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.

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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

2YO Handicaps

Prompted by Matt Bisogno’s article on the up coming Nursery season I decided to have a look at a race category that I have not examined before. Matt’s excellent article had lots of food for thought, the only thing I questioned was that he had not split the data into train test partitions. This is something I think is not solely for Machine Learning applications but can also be applied to system building. So with this in mind I examined Nursery races from 2009 to 2015 inclusive using the first two years as a data run in. Any theories could then be tested on 2016/17.

My only port of call on this topic is trainer performance in these races and it may well uncover a classic example of Nick Mordin’s black is white and white is black in the world of betting.

Taking trainers strike rate in 2yo races generally and then also logging their strike rate in 2yo handicaps proved very interesting. At first I set the check to those runners where the trainer had a better SR in Nurseries than overall 2yo races. Prepare to enter Mordin world.

Bets 4866 PL -586 ROI% -12.04 VarPL -53 VROI% -7.18%

Now if we take a look at those trainers with an SR% less than their overall 2yo SR%

Bets 5326 PL +616 ROI +11.5 VarPL +54.3 VROI +6.82%

All the above are to BFSP before commission.

OK so how did this theory pan out on 2016 and 2017, well not too bad.

Bets 2155 PL +123 ROI 5.7% VPL +3.8 VROI +1.18%

Certainly this subset are worthy of further attention when deciding a bet. It would appear that punters overbet trainers with obvious good records in Nurseries whilst run of the mill records are ignored to heavily even amongst some big name trainers.

Examples of trainers to avoid would be S Bin Suroor and Roger Varian. They do well with their Nursery runners but the public seem to know that.

Trainers to watch out for in 2018 are

A B Haynes
A Berry
A Carson
A M Balding
A P Jarvis
A Stronge
Amy Murphy
Andrew Reid
Archie Watson
B Curley
B Haslam
B J Meehan
B Smart
B W Duke
C Allen
C G Cox
C Hills
C R Dore
C W Fairhurst
D Carroll
D Donovan
D J Coakley
D J S Ffrench Davis
D Kubler
D McCain Jnr
D Morris
D O’Meara
D O’meara
D P Quinn
D R C Elsworth
D Simcock
D W P Arbuthnot
Dr J D Scargill
E J Creighton
E J O’Neill
Ed De Giles
Eve Johnson Houghton
F J Brennan
G A Butler
G A Swinbank
Garry Moss
George Scott
H A McWilliams
H Palmer
H Spiller
Henry Spiller
Hugo Palmer
I Mohammed
I Semple
J Akehurst
J D Bethell
J G Portman
J Hetherton
J Howard Johnson
J Hughes
J J Quinn
J L Eyre
J Noseda
J Pearce
J Ryan
J W Mullins
J W Unett
K A Ryan
K Dalgleish
K R Burke
Kevin Frost
L McAteer
L Smyth
M A Jarvis
M Al Zarooni
M D I Usher
M Dods
M E Rimmer
M G Quinlan
M Johnston
M Murphy
M P Tregoning
M R Channon
M S Tuck
M Walford
Mark Gillard
Micky Hammond
Miss Gay Kelleway
Miss J R Tooth
Miss Jo Crowley
Miss L A Perratt
Mrs H S Main
Mrs I G-Leveque
Mrs K Burke
Mrs K Walton
Mrs L C Jewell
Mrs L J Mongan
Mrs L Stubbs
Mrs L Williamson
Mrs Marjorie Fife
Mrs S A Watt
N Wilson
O Stevens
Ollie Pears
P C Haslam
P Charalambous
P D Evans
P Hedger
P J Makin
P M Phelan
P McCreery
P R Chamings
P W Chapple-Hyam
Pat Eddery
Pat Morris
Patrick Morris
R A Fahey
R A Teal
R Brisland
R Curtis
R D Wylie
R Eddery
R G Fell
R Hannon
R Johnson
R M H Cowell
R M Whitaker
Richard Hannon
Richard Spencer
Robyn Brisland
S A Callaghan
S Dixon
S Durack
S Kirk
Sir H R A Cecil
Sir Michael Stoute
Stef Higgins
T B Coles
T D Barron
T D Walford
Tom Dascombe
W G Harrison
W G M Turner
W J Haggas
W J Knight
W J Musson
W Stone

What is a Good Price ?

