A Brave New Betting World

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So you are off to the races on Saturday. It’s 2025 and your smart friend has invited you. You are travelling in his fully auto piloted hydrogen car, the two of you having a beer in the back seat while the car finds its way to Ascot. Your friend is excited because he has the 2/1 fav ante post at 5/1 to win 5,000 WeChatDig the current goto digital currency backed up by Blockchain technology. He is pissed off though because as a pro gambler Blockchain’s logging of every transaction in a Government accessible log means the threat of taxing the winnings of serious gamblers is now almost ready to go live. This is infuriating as he has not yet fully paid off the final payment on his chipped brain implant that enables him personal neural access via 5G to any information and data that desktop Google used to provide, an invaluable facility for plugging in late race information, via his own brain, into his own personal deep learning Neural Network which runs on his Quantum Computer phone. But you are not worried about all this, you are just pissed off that you are half way to Ascot and you forgot to pick up a copy of the Racing Post from the newsagent.

Which of these two do you feel most aligned with, because if it’s the Racing Post reader then you are going to pretty soon feel like you wandered into one of Chevley Park’s stud farm boxes and got mistaken for a mare. The betting landscape is changing although you would not think so listening to the popular media still driven by a fool in the ring and droves of writers/tipsters ‘burning the midnight oil’. I am struggling to think of one media contributor has input which reflects the current direction of successful pro betting.

Is it all gloom?, if you are not part of the Artificial Intelligence approach to sports betting are you just one of the monkeys trying to type a Shakespeare play ?. Probably, if we are talking meaningful amounts of money here, after all we have to remember that the small bet turnover, large bet size avenue has been closed down by bookmakers.

So how best to re skill and create the best possible chance of punting success (please don’t suggest getting your Racing Post delivered). The first thing I would do is learn to code. For some people this is unattainable but for others a few months on line could get you up and running especially if the course was geared towards the application to sports betting. The second approach is to gain access to AI through a more intuitive, easy to use interface. On both of these points I have been considering offering some input. The former would be in the shape of a one or two day course in Python programming covering topics and skills that I use on a daily basis. On the latter I have made some progress on a prototype. You can access a short video demo’ at https://www.youtube.com/watch?v=EEqxwJqjx7g

You may think what I am describing is alarmist, a fantasy even if you cherry pick the most likely points of the message. Well do not expect to read something similar in the popular media. Their job is to protect their sponsors by keeping you ill informed and locked into a way of betting that can only lead to one result. You can choose to know a lot about Racing or a lot about data, I know which one I would choose.

Using GroupKFold to access races in Python

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Apologies to those of you not interested in Machine Learning and Python but I wanted to get this out there so that if my code is incorrect (it appears to work) or perhaps there is a more efficient approach, then some one will kindly put me right.

When you build a ML model you can use a technique called K fold cross validation. It simply means splitting you data into, as an example, 5 partitions, and then training your model on partition 1 to 4 and testing it on partition 5. This happens five times with each times having a different test partition eg- train on partition 1,2,3 and 5 but test on 4.

Now the problem is that with race data you really want to split on whole race boundaries. You do not want, for example, half the field from a given race ending up in the train data and half in the test data.

To handle this we can use GroupKFold

The idea behind GroupKFold is that the data is first grouped on an identifier, in our case the RaceId and then the folds or partitions as I called them are created on the groups. Below is the small sample data, followed by the code.

RaceId,Track,Horse,Dlto,Penulto,Age,Rating,Bfsp,FinPos
1,Nemarket,Mill Reef,13,56,4,85.5,3.5,1
1,Newmarket,Kingston,34,23,4,76.2,7.5,0
1,Newmarket,Nijinsky,27,,4,95,10.2,0
2,Sandown,Red Rum,98,23,5,90,5.4,0
2,Sandown,Henbit,101,54,4,85,20.4,1
2,Sandown,Troy,22,32,4,98,1.9,0
2,Sandown,Wollow,36,23,4,87,2.2,0
2,Sandown,The Minstrel,44,67,4,88,5.8,0
2,Sandown,Try My Best,34,53,4,82,3.2,0
2,Sandown,Tromos,62,73,4,65,6.2,0
3,Bath,Sea Pgeon,47,35,4,81,20.0,1
3,Bath,Monksfield,59,5,4,78,11.4,0
3,Bath,Night Nurse,12,15,6,62,4.2,0
3,Bath,Birds Nest,14,78,5,53,3.2,0
4,York,Frankel,25,17,4,100,1.9,1
4,York,Brigadier Gerard,23,67,3,90,2.9,0
4,York,Dubai Millenium,89,23,4,85,4.8,0
5,York,Posse,23,56,4,82,5.6,0
5,York,El Gran Senor,32,21,4,100,2.3,1
5,York,Radetsky,67,21,7,70,12.4,0

