Should We Avoid Low Class Races

One of the most repeated myths, especially on TV, is that punters would be best served betting on higher class races as lower class races are unreliable due to the erratic nature of lower class animals or perhaps the shear level playing field of modest form. Why this has come about I have no idea as I have long since smelt a rat about this piece of advice. Whether its bookmaker propoganda, as I am pretty sure they want you to bet on high class races, is anyone’s guess. The real question are higher class horses more reliable from a punting point of view ?.

For the purpose of this article I will take a punter view of reliable, namely that punters tend to overbet the last run and usually get swept up with placed horse last time out in preference to those with a more chequered last run figure. If we take a look at the profit and loss to backing to Betfair SP runners who placed first second or third last time out we would expect, if the message is correct, to find less loss in higher class races. Indeed the lower class figures should show that we lose more following ‘form’ in lower class races. Is this the case for the last 5 years Handicaps on the flat?

Class 2 6969 bets PL -346.77 ROI -4.97%

Class 3  6264 bets PL -401.87 ROI -6.41%

Class 4 12768 Bets PL -530.69 ROI -4.15%

Class 5 15475 Bets PL -353.48 ROI -2.28%

Class 6 13994 Bets PL -459.2 ROI -3.28%

The least loss occurred in class 5 and class 6 the very races the media is telling you to avoid and very races bookmakers do not really want you to bet in. Ask yourself this why do some bookmakers state in their new max liability rules that they will take a bet but only in class 4 and above. Enjoy Kempton tonight, I like Hombre Casado and Cool Strutter in the last two handicaps.

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Form Reading Systems and Machine Learning

So what is the difference between Trevor picking horses based on

1.Ad hoc reading of form

2. A system

3. A machine learning model.

First of all Trevor’s method is number 1 and probably highly subjective, even if you told him to focus on the same criteria eg trainer form, as a machine learning model there would be no guarantee that he would interpret the data correctly of if he would interpret it the same way on every day he read form. In an attempt to introduce some sort of rigour and consistency he may attempt to devise a system.

Flatstats tweeted the following data the other day.

Best Value Jockey at Wolverhampton: Rossa Ryan

21/77, 27% strike rate, 1.59 A/E, 57% profit

6:15 Brockley Rise 6:45 Ventura Island 7:15 Fox Power

Based on this Trevor may decide to back all Rossa Ryan mounts at Wolves from now on. He may even decide to back all best value jockeys at every track from now on. This would be an example of a system. It would be a tricky system to follow as Trevor would have to have access to or keep updated figures on who is the top value jockey at each track.

Trevor however gets up one morning, especially when things have not been going too well with the system and wonders if he can integrate trainers into the system. He likes following trainers and thinks it could add value to the system. The problem is that keeping updated values for both trainers and jockeys is going to complicate things further. Also how do you combine the two inputs, should it be a system in which the top valued jockey is riding for the top valued trainer?. That might be too restrictive. What about top valued jockey riding for one of the top three trainers at the track. You can see how just introducing an extra variable has made the possibilities far more complex. Perhaps he should go for trainers over a minimum strike rate at the track.

This is where Machine Learning steps in. With ML we can feed in to an ML algorithm values for our two variables. Maybe initially this would be jockey strike rate at the track and trainer strike rate at the track. Let us say we do this for 2010 through to 2016 and each line of data looks like

10,12,0,3.5

8,9,0,10.5

12,11,1,2.6

From the first 3 lines of this data which we shall say is for Wolves only we can see that the first runner had a 10% jock on board riding for a 12% trainer (all figures for Wolves) and the horse finished out of first place at odds of 3.5. The third line is a 12% jock for an 11% trainer who finished first in this race. There would be many lines of such data for the period which we call the training period because we will use this data to train a model to hopefully find meaningful relationships between the two inputs and the output (win/lose). The odds would not be used in the model other than to test how much profit or loss it made. Furthermore we can get the model to predict the percentage chance a horse with a certain jock percentage and trainer percentage has of winning a future race and hence see how we would have done backing perhaps when a horse is above a certain threshold.

Once we have trained the model we can then see how it performs on new unseen data which we call the test data. For us this may well be data for 2016 to 2018. This will give us a far more realistic idea of how well this model performs.

Sometimes data we want include is not in the correct form. Machine learning models in some applications like Python need all data to be in numeric form. So if we included a third field say headgear where b means blinkered and b1 means blinkered first time and so on. Here we have a problem with non numeric data but it is fairly straight forward to convert the data into numeric representations.  There is also the possibility that our two inputs are correlated in that better jockeys tend to ride for better trainers. This highlights that careful thought is needed about how we pick data to include. These are perhaps topics for another post. This post was an attempt to clarify the difference between the three modes of bet analysis.

Recent Flat Trainer Form

In response to a recent twitter exchange I am posting this up from an old article I wrote in 2010. I had previously written and shown that NH figures seemed to say the opposite ie one was better off backing trainers ‘in form’. Interestingly the flat figures below also show what Joseph Buchdahl’s football figures showed for teams in or out of form ie that there is sweet spot before positive results swing the other way.

 

Last month I took a look at how recent trainer form affected the performance of runners and the pound in your pocket. It showed that some improvement on your bottom line can be obtained from looking at recent strike rates of trainers although not enough to produce any outright profits. Last months article focused on National Hunt racing. This month I will take a similar look at Flat trainers and see if any similar trends emerge.

As with last months article I will look at trainers with an overall strike rate of more than 10%, but this time on the Flat. This would hopefully rule out small trainers with very few runners which could in turn skew the results.. My first port of call was to see how these trainers faired when their recent form was inferior to their overall form. To do this I plumped for the last 25 runs as an indicator of recent form. A broad brush approach would therefore initially be to state that any ratio ie overall SR% / last 25 SR%, greater than 1 would mean ‘out of form’. Yes very broad brush I know but we have to start somewhere. The results were as follows for all horses going off under 8/1 for the years 1999 to 2005.

 

All horses regardless of recent form :-

Bets                          Wins                           PL                        ROI%

48469 10846 -4674 -9.6%

 

Strike rate ratio > 1

23077 5160 -1963 -8.5%

 

Slightly better returns for those trainers on the wrong side of the ‘in form’ ratio. If we increase the ratio to greater than 1.2 ie more significantly out of form, we have

18179 4018 -1427 -7.85%

 

Further improvement in the returns !.

Here are a full set of ratios :-

 

Ratio                Bets                      Wins                PL                       ROI%

>1.4 12524 2779 -876 -7.0
>1.6 9960 2192 -735 -7.3
>1.8 8133 1770 -675 -8.3
>2.0 6795 1462 -631 -9.28
<1.0 24854 5562 -2708 -10.8
<0.8 15632 3519 -1463 -9.3
<0.6 7162 1609 -586 -8.19
<0.4 2070 422 -277 -13.4

 

The interesting figures from the above are the improvement shown when a trainer is out of form but not drastically so ie when the ratio of strike rate to recent strike rate is somewhere between 1.4 and 1.8. In fact betting those within that band produced :-

Bets                            Wins                           PL                        ROI%

4391 1009 -201 -4.5%

 

Using the above band for the following 3 years ie 2005 to 2008 produced for those 3 years :-

Bets                            Wins                           PL                        ROI%

2661 606 -97 -3.6%

 

Certainly -3.6% is surmountable using Betfair.

