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п»їSports betting statistical analysis using.
Sport betting is a form of wagering on the outcomes of traditional probability games such as cards, dice, or roulette as well as on the outcomes of sporting events such as football or baseball. Betting results are resolved at the conclusion of the event and generally requires that neither of the parties involved in the wager has any influence on the event in question. A private citizen wagering on the outcome of a football match between Manchester United and Chelsea would be considered a form of sports betting, while the team owner making that same bet would not be considered a form of sports betting.
Sport betting has been popular for centuries. The earliest forms of contemporary sports betting revolved around cock fights in the late 17 th century, while wagering on horse races became highly popular between the 19 th to 20 th centuries. In 1960, television gave birth to a new era in the history of betting on team-based sports, and the longstanding 10% tax on sports betting was eliminated in 1974, leading to even more popularity. In 1990s, the advent of the Internet facilitated online sports betting, creating an increasing need for sophisticated statistical tracking, like regression analysis, to develop winning strategies for wagering.
Traditional gambling and sports betting: Are they same?
Sports gambling: Profitable or not.
In 2011, the gross gaming revenue of the global gambling industry was $368.4 billion, with 8.60% of this total earned from wagers made over the Internet. Online gambling includes all Internet-based portals that provide lotteries, poker, casino games, bingo, and sports betting. Sports betting remains most popular form of online wagering, representing 43% of gambling revenue earned from Internet sources, with a total market of $13.66 billion.
Figure: Worldwide gross gambling revenue.
**source: Statistical Methodology for Profitable Sports Gambling.
Methods used in sports betting system.
Various methods can be used to generate a sports betting system, although most experts agree that the most widely used method is regression analysis. Regression analysis can be used to establish the important factors and variables which will influence the overall outcome of a sporting event. Multivariate linear regression, logistic regression, and multiple regression analysis can all be used to calculate the probability of any outcome, and since determining the outcome of a sporting event requires analyzing a high number of variables, regression analysis provides a suitable framework for defining and assigning a value to these variables. For example: A multivariate linear test on American football games was conducted by NFL. The result showed that the most important variable – the variable with the highest influence over the outcome of the match – was “passing efficiency”. Recent movies and bestseller titles like Moneyball have delved into the world of statistical analysis, driving increased interest in the use of regression analysis for sports betting.
Logistic regression analysis.
Logistic regression is a forecasting technique that provides a probability percentage for a given variable. For example, if one wants to calculate the probability of a team winning the 59 th game of the season, they would analyze the last 58 games to obtain the team’s point differential or margin of victory (MV or MOV). Margin of victory is a statistical term which indicates difference between the number of points scored by the winning team and the number of points scored by the losing team. A smaller MV represents a close match, and by using statistical software like SPSS, the following equation can provide the percentage chance that the team will win, based on MV scores:
(e is known as euler’s number, roughly 2.72).
A percentage chance of winning can also be determined in Microsoft Excel by using this equation:
Multiple regression analysis in sports betting.
Multiple regression systems are widely considered the most reliable modern sports betting system. This core of MRA is built on a timeless logical assumption: “what’s past is prologue”. This means that one must know the past to know the future. To create a multiple regression betting system, one must have reliable data regarding past information of the players and teams, meaning that trustworthy historical data is crucial to building an effective multiple regression system.
An example of using a multiple regression system in sports betting.
A sports bettor will wager on the final match between Team A & Team B.
Regression #1: Bettor finds that Team A won the regular series against Team B by 3-1 during the first match of the year.
Regression#2: Bettor finds that Team B crushed Team A in a recent playoff match.
Regression#3: One player of Team A is Player X, and Player X has never won against Team B.
Since both teams have scored a victory, bettor determines that the key variable is the presence of Player X, and decides that Team B will win the match. Thus, by using multiple regression analysis, bettor is able to analyze the events of the past and extrapolate the most probable future.
To utilize multiple regression methodology in a betting system, one needs to posses consistent and reliable data on the past performance of both teams and players (“Multiple Regressions”:2013). Without an extensive and dependable source of historical data, the bettor will not be able to regress into the past to determine probable outcomes of future events (“Multiple Regressions”:2013).
To develop a multiple regression system, mining data from an online sports book that can offer accurate historical sports data in a format that is easily accessible and actionable is highly recommended. These sports books also provide step by step rules for implementing regression analysis techniques in sports betting.
Note that regression analysis methodology is also employed by most casinos in an effort to generate probabilities that favor the house – for similar reasons, sports books use regression analysis to provide sports betting enthusiasts with the same advantage. While we all know that no future event can be predicted with 100% accuracy, a comprehensive regression analysis system can be used by sports boo developers to calculate probabilities that are highly reliable.
Problem of using regression analysis in sports betting.
