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Data exploration assignment ppt

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Ramachandran N
Ramachandran N

The document analyzes data from thoroughbred horse races to provide suggestions to clients. It finds that average handle peaks on Wednesdays, Fridays, and Saturdays. Handle increases steeply when purses are over $125,000 and when races are scheduled between 11am-12pm and 7-8pm. The analysis also finds the handle is maximized with 11 races per day and 4-12 runners per race. These findings inform recommendations to clients on optimizing scheduling and payouts to increase profits.

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Data Exploration Thoroughbred Horse Racing N RAMACHANDRAN
Transform Qualitative Variables to Quantitative • Transforming the qualitative variables which have a significant impact on the handle : 1.Race_Type:dummy_allowance , dummy_handicap , dummy_stakes , dummy_maiden , dummy_starters,dummy_claiming 2.Age Restriction:dummy_is2allowed , dummy_is3allowed, dummy_is4allowed, dummy_is5allowed, dummy_isg5allowed 3.Surface : dummy_dirt , dummy_turf 4.Track Id:dummy_AD ,dummy_CD , dummy_CRC, dummy_FG
Derived Variables • Hour of race : Getting the hour of race in 24hr format • Day of race : Getting the day of the week (1: Sunday , 7: Saturday) • Month of race :Gettting the month of the race(1:Jan , 12:Dec)
Summary Statistics • No missing values .Some of the data not available for conditions_of_races , sex_restriction are assumed to mean that there are no conditions or restrictions and hence the field is blank. • Proc means and proc freq data on the expected lines .Nothing unusual to be reported from the data.
Graphical Analysis • Compared different independent variables to the dependent variable handle and generated some charts.
0 100000 200000 300000 400000 500000 600000 1 2 3 4 5 6 7 HANDLE DAYOFWEEK Average Handle vs Day of Week • The data below shows that the average handle peaks on Wed , Fri and Sat.(Sun =1 and Sat=7)
0 500000 1000000 1500000 2000000 2500000 3000000 3500000 4000000 4500000 5000000 7000 10000 13000 16000 19000 22000 24500 27500 30500 33500 36500 40000 43500 46500 48300 50000 52500 55000 59500 61500 65000 68200 75000 125000 400000 Handle Purse_USA Average Handle vs Purse_USA • There is a steep increase in the Handle when the total prize money increases above 125000.
0 100000 200000 300000 400000 500000 600000 700000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Handle Race No Average Handle vs Race No • The average handle increases with the no of races till the race no 10 or 11.The client is advised to restrict the number of races to 11.For the cases of more than 11 races in a day , the returns are not that great.Race no 15 is an outlier .
Average Handle vs No of runners • The average handle increases from no of runners from 4 to 12 and the client is suggested to keep this range to maximize profits. 0 100000 200000 300000 400000 500000 600000 700000 800000 900000 1000000 3 4 5 6 7 8 9 10 11 12 13 14 HANDLE No of Runners
0 200000 400000 600000 800000 1000000 1200000 11 12 13 14 15 16 17 18 19 20 HANDLE Hour of Day Average Handle vs Hour of day • The data shows a significant high value of the handle where the 1st race are in the range 11-12 pm and the last race occurs in the time 7- 8 pm. The client can be suggested to schedule the races as such.
Handle Value Graph • All the high values of the handle look like an outlier but the reason behind them is that they are mostly placed on the weekends (ie on holidays) 0 1000000 2000000 3000000 4000000 5000000 6000000 1 177 353 529 705 881 1057 1233 1409 1585 1761 1937 2113 2289 2465 2641 2817 2993 3169 3345 3521 3697 3873 4049 4225 4401 4577 4753 4929 5105 5281 5457 5633 5809 5985 6161 6337 6513 6689 6865 7041 7217 7393 7569 7745 7921 8097 8273 8449 8625 8801 8977 9153 9329 9505 9681 9857 10033 10209 10385 10561 handle
Handle vs Track Id • From data it can be inferred that the average handle at Churchill Downs in the state Kentucy is significantly greater than its peers. 0 100000 200000 300000 400000 500000 600000 700000 800000 900000 AP CD CRC FG Count of handle Average of handle2
Anomaly Detection • In the handle graph(11th slide) , there are some spikes in the values which turnout to be weekends when high transaction handle occurs , so could not be termed as an outlier. • There is only one day(26-Oct-04) where we have no of races =15 , so that can be an outlier .
Suggestions for client(Summary) • As described in the few graphs and histograms , some of the things the client should take into account are : • 1.Wed , Fri , Sat , Sun : are the highest gross handle days in a week. • 2.Steep increase in handle when the purse is higher than 150000$. • 3.Restrict the no of races to 11/day. • 4.Average handle increases when the no of runners are in 4-12 range. • 5.Value of the handle is significantly high if the first race is in 11-12pm and the last in 7-8pm range.

