How to read betting lines? A minus symbol in betting odds tells you that you need to wager that much in order to win a $100 profit. A $100 bet at -110 would earn you a $91, while a $10 bet at -110. Odds represent the likelihood of an outcome occurring. In sports betting, each team is assigned odds that represent the likelihood of them winning the game. When the odds for two teams are even, meaning 1 to 1, it means that each team is equally as likely to win the game. So the odds for males are 17 to 74, the odds for females are 32 to 77, and the odds for female are about 81% higher than the odds for males. Now we can relate the odds for males and females and the output from the logistic regression. The intercept of -1.471 is the log odds for males since male is the reference group (female = 0).
- How to Read American Odds American odds are centered around winning or wagering $100 on a given bet. If You’re Betting a Favorite: The odds for favorites will have a minus (-) sign in front, and indicate the money you need to risk to win $100.
- Odds are displayed in either American, Decimal, or Fractional formats, and serve two purposes: They signal the implied probability of the outcome they are attached to They indicate how much money you could win betting on that outcome.Be careful relying on the odds alone when evaluating the probability an event will occur.
Grab your helmets and jerseys as you have reached the greatest NFL odds on the Internet. MadduxSports.com and its 14 offshore sportsbook partners are proud to bring you live point spreads for all of the NFL football games for this week. In addition to offering the live betting lines, the sources they come from are first-class, first-rate betting sites that have earned the stringent approval from the Maddux Sports handicappers. We go through the rigorous process of selecting quality sportsbooks so payouts for the visitors of our sites are of the utmost ease. With what you are getting below, most sites charge for, but not at Maddux Sports as all of these NFL football odds are complimentary just for being a visitor. Just remember to bookmark us for future reference. Now that we got all that out of the way, how does this free NFL lines feed work you ask. Well it is very simple. We have sorted the different point spreads in a few tables/categories. The first is the standard NFL game spread and total that most of you are accustomed to. The second layout is the NFL money lines (for more on what moneylines are scroll down to the section). Next we bust out the current 1st half lines and after that the second half lines (which are posted once the games go to halftime). A new feature that at the time no other lines feed offers is quarter betting lines for all four quarters of the game. For those that are still reading now you know what we offer, how about how do you use it? Simply find the game you have your eye on and check out each books point spread to find your best line and best option to bet. You can than click on the sportsbook name with the point spread you like and be transported to the book of your choice. Please note some users will need to hit the scroll bar at the bottom of the page to view all the books depending on your computers resolution. The NFL football odds presented here are and always be free of charge. The live NFL lines runs from day 1 of preseason football all the way through the super bowl. Each and every NFL team including the Patriots, New York Jets, Cowboys, and Packers are covered. By having this line service at your disposal you can always say you never took a bad number and always did everything in your power to be make a great NFL bet. Remember that half points especially around key numbers like 3,4,6,7 and 10 turn pushes into wins and losses into pushes. We advise having sports betting accounts with no less than 3 of the different books beneath. That does it for the how to guide, feel free to start browsing the NFL odds below. This season don’t let your hopes of point spread glee come down to referees or quarterbacks do your best to be a sharp sports bettor by shopping for soft lines. |
Odds and odds ratios are an important measure of the absolute/relative chance of an event of interest happening, but their interpretation is sometimes a little tricky to master. In this short post, I'll describe these concepts in a (hopefully) clear way.
From probability to odds
Our starting point is that of using probability to express the chance that an event of interest occurs. So a probability of 0.1, or 10% risk, means that there is a 1 in 10 chance of the event occurring. The usual way of thinking about probability is that if we could repeat the experiment or process under consideration a large number of times, the fraction of experiments where the event occurs should be close to the probability (e.g. 0.1).
The odds of an event of interest occurring is defined by odds = p/(1-p) where p is the probability of the event occurring. So if p=0.1, the odds are equal to 0.1/0.9=0.111 (recurring). So here the probability (0.1) and the odds (0.111) are quite similar. Indeed whenever p is small, the probability and odds will be similar. This is because when p is small, 1-p is approximately 1, so that p/(1-p) is approximately equal to p.
But when p is not small, the probability and odds will generally be quite different. For example if p=0.5, we have odds=0.5/0.5=1. As p increases, the odds get larger and larger. For example, with p=0.99, odds=0.99/0.01=99.
