Just Think Then Did It (actually)

Just spent a sunny afternoon (that’s dedication for you) data munging and setting up the ML approach to pace comment analysis I outlined in the previous blog post. I was curious to see if an ML approach would categorize horses positions better than my pace program that I already use. To do this I used data from TPD which gives an accurate data source for horse positions within a race. Once I had mapped early position I could designate horses as Leaders. trackers, mid division or hold ups. The next task was to feed this data along with the corresponding race comments, 15844 total lines of data, to an ML analyser after first splitting into train and test splits. The algorithm was asked to learn what category a horse was based on its race comment from the training data. here is an example line or two.

led until over 1f out weakened final furlong,1
tracked leader led over 1f out headed inside final furlong kept on same pace,2

I then asked it to predict the positional number ie 1 to 4, on the comments held out in the remaining test data. It turned out that it did reasonably well with an accuracy of 69.5%. Well I say reasonably well, the truth is I have never put my own pace figures to the test against actual TPD data. This was the next step, checking my pace programs performance against the TPD data.

Across the whole test file my program came in at 68.2% accuracy. Not a huge difference between the ML algorithm but what was very useful about this exercise is that it allowed me to check how my pace program, and of course the figures on the SmarterSig site, do against the various categories of 1 to 4.

Predicting leaders it did OK at 69.6% accuracy
Predicting Trackers it came in at 81.5% accuracy
Predicting Mid div’s it achieved 44.7%
Predicting hold ups it managed 73.9%

This is very useful information as it allows me to reexamine the pace program and potentially fine tune it or possibly jump ship to the ML side of processing. The mid div’s seem to need a bit of TLC although we will always be a hostage to how race comment compilers write up.

Now time for beer before the sun go’s down

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Just Think Then Do It (maybe)

It is a glorious Sunday morning and I am sat in the garden at 8am barefoot and earthing.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3265077/

Some times it best to just stop and think and then when you have done that think a bit more. Of course Nike would like you to just do it but then again increasing their sales is dependant on your compulsion to buy.

While I was thinking I hit upon a rather simple and obvious idea that I had been pondering over the last few days. I produce Pace Figures daily on the SmarterSig web site and even if I say so myself, they are rather good. They should be I have tended them carefully over the last 10 years, tweeking them when ever I spot a miss-reading of a race comment. They form an integral part of my daily betting. I have however been pondering another recent interest of mine which is Machine Learning and sentiment analysis. Sentiment analysis is the adoption of machine learning techniques to analyse text and derive meaning. If you have read my earlier blog on this you will get the picture. What I am now pondering is could a machine learning approach produce better predictions of pace than my hard wired program approach. Furthermore it would also be capable of self updating or learning. For those of you not familiar with ML it will involve feeding the program as many examples of race comments as you can muster along with a an outcome for each comment. this outcome for example could be 1 for held up 2 for tracked and 3 for led or perhaps some other variation. Now the off putting aspect to this idea is that you would need to sit and watch a lot of races in order to really accurately tag each comment with the correct pace position. Now even bare foot in the garden such a task would guarantee a reversal of that Nike philosophy. However the rather obvious occurred to me while I wasn’t ‘just doing it’, the TPD tracking data gives me as accurate a track on race position as you can get. Marry this with race comments plug into the sentiment analysis and potentially thousands of race comments can be accurately machine trained. Once trained the system can then be used to predict future race pace. If TPD extend to all tracks then there would be no need for this and of course there is no guarantee that this will outperform my carefully hand crafted pace figure program but it sure as hell beats ‘just dont do it’.

Sectionals and GDB

Spent a few hours courtesy of TotalPerformanceData looking at sectional times for the various courses they cover. I was working on creating average split times for the various split or gate points as they are called for each track by the winners of the races. So in other words what is the average split times for achieved in 2017 for Wolves 7f winners

Final furlong 12.07368421 secs
2f to 1f 11.87368421
3f to 2f 12.33026316
4f to 3f 12.25657895
5f to 4f 11.46710526
6f to 5f 11.83289474
Start to 6f 16.58157895

The beauty of AW racing is that we do not have as much going variation to contend with although this can be eventually factored in with averages for each going type. Class may also have to be factored to get a very fine level of detail on how fast or slow a race was run at different points.

