner per year.

3. Measurements • There are no definite rules be used to judge. • Nevertheless, many measurements could be used to judge whether a pitcher is good or not. • Wins • ERA • WHIP • G/F etc.

4. Aim of the study • To analysis the historical statistics of pitchers. • Building a predictive model. • To predict the Cy Young Award winner of the year in the future.

5. Data mining procedure • Ten data mining methodology steps

6. Step 1：Translate the Problem • Directed data mining problem • Target variable: Cy Young Award • Classification • Decision tree • Purposes • Gambling game • Predictive activities

7. Step 2：Select Appropriate Data • Just MLB statistics data (1871 ~ 2006) • Cy Young Award: 1956 ~ 2006 • total 21456 records • List of Cy Young Award winners • “Time” factor • 1999 as the dividing year. • Because of the emerging items. • Variables: to remove the items that are not representative of a pitcher.

8. Step 3：Get to know the data • The materials that we used all come from MLB official site • These data have already been disclosed for a lot of years • The quality of data is very good • some attributes has value since 1999

9. Step 4：Create a model set • We divide the data into training data and testing data • We do not create a balanced sample • The record of MLB is not the seasonal materials • we will pick the materials since 1999

10. Step 5：Fix problems with the data • These data are taken from MLB official side • No missing values • single source

11. Step 6：Transform data to bring information to the surface • There are no combinations of attributes • We delete some attributes • We add a attribute-Year • We add a attribute (CyYoungAward_Winner) for classification

12. Step 7：Build Models • Tools Used • Weka Crash Problem • Blank Attributes • Build Model • Handling Blank Attributes

13. Tools Used

14. Weka Crash Problem • Raw data • 21456 data instances • 42 attributes • Weka crashed during model construction • Give Weka more memory

15. Blank Attributes

16. Build Model • MLB 1956~2006 • with blank attributes • ADTree • MLB 1956~2006 • without blank attributes • ADTree • MLB 1999~2006 • ADTree

17. Handling Blank Attributes

18. 1956~2006, with blank attributes, ADTree

19. 1956~2006, with blank attributes, ADTree

20. 1956~2006, without blank attributes, ADTree

21. 1956~2006, without blank attributes, ADTree

22. 1999~2006, ADTree

23. 1999~2006, ADTree

24. Step 8：Assess Models(1/2) • Not good enough for gambling

25. Step 8：Assess Models(2/2) • Some attributes are more important

26. Step 9：Deploy Models • To implement a computer program with the built model. • To predict the Cy Young Award winner more easily.

27. Step 10：Assess Results • To compare the predictive and the final Cy Young Award winner directly. • Not “business” but “interest”. • Assessment from the judgment of the person.

28. Conclusions • We have used the classification technology to set up the model of predicting • We find the accuracy of the built model is not high • Some factors that we are not to consider • It can not use in the place with essential benefits • Just for fun