When it comes to which trainers are best to follow when ‘out of form’, here is a list of profitable trainers when the ratio is set to > 1.4 and < 1.8

Trainer                 Profit

T D Barron 39.53
Saeed Bin Suroor 38.03
M A Jarvis 27.39
W R Swinburn 24.75
T G Mills 21.01
N A Callaghan 19.43
Andrew Reid 19.3
M C Pipe 16.21
J Noseda 15.28
R Hannon 12.93
P R Webber 11.75
N J Henderson 10
Ferdy Murphy 8
L Lungo 7.5
M A Magnusson 7.25
N J Vaughan 7
Tom Dascombe 7
M Wigham 6.75
D R Lanigan 6
R M Beckett 6
H Morrison 5.91
H Candy 5.24
Miss S J Wilton 5
Carl Llewellyn 4.87
John M Oxx 4.87
D R Loder 4.86
J W Payne 4.75
A C Stewart 4.5
E S McMahon 4
Miss Tor Sturgis 4
S P C Woods 3.88
M Pitman 3.33
D J Daly 2.5
George Baker 2.5
A P O’Brien 2.36
R Brotherton 2.25
Paul Johnson 2
A M Balding 1.72
W Jarvis 1.7
P W Chapple-Hyam 1.62
M Botti 1.33
D Flood 1.22
R J Smith 1.1
M Johnston 0.12

 

Good luck with the above figures. One thing for sure most media pundits are guessing when they refer to trainer form.

Educating Trevor 2

As a retired gentleman Trevor likes a bet, in fact I first met him in a Bookmakers shop around ten years ago. He wasn’t a profitable punter but his daily walk into town for a bet, some banter and a cup of tea was a regular ritual. Trevor has one quality however that Bookmaker’s do not like, he has a brain in his head. He demonstrated this by expressing to me an interest in becoming more successful with his betting. How many punters have you met who seem to want to validate their own failure rather than improve their own betting, Trevor was not one of them and he was about to take his first steps towards profit.

He first asked me what was the single most important thing he could do to improve his bottom line, how could he at least perhaps break even and maybe even win long term. I asked him how he bets, what is his daily routine. It is a familiar one to us all. He makes his picks around 8am, has some breakfast and then strolls into town around 10am to his favourite Bookmaker which at the time happened to be Paddy Power. It sometimes changes, if the manager makes him unhappy for some reason. He will then switch to Ladbrokes, Coral, Fred or Hills. If there is one thing FOBTs have given punters its more shops to choose from, but it is probably the only thing. Trevor will write out his bet taking the odds the bookmaker is currently offering at around 10.30am.

So what can Trevor do that will improve his bottom line. Study more form? Follow a tipster? Invest in some fancy speed figures and follow them? My first line of advice was none of these. What all punters should do to improve their betting is to take the best price available. I suggested that Trevor checked which Bookmaker has the best odds amongst his Bookmakers and make the effort to grab those prices. If he could include online bookmakers then his prospects would be even better. Trevor managed to do this (although not via online books) and admitted that he felt better off financially. The second thing I got Trevor to do was check out Betfair and once again Trevor was not only open to this idea but came back to me amazed at some of the prices he was getting.
Okay, so far this is all anecdotal although I can assure you perfectly true. The real question is how much better off is Trevor and could he do even better with his bet placement choices? To test this I gathered 10:05 am prices for all High street and online Bookmakers for a period of just over 2 weeks starting around the St Ledger meeting. I wanted to see how better or worse off the profit would be with various bet placement strategies. First of all I wanted a baseline to compare to. I decided on profit and loss to Bookmaker SP on all horse who had a 10:05am average price of less than decimal 10.0 (9/1). To keep things simple I only considered races where, from 10:05am onwards, there were no non runners up to race off time. Both Flat and NH were analysed.

AM av’g Price < 10 in betting to bookmakers’ SP £1 bets

Bets                              PL                 Returns to £1
2090                        -£192.86                    £0.91

From the above we can see that during this period if we had 2090 £1 bets we would have lost £192.86 which equates on average to getting 91p back for every pound bet. What we would really like to see from a betting strategy is that last column to be above £1.00 or with this article we are looking to see what method of bet placement moves us closer to £1.00 when betting blindly.
So how can we improve matters before we even consider how we are picking our bets? Well what if we do what Trevor is now doing. What if we bet at best 10:05am prices rather than Bookmakers’ SP. The first question is, which Bookmakers should we realistically consider? Trevor is not going to have accounts with every available checkable Bookmaker. Perhaps a look first at betting to best price at 10:05am with the main High street shops, namely Ladbrokes, Coral, Hills, Fred and Power.

AM av’g price < 10 in betting to best 10:05am price with main high street firms

Bets                                PL                       Returns to £1
2090                         -£124.73                          £0.94

Excellent a small improvement: a loss of £124.73 which equates to a return on every pound of 94p.
However what if Trevor is willing to really do the leg work and open accounts with all online Bookmakers? For these results I have excluded all exchanges except Betfair exchange and Betfair Sportsbook.

          AM av’g price < 10 in betting to best 10:05am price with all firms

Bets                              PL                          Returns to £1
2090                         -£36.69                            £0.98

It is worth noting that we will need larger samples than this to feel confident about whether that 0.98 is a realistic figure but what we are really interested in at this stage is the relative behaviour of these numbers. Is one strategy is better than another and by how much.
Let us be a little more realistic. Trevor may not want to have over twenty online accounts. Perhaps he is only prepared to open two or three. The question now becomes one of which two or three bookmakers should he open online accounts with. A count of how often each bookmaker is top priced or joint top priced on a race should tell Trevor where to have his two or three accounts. The chart below shows this count for the period under examination.

toppricedbybookie

The above chart suggests Trevor should have accounts with BetVictor, Betfair and Bet365 in addition to his high street shops. If Trevor makes the effort and adds these three online bookmakers to his access he now has the following figures.

 AM av’g < 10 betting to best 10:05am prices with High st + Three online

Bets                         PL                  Returns to £1
2090                     -61.97                      £0.97

Let us now take a look at how Trevor would have performed if he took the easy option and simply placed all the bets at Betfair SP. This can be done with a single click in the morning and then the rest of the day is his.

          AM av’g < 10 in betting to Betfair SP after 5% comm’ deducted

Bets                          PL                Returns to £1
2090                      -27.48                    £0.98

Betting all horses with an 10:05am average price less than 10.0 in the betting at Betfair SP has produced a loss of £27.48, which equates to a return of £0.98 for every pound invested. This is equal to best price with all bookies but adjusting commission from 5% to 4% places it ahead of best AM price with all bookmakers.

