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|Mathematica sports betting||The Logic Of Sports Betting. If you would rather back England, then you should wait to see if they get through the group stages where they are favourites. I tested mathematica sports betting models, refined them, and before too long I started to make money. Read more Read less. For any game of chance, the probability model is of the simplest type—the sample space is finite, the space of events is the set of parts of the sample space, implicitly finite, too, and the probability function is given by the definition of probability on a finite space of events:.|
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It's an albatross around the neck of fair-minded punters the world over. But baseball betting still operates according to odds , a system that is slightly more favorable to the common man. More important, those odds vary primarily according to each game's pitching matchups; if you have a contrarian notion that a certain ace is about to have an off game, you can really clean up. Horse racing, meanwhile, is a great way to make money if you're willing to put in time at the track.
As my relative notes, 98 percent of the people who bet on horses have no idea what they're doing; they plunk down money based on gut feelings, past performance, or cuteness of a competitor's name. You can run rings around those folks if you're willing to attend morning workouts with stopwatch in hand, as well as understand when it's appropriate to take a risk on a parlay. My relative's share in a successful Pick Six gambit is what put home ownership within his reach.
So why does pro football remain America's favorite sport upon which to wager? My relative has a theory about this which rings true, which he once expressed to me with a shrug: "People don't pay much attention to numbers. No, we don't. Because our nation has collective math anxiety, we put our trust in those who claim to understand what's really going on beneath the surface. But more often than not, such self-proclaimed experts are simply working a self-serving agenda, and don't really possess the wisdom which they claim.
Truth be told, I'd rather have such charlatans conning us into bad sports wagers than haphazardly creating securities. But given how little scrutiny we give to the basic mathematic wisdom of everyday transactions, I fear that we'll inevitably get fooled again—and again, and again.
Want more of my take on the intersection between gambling and math? Check out my recent ruminations on jai alai , underground Filipino lotteries , and Balinese cockfighting. We want to hear what you think about this article.
Submit a letter to the editor or write to letters theatlantic. Skip to content. 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. Thus, in order Y to become larger than 1, either X or Z should increase. 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. We have now demonstrated how a single mathematical equation distinguishes winners from losers.
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:. Indeed, it is a totally respectable view, no objection on that. 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. 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.
A system that makes 5 points out of a sample of bets may be satisfactory. Yet, I would rather have a system that makes points tested on a sample of 10, games! And that is where mathematics and statistics make a huge difference in sports betting. Sign in. Log into your account.
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We computed and added to the data several specific features, such as ratings, head to head statistics, that mostly affect the rightness of corresponding event. After adjusting the data we used built in Mathematica functions Predict and Classify to predict the events. These functions are used to give suggestions on the outcome of the match.
We then tested our predictions on the actual odds of our matches. The odds were crawled from another website which contained all of the winning odds of Spanish League matches played in between to This data allowed us to simulate betting and try to estimate the profitability of our program.
Finally, based on our calculations we suggest betting strategies that satisfy the most optimal risk-profit relationship. To start with, we imported our csv formatted match data and the ratings into Mathematica. Below is shown a sample of the match data csv file. Then we used the function SemanticImport, which returns a Mathematica dataset.
Then we did data cleaning to make it easier to work later. Now data is ready. The code below adds a new column named GoalsTotal to our dataset to provide the total goals for every match. Afterwards with the function addRating we add the ratings for each club in the league. Now that we have added the ratings and total goals we can start to compute some individual features for every team and add them to our dataset.
To accomplish that we first group all the matches by home and away status for every team. Afterwards we add to our dataset a very important data column - the individual total goals average per match. This feature is essential for predicting the total goals of a certain match the prediction of total goals is not included in this post, it will be published later. Another powerful parameter for predicting the total goals is the total number of goals that were scored during head to head clashes of two specific teams.
One of the most influential factors that affect the outcome of a match is the home and away statuses of the teams. The reason behind is that relatively weak teams tend to play better at home stadium. Continuing the data transformation we added the outcomes of the matches.
We give them numerical values: 1 for the win of the home team, -1 for the win of the away team and 0 for draw. This choice makes no difference in case of the classification done by the function Classify. However, it is crucial for predicting the outcome with the function Predict. Obviously, when needed, we can always remap the numerical values to the three outcome categories.