There is one glaring problem in using regression analysis to predict outcomes of sporting events: the differentiation between correlation and causation. Regression analysis is effective at identifying a correlation between events, but cannot properly identify whether one event is caused by another. For example: regression analysis can be used to show that every time Team A loses, player X does not score a goal. However, regression analysis cannot be used to conclude that Player X not scoring a goal is the cause of Team A losing the match.
In other words, regression analysis can be used to determine probable future performance based on defined past outcomes, but is unable to define causes for past outcomes. Ultimately, the effectiveness of any multiple regression system relies entirely on the proper selection and comparison of variables.
Other betting systems.
In addition to multiple regression analysis, there are two other commonly used wagering methodologies: the arbitrage betting system and the use of statistical anomalies. Arbitrage betting is designed to generate profit without taking a loss (“Multiple Regressions and Statistical Anomalies”:2012), and in most cases the result of sports event is not considered. Naturally, profits are not guaranteed, but arbitrage is a straightforward strategy that can easily be learned by novice bettors.
When implementing a strategy around statistical anomalies, the bettor seeks to gain a competitive advantage by diverging from seemingly sound predictions by introducing variables that are often overlooked by other forms of betting systems. Using this tactic successfully requires a careful study of both teams and players, as well as a variety of incidental variables, such as weather, crowd sizes, health conditions, or injuries. By using this methodology, the bettor is attempting to determine how individual players and teams deal with anomalous situations not generally encountered during a match (“Multiple Regressions and Statistical Anomalies”:2012).
While regression analyses can help a bettor identify and define the variables that may affect the outcome of any given match, determining which variables to measure and compare is the central challenge in building a winning regression system. Therefore, regression analysis in sports betting is based upon not only a comparison of reliable past data with future events, but in deciding which variables may potentially alter the probabilities of those future events.
Mathematics and Statistics in Sports Betting.
Don't be afraid of Mathematics and statistics when betting on sports. You may ignore teams' stats but you cannot afford ignoring these simple calculations.
Some mistakenly believe that mathematics and statistics are insignificant in sports betting. The truth is that just like in casino games, the effectiveness of a sports betting system to generate profits depends strictly on mathematics .
Even when there is no obvious system, as if a player bets blindly, the bettor may inadvertently wager using math correctly! Even in the case when betting decisions are influenced by the news, predictions and rumors, a player’s profitability is directly depended on math .
On the other hand, statistics may be applied when we create a system, but are mainly used in the study of the system’s results, as in testing the credibility of the followed methods.
Mathematics in sports betting: Just a simple equation.
The truth is that for most people betting on sports is more like a hobby – as it should be.
However, if sports bettors spent some time on making the following very simple calculations, it would be possible to minimize the losses from betting and, why not, stop being an expensive hobby.
There is no doubt most players lose a lot of money betting , either online or offline. The majority of players do not record the results of their bets. In other words, they do not systematically track what comes in and what goes out on their betting account each month. This is one of the 10 reasons we lose in sports betting.
Going back to the topic of mathematics in betting, coming out a winner in sports betting depends on a very simple equation .
Consider the average of odds you bet on, let us say 2.00. Now think how often your betting tips win. Suppose the answer is 45 percent. This means that for every 100 bets, you win 45 bets corresponding to 45 units profit (since you are betting on 2.00 average odds). At the same time, you lose 55 bets, which translate to a loss of 55 units.
If you wager €10 per bet on average (in this case one unit equals €10), after 100 bets you would make €450 on your successful predictions and lose €550 on the rest. So you will find yourself to be losing a total of €100. If you bet 100 times each month (about 3 matches on average per day), sports betting costs you about €1,200 every year ; more than a regular monthly salary for most people.
The above example can easily be represented by the general equation Y= X*Z, where X is the average odds of betting and Z is the success rate of predictions. If the product (Y) is greater than 1, you will be a winner in sports betting in the long run. Otherwise, the smaller than 1 the quicker you lose your capital.
These are actually the only math to have in mind – for the bettors who want to bet merely for the enjoyment. The result (Y) in that mathematical equation distinguishes players between winners and losers and betting systems between profitable and money-losing.
Write down all the matches you bet at odds of 2.00. Then verify how many of those bets have worked well. If it is 50 percent or less, the news is not good. However, if it is over 50 percent then, in general, you pick your bets correctly .
How do I win in sports betting? The answer lies on the equation!
We all wonder how come some people manage to win in sports betting. That is the same as asking how the Y in the equation above is larger than 1. The answer of course lies on X and Z.
In the example above , we saw that X equals to 2.00 and Z to 0.45 (45 percent). Thus, in order Y to become larger than 1, either X or Z should increase. This means we should choose higher odds than 2.00 or increase the success rate of our forecasts.
So there are two solutions:
to stick to the same method of picking our bets looking, however, for better odds ; or to improve our betting system’s winning probability .
