STAT 2011 - NFL Game Reports

Accept no substitutes.

Dallas vs Philadelphia, October 30th, 2011

Game Report's are where the STAT handicapping application truly shows it's power, offering bettors in-depth situational trend analysis and industry leading confidence percentages that quickly reveal if either side happens to be a good play versus the Vegas line. They have 2 main components: Situation Boxes that detail each individual trend. And, a Top Header that summarizes the active trends and provides a Confidence percentage.

Situation Boxes

ATS Records: This section shows how trends have performed versus the spread historically, in the last 3 seasons (L3S) as well as in the current season (CS). Additionally, how the team in question has fared in the past 3 seasons in the applicable situation is also provided.

ASMR: Stands for Average Spread Margin Rating. Typically, teams cover the spread by an average of just over 10 points. For situations with a record above .500, a positive ASMR signifies that teams are covering by more than this average, while a negative value means they are covering by less than the average. In the case of situations with a record below .500, a positive ASMR signifies that teams are typically losing by an amount in excess of the average spread loss, while a negative value means the opposite. The bottom line is this: regardless of the trends record, a positive ASMR essentially indicates a larger-than-normal overall difference against the spread.

HM%: Percentage of home teams that have been selected

DG%: Percentage of Underdogs.

DIS%: Distribution % is the percentage of teams in the league that have been involved in this situation at one time or another.

ALN%: Alone % shows how often a situation is alone in it's selection, meaning, there are no other trends present that are playing in the same 'direction'.

After the stats, the top 4 teams that this situation has played either on, or against, are listed (with the actual number of hits shown underneath the team name) and also, the situations that are most likely to be present in the same game (situation # and %'s are shown)

All the data that is shown in each Situation Box is there for a reason and most of this information will be used in the calculations shown in the Top Header, explained below.

Top Header

Situations For: Pretty self explanatory. Teams with at least a +3 advantage have been excellent bets historically.

Net Win Differential: The sum of the ATS records for all the situations present in the game. As an example: If Dallas was in a positive situation with an 86-23 record and their opponent was being helped by a 56-13 situation, Dallas would have the edge in NWD by virtue of their more powerful situation (86 - 23) - (56 - 13) = +20.

T-Values: Are a much better way of assessing a situation than the more simple NWD. In this case, they are calculated by subtracting losses from wins and dividing this figure by the square root of the total number of games involved. A trend with a 33-3 ATS record would therefore have a T-value of 5 ((33 - 3) / Sqr(36)) as would a situation with a record of 75-25 ATS ((75 - 25) / Sqr(100) = 5). Doing this for every trend involved in the game provides the 'T-Value Advantage' which is used as the basis for the final Confidence percentage. But wait, there are still another 6 factors that need to be considered:

#1 - Condition Ratio (CRAT): 2 different situation's with identical records of 60-20 ATS might have the same 'T-value', but, their chance of success in future games could be very different depending on the number of conditions that make up the premise of each one. As more and more conditions are added to a situation, its record will typically improve. The flip side of this; however, is a corresponding decrease in confidence due to the more complicated logic involved. To put things simply: a situation with a record of 60-20 ATS that was built on only 2 conditions will probably produce far better results down the road than a 60-20 trend that has 6 different stipulations. CRAT basically compares T-values with the exact number of conditions involved. A situation with a T-value of 6, as an example, will normally use 6 conditions (the average ratio is actually 1 : 1.1). Situations that produce good results based on a lower-than normal number of conditions will receive a boost, while those that are more complicated may be penalized.

#2 - Average Spread Margin Rating (ASMR): (Explained in more detail above) Situations that have an ASMR below zero are penalized while those with an ASMR above zero receive a bonus.

#3 - Performance in Recent Games (RGAM): After studying patterns related to NFL situational analysis for the better part of 15 years, it's become fairly clear (to me, anyway) that recently played games (i.e., from the current or last 3 seasons) do offer a better indication of which way a situation is headed than results from before this time period. Statistical regression to the mean, an effect of the 'law of averages', must also be taken into consideration and based on these 2 factors, STAT weights games from the current season higher than last season, and games from the last 3 seasons, higher than those played before this time period. As a result, a situation with a historical record of 26-0 that started the season 0-3 would see an accelerated reduction in it's T-value well beyond a re-calculation based on overall record alone.

#4 - Previous history for this Team (TREC): Has the current team been involved in this situation before? And if so, what were the results? As with Recent Games, extra weight is applied to past games involving the current team (as long as they occurred within the L3S).

#5 - Team Distribution (TDIS): Does the situation usually select the same 2-3 teams or is it spread out across the league? Situations that are spread out evenly across the league are less likely to be affected by team personnel and game-plan changes and are more stable as a result.

#6 - Situation Overlap: Some situations and systems can overlap with each other's results. Certain situations in the Playoffs and on Monday Night, for instance, can have similar premises while other situations may act as a 'subset' of a larger one. In such cases, STAT adjusts the T-values for competing situations down, in order to give a more accurate assessment of their total effect on a game.

Confidence %: Adding the original T-value advantage together with the 6 values in each of the adjustment columns gives us a total which is used to help determine the expected percentage chance that the team in question will cover the spread. Games where the CP% surpasses the 55% mark have proven to be very accurate over many seasons and it's these games that I tend to focus on more than any others.