Another important advantage of using numeric outcomes is that it allows to add Head to Head statistics feature which numerically shows which team has advantage over its opponent in previous matches. Eventually we are going to compare the accuracies when using categorical and numerical outcomes.
In this phase of the data processing we add one more valuable feature which is Head to Head statistics. It basically illustrates the advantage of Team1 over Team2 during their previous matches. Then we filter the data based on the minimum number of total matches played between two specific teams.
Now we can initialize and train our classifier. There are 3 categories for the outcome of the match, namely 1 for Team1 victory, -1 for Team2 victory and 0 for the Draw. Here the dataset is separated into the training set and the test set. The columns which represent features that are relevant in predicting the outcome are HomeStatus , Team1Rating , Team2Rating and head to head statistics, labeled as H2H. Here we use the function ClassifierMeasurements to check the accuracy of our classifier.
This number shows how powerful is the head to head statistics feature. Now let's look at the confusion matrix plot which is very useful feature of ClassifierMeasurements function and allows to analyze the wrong classifications of the created model. From the plot we can see that our classifier does not tend to predict Draw as an outcome. Let's analyze the results a bit. By subtracting the outcomes of actual and predicted numerical classes we get a list named difference which contains integers from -2 to 2.
If the subtraction of actual and predicted values is 0 then the classification was valid. Numbers -2 , -1 , 1 and 2 are the matches which were classified incorrectly. The rest of the matches were classified incorrectly but with a little difference. The matches that have value 1 in the difference list were classified wrongly with just 1 step of miss.
Let us bring an example to make this a bit clearer. Let's take a match to which corresponds a value of 1 in the difference list. If our classifier predicted victory of Team1 and it missed obviously that means the match was drawn, because the difference of actual and predicted values is 1. At this part of the project we use a different approach to find out the outcome of the match. Similar to the method used in previous section we prepare the training and test sets.
We train the function and then we use the function PredictorMeasurements to test our prediction on some test data. So we got numerical values for the outcomes of the matches. It gave values ranging from roughly - 0. Later on we will write a function to convert the numerical values to categories but for now let us analyze the ResidualPlot of our predictor. The plot above shows the difference between actual and predicted values for the outcome of the matches of the test set.
Every point represents a single game. Moreover points that lie near the 0 axis are almost perfect guesses. All other points have a certain amount of distance from 0 which basically shows how far the predictions were from the right guess. Continuing with the outcome predictor, we must convert the numerical values of the predictor into 3 categories based on the results of the residual plot.
For now we only will create a very rough approximation to see how much accuracy we can get. Here we classified the predicted numbers in range Moreover numbers greater than 0. Similar to the method we used in case of the Classifier , we try to find out the accuracy of prediction as well as how bad our predictor did in the cases of wrong predictions.
Although the accuracy of our classifier has decreased slightly we see a fundamental improvement in totally missed guesses which are represented with number 2. Another advantage that the predictor has over the classifier is that now it also guesses reasonable amount of draws. Those advantages are very effective in betting, because draws are considered difficult events to predict and usually odds for draws are much higher than those for wins.
As it was mentioned above, our data has all the odds for the outcomes of all the matches of the league. Here is a sample of the odds data. Now let's simulate simple bets. To do that we randomly choose 10 matches and give them to our predictor to guess the outcome. As a result we get numerical values which we then convert into three classes with the help of the piece-wise function.
After that we get a list containing 1 s, -1 s and 0 s. Those are the outcomes that the predictor guessed for randomly selected 10 matches. Let's remember how we determined the correct guesses. We simply checked if the difference between predicted and actual class is 0 or not.
Therefore we construct a function that takes the difference between actual and predicted classes as an argument and returns 1 if and only if the difference is 0. Than we multiply the result of that function to the corresponding winning odds. This way we simulate a bet of 1 dollar for every match. That function is. So the question is weather we actually win? To figure that out we do Total of the won money and then subtract from it the 10 dollars we have bet in total 1 dollar for a match.