In the first case, odds comparison is crucial, while in the latter we should work on the parameters and variables of our system.
You can read more about the relationship between the odds and probability in the article about how we select the right bets online.
Statistics in Sports Betting.
We have now demonstrated how a single mathematical equation distinguishes winners from losers. Since we are on the awkward subject of math, let us say a couple of things about statistics and odds.
There are quite a few posts that I read online from time to time that advise players not to follow the statistics, if they want to win in sports betting. They claim that statistics are there to be challenged, as historic data and the frequency of a team scoring, for example, do not have any effect on our sports betting performance. As they say:
[quote type=”center”]The ball spins without knowing any laws or statistics![/quote]
Indeed, it is a totally respectable view, no objection on that. Nevertheless, we must not forget that there are a large number of applications and innumerable excel sheets dealing with statistics in sports betting; a fact that, if no other, demonstrates that a great percentage of players are trying to beat the odds through the analysis of teams’ and players’ statistics. By completely rejecting the notion of statistics in sports betting is like deploring those who follow it.
Moreover, we should consider the fact that in every sport event, statistics are reported during the event. At the same time, major sports news sites keep statistical data for many years to come.
Yet you might say: well Jim, you already answered that question yourself. Statistics just sell to a whole lot of people who think they may become winners following statistical models. They are giving them the necessary hope to keep them into the game, to keep them interested.
This is indeed an explanation that perhaps I should write about in the future. Having said that, I must also mention that at times betting systems emerge, which rely exclusively on the statistical analysis of the games. Moreover, in several movies statistics are shown as the Holy Grail of betting . What the heck, a part of these allegations must be true.
Nevertheless, statistics in sports betting are applied extensively when building or improving a particular betting system . Now, I am not talking about the input variables of a system, such as statistics used in tennis matches. I’m referring to the statistical analysis of the system’s actual performance, such as the drawdown.
By studying the reliability of the system, based on statistical aggregates, we are confident for the betting system’s future performance , while confirming the proper functioning of our system. A system that makes 5 points out of a sample of 100 bets may be satisfactory. Yet, I would rather have a system that makes 250 points tested on a sample of 10,000 games! And that is where mathematics and statistics make a huge difference in sports betting.
Sports Betting - Statistics & Facts.
The global gross gaming/gambling yield amounts to more than 400 billion U.S. dollars each year. Gross win from gambling represents the amount of money the gambling operation keeps from the customer’s stakes, wagers, bets etc. less the winning paid out to the customer and before deducting operating expenses. With about one third of the global gambling gross win, Asia is the biggest market for gambling and sports betting in particular. Probably the fastest growing segment of the industry is the online / interactive category, as the global online gambling market has grown at a consistent rate over the last few years from around 20 billion U.S. dollars in 2009 to more than 40 billion U.S. dollars by 2016.
Sports betting services are provided by companies such as William Hill, Ladbrokes, bet365, bwin, Paddy Power, betfair, Unibet and many more through their websites and in many cases betting shops. In 2015, William Hill generated around 2.37 billion U.S. dollars in revenue with about 13.26 billion U.S. dollars in total being staked / wagered with the company.
According to a Statista survey, almost 50 percent of people 18 years and older in the United States have placed a bet on a sports event at least once in their life. That share is even higher amongst those that watch at least some March Madness games.
This text provides general information. Statista assumes no liability for the information given being complete or correct. Due to varying update cycles, statistics can display more up-to-date data than referenced in the text.
Interesting statistics.
In the following 8 chapters, you will quickly find the most important statistics relating to "Sports Betting".
Applying Data Science to Sports Betting.
Jordan Bailey.
Sep 18, 2018 В· 7 min read.
In the past few months, I took a class in Data Science through General Assembly, a coding academy. We primarily coded in Python, with extensive use of Python libraries designed for mathematical computation & statistical analysis (Numpy, MatPlotLib, Pandas, and others). I wanted to write this article to detail the motivations, methods, & findings of a data science project I created as part this class, in a way that was more accessible to a non-technical audience.
Introduction.
For our final project, w e were directed to use the tools we developed over the course to come up with a problem which could be addressed using a machine learning model.
I will go through my workflow throughout this project, starting with stating the problem I set out to address, looking at my method of acquiring the data, moving through how I structured my dataset, and ending with an explanation of the models I created and an interpretation of their results. I’ll conclude with some lessons I learned in the process.
The Problem.
I’m a big fan of NBA basketball. The idea for this project occurred when trying to think of a way to use basketball statistics in a machine-learning context. I initially thought of using box score statistics from previous games to make a prediction as to whether a particular team would win or lose. I am also a big fan of sports betting (in theory, less so in practice), so I thought it would be more interesting to try and make a prediction using box score statistics, with the idea I could use that prediction to inform a bet.