I made the mistake or my partner made the mistake of arranging a lunch date with another couple at our home on the day of the England v Sweden quarter final. Luckily the guy was a football fan so we peeled off at 3pm for the football minus the cigars and snooker or is it billiards that gentlemen play?.
When we returned to announce that England were through to the semi finals which we never would have managed if it was not for Brexit, my partner got rather annoyed that I had not put that fiver on for her when England were 8/1 earlier in the competition, lamenting the fact that they were now around 11/4. Her friend mentioned that when they go racing she never backs the favourite which is always about 2/1, its just not worth it. Suddenly I saw an opportunity for some basic betting tuition and could not resist a quick insight into the error of her statement. I pointed out that if I was to offer her 2/1 on it not raining tomorrow (we are in the middle of a heat wave extending for at least another week)would she not rush to the cash till ?. The answer was no because for the amount she puts on it is not worth having a pound on to win two. I pointed out that her betting strategy is being governed by how much she might win and not what chance does she think she has of winning compared to the odds I am offering. I was expecting or hoping for a light bulb moment but it wasn’t to be, she still could not even bring herself to admit that it made perfect sense and maybe she should bet according to chance and not how much she may win.

What astounded me about this was here we had an educated person who simply could not grasp or maybe accept that her perspective was totally wrong. Her point of view is that she takes x pounds to the races and is prepared to lose it as part of the days cost. I would suggest that in the long run she is aiming to lose it. Part of the problem is that at school we are not taught to think in terms of chance and probabilities. We come away with a ‘it will happen’ or ‘it wont happen’ view of the world. Politicians suffer from this as much as anyone as highlighted in the excellent book Superforcasting by Philip Tetlock and Dan Gardner.

For four years I ran the UK’s most successful tipping line in terms of level stake profit and as voted by The Secret Betting club. During this period I told everyone to have more on all the odds on shots. I made this suggestion because they made each year between 12 and 20 points profit and furthermore being odds on a bookmaker was likely to take more. I suggested that this boost would pay for the service and hopefully a few changed their perspective on odds on shots as my suggestion was prompted by a complaint after an odds on shot got beat.

In my view there are three approaches to price. You are either good at spotting overpriced horses which is why I backed Saxon Warrior at 11/4 yesterday, beaten a head. The second approach is that you have an algorithmic based approach of producing an oddsline or the third is that you have created a model or system that you are confident consistently underbets the selections and you are also confident that this is unlikely to change.

Adopt one of them but forget about how much you are likely to win.

Sartin and UK Sectionals

In the mid 1980’s the Sartin methodology began to gather a lot of attention across the pond with both punters and journalists. The method revolves around essentially finding meaning within the rich source of sectional data available within US racing. Here in the UK we are only just starting to scratch the surface with Turftrax and Total Performance Data more recently starting to cover tracks.
The Sartin method initially has a focus on the four data items.

1st fraction
2nd fraction
final fraction
X factor

X factor is a calculation based on 1st fraction and final fraction but let’s not worry about that for now.

This article is not going to be pure Sartin but rather a quick look at sectional calls and how predictive they might be, having said that I will take a look at the X factor number although it is worth bearing in mind that Sartin does all his calculations using horse speed in feet per second. I would need to be a bit more confident about the measurements in the data before doing conversions but using actual times might still be informative.

I decided to look at a subset of data namely Wolverhampton 7f races. The approach centred around compiling averages or pars for first fraction, second fraction and final fraction calls. The Wolves 7f data alas starts at 4f which meant i decide to make the first call 4f, the second call 2f and the final call time obviously 2f to the finish. So just to recap we have a first section of 3f a mid section of 2f and a final section of 2f. These averages were based on horses winning races or getting beaten a head or less.

Now the first check I made was how did these winners do next time out if they were above par on the first section and above par on the final section. I am focusing on these sections as Sartin’s factor X revolves around them. Now I would expect them to do pretty badly being above par in both sections. By the way there is no allowance for class or conditions here yet, its very rough and ready.

Bets 16 PL +14.95 to BFSP

Now let’s consider those that ran below par for the first section and above par for the last.

Bets 50 PL +3.58 to BFSP

How about above par for the first section but below par for the last.

Bets 46 PL -9.77 to BFSP

Finally below par in both sections.

