Is Wind The New Going ?

I wrote a few blog entries ago about the performance of pace at the new Newcastle AW track. I thought I would update matters with performance figures up to the end of 2016. For Newcastle therefore that is about 8 months of racing on the AW. A reminder that all my figures are based on pre race pace prediction as per Smartersig pace figures.

In addition to an update I thought it would be interesting to include some reflection on the effect of the wind during this period. To do this I compiled what I will call a ‘degree of separation’. This simply means the number of wind degrees the wind varies from a tail wind at Newcastle. Let me explain, a tail wind at Newcastle is South East or SE for short. This means that the wind is blowing from the south east. The dividers between South and East are as follows

South South East
South East
East South East

Similarly going west from south we have

South South West
South West
West South West

So from the above we can say that East is 4 degrees of separation from South as is West. South South East is 1 degree of separation from SE and so on.

Data on wind direction on race days along with wind speed and temperature were collected.

First of all the results from blindly backing to BFSP the various broad category of pace regardless of conditions or price.All figures pre commission

Led last time 247 Bets PL -83.6 ROI% -33.8%
Tracked 652 Bets PL -49.1 ROI% -7.5%
Held Up 890 Bets PL -52.8 ROI% -5.9%

From the above we can see that leaders have an appalling record.

Now lets see what effect the wind has had. As a broad brush stroke I have classed a degree of separation of less than 4 as a tail wind although of course a 3 for example would be something of a cross wind. On the other hand DOS above 4 is classed as a head wind.

First of all a tail wind

Led 63 Bets PL-33.3 ROI% -53%
Tracked 157 Bets PL -38.06 ROI% -24.4%
Held up 233 Bets PL -17.4 ROI% -7.4%

Now lets take a look at the head wind situation

Led 171 Bets PL-42.9 ROI% -25.1%
Tracked 454 Bets PL +10.2 ROI% +2.2%
Held up 601 Bets PL -22 ROI% -3.6%

The first thing to notice is the far greater number of selections when there is head wind suggesting that Newcastle gets more than its fair share of head winds down the straight. This coupled with the long straight may be the reason for the poor performance of leaders. Strangely enough the wind speed at this stage did not seem to add much value.

This area opens all kinds of possible avenues of research and I have already compiled data for all UK flats tracks. More obscure areas of enquiry might be aspects such as do greys do better in hot temperatures than dark horses or is all this just blowing wind up our ……………….

All comments welcome below

One Step Two Step or Half Step

I was reading the article linked below last night which revisits the idea that when creating a set of ratings for horse racing one can gather a set of horse features for a given race. For example the Jockey strike rate of each mount along with the draw position along with … you get the picture. Now the the difference between a one step and a two step created model is that with a one step you include as a feature of each horse it’s starting price be that bookmaker or Betfair. The problem with this approach is that the SP can swamp the attention of your chosen model building algorithm. Not surprising really given the well documented effect on winning SP has. Short priced horses win more often and even shorter priced horses horses win more often than simple short priced horses and so on.

The two step approach chooses to get round this by building the model using only what is called the fundamental features, in other words we take out the SP and focus on the characteristics of the horse. Once we have built this model and produced a set of ratings for a given race we then proceed to step two. In step two the SP is introduced to the results of step 1 in order to build a final model, this means the SP has not had a chance to bully the fundamental features as they were examined in step 1.

All this can lead to producing an evaluation of the chance of each horse winning and hence a betting strategy based on backing those with longer odds than their predicted chance according to the model. For many people using there own ratings or someone else’s the odds line production can be a daunting problem, but do we really need to worry about that stage. Can we just forget the oddsline component ?.

If a set of ratings is profitable to top or top two rated do we need to oddsline it, perhaps not. Creating an oddsline may well create fewer bets and perhaps a more impressive ROI% but what if backing the top two had created pretty much the same profit but from twice or three times as many bets ?. I am suggesting that the non oddsline approach can have its merits in our UK set up. In the US where a 17% takeout has to be overcome along with no facility to take a price an oddsline is an essential tool as I see it but here in the UK we bet to a 1% takeout (OK a little more if you are paying 5% commission on Betfair). Furthermore the dreaded premium charge looms over us if we get successful on Betfair and here is where the non oddsline approach has some merit. The larger number of bets generated and fluctuations in the profit rate will offer some safeguard against premium charges. that higher ROI% from fewer bets will in time be more likely to change a non premium charge account into a PC one. A slower burning larger turnover account will have a much grater chance of avoiding PC. In fact I would encourage all break even type bets to be left in your betting portfolio to add extra protection.