The code

import numpy as np
import pandas as pd

from sklearn.ensemble import RandomForestClassifier

m = RandomForestClassifier()

from sklearn.model_selection import GroupKFold

## read in the data ##
df = pd.read_csv(‘testcsv.csv’)

## take a look at the data ##
print (“The input data”)
print (df)

## keep it simple remove rows with missing data ##
df = df.dropna(axis=0)

print (“df”)
print (df)

## create group indices ##
groups = df[‘RaceId’]

## take a look at what this produces
print (“The groups indices”)
print (groups)

## pull out the fields required for the model ##
X = df[[‘Dlto’, ‘Penulto’]]
y = df[[‘FinPos’]]

print (“y”)
print (y)

## create instance of groupKFold set n_splits as required ##
gkf = GroupKFold(n_splits=2)

## loop through n_splits times createing index’s into the arrays for the allocated rows ##
for train_index, test_index in gkf.split(X, y, groups=groups):

print(“TRAIN:”, train_index, “TEST:”, test_index)

X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]
print (“— X train test —-“)
print(X_train, X_test)
print (“— Y train test—“)
print (y_train, y_test)

## train a model on X_train using y_train as target features ##
y_train = np.ravel(y_train)
model = m.fit(X_train,y_train)

## test model on X_test ##
preds = model.predict_proba(X_test)
print (‘Predictions’)
print (preds)

print (“————–Split done———–“)

NOTE – When viewing the output you are probably expecting the dropna command to physically remove row number 2 as it contains a NaN, it certainly does not appear when the df is printed out. But if you examine the indices and the group links into the df you will see that it still links in to row 3 for Red Rum. In other words it does not shuffle down the df entries when executing a dropna. I was fooled by this for a while. Thanks to @amuellerml for nudging me in this direction

A Brief Look Back

My first exposure to horse betting could have been a disaster or a blessing depend on where you end up 40 years down the line. I met a friend at College back in 1975 and as an ex pro footballer he had already caught the bug via long afternoons after training with nothing to do. My first bet was 3 x dollar doubles and a dollar treble. I think it was shillings actually but we called then dollar doubles for some unknown reason., Yes you guessed it, they all won. I went into that 1976/77 winter hoping The Minstrel would win everything and his stable companion Cloonlara would do likewise in the filly classics. Cloonlara never trained on but the Minstrel won the Derby.

I remember being fascinated by the wonderful names of those 2yos, Tachypous still sticks in my mind and also the wonderful secret code of race reading appealed to my psyche. What did nearest finish mean or hld up.

I am a great believer in the idea that your the people you hang out with will greatly influence your betting and although me and my mate had some great success along with failures I had begun my betting career with a strong bias against handicaps which I now realise was a mistake. My belief that there was no point arguing with the handicapper who’s full time job was to make all the horse’s theoretically dead heat was incorrect but it took me a few years to realise this.

Years passed with mainly losing years although my pre disposition to be non addictive and the thought I put into bets even from an unsound perspective generally meant I lost small. The media had already polluted my thinking, straight jacketing my outlook into finding winners instead of finding value. I remember a line from Van De Wheil who some of you may remember writing in the Raceform Handicap book readers letters page. “There is a winner in every race but is there a bet” or words to that effect. I think VDW should have spelt that out a little more clearly instead of expecting everyone to understand what he meant.

I have always had an interest in crowd betting or the wisdom of crowds perhaps due to my first really great betting year. My buddy from college and I teamed up in 1984 and for a flat season bet together with a no bet unless we agree rule. It probably helped that he was in Brecon Wales and a new wet behind the ears bookmaker had opened who thought it was a safe idea to bet in the morning to Sporting Life forecast SP’s…..DOH!. By the end of the season he offered to pay us by giving us his car which we refused in favour of installments. Late in the season we met up in Rotherham where by then I was lecturing at a college in Computer Science and split up the money over the table in an Italian restaurant. I still think this approach has potential for a small group of punters perhaps with bet suggestions put forward and then annonymous voting on bet or no bet.