Bets 21 PL -18.05

Finally Sartin’s X factor is calculate by the simple formula

(1st fraction + final fraction) / 2

Calculating Pars for factor X and then a ratio for each winning horse via

ratio = horse X factor / Par X factor

We have for a ratio below 1

71 bets PL -14.47 to BFSP

For a ratio above 1

62 bets PL +5.18

The message, if there is one to be had, from the above figures, is that horses that have had tough races on the clock do worse next time out whereas winners who have had easy races on the clock fair better. This is just a hypothesis and would need testing on larger data.

But wait a minute, maybe we are just looking at the wrong fractions. I mean Tom Brohamer states that when a horse is top on the mid fraction and top on the final fraction it is time to loosen the betting belt. So how do those horses that win and beat par on these last two sections actually do next time out.

Bets 24 PL +11.14 to BFSP

None of this is pure Sartin or Brohamer but it does perhaps demonstrate that there is plenty of new rich veins of data coming online and those that ignore it need to be sectioned.

Field Size as a Short Cut

Hugh Taylor had a nice winner yesterday in Buccaneers Vault at 9/1 EP. Here is what he had to say about the horse.

“Dropped in from his wide draw at York, he was last turning for home, but made smooth headway towards the far rail in the straight. The race unfolded up the centre of the track, however, and although he pulled well clear of those who raced close to him, he was unable to land a blow.That was enough to suggest he’s in good form, and the return to 6f will suit”

Looking at the race on video his account is pretty much accurate and was there for all to see but not everybody saw it. Hugh has admitted that the cornerstone of his methods is looking for horses that ran better than the general public might interpret and therefore go off at bigger odds next time than they should. An approach everyone should try to emulate, but not everyone has the time of day to study videos of all yesterdays racing and make notes/alerts.

One way of short cutting the practice is to specialise in a particular distance. If you have a favoured area, say sprints then obviously choose that area. If you have another angle like pace bias over 8f then choose 8f races. This is an approach being used in an experiment currently being conducted by a group.

There may however be an additional filter which can be adopted to reduce the workload without reducing the accuracy and that is previous run field size. below is a list of the AE values for runners in handicaps having run in a handicap previously. The AE’s are constructed by number of race runners in previous race and the displayed values are for at least 100 sample races.

4 ran 1.01981618
5 ran 1.012712218
6 ran 0.954235887
7 ran 1.013166246
8 ran 0.944577488
9 ran 1.025891188
10 ran 1.01479714
11 ran 1.009386069
12 ran 0.999026866
13 ran 1.001438238
14 ran 1.029168591
15 ran 1.041550091
16 ran 1.016391039
17 ran 1.034566678
18 ran 0.971012869
19 ran 1.1053776
20 ran 1.093988019

What the graph shows is that runners LTO in 6 runner races have next time out an AE of just over 0.94. Anything above 1.0 is a sign that the public underbetting and that the horses are going off bigger than they should. The data is from 2009 to 2013 and shows that if we specialise in our video watching on runners of 14 or more we will not compromise our note taking value, perhaps even improve it, but we will cut down the number of races we have to study. There is a lot more going off in a large field as Mr Taylor discovered to his benefit.

Not everyone likes big fields to bet in but you should be studying them after the race.

Raw Sectionals

Sectional data provided by TPDZone is now available on ATR and it is providing a rich vein of analysis and fascination. Simon Rowlands is the guru on this topic but after some conversations with him it struck me that there is another way to look at the data which may prove interesting. The ATR approach is to look at sectional times, in particular the final section, as a ratio of the rest of the race. This proves a valuable insight into how energy was used through a race and and perhaps who won/lost because of or in spite of energy use. The problem is that it does not readily allow you to answer questions like who will lead between horse A who led at Wolves last time and horse B who led at Lingfield. The topology of the courses are so different that raw times run over the first part would be meaningless but times relative to actual PARS for that section and course might show that one has a better chance.
In addition assessing the class of horses is difficult via ATR, at a glance you cannot see whether horse A has just run in a class 6 but has posted a class 4 or 5 run.
To address this I compiled PAR times for each
course/sectional/going/class/distance
I then looked at a recent performance and tweeted that the winner compared to these PARS looked about class 4, the grade it was running in. My next step was to see if I could find a runner who had posted something above class for these pars. I did not have to look far. The first race I looked at over 7f at Wolves showed the class 6 runners leading through the first fraction in a slightly sub class 4 speed. Now given that the first 3 through this gate also finished in the first 3 suggested they may well be above class 6. Only one has run since winning ‘easily’ in class 6. The other two are yet to run. Comment below if you would like to know their names. Sorry to be a tease but I do get fed up with writing and getting little or no feedback.

