An NFL Sunday can be extremely busy. With the RedZone channel more popular than ever and up to ten simultaneous games occurring at the same time, many bettors become focused on the result and not what happens in a play-by-play context. This article explains how you can revaluate games to help your betting.
- Seek to validate
- Considering game state
- The influence of big plays
Seek to validate
After the games are complete on Sunday afternoon, I revisit each box score to validate performance. The majority of the validation occurs at a team level basis, but in some situations, it can get granular on a player by player basis.
My intention of doing this is to understand how teams performed and why the result ended as it did. For many bettors, it is easier to accept the final result than it is to understand the final result. Knowing how teams play is more important than knowing how teams played. In the example below, I will walk through my process, step-by-step, of reading an NFL box score using the NFL Week 9 game between the Arizona Cardinals and San Francisco 49ers.
Establish a baseline
I like to establish a baseline before beginning. What did the market open at, and where did it close? Movement in odds during the week are a reflection of opinions within the open market. I want to validate the difference of opinion and see if it holds in the market.
At Pinnacle, San Francisco opened as a -7 point favourite and were bet up to a closing price of -10.5. In this game, there was a clear shift of opinion supporting the San Francisco 49ers. My job as I work my way through the box score is to validate the support of San Francisco.
Look at the high-level numbers
I start with the basic high-level statistics to look at the game in a glance. The five numbers I focus on are total plays, total yards, yards per play, yards per pass and yards per rush. Total plays show who controlled the football, total yards shows the output, yards per play shows the overall efficiency of those plays, and yards per pass/rush show specific play output.
The numbers for San Francisco vs. Arizona were:
I like to take the sum of the total yards (768) and divide by 15 which is the league median for yards per point. This calculation puts the game into perspective from a pace and production standpoint. In this game, there were 53 points scored which were 1.8 points higher than the expected output from the league median number.
I then like to take the differential in yards per play (Arizona +1.1) and divide by 0.2, which comes from a regression run on scoreline compared to yards per play differential. In this game, the yards per play differential suggests Arizona should have won by 5.5 points which is eight points different from the result.
At this point, I am looking at this game as near equal to the scoreline and total output, but potentially misleading showing a 49ers victory. Anytime there is a discrepancy, I want to seek to validate and determine why. Often it is several fortunate circumstances working in favour of one team or yardage gained late in the game to pad the numbers.
Throwing the football can be a good indicator of a box score being misleading – or not – as passing yards are significantly more efficient than rushing. In this box score, Arizona were ahead by 1.1 yards per play, but San Francisco were 0.6 yards per pass better.
Considering game state
After evaluating the high-level stats, I begin to look at a drive-by-drive breakdown. The first thing I look for is how many drives occurred in a non-neutral game state. A neutral game state is any play or drive that occurs when a lead for either team is seven points or less.
Non-neutral game states – especially late in the game – often result in drastically different play-calling both for the team leading and trailing. For a team with a lead of eight points or more late in the game, it is more beneficial to exchange time for yardage to increase the probability of winning.
Box score performances can be misleading if a team gained a large portion of their yardage in a negative game state (trailing by eight points or more) in the second half as they frequently will see more conservative defensive schemes and coverages.
In this game, there were nine drives in the second half. Each drive except for the last of the game started with San Francisco leading by eight points or more. Arizona gained 245 (68%) of their 357 total yards in a negative game state.
Great teams earn a big lead early in the game and hold on to the lead for the duration. Despite losing by just three points, my take away is that Arizona gained just 32% of their yardage in a neutral game state and never earned a positive game state in comparison to 100% for San Francisco.
The influence of big plays
Any big plays gaining a large number of yardage at one time will skew the per-play numbers. Explosive offensive play is an asset, but parsing out large plays can be an indication of luck within games.
On the second to last drive of the game when trailing by 11 points, Arizona gained 88 yards and seven points on one pass. The other plays in the game that gained more than 20 yards were 36, 21, 21 and 20. In order to see how much of a difference that play made in the final numbers, I like to remove it and recalculate the five main stats. The below table is the same as the above but with Arizona’s 88-yard pass to Andy Isabell removed.
By parsing out the one big play, the game takes on a very different look that reflects the state of the other 118 plays. The total yards in the game is equal to the actual points scored (45 vs 45.2), the projected scoreline dividing the yardage total for each by 15 is San Francisco 26 – 17 Arizona, compared to 28-17.
The yards per play differential of San Francisco +0.6 suggests a 49ers victory by three points, reflective of the actual score, but not of the box score above. As mentioned above, the game state dictated a lot of the play calling within this game.
The San Francisco 49ers were in a neutral game state for 34 of their 69 total plays. In those 34 plays, San Francisco gained 7.4 yards per play. Substituting the 7.4 for the 6.0 makes a differential of 2.0 or a 10-point expected winning margin, which is equal to the winning margin produced by the yardage gained.
Turnovers, short fields and defensive scores
If the game state stayed neutral for the full game and there were no big plays to parse out, chances are any discrepancies can be explained by turnovers, short fields or defensive scores.
Teams that win the turnover battle win 80% of games in the NFL. Identifying the turnover differential is something that can explain a lot of any discrepancies in the box scores. A turnover will often result in a short field, or reduced number of yardage to gain for a touchdown or a defensive score.
Often teams will be opportunistic taking advantage of turnovers and a short field to score at a higher rate. Arizona and San Francisco did not have any turnovers, so evaluation of the box score is clean in this respect.
Red zone inefficiency
Although the media marks wasted red zone chances as an inefficiency, continuous drives into the red zone can often suggest a good performance. I like to look at the length of each drive and see if a team was lucky or unlucky near the opponents’ goal line.
Each box score in the high-level stats will have a red zone conversion rate which expresses the number of drives inside the opponents 20-yard line compared to the number of touchdowns scored. The media focuses on the conversion percentage, but I prefer to focus on the attempts.
With more data available, it has become evident that red zone conversion is quite random, but red zone attempts are reflective of skill. From an expected points basis, the more snaps you take close to the opponents’ goal line, the more will you score.
I like to focus on the yard line of where first down snaps occurred. San Francisco were 2-2 on red zone trips, but Arizona were 2-3. They had a 1st and ten from the San Francisco 15-yard line but settled for a field goal from the San Francisco 18. This outcome is a minor example of a team failing to capitalise on an opportunity leaving just shy of four expected points on the field.
Why these considerations matter in NFL betting
The goal of working through the box score was to validate the move from San Francisco -7 to San Francisco -10.5 through the performance within the game. By settling for looking at the final score and high-level stats, bettors can make a strong case that the move was unwarranted and the Cardinals should have won the game by multiple points.
However, by working through the box score, it becomes quite clear that San Francisco dominated the game from start to finish and Arizona was the benefactor of one big play, and much of their output was from the most favourable situation in a negative game state.
In total, the above work took me between 15 and 30 minutes. I can work through an entire Sunday in two to three hours. I highly recommend it to all betting on the NFL.
- Evaluate high-level stats (total plays, total yards, yards per play/pass/rush)
- Divide total yards by 15 and yards per play differential by 0.20
- Identify any discrepancies and any early large leads
- Look drive-by-drive to sum up yardage gained in a negative game state
- Parse out any big plays and recalculate totals
- Account for turnovers, defensive scores and short fields (opportunistic)
- Tally the number of first down attempts inside the opponent red zone