Once I learned that Andrew Mack had begun work on the second volume of his Statistical Sports Models in Excel book series and that it would be published in a matter of months, I realised that I was yet to share my thoughts on his first instalment.
This is a book review that should have been published long ago. Not because I read volume one back in August of last year, but because of the valuable lessons it can teach bettors and the more people that are aware of those lessons, the better.
One of many ways to bet
Although many bettors don’t like to believe it, there is no goose that lays the golden eggs in sports betting. There are various different ways to approach betting on sports and each have their own merits and potential pitfalls. In his book, Andrew Mack focuses on one particular approach that can lead to success when the required time and effort is put in to get the desired results.
The world of data analytics and modelling in sports betting is nothing new. However, breaking down the odds in a betting market through analysis of data sets and using simulations and models to predict future outcomes has very much taken over the traditional handicapping we might have seen at sportsbooks less than a decade ago. Andrew even references Pinnacle’s own Marco Blume and the fact that he’s talked about how our traders are using R and Python for pricing and trading.
Much like building models and setting your own odds to compare against the market is one of many ways to bet, there are countless methods and programs to use within the wide-ranging term of sports modelling. As the title suggests, this book focuses on Excel rather than a detailed analysis of other programming languages like the aforementioned R and Python.
Honing in on one specific language may alienate those that have already discovered they are better suited to alternative options, but it also allows the book to delve straight into the real detail without generalising. There’s nothing more off-putting for a reader, especially a novice in this field like myself, than having to wade through materials or examples that aren’t applicable and can’t be used to help improve your understanding.
Don’t expect a guide on how to win in sports betting
It is an outlandish claim for any book to say it offers a simple solution to winning in sports betting. However, the problem we have is that there are so many people out there wanting to believe it’s possible - and this makes books that appeal to a get rich quick mentality possible and it’s why they are probably some of the best-selling books out there.
Statistical Sports Models in Excel is at the opposite end of that spectrum. It is laden with warnings about how difficult it is to achieve positive expected value with your bets and that it will likely take a lot of mistakes and a lot of failure before you even get close to that point.
There are no convoluted sentences, concepts are communicated clearly and for each example used there is a thorough-guided process with additional visual cues.
I think what Andrew’s book offers is a big step in the right direction for breaking down something that can often be quite daunting to bettors. Many people will be so put off by the technical jargon and lack of user-friendly interfaces for a lot of the necessary software or applications that they won’t even begin the journey of modelling in sports betting.
What this book does is open the door to those with an interest who haven’t quite made the jump to getting their hands dirty yet, or those who have tried but require additional help.
The fact that Andrew is happy to give this information away shows that there is little to no value in betting with the actual examples shown. There is value, however, in learning about the benefits of sports modelling, where to access data, how to use it and where these newfound skills can take you (as well as how much additional work is required).
An important word of warning
It is important for those interested in reading this book that, as Andrew states in the very opening, sports betting is “fraught with difficulty”. This obviously means you should expect challenges and periods of difficulty if you hope to reach the end goal of achieving consistent profits.
This book is certainly a great place to start and while the level of complexity does follow a natural progression through the book so as to not put off the reader as they develop their skillset, it will be quite dense and require a bit of hard work to get through at points. I think you’ll be hard pressed to find any literature on this subject matter that doesn’t feel like that in parts.
The key to enjoying Statistical Sports Models in Excel is to treat it as an exercise book or textbook you might find in school. It will take work to get through it and when things don’t quite click, it might be best to take a break rather than ploughing through. It is worth persevering though and because it feels more personalised as a reader, it makes it much easier to stick with compared to the dull equivalents that might be handed out in class.
The key is in the simplicity
While I feel it is worth warning potential readers about the complexity of a book like this, it has to be said that keeping it as simple as possible is one of its standout features. There are no convoluted sentences, concepts are communicated clearly and for each example used there is a thorough-guided process with additional visual cues.
This focus on simplicity doesn’t hinder the breadth of sports that can be covered or the different models that are shown within Statistical Sports Models in Excel either. This approach also allows the reader to actually use the book as a workbook and while I don’t bet on a regular basis, I still found myself reading along with Excel open on my computer to see how easy it is to implement the teachings.
Statistical Sports Models in Excel is laden with warnings about how difficult it is to achieve positive expected value with your bets and that it will likely take a lot of mistakes and a lot of failure before you even get close to that point.
The text is cleverly structured and formatted to speak to the reader as they develop as a modeller. Only once you’ve got through the introduction that includes a walkthrough for setting up Excel and utilising the available add-ons (some are free and some come at a cost) and have your “Excel modelling toolbox” can you begin to look at working on actual models.
By the time you’ve got to trying out a Game Scores Standard Deviation Model, the earlier work on the Bradley Terry Model and Team OLS Optimised Rating (TOOR) model make the process much more straightforward. This isn’t just because of the similarities between these models, but the fact that the basics are covered first and with each step throughout the book do you build on your knowledge.
Of course, building these models is only one small step towards the end goal for the majority of people reading this. Developing your skills and building new models with other data-sets is needed to take things to another level, but starting out with a simple approach to building the foundations will certainly stand the readers in good stead.
I would strongly recommend anyone who finds themselves as an observer in conversations about modelling in sports take the time to read Statistical Sports Models in Excel. It’s benefits will most likely become even more apparent if you take a practical approach and follow the book as a guide. I, for one, am definitely looking forward to volume two in this series.