My initial thought was to go down the same line as others and start trying to determine fast/slow run races and then another idea occurred to me. Why not allow Machine learning to take the strain. We are after all interested in which horses to back next time. With a more manual approach we have to determine what type of race a horse has just run in and then figure out which types of performance are best backed next time.

To do this we could feed a Gradient Descent Boosting ML algorithm or some other ML of choice with a file of the following data.

All 7f races at Wolves

Deviation from split 1 average,dev from split 2 average,…….,
dev from final split average,FinPos next time out

It could be possible that with such data machine learning might do a reasonable job of predicting the best split time runs for next time out results. It would not just be a case of best overall time but also the manner in which it was run.

I must confess to not having thought this through fully just yet, it is very much a work/thought in progress and I welcome views.

Guineas Trials Are They Relevant

Its Guineas week and many a pundit is pouring over the trials run over the last few weeks but are they very helpful from a betting perspective. I decided to take a look at the pound in your pocket effect as I was particularly taken by Saxon Warrior ahead of Saturdays race. He held off a good hold up horse when running prominently in a fast run Racing Post trophy at the back end of last season, but if he runs as promised then he will be another OBrien runner without a prep race.
An AE value is a measure of how under or over bet a factor is. If we took 10 white faced horse where 5 went off at 2.0 and 5 went off at 3.0 BFSP we would expect according to the market to see 4.16666 winners, thats

5 X (1 / 2.0) + 5 X (1 / 3.0)

If say 6 of them won then they are doing better than the market suggests, whereas if say only 2 won then they are under performing in relation to the market.

AE Values above 1.0 (Actual win divided by expected wins) suggest that the market is under-betting them and we want to be with them in the future.

Taking horses that have run in the Guineas since 2009 and dividing them into prep runs for those having run within 55 days and anything above being a no trial runner we have the following AE values

Prep runs AE = 0.75
No prep AE = 1.26

Horse without a prep run have been significantly under bet in previous Guineas

Interestingly pretty much the exact opposite is the case for fillies in the 1,000 guineas. Perhaps trainers are reluctant to push fillies at home so early.

I am not a big fan of race trending but could not resist a quick look at the draw across bot Guineas and the AE’s for high, low and middle came out at

High 1.04
Mid 0.68
Low 1.37

Horse that are of interest this year are

Saxon Warrior Won
Elarqam
Ghaiyyath
Headway
Barraquero
Tip Tie Win
Murello

Short list for the 1k guineas tweeted out before the race

Altyn Orda
Anna Nerium
Billesdon Brook Won BFSP 168.1
Dans Dream
I can Fly
Sarrochi
Sizzling
Soliloquy

Gradient Descent Boosting

The new kid on the block in the world of machine learning is deep learning but the among the shallow learners the method kicking ass in the world of Kaggle is Gradient Descent Boosting, and I have to admit I am a convert.

Up until now I have not quite seen the gains from ML techniques over non ML methods of forming ratings such as AE values. The old adage that data is king and the method will never polish a turd to such an extent that it will shine has never been in doubt to me. Gains form ML techniques have seemed minimal but that may be about to change in my world.

I fed a set of data into a Sklearn Random Forest machine learning algorithm. If you are not familiar with Random Forests they are basically a tree based algorithm but their strength comes from the fact that they form multiple trees on subsections of the input data. Branches are also split on random sub sets of the available fields within the data. This means that a single more dominant feature or field within the data is less likely to swamp the decision making of the tree’s and hence bias the overall model towards one field within the data fields.

Working with BFSP as a model evaluator I checked first the performance of top rated horses using the model on fresh unseen data. This produced after commision

8031 bets PL -33.6 Points ROI -0.41% VarPL -3.96 VarRoi -0.31%

Now creating a Gradient Descent model and applying it to the same data produced

9250 bets (more joint tops) PL +713.7pts ROI +7.7% VarPL +100.5 VarRoi +5.54%

Splitting at roughly the mid point of rating values to split the runners roughly in half produced for the GDB method

33536 bets PL +861 ROI +2.56 %

By contrast for the Random Forest it produced

33155 bets PL -784 pts ROI -2.36%

How does Boosting differ from plain old Random Forests. Random Forests rely on a technique called bagging. A selection of the input data is placed in a ‘bag’ and the random forest algorithm works on this bag or subset of data. Another bag is then selected and a Random Forest algorithm works on this data and so on. The results are then averaged across all the trees to produce a final prediction. With Boosting however an extra step takes place. When the first bagged set of data is analysed, weights are handed to the data ready for selection for the second bag of data. Those data items that were predicted poorly in the first bag are prioritized for inclusion in the second bag in the hope that within this new mix they will be predicted more efficiently. Kind of like you doing a random set of revision questions from a set of questions and then when I select a second random set for you to try I increase the chance of selecting the ones you got wrong in the first revision test.