By the late 80’s I was lecturing in Computer Science at De Montfort University, in fact before that in 1987 I had spent many months travelling to London to spend hours in the Colingdale newspaper library researching past copies of the Sporting Life for a wonderfully naive system I had put together. I recall at the time thinking that if this data could only be in machine readable form my computing skills would allow me to unpick it. I did not realise that a couple of guys would implement this in the mid 90’s and revolutionize the way we look at betting. The system had a poor year when I took it into the field and if it was not for Mystiko winning the Guineas that year (a non system bet) I would have been out of the housing market for a good while.

I was modest on the betting front during the 90’s as I was still lecturing at the University and I had opened a restaurant with my partner but in the late 90’s I discovered the first of probably two life changing decisions. The first was RSB a computer package that allowed one to interrogate dozens of horse racing variables and build systems based around them. For me and others betting had been akin to landing in a foreign town and then trying to find the railway station whilst having your nose constantly 2 inches from the ground. RSB was like being handed Google Maps. For sure there was a lot of backfitting suicide conducted by many of its users. Never have so many jockeys on certain course’s on soft ground on a Tuesday afternoon only in races with less than 10 runners been backed by hopeful naive punters but when taken with education from other sources RSB allowed a birds eye view of what really had value in Race betting. That education I mentioned came from my second smart move.

Reading a Russell Clarke article one day I discovered a place for serious minded bettors to exchange ideas and get help. It was a monthly magazine called SmartSig and an accompanying email forum for discussion. There were plenty of smart punters on there and it was through SmartSig that I began to learn rudimentary language and rules that pro bettors work to and not the misplaced find a winner mentality of the mass media. I contributed a few articles to SmartSig and attended some of the get together’s such as the lecture we all attended on Bayes theorem given by Professor Speigelhalter in Oxford.

During the noughties I gradually moved away from Lecturing and took up full time betting. This had its own complications to overcome. I also had taken over running SmartSig after the retirement of the owner Stef and an aborted relaunch by a purchaser. Renamed as SmarterSig it is still running in a diluted form to this day. During this period I also organised and ran two sports betting conferences which as far as I know were the first of its kind in the UK, certainly outside academic circles.

It is not clear when you start betting with bookmakers that if you win they will refuse your bets but I quickly discovered this to be the case. To navigate around the problem I moved from online to betting shops in the High street, taking up camp in a Costa coffe shop from 9am to 12 noon, popping out occasionaly to take a price on a horse. I was having around 3,500 bets per year and averaging around 14% on turnover profit. I soon discovered however that shops ban you quicker than online and I was soon struggling to get bets on. At this point I hired a team od students from the University, paying them £5 per hour to do the running. I calculated that I was still well ahead despite the employee overhead. Two problems arose with this tactic. The students got banned and Costa became suspicious that guys rapidly leaving and returning with wads of cash while I sat orchestrating over cups of coffee looked suspiciously like a drug operation and Costa Coffee came in line with Ladbrokes et al and banned me. To this day if you go in a UK Costa Coffee shop and try to access a betting site through there wifi you will be declined. Like to think I have contributed something to the comfort of Costa Coffee drinkers

Things had to change and I decided after a chat with a guy I knew from SmartSig who ran a tipping service that the best course would be to run a service myself and let the client manage the headache of getting on. I proofed all bets to a reputable proofing service and for 3 out of 4 years actually won their accolade of top overall tipster which included all sports not just horse racing. Part of what helped me win those awards was my approach was somewhat different to other tipsters who had no doubt been brought up on the false notion that selectivity, a few bet per month was the sign of a sound tipster. I was a volume bettor by their standards and the evaluation formula used on the proofing service favoured volume. This idea of volume was to be pivotal in my next step.

After a health scare in 2013 I decided to shelve the tipping line. Maybe I had underestimated how stressful having clients can be although at the time it did not seem to bother me. My replacement however was already in the pipeline, Starbucks, no only joking. I decided that Betfair betting was the only way to go but liquidity in the morning where I had been earning my 14 or so percent was thin so I needed to replace 3,500 bets and 14% with many many more bets and perhaps 2% ROI. This would mean bet selection and placement automation. You cannot physically place the number of bets/lays I was anticipating with a manual approach or at least not without a serious Vitamin D deficiency. I did my own coding in a mixture of Perl and Python and for the last 5 years or so have been betting solely on Betfair to my own horse ratings.