Fractional odds and gambling
Particularly in the world of gambling, odds are sometimes expressed as fractions, in order to ease mental calculations. For example, odds of 9 to 1 against, said as 'nine to one against', and written as 9/1 or 9:1, means the event of interest will occur once for every 9 times that the event does not occur. That is in 10 times/replications, we expect the event of interest to happen once and the event not to happen in the other 9 times. Using odds to express probabilities is useful in a gambling setting because it readily allows one to calculate how much one would win - with odds of 9/1 you will win 9 for a bet of 1 (assuming your bet comes good!).
Odds ratios
In the statistics world odds ratios are frequently used to express the relative chance of an event happening under two different conditions. For example, in the context of a clinical trial comparing an existing treatment to a new treatment, we may compare the odds of experiencing a bad outcome if a patient takes the new treatment to the odds of a experiencing a bad outcome if a patient takes the existing treatment.
Suppose that the probability of a bad outcome is 0.2 if a patient takes the existing treatment, but that this is reduced to 0.1 if they take the new treatment. The odds of a bad outcome with the existing treatment is 0.2/0.8=0.25, while the odds on the new treatment are 0.1/0.9=0.111 (recurring). The odds ratio comparing the new treatment to the old treatment is then simply the correspond ratio of odds: (0.1/0.9) / (0.2/0.8) = 0.111 / 0.25 = 0.444 (recurring). This means that the odds of a bad outcome if a patient takes the new treatment are 0.444 that of the odds of a bad outcome if they take the existing treatment. The odds (and hence probability) of a bad outcome are reduced by taking the new treatment. We could also express the reduction by saying that the odds are reduced by approximately 56%, since the odds are reduced by a factor of 0.444.
Why odds ratios, and not risk/probability ratios?
People often (I think quite understandably) find odds, and consequently also an odds ratio, difficult to intuitively interpret. An alternative is to calculate risk or probability ratios. In the clinical trial example, the risk (read probability) ratio is simply the ratio of the probability of a bad outcome under the new treatment to the probability under the existing treatment, i.e. 0.1/0.2=0.5. This means the risk of a bad outcome with the new treatment is half that under the existing treatment, or alternatively the risk is reduced by a half. Intuitively the risk ratio is much easier to understand. So why do we use odds and odds ratios in statistics?
Logistic regression
Often we want to do more than just compare two groups in terms of the probability/risk/odds of an outcome. Specifically, we often are interested in fitting statistical models which describe how the chance of the event of interest occurring depends on a number of covariates or predictors. Such models can be fitted within the generalized linear model family. The most popular model is logistic regression, which uses the logit link function. This choice of link function means that the fitted model parameters are log odds ratios, which in software are usually exponentiated and reported as odds ratios. The logit link function is used because for a binary outcome it is the so called canonical link function, which without going into further details, means it has certain favourable properties. Consequently when fitting models for binary outcomes, if we use the default approach of logistic regression, the parameters we estimate are odds ratios.
How To Read Spreads
An alternative to logistic regression is to use a log link regression model, which results in (log) risk ratio parameters. Unfortunately historically these have suffered from numerical issues when attempting to fit them to data (see here for a paper on this). However there is also a more fundamental issue with log link regression, in that the log link means that certain combinations of covariate values can lead to fitted probabilities outside of the (0,1) range.
Sports Betting Odds Explained - How To Read Betting Lines
Case control studies
Reading Odds Betting
In case control studies individuals are selected into the study with a probability which depends on whether they experienced the event of interest or not. They are particularly useful for studying diseases which occur rarely. A case control study might (attempt to) enroll all those who experience the event of interest in a given period of time, along with a number of 'controls', i.e. individuals who did not experience the event of interest. In a case control study the proportion of cases is under the investigator's control, and in particular the proportion in the study is not representative of the incidence in the target population. As a consequence, one cannot estimate risk or risk ratios from case control studies, at least not without external additional information. However, it turns out that the odds ratio can still be validly estimated with a case control design, due to a certain symmetry property possessed by the odds ratio.
Rare outcomes
Cached
When the event of interest is rare (i.e. the probability of it occurring is low), the odds and risk ratios are numerically quite similar. Thus in situations with rare outcomes an odds ratio can be interpreted as if it were a risk ratio, since they will be numerically similar. However, when the outcome is not rare, the two measures can be substantially different (see here, for example).
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