In these quiet times for sport and betting, it’s an ideal time to look to develop our knowledge and skill sets and as part of that, in this new 2-part series I want to walk you through how to use Monte Carlo simulations to evaluate tipsters.
Much of this work has been ported over from other investment worlds and the outputs provided help us make more informed decisions on just which tipsters offer the best chance to make you a profit betting.
The full SBC Advanced Tipster Analytics Guide on Monte Carlo Simulations and the outputs for each tipster are available to full SBC members, yet in this 2-part article, I want to outline what they are and how exactly they can help when evaluating a tipster.
The good news is that you DO NOT need to understand any complicated mathematics to benefit from exploring these advanced outputs. Simply follow the easy to understand explanations we give and the context and scores we put the ratings in.
These advanced analytics are ideal if you are interested in topics like losing runs, risk, whether a tipster is lucky or skilful, comparison to other investment returns and volatility.
Over the course of this 2-part article as well as explaining the usage of Monte Carlo simulations behind the analytics, I will also be explaining the outputs they provide, which include:
- Historical maximum drawdowns
- 99th and 50th percentile drawdowns
- 50% bankroll drawdowns
- Likelihood of a losing year
- Risk reward & Capital risk ratios
- Dispersion factor
- Sharpe ratio
- P-values
If this all sounds complex – don’t worry it isn’t and please don’t stop reading!! You honestly don’t need a degree in maths to figure out how they can help you.
Instead let me guide you through each of them and explain what they are and how they can help you make better decisions for choosing the tipsters you follow.
Introducing Monte Carlo Simulations
When the SBC team reviews a tipster, the first port of call is to explore the live record of performance obtained to date. Usually this encompasses at least a couple of years data, if not more and often at least 1000 to 2000 past actual bets.
As useful as it is to explore this live actual record of performance, from an analysis perspective, the amount of data we have – even if 2000 bets is often insufficient to draw 100% concrete conclusions from.
This is where running Monte Carlo simulations for each tipster comes in as they replicate the profile of the service over many million data points.
So rather than calculate what might happen when analysing 1000 or 2000 live bets, we do so over say 90 million – a much more useful data set.
The results of each Monte Carlo simulation can help to answer key questions for any given tipster such as:
- Is a betting record more likely to be based on luck or skill?
- What kind of losing runs or drawdowns might you realistically expect?
- How do their live results compare to their simulation results?
- Have they benefited from some freak results and outliers that are unlikely to be replicated?
- What size betting bank do you REALLY need?
Ultimately, all the outputs from these simulations helps to give us a greater understanding of the quality of any given tipster. Certainly, it is far more useful than just scrutinizing the live results only.
All SBC reviews now include the results from our Monte Carlo simulator outputs as standard, hence why I wanted to illustrate what they mean and how you can interpret them in this special 2 part article.
How These Simulations Work
Monte Carlo simulations have existed in several forms for around 90 years and have been used in many walks of life such as medicine, insurance, space, oil exploration, nuclear weapon experimentation and even for general election modelling!
The simulation is a mathematical technique which is used to understand the impact of risk and uncertainty.
The main principle is the application of randomness and volatility to test theories of probability.
They are absolutely ideal for evaluating historic betting patterns and to predict best and worst-case scenarios.
When we at the SBC take a tipster’s proofed selections and provide statistical analyses by, for instance, season, month, course, race type and odds banding, we augment this by taking several copies of the key data fields and then modelling these through a computer algorithm which generates ‘virtual seasons’ each of the mean number of bets.
Typically, this might create, say, 100,000 seasons which could produce in the region of 90 million bets!
The greater the number of simulations that are run, the more confident and positive one can be of the accuracy of the outcomes.
It often takes 5-6 hours to run these Monte Carlo simulations for each tipster and a fair bit of processing power to get there but the results are often well worth the wait!
