All You Need to Know About NFL Props (Part II of II)

All you need to know about NFL Props (Part II of II)

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Last week's Pinnacle Pulse, introduced some ideas about how to gain an advantage from the increasingly popular 'Which team will score first' prop. This week, we're sticking with the theme and providing further hints on how to successfully price other common props available at PinnacleSports.com.

One of my personal favorites is the proposition: 'Will the first score be a Touchdown or FG/Safety'. As a starting point for weighing this bet, you should look at how many TDs, FGs and Safeties have been scored by all teams in the season to date. Before this week's Monday night game, the frequencies of each scoring play were as follows:

Fig 1. (up to Wk 13) Touchdowns Field Goals Safetys

Frequency 848 546 13

% 60.3 38.8 0.9

This provides a good 'baseline', but there are two adjustments to be made to refine the data for an accurate assessment of the proposition.

First, the last two possessions of the half are more likely to result in a field goal than at any other time in a football game (except overtime). This bias isn't simply due to time running out - in many cases, teams will use conservative play-calling to ensure a field goal attempt. As a result, 50% of the time the last score of a half is a field-goal.

Since close to 40% of all scores are FGs or Safeties (fig 1), and it's been established that the final score of each half is a FG 50% of the time, we're in a position to work out how likely the first score is to be a FG by using simple algebra and the average number of scores by game.

We can work out an 'average' score value using the %'s from fig 1.

(60% * 7 points/TD + 40% * 3 points/FG = 5.4 points/possession).

Historical data indicates that a 'typical' game averages a total of 41 points, so you would expect 7.6 scores - average total points scored/average score per scoring possession (41 / 5.4). You'd also anticipate 3.04 FGs - average number of scores * FG frequency % from fig 1 (7.6 * 0.4). From this we can work out the odds (x) of a FG being scored in any scoring possession, excluding the last score of the half.

3.04 FGs (per 7.6 scores) = 6.6 scores not at end of half * (x) + (1 end of half score) (0.5)

X = (3.04 - 0.5) / 6.6 = 0.385 (38.5%), or about +160 for a FG.

Now, you have to adjust this number for the actual match-up. For example, take a look at next week's Monday Night match-up between the Bears and Rams. You need to look at TDs and FGs for each team's offense and defense for this season.

Fig 2 Scored Conceded

N/A TD's FG's TD's FG's

Bears 34 26 15 15

Rams 23 27 33 18

Total 57 53 48 33

Since we do not care who scores what (only which is scored first), we simply add the total TDs and FGs from Fig 2 - 105 TD's (57+48) versus 86 FG's (53+33). 45% of the scores involving these two teams were Field Goals, compared to 40% for the league average. If 45% were the result of thousands of games, you would expect that to go forward. However, each team has only played 12 games, so expect this percentage to 'revert to the mean'. One way to do this is to average the two (40 + 45) / 2) which suggests 42.5% of all scores in this match-up will result in a FG. For the first possession, start with the baseline first possession FG score frequency of 38.5%, and multiply by the mean FG expectancy for these teams calculated above (42.5/40 *0.385) = 40.9%. This is equivalent to +144, which is my price for a FG being the first score of the game.

Once you have your no-vig price (+144/-144), try to find a line that is 10 cents off. If you found 'first score TD' at -134 or better, or 'first score FG/Safety' at +154 or better, it might warrant a play in this match-up.

There are also several ways to improve this result. You can track safeties, or do statistical studies on scoring for many years to plug more accurate averages into the calculations. There are other statistical methods you can use to get even better results. However, when you're tackling props, you want to evaluate them without spending an inordinate amount of time as you'll make more money by pricing 10 different props well, than by analyzing 1 prop perfectly.