How behavior analysis can help


Behavior is everything we as people do, including when looking for sports betting patterns. Behavioral analysis is about understanding WHY we do what we do.

Whether it’s individuals or groups of people, there are many variables in our environment that impact our behavior and how we react to certain things. Sports are no different. Players and coaches try everything they can to control the environment around them to produce optimal results. They do this to ensure optimal results.

Sports betting is also no different in terms of how bettors take into account different player or team behaviors before placing their bets. Bettors generally place bets when they have the best understanding of all the different variables that could impact the environment of a particular game.

To do this, bettors often turn to analysts (like the big ones here on Professional football network) to get valuable information and trends that could affect the outcome of a match. Bettors also look for sports betting patterns for projections and predictions that have strong correlations with actual results.

What is a sports betting model?

Sports betting models take an incredible amount of data points into account in relation to the outcome they are trying to project. Likewise, some betting models only select specific data points that correspond to what they believe to be most valuable for the outcome they are trying to project. In football, for example, these data points could be things like yards gained, yards given up, penalties, starting position on the pitch, points scored, dew point at kickoff, etc

Those who create models attempt to place relevant values ​​on these data points to compose their model to a level of precision that allows them to make reliable predictions. As a behaviorist, I try to identify specific team behaviors as data points to include in my model. Things like points scored and points dropped, among others.

I use these points to project winners against the spread (ATS), over/unders, etc. You can follow the success of my model each week by following me on pikkit. Pikkit is a community for bettors to interact around the bets they place, as well as my favorite bet tracking app there, so everyone can see how I’m doing.

Using Behavioral Analysis to Help Sports Betting

Applied behavior analysis (ABA) is often mistakenly associated with a model of therapy for people with autism. However, behavior analysis is a hard science based on collecting observable events, mapping that data, and finding patterns that lead to projections of future behavior.

Sound familiar?

As a behaviorist, I work directly with many NFL players on movement technique and behavior for their positions. Additionally, I observe the team behaviors of the 32 teams in the 32 different environments in which they operate on a weekly basis. Some of these behaviors are more useful than others in determining winners and losers.

I took the data collected in my observations and put it into a model using a Standard Celeration Chart (SCC), which is a logarithmic chart measuring how fast something is getting better or worse. worsen.

This model gives me projections for expected team behaviors in a few different areas which I use to bet against the spread, moneyline, over/unders and even some parlays. I even use it for some fantasy projections I play Underdog Fantasy. I love the programming feel of the games and the combination of some big win projections. All of this is based on the principles of behavior analysis.

This is a six-week chart of Cincinnati Bengals data on how many points they allowed per game as a team. This graph shows how quickly their team is accelerating in giving up more points (which is not good for a defense), the likely range of outcomes indicated by the green “bounce” lines, and the average, or level, of those data.

Explain level, acceleration and rebound

Part of adhering to the principles of Applied Behavior Analysis means knowing what we are looking at when we analyze trends, projections, and expected outcomes. It is very important that we know the definitions of the key terminology used, not only for the person doing the modeling, but perhaps even more important for you, the audience.

Below are three key terms to be aware of when analyzing the chart above:


The level on these graphs simply refers to the average of the data points consumed. It does not indicate trends or projections, just an average of the cumulative data. While it’s useful to know this, it doesn’t tell us how quickly or quickly something is getting better or worse. Also, it does not take into account actual score predictions but may play a role in over/unders.


When you see acceleration on these graphs, think of acceleration or deceleration. Simply, acceleration refers to the rate at which something grows (better) or decays (worse). This is represented by a multiple (x1.1) or a divisor (÷2). Visually on the chart, it is the solid, dotted black line. We use it as part of what makes a projection or prediction.


Bounce is exactly what it sounds like. This is the “bounce” of the data. Think of a basketball being dribbled. Larger distances between two data points are a big bounce, smaller distances are a small bounce. Bounce rate is the representation of data consistency. The more small bounces, the lower the bounce rate, the more consistent the data, the more reliable the projection.

The reverse is true when it comes to bigger bounces. On the chart, this is always represented in multiples (x2). Visually on the chart, these are the solid and dotted green lines. They represent the high and low ends of the range of results. The acceleration line will always divide the rebound rate.

If you need to consume this information and then place a bet on the Sportsbook bar stool app with a promo because you liked what you saw, it is very important that you understand what you are watching.

High doesn’t always mean good, and low doesn’t always mean bad!

Frequently Asked Questions

Can you use this model to bet on every game played?

The short answer is yes. However, don’t forget the bounce rate. Personally, I wouldn’t place a bet on a game where a team has a rebound rate greater than x3 or a team has a two-way acceleration greater than 2. This indicates high levels of chaos and outlier possibilities. If you follow me on pikkit I will always say what games I bet on and what those numbers are as to how I picked those specific games. If I deviate from this, I will always be transparent about it.

What does a 90% confidence level mean for the bounce rate on the chart?

This represents how “confident” we are in calculating that particular bounce rate. 90% is the standard for calculating a bounce rate, which means we are 90% confident that this is the correct result range for a throw. Anything outside of this range would be considered an outlier.


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