Can Forecasters Forecast Successfully?: Evidence from UK Betting Markets

Journal of Forecasting, 19(6): 505-513I am occasionally blog on a paper that is of interest. Well, of interest to me. The latest paper to catch my eye is (other papers I have commented on can be seen here).

Leighton Vaughan Williams (2000) Can Forecasters Forecast Successfully?: Evidence from UK Betting Markets, Journal of Forecasting, 19(6), 505-513 (doi).

The reason that this paper was of interest is because I was reading Leighton’s book (Betting to Win : A Professional Guide to Profitable Betting, High Stakes Publishing, 2002, ISBN: 1-84344-015-6) and this paper was mentioned. Many (many, many) years ago, long before I was an academic, I went through a phase where I collected all sort of horse racing systems. My idea was to test them all out to see if any of them worked. I never actually placed a bet and I never really tested any systems as they generally involved too much time to collect all the data, process the data etc. etc.

Since then I have still thought that it would be interesting to look at various horse racing systems to see if they worked.

This is what this paper does. Unlike my idea though, it takes tips from services that you subscribe to, either by paying money or by contacting them using a premium rate phone service. This seems a lot more sensible, rather than having to enter all the data yourself.

This paper looks at the performance of tipping services, with the analysis being carried out in 1995. Five services were compared. Four of these were subscription based. That is a fee is paid, and you gets tips at various times. In 1995, these services cost at least 99 GBP per month, which seems a lot to me now, let alone in 1995. The other service was a premium rate phone number, where you phone up to receive the tip and the costs of the phone call effectively covers the cost of the service. These five services were chosen as they were amongst the top tipping services as assessed by the Racing Information Database (I have tried to google this and am not sure that it is in existence anymore, but would be willing to be corrected, and update this post to provide a link).

The paper goes through each of the tipping services and evaluates how many tips were provided (and over what period – some, for example, were analysed over three months, others over six months – I think the period was probably chosen to ensure that a sufficient number of tips were analysed as not all services provide tips at the same interval), any conditions associated with the service (for example, only bet if a certain price is available), the profit (or loss) from investing in that service etc.

The good news is that all the tipping services produced a pre-tax profit when used with the relevant staking/price plans. Leighton also makes the point that none of these profits could be said to be significant. It was also interesting to note that increased profits could have been achieved if some of the lesser supported tips were ignored. Of course, this would be a hindsight examination and the obvious question would be, when in play, what tips do you ignore, and what ones do you actively bet upon? There is also evidence that you should use a variable staking plan, rather than a flat stakes method.

If you use a tipping service, there are also other factors to take into account. There is an upfront investment (which you may never recover). Unlike an academic study, you will probably only choose one and which one do you choose? There is also (as pointed out by Leighton, in chapter 20 of his book) the fact that you have to take what the tipping services advertise with a pinch of salt. As an example, a service might only say bet if you can get a price of 4-1 or better. What happens if that price is almost impossible (or even actually impossible) to get, will the service still include that in their results if the horse should win? And what if there was a price available (even for a few seconds) at 6-1, would the service return that as the price you could have got even though, unless you were very quick, or very lucky, you would have struggled to get on at 6-1.

It is twelve years since the paper was published, and seventeen years since the analysis was carried out and things have moved on. Tipping services (I suspect) come and go, technology has moved on, the tax regime has changed and there are now many other ways to bet which were not so predominant at the time. I am thinking specifically of spread betting and betting exchanges. These have, undoubtedly, made a big difference to the industry.

I am not up to date with the scientific literature in the area of sports forecasting and I suspect that there are many papers out there that provide various comparisons and analysis. If you know of any, or know of a good review paper in this area, I would be very interested if you could post a comment giving the reference.

I would also be interested in hearing from any professional tipping services (no matter what sport, but UK based as I don’t claim to understand American sports or markets) who wish to subject their service to scientific analysis. Note, this is not an open invite to advertise your service on this blog. I get enough spam as it is (and moderate it out) and I don’t want the comments box filled up with lightly disguised adverts to various web sites that claim to make millionaires from people who subscribe. But serious enquiries are welcomed.


Prediction of sporting events: A Scientific Approach

My final year undergraduate dissertation project (many years ago) attempted to predict the outcome of horse races using Neural Networks. I briefly blogged about it in June 2009 (

The result of the project was (in my view) encouraging but was lacking in a couple of areas. The data was incomplete (the starting prices were not available so I had to make some assumptions and it would have been more useful to have studied a greater number of races). I would also have liked to have tried some other prediction methods, beyond just neural networks.

Since doing that project I have maintained an interest in predicting sporting events, although sports scheduling (e.g. 10.1016/j.cor.2009.05.013 and 10.1057/palgrave.jors.2602382) has seemed to have taken up more of my time. But I have always wanted to return to prediction, utilising Operations Research methodologies.  As such, I maintain a database of any literature that I see on the topic. This incudes the scientific literature, as well any newspaper cuttings, useful web sites etc.


One of the problems that serious sports forecasters face is being taken seriously. A quick google for sports prediction (or many other similar terms) will bring up many sites offering services that (supposedly) enable you to make money. The services typically involve investing in some system, or subscribing to a service where you are sent the predictions for you back in whatever way you see fit.

Of course, if we were sceptical, we might assume that many of these services are really there to make money for the people selling the service, rather then those who are buying. I am sure that there are some services out there that make money for both the seller and the buyer, but the challenge is to find out which services offer value for money before you go bankrupt in the pocess!