The following short video does a pretty good job of describing the process. Needless to say the above results have sparked my interest

Cadence and the Guineas Favourite

Simon Rowlands is always a thought provoking read and he recently extended his sectional coverage to embrace stride length and frequency a new addition to horse racing data which I wrote about a month or so ago. His piece on this can be found at

http://www.attheraces.com/blogs/sectional-spotlight/12-April-2018

I do not wish to steal Simon’s thunder as I guess he will have more articles in the pipeline but in the above piece he did not mention a number of variables that may need to be accounted for when accessing this new data. Going and class are two obvious ones and I want to touch on this with regard to the Guineas favorite that Simon mentions in his article, Saxon Warrior.

Saxon Warrior recorded a strides per second of 2.36 when winning the Racing Post Trophy and the RPT always conjurs the question of whether the winner is a Derby horse or a Guineas horse. Simon does not actually come down on either side as rightly so one cannot say that because Saxon Warrior recorded an SPS of 2.36 over a mile he would not record an SPS quite happily that would win him a Derby but the real question at this stage is does his SPS give him a chance of winning the Guineas ?.

So far the average SPS of horses winning over 12f at class 2 is 2.32 whereas winning at 12f at class 6 is 2.26. Clearly lower class horses cannot muster the same stride speed. By contrast 8f horse at class 2 have an average SPS of 2.35 with class 6 at 2.33.

The striking feature of these averages is that over 12f a class 2 performance average is not strikingly different from an 8f class 2. Compare this with a 5f class 2 at 2.49. Whether the differences are not linear or perhaps the going really needs to be factored in is a topic I can write about later.

From here we can see that Saxon would appear to be certainly a genuine class 2 or higher over 8f. Now you may say that’s obvious as he won a class 1 at a mile but this is not the case. If last year just happened to be a year of staying horses running the Post then he may have won it and yet only clocked an SPS of a class 3/2 animal.

I would say to any Saxon Warrior fans, do not be put off his Guineas chance simply because he is a Post winner.

Does Hugh Taylor Eat Breakfast ?

I have to confess that in my interview with Hugh for my book, The Newmarket Wizards, it did not occur to me that I should ask such a question. What would be useful to know is does Hugh ever skip breakfast, as knowing which days he skips breakfast might be more valuable than any other variable we could choose to look at. Eating or not eating breakfast may also be something you might wish to consider from your own betting and indeed health perspective.

I have recently investigated the blood glucose (bare with me I will get on to betting) effect of various breakfast meals on myself.

https://heartattackandthenhs.wordpress.com/2018/01/23/personalized-diet/

The results astonished me and made me realize that breakfast is the one meal we all tend to adopt habits around. In other words it is the easiest meal of the day to control and yet it could be the most damaging. Research shows we are all most insulin resistant in the morning and yet it is breakfast when we carb’ load. Breakfast is also a meal which naturally breaks a perfect opportunity to fast for around 18 hours and the research around fasting and longevity is pretty well bullet proof.

But what if all this health talk does not motivate you to skip those cereal killers or at least replace them with low glycemic alternatives like eggs. Maybe appealing to your punting instincts will do the trick.

Always gamble on an empty stomach. Sounds ridiculous and certainly the exact opposite to what most people do and yet a research team from Utrecht in 2014 looked at this very phenomena. They took two groups of University students and fed half a decent breakfast whilst the other half had no breakfast. They were set a standard well know risk assessment test known as the Iowa Gambling Task.

https://en.wikipedia.org/wiki/Iowa_gambling_task

The task involves picking cards from four piles of cards with each pile having varying reward and risk. The task was to see if the myth that suggests that when we are hungry we take bigger risks and have poor grasp of risk/reward was actually true. This idea is derived from the fact that when we are hungry we seek food more irrationaly and when we are sexually aroused we take bigger risks seeking sex.

