Sports Betting Model To Predict Spreads

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How To Build A Predictive Betting Model
Building a sports betting model can be difficult work. We won’t lie to you. It can mean long hours of tediously entering data, sorting spreadsheets, setting up databases, testing, re-testing and re-re-testing.
All this, with no promise that you will eventually ‘crack the code’.
Would you expect anything different? Bookmakers have the sharpest minds working for them day in day out, using everything at their disposal in order to compose the odds that millions of people are trying to beat. You think you’re going to come up with a system to beat them in a couple of afternoons of analysis? It’s not going to happen. It takes times and dedication, a sharp mind and persistence. But then, half the thrill is in the chase.
More often than not, the end product is worth the time and effort, even if it’s just for the many lessons you will learn along the way, both practical and theoretical. But we can tell you, while no model you build will be light work, the first model is always the hardest.
Where do you start when building a sports betting model? What are the key elements to developing a profitable betting model? In this article we discuss the basic to consider when starting to develop a sports betting model.
If you want to build a sports betting model, this is what you need to learn:
If you’re serious in your ambition to build a sports betting model, just know this, it can be difficult work. We won’t lie to you. It can mean long hours of tediously entering data, sorting spreadsheets, setting up databases, testing, re-testing and re-re-testing.
All this, with no promise that you will eventually ‘crack the code‘.
Would you expect anything different? Bookmakers have the sharpest minds working for them day in day out, using everything at their disposal creating betting algorithms to set the odds that millions of people are trying to beat. You think you’re going to come up with a sports betting model, cunning enough to beat them in a couple of afternoons of analysis? It’s not going to happen. It takes time and dedication, a sharp mind and persistence. But then, half the thrill is in the chase.
More often than not, the end product is worth the time and effort, even if it’s just for the many lessons you will learn along the way, both practical and theoretical.
We know successful bettors who have built a number of statistical betting models over the years, developing everything from a football betting model to a basketball betting model, a baseball betting model to a ice hockey betting model. And we can tell you, while no sports betting model you build will be light work, the first model for sports betting that you build is always the hardest.
In this article we will discuss the fundamental things to consider before getting started on a sports betting model. And in doing so, try to impart to you some of the lessons we have learnt along the way in the hope that it saves you some time and frustration.
It’s pretty elementary, but you would be surprised by the number of people who miss the point and don’t quite grasp what any sports betting model is trying to achieve.
Well, each of the betting models we have developed attempt to assess the current ‘potential’ of a team or participant, which is then compared to its opposition in an attempt to gauge the likely outcome of the contest.
What you’re essentially trying to do with a betting model, in very basic terms, iscreate an independant point of reference from which you can ascertain the probability of all possible outcomes in a given match or contest.
Ideally you want your betting model to be able to recognise value in a given betting market. In other words, you want it to give a truer expression of a team’s potential or ‘form’ than what the bookmakers odds do.
Once you’ve developed your model, for whatever sport or league you are looking to bet on, you’ll be surprised how often it can identify value in the market. Will it always get it right? Of course not. But a fully developed statistical betting model will show you opportunities that the general betting public simply wouldn’t consider.
Yeah we know, it sounds like homework. Bor-ing. But you’re not doing yourself any favours unless you understand the fundamentals of probability theory. And it’s not so much about learning and grasping theory, although it’s important. It’s equally as much about inspiration.The more you read about and understand probability theory, the more imaginative you’ll become with your betting models. You’ll come up with all sorts of interesting and creative things to do with the numbers, taking angles you hadn’t even considered.
Sure you can probably get by developing a predictive model with basic maths. Maybe. But it’s not going to be the cunning bookie killing machine that you’ve always imagined having at your disposal.
A successful bettor once told us his first betting model was developed using graph paper. Yeah, that’s right: graph paper. It was clumsy. It was inefficient. But it’s all he knew. Then after he discovered spreadsheets, and from there databases and from there some very basic Php programming. No you don’t have to be a programming wiz to build a sports betting model. Most successful bettors are not. But the more you do know about spreadsheets and the like, the better off you will be and the more powerful your testing and analysis will be. And perhaps most of all, the more efficiently you will make use of your time.
So at the very least, know how to throw a spreadsheet around and learn how to make the data dance. And from there, work your way into building databases and writing queries. Trust us. You’ll be glad you did.
If you’re starting to develop your first betting model or system, we would recommend you begin with not only a sport you know well, but a league you know well. And by knowing well, we mean like a ruthless expert. If you don’t understand the fundamentals of the sport or league, it’s very difficult to know where to begin in your analysis and very difficult to know how to assess the performance of the sport’s participants. And by understanding the fundamentals we also mean have a clear and comfortable understanding of the betting markets for that sport. The markets that you are going to attack is at the very core of your betting model’s identity. Is that market head to head betting? Is it line betting or handicap?
