final case study powerpoint
TRANSCRIPT
![Page 1: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/1.jpg)
Final Case StudyPredictive Modelling for Equestrian Sports
N RAMACHANDRAN
![Page 2: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/2.jpg)
Average by Stake Indicator
0 50000 100000 150000 200000 250000 300000 350000 400000 450000 500000
All
AP
CRC
FG
Handle by Stake Indicator
Y N
![Page 3: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/3.jpg)
Average Handle by Day of Week
0
50000
100000
150000
200000
250000
300000
350000
Sun Mon Tue Wed Thu Fri Sat
Handle vs Day of week
All AP CRC FG
![Page 4: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/4.jpg)
Average Handle by Hour of day
0
50000
100000
150000
200000
250000
300000
350000
400000
1 2 3 4 5 6 7 8 9
Handle vs Hour of day
hour_of_day All AP CRC FG
![Page 5: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/5.jpg)
Average Handle by No of runners
0
100000
200000
300000
400000
500000
600000
700000
800000
3 4 5 6 7 8 9 10 11 12 13 14
Handle vs No of runners
All AP CRC FG
![Page 6: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/6.jpg)
Average Handle vs Race Number
0
200000
400000
600000
800000
1000000
1200000
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Handle by Race Number
All AP CRC FG
![Page 7: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/7.jpg)
Average Handle by Month
0
50000
100000
150000
200000
250000
300000
350000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Average Handle by Month
All AP CRC FG
![Page 8: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/8.jpg)
Variables and their influence on the handle
Variables influencing Handle
All AP CRC FG
Purse_USA +ve +ve +ve +ve
Number of runners +ve +ve +ve +ve
Holiday +ve +ve +ve -ve
Weekend +ve NA +ve +ve
Race Type -ve +ve -ve +ve
Age Restriction -ve -ve NA +ve
Sex Restriction -ve -ve -ve -ve
Race Number +ve -ve -ve +ve
Hour of day +ve -ve +ve +ve
Track_Condition -ve -ve NA NA
Wager Type +ve +ve +ve +ve
![Page 9: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/9.jpg)
Linear Regression
• The analytic modelling used to predict the handle values is Linear Regression .Since the handle is a continuous variable , this is the best method to understand the predict the values.
• Following are the charts that show the results of the predicted values and the error with respect to the original handle values .
• (The details of the variables used in the regression are in the Excel files.)
![Page 10: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/10.jpg)
Predicted Handle vs Handle with All Track Ids
![Page 11: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/11.jpg)
Original Handle vs Errors for all Track Ids
![Page 12: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/12.jpg)
Predicted Handle vs Original Handle for track AP
0
200000
400000
600000
800000
1000000
1200000
1400000
0 200000 400000 600000 800000 1000000 1200000 1400000 1600000 1800000 2000000 2200000 2400000 2600000 2800000 3000000 3200000 3400000
predicted_handle
![Page 13: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/13.jpg)
Original Handle value vs Error for Track AP
-600000
-400000
-200000
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1800000
2000000
2200000
2400000
2600000
2800000
0 200000 400000 600000 800000 1000000 1200000 1400000 1600000 1800000 2000000 2200000 2400000 2600000 2800000 3000000 3200000 3400000
difference
![Page 14: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/14.jpg)
Predicted Handle vs Original Handle for track CRC
0
100000
200000
300000
400000
500000
600000
700000
800000
0 100000 200000 300000 400000 500000 600000 700000 800000 900000 1000000 1100000 1200000 1300000
predicted_handle
![Page 15: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/15.jpg)
Original Handle value vs Error for Track CRC
-400000
-200000
0
200000
400000
600000
800000
1000000
1200000
0 200000 400000 600000 800000 1000000 1200000 1400000
difference
![Page 16: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/16.jpg)
Predicted Handle vs Original Handle for track FG
0
100000
200000
300000
400000
500000
600000
0 100000 200000 300000 400000 500000 600000 700000 800000 900000 1000000 1100000 1200000 1300000 1400000 1500000 1600000 1700000 1800000
predicted_handle
![Page 17: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/17.jpg)
Original Handle value vs Error for Track FG
-400000
-200000
0
200000
400000
600000
800000
1000000
1200000
1400000
0 200000 400000 600000 800000 1000000 1200000 1400000 1600000 1800000
difference
![Page 18: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/18.jpg)
Important Points
• The predicted values for the range upto handle = 700,000 is predicted with a good accuracy.
• The model does not do a good job of predicting higher values of handle.
• The Handle values vs error graph shows most of the values symmetrically placed along the x axis , the error are random and therefore there is not any collinearity issue.
• Adj R sq is in the range 0.60 – 0.75 for all the different analysis.
![Page 19: Final case study powerpoint](https://reader034.vdocuments.mx/reader034/viewer/2022042716/55aa155c1a28abf6108b4586/html5/thumbnails/19.jpg)
Ideal Variable Values to Maximize Handle
Ideal Values for the maximization of Handle
All AP CRC FG
Number of runners 14 14 13 13
Holiday 1 1 1 0
Weekend 1 0 1 1
Race Type STK STK STK STK
Age Restriction 4U 34 35 3
Sex Restriction No Restriction No Restriction No Restriction No Restriction
Race Number 3 9 6 2
Hour of day 7 1 2 2
Track_Condition FT GD FT FT
Wager Type E E E E
Month Jan Aug Jan Jan
Day of Week Wed Wed Mon Thu