If you have read any of my other blogs entries you will know that even losing punters should be migrating to Betfair where they will lose less. What does the future hold/, well I have a keen interest in Machine Learning and its application to betting but I guess ultimately everything depends on where this sport is heading. At the moment people are not being replaced in the area of horse betting and we are losing market share. I think many of the current promotion horses being ridden by people are well intentioned and certainly welcomed by me eg sectional times. The real issue however is overall profile of horse betting. Every element of Racing is covered with grace and respect but you will have noticed that when it comes to betting a fool is rolled out to talk about it. Usually the fool in the ring is the biggest culprit, the fools on bookmaker adverts are to be expected. We need betting to be viewed in a more favourable light, one which mirrors stock marketing investing. This first of all requires allowing people to bet and certainly not protecting and covering up those that don’t allow punters to pace bets within reasonable risk managed quantities. Ultimately though we need to push the exchanges as the only place in town for all punters who have ambitions of winning or at the very least losing more slowly.

Machine Learning What Should We Predict

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I have been playing around with an ML model today and the purpose of this post is to hopefully promote some discussion about potential target fields.

When you feed data to an ML algorithm you need to define input features eg is the horse a course winner along with a feature that the inputs have to predict. It is with the latter that I was running a simple experiment. I ran a model on four different target features to get a feel if one stood out from the others. The four varieties were as follows.

1. Good of old fashioned 1 if the horse won and 0 if the horse lost
2. 1 if the horse won or came second 0 otherwise
3. 1 if the horse won or came second or finished 3rd in a race with more than 8 runners
4. 1 if the horse finished in the first 4 and out ran its odds 0 otherwise

In the last case outran its odds simply meant that the horse was positionally longer in the odds than its finishing position. For example a horse finishing 2nd but went off fav would be a 0 whereas a horse finishing 4th and being 5th in the betting gets a 1

I tested for both how the top rated performed and how simply backing horses above a threshold performed. This is a quick and dirty measure but the objective is to foster some discussion hopefully on other measures for target variables.

Option 1 produced

Toprated 7998 bets 1323 wins PL after comm’ +514pts ROI +6.42% Varpl +40.6

Option 2 produced

8012 bets 1328 wins PL +265.3 ROI 3.3% Varpl +43.9

Option 3 produced

8028 Bets 1365 wins PL +413 ROI +5.15% Varpl +91.5

Option 4 produced

8056 bets 1201 wins PL +235.9 ROI +2.92% Varpl + 67.79

When it came to simply backing any horse above a certain threshold on the ratings option 3 performed best followed by option 2 and then option 1 and finally option 4

The reason for trying the various options is that unbalanced data can effect the performance of ML algorithms although the Gradient Boosting Tree based algorithm I am using suffers least. An unbalanced data set simply means fewer 1’s than 0’s. The closer you get to 50-50 on the target 1’s and 0’s the more balanced the data is. Clearly adding placed runs increases the balance.

The question however is are there other options worth throwing at the algorithm. I would be happy to receive any suggestions on other possible target fields in the comments section.

Pinatubo Gift Horse or Bet Trap

You could not escape Pinatubo’s stunning win in Ireland recently when he demolished the opposition. His leg speed made Too Dran Hot look like a candidate for next years Cheltenham Gold Cup. Nor could you perhaps ignore the avalanche of bookmaker offers for next year. I think he is 5/4 for the Guineas as I write. A recent article was posted on Twitter citing the dangers and gloom around these 2yo hotpots. For sure the Tromos, Arazi and Celtic Swings of the past are fresh still in our minds but is this because we harbour gloom more than we do success. When was the last time anyone cited the great 2yo’s that went on to succeed as 3yos. Also what do we mean by succeed, well as a punter we want to know if they are a trap or a decent betting proposition.

