ARAZI91
Mare
Trainer & Jockeys Only Runner or Ride That Day.
Not really a system of sorts , more an angle but one you occasionally see dropped into some TV or Press pundits assessment of a race - the Trainer or Jockey's only ride or runner of the day. Logically you would think it would be more significant if attached to the larger scale operators who have multi-runners every day and all over the country. But is it.? Well as usual in horse racing , nothing is as clear cut - it seems it is significant for some but not so for others. Hopefully this research might try and shed some light on the names who contend and those who pretend. If it helps cash a few winners and avoid a few losers , all the better
By the way , no fancy pants data analysis 'R' type stuff here. I like to keep it nice and simple - look at the contrasts and compare. All data reported from HRB and then bunged into an excel spreadsheet for presentation with my very limited skills , so you real data scientists on here please be easy on me
So i'll start with the trainers.
Simple premise - compare all occasions where a trainer had one and only one runner on that racing day against his overall record.
Data is all UK runners only from 2003 up to yesterday and covers all codes.
I only used trainers who had at least 100 runners from 2016.
Now the messy bit - trying to explain an excel table
I sorted the table by largest difference in win percentage from "single runner days" to overall strike rate and this is the light blue column - "Win % Diff"
Yellow columns relate to overall trainers record and orange relate to trainers"single runner days"
The columns are in the following order - win % , Runs , BF P & L & BF ROI (within these there are two columns each - One for All Runs - Yellow and One for just the trainer having the one runner - Orange) - then a single % of runners column (this tells you how many times the trainer % wise has just the 1 runner on a day compared to all his runners)
After that is the Light Blue column "Win % Diff"
You will see a split after that (white blank column) and then another 3 columns - this is data only from 2016 to current and was just a check to compare strike rates and again the "Win % Diff" to see if results and trends were holding up or not.
There were 404 trainers on the list and the average difference in win % was a paltry 0.52% in favour of a trainer having only the one runner that day.This tells me that for the majority it don't make much difference if its the trainers only runner of the day. But from top to bottom there were some interesting discoveries at both extremes.
First off - the top of the table - Those trainers who have the largest difference in win %
ALL = trainers record overall (yellow)
1= trainers record with only having 1 runner that day.(orange)
(I have left off number of winners on the sheet , so just runners and win%)
Aiden O'Brien tops the table with his UK " only one runner today" hitting at around 30% from a small sample of only 63 runners - this compared to his overall strike rate of 16.58% was the largest jump. The end 3 columns show that since 2016 this has held up with a near identical difference in win %. You will also see that AOB only has a "single runner" in a day in the UK 4.56% of the time. The market also has these usually covered.
I thought the Hannon Jnr stats were quite significant - compared to his overall SR of just under 14% - when he has only the one runner in a day they win around 23% of the time from a sample of 167 (3% of runners) - BF ROI of 8% and the data holds up quite well from 2016 too.
And it is similar down the table - Bin Suroor overall jumps up 7.5 % with single runners and has a decent size sample (14% of all runners) - from 2016 there is a dip here but still positive. Paul Nicholl's was similar.
The big one for me was Charles Hills - overall his SR is 12.5% - but when he has only the one runner in a day this jumps up to around 18% - and the 28% ROI on BF looks really good against his overall ROI. The data from 2016 showed a slight increase in win% difference which was a positive.
Gosden , Pipe , Channon & Stoute all had positive difference's over 4% in SR - although some dipped since 2016.
Dascombe , Bell , C Appleby , Ryan , Meehan & Ed Dunlop's runners could also arguably be upgraded when you see it's their only runner of the day.
Beckett shows up well too and the data just from 2016 shows an increase to 23.5% - similar applies to Phillip Hobbs as well.
And onto the bottom of the table
Some big scale operators on there like O'Meara , McCain Jnr , Michael Appleby and the data shows their single runners under-perform to different degrees compared to their usual standards.
Gordon Elliot with a smallish sample compared to his total runners has the largest drop in overall performance.
