Home Field in Divisional Games.

by Denis O'Regan.

Home field advantage exists in every professional sport.For example in association football in the UK teams acquire upwards of 60% of their league points at home and on average win just under half of their home games.Home field is worth around 0.4 of a goal.

However,if you restrict the soccer samples to local derbies then the figures look significantly different.Local derbies are contested between very close geographical rivals (usually,but not always from the same city).The games therefore are of great importance to the fans,the players are familiar with their opponents and their surroundings and travel fatigue is eliminated.

In such games goals overall are less plentiful and home field advantage appears to be greatly reduced.Home sides now only win around 40% of the games (there are more draws) and the average margin of victory for the home side is nearer to 3 tenths of a goal.

Divisional games in the NFL appear to share many characteristics with local soccer derbies.Travel times are shorter,the away team is familiar with their opponents stadium and with their opponents and the game has heightened importance both to the fans and to the team.To get to the post season you first must top your division.

I therefore compared the scoring in divisional and non divisional games since the league went to 8 divisions of four teams.

Scoring is depressed in divisional games compared to non div ones,but the difference is small.Non divisional games averaged 42.9 points per game compared to 42.2 ppg for divisional ones.

It's when you start looking at the margin of victory for the home sides in the two types of matchups that potentially significant differences become apparent.The average scoreline in divisional games is 22.0-20.2 in favour of the home team for an average home field advantage of only 1.8 points.That compares to around 2.7 points for the league as a whole over the same timescale and 3.1 points for all non divisional games,again since 2002.

Wins and losses also confirm the depressed HFA in divisional games.Home teams win just 54% of divisional games (which tallies well with the 1.8 point average margin of victory),compared to 58% for the league overall and 59.5% for non divisional games.

Divisional games appear to be just as atypical when you look at how the HFA is distributed over the 4 quarters of the game.It's long been established that HFA appears to be at it's strongest in the first quarter and consistently declines to be at it's lowest in the 4th.This is evident in non divisional games.

In these type of games 39% of the HFA is gained on average in the first quarter,35% in the second,19% in the third and 6% in the 4th.

However the divisional games don't follow this pattern.Comprising 680+ games,40% of the HFA is accrued in the 1st Q,31% in the 2nd,ONLY 5% in the 3rd and 25% in the 4th.

The average points scored are

1st Q,home side 4.6 pts,away side 3.9 pts.
2nd Q,home side 6.7 pts,away side 6.1 pts.
3rd Q,home side 4.4 pts,away side 4.3 pts.
4th Q,home side 6.2pts,away side 5.7 pts.

and these numbers are reflected in the win/tie/loss figures for each quarter.

1st Q,home side "wins" 42% of the time,away side "wins" 35% of the time and 24% of games are tied after one quarter.

Re setting the game to 0-0 at the start of the 2nd Q,home sides "win" 46%,away sides 40% and 14% are tied.(The lower rate of ties reflects the higher scoring in this quarter).

3rd Q,home side "wins" 39%,away sides 38% and 23% ties.

4th Q,home side "win" 43%,away sides 39% and 17% are tied.

It's easy and probably dangerous to attribute reasons for the closeness of the third quarter and the surge of home field advantage in the fourth compared to other non divisional games.(The team trailing at the half is more likely to be the visitor,realising the importance of the game,they make considerable adjustment at half time and "up" their game to get back into contention.However,the effort takes it's toll in the 4th and increased crowd involvement sees the home side well back on top).

Plausible,but probably not even half the story.The numbers may just be a fluke of nature and they certainly aren't mirrored equally across every division. (For example visitors dominate the 4th Q in the AFC North and away sides "win" that quarter over 50% of the time).

Further superficial investigation suggests that visiting teams in divisional matchups are more efficient at throwing the ball than you would expected compared to visitors in non divisional matchups and the home side commits more penalties than is usual.

Whether these game stats cause the wins or the wins cause the games stats is,as ever open to debate.

Running Back Overuse

by 'DeltaWhiskey'

INTRODUCTION

The following explores the notion that running backs are subject to "wear and tear." Football Outsiders has popularized and continues to promulgate the notion that carrying the ball over 370 times a season places a running back at "greater risk for significant decline or injury the next season." A full explanation of the "Curse of 370" can be found here. Brian Burke, at Advanced NFL Stats, has suggested the "Curse" is more of a "Myth," noting that the Football Outsiders made several statistical errors in developing the theory and that regression to the mean better explains "The Curse." Mr. Burke's critique can be found here.