Many people will be surprised to find that the easy Betfair SP option outperformed all the other scenarios when realistic commission is used. Furthermore there is no chance of you getting your account restricted or closed betting to Betfair or Betfair SP. Seems that the old adage of don’t work hard work smart, applies to your betting as well.
Finally is there any Bookmaker based betting that can beat Betfair SP? Well so far there is one but it would rely on getting Best Odds Guaranteed (BOG) with any of the high street Bookmakers at 10:05am. Where BOG is offered on a race you can take a price and if the SP is bigger your bet will be settled at the bigger price. I am unconvinced that this is likely, my understanding is that they all have various time and race caveats when offering BOG and of course it is the first thing to go when you show any sign of intelligent punting.

   Av’g price < 10 in betting to best high street only and BOG at 10:05am

Bets                     PL              Returns to £1
2090               +58.96                  £1.03

If you are not a long term winning punter then these figures suggest that when and how you bet can make a major impact on your loss figures. What if you are a winning punter?. Unless the current bookmaker climate changes you may well be forced onto Betfair anyway but in the meantime I would advocate placing small breadcrumb bets on Betfair alongside your normal bets to gauge the difference in overall performance.

If there is interest in this article I will follow it up with a look at whether the intelligence of the market or wisdom of the crowds can be harnessed to produce a profit from the data. Twitter @SmarterSig

Key Race Analysis 2

OK so I have set the scene for what I mean by key race. In this entry I want to look at some numbers for various key race performance. I am going to look at the first runner coming out of a handicap and how subsequent runners fair when the first runner to appear has run well when up in class, down in class or in the same class. I will look at this from two angles, backing all runners coming out of the race and only backing the next runner, that is to say the second runner coming out of the race. The reason for the latter is that perhaps any benefit gets eroded if a race is more obviously becoming a key race and perhaps using only the second runner as a bet may produce better figures.

Now our intuition may lead us to think that the first runner coming out of a race and running well in a more valuable race would be the stronger indicator, so lets see how things worked out to bookmaker SP.

First of all backing all runners from the second runner onwards, next time out after the first runner ran well –

1. Up in class = Bets 11495 PL -2677 ROI -22.4%
2. Same class = Bets 20442 PL -3490 ROI -17.07%
3. Down in class = Bets 12913 PL -2278 ROI -17.6%

This seems to contradict our instinct with a good run up in class from the first emerging horse paving the way for greater loss than down or same class.

Now backing only the second runner from a race by the above categories

1. Up in class = Bets 1687 PL -353 ROI -20.9%
2. Same class = Bets 2811 PL -447 ROI -15.9%
3. Down in class = Bets 1740 PL -40.2 ROI -2.31%

The variable PL on down in class was +4.87 so that -40.2 does not seem to have been facilitated by a big priced winner or two.

This has a touch of the Mordin philosophy about it. Whats is intuitive is wrong and what is counter intuitive is better. This is because we are dealing with a market and often what seems right also seems right to the market.

Comments welcome

Key Race Analysis 1

Key races is a term that probably originated around American racing although it would be fair to say that we have always been informally aware that some races produce lots of good future form whilst others produce a steady stream of losers. By winners and losers here I am talking about next time out runs. Clearly if we knew that the 2.30 this afternoon was going to have 3 winners next time out from the 8 runners we would be in a position to make money provided the odds where generous enough.

The SmarterSig site has a key race page and others also try to highlight hot races from previous form but what I want to look at in this blog is how useful are they when it comes to seeking out profit. First of all I need to define what parameters I am using so that everyone understands what is being measured. This topic can have people going up blind alleys of misunderstanding from the get go if a little thought is not spent on the method. It is also an analysis that can take many different forms and this is by no means the only slant on the topic.

Firstly I am going to look at handicap races that have had one runner come out of them. Sure more runners will appear but it is at this one emerging runner point that I will mark the race down as being a certain category of race. Let me explain with an example. At the time of writing on 22/08/2018 Surrey Blaze is running at York. His last run was in a race that has produced one future runner namely Trouble and Strife who came 2nd beaten 1.25 lengths over 14f, he then came out in the same class of race and ran well to finish 2nd again beaten 2.25 lengths over 14f.

These are some explanation needed here. First of all I will define the same class as being within 0.5k of prize money either way. Anything greater than 0.5k will be classed as a class rise and below a 0.5k drop will be classed as a class drop. We can argue about these values and they can be adjusted but for now they will do fine. I also want initially to bucket races as key races where a good run has come out of a next time out class drop, next time out class rise and next time out class same so that we can compare them as markers. For example do people overbet subsequent runners just because a race has produced a good run next time out in a higher class from its first emerging runner ?. Also what if the first emerging runner runs in a lower class and runs poorly, does this mark the previous race as a donkey race ?.

Next stop was defining ‘good run’. Again there are a few ways to do this, I chose a good run to be where the distance beaten was less than the race distance multiplied by 0.2. So for 5f its less than 1 length and for 10f its less than 2 lengths.

Now the question remains is it more favourable to blindly back horses where the first emerging horse has run well in lower, higher or the same class. Also what other tweeks or parameters might be of interest. One tweeter suggested looking at percentage of horses beaten in subsequent next time out races as a way of comparing rather than ROI%. Have your say in the comments below and in the next post we will check out some numbers.

Betting Fast and Slow

Excuse the play on an excellent book that you should all read called Thinking Fast and Slow. I have mentioned this book before in an earlier blog but that is not the topic I am talking about today. Another book did howver prompt todays theme, the book is called The Signal and the Noise although I am not quite sure how I moved from ending a chapter in the book to investigating pace and time in relation to winner finding. It may have just been a motivating factor rather than a particular subject matter.

We are often told that slow run races are unreliable and that even run races are best, even fast run races can be frowned upon. Now I know where you are positioned in these different kind of races may help or hinder a horse in winning next time out but what I wanted to do for the time being is ignore the fine detail and simply look at whether the different pace categories as defined by ATR are better or worse when it comes to providing future winners. Let’s face it anything that narrows the field but increases the winner finding is bound to be beneficial unless the market has latched onto it.

The baseline for consideration was to be how horses that had run in a pace defined race did next time regardless of the pace. I initially started out looking at only horses that had placed in a pace defined race. This produced a strike rate of 17.17%.

I then looked at how the strike rates vary according to the pace of the last race for the above horses. This produced the following

Ev-Fs runs = 376 SR% = 15.95%
Ev-Sl runs = 507 SR% = 15.77%
Ev runs = 1299 SR% = 16.55%
Fast runs = 345 SR% = 21.15%
Slow runs = 408 SR% = 18.62%

This seems to fly in the face of conventional logic so I next looked at the performance of all horses next time out coming from pace races regardless of whether they placed or not.

