## Prob-O-Matic

Hey kids! See what happens to an old programmer who's retired with too much time on his hands?

So one of the things I like to do (besides cheerleading for the Cardinals) is creative number crunching with sport statistics.

A few years back I came up with a concept for a program (written in the 'C' language), that has an interesting twist with the relative values of team statistical categories for prognosticating NFL games. One of the reasons I like doing this stuff is just basically because it's fun and interesting to program, and in this case showed many interesting results.

Since it deals entirely with trends and probabilities, I've named it 'Prob-O-Matic'

By the way, no great claims are made about the program (other than interesting data and results), which doesn't factor in injuries, weather, intangibles or subjectives - it's strictly objective and deals with probabilities as you will see.

Probabilities Screen (After Week 11 / 2015)

The top section displays the records of NFL teams relating to home-field advantage and performance of betting favorites, showing win-loss records in addition to records against the spread (ATS). Notice the poor showing of favorites to cover the spread this season.

The second section lists league tendencies pertaining to Point Total results, breaking down overs (+), unders (-) and scoring averages in both indoor and outdoor games (also notice the disparity between indoor and outdoor games).

Not all statistical categories are equal of course so it seems logical that in order to predict game results based on team statistics, that a relative value of category importance (weight) would be beneficial.

The bottom left section "Won / Lost" displays a breakdown of the relative importance of 10 statistical categories pertaining to winning and losing. For example, teams that out-perform the opposing team in Points Scored during a game, are victorious (not surprisingly) in 100 percent of those games! Conversely, teams that out-perform the opposing team in Interceptions have won 73.1 percent of those games.

Another important factor would be to determine how reliable teams are when showing an advantage in a statistical category (pre-game) to indeed out-perform their opponent in that category in the upcoming game.

The "Reliability" section displays the results of teams who had a significant advantage in a category prior to the game and their ability to repeat that performance in that category. For example, teams with a statistical advantage over their opponent in the Pass Percentage category (both offensive and defensive statistics are utilized), go on to out-perform their opponent in Pass Percentage 63.7 percent of the time.

Now, based on the trends we've collected, a secret formula generates the relative statistical values (weightings) for each category adjusted to a percentage basis of value. (As shown below).

These values are highlighted in white in the middle column and represent the values used by the program for prognosticating. As you can see, the Rushing Yards category is worth 12.2 percent value and slightly greater than the value of First Downs, Passing Yards and Penalties combined.

Along with the relative weighting of categories, another factor needed is the degree of statistical superiority a team has over the opponent per category. In order to achieve this, standard deviations for each category are calculated and displayed in the right white column next to the league averages.

It's generally accepted that odds-makers assign 3 points to the home team when setting pointspreads, however based upon the win percentage of home teams against the spread, an adjustment would seem in order. The Home Field Advantage value has been adjusted accordingly to +1.6 points (as shown in white) that represents the actual point differential of home teams over visitors.

Also notice that average combined scores in indoor games are 1.5 points higher than league average, while outdoor games average half a point lower than average. These values are utilized in the Points Total Projections (see below).

All the above values are subject to change with the addition of new weekly data, thereby making the prognosticating formula self-adjusting.

Matchup Screen

The upper portion of the screen displays miscellaneous Win/Loss records of each team above the team's per-game statistical averages by category. Numbers in white represent very good stats while numbers in blue represent very poor stats.

The arrows in the center indicate a significant statistical advantage in that category (the stronger the advantage the greater number of arrows).

The bottom line displays the projected outcome by factoring the relative weight of each category combined with the team's degree of superiority in that category, plus Home Field Advantage.

These diagnostic details can also be viewed (at the risk of too much information).

The leftmost column of all white numbers is a repeat (from the Probabilities Screen) of the relative weight of each category. The rightmost white column lists the degree (units) one team has the advantage over the opponent statistically in that category.

These two sets of numbers generate the amount of points credited towards the pointspread for that team as shown in the middle white column (negative values indicate advantage to the visiting team).

Also displayed is the current Home Field Advantage adjustment while the bottom line lists team's rating histories (the beloved Arizona Cardinals in this case).

Power Rankings

And last but not least, the Power Rankings screen displays sorted team ratings (using the formula described above), producing relative team rankings.

Disclaimer

No miraculous claims are made about this program which is merely an interesting exercise in attempting to squeeze out winning results (on the whole) by creative use of statistics, trends and probabilities, and should be used for entertainment purposes only. And that's not really me in the picture by the way.

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Update: Prob-O-Matic helped the author win the WalterFootball ATS Pick'em Contest for the month of November, 2015. :)

<em>This is a FanPost and does not necessarily reflect the views of Revenge of the Birds' (ROTB) editors. It does reflect the views of this particular fan though, which is as important as the views of ROTB's editors.</em>