The following further examines the validity of the "Curse of 370" and the
possibility that "overuse" of running backs may be a real phenomenon that
results in a performance decline.

METHODOLOGY
The primary sources of data for this study were Pro-Football-Reference.com's
list of "Single-Season Rushing Attempts Leaders." This list contains the Top 250 single-season records for attempts. When accessed for this study, the number of attempts ranged 286 to 416, with a mean of 326 and SD of 30. To test the potential effects of overuse theory, two groups were identified; Group 1 consisted of players who were one or more SD above the mean (356 or more carries). Group 1 was compared to Group 2, which consisted of running backs who were more than one SD below the mean (295 carries). This break conveniently resulted in two groups with 43 members each. It should be noted that several running backs' data were discarded because they: a.) didn't play the following year (e.g. Ricky Williams) or made the list in 2008 (e.g. Michael Turner). Also, Quarterbacks that made the list, were discarded.

For each RB, the data from individual player "cards" at Pro-Football-Reference.com was utilized. Data was gathered for the relevant season (i.e. the year they rushed either 356 or more carries, or less than 295 carries = "Year") and the following season ("Year+1"). This design allowed for testing of the hypotheses via Repeated Measures 2x2 factorial designs.

Two sets of statistical analyses were conducted. The first looked at the issue of overuse utilizing readily available standard measures of running back performance utilizing data from the above described data source. The second set of analyses, utilized Football Outsiders measures to assess
running back. This second analysis required creating a subset of data from the first because Football Outsider's data extends only back to the 1995 season. Therefore, all running back performances prior to 1995 were discarded from these analyses (sorry Jim Brown fans). This conveniently left two groups of 20 each after one of Jamaal Anderson's season was
discarded because he did not have enough carries in Year+1 to generate Football Outsider statistics. Football Outsider data for each RB was "Year"
and "Year+1" included the following measures: individual DVOA and DYAR.

DATA ANALYSIS
Standard Statistics
# CARRIES: The first analyses simply addressed whether or not the two
groups being analyzed differed in the number of carries.

Group Means
Group Year Mean Year+1 Mean
One 377.16  282
Two 290.18  240.74

Note that the average number of carries for Group 1 is actually greater than
370.

ANOVA results yielded the following
Between subjects F =39.63, df (1,84), 0.0001
Within subjects F=46.28, df (1,84), 0.0001
AxB F=4.63, df(1,84), 0.05

These results indicate that in terms of number of carries, there is a statistical difference between the Groups, between years and a year by group interaction. These results suggest that not only is there a difference from "Year" to "Year +1," but that the magnitude of the decrease is significantly different between the groups.

# of GAMES: A central component of "The Curse" proposes that more carries (specifically greater than 370) will result in a decline in the number of games a running back will play the following year (Year+1). The following analysis compared the two groups' number of games played in Year and Year +1.

Group Year Mean Year+1 Mean
One 15.674  13.534
Two 14.953  13.116

ANOVA results yielded the following
Between subjects F =1.95, df (1,84), p <ns.
Within subjects F=27.99, df (1,84), p<0.0001
AxB F=, df(1,84), p<ns.

These results indicate that high use, whether above or below 370 carries results in a decline. This is most consistent with Mr. Burke's findings. There is no statistical difference between the groups, nor is the magnitude of decline between the groups different. If a "Curse" exists, the bar is much lower than 370.

Y/A: Next, yards per attempt were assessed. Yards per attempt provide an indicator of a RB's effectiveness on a carry per carry basis.

Group Year Mean Year+1 Mean
One 4.3419  4.086
Two 4.3163  4.093

As pointless as it seems, here are the results of the analysis.
Between subjects F =0 df (1,84).
Within subjects F=10.74, df (1,84),0.01
AxB F=0.04, df(1,84),

In sum, a running back that carries a lot, on average, will experience a decline the following year - again, there is nothing magic about 370.

"DVOA Era Statistics"
Perhaps the measures utilized above are simply too crude to properly detect the effects of "370." To address this possibility, similar analyses were conducted utilizing Football Outsiders stats. First a series of analyses utilizing "Standard Statistics" were conducted to ensure that the sub-groups being analyzed were relatively the same as the original groups.