Unfortunately, there are not that many scientific papers that consider how to predict the outcome of sporting events, at least as a way to return a monetary profit. There are some, of course. For example an article that appeared last year in the International Journal of Forecasting


S. Lessmann, M-C. Sung, and J.E.V. Johnson (2010) Alternative methods of predicting competitive events: An application in horserace betting markets, 26:518-536, DOI: 10.1016/j.ijforecast.2009.12.013


considered how to predict horse races. The motivation of the article was actually to try and predict competive events such as political elections and (of course) sporting events, although the paper was really a large scale (1000 races,  12,092 runners) study. The paper concluded that their proposed model was able to provide an increase in wealth of just over 528% if using a Kelly (Kelly, J. L. (1956). A new interpretation of information rate. The Bell System Technical Journal, 35, 917–926) strategy, with reinvestment.


Considering other sports, such as football (the UK version), a couple of examples of predicting matches can be found in Economics, Management and Optimization in Sports, Springer, 2004, ISBN: 3-540-20712-0

In Using Statistics to Predict Scores in English Premier League Soccer (John S. Croucher, pp 43-57), various models are presented that attempt to fit the number of goals scored by each team. The best model found had a Poisson distribution.

Another paper from the same book (Modelling and Forecasting Match Results in the English Premier League and Football League (Stephen Dobson and John Goddard, pp 59-77)) considers about 30 seasons of data. This paper also uses a statistical method, but assigns probabilities to win, lose or draw, rather than trying to predict the number of goals scored. This paper also provides a good overview of previous work in this area.


I suppose, the stock market is one area that has been widely studied with respect to prediction, with an eye on turning a profit. There have been hundreds (if not thousands) of papers that look at ways to predict stock prices, interest rates, inflation etc.


Maybe, not surprisingly, there has been limited reporting in the scientific literature as to whether anybody has made (or makes) money from the methodologies that they have developed. After all, if you have a successful system, why tell everybody about it (which is one of the major arguments as to why would you buy a system/tips from a service on the internet).


What I would actually like to see is a lot more scientific papers not only reporting their predictive systems but also how much money was made, over what period of time and if the system is in daily/weekly use at the time of writing.

Of course, the system needs to reproducible (as should all good scientific writing).

However, if the system is successful, the author(s) might be unwilling to reveal its secrets but might still want to let the world know about its effectiveness. Under these circumstances I have a few ideas as to how this could be done.

  1. In the run up to publishing the paper, the authors make a series of predictions and lodges them with a reputable source. This could be another scientist, a lawyer, or even published on a web site that can be verified from a date/time point of view. The important thing is to ensure that the predictions can be verified as being made in advance of the event. If these predictions were made over a period of (say) six months, then this could form part of the results presented in a paper.
  2. It is, of course, uderstandable that authors do not want to publish the full details of their winning methodology in a scientific paper but, as scientists, we like to publish our work. The scientific community should be understandable of this, in the same that they are accepting that sometimes certain factors must be kept confidential due to commercial sensitivities. Therefore, the general methodology could be described but omitting key points (and being upfront about that) but, if combined with 1), above, then this could still make a contribution to the scientific literature.


An attractive alternative would be to run a prediction competition (see Kaggle, who are doing excellent work in this area), where competitors are given a set of data and asked to provide predictions on the outcome of sporting events; ideally those that have not taken place yet.


In summary, I would really like to see more reporting (on a sicientific basis) of sports predictions which are unasheamedly about trying to return a profit, as this is an under represented area at the moment. Why not have a go?


Note: I entered this blog entry into the INFORMS blog competition. The March 2011 competition was O.R. and Sports.

Horse Race Prediction with Neural Networks

I was sorting through some old papers recently and I came across my undergraduate final year dissertation. I recall that it started as a project about genetic algorithms but quickly turned into a project that used neural networks to predict the outcome of horse races.

I trained a back propagation network and used the final network to predict the outcome of (selected) races that the network had not been trained on. One of the biggest challenges was finding suitable data. I was lucky enough that a couple of companies (Timeform and Raceform – thank you) sent me their databases which made the data collection side of things a lot easier than it might have been.

One item that was missing from both datasets were the starting prices. Due to this I could not really judge if the predictions would result in a profit. However I did a few calculations and assumed that the average odds were either 2/1 (3.00), evens (2.00) or 1/2 (1.50) (see note, for a description of the odds calculation). I also made an assumption that the odds would also capture any tax that had to be paid.
Using these figures it was possible to make a profit even when the average odds were as low as 1/2 (1.50).

I wonder if it really is possible to develop a prediction system that can make a profit from backing horses? Although my undergraduate dissertation suggested that it is, it would need a lot more development, testing and analysis.
I would also like to investigate other methodologies, in addition to neural networks – but that needs a little more thinking about.

Of course, it’s not possible to predict the result of every race but you only need to predict enough races, at good enough odds, to show a profit.

One of the issues when betting is the amount of tax you have to pay but with new methods of betting (such as spread betting and betting exchanges) becoming ever more popular, perhaps this might not be so much of an issue.
I know that betting exchanges (such as betfair) still charge a tax but at least you are betting against other punters and are not limited by the odds being offered by the bookmakers.

I’ll keep this one of the back burner for a while, but I think there is some potential in exploring it further.

Note on odds: I have shown the odds in two ways. The UK way of expressing odds is (for example) 2/1 which means you have to place a stake of 1 unit to win 2 units. You also receive back your stake. So if you bet 1 unit at odds of 2/1, and the horse wins you receive 3 units back (the 2 units you won + your 1 unit stake = 3 units; less any tax – but let’s ignore that for the purpose of this discussion).
Another way of expressing odds is the decimal format (which I have shown in brackets). This is used, for example, on betfair. This says how much you will receive for 1 unit, including getting your stake back. So if you bet 1 unit at odds of 3.00, and the horse wins, you get 3 units back.
So the two ways are just the same way of expressing the same thing, but you might be more used to seeing one system over another, depending on where you live/bet.