12 Outputs to Rate Tipsters By
By running the Monte Carlo simulations against each tipster analysed and reviewed, we are able to produce detailed results that provide a number of very useful outputs – 12 in total.
Over this 2 point guide, I will provide a description of each output with our reasoning as to why they are useful analytical tools and their application, together with some worked examples from our recent investigations showing how you can use them to make better decisions.
1) Strike Rate
This first output is a simple one as the strike rate is simply the overall number of winners divided by the tips given, expressed as a percentage.
What To Look For: Whilst there is isn’t really a ‘good’ or ‘bad’ strike-rate, this output is important as those tipsters with a lower strike-rate often require more patience, yet bring higher rewards and vice versa.
2) Return on Investment (ROI)
Another standard output whereby the ROI indicates the actual profit as a percentage of total stakes. ROI is always useful as it puts profit into context.
What To Look For: The type of ROI you can make often varies based on the strike-rate and the type of sport the tipster specialises in.
It isn’t rare to see a top racing tipster hit 30% ROI+ (caveated by the issues of getting on) vs that from a football tipster, whereby anyone making 5% ROI+ is doing very well (caveated by the fact it’s often much easier to get your bets on).
Therefore, the ROI needs to be judged in context against the markets tackled and the profile of tipster.
3) Historical Maximum Drawdown
Moving onto the first of the more advanced outputs and it’s important to understand what a drawdown is as this is a term we use regularly.
A drawdown is quite simply the worst historical run that has ever taken place – the peak-to-trough decline in bankroll experienced over a period of bets.
What To Look For: A tipster service with a strong record will have a historical maximum drawdown that is easily absorbed by the betting bank suggested. It’s a useful output to examine as if you struggle with long losing runs, you want to avoid tipsters that suffer occasional large drawdowns.
4) 99th Percentile Drawdown
Building upon [3] above, what this output does is effectively list all of the seasonal drawdowns found in the simulations and then takes the 99th percentile figure. It is useful as should a drawdown exceed this 99% figure; it indicates there is a problem or change in the service to be aware of.
What To Look For: We like to see similarity between a tipster’s live record and the results from the Monte Carlo simulation. If a tipster suffers a drawdown that equals or goes beyond this 99% figure – it could suggest a problem.
Recent Examples: We recently analysed a long-term profitable racing system, which over 9 years’ worth of live results had suffered a heaviest drawdown of 109 points. The 99th percentile figure came out at 119 points – suggesting the live tipping history was very similar to the simulation results. A very good sign.
Compare this with our analysis on another racing tipster recently that didn’t attract a high SBC rating. Although their worst drawdown to date was 63 points, the simulations reveal that 139 points would be needed to accommodate the likely potential drop at the 99th percentile. Suggesting that followers of this tipster should prepare for a worse run than that which has been seen in live results.
Read Part 2
Click here to read part 2 of this article, where I cover 8 further outputs from the Monte Carlo simulations including:
- 50th percentile drawdown
- 50% bankroll drawdown
- Likelihood of a losing year
- Risk reward ratio
- Capital risk ratio
- Dispersion factor
- Sharpe ratio
- P-values
SBC Helping Inform Your Betting Over The Next Few Months
Whilst there isn’t a huge amount for us to bet on right now, we are using this quiet betting period to publish a series of quality reviews, articles and insightful guides to help deepen the expertise of SBC members.
Mindful of the fact that many of you are using this time without sport to improve your knowledge and understanding of the betting and tipster world, our role is to help fill that gap with informative, educational and quality content.
Tackling everything from helping develop and understand your betting risk profile, the psychology of a winner, Advanced Tipster Analytics and guides to betting on new sports, you can expect to read a lot as an SBC member over the next few months.
As ever, membership comes with a full 30 or 90 day money back guarantee, so if you are looking to develop your betting skills, do consider a Smart Betting Club subscription.
Best regards
Peter Ling
Smart Betting Club Owner and Founder
