Sports Betting Model To Predict Spreads

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The proposed government legislation, if passed, would allow gamblers to place a bet on the outcome of a single sports game, like a football match or a hockey game. Currently, sports bettors in Canada are limited to "parlay" bets — meaning they have to place bets on more than one game, what is spread sports betting pick the how much is wagered on illegal sports bets team in each contest, to see any sort of windfall. The odds of a winning parlay bet are low. Liberal MP Irek Kusmierczyk, who was elected last fall to represent Windsor-Tecumseh, said in a Facebook post Wednesday that he's been "working hard since day one" to push the government to make the necessary Criminal Code changes, which could allow casinos like Caesars Windsor or racetracks like Toronto-based Woodbine to offer enhanced sports wagering. A spokesperson for Lametti declined to comment on legislation that has not yet been introduced in Parliament. While provinces and territories control gambling operations in Canada, all operators work within the limits of the federal Criminal Code, which addresses gambling regulations and laws. Government legislation is often easier to pass in Parliament than private member's bills because the government has more levers to pull to get bills through both houses of Parliament in a timely manner.
In this blog post, I will guide you through the steps to create a predictive algorithm using common machine learning techniques:. Since the NFL season is currently about halfway through, it provides an intriguing and relevant source of data upon which we can build our models.
The quickest way to get up and running is to install the NFL Game Predictions Python environment for Windows or Linux , which contains a version of Python and all the packages you need to follow along with this tutorial, including:. All of the code in this tutorial can be found on my GitLab repository here. When creating a model from scratch, it is beneficial to develop an approach strategy that clearly delineates the goal of the model. Doing so clarifies which data should be used, how to manipulate the data to construct a training set, and where to obtain the data.
Because our goal is to predict the outcomes of NFL games in the season, the first thing we need to define is the statistical metrics that can best determine whether a team wins or not:. Our model should include all these in-game statistics, among others.
In addition to the standard in-game statistics, we can use external metrics within the model. There are many third-parties that construct their own metrics based on the same in-game statistics, qualitative rankings from experts, historical team rankings that go back decades, and even the exact players that are on the field during each game.
Some of these include:. Each has its own methodology, but all have been proven successful at predicting game outcomes. Our model should incorporate one or more of these external metrics. Our approach is to construct a dataset where each row represents a single game between two teams, and the columns are based on the aforementioned metrics.
The result of each game is given by either a 0 for a home team victory or a 1 for an away team victory. We can use logistic regression to make a prediction a probability between 0 and 1 of the away team winning or losing. Because the season is only half-way through, it is interesting to see if we can build a model using the games that have already been played to predict the games that will be played in the remainder of the regular season.
To obtain the data for the season, we first need to import the sportsreference package. The former gives the statistical information for a given game, while the latter provides the game information teams playing and who wins if the game has already been played. It is also worth noting that the nature of the NFL changes from year to year. As a result, the weights corresponding to each feature in our model can differ from season to season.
For more information on how to use the sportsreference package , refer to its documented capabilities. To see how these methods work in practice:. The first argument is the week of the NFL season week 1 , and the second is the season itself season. You should see something like this:. The first entry in the dictionary is a unique game string. We can use the game string given by boxscore to obtain the statistics for the game:.
There are 58 columns worth of statistics in the dataframe. Once a game is played, this dataframe is populated. If a game has not been played, an empty object is returned. The column names can be listed by running:. We also need to extract the schedule, so we know which teams are playing in the coming weeks. This is perhaps the easiest place to start. We can write a function that loops over each week, and each game within each week, to extract the schedule:.
This dataframe provides the basis for our final dataset, as each row corresponds to a game. The two inputs specify which weeks to loop through in a given season. The output should look something like this:. Thus far we have written functions that allow for the extraction of the NFL schedule, along with the in-game statistics of the games that have been played.
So, for example, if a game is played in week 6 between the Tennessee Titans and the Houston Texans, the features should represent how the Tennessee Titans and Houston Texans have performed in weeks 1 through 5. So for the games in week 2, the statistics are solely given by the week 1 results. For the week 3 games, the statistics are the average results from weeks 1 and 2.