All horse SR% = 11.28%

Ev-Fs runs = 1095 SR% = 10.86%
Ev-Sl runs = 1233 SR% = 10.21%
Ev runs = 3599 SR% = 11.22%
Fast runs = 1018 SR% = 11.88%
Slow runs = 905 SR% = 12.81%

The bias within Fast and Slow has not been removed but interestingly the deficit within Ev-Fs and Ev-Sl still remains. Its not as though as we move away from an even pace things get better, both exhibit a kind of W shaped curve.

Further data and angles of investigation are needed on this and comments are welcome but for the time being dont be too fast to dismiss slow races.

Betfair AM Liquidity

A fairly frequent question that I have come across lately is whether the Betfair morning market is losing or gaining liquidity. In fact some are wondering whether the pre race market is losing liquidity. To check this out I took a look at average race traded totals by race type. Handicaps would probably be a good indicator of how things are going in terms of liquidity. First up is Flat handicaps, we are only 7 months into 2018 so the average for this year need to be taken with a pinch of salt. Readings are taken at 11am to avoid any clashes with early winter start times.

 Flat Handicap Average AM Race Total Traded

It will be interesting to see if 2018 recovers, if it does not then the trend seems downwards.

Now lets take a look at Hcp Chasers and see if there is a different picture.

NH Handicap Chase Average AM Race Total Traded

Again there seems to be a corresponding dip in liquidity. The questions is why has this happened. Has there been a reduction in advertising ?. Does it tie in with Power taking over ?. Are there other possible explanations ?. I would love to hear your theories in the comments section to this blog.

Distance Movers by Trainer

The three year old Burn Some Dust has just done exactly that and won the 2.40 at Newcastle for Brian Ellison. The most interesting thing about this horses previous few runs is that he was taking a hike up in trip by a good four furlongs and he had run as a three year old so its not simply a case of 2yo race distances anchoring his previous profile. This set me wondering which trainers have what probably requires a fair bit of bare faced cheek to run a horse four furlongs short of his best before winning. The first name that probably pops into your head is Sir Mark Prescott but he is not top of this both in terms of numbers and percentage of overall handicap runners.

Here are the top 20 in terms of pure runner numbers.

Trainer Hcpruns +4f %ofhcps PLtoBFSP ROI%
M Johnston 6967 161 2.310894216 245.7136 152.6171429
B Ellison 2969 147 4.951162007 2.9844 2.030204082
A W Carroll 3590 105 2.924791086 -71.8638 -68.44171429
R A Fahey 7764 100 1.287995878 -35.3568 -35.3568
Ian Williams 2161 80 3.70198982 147.8438 184.80475
P A Kirby 927 71 7.659115426 0.9834 1.385070423
D O’Meara 3676 70 1.904243743 -20.5762 -29.39457143
M Appleby 3231 68 2.104611575 11.433 16.81323529
H Morrison 1847 65 3.519220357 -2.9436 -4.528615385
T D Easterby 4839 63 1.301921885 -43.859 -69.61746032
M W Easterby 3187 61 1.91402573 5.7404 9.410491803
J S Goldie 3035 60 1.97693575 -35.3804 -58.96733333
G L Moore 2444 60 2.454991817 -47.4062 -79.01033333
G A Swinbank 1623 58 3.573629082 24.246 41.80344828
P D Evans 4278 56 1.309022908 -21.4922 -38.37892857
S Mark Prescott 1200 56 4.666666667 -12.2876 -21.94214286
M R Channon 3267 48 1.469237833 3.1878 6.64125
K Dalgleish 2562 46 1.795472287 -5.1716 -11.2426087
D M Simcock 1752 45 2.568493151 -5.1018 -11.33733333
A M Balding 3031 44 1.451666117 39.255 89.21590909

In terms of percentage of hcp runners and taking only trainers who have had at least 20 runners go up in distance by four furlongs, the trainer with the highest percentage is Tim Vaughan at 9.25% but he clocks a 2.62pt loss for those 21 distance movers.

Looking for profit amongst the data however will take a bit more digging than the time since the race finished an hour ago if not for any other reason than the PL column is propped up quite a bit by Mark Johnson who’s horses seem to be trying a little bit harder when they go up four furlongs.

NH Chases Course Similarity

Read an interesting article recently on course similarity using course data such as incline to finish, tightness of bends etc. A clustering algorithm was used to group like courses together so for example with NH racing Aintree and Kempton were deemed similar and hence should in theory mean that horse who win at Kempton can be followed at Aintree and vice versa.

I decided to take a different route and see if the horses themselves can tell us which courses are good or bad for switching to. The method I employed was quite simple. Take a look at all NH chase races from 2009 to date and as this data is reviewed log winners by track after first checking if each horse running in a handicap chase has won a race before and for each race won increment by the appropriate amount the actual wins and expected wins for the key combination of track_won_at#track_running_at

This will give us a set of AE values for each track combination. Lets take an example, say Jackthejumper has won at Ayr, Catterick and Newbury and he is next running at Perth. The expected wins and actual wins for Ayr#Perth Catterick#Perth and Newbury#Perth will be updated. If he won at Perth then 1 will be added to each of the three actual wins and for the expected wins 1 / BFSP will be added to each of the three expected win keys.

I then printed off all AE values where the expected wins was greater than 5 just to weed out the very small samples. Here is a list of the results in order of AE value. The larger AE values towards the top are the more similar courses (in theory). What might be useful is to check which courseA#courseB with a good AE value also has a good courseB#courseA AE. I leave that for the reader.