Standard Statistics
#Carries:  
Group Year Mean Year+1 Mean
One 375.5  291.4
Two 291.65  242.7

ANOVA results yielded the following
Between subjects F =19.83, df (1,38), p <0.0001
Within subjects F=17.11, df (1,38), p<0.001
AxB F=, df(1,38), p<ns.

These results suggest the groups are different in the number of carries. The nonsignificance of the interaction effect is the only difference and suggests that the previously detected difference may be due to a larger sample size.

#Games

Group Year Mean Year+1 Mean
One 15.5  13.85
Two 15.4  14.2

ANOVA results yielded the following
Between subjects F =.04, df (1,38).
Within subjects F=6.89, df (1,38), 0.01
AxB F=.17, df(1,38), p<ns.

These results are similar to those found above.

Y/A (Yards/Attempt)
Group Year Mean Year+1 Mean
One 4.355 3.94
Two 4.165 3.89

ANOVA results yielded the following
Between subjects F =.52, df (1,38),.
Within subjects F=11.33, df (1,38), p<0.01
AxB F=0.48, df(1,38), p<ns.

Results are again similar to above. These results then suggest that the sub-group of players, post 1995, are statistically, fairly similar to the group as a whole.

DVOA Statistics
To test validity of Football Outsider's "Curse of 370," it seems appropriate to utilize some of Football Outsider metrics. For an explanation of these metrics - http://www.footballoutsiders.com/info/methods.

Individual DVOA: This analysis tests the difference in DVOA between running backs in Group 1 and Group 2 based on Year and Year+1. DVOA is usually expressed as percent, in the following, it is expressed in decimal fashion.

Group Year Mean Year+1 Mean
One 0.0755  -0.026
Two -0.000  -0.062

ANOVA results yielded the following
Between subjects F =6, df (38,1), p <.05.
Within subjects F=13, df (38,1), p<0.001
AxB F=1.0, df(38,1), p<ns

These results suggest that there is a difference in Year and Year+1 (wear and tear or regression) and a difference between Group 1 and Group 2; however, the ns interaction effect indicates that the magnitude of the wear and tear is nonsignificant between groups. That is, the 0.1015 drop between Year and Year+1 observed in Group 1 is not significantly greater in magnitude than the approximately 0.062 drop observed in Group 2.

DYAR

Group Year Mean Year+1 Mean
One 260.1  93.05
Two 105.95 55.45

ANOVA results yielded the following
Between subjects F =7.06, df (38,1), p <.01.
Within subjects F=10.46, df (38,1), p<0.01
AxB F=3, df(38,1), p<ns

These results are similar to those for DVOA.

DISCUSSION

This study analyzed "the Curse of 370" by comparing two groups comprised of RBs who accumulated enough carries to be ranked in the top 250 single-season attempts list. The first group consisted of 43 running backs that were at least one SD above the mean in number of carries. The second group consisted of 43 running backs who were one SD below the mean. This process yielded two groups that had a high likelihood of being significantly different regarding the number of carries in a season, and if a high-number of carries is the cause for decline in subsequent seasons, then there should be significant detectable differences in the magnitude of decline. None of the results suggested this to be the case. Simply stated, this study did not detect any evidence to support "the Curse of 370." The study did find that a large number of carries are associated with decline the following year; however, that decline does not become magnified as the number of carries increases - at least not in this data set.

Mr. Burke has asserted that the decline is probably due to regression to the mean; however, further analyses utilizing this data set suggest other factors may be associated with the decline and will be examined in future posts.

Rating Quarterbacks

by Jason Winter of Defensive Indifference

There’s no really good existing way to rate quarterbacks, from a statistical standpoint. Most people understand the flaws with the widely known statistic, though there are alternatives. Adjusted Yards Per Attempt is one of the simpler ones. To review, that’s:

AYA = (Passing Yards + 10*TD passes - 45*Interceptions)/(Pass Attempts)

It is, essentially, a QB’s “yards” (counting TD passes as 10 yards and interceptions as -45 yards) divided by his attempts. Seems logical enough, and fits with other generally accepted “average” stats (like yards per carry for running backs or yards per reception for wide receivers).

But it’s always bugged me how pretty much every stat ever used to rate quarterbacks only accounts for their passing numbers. Since the days of Fran Tarkenton, carrying forth into the days of Randall Cunningham, and, more recently, with Michael Vick and Vince Young, people have argued that rushing stats should play a role in whatever system exists to rate quarterbacks. It makes sense; how can you say a QB with a 6.5 AYA (or 88.7 passer rating) for the season who runs 40 times for 40 yards is the same, statistically as one with a 6.5 AYA who runs 40 times for 200 yards? Isn’t the second guy better?