Here is the link to the paper, comments welcome as usual.

AI Ratings Update

Stef the original creator of Smartsig produced a set of ratings using a neural network. The ratings were based on the finishing positions in the last 3 runs of each horse along with the days since last run. This data was fed into an individual Neural Network for NH hurdles, NH chases, AW flat races and AW turf races. A typical line of data would look something (I guess) like

5 1 3 76 0

Showing that a horse had come 5 (all places above 4th represented as 5) in its third last race. 1st in its second last race. Third in its last race. Ran 76 days ago and in this coming race was not a winner.

These ratings to my mind were not intended as point and fire set of ratings but more as an illustration of how AI can be used and perhaps even as a starting point for further study either using traditional form study or AI methods. They have been published daily with Stef’s permission on the web site.

I thought it would perhaps be time to play around with them a little further and perhaps attach some performance figures to them. I was wondering if the above representation was indeed the best configuration. I chose to use a Random Forest as an Machine Learning vehicle simply because scikit-learn and Python do not have a readily available NN module.

The first thing I did was create a file for AW handicaps based pretty much on Stef’s layout of placings being 1 to 5 where 5 means anything outside the first 4. Days since last run were left as is. It is inevitable that some horses will not have 3 runs and in these cases I opted within Python to replace the values with the mean of the whole column. So a missing third run would be replaced with the mean for all third runs of all horses in the set. This is needed as Python and Scikit Learn do not allow missing values unlike the package R.

The next step was to train the forest on 2011 to 2013 data. Once this was done I tested the model on 2014 to mid 2016. I was hoping perhaps that to BFSP top rated horses might get close to break even as I recall that the original AI ratings top rated lose about 8 or 9% to bookie SP. I was pleasantly surprised to find the following

Top rated bets 4103 PL after 5% comm +215.3 ROI 5.24%

The bottom rated horses produced

7384 bets PL -927 ROI -12.55%

Encouraged by this I went on to try a modification to the placings data using the position of a horse in a race as a percentage of the runners in the race. So first of 2 would be 0.5 whilst first of 10 would 0.1. Placings were not cut of after 4 so for example 5th of 10 would be 0.5. I was hoping that this extra information would produce better results but as is often the case in this game more can mean less.

Toprated bets 4254 PL +5.7 ROI 0.13%

Finally I tried a hybrid of the above two methods. Placings 1,2,3 and 4th would be expressed as a percentage of total runners in a race whilst 5th plus would be represented as 1. This produced the following results

Toprated bets 4118 PL +68.5 ROI 1.66%

If there is interest in these ratings via the comments below I would be happy to produce them alongside the AI ratings and maybe extend them into other codes of racing. Any feedback below is most welcome.

Jockey Ratings

I have been pondering recently over the relative merits of different jockeys. Perhaps it is the sad untimely death of Walter Swinburn and the praise heaped upon him now he has gone that has prompted me or perhaps I have always wondered how Racing Research go about formulating jockey ratings as i seem to recall they had Ray Cochrane as their best jockey in one particular year. What ever method we choose for rating jockeys it has to be first and foremost objective and logical. Simply checking strike rates does not account for the fact that jockeys have different stables which prompt those strike rates. Is a top jockeys with a top yard better than a mid strike rate jockey finding mounts where he can ?. The latter could be the better jockey but the strike rate would not show this.

One option is to use the market as a measure of jockey ability. The problem with this is that the market tends to overbet and underbet certain jockeys. One way to try and iron this out is to take a look at the AE values of jockeys and then compare this with the AE values of all jockeys with the same overall strike rate. Perhaps a jockey with a 12% strike rate should get an AE value equal to that of all jockeys with a 12% strike rate. If it was lower then this would indicate that he is not booting home as many winners as his fellow 12% jockeys on average. If not an absolute measure of pure ability it might indicate who we should avoid and who we should look twice at.

I carried this out on the jockeys with a minimum of 1000 runs from 2012 onwards. I used linear regression to smooth out the strike rate AE values and then compared the jockey AE vales with the strike rate AEs. The league table of jockeys, ranging from top to bottom came out as follows, with William Twiston Davies as the number 1 jockey.