PrevWinCourse#ThisRunCourse | Actual Wins | Expected Wins | AE Value

Haydock#Carlisle 13 5.347447247 2.431066526
Ayr#Doncaster 12 5.273885622 2.275362202
Wincanton#Aintree 11 5.090120406 2.161049076
Uttoxeter#Sandown 12 5.775310313 2.077810429
Fakenham#Stratford 11 5.70695874 1.927471443
Bangor-on-Dee#Perth 13 6.861941094 1.894507665
Newbury#Aintree 14 7.392467964 1.893819502
Fontwell#Warwick 12 6.405885819 1.873277223
Worcester#Kempton 10 5.49012075 1.821453563
Ludlow#Chepstow 10 5.502147437 1.817472199
Lingfield#Chepstow 16 9.327672801 1.715326035
Catterick#Cartmel 9 5.311389399 1.694471884
Uttoxeter#Carlisle 11 6.539082471 1.68219319
Taunton#Uttoxeter 9 5.406941112 1.664527098
Newcastle#Musselburgh 11 6.61289466 1.663416789
Hereford#Wincanton 9 5.420903497 1.660239848
Wincanton#Uttoxeter 9 5.4613611 1.64794084
Cartmel#Perth 12 7.405378545 1.620443834
Wetherby#Cheltenham 8 5.001351059 1.599567778
Plumpton#Ffos 12 7.598968862 1.579161623
Worcester#Fakenham 9 5.752235946 1.564608977
Ffos#Wincanton 10 6.394612153 1.563816501
Southwell#Plumpton 10 6.438225245 1.553223073
Exeter#Newbury 8 5.188170294 1.541969432
Perth#Hexham 11 7.138453061 1.540950106
Hereford#Chepstow 8 5.28282442 1.514341451
Chepstow#Stratford 8 5.292658311 1.511527767
Chepstow#Towcester 8 5.318448794 1.504197993
Wincanton#Ludlow 9 6.006361055 1.49841142
Warwick#Fakenham 11 7.366663395 1.493213333
Sandown#Cheltenham 15 10.19186757 1.471761666
Ffos#Newton 15 10.23911361 1.464970561
Wetherby#Carlisle 18 12.30434347 1.462898045
Leicester#Kempton 12 8.205335235 1.4624631
Cartmel#Market 9 6.163739299 1.460152606
Fontwell#Newton 21 14.43050921 1.455250102
Ffos#Market 10 6.910240901 1.447127552
Wincanton#Ascot 10 6.926880299 1.443651336
Plumpton#Sandown 10 6.930389959 1.442920248
Worcester#Cartmel 10 6.966864738 1.435365889
Uttoxeter#Newbury 10 7.003112115 1.427936585
Chepstow#Uttoxeter 15 10.50970268 1.427252555
Hereford#Huntingdon 10 7.033295644 1.421808567
Carlisle#Kelso 25 17.68715728 1.41345495
Fontwell#Sandown 8 5.705251814 1.40221681
Worcester#Warwick 15 10.72043372 1.399197121
Towcester#Huntingdon 12 8.601468542 1.395110607
Leicester#Warwick 10 7.243106182 1.38062314
Uttoxeter#Bangor-on-Dee 14 10.14083028 1.380557568
Wetherby#Musselburgh 9 6.51937043 1.380501399
Newton#Plumpton 10 7.255243796 1.378313435
Stratford#Newton 19 13.80362568 1.376449959
Worcester#Towcester 8 5.814765223 1.375807912
Stratford#Fakenham 7 5.122827219 1.366432968
Haydock#Wetherby 15 11.0434353 1.358273001
Fontwell#Exeter 16 11.83131509 1.352343326
Wincanton#Huntingdon 8 5.916818391 1.352078004
Ludlow#Leicester 8 5.927773278 1.349579281
Market#Catterick 7 5.195170327 1.347405294
Chepstow#Bangor-on-Dee 8 5.954612017 1.343496432
Ffos#Chepstow 20 14.92475641 1.340055372
Wetherby#Doncaster 10 7.476196319 1.337578573
Exeter#Stratford 7 5.245456358 1.334488274
Fontwell#Ludlow 7 5.24572131 1.334420871
Newton#Ludlow 9 6.762019796 1.330963273
Fontwell#Kempton 10 7.518967771 1.329969792
Uttoxeter#Market 22 16.54503928 1.329703703
Warwick#Newton 7 5.266740117 1.329095388
Sedgefield#Carlisle 17 12.81475792 1.326595486
Worcester#Market 16 12.0839784 1.324067246
Uttoxeter#Wetherby 12 9.063791412 1.323949267
Carlisle#Haydock 9 6.806931394 1.322181682
Stratford#Fontwell 10 7.583137483 1.318715376
Uttoxeter#Fontwell 17 12.96141646 1.311585046
Exeter#Newton 7 5.351957552 1.307932646
Fontwell#Hereford 9 6.884894022 1.307209664
Wincanton#Exeter 13 9.96126494 1.305055139
Worcester#Newton 26 20.07795003 1.294952919
Ayr#Wetherby 15 11.58435527 1.294849791
Huntingdon#Stratford 8 6.191218578 1.292152732
Newbury#Ascot 9 6.982876633 1.288867106
Hereford#Uttoxeter 10 7.801363014 1.281827289
Ffos#Southwell 10 7.808155985 1.280712119
Market#Wetherby 11 8.611710982 1.277330373
Newton#Market 9 7.048707161 1.276829891
Musselburgh#Newcastle 7 5.485531242 1.276084246
Ludlow#Market 10 7.845549051 1.274608053
Southwell#Fakenham 14 10.99230181 1.273618596
Wetherby#Newcastle 17 13.36446105 1.272030345
Newbury#Exeter 8 6.290774542 1.2717035
Wincanton#Stratford 8 6.297587645 1.270327695
Leicester#Ludlow 10 7.878631994 1.269255882
Leicester#Market 10 7.918380612 1.262884482
Market#Ludlow 11 8.710329576 1.262868403
Lingfield#Wincanton 11 8.721293582 1.261280783
Kempton#Fontwell 12 9.517102181 1.260888007
Towcester#Wincanton 10 7.940357122 1.2593892
Newcastle#Sedgefield 18 14.41995039 1.248270592
Huntingdon#Towcester 12 9.614994296 1.248050662
Wincanton#Newbury 11 8.817937264 1.247457276
Newton#Stratford 20 16.09769677 1.242413762
Chepstow#Newbury 10 8.052115652 1.241909634
Newton#Uttoxeter 14 11.2825972 1.240849049
Wincanton#Chepstow 17 13.74227191 1.237058916
Huntingdon#Fontwell 15 12.12797784 1.236809648
Leicester#Towcester 16 12.93674293 1.23678735
Kelso#Haydock 7 5.674948191 1.233491437
Worcester#Sedgefield 8 6.496687814 1.23139671
Stratford#Uttoxeter 15 12.18691955 1.230827851
Warwick#Sandown 7 5.6968092 1.228758021
Newcastle#Kelso 23 18.72832616 1.228086259
Warwick#Towcester 9 7.330733919 1.227707907
Worcester#Ludlow 10 8.146910315 1.227459198
Market#Kempton 8 6.526993392 1.225679194
Stratford#Market 11 8.993258898 1.223138367