But if we get into adding rushing yards to a quarterback’s numbers, why shouldn’t we add other things, as well? Isn’t it better if a QB takes 20 sacks instead of 50, or if he fumbles five times instead of nine (assuming identical attempts)?

Ah, but now that opens up another can of worms. Many people would argue that some sacks – not all, but a fair number – are the fault of the offensive line and not the quarterback. You could make similar arguments regarding fumbles, many of which occur as the result of sacks. Why should those numbers be counted against a quarterback in any “comprehensive” statistic?

My response is: Why should passing yards be counted? Or touchdown passes? If we’re going to credit the QB for a completed pass, and assign him positive stats based on his good play, why shouldn’t we blame him for a bad play, like a sack or fumble? True, the negative play might not have been his fault entirely. But no completed pass is ever completely due to his efforts – the receiver and offensive line likely had a role in it, as well – and nobody would suggest we not include passing yards in our rating of a quarterback. If we can’t determine what percentage of a quarterback’s “good stats” are due solely to his efforts and adjust his numbers downward as a result, there’s no reason we should exclude his “bad” stats just because some part of them wasn’t his fault. Just as it’s clear that some QBs are better at running than others, it’s clear that some QBs are better at not taking sacks or not fumbling than others? Why shouldn’t the QBs who are good at this be rewarded and the ones who are bad at it penalized?

Even if we accept that some guys are “just playing on a bad team” or behind a bad offensive line and want to make adjustments for that, why shouldn’t we make adjustments for players with good offensive lines or receivers? Would Tom Brady have thrown 50 TD passes in 2007 if he’d been playing for San Francisco instead of New England and been throwing to Arnaz Battle and Darrell Jackson instead of Randy Moss and Wes Welker? Of course not.

That comes to the heart of this system, however. It’s not meant to show who the “better” QB is, not from a strict interpretation of the word. Tom Brady would have been the exact same person and same player in San Francisco, but his stats would have been lower, due to having worse teammates and coaching. A quarterback who takes a lot of sacks puts up worse numbers than one who doesn’t, all other things being equal. He may or may not actually be worse, but his stats should suffer, just as a quarterback who has Randy Moss and Wes Welker to throw to should have better numbers than one who didn’t (and we might get a chance to see how Matt Cassel does when he doesn’t have those two to throw to). Rather, this system is meant to show which quarterback had the best statistical year, taking all available statistics into account and not biasing the results with any judgments based on credit or blame. If it’s on the player’s stat line, it counts, whether for good or bad.

We can use AYA as a baseline for our “new” stat, which I like to call Total Yards per Attempt (TYA). We can stick with the basic premise of Yards/Attempts, but we’ll need to add a few things to each side. Our numerator has to include:

Passing yards
Rushing yards *
Passing touchdowns
Rushing touchdowns *
Interceptions (negative)
Sack yardage (negative) *
Fumbles (negative) *

And the denominator includes:

Pass attempts
Rush attempts *
Sacks *

(* indicates new statistic. And yes, I know I could add receiving stats, but those are so rare for QBs that I think we don't lose much by leaving them out.)

Rushing yardage can be added easily enough. Rush TDs can have the same weight as passing TDs, 10 per. Sack yardage can simply be negative yardage. Pass attempts, rush attempts, and sacks present no problems, either.

What to do about fumbles, though? Not every fumble results in a turnover, so they should be weighed less than an interception (which is always a turnover). Yet I don't know of any easy place to find the stats on individual quarterbacks' lost fumbles, and I can't assign a -45 per lost fumble for each quarterback, some of which are recovered by the offensive team.

Looking at the last few years, I find that, overall, fumbles (by any player) are recovered by the defense about 2/3 of the time (usually 65-70% per season). That's a convenient enough result, since 2/3 of 45 is an even 30. Works for me. Thus, fumbles are worth -30 to a quarterback.

(I realize, too, that fumbles by a quarterback are generally worse than an interception, since they often occur on or behind the line of scrimmage, as opposed to downfield, but unless there’s something in The Hidden Game of Football (which I haven’t read) or anyone else has any other data out there to assign better values to fumbles, I’ll go with what I’ve got.)