William Twiston-Davies
J Quinn
J P Sullivan
F Norton
P Cosgrave
D Allan
R Winston
P Hanagan
Martin Lane
David Probert
P Makin
L P Keniry
S De Sousa
Oisin Murphy
G Baker
P McDonald
Jim Crowley
D Tudhope
A Mullen
William Carson
P Mulrennan
D Sweeney
J P Spencer
S Donohoe
R Kingscote
J Fanning
A Kirby
Andrea Atzeni
T Eaves
Hayley Turner
L Morris
S W Kelly
T Hamilton
T P Queally
R Hughes
R L Moore
G Lee
R Havlin

The Nature of Expert Gamblers

The topic covered here is probably the most fundamental problem that most gamblers fail to overcome. First of all let me say that you must first be playing a game in which the odds can be larger than the actual probability at certain points in the game. This is the first fundamental flaw amongst gamblers. They prefer simplicity citing complexity as the enemy when in actual fact the opposite is true. The greater the complexity the greater the opportunity, usually. Just ask a punter why he plays the slots in preference to horse racing and he will say that he has a better chance of winning !, what he really means is that he has fewer variables to consider. The other problem that faces gamblers or at least those playing a game which offers opportunity, is that their approach is based around finding winners and not finding value. In horse racing they focus on whats going to win rather than is their a bet in the race. This is not surprising as their first exposure to Racing is via ‘experts’ in the media who operate in exactly the same way. Finally one thing the speaker did not touch upon which I think might be interesting is what value or opinion do successful gamblers have towards money. My guess is that ironically they are less driven by it than people think.

Cracks in Thistlecrack

Thistlecrack has just won at Newbury in his third novice chase and opinions are as split as ever. Should a novice really be 7/2 favourite for the Gold Cup ?. Some think he is the new heir to the throne especially with so many doubts amongst main contenders whilst the doubters just feel that the price is plain daft.

I am tending towards the latter after today. An examination of his final sectional and comparing with O Maonlai, a handicap winner rated 133 later in the day, does not make for a 7/2 gold cup favourite. Given that O Maonlai ran just less than a furlong shorter, the two overall times work out to be pretty similar. The final sections however paint a different picture. I will admit that Thistle won pretty easily but his final section of 32.84 does not match the quicker O Maonlai at 32.2. I would content that the handicapper O Maonlai would have given him a fair race based on this data and given that Thistle will have to contend with far superior opposition at Cheltenham his price does look a tad crazy for March.

Betfair API NG Session 12

In this session we will look at how to access Irish racing in addition to UK racing.

This is a fairly simple process and involved editing one line in our MyAPILib library that we created. This will hardwire so to speak our library to pick up Irish and UK races in the same way that the library at the moment is hard wired to pick up only UK races. It might be better to write the subroutine so that we pass it a parameter indicating what markets to pick up.

First let us take the simple approach and simply change the routine to gather Irish and UK races. In our library we need to edit only one line within the getEvents subroutine. This line will now read as

market_catalogue_req = ‘{“jsonrpc”: “2.0”, “method”: “SportsAPING/v1.0/listMarketCatalogue”, “params”: {“filter”:{“eventTypeIds”:[‘+eventId+’],”marketCountries”:[“GB”, “IE”],”marketTypeCodes”:[“WIN”], “marketStartTime”:{“from”:”‘ + now + ‘”,”to”:”‘ + to + ‘”}},”sort”:”FIRST_TO_START”,”maxResults”:”100″,”marketProjection”:[“MARKET_START_TIME”,”RUNNER_DESCRIPTION”, “RUNNER_METADATA”, “EVENT”]}, “id”: 1}’

Notice how the marketCountries parameter has IE added to it. API NG takes two letter country codes as specified by ISO 3166 at

To make the getEvents sub routine more generic we need to modify it to receieve an extra parameter stating the required markets

def getEvents(appKey, sessionToken, eventId, reqMarkets):

Now we can call this routine with the following modified call

myMarkets = ‘”GB”,”IE”‘
races = myAPILib2.getEvents(appKey, sessionToken, horseRacingEventTypeID, myMarkets)