Fontwell#Wincanton 18 14.73073333 1.221935092
Uttoxeter#Warwick 16 13.10519834 1.220889573
Hereford#Towcester 9 7.383289982 1.218968782
Southwell#Warwick 8 6.566394896 1.218324534
Newton#Exeter 7 5.749357855 1.217527275
Ayr#Haydock 7 5.774787541 1.212165807
Uttoxeter#Ludlow 9 7.429119183 1.211449134
Ludlow#Uttoxeter 9 7.432428003 1.210909813
Worcester#Huntingdon 10 8.265682062 1.209821516
Newcastle#Ayr 26 21.63868338 1.201551848
Huntingdon#Plumpton 11 9.154983288 1.201531412
Ludlow#Fontwell 6 5.017647991 1.195779379
Warwick#Chepstow 10 8.363296657 1.19570074
Stratford#Chepstow 6 5.036320795 1.191345874
Hexham#Ayr 17 14.30287869 1.188571921
Haydock#Newcastle 6 5.04975186 1.188177195
Leicester#Plumpton 6 5.050696485 1.187954972
Kelso#Carlisle 14 11.78512292 1.187938395
Newton#Towcester 7 5.894383219 1.187571242
Worcester#Uttoxeter 21 17.69257961 1.186938279
Wincanton#Hereford 6 5.055512673 1.186823254
Stratford#Bangor-on-Dee 6 5.058457706 1.186132285
Stratford#Worcester 17 14.37491332 1.182615826
Wetherby#Kelso 17 14.39305734 1.181125011
Market#Sedgefield 12 10.16675019 1.180318172
Leicester#Wincanton 9 7.634631099 1.178838883
Ayr#Musselburgh 10 8.504510052 1.175846691
Market#Southwell 17 14.45862714 1.175768614
Fontwell#Uttoxeter 13 11.0568934 1.17573712
Chepstow#Cheltenham 10 8.508726952 1.175263944
Exeter#Fontwell 12 10.21500346 1.174742627
Plumpton#Wincanton 13 11.10047769 1.171120772
Uttoxeter#Worcester 21 17.98889229 1.167387056
Newton#Bangor-on-Dee 6 5.142999066 1.166634472
Market#Huntingdon 13 11.14955425 1.165965895
Warwick#Newbury 7 6.006201255 1.165462112
Fakenham#Huntingdon 6 5.154323888 1.164071201
Folkestone#Plumpton 9 7.744183507 1.162162543
Worcester#Fontwell 14 12.06693318 1.16019537
Exeter#Wincanton 19 16.39640635 1.158790506
Uttoxeter#Ffos 14 12.0865029 1.158316853
Haydock#Kelso 7 6.047381315 1.157525817
Market#Warwick 7 6.047900515 1.157426446
Musselburgh#Wetherby 6 5.193821965 1.15521865
Perth#Sedgefield 6 5.200907921 1.153644727
Taunton#Chepstow 10 8.686259275 1.151243554
Ludlow#Cheltenham 7 6.086441906 1.150097234
Wincanton#Warwick 7 6.087814366 1.149837952
Wincanton#Sandown 12 10.44703462 1.14865131
Kempton#Cheltenham 8 6.971626242 1.147508447
Hereford#Ludlow 7 6.101569948 1.147245719
Kempton#Ascot 6 5.237048786 1.145683427
Huntingdon#Newton 9 7.8713456 1.143387733
Lingfield#Uttoxeter 6 5.249312166 1.143006895
Hereford#Fontwell 12 10.51011053 1.141757736
Perth#Kelso 12 10.52214728 1.140451628
Newcastle#Hexham 21 18.48081211 1.136313701
Exeter#Chepstow 14 12.33138543 1.135314445
Leicester#Fakenham 7 6.169156067 1.134677081
Newton#Fontwell 19 16.8761338 1.125850282
Plumpton#Fakenham 10 8.892657758 1.124523205
Chepstow#Exeter 13 11.5639693 1.12418147
Towcester#Ffos 10 8.903914993 1.123101468
Plumpton#Uttoxeter 8 7.125919781 1.122662091
Taunton#Fontwell 12 10.71041632 1.120404627
Catterick#Sedgefield 23 20.55300905 1.119057552
Uttoxeter#Sedgefield 8 7.200870723 1.110976757
Market#Cartmel 10 9.019156007 1.108751195
Uttoxeter#Towcester 11 9.961165022 1.104288502
Wincanton#Taunton 9 8.177466078 1.100585428
Uttoxeter#Cheltenham 7 6.368602848 1.099142177
Worcester#Exeter 6 5.476188813 1.095652507
Hereford#Plumpton 6 5.495670614 1.091768489
Plumpton#Southwell 11 10.09207064 1.089964625
Taunton#Newton 10 9.193750898 1.087695339
Towcester#Taunton 7 6.475724256 1.080960171
Leicester#Fontwell 9 8.360800565 1.076451941
Taunton#Ludlow 8 7.451900873 1.073551586
Ayr#Aintree 6 5.591007776 1.07315179
Uttoxeter#Southwell 16 14.91856762 1.072489022
Wincanton#Cheltenham 11 10.2565721 1.072483077
Towcester#Plumpton 9 8.39639739 1.071888285
Kelso#Wetherby 13 12.15647506 1.069388942
Hexham#Carlisle 20 18.71029122 1.068930449
Ffos#Stratford 8 7.496404687 1.067178245
Perth#Musselburgh 8 7.515601054 1.064452456
Fakenham#Market 7 6.587114874 1.062680724
Sedgefield#Kelso 12 11.32878734 1.059248412
Fontwell#Taunton 7 6.616235743 1.058003413
Southwell#Cartmel 6 5.674093233 1.057437683
Carlisle#Hexham 21 19.86466076 1.057153719
Chepstow#Plumpton 6 5.717422439 1.049423943
Plumpton#Towcester 8 7.626588735 1.048961768
Bangor-on-Dee#Southwell 6 5.721960306 1.048591685
Catterick#Wetherby 10 9.546867981 1.047463945
Cartmel#Hexham 6 5.734918159 1.046222428
Fontwell#Southwell 10 9.559364439 1.04609465
Perth#Ayr 9 8.657463301 1.039565481
Kelso#Hexham 18 17.32952657 1.038689657
Huntingdon#Wincanton 7 6.785750656 1.031573418
Plumpton#Newbury 7 6.805857303 1.028525825
Sandown#Newbury 8 7.779862626 1.028295792
Newcastle#Wetherby 16 15.65284961 1.022178095
Hexham#Kelso 18 17.62561824 1.021240773
Newcastle#Catterick 8 7.874360366 1.015955535
Plumpton#Warwick 8 7.887900507 1.014211575
Wincanton#Worcester 8 7.913840574 1.010887182
Sedgefield#Southwell 12 11.8960526 1.008737974
Catterick#Newcastle 6 5.95634452 1.00732924
Warwick#Wincanton 7 6.956582256 1.006241246
Lingfield#Warwick 6 6.000777568 0.999870422
Chepstow#Sandown 6 6.002002082 0.999666431
Plumpton#Stratford 8 8.029067201 0.996379754
Uttoxeter#Kempton 5 5.02384028 0.99525457
Lingfield#Towcester 5 5.024956354 0.995033518
Sandown#Fontwell 6 6.050695977 0.991621463
Musselburgh#Kelso 9 9.102427443 0.988747239