So, putting it all together, we get the following formula:

TYA = (Passing Yards + Rushing Yards - Sack Yards + 10*TD passes + 10*Rushing TDs - 45*Interceptions - 30*Fumbles)/(Pass Attempts + Rush Attempts + Sacks)

Which appears to take pretty much every QB stat into account.

To close, here’s the leaders in TYA for 2008, along with their rank in passer rating. The average among these quarterbacks was 5.04; figuring the average for the entire league is difficult because it requires individually looking up rushing numbers for every quarterback, as well as excluding players who threw passes and whose rushing numbers would skew the results (like running backs).

Rank	Quarterback	TYA	PR Rank
1	Philip Rivers	6.6	1
2	Drew Brees	6.52	4
3	Chad Pennington	6.39	2
4	Peyton Manning	6.19	5
5	Jake Delhomme	5.86	18
6	Kurt Warner	5.82	3
7	Matt Ryan	5.8	11
8	Jay Cutler	5.8	16
9	Jeff Garcia	5.55	9
10	Aaron Rodgers	5.51	6
11	Matt Schaub	5.49	7
12	Donovan McNabb	5.46	14
13	Tony Romo	5.23	8
14	Matt Cassel	5.19	10
15	Kerry Collins	5.18	23
16	Seneca Wallace	5.11	13
17	Eli Manning	5.1	15
18	Jason Campbell	4.9	19
19	Trent Edwards	4.83	17
20	Shaun Hill	4.76	12
21	David Garrard	4.66	20
22	Tyler Thigpen	4.57	27
23	Kyle Orton	4.56	25
24	Dan Orlovsky	4.28	30
25	JaMarcus Russell	4.23	26
26	Joe Flacco	4.17	22
27	Ben Roethlisberger	4.03	24
28	Brett Favre	3.91	21
29	Gus Frerotte	3.83	28
30	Marc Bulger	3.81	31
31	Derek Anderson	3.24	33
32	JT O'Sullivan	3.12	29
33	Ryan Fitzpatrick	2.94	32

Breaking Down the Superbowl.

by Denis O'Regan

With the Superbowl over and every conceivable statistic from the game duly recorded,I though it would be a good time to try to develop a measurement of individual contribution to the game.

I've had an idea about the methodology to use for a couple of seasons,but the appearance on this site of Brian's win probability calculator has made the application much more feasible.

Basically,every point on the field has an average value in terms of the number of points a team can expect to score.Therefore,the point of the snap will be worth say X points and the end of the play will be worth Y.The difference between the two,be it positive or negative will be the 'worth' of the play in terms of increased or decreased expected points.

Passing plays are potentially more interesting than running plays because they can be further broken down into their component parts by taking yards after the catch into account.You could think of the points difference from the point of the snap to the point of the catch being attributed to the passer (with the assistance of the catcher) and those from the point of the catch to the end of the play as belonging to the receiver (with the assistance of the passer).

Overall it's a way of defining individual plays,and therefore individual players contributions to a game in the more readily recognizable currency of game points.It also puts into context plays for big yardage gains on third and long that don't get to the first down marker.

Here's how these calculations look for the two outstanding performers from this month's Superbowl.

Larry Fitzgerald caught 7 passes,his yards after the catch numbers changed the points expectancy for his team by a cumulative 7.7 points.Kurt Warner's contribution on those completions came to a cumulative 3.9 points and incompletions reduced the team's expected points by 0.6.

Combined,the Warner/Fitzgerald duet altered Arizona's points expectation by 11 points.That's 1.4 points per pass attempt.

Pittsburgh's SB MVP Santonio Holmes only combined with Ben Roethlisberger to advance the Steelers expected points by 8 points.Those 8 points were made up from 9.6 yac points,3.1 QB yards points and minus 4.7 points from incompletions and interceptions.Overall Holmes averaged 0.6 points per pass attempt.

Miller contribute a net gain of 3.7 points,Ward 3.1 points,Breaston 4.7 and Boldin(as a result of being the intended recepient of Warner's 100 yard interception return) minus 6.9 points.

Neither side's primary running back contributed positive points to their respective teams points expectation when they ran the ball,although they fared better as pass receivers.

The method can be expanded at look at how whole units perform and looks especially attractive as a way of quantifying the punting unit.

How Teams Try To Win

by Denis O'Regan

One of the more obvious ultimate aims of a NFL team is to score enough points to try to guarantee victory over it's opponents.However,it is equally apparent that at certain times during a game teams have other objectives that take preference over maximizing the score.Running the ball to run out the clock when they already have a large lead,for example.