The market_catalogue_req initialisation line will now need changing to

market_catalogue_req = ‘{“jsonrpc”: “2.0”, “method”: “SportsAPING/v1.0/listMarketCatalogue”, “params”: {“filter”:{“eventTypeIds”:[‘+eventId+’],”marketCountries”:[‘+reqMarkets+’],”marketTypeCodes”:[“WIN”], “marketStartTime”:{“from”:”‘ + now + ‘”,”to”:”‘ + to + ‘”}},”sort”:”FIRST_TO_START”,”maxResults”:”100″,”marketProjection”:[“MARKET_START_TIME”,”RUNNER_DESCRIPTION”, “RUNNER_METADATA”, “EVENT”]}, “id”: 1}’

Comment Commentary

When I think back to my earliest memories of betting on horses I recall the an Autumn day when a colt called The Minstrel powered up the Newmarket heath to beat Saros in the Dewhurst Stakes. Somewhat fitting given that we watched Churchill win yesterday’s race of the same name. The other nostalgic memory I have is my fascination with the new language I had to master to understand this conundrum of a sport. What did ‘ro’ mean or ‘nearest finish’ and how did the latter differ from ‘finished well’ or ‘finished fast’.

I would imagine that there are plenty of seasoned observers who even today do not fully understand the meaning of some of them. Given the way I now think about betting the meaning of what each of these peculiar abbreviations and phrases mean is of less importance compared to how the public use them and what effect they have on the odds of horses. In other words their worth is more important than their meaning although perhaps in this video age their worth is perhaps a little less of a currency than it used to be.

One member of the Smartersig forum asked about this topic today in relation to a past article to which I replied that I had done some analysis using both AE values and also a Kth nearest neighbour machine learning approach. The latter had not produced any revelations but the AE values did highlight some interesting observations. I also mentioned that Peter May had tries to formalise Raceform comments when he worked there, something I think should be implemented so as to avoid different race readers using varied comments to mean the same thing. He also responded to a particular example and gave a definition of what the terms actually meant. The extract is copies below.

“Nearest finish” means the horse ran through beaten runners to secure its
best position at the end of the race; “finished well” implies the horse
improved at the end of the race to get the better of horses that were not
necessarily beaten at the time. I’d rather be on a “finished well” next
time out than a “nearest finish” unless the latter was a non-trier and
merely running for a handicap mark.

Now, who wants to do: “stayed on well” and “ran on well”?

My reply to the above was

You would be right to value Finished well higher than nearest finish, the figures below show that it has a far better AE value although both are > 1
Comments from the previous race applied to next race

Comment                                                                           actual wins Expected wins AE
NEAREST FINISH 460 431.4271 1.066229
FINISHED WELL 38 29.68467 1.280122

Sadly combining all AE values for sub comments within a race comment and then using these next time out for a horse does not produce an automatic ticket to wealth. Betting only those below 8/1 for example will lose you around 2% less in the pound for those with an overall AE > 1 compared to those with an AE less than 1. It may be possible however to tease out profitable combinations from the figures and some of the values do highlight the old adage that more likely winners does not always mean better overall results to the pound. There is often a reverse effect to the pocket than the one we expect. For example which would you rather back, ‘stayed on same pace inside final furlong’ or ‘stayed on well’. Below is a list of the top 100 based on expected win numbers.