The results were inf act the exact opposite. Fasting members were significantly better at the task in terms of evaluating risk/reward than those who ate breakfast. Their decision making skills were more finally honed even when allowing for other factors. We bet in a world where Betfair offers us less than 2% over-round and therefore slight gains and edges can push us into profit. Skipping breakfast before making those betting selections just might be one of them. Does Hugh Taylor eat breakfast ?, I don’t know but he would do well to stay away from the fridge after issuing a ‘possibly one more bet’ message.

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0111081

Horse Stride & Confirmation 5

Today’s Lingfield Winter Derby poses more unknowns than known’s in terms of stride length and frequency. Many horses have not yet registered any data which means we are guessing in terms of size, class etc. Is there anything we can gleam from the data so far, well lets take a look.

One possible measure of class is stride length X Srides per second. This number does not tell us how long they maintained this number but it still may be an indicator of class. If it does then over time we should see some nice averages emerge over various official class levels.

At the moment the average ‘power stride’, for want of a better term, for the conditions of today’s race at Lingfield are 59.18. Class 2 has an average of 59.8 but this unexpected larger number may simply be due to sample sizes so far.

How do the horse today measure up?. The Obrien favourite has no data and is taking a hike in class on official figures. If Ryan Moore was not on board I am guessing that he would not be favourite. The interesting horse is Utmost, a market mover into second place at around 9/2. He has a power stride of 58.4 when winning at Lingfield which does not look good enough but his previous defeat comes out at 60.1. If this was the other way round I might be more bullish but for me he looks vulnerable as I suspect the winner of this race will be better than the average of 59.18 or even 59.8. Utmost also did his figures from two races that were overall Even to Slow. My feelings at this stage is this would elevate the so called power stride rating.

The other interesting readings are for Mr Owen who comes in a 57.7 which is above the class 2 (no class 1’s yet at Wolves) average of 57.4 but this was also done off a slow pace which may well have boosted his power stride number.

Master the World does not look likely to figure with a power stride of 58.19 well below class 1 or 2 for Lingfield. Again his figure was from an Even to Slow race.

Suggestions, well not with confidence but maybe a chance on Khalidi EW

Horse Stride & Course Confirmation 4

Are female horses really smaller than male horses and do larger horses (fewer strides per second) really carry weight better than smaller horses. If they do then identifying them early will pinpoint horse with the ability handle weight rises more efficiently. It would also seem that a modification on average stride lengths per distance will be needed. At the moment there is only AW racing and although Southwell and its more demanding surface presents a spanner in the works, grass racing and its different goings will prove problematic unless I allow for them. You cannot compare a horses strides per second on heavy ground with a stride per second average for all horses across all grounds.

Continuation of this thread on horse size and stride data will continue on the SmarterSig email forum at

https://groups.yahoo.com/neo/groups/SmarterSig/info

Horse Stride & Course Confirmation 3

A good big un will always beat a good little un. A commonly used phrase but is there any truth in it when it comes to horse racing ?. Of course as punters we are not interested in whether a big un will beat a little un, we are interested in whether the public’s perception of horse size presents any betting possibilities. If big horse do beat small horses more than they should but the public overbet them to such an extent that smaller horses go off at too large a price then I am with the small horses even if they win less frequently. The one major overall edge we have is that the crowd is looking for winners whilst we are looking for profit.

The ATR stride data offers an opportunity to explore this question in a way that was not possible previously. Stride patterns will probably be fairly indicative of horse size and we can use this to check the performance of big v small.

The data has only been made public since the last month or so of 2017 so checking going forward has a long way to go before meaningful samples can be compiled, but we can look back at overall ROI% of horses according to size which may offer some clues. First of all I have only so far been logging horses who have been placed in a race in order to get an accurate handle on stride length and frequency. By definition these horses are successful animals and will unsurprisingly come out retrospectively to be profitable animals overall.

First of all checking the PL of ‘large’ horses or at least below one standard deviation on stride length per second revealed that historically they have had collectively 1848 runs and to BFSP logged a profit of +359 points. In other words if we could go back in a time machine and bet them we would have made a ROI% of 19.4% before comm’.

By contrast small animals (quicker stride patterns) have run 2792 times and logged a pre comm profit of +615 returning +22%

It would seem therefore that small horses from a betting perspective have done slightly better historically than larger horses when examining from the poll of proven horses. The real acid test will be going forward but this will take some time and how and where these two horse types are best bet will be the intruiging question.

FOOTNOTE- The lack of feedback, I have to admit is making me wonder whether I am wasting my time blogging. If you have any comments pro or con then please feel free.