What follows tries to identify the different stages in a game and tries to pinpoint the tactics used by teams when they are actively trying to score points.

There's a multitude of factors that determine a team's approach during a game,but I'll concentrate on ones I consider most influential.

Firstly,down and distance.These two factors can be reasonably broken down into predominately passing or running plays.To try to eliminate any in built play calling bias as a result of down and distance I decided to look exclusively at 1st and 10 plays.It's not an obvious running or passing down/distance and it also provides a hefty sample size for each team.Everyone gets a first and 10 sooner or later.

Next the current score.It's well documented that teams favour the run when well ahead and the pass when well behind.So I further broke the first and 10 plays down by the current score.I looked at the ratio of runs to passes when teams trailed by 2 or more scores,trailed by 1 score,where tied,led by one score and finally when they led by 2 or more scores.

And lastly I decided to include a teams offensive strength.Even poor offensive teams are likely to be better at running the ball compared to passing it or vice versa.I was simply interested in which offensive skill a team did better at and by how much compared to their weaker discipline.

I firstly compiled a run attempt/pass attempt ratio for all 32 teams from the 2007 season,to confirm that teams favour the run when well ahead and the pass when well behind.

And they do.

On average teams throw around two passes for every one run when they trail by 2 or more scores on 1st and 10.

When down by 1 score the ratio has moved closer to parity,but on average 1.2 throws are still made for every one run.

Running is favoured when teams are tied.1.2 runs for every one pass.

That increases to 1.5 runs to 1 pass if teams lead by a score.

Lead by 2 or more scores and runs start to outweigh passes by almost 3:1.

This progression from throwing when behind to running when in front is mirrored by all 32 teams.

However,this carn't be the whole story.There must be periods of the game where teams are trying to maximize the points they score and they must be trying to do this by a combination of maximizing their yards per play and increasing their chances of continuing drives.It further seems reasonable that they attempt to do this by playing to their offensive strengths.Playcalling when trailing or winning big,seems to be dictate more by the state of the game than a team's offensive strength.So the next step was to see if a team's offensive strength dictated how a team played when the game was close,say within a score either way.

Initially,I chose two teams with widely differing offensive styles.Minnesota ran the ball extremely well and passed it relatively poorly,while the reverse was true for Indianapolis.

If offensive strength did play a part in playcalling as well as the state of the scoreboard,then it seemed likely that as these two teams went from trailing to winning,you would see Minnesota committed earlier to the run (their relative offensive strength) ,while Indy would stay with the pass (their strength) for longer.

And that's what happens.

Minnesota are already running more than they pass when they still trail by 1 score (the league as a whole are still passing more than they run) and Indy are still passing almost as often as the run even when they lead by 1 score (the league as a whole become more frequent runners around when the scores are tied).

Having seen that two teams with polar opposite approaches to offense tend to go to their strengths in close games the last step is to see if there's a general league wide tendency for teams to rely on what they do best.To do this I calculated the strength of the correlation between what a team does best on offense and how often they attempt to do it split by current score.

When the 32 teams trail by 2 or more scores there is no correlation between the two conditions. There appears to be no evidence that teams that run better than they pass run more often in these situations(correlation of 0.01).The same applies to better passing than running teams (correlation of -0.04).It appears that the situation of being 2 scores or more adrift,strongly dictates play calling,everyone has to pass whether it's their most potent attacking force or not and it appears to be a haphazard process.

However,when down by just 1 score teams are able to start to go to their strengths.Teams that pass much better than they run,tend to pass more often than other teams in this situation.When teams trail by a score the correlation between passing well and passing often is 0.35.

The correlation is similar when scores are tied and peaks at 0.47 when teams lead by a score.(Presumably they recognise that one score isn't a decisive lead and they need to press home their advantage and the best way to achieve this is to do what they do best and do it more often than league average).

Once teams lead by 2 or more scores the correlation becomes entirely random again and playcalling mirrors what happens when teams are trailing by 2 scores.Running becomes predominant and teams effectively forget where their strengths lie.Their gameplan is no longer focussed on increasing their score,it's more about shortening the game by keeping the clock running.

The situation for running the ball is identical.The better a team is at running the ball compared to passing it,the more they pound the ball when the scoreboard is within a score either way.Once the lead or deficit becomes larger,they randomly apply the doctrine of pass if you're behind and run if you're ahead and the reasonably strong correlation disappears.