For each game in question, this process of aggregation is done for both the away and home team, then merged onto each respective team. Following this, we calculate the differential statistics between each team. For our model to be able to predict which team wins, we need features that represent differential performance between the teams, rather than absolute statistics for each team in separate columns. If a game has not yet occurred e. The final dataframe should look like the following:.
The rating is essentially a power rating for each team, based on their historical head-to-head results. This includes not only games from the current season but all previous seasons. But in addition to the power rating, they also include a few corrections, including a correction for the quarterback playing in each game. While their models achieved an accuracy of This fact made me realise something.
Bookmakers have their own data science team. Before I write the first line of code I was determined to find out if this was really feasible. At some point, I thought that maybe it was not legal to use your own algorithms, to which a simple Google search answered that it is allowed. Then I thought about bookmakers and how they regulate or limit the amount you can bet. This dissertation is where my research stopped. This paper explained how the authors attempted to use their algorithm to monetize and found two main barriers.
Therefore, as your ML model points you towards the more certain results, you might always end up with a low benefit. Second, and even more important:. Consequently, when you start to win often, bookmakers will start discriminating against you and restraint the amount of money you can bet.
You have to dedicate a lot of time and effort to make many bets and withstand being flagged by bookmakers. My conclusions are that developing ML models for sports betting is good only for practice and improvement of your data science skills. You can upload the code you make to GitHub and improve your portfolio. However, I do not think it is something that you could do as part of your lifestyle in the long term. Because at the end bookmakers never lose.
Ultimately I ended up not doing a single line of code in this project. I hope that my literature review helps illustrate others. Follow me on LinkenIn. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Make learning your daily ritual. Take a look. Get started. Open in app. Sign in. Editors' Picks Features Explore Contribute. My findings on using machine learning for sports betting: Do bookmakers always win?
A naive money-oriented idea? Manuel Silverio. Written by Manuel Silverio. PhD in Digital Transformation. Sign up for The Daily Pick.
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Each model is automatically tracked and users have access to a backtesting tool to view every projection made this season. Past results can be viewed day-by-day, or backtested with a variety of custom criteria. While most MLB models make projections based on how a team's been hitting as a whole, our offensive projections are based on each and every player included in that particular team's lineup for the day.
This means our model waits for each lineup to be posted usually within a few hours before first pitch , then analyzes it on a player-by-player basis. This method is to ensure the highest accuracy in predicting a team's performance. The challenge of MLB is analyzing advanced data to determine which players have been lucky and unlucky in relation to their actual performance.
Much like a player projection system, our model identifies a "true" performance level for players and projects games accordingly. It's a stat that takes a stab at measuring overall efficiency on both the player and team level. From NBA. In fact, a team's PIE rating and a team's winning percentage correlate at an R square of. We also believe recent team play is a better predictor of a team's future performance than their play from several months ago, and as such, recency is more heavily weighted.
Our NBA model doesn't care about a team's record. It objectively measures a team's efficiency throughout each game, from start to finish, possession by possession - regardless of whether it ended as a win or loss. A team being on an slide doesn't mean much if they played top teams competitively.
Conversely, a team being on a 4 game win streak where they barely beat bottom-feeding teams doesn't paint the whole picture either. Objective measures of efficiency are what gives our model the ability to find value in over and underpriced lines in the market as a result of the general public's ignorance. College basketball teams only play about 30 games with the same player group before the season is over and turnover begins, so interpreting the right metrics is important for any model.
How we generally interpret basketball here at SMA is by putting more weight into metrics with less volatility than whether or not the ball falls into the hoop.. Shot proximity is important in this regard, so for example, the model doesn't like long two point jumpshots. The college three-point line is about 2 feet closer than the NBA's depending where you shoot along the arc , so the most efficient teams need to be taking threes or taking high-percentage, close-proximity shots.
Team passing abilities are important along these lines of thinking too, and so is offensive rebounding ability. There's just not enough of a sample
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