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Data exploration assignment ppt

  • 1. Data Exploration Thoroughbred Horse Racing N RAMACHANDRAN
  • 2. Transform Qualitative Variables to Quantitative • Transforming the qualitative variables which have a significant impact on the handle : 1.Race_Type:dummy_allowance , dummy_handicap , dummy_stakes , dummy_maiden , dummy_starters,dummy_claiming 2.Age Restriction:dummy_is2allowed , dummy_is3allowed, dummy_is4allowed, dummy_is5allowed, dummy_isg5allowed 3.Surface : dummy_dirt , dummy_turf 4.Track Id:dummy_AD ,dummy_CD , dummy_CRC, dummy_FG
  • 3. Derived Variables • Hour of race : Getting the hour of race in 24hr format • Day of race : Getting the day of the week (1: Sunday , 7: Saturday) • Month of race :Gettting the month of the race(1:Jan , 12:Dec)
  • 4. Summary Statistics • No missing values .Some of the data not available for conditions_of_races , sex_restriction are assumed to mean that there are no conditions or restrictions and hence the field is blank. • Proc means and proc freq data on the expected lines .Nothing unusual to be reported from the data.
  • 5. Graphical Analysis • Compared different independent variables to the dependent variable handle and generated some charts.
  • 6. 0 100000 200000 300000 400000 500000 600000 1 2 3 4 5 6 7 HANDLE DAYOFWEEK Average Handle vs Day of Week • The data below shows that the average handle peaks on Wed , Fri and Sat.(Sun =1 and Sat=7)
  • 8. 0 100000 200000 300000 400000 500000 600000 700000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Handle Race No Average Handle vs Race No • The average handle increases with the no of races till the race no 10 or 11.The client is advised to restrict the number of races to 11.For the cases of more than 11 races in a day , the returns are not that great.Race no 15 is an outlier .
  • 9. Average Handle vs No of runners • The average handle increases from no of runners from 4 to 12 and the client is suggested to keep this range to maximize profits. 0 100000 200000 300000 400000 500000 600000 700000 800000 900000 1000000 3 4 5 6 7 8 9 10 11 12 13 14 HANDLE No of Runners
  • 10. 0 200000 400000 600000 800000 1000000 1200000 11 12 13 14 15 16 17 18 19 20 HANDLE Hour of Day Average Handle vs Hour of day • The data shows a significant high value of the handle where the 1st race are in the range 11-12 pm and the last race occurs in the time 7- 8 pm. The client can be suggested to schedule the races as such.
  • 11. Handle Value Graph • All the high values of the handle look like an outlier but the reason behind them is that they are mostly placed on the weekends (ie on holidays) 0 1000000 2000000 3000000 4000000 5000000 6000000 1 177 353 529 705 881 1057 1233 1409 1585 1761 1937 2113 2289 2465 2641 2817 2993 3169 3345 3521 3697 3873 4049 4225 4401 4577 4753 4929 5105 5281 5457 5633 5809 5985 6161 6337 6513 6689 6865 7041 7217 7393 7569 7745 7921 8097 8273 8449 8625 8801 8977 9153 9329 9505 9681 9857 10033 10209 10385 10561 handle
  • 12. Handle vs Track Id • From data it can be inferred that the average handle at Churchill Downs in the state Kentucy is significantly greater than its peers. 0 100000 200000 300000 400000 500000 600000 700000 800000 900000 AP CD CRC FG Count of handle Average of handle2
  • 13. Anomaly Detection • In the handle graph(11th slide) , there are some spikes in the values which turnout to be weekends when high transaction handle occurs , so could not be termed as an outlier. • There is only one day(26-Oct-04) where we have no of races =15 , so that can be an outlier .
  • 14. Suggestions for client(Summary) • As described in the few graphs and histograms , some of the things the client should take into account are : • 1.Wed , Fri , Sat , Sun : are the highest gross handle days in a week. • 2.Steep increase in handle when the purse is higher than 150000$. • 3.Restrict the no of races to 11/day. • 4.Average handle increases when the no of runners are in 4-12 range. • 5.Value of the handle is significantly high if the first race is in 11-12pm and the last in 7-8pm range.