Comment Actual Wins Expected Wins AE
CHASED LEADERS 2671 2587.35969 1.03232651
HELD UP 2231 2265.561685 0.984744761
TRACKED LEADERS 1873 1871.609995 1.000742679
RIDDEN OVER 2F OUT 1785 1820.174781 0.980675053
TOOK KEEN HOLD 1481 1361.466088 1.087797936
RIDDEN OVER 1F OUT 1356 1354.724618 1.000941433
LED 1182 1243.74324 0.950356924
WEAKENED INSIDE FINAL FURLONG 1050 1026.676437 1.02271754
IN TOUCH 979 1011.940749 0.967447947
SLOWLY INTO STRIDE 933 974.9412843 0.956980708
PROMINENT 1008 945.7114615 1.06586421
HELD UP IN REAR 856 920.3450153 0.930085985
MIDFIELD 849 891.8348109 0.951970017
HEADWAY OVER 2F OUT 824 837.1442708 0.984298679
DWELT 782 772.8943664 1.011781214
KEPT ON 702 726.5056188 0.966269196
WEAKENED OVER 1F OUT 683 700.8964292 0.974466371
HELD UP IN TOUCH 725 700.8810366 1.03441235
MADE ALL 639 650.0103887 0.983061211
RIDDEN 2F OUT 631 637.7561372 0.989406394
RAN ON 624 637.5882859 0.978687993
IN REAR 633 632.1035041 1.001418274
SOON RIDDEN 607 632.0653357 0.960343758
TRACKED LEADER 643 626.1577366 1.026897797
SOON WEAKENED 576 598.9307325 0.961713882
HELD UP TOWARDS REAR 576 567.435946 1.015092548
HEADWAY OVER 1F OUT 532 564.6852995 0.942117672
CLOSE UP 574 560.7940987 1.023548574
STAYED ON 569 548.0675889 1.03819312
STEADIED START 467 527.8751126 0.884678949
HELD UP IN MIDFIELD 460 490.6925151 0.937450615
CHASED LEADER 518 470.069006 1.101965868
NEAREST FINISH 460 431.4270504 1.066228925
LED OVER 1F OUT 431 420.5246004 1.024910313
KEPT ON SAME PACE 403 409.498403 0.984130822
HEADWAY 2F OUT 368 389.0303672 0.945941579
KEPT ON FINAL FURLONG 369 382.5430579 0.964597298
ALWAYS TOWARDS REAR 346 377.4591406 0.916655507
ALWAYS PROMINENT 382 373.7864294 1.021973967
HEADWAY 3F OUT 371 372.4799733 0.996026704
RIDDEN WELL OVER 1F OUT 374 371.5040007 1.006718634
RIDDEN OVER 3F OUT 350 369.287482 0.947771092
TOWARDS REAR 373 368.4816783 1.012261998
IN TOUCH IN MIDFIELD 374 362.3074659 1.032272407
RIDDEN 3F OUT 379 356.4176777 1.063359153
HEADWAY OVER 3F OUT 343 333.6436157 1.028043049
EFFORT OVER 2F OUT 326 329.1876265 0.990316688
TRACKED LEADING PAIR 353 313.414136 1.126305292
LED OVER 2F OUT 326 310.5600987 1.049716307
RIDDEN OUT 333 301.1864019 1.105627605
KEPT ON INSIDE FINAL FURLONG 284 290.6621099 0.977079538
ONE PACE 284 284.4649458 0.998365543
RAN ON WELL 300 282.0159815 1.063769501
RIDDEN TO LEAD OVER 1F OUT 293 268.7380356 1.090281096
STAYED ON WELL 260 268.7236763 0.967536629
STAYED ON SAME PACE INSIDE FINAL FURLONG 282 268.1975814 1.051463621
HELD UP IN LAST PAIR 250 261.3892469 0.95642802
LED INSIDE FINAL FURLONG 272 260.428051 1.044434342
NOT REACH LEADERS 289 260.0312104 1.111405049
NO IMPRESSION 270 256.222184 1.053772924
SOON LED 287 253.9916809 1.129958269
READILY 260 242.4380556 1.072438893
DRIVEN OUT 212 237.5200836 0.892556102
HEADED INSIDE FINAL FURLONG 230 236.4138702 0.972870161
HEADED OVER 1F OUT 232 233.9553457 0.991642227
RACED KEENLY 234 229.4599047 1.019786007
RIDDEN ALONG OVER 2F OUT 208 228.9571069 0.908467105
COMFORTABLY 234 224.0209046 1.044545376
STAYED ON SAME PACE 226 221.8574082 1.018672317
BEHIND 225 221.6497903 1.015114879
DRIVEN OVER 1F OUT 231 220.145094 1.049307962
NEVER TROUBLED LEADERS 210 215.2400129 0.975655024
NO EXTRA INSIDE FINAL FURLONG 239 213.8537856 1.117586015
SOON BEATEN 213 212.5922548 1.001917968
KEPT ON SAME PACE FINAL FURLONG 210 211.0349585 0.995095796
RIDDEN ALONG 2F OUT 198 203.6188192 0.972405207
RIDDEN AND HEADED OVER 1F OUT 190 200.915252 0.945672358
HELD UP IN LAST TRIO 188 198.4807439 0.94719516
PUSHED ALONG OVER 2F OUT 213 197.7588924 1.077069139
LED 2F OUT 208 196.9414763 1.056151319
DRIVEN OVER 2F OUT 192 195.4939592 0.982127534
KEPT ON WELL 190 190.0810739 0.999573477
STAYED ON FINAL FURLONG 193 182.1342079 1.059658162
EFFORT 2F OUT 171 180.8516812 0.945526184
STAYED ON INSIDE FINAL FURLONG 161 179.4288736 0.897291483
NEVER DANGEROUS 178 178.4931228 0.997237301
TAILED OFF 164 171.4225248 0.956700412
NEVER ON TERMS 154 170.1821368 0.904912836
NEVER ABLE TO CHALLENGE 173 168.309055 1.02787102
STAYED ON SAME PACE FINAL FURLONG 149 165.9114395 0.898069479
SOON CLEAR 164 163.855825 1.00087989
NO CHANCE WITH WINNER 149 160.0478459 0.930971605
RIDDEN ALONG 3F OUT 171 158.544772 1.078559689
TOOK KEEN HOLD EARLY 148 157.3422748 0.940624509
WEAKENED OVER 2F OUT 140 156.5396692 0.894341995
HELD UP IN LAST PLACE 134 156.2194758 0.857767569
PULLED HARD 168 155.8111567 1.078228309
RIDDEN HALFWAY 148 153.9541078 0.961325437
PUSHED ALONG OVER 3F OUT 165 151.0215281 1.092559466