I had a quick look back at 2yos that had logged an official rating greater than 115. Seemed like a starting point given that Too Darn Hot logged 116. Although my data go’s back to 1999 I suspect the ratings may have changed in scale along the way as I have no qualifiers prior to 2008. The total number of bets on these horses as 3yos amounted to 50 with PL of +29.6 to Bookmaker SP. Pretty impressive especially as the Betfair SP would be a good deal better. The AE value by the way was 1.46

Here are this years bets (English races only)

11/05/2019 Anthony Van Dyck (IRE) 1 2
16/05/2019 Too Darn Hot 2 1
01/06/2019 Anthony Van Dyck (IRE) 1 6.5
18/06/2019 Too Darn Hot 3 2
21/06/2019 Pretty Pollyanna 7 10
13/07/2019 Pretty Pollyanna 4 8
27/07/2019 Anthony Van Dyck (IRE) 10 7
31/07/2019 Too Darn Hot 1 1
24/08/2019 Pretty Pollyanna 2 0.9

What if we extend the range a little to say greater than 110.

314 bets PL +0.41 AE = 1.34

Again not too bad and clearly shorter priced horses did well.

Finally 2yo’s that were rated above 100 produced a further reduction in overall performance.

2364 bets PL -691.27 AE = 1.04

It would appear from the above that great 2yo records are not something to be feared and somehow we have to get over Tromos even if you did back him for the Guineas

Betfair It’s a No Brainer

Some time ago I wrote an article which described why all punters would be better off betting on Betfair than wasting their time betting with bookmakers. You can re read the article here https://markatsmartersig.wordpress.com/2018/10/05/educating-trevor-2/

What surprised me about this article is that no one challenged me with the argument that my data was not based on intelligent selections but merely looking at all horses. To rectify this and hopefully convince a few more people that Betfair is the only game in town I decided to take a look at how our newspaper tipsters had faired with their naps during 2019 based on best early morning price at 10am versus Betfair SP. The bookmakers used for checking best 10am price were as follows

Bet365,Sky Bet,Ladbrokes,William Hill,Marathon Bet,Betfair Sportsbook,Paddy Power,Unibet,Coral,Betfred,Boylesports,Totesport,Black Type,Betstars,Betway,BetBright,10Bet,Sportingbet,188Bet,888sport,Bet Victor,Sportpesa,Spreadex

As you can see I have been very generous in the sense that no punter is going to have accounts open whilst taking top prices amongst all those bookmakers, nevertheless lets see how our paper pundits faired against BFSP. As usual for simplicity I have considered only races where there were no NR’s between 10am and the off.

Backing all selections to best AM price produced the following results across all tipster naps.

5444 Bets PL -299.67 pts ROI -5.5%

Now backing the same selections to BFSP we have

5444 bets PL -277.35 pts ROI -5.09$ after commission deducted.

Even with the world of bookmakers at their disposal our newspaper tipsters could not beat BFSP and in the long run neither will you because even if you are sharper than our tipsters your accounts will be closed down quicker than a 5f sprint on fast ground at Epsom.

If there is interest I will later drill down into individual tipsters to see if there are any patterns. One area of interest may be the profile of the tipster, perhaps lower profile tipsters working for the Carlisle Evening Gazzette do better than Racing Post ratings. For example Paul Kealy has posted a profit 36.45 to BFSP after commission

The Arc 2019

I rarely tip on this blog but if you scroll back you see that my record is pretty decent. I have also mentioned that I think punters would do better if they did nothing else but formulate a perspective on certain races and use this each year. This was the reason I backed Too Darn Hot in the Sussex at 10/1 and it is the reason I wish to talk about the coming Arc in Paris.

The Arc in my opinion is, ground allowing, a 12f race for 10f/12f horses in contrast to 12f/14f horses of which there have been many top class fancied runners in the past. I have had many winning bets in this race based on this supposition. By contrast I abandoned the Ledger many years ago due to my lack of perspective.

This year I am going to commit Racing heresy by suggesting that Enable is just too darn low in the betting. She has won two Arcs from pole stall and track positions but more importantly I agree with the handicapper. She is scrambling home lately from horses that in recent pantheon of racing I would not consider to be the highest of class.

If I am right who is over priced for this race. Japan is the second favourite and although I have been wondering when Japan (the country) would eventually win the Arc, I am not convinced that its namesake will do that this year. The final sections of the International seem to back up the eye in that Japan was scrambling home ahead of a whole group of horses. You could argue that Mark Johnsons horse might have won if he had got a better run. Sure Japan will benefit form going back to 12f but I would want more tactical speed from my Arc selection, and its this tactical 10f speed where we find my selection.