Wetherby#Uttoxeter 8 8.096713558 0.988055208
Ffos#Bangor-on-Dee 6 6.092920021 0.984749509
Hexham#Cartmel 12 12.20480977 0.98321893
Plumpton#Chepstow 10 10.17067889 0.983218535
Newton#Wincanton 11 11.23558928 0.979031872
Kempton#Worcester 6 6.136030969 0.977830788
Uttoxeter#Fakenham 6 6.1373473 0.977621064
Plumpton#Huntingdon 6 6.140536214 0.977113364
Ascot#Cheltenham 8 8.197921491 0.975857113
Sedgefield#Catterick 14 14.36884322 0.974330347
Chepstow#Ffos 14 14.38212356 0.973430658
Towcester#Market 12 12.33270955 0.973022185
Bangor-on-Dee#Uttoxeter 7 7.199732326 0.972258368
Musselburgh#Hexham 9 9.25871023 0.972057638
Newbury#Wincanton 8 8.238544754 0.97104528
Southwell#Newton 9 9.329075176 0.964725852
Plumpton#Exeter 7 7.272688472 0.962505135
Hexham#Market 6 6.242250039 0.961191872
Kelso#Perth 20 20.82496669 0.96038569
Market#Perth 7 7.303269236 0.958474866
Leicester#Huntingdon 8 8.350496288 0.958026891
Ffos#Fontwell 9 9.435381058 0.953856547
Kelso#Doncaster 5 5.258003996 0.950931191
Kelso#Market 5 5.267224342 0.949266573
Huntingdon#Leicester 7 7.388744629 0.947386918
Southwell#Worcester 10 10.55771599 0.94717456
Market#Uttoxeter 16 16.92508177 0.945342552
Sedgefield#Cartmel 14 14.83340893 0.943815415
Towcester#Leicester 9 9.560845954 0.941339296
Towcester#Chepstow 8 8.504848832 0.940639882
Towcester#Worcester 7 7.450501127 0.939534117
Sedgefield#Perth 9 9.588833456 0.938591753
Ffos#Warwick 6 6.40278635 0.937092021
Stratford#Ludlow 6 6.407804355 0.936358176
Ayr#Newcastle 19 20.29601698 0.93614427
Carlisle#Newcastle 12 12.82620143 0.93558487
Wetherby#Market 11 11.78601351 0.933309638
Chepstow#Haydock 6 6.429922512 0.933137217
Newbury#Sandown 12 12.87745602 0.931861074
Carlisle#Ayr 15 16.11970271 0.930538253
Newcastle#Carlisle 16 17.19701503 0.930394023
Newbury#Kempton 7 7.525865433 0.930125587
Carlisle#Perth 12 12.90496382 0.92987475
Fontwell#Market 15 16.20294411 0.925757683
Southwell#Leicester 9 9.733146509 0.924675283
Market#Stratford 8 8.669204936 0.922806654
Chepstow#Wincanton 9 9.76969819 0.921215766
Ffos#Worcester 12 13.04273176 0.920052656
Ludlow#Stratford 8 8.703219382 0.919200085
Towcester#Fontwell 14 15.3288236 0.913312095
Newcastle#Perth 6 6.573220672 0.912794549
Hexham#Newcastle 13 14.24834323 0.91238678
Uttoxeter#Newton 10 10.97965664 0.910775294
Leicester#Uttoxeter 8 8.785888187 0.910551083
Wincanton#Newton 12 13.20428383 0.908795975
Lingfield#Plumpton 13 14.35373317 0.905687729
Chepstow#Fontwell 8 8.847988889 0.904160267
Sedgefield#Musselburgh 7 7.772289663 0.900635502
Southwell#Market 15 16.6560286 0.900574823
Towcester#Warwick 8 8.902129652 0.898661367
Market#Ffos 6 6.68081888 0.898093498
Perth#Wetherby 5 5.578032709 0.896373374
Hexham#Sedgefield 16 17.87732589 0.894988439
Wincanton#Fontwell 14 15.65967904 0.894015769
Southwell#Stratford 12 13.44446512 0.892560611
Sedgefield#Hexham 19 21.31012481 0.891594966
Ayr#Hexham 9 10.10239513 0.890877845
Sedgefield#Market 13 14.6058001 0.890057368
Hexham#Uttoxeter 5 5.634219498 0.887434365
Kelso#Musselburgh 9 10.15663453 0.886120296
Cheltenham#Sandown 7 7.909945711 0.884961826
Fontwell#Worcester 11 12.4348828 0.884608257
Wetherby#Haydock 9 10.18103315 0.883996729
Warwick#Plumpton 6 6.814176305 0.880517282
Worcester#Ffos 8 9.100610109 0.879061942
Stratford#Southwell 10 11.43294526 0.874665256
Plumpton#Taunton 6 6.865501505 0.8739347
Market#Leicester 6 6.865948073 0.873877859
Ayr#Perth 11 12.59456647 0.873392509
Kempton#Stratford 7 8.028328382 0.871912516
Perth#Carlisle 6 6.902635648 0.869233189
Musselburgh#Market 5 5.764812902 0.867330837
Folkestone#Fontwell 5 5.770646843 0.866453993
Musselburgh#Carlisle 5 5.773749988 0.865988311
Cartmel#Sedgefield 7 8.084788003 0.865823569
Wincanton#Kempton 8 9.283199624 0.861771838
Plumpton#Fontwell 42 48.99538651 0.857223567
Lingfield#Fontwell 11 12.83478821 0.857045696
Sedgefield#Newcastle 10 11.69641973 0.854962479
Wetherby#Sedgefield 9 10.53004561 0.854697153
Southwell#Towcester 8 9.3658525 0.854166772
Plumpton#Newton 10 11.71114495 0.853887476
Ludlow#Wincanton 6 7.059086417 0.849968345
Chepstow#Market 5 5.882783489 0.84993779
Bangor-on-Dee#Market 6 7.10288524 0.844727149
Kempton#Wincanton 6 7.108627831 0.84404475
Wetherby#Ayr 10 11.90478797 0.839998161
Warwick#Worcester 5 5.979521822 0.836187265
Fontwell#Fakenham 10 11.96058748 0.836079333
Kelso#Sedgefield 11 13.16096611 0.835804903
Market#Newton 5 5.99564911 0.833938062
Uttoxeter#Leicester 6 7.218352839 0.831214563
Market#Hexham 5 6.018990156 0.830704133
Southwell#Uttoxeter 15 18.17989299 0.825087365
Catterick#Kelso 9 10.91929609 0.824228954
Hexham#Perth 12 14.6066274 0.821544883
Kelso#Ayr 14 17.09730028 0.818842728
Newton#Southwell 5 6.107240399 0.818700374
Huntingdon#Market 13 15.89095602 0.818075387
Southwell#Fontwell 13 15.94776599 0.815161196
Plumpton#Worcester 7 8.592905302 0.814625526
Chepstow#Newton 5 6.161270111 0.811520987
Taunton#Exeter 7 8.655566254 0.80872814
Exeter#Cheltenham 5 6.183003137 0.808668521
Fontwell#Towcester 9 11.21611948 0.802416559
Market#Carlisle 5 6.24823686 0.800225746
Exeter#Kempton 4 5.032357666 0.794856063
Fakenham#Fontwell 6 7.554630354 0.794214901
