Regression Analysis in Sports Betting Systems – Part Two

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.

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