Scottass has been pretty much overlooked due to his form being abroad and the lack of runs from the placed horses but the clock paints a picture. The overall time of the race was very good, over 5 secs below standard and his final 400 metres was 23.09 secs. What does this mean, well on the same day Pellegrine won a G3 in a slower overall time with a final 300m of 19.2 secs compared to a final 300m for Sottass of 17.72 sec. On similar ground Study of Man clocked 24.7 for his final 400m in last years French Derby. Finally again on similar ground but at Longchamp, who’s terrain in the straight looked so similar to Chantilly that I had to double check they did not run Enables second Arc at Chantilly, Enable clocked 23.9 for the final 400 metres.

Dont misunderstand, Enable is an obvious player. her final 400 on GS at the Chantilly Arc in 2017 clocked an impressive 24.2 but none of this seems warrant 4/5 with bookmakers and 8/1 Sottsass. She is good but she is no Treve.

I have backed him EW at 8/1 on one of my rare excursions to a bookmaker where once again I found that £200 EW at 8/1 is far too big a bet for the big High st bookmakers. Once again making a mockery of the TV lies about monster laid bets.

Good luck with whatever you fancy I hope at least you are well drawn

Why Betting Systems Do Work

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Peter Thomas had an article in the Racing Post recently which had a general mocking outlook upon racing systems and those that pursue them. This is the same guy who in a previous articles some years ago described Nick Mordin as mad as a box of frogs or words to that effect. What struck me about the recent article

https://www.racingpost.com/news/racing-revealed/some-old-wives-tales-that-have-perplexed-punters-since-the-beginning-of-betting/367491

was firstly, why the hell am I reading the RP, a rag who’s biased reporting has resulted in me not picking it up in quite a number of years but more importantly I was struck how little the author seemed to know about systems. I come across this quite a lot, if a person does not understand something they decide not to increase their understanding but instead justify it by arguing for its worthlessness. Psychologist will have a term for this but it escapes me at the moment.

Do betting systems work and even if they don’t is pursuing them a worthwhile task?. The last part of that sentence might seem to demand an obvious NO!, but I will endeavor to convince you otherwise. Let us start with the first part of the question, are there any successful systems?. We must first define a system. My definition would be a set of rules which if followed to the letter by a number of people would result in all coming to the same decision or selection. Those rules may be simple or perhaps complicated but whichever they may be you and I would end up with the same selections if executed with the same data at the same time. They do not have to produce one bet per race or even one bet in all races but they must produce a profit long term and here is the important bit which Mr Thomas must have overlooked. That profit can be to any pre determined available price including Betfair SP although that price cannot be determined with hindsight.

Nick Mordin, the doyen of system writing, was himself collared on track by Big Mac’ some years ago as Nick promoted his book ‘Winning Without Thinking’. Mac eventually tried to get him to to admit that winning systems were really a myth and asked him to declare one on TV if such a thing did exist. Nick suggested backing all female jocks on the all weather as the betting public were still treating them as grossly inferior to male jockeys and hence sending their mounts of at inflated prices. This was true at the time (I have not checked recently) and is a good example of how being aware or even anticipating a system before it unfolds can be profitable but equally one must be aware of its potential shelf life.

There are benefits to system development beyond the uncovering of a successful system, one of which is the journey itself. Analysing data from a system perspective is a great introductory way to get a feel for the terrain you face as a punter. The amount of loss you need to overcome in order to become profitable and more importantly what factors move you closer even if in isolation or combination they do not quite achieve long term profit. The study will give you a greater feel for the biggest hurdle all punters have to overcome, namely variance. In other words the kind of winning and losing periods once can expect to experience even with a decent looking system.

Personally I stated off analysing systems before moving onto more advanced methods such as Machine Learning. I spent many hours back in the 1980’s in Colingdale Newspaper library pouring over back copies of the Sporting Life to produce a system that was doomed to mediocrity. I remember thinking with my Computer skills if I could only get hold of electronic data. The guys at RSB in the mid 1990’s did exactly that and system investigation moved on for Joe System.

Systems do work, they work in the sense that there are rule based methodical betting methods that produce consistent profits and they work because they give people a valuable apprenticeship in understanding data. Of course there are good and bad methods to developing systems as there are with other more sophisticated approaches but do not be put off the journey by a doubting Thomas.