The NR Conspiracy

In the 8.10 at Chelmsford today there are bang on 16 runners in a handicap. Bookmakers do not like 16 runners in handicaps, perhaps even more so than 8 runners in a handicap. It always seems however that they have little to worry about. Doesn’t one always come out and drop the runners to 15 or less. The punter friendly each way terms of 16 runner handicaps seemed to be inevitably snatched away from them by a non runner or two.

Is something going on here or are we imagining it. Is this the equivalent of a football manager bung, with trainers getting a brown envelope for saving the bookmakers thousands of pounds ?.

Lets look at some figures covering 2009 to 2011 inclusive for handicaps across both Flat and NH.
It shows the number of decs, number of races for that many decs, number of NRs and the NR percent expressed in relation to total decs for that dec size race.
This is for hcps only.
We can see that for 8 dec races there was 1646 such races and they were subject to 1058 NR’s which is 8.03% ie 1058 / (1646 * 8).
This is below the total NR percentage across all races of 8.62%
16 decs by comparison stand at 10.39%
Is there a confounding reason why 16 runner races would get more NR’s as a percentage ?
There does seem to be a spike around the 15,16 and 17 dec’ mark although quite why 15 decs should be spiking is a puzzle

Decs NumOfRaces NRs NRpercent
1 1 0 0
2 3 0 0
3 22 6 9.090909
4 148 30 5.067568
5 468 143 6.111111
6 863 375 7.242178
7 1199 580 6.910521
8 1646 1058 8.034629
9 1554 1104 7.893608
10 1630 1374 8.429448
11 1550 1416 8.304985
12 1740 1769 8.472222
13 1312 1535 8.999765
14 1369 1756 9.162058
15 529 853 10.74984
16 613 1020 10.39967
17 420 738 10.33613
18 247 397 8.929375
19 61 95 8.196721
20 170 260 7.647059
21 22 53 11.47186
22 32 61 8.664773
23 7 2 1.242236
24 28 25 3.720238
25 3 4 5.333333
26 3 11 14.10256
27 10 18 6.666667
28 10 17 6.071429
29 8 9 3.87931
30 7 6 2.857143
31 2 9 14.51613
32 2 7 10.9375
34 2 3 4.411765
35 2 3 4.285714
36 1 4 11.11111

Newcastle AW Pace

It is perhaps a little early to be evaluating the pace angle at the new Newcastle AW track but I thought an update on how things are measuring up might be in order.

So far working to BFSP before commission and using SmarterSig pre race pace figures and I emphaseise pre race here, we have the following figures

Hold up ie Less than 1.4 pace figures for prior race runs 372  wins 31 PL -86pts

Prominent ie pacefig greater than 1.4 and less than 3.2  runs 308 wins 35 wins PL +47.7

Led ie pacefig greater than  3.2 runs 114 wins 9 PL -59.55

Horse with a prominent run in their previous race as opposed to held up or led have done very well next time out at Newcastle so far although the duration and sample sizes are very small.