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 59th 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:
1 / (1 + EXP(-(-0.0039+0.1272*[MOV])))
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.
Built it with a couple sites with a friend using:
http://cran.r-project.org/bin/macosx/ pretty much free version of SAS
. This shyt gonna be my ruin
hold up what you betting on?
Some games Im thinking about playing on Saturday. Ive been lurking covers per your suggestion
is that a BETDSI or Bookmaker betting ticket I see?
What other stats did you use in your model?
Three of those better be the LAkers, Cavs, and Wizards over