My decision for the ultimate direction of the project was to use NBA box score statistics from previous games to train a logistic regression model. This model would return a prediction as to whether a game’s total score would be Over | Under a point total set by a bookkeeper. The hope was I could use the predictions over the course of an NBA season to inform a long-term betting strategy.
Context.
For every NBA game, a sports book will set a point total, which is the book’s prediction of the final score. The sports book challenges bettors to bet either an OVER, if the bettor believes the total final score of the game will be higher than the total set by the book, or UNDER, if the bettor believes the total final score of the game will be lower than the total set by the book. If the game’s total final score is equal to the point total set by the book, that’s called a PUSH, and the book returns the bet. (For a more detailed explanation of the sports betting context behind my project, please see my Technical Report)
Acquiring Data.
Structuring the Data.
My goal was to return an Over | Under prediction for an NBA game, based on the box score statistics for the previous games. To do this, I structured my dataset in a way that each individual game was represented as the box scores for the 3 prior games for each team, for a total of 6 prior games. I made a small example below for reference:
#: represents number of games back from the current game.
Current Game: A vs B.
Represented As: 1 A vs O, 2 A vs O, 3 A vs O, 1 B vs O, 2 B vs O, 3 B vs O.
By setting up my data up in this format, I hoped to create a model which would pick up on a relationship between the prior 3 games of box score statistics and the total score of an NBA game. In addition, I thought there may be bias on the part of the bookmakers in terms of adjusting the point totals based on the performance of the teams’ previous games; bias that my model could detect.
Modeling.
The process of taking this dataset and drawing insight from it involves creating a model. (See Glossary) The specifics of the model and how I created it are fairly technical, so I’m going to refer people who are interested in those details to the Modeling section of my Technical Report. At the end of the process I had created 2 models, one which returned a prediction on whether an NBA game would be OVER a set line or not, and one which returned a prediction on whether an NBA game would be UNDER a set line or not.
Results.
With the predictions from my models, I could analyze how often my model returned correct predictions. My models, making a prediction on all the NBA games in a season, were not significantly better than the average in determining whether a game would be Over | Under. Therefore, I set up what I termed a confidence threshold; my model returned probabilities for the chance of a game being Over | Under, and if the probability of a prediction was above the threshold I set, the prediction was “confident.”
Here is a breakdown of my results, with a confidence threshold of 62%:
Predicted confidently: 88 games Predicted correctly: 52 games Accuracy: 59.09%
Predicted confidently: 96 games Predicted correctly: 52 games Accuracy: 54.16%
I set the threshold at 62% because that point optimized both prediction accuracy and the # of games that were predicted “confidently”
My model did better in accurately predicting games that were Over as opposed to those that were Under, for the 2018 NBA season.
I have a guess for this phenomenon:
Basketball point totals have been increasing over seasons. Looking at the past 5 years of NBA games, the average score has increased by.
5 points per team.
Simulation.
I created a basic simulation, combining the predictions for each game in the 2018 NBA season. Starting with $10,000, and making a bet each time my models predicted a confident bet (as above, set at the threshold of > 62% probability), I show how my model performs informing a betting strategy over the course of the season:
The final total for the betting strategy informed by my model’s predictions was $11,880 , with an accuracy of 56.52% on the bets that were predicted confidently.
Conclusions.
Working with time series data was more difficult than I expected. Because I chose to represent an NBA game as 6 different sets of statistics, I had to coordinate a large amount of data along a time dimension. One of our instructors said 90% of the work in data science is cleaning and arranging data, and that was certainly true for this project. Although my models were successful in returning enough accurate predictions to return a profit, I would not use these model to inform a long-term betting strategy, or I would maybe use the OVER model I created in conjunction with other strategies. My reluctance stems from the limited # of games the models were able to predict confidently. Also the fact that I had to manually target the level at which the models were maximally effective is a caveat to my results. I had a good time working with sports betting data because the results were visible, and I could show clear benefit to the work I did.
Glossary.
I recognize the concepts present in the Data Science portions of my project can be difficult to grasp, so I’m breaking down some of these concepts and how they’re used in my project here.
Data Science — The interdisciplinary practice of using scientific methods, algorithms, and systems to draw insights from data. Combines elements of computer programming, statistics, mathematics, machine learning, as well as domain-specific knowledge. Web Scraping — The process of writing code to pull data off of a website. For a look at the python code I used to scrape the data, see my Web Scraping Notebook Over | Under — The bar between the word over and under can be read as “Over or Under” Model — A model is a system of making a determination about the value of an unknowable data point, using information you have available about the characteristics of other data points. Logistic Regression — The process of determining a relationship between a set of data points, where each data point falls into one of two categories. This relationship can then be used to make predictions on the category of additional data points. Can be thought of as predicting classification into one of two categories (in this case, classifying each game as either OVER or UNDER)
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