Does a Good Big Un Beat a Good Little Un

There was an interesting post which I only picked up after the Derby from Simon Rowlands regarding the stride length of the Derby winner pre Derby run. At around 26+ it is very high, only 6.7% of horses have registered a stride length in excess of 26. There would have been two ways of looking at this pre Derby, firstly does it have any significance for him staying but perhaps more importantly will he handle the track. Many would have assumed no based on such a high stride length as its generally thought that the undulations of Epsom do not suit the big strider. This set me pondering over the TPD data and whether stride length as an indicator of size has any bearing on track preference. After digging around I felt that there is simply too little data yet to really start making predictions but what I felt I could do at this early stage in the emerging life cycle of TPD data is whether a big horse holds any betting advantage over small ones.

Calculating the average stride length of horses by track/distance/going/class then enabled me to compile a list of horses that posted a placed run that was greater than the average for the race they were running in. These would be deemed large horses although clearly those just above average are more average than large. Similarly those below average were deemed small.

How would we have got along betting them blindly in their following races.

Large horses produced 2783 bets and a ROI of -4.73% to BFSP
Small horses produced 3179 bets and a ROI of -8.9% to BFSP

This difference was more pronounced when looking at bet runs only in handicappers

Large horses ROI -1.73%
Small horses ROI -8.36%

That’s a huge difference if it holds up and one wonders if handicapped of the future may allocate penalties based on horse size ?.

Superforecasters

What makes a good forecaster, be it horse racing, political predictions, social movements or perhaps currency fluctuations ?. We probably all have an opinion on this one. Some would say intelligence, maybe IQ, they would be wrong. Being smart is no disadvantage but it is not the main driver behind the super forecasters out there. Perhaps it’s the men and women on TV ?. Almost certainly not, they are selected on the basis of how much air time they can accurately consume. The book I have almost finished attempts to shine a more objective light on what makes people good forecasters and seeks out those in the general public that fall into the category. Superforecasting: The Art and Science of Prediction.

The people in charge of this project simply advertised for volunteers (actually they got some gift vouchers at the end of the year) to become subjects in an experiment designed to find out who could become accurate forecasters and most important why they had such traits. Once the individuals had been tested before selection they were assigned periodically questions such as will the left or right party win the next Honduras election ?. What are the chances of Italy leaving the EU or defaulting on their debt. Members had to assign confidence levels to their answers and were allowed as time progressed to update their answers. Interestingly people who were diligent at updating tended to be the best forecasters when their objectively based scores were compiled.

It is a must read for anyone involved in forecasting and one of the most interesting points for me was when after a year and before they knew the ranking of forecasters, the people running the experiment decided to run groups. They randomly compiled groups to operate even thought they knew the dangers of group think and group fallout. Despite this fear the groups performed better than individuals and later when they compiled groups of super forecasters they too performed better than individual super forecasters, something I have found myself.

So what qualities make up a super forecaster and do they apply to horse betting ?.

1. Cautious – Nothing is certain, they are able to think in terms of percentages
2. Humble – Reality is infinitely complex
3. Nondeterministic – What happens is not meant to be and does not have to happen
4. Actively open minded – Beliefs are hypothesis to be tested, not treasures to be protected
5. Intelligent and Knowledgable With a Need For Cognition – Intellectually curious, enjoy puzzles.
6. Reflective – Introspective and self critical
7. Numerate – Comfortable with number

Within their forecasting they tend to be

8. Pragmatic – Not wedded to any ideas of agenda
9. Analytical – Capable fo stepping back and considering other views
10. Dragon Fly Eyed – Value a wide range of views which they then synthesize
11.Thoughtful updaters – When facts change they change their minds
12. Good Intuitive Psychologists – Aware of checking for personal biases

In their work they tend to be

13. Growth Mindset – Believe its possible to get better
14. Grit – Determined to keep at it however long it takes

I am sure you will tick a few of those as supremely relevant to horse betting. At the moment I am running a similar forecasting group in horse betting. Each member is assigned a specialist distance eg 5f and is asked to make selections to the group based on morning value prices. I hope to report back on this later in the year. By the way we have one vacancy in the group to cover 6f races.