In other words, the manner in which you decide to assess a team’s performance is going to be determined by the betting market you want to find value in. So know the sport’s betting markets as well as you know the sport itself.
You must also keep in mind bookmaker limits and market liquidity. The amount of money you can get down on a particular league or bet type is something to consider before spending hours building your betting model. Sure, you might make a killer model for Polish 2nd Division football. But are you going to be able to bet at a rate that makes the time spent on the model worthwhile?
Personally we would stay away from the more obscure leagues, at least in developing your first model. For one thing, mainstream bookmakers are far more sensitive to successful betting in these sorts of leagues. They will move quickly to restrict your betting if they feel you’ve got an edge in a league that they would readily admit to not knowing as well as they should. Plus bet limits in these leagues usually begin pretty low anyway. And even if you move your action to a betting exchange like Betfair, you’re going to have trouble getting your money matched in the lower leagues.
So aim high. Shoot for the big time. This is where the money is. And it’s where the challenge is too. Build a betting model that will give you options and one that will provide for you long-term.
Your model is going to need data. At the very least that means final scores, but ideally it means meaty in-depth stats that you can breakdown and incorporate into an algorithm. And most of all – historical odds for which to test your model on. Where can you get the data you need in the format you desire? Is it readily available in spreadsheet form? Well, that can be the tough part. There are plenty of sources on the net for statistical data for a wide number of leagues. Some are free. Some will cost you a pretty penny. It’s worth spending hours trawling the web for sources. You’ll often find the best sources in places you’d never expect, tucked away in the far reaches of the internet.
But you won’t always find exactly what you are after, especially if you’re looking to make a betting model for more obscure sports or leagues. So there is always the option of doing your own data entry, even if it’s to augment a data source from another provider. Personally, we’d advise this only as a last resort. Because to be perfectly honest – data entry sucks. We would also recommend becoming familiar with data scraping software. This software will allow you to scrape data from websites directly into spreadsheet format. Learning how to do this (and it’s fairly simple these days with the great range of intuitive software available) will save you hours if not days or weeks in data collection.
But if you are going to head down the path of manual data entry and begin your own data source from scratch, just remember to repeat this mantra: “You only have to do it once. You only have to do it once.” It helps.
Oh, and remember to click ‘Save’ often. (The horror!)
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The explosive rise and spread of legalized sports betting in the United States has prompted many to develop (or renew) an interest in a more quantitative, data-driven methodology for predicting the outcomes of sporting events.
Historically, any sportsbook operators generating their own odds (as opposed to just copying bet365 or Pinnacle markets) or posting live projected win probabilities (e.g. ESPN Gamecast) has done so using some form of stochastic simulation, most commonly a Monte Carlo simulation. This is a method for iteratively evaluating a deterministic model using sets of nondeterministic (i.e. random) numbers as inputs.
To make sure we all understand what this means in this particular context, suppose I asked what the probability was of rolling a 1 on a single throw of a six-sided die. You likely know that immediately to be 1/6 or approximately 16.7%. However, for the less quantitatively-oriented, we could:
Of course, no one is going to sit around a roll a die 100,000 times, nor could they do so in an independent and identically distributed (iid) fashion. Fortunately, all of your favorite spreadsheet programs and programming languages allow for this to be done many many times, very very quickly. You can see the syntax for one iteration in Excel and Python underneath the graphic above, just for the sake of illustration.
An Example: Yankees vs. Red Sox (June 29, 2019)
How does this apply to sports? Believe it or not, most odds, win probabilities, and score projections are generated by simulating matchups between teams or players in this exact fashion. We can illustrate this using as an example the matchup between the New Yankees and Boston Red Sox that took place at London Stadium in the United Kingdom on June 29th, 2019. If you want to follow along or toy around yourself, you can download the macro-enabled Excel workbook here.
Congratulations, we have now “simulated” a matchup between the Yankees and Red Sox in which the Yankees won by a score of 10.147 to 7.945.
However, just as with our dice example, we want to run this simulation not once, but, perhaps 10,000 times:
Finally, we can calculate the frequency with which each time won in our simulated matchups and convert or compare those to odds for betting purposes.
A team that wins with a probability of 53.8% would be represented at fair odds of -116 (1.86, for international readers), while their counterpart with a win probability of 46.3% would be represented at fair odds of +116 (2.16). With standard juice applied, this would likely hit the market at Yankees -129, Red Sox +107.
If you’re noticing that the two probabilities add up to 100.1%, that is only because of rounding for the sake of this graphic.