Market#Aintree 4 5.039202539 0.79377639
Warwick#Uttoxeter 7 8.826267503 0.793087225
Chepstow#Warwick 10 12.62309447 0.792198777
Towcester#Uttoxeter 8 10.17557371 0.786196457
Plumpton#Lingfield 10 12.73505058 0.785234415
Musselburgh#Sedgefield 4 5.100998382 0.784160217
Worcester#Stratford 15 19.15009821 0.7832858
Ayr#Kelso 15 19.315898 0.776562394
Hexham#Musselburgh 8 10.30826599 0.77607621
Warwick#Stratford 5 6.45528331 0.774559343
Worcester#Bangor-on-Dee 4 5.165374127 0.774387276
Hereford#Market 6 7.783241839 0.770887006
Ffos#Ludlow 6 7.792200119 0.770000758
Uttoxeter#Stratford 12 15.58769376 0.769838065
Worcester#Southwell 10 13.02889186 0.767524983
Taunton#Wincanton 11 14.36465266 0.765768603
Uttoxeter#Haydock 7 9.177960291 0.762696697
Kelso#Cartmel 7 9.19314318 0.761437069
Leicester#Bangor-on-Dee 4 5.306416645 0.753804359
Plumpton#Kempton 5 6.647192621 0.752197249
Hereford#Worcester 6 7.985685814 0.75134436
Worcester#Leicester 4 5.347831517 0.74796672
Fontwell#Huntingdon 6 8.02461653 0.74769928
Southwell#Ffos 6 8.031990224 0.747012861
Newton#Cheltenham 4 5.358991618 0.746409079
Sedgefield#Uttoxeter 5 6.70394111 0.745829941
Hexham#Catterick 6 8.078250441 0.742735082
Carlisle#Market 4 5.395300306 0.741385979
Huntingdon#Worcester 6 8.176206154 0.733836683
Carlisle#Wetherby 9 12.37408305 0.72732662
Sandown#Ascot 5 6.905566017 0.724053609
Taunton#Worcester 5 6.920856729 0.722453909
Newton#Worcester 15 20.78412436 0.721704689
Taunton#Southwell 4 5.57583853 0.717380889
Sedgefield#Ayr 5 7.015143931 0.712743751
Leicester#Stratford 4 5.648963718 0.708094475
Musselburgh#Ayr 5 7.063175034 0.707896941
Leicester#Worcester 4 5.653626522 0.707510477
Towcester#Exeter 5 7.084404958 0.705775577
Ayr#Cartmel 4 5.671175926 0.705321092
Uttoxeter#Chepstow 8 11.37517463 0.703285906
Fontwell#Ffos 8 11.41545133 0.70080453
Warwick#Fontwell 8 11.42904558 0.699970959
Uttoxeter#Cartmel 5 7.148120631 0.699484558
Wetherby#Hexham 6 8.597015432 0.697916626
Fakenham#Southwell 7 10.04504144 0.696861237
Hereford#Ffos 4 5.78101565 0.69191994
Carlisle#Sedgefield 8 11.57574842 0.691100023
Huntingdon#Ludlow 5 7.246473699 0.689990775
Perth#Cartmel 6 8.701977722 0.689498433
Southwell#Wetherby 5 7.263992462 0.688326705
Ascot#Sandown 5 7.264400185 0.688288072
Kempton#Sandown 5 7.297410982 0.685174511
Huntingdon#Southwell 9 13.18238852 0.682729081
Kelso#Newcastle 8 11.75485489 0.680569864
Fontwell#Lingfield 7 10.36048363 0.675644135
Market#Fontwell 6 8.916456414 0.672913063
Wincanton#Ffos 4 5.950176102 0.672249011
Exeter#Warwick 4 5.959645634 0.671180846
Fontwell#Plumpton 19 28.37029307 0.669714619
Fontwell#Stratford 10 14.95282614 0.668769897
Wincanton#Plumpton 4 6.003883884 0.666235403
Catterick#Southwell 4 6.018416559 0.664626644
Newbury#Cheltenham 10 15.15928215 0.659661843
Ludlow#Ffos 4 6.096104442 0.656156737
Musselburgh#Perth 8 12.24322654 0.653422525
Cheltenham#Newbury 4 6.185809189 0.646641349
Southwell#Sedgefield 7 10.91131556 0.641535841
Fontwell#Chepstow 6 9.353031952 0.641503208
Wetherby#Perth 5 7.805026864 0.640612786
Market#Bangor-on-Dee 5 8.067460863 0.619773691
Fontwell#Leicester 4 6.524014206 0.61311945
Catterick#Market 4 6.535710932 0.612022172
Ayr#Carlisle 7 11.45000294 0.611353555
Southwell#Ludlow 4 6.599550794 0.606101858
Market#Worcester 5 8.322908103 0.600751557
Fontwell#Bangor-on-Dee 3 5.02461971 0.597060111
Kempton#Ludlow 3 5.028247121 0.596629387
Haydock#Ayr 4 6.705151061 0.596556284
Southwell#Chepstow 3 5.055177553 0.593450966
Catterick#Hexham 3 5.072701316 0.591400876
Ludlow#Ascot 3 5.078854947 0.590684324
Towcester#Southwell 6 10.23617535 0.58615643
Ludlow#Worcester 5 8.545451995 0.585106558
Exeter#Sandown 3 5.224400676 0.574228545
Newbury#Chepstow 7 12.2411768 0.571840446
Ffos#Uttoxeter 6 10.53015686 0.569792082
Ludlow#Warwick 3 5.280973182 0.568077113
Lingfield#Ffos 3 5.317148874 0.56421215
Huntingdon#Kempton 4 7.099174222 0.563445814
Huntingdon#Ffos 3 5.342484965 0.561536442
Market#Fakenham 3 5.374121566 0.558230766
Uttoxeter#Lingfield 3 5.609046998 0.534850216
Sandown#Kempton 3 5.697015289 0.52659153
Uttoxeter#Exeter 3 5.718641483 0.524600119
Southwell#Huntingdon 5 9.568466739 0.52254976
Kelso#Catterick 4 7.673616483 0.521266603
Towcester#Newton 3 5.896708694 0.508758386
Leicester#Southwell 6 11.88889541 0.504672621
Worcester#Chepstow 3 5.988405475 0.500968081
Huntingdon#Fakenham 3 6.408370089 0.468137757
Newbury#Haydock 3 6.834896902 0.438923958
Ludlow#Taunton 3 7.1095456 0.421967896
Huntingdon#Uttoxeter 4 9.674948551 0.413438891
Hexham#Wetherby 4 9.70049982 0.412349887
Sedgefield#Wetherby 6 14.98656996 0.400358455
Newbury#Warwick 2 5.237540997 0.381858586
Market#Wincanton 2 5.254989997 0.380590639
Newcastle#Haydock 3 8.424026382 0.356124241
Warwick#Southwell 3 8.553138016 0.350748461
Carlisle#Catterick 2 5.769918233 0.346625363
Warwick#Ludlow 2 6.387829833 0.313095379
Cheltenham#Aintree 2 7.845861281 0.254911466
Newton#Ffos 2 7.99413896 0.250183292
Uttoxeter#Wincanton 2 9.49407801 0.210657633
Haydock#Cheltenham 1 5.783816291 0.172896225
Stratford#Ffos 1 6.346452126 0.157568352
Wetherby#Catterick 1 7.092610561 0.14099181