Final case study powerpoint
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Case study to build a predictive model for the Equestrian racing data and help the client to increase profits.
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Final case study powerpoint
- 1. Final Case Study Predictive Modelling for Equestrian Sports N RAMACHANDRAN
- 2. 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
- 3. 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
- 4. 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
- 5. 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
- 6. 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
- 7. 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
- 8. 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
- 9. 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.)
- 10. Predicted Handle vs Handle with All Track Ids
- 11. Original Handle vs Errors for all Track Ids
- 12. 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
- 13. 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
- 14. 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
- 15. 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
- 16. 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
- 17. 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
- 18. 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.
- 19. 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