Ill post up the jockey table next and there are some big names again at both ends of the table.
Not really a system of sorts , more an angle but one you occasionally see dropped into some TV or Press pundits assessment of a race - the Trainer or Jockey's only ride or runner of the day. Logically you would think it would be more significant if attached to the larger scale operators who have multi-runners every day and all over the country. But is it.? Well as usual in horse racing , nothing is as clear cut - it seems it is significant for some but not so for others. Hopefully this research might try and shed some light on the names who contend and those who pretend. If it helps cash a few winners and avoid a few losers , all the better
By the way , no fancy pants data analysis 'R' type stuff here. I like to keep it nice and simple - look at the contrasts and compare. All data reported from HRB and then bunged into an excel spreadsheet for presentation with my very limited skills , so you real data scientists on here please be easy on me
So i'll start with the trainers.
Simple premise - compare all occasions where a trainer had one and only one runner on that racing day against his overall record.
Data is all UK runners only from 2003 up to yesterday and covers all codes.
I only used trainers who had at least 100 runners from 2016.
Now the messy bit - trying to explain an excel table
I sorted the table by largest difference in win percentage from "single runner days" to overall strike rate and this is the light blue column - "Win % Diff"
Yellow columns relate to overall trainers record and orange relate to trainers"single runner days"
The columns are in the following order - win % , Runs , BF P & L & BF ROI (within these there are two columns each - One for All Runs - Yellow and One for just the trainer having the one runner - Orange) - then a single % of runners column (this tells you how many times the trainer % wise has just the 1 runner on a day compared to all his runners)
After that is the Light Blue column "Win % Diff"
You will see a split after that (white blank column) and then another 3 columns - this is data only from 2016 to current and was just a check to compare strike rates and again the "Win % Diff" to see if results and trends were holding up or not.
There were 404 trainers on the list and the average difference in win % was a paltry 0.52% in favour of a trainer having only the one runner that day.This tells me that for the majority it don't make much difference if its the trainers only runner of the day. But from top to bottom there were some interesting discoveries at both extremes.
First off - the top of the table - Those trainers who have the largest difference in win %
ALL = trainers record overall (yellow)
1= trainers record with only having 1 runner that day.(orange)
(I have left off number of winners on the sheet , so just runners and win%)
Aiden O'Brien tops the table with his UK " only one runner today" hitting at around 30% from a small sample of only 63 runners - this compared to his overall strike rate of 16.58% was the largest jump. The end 3 columns show that since 2016 this has held up with a near identical difference in win %. You will also see that AOB only has a "single runner" in a day in the UK 4.56% of the time. The market also has these usually covered.
I thought the Hannon Jnr stats were quite significant - compared to his overall SR of just under 14% - when he has only the one runner in a day they win around 23% of the time from a sample of 167 (3% of runners) - BF ROI of 8% and the data holds up quite well from 2016 too.
And it is similar down the table - Bin Suroor overall jumps up 7.5 % with single runners and has a decent size sample (14% of all runners) - from 2016 there is a dip here but still positive. Paul Nicholl's was similar.
The big one for me was Charles Hills - overall his SR is 12.5% - but when he has only the one runner in a day this jumps up to around 18% - and the 28% ROI on BF looks really good against his overall ROI. The data from 2016 showed a slight increase in win% difference which was a positive.
Gosden , Pipe , Channon & Stoute all had positive difference's over 4% in SR - although some dipped since 2016.
Dascombe , Bell , C Appleby , Ryan , Meehan & Ed Dunlop's runners could also arguably be upgraded when you see it's their only runner of the day.
Beckett shows up well too and the data just from 2016 shows an increase to 23.5% - similar applies to Phillip Hobbs as well.
And onto the bottom of the table
Some big scale operators on there like O'Meara , McCain Jnr , Michael Appleby and the data shows their single runners under-perform to different degrees compared to their usual standards.
Gordon Elliot with a smallish sample compared to his total runners has the largest drop in overall performance.
Ill post up the jockey table next and there are some big names again at both ends of the table.
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