Marginal Gains in Betting

The concept of marginal gains in sport is probably best epitomised by Sky Sports cycle racing team and David Brailsford. Their attention to detail and the extraction of small marginal gains wherever possible underpinned the road success of UK cyclists. I prefer however the tale of Takeru Kobayashi and the world of speed hot dog eating. The world record for the New York Coney Island hot dog eating contest was 25.125 hot dogs. When hot dogs get measured down to the three decimal places you have to sit up and take the sport seriously, especially when in horse racing we cannot even measure the distance or going of races with any accuracy. Many rotund Americans (is there any other type) had tried and failed to beat the record and when Kobayashi turned up as a rather slim even small outsider he was not taken seriously. Kobayashi had however done his homework and applied the philosophy of marginal gains to his already reasonable speed eating skill. He experimented with eating the dog whole and then in half. He checked out the performance of dipping the buns in water. He tried different water temperatures and even water with oil. Any form of drink is allowed in the competition but no vomiting. He played around with stomach exercises to limit vomiting and impressively all these various experiments were entered in a spread sheet and analysed. When he got to Coney Island those New Yorker’s never stood a chance. In the allotted twelve minutes Kobayashi ate 50 hot dogs, pretty much doubling the previous record.

Bettors can learn a great deal from Kobayashi. Small gains can soon compound to impressive differences in end of year results and as a serious punter you should always be on the look out for any form of improvement. Allow me to give you a concrete example. A few years ago I was betting into Betfair at 1 minute to off time along with Betfair SP. The latter was more of a marker bet to gauge and difference between the two which was quite significant in favour of BFSP. Aware that I wanted to get closer to BFSP but equally aware that increasing bet size may result in cannabalising my own BFSP if I took this route I looked at ways to improve the returns on the one minute bets. First call was checking using weight of money on the price as I made bets. A horse with weight of money at the bet price indicating a drift would result in me placing the bet at one tick higher. The other bets would be placed at the price. Here are the results for 2018.

BFSP bets return +2.9%
Blindly backing at 1 min +1.03%
Backing using weight of money +1.41%

Niggling at the back of mind hwoever was whether weight of money was really the dominant factor in improving the returns. I therefore tried simply placing bets at one tick up regardless of weight of money.

One tick regardless +1.72%

These gains do not sound much but at the level of activity I pursue they add up to serious improvements.

Coney Island here I come

Derby Profile 5

Final stage of our analysis and we need to bring together all the individual AE values for each horse on each of the four features, draw, jockey, pace and last race distance. This analysis has really been a about illustrating a method of evaluating horses. The fact that it happens to be the Derby is not particularly relevant but given it is a high profile race it probably helps to sell the lesson and of course we are all more likely to be familiar with the horses in question and the race.

To get a final AE value for each horse we need to multiply the individual AE values for each horse. In other words we need to multiply a horses

pace AE x Jockey AE x Draw AE x Last race distance AE

With these small samples some AE’s can come out zero dues to no wins which would of course render a horses final AE as zero, a rather unrealistic value. There are only one or two of these so I have used an AE of 0.8 knowing they do not artificially elevate a horse into a high ranking position.

Horse Draw Jock LastDist Pace Total
Sir Dragonet 1.02 0.72 2.04 1.69 2.531
Madhmoon 1.5 1.15 1.28 0.89 1.965
Anthony Van Dyk 1.5 0.72 2.04 0.89 1.960
Broome 1.5 1.15 0.64 1.69 1.865
Norway 1.02 0.72 2.04 0.89 1.333
Hiroshima 0.32 1.15 2.04 1.69 1.268
Bangkok 1.02 1.15 0.64 1.69 1.268
Humanitarian 1.5 1.15 0.64 0.8 0.883
Japan 1.02 0.72 0.64 1.69 0.794
Circus Maximus 1.5 0.72 0.64 0.89 0.615
Line of Duty 0.32 1.15 0.64 1.69 0.398
Telecaster 0.32 1.15 0.64 0.89 0.209
Sovereign 0.32 0.72 0.64 0.89 0.131

The interesting rating is that of Telecaster who looks to have a lot to do from his draw. If he gets lit up as he has in his last two runs he will find the draw and that hill quite taxing.

Let me know who you fancy in the comments section.