While most MLB models make projections based on how a team's been hitting as a whole, our offensive projections are based on each and every player included in that particular team's lineup for the day. This means our model waits for each lineup to be posted (usually within a few hours before first pitch), then analyzes it on a player-by-player basis. This method is to ensure the highest accuracy in predicting a team's performance.

1. Excel Model For Nfl Betting
2. Nfl Betting Model Excel

NFL Odds Spreadsheet Makes It Easy. After years of being tired of switching back and forth between Excel and my browser to see how the lines compared to my model, I decided to build something that would make things easier. NFL totals betting is rather self-exploratory. Also known as over/under, this form of betting involves placing a wager on the total number of points scored by both teams combined in a game. Building the 2019 SportsGrid NFL Betting Model There’s a well-established process to modeling out NFL games and our approach. To start by paraphrasing Richard Feynman, the key to science is you.

The pitching/hitting evaluation component of the model uses advanced MLB metrics that go way over the casual baseball fan's head. Exit velocity, batted ball profiles, splits, plate discipline metrics, park factors, performance with or against certain pitches/velocities (combined with pitch usage rates), BABIP, FIP/xFIP, SIERA, and wRC+ are among the many metrics incorporated in the model. The challenge of MLB is analyzing advanced data to determine which players have been lucky and unlucky in relation to their actual performance. This is something that public/square bettors are very poor at figuring out, leaving a lot of value on the table in the betting market. Much like a player projection system, our model identifies a 'true' performance level for players and projects games accordingly.

Are you curious whether or not your favorite football team will win this week? What if I told you there was a way to predict the outcome of every match up in the NFL? With the use of Microsoft Excel and Data Analysis, that is precisely what I did for Week 12 of the 2018 NFL season and I will show you how I did it.

Before I dive into my method and results, I want to credit Mr. Terrence Davis and his article 'PREDICTING NBA FAVORITES WITH MICROSOFT EXCEL and DATA SCIENCE' for inspiring me to start this project.

How To Build A Predictive Betting Model. Building a sports betting model can be difficult work. We won't lie to you. It can mean long hours of tediously entering data, sorting spreadsheets, setting up.

Click HERE to access the full Microsoft Excel Workbook.

Data Collection

Prior to making any predictions, the first thing you must do is collect your data. I am looking to predict the outcomes for each of the games during Week 12 of the 2018 NFL season. So, I determined that the 2018 Team Offense and Defense statistics of each team (before Week 12) would serve as a good indicator of how each team ranks so far. In regards to NFL data, Pro Football Reference is a wonderful resource that offers an abundant amount data. From here I was able to import the 2018 Team Offense and Defense data to Microsoft Excel with ease by using the 'Get as Excel Workbook (experimental)' feature.

Determining the Relevant Variables

Too much irrelevant data can be a problem. After importing the Team Offense and Defense tables from Pro Football Reference to Microsoft Excel, I was overwhelmed by the twenty-five columns of statistics. Therefore, I needed to filter out the data into only the variables that I deemed most relevant to a team's ability to win.

What are the most important variables that are used in determining an NFL team's ability to win? The answer to this question varies depending on your opinion. Personally, I feel that these variables are the best determinants of an NFL team's ability to win:

• Margin - Difference between Points For (PF) and Points Against (PA)
• Sc% OFF - Percentage of drives ending in an offensive score for
• TO% OFF - Percentage of drives ending in an offensive turnover
• YdsPen OFF - Penalty yards committed by offense
• Sc% DEF - Percentage of drives ending in an offensive score against
• TO% DEF - Percentage of drives ending in a defensive turnover
• YdsPen DEF - Penalty yards committed by defense

Using only these variables, you can now transform your data into a less intimidating table:

By carefully studying the variables, you may conclude that the Margin variable is determined by the other six variables. Therefore, the dependent variable of my data set is Margin and the independent, a.k.a. explanatory, variables are Sc% OFF, TO% OFF, YdsPen OFF, Sc% DEF, TO% DEF, YdsPen DEF. This information will be important for the next step.

How to Make Use of the Data

With all of this data, you need to create a relationship between your variables that will serve as a formula for computing your ratings. Linear regression is a good way to model the relationship between two variables (dependent and independent) of a data set. Since we previously determined that this model contains a dependent variable that is explained by several independent variables, linear regression is the method that we will use.

In Microsoft Excel, you can run a linear regression by going into the Data tab, then clicking Data Analysis and scrolling down to Regression. The Input Y Range (dependent variable) in my model is the Margin column. The Input X Ranges (independent variables) are the columns containing Sc% OFF, TO% OFF, YdsPen OFF, Sc% DEF, TO% DEF, YdsPen DEF. Once your linear regression is set-up, simply press OK to see your results.

The results of this linear regression were good. The regression output determined that there was an R Squared value of 0.9077 which, as expected, tells me that there is a strong relationship between the X and Y Ranges. Using the coefficients in the regression summary, this is the formula that I will use to determine each teams' rating:

Here is how you can read the formula:

Excel Model For Nfl Betting

• Every 1% increase in offensive scoring percentage increases the team rating by 641.938
• Every 1% increase in offensive turnover percentage decreases the team rating by 61.934
• Every 1 yard increase in offensive penalty yards decreases the team rating by 0.015
• Every 1% increase in offensive scoring percentage against decreases the team rating by 691.225
• Every 1% increase in defensive turnover percentage increases the team rating by 163.596
• Every 1 yard increase in defensive penalty yards increases the team rating by 0.028 (positive value for this coefficient is counter-intuitive)

Ranking Each Team

With a formula in place, all you have to do now is calculate the rating of each team by inputting the variable data into the rating formula. The VLOOKUP() and SUM() functions in Microsoft Excel make this an easy task.

Now, you can rank the teams based on their rating. The higher the rating, the better the rank (1 = Best, 32 = Worst).

Predictions & Results

With each teams' rankings calculated, I simply created a table with each of the Week 12 match ups and predicted the winner based off of which team had the better rank.

Nfl Betting Model Excel

As you can see, this ranking system correctly predicted 11 out of 15 games! In comparison, ESPN's Week 12 Power Rankings correctly predicted 10 out of 15 games.

Conclusion

Obviously, there is no perfect method for predicting the outcome of a football game or any sports event for that matter. There are simply too many unpredictable variables to account for. However, by choosing your variables wisely it is evident that you can make a quality prediction.