Sports Betting Analysis

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Sports Betting Analysis

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Sports Betting Analysis Video

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A gamble is said to have value when the implied probability based on the odds is a lower percentage than the likelihood you calculated from your own analysis.

But how do we accurately determine the probability so that we have a number to compare against the odds? We have to create betting systems based on statistical analysis and probability distributions.

This guide is meant to help you understand how handicapping is done and get you started. When we talk about statistical analysis as it relates to sports betting, we are usually talking about regression analysis.

Regression analysis is a set of processes used to determine the relationship between a dependent variable and one or more independent variables.

It may take some time and lots of playing around with large sets of data, but the reward will be worth it. You can do this for lots of factors to get an idea of which variables impact winning and losing, and to what degree.

The more statistically significant a variable is, the more likely you can trust it to correlate to winning. Because there are always numerous variables at play at once, all of which impact the outcome of a game or contest to some degree, multiple regression analysis is the system most commonly used for sports betting.

Rather than just pick a single statistic, this system considers numerous regressions to predict a future outcome based on past data.

The handicapper analyzes these regressions and extrapolates a predicted outcome. Based on the data, New Orleans is expected to have worse defense on the road, while the Rockets excel at home.

Despite the Pelicans winning the most recent contest between the two teams, the data seems to suggest the Rockets will be taking the upcoming game in Houston.

The key to reliable regression analysis is having extensive historical data sets to manipulate.

You want as many statistics available as possible, both for the teams and individual players. From there, you can begin narrowing your focus to specific factors that strongly correlate to precise outcomes.

Logistic regression is a method for analyzing data in which the outcome is determined by one or more independent variables. This form of analysis can be used to obtain an odds ratio despite having more than one explanatory variable.

The calculated result is the impact the combination of variables has on the desired outcome, represented in an odds ratio.

Any time you are working with statistical analysis, you must remember that correlation does not necessarily mean causation. Regression analysis can be used to find variables that correlate, like home field advantage and winning, but it cannot prove that winning is caused by playing at home.

Probability distributions are methods of providing the likelihood of the occurrence of different possible outcomes.

Rather than just solving for the most probable outcome, they provide the likelihood of each possible result. Graphical models may then be used to represent the range of probabilities.

Bayesian networks are a type of graphical model that can be used to make predictive distributions. The networks are broken up into levels consisting of variables that may impact the outcome of a match.

For example, if we are trying to base our prediction on team strength, level one would be comprised of values for historical inconsistency, team performance, and average goals per game.

For level two, we would consider those previous factors, but also look at the injuries for each team. Then we forecast the two teams again based on this additional filter.

These additional variables are added to the other levels to comprise level three. We then make a final forecast.

Poisson distribution is a predictive method typically used in soccer or hockey betting, or for NFL prop bets based on things like sacks.

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