We won 7. However, do not rush to gamble. This process is purely probabilistic. To gain a better understanding of how profitable this kind of betting is, one needs to perform a sufficient number of betting simulations. Then only after analyzing the results and after calculating mean profit it would be possible to speak about long term wins.
However, the significance in the numeric approach to the prediction of the outcome is that we can decide on what event to bet based on numeric predictions. Let me bring an example. I recommend it as part of your education in sports gaming. There were times especially with the numbers and charts that I got flashbacks from my stats class in college and started to break a cold sweat.
Read more 2 people found this helpful Helpful Report abuse John Hughes 5. I so wish I knew this stuff years ago before I lost every sports bet I've ever made. I so wish I had something to bet on now. I've read a lot of books on sports but I've never read anything about betting before. I can't imagine there is anything better out there - at least not on a math level I could understand.
The author is funny and smart - they didn't include his picture so I don't know if he's good looking too. One day sports will be back and I will be ready for it! Read more 4 people found this helpful Helpful Report abuse Lindsay Deacon 5. Reviewed in the United States on July 13, Verified Purchase As a newbie to sports betting, I wasn't sure what to expect but I was immediately impressed with how much information this book has to offer.
The book is really well-organized and well-written. Topics are broken down into easy-to-understand concepts and strategies, and I felt like I've learned a lot, even when I am just flipping through -- and it never felt boring. This is a reference guide that could be used over and over so I'll be ear-marking pages, especially for when sports' seasons start back up again!
Read more 3 people found this helpful Helpful Report abuse Fernando Acosta 1. Your browser does not support HTML5 video. This book goes deep on spreads, moneylines, even football offenses. It touches on baseball and odds and betting times. This thing is the definitive source of sports betting. I never bet professionally, I play football parlays with my cousins and may have bet a couple of friends on the game but after reading this it will give you the courage to bet professionally when you feel the urge.
It is difficult to find all of this online without a ton of work, not to mention finding sources you trust. While I understood the basics of betting football and basketball, this book helped me understand the "why" behind the spreads, what they mean, and how to use them effectively for your particular goal across multiple sports.
Read more 3 people found this helpful Helpful Report abuse Brian T 5. Reviewed in the United States on July 12, As a fairly knowledgeable sports fan but not an expert on sports gambling, I found this book to be very helpful in explaining all of the intricacies of placing sports bets. It's one thing to know a simple betting line or future bet to win the Super Bowl for example , but another to determine what are the better bets to make either online or in person.
Definitely feel like a better educated gambler the next time I'm actually able to bet on sports. Great read! Read more One person found this helpful Helpful Report abuse See all reviews What other items do customers buy after viewing this item? Page 1 of 1 Start over Page 1 of 1 Previous page Next page There's a problem loading this menu right now.
Read more Read less. Kindle Cloud Reader Read instantly in your browser. Frequently bought together. Add all three to Cart Add all three to List. One of these items ships sooner than the other. Show details. Ships from and sold by Amazon. Customers who viewed this item also viewed. Page 1 of 1 Start over Page 1 of 1. Previous page. Josh Appelbaum. The Logic Of Sports Betting.
Ed Miller. Pat Hagerty. Statistical Sports Models in Excel. Andrew Mack. The Complete Guide to Sports Betting: The six key betting principles that professional bettors use to ensure profit at the sports book. Kevin Dolan. Next page. More items to explore. Then One Day Chris Andrews. Wayne L. Picking Winners: A Horseplayer's Guide. Andrew Beyer. Register a free business account. From the Inside Flap Understand odds, moneylines, and point spreads Wager on baseball, football, and basketball Gather and study data like the pros Can sports wagering be profitable?
Bet online or at a sports book Learn techniques for turning a profit Develop systems to succesfully bet on all the major sports Manage your bankroll Avoid bad betting habits Use fundamental and numbers-based analysis. Understand odds, moneylines, and point spreads Wager on baseball, football, and basketball Gather and study data like the pros Can sports wagering be profitable?
About the Author Swain Scheps has written extensively on gambling topics and is a veteran sports bettor and industry expert. Follow him on Twitter: swainscheps. Read more. Shop our favorite brands. Check out our wide selection of third-party gift cards.
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