The market on bet365 opened at Yankees -135, Red Sox +114, which is quite close to what our very primitive model has suggested thus far, though there may have been some luck involved. At this point, it is too early to tell but skepticism is often the prudent viewpoint.
This tactic of first creating a mathematical representation of an event, and then iterating through it over and over is a standard part of any data scientist’s toolkit and is used to make predictions that you might interact with regularly. Weather forecasts and economic projections are two very common examples, but for some it is not entirely intuitive why the method is effective in the first place. If this does not describe you, feel free to scroll down to the section titled Home Field Advantage.
We recall from statistics that normally distributed data sets follow a bell curve, under which a specific proportion of values can be found within a given distance from the mean.
Using only the mean and standard deviation of any normal distribution, we can construct a chart like the above. In the context of the model we’ve built thus far, this chart can be read as the likelihood with which the Yankees (or a team statistically identical to the Yankees) would score any number of runs against the Red Sox (or a team statistically identical to the Red Sox). Since normal distributions are symmetric about the mean, we would say there is a:
You’ll notice that in the case of the Yankees at this point in 2019, moving two standard deviations to the left of the mean yields a prediction of a negative score (-0.860). We will come back to this momentarily.
When we were building our simulation, we used the inverse of the cumulative normal distribution function along with 3 parameters. The cumulative normal distribution function itself shows, for a given distribution and value of x, the probability of a randomly selected value being less than x. In other words, what percentage of the data falls to the left of x. One final way to internalize this that is relevant here is to imagine choosing a value x along the x-axis and then asking what the probability is that a randomly selected point under the curve will be to the left of that x value.
The inverse of this function does…the inverse. It allows us to input a distribution (parameterized by a mean and standard deviation) and a percentage or probability, and then produces as output the x value for which the supplied percentage of data falls to the left.
The percentage or probability that we are supplying, in this case in Excel, comes from a (pseudo) random number generator that outputs a value greater than or equal to 0 and less 1, notated mathematically as [0,1). When the simulation was run to construct these graphics, that random value produced by Excel for the Yankees was ~0.95234.
One interpretation of this is that, for a bell curve representing a distribution with a mean of 5.142 and standard deviation of 3.001, 95.234% of the values fall to the left of 10.147. Another way of saying this is that the Yankees (or a team statistically identical to the Yankees) will score 10.147 or fewer runs against the Red Sox (or a team statistically identical to the Red Sox) in 95.234% of matchups.
As long as the assumption of a normal distribution holds, iteratively using random values between 0 and 1 to generate simulated scores for each team will give us a good approximation of how the game we parameterized is likely to play out (hopefully). For other sports and events, a normal distribution may not be ideal. For very low-scoring sports like soccer, it may make more sense to use a Poisson distribution or a negative binomial distribution to model the game, rather than a normal distribution.
In this particular case, as was briefly referenced earlier, it is possible to obtain a negative number of runs scored. For the purposes of the model we are building, we will employ logic that converts any negative scores to a score of 0.000. This is a place where we see the art of modeling truly fusing with the science. Perhaps a simulated score of Yankees: -4, Red Sox: -3 is just as informative as a simulated score of Yankees: 5, Red Sox: 6. Or, perhaps one wants to completely remove the entire simulated matchup in the case that either team produces a result that would be impossible in the real world. Stepping back for just a moment, we see this is not the only aspect of our model that leaves room for creativity and also for error. We did not take into account injuries, momentum, weather, stadium dimensions, starting pitchers, and a host of other important data points that may impact our predictions.
Often, one of the best ways to learn to build models is to start with the minimum viable product and then incrementally add to and scale it. In this case, we will first look at incorporating home field advantage as a feature of our model.
During the 2018 MLB regular season the home team won 1,277 games (52.6%), while the away team won 1,149 games (47.4%). Thus, someone building a model amidst the 2019 season may ascribe a 2.6% incremental win probability to a team playing at home, all else being held equal.
This is not a home game for the Red Sox, as it was played in London at a neutral site, but suppose it was. One option would have been to only use Runs Scored and Runs Against data from Red Sox home games and Yankees away games in the first place, so that no further modifications need to be made. In some cases, this would be a viable approach, though we may hesitate to think this is one of them, especially because of the limiting factor it has on an already-small sample size.
Suppose our research revealed that, all else equal, home field advantage can be expected to add 0.32 expected runs to a team’s output, after adjusting for the quality of the opponent’s defense. It is particularly easy to incorporate this into our model by simply adding 0.32 to the Adj. Runs Scored for the team with home field advantage.
This occurs before the value is passed as a parameter to the inverse of the cumulative normal distribution function, (NORM.INV, in Excel parlance), so no further adjustments are needed. The same can be said for any factor that adds or subtracts a specified absolute contribution to or from the expec
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