Tempo-Free Football: Points Per Possession
Yesterday I did some quick research on an improved red zone efficiency metric, which generated some discussion on other potential ways to look at a team's offensive productivity. One of the suggestions that immediately intrigued me was points per possession (thanks Gene). This metric is becoming more and more popular in basketball; I'm sure several of us have read a decent amount on this from Pomeroy, Gasaway (formerly Big Ten Wonk), etc., as Brian references their tempo-free stats on occasion. Dylan at UMHoops uses them as well, for those of you who follow (if you don't, you should).
Points per possession would seem like a pretty easy number to come up with. Well, total points scored is easy to find, but number of possessions... not so much (if anybody has a source for this data, I'd love it if you would share). To get there, I looked at all possible ways that a possession can come to an end (once again drawing on things learned from Pomeroy and Gasaway): Punt, Turnover (downs, int, fumble), Score (and thus a kickoff). So to determine total possessions, I used:
Punts + Turnovers + Kickoffs - Number of Games (each team has one kickoff per game that was not the result of a possession).
There are still some flaws with the above:
- I currently have no way to account for a possession that ends at the end of a half.
- A muffed and lost punt (or a Gordon/Vinopal special: pick -> lost fumble) will show up as a possession (turnover), but these probably shouldn't be taken into account when considering offensive efficiency.
- Points scored without the offense's involvement (pick-six, punt/kick return etc) should not count towards offensive efficiency. I am not sure whether or not the NCAA's "Team Scoring Offense -- Total Points" adjusts for this or not.
I welcome any suggestions or further critique.
(And since this is MGoBlog) Well, that's a lot of words, how about a...
Chart
Rank | Team | Kickoffs | Games | Punts | Turnovers | Possessions | Points | Points Per Possession |
1 | Nevada | 32 | 4 | 8 | 5 | 41 | 179 | 4.37 |
2 | Stanford | 36 | 4 | 7 | 5 | 44 | 192 | 4.36 |
3 | Indiana | 22 | 3 | 9 | 1 | 29 | 124 | 4.28 |
4 | Ohio St. | 36 | 4 | 14 | 3 | 49 | 197 | 4.02 |
5 | TCU | 30 | 4 | 13 | 6 | 45 | 178 | 3.96 |
6 | Michigan | 28 | 4 | 13 | 6 | 43 | 165 | 3.84 |
7 | Oklahoma St. | 30 | 3 | 12 | 6 | 45 | 171 | 3.80 |
8 | Air Force | 26 | 4 | 11 | 5 | 38 | 144 | 3.79 |
9 | Kentucky | 27 | 4 | 13 | 3 | 39 | 147 | 3.77 |
10 | Alabama | 28 | 4 | 11 | 7 | 42 | 158 | 3.76 |
11 | Utah | 30 | 4 | 15 | 7 | 48 | 177 | 3.69 |
12 | Wisconsin | 29 | 4 | 13 | 5 | 43 | 158 | 3.67 |
13 | Oregon | 41 | 4 | 18 | 9 | 64 | 231 | 3.61 |
14 | Boise St. | 24 | 3 | 7 | 6 | 34 | 121 | 3.56 |
15 | Houston | 32 | 4 | 12 | 10 | 50 | 177 | 3.54 |
16 | Florida | 27 | 4 | 11 | 9 | 43 | 151 | 3.51 |
17 | Southern California | 26 | 4 | 12 | 9 | 43 | 148 | 3.44 |
18 | Florida St. | 26 | 4 | 11 | 8 | 41 | 141 | 3.44 |
19 | East Carolina | 22 | 3 | 15 | 3 | 37 | 127 | 3.43 |
20 | Iowa | 25 | 4 | 17 | 6 | 44 | 144 | 3.27 |
21 | Nebraska | 28 | 4 | 16 | 11 | 51 | 160 | 3.14 |
22 | California | 27 | 4 | 14 | 9 | 46 | 144 | 3.13 |
23 | Mississippi | 26 | 4 | 13 | 12 | 47 | 144 | 3.06 |
24 | UTEP | 22 | 4 | 11 | 8 | 37 | 113 | 3.05 |
25 | Auburn | 23 | 4 | 16 | 8 | 43 | 131 | 3.05 |
26 | Hawaii | 29 | 4 | 12 | 11 | 48 | 146 | 3.04 |
27 | Army | 22 | 4 | 18 | 3 | 39 | 118 | 3.03 |
28 | North Carolina St. | 28 | 4 | 20 | 6 | 50 | 151 | 3.02 |
29 | Clemson | 21 | 3 | 17 | 4 | 39 | 117 | 3.00 |
30 | Michigan St. | 27 | 4 | 20 | 6 | 49 | 147 | 3.00 |
31 | San Diego St. | 30 | 4 | 18 | 7 | 51 | 153 | 3.00 |
32 | South Carolina | 24 | 4 | 13 | 8 | 41 | 123 | 3.00 |
33 | Missouri | 30 | 4 | 17 | 8 | 51 | 151 | 2.96 |
34 | Tulsa | 30 | 4 | 15 | 10 | 51 | 151 | 2.96 |
35 | Miami (FL) | 19 | 3 | 8 | 10 | 34 | 100 | 2.94 |
36 | Idaho | 23 | 4 | 13 | 11 | 43 | 126 | 2.93 |
37 | Virginia | 18 | 3 | 12 | 6 | 33 | 96 | 2.91 |
38 | Fresno St. | 20 | 3 | 15 | 5 | 37 | 107 | 2.89 |
39 | Georgia Tech | 23 | 4 | 11 | 13 | 43 | 124 | 2.88 |
40 | Syracuse | 24 | 4 | 18 | 7 | 45 | 129 | 2.87 |
41 | Arizona | 27 | 4 | 17 | 8 | 48 | 137 | 2.85 |
42 | South Fla. | 18 | 3 | 8 | 11 | 34 | 97 | 2.85 |
43 | Virginia Tech | 25 | 4 | 11 | 8 | 40 | 114 | 2.85 |
44 | Texas A&M | 23 | 3 | 13 | 11 | 44 | 123 | 2.80 |
45 | Central Mich. | 22 | 4 | 16 | 9 | 43 | 120 | 2.79 |
46 | Arizona St. | 28 | 4 | 15 | 13 | 52 | 145 | 2.79 |
47 | Maryland | 26 | 4 | 22 | 6 | 50 | 138 | 2.76 |
48 | Arkansas | 24 | 4 | 13 | 14 | 47 | 126 | 2.68 |
49 | Kansas St. | 24 | 4 | 20 | 6 | 46 | 123 | 2.67 |
50 | Wake Forest | 24 | 4 | 21 | 8 | 49 | 131 | 2.67 |
51 | Northwestern | 25 | 4 | 19 | 5 | 45 | 120 | 2.67 |
52 | Oklahoma | 27 | 4 | 23 | 5 | 51 | 136 | 2.67 |
53 | Minnesota | 22 | 4 | 13 | 10 | 41 | 106 | 2.59 |
54 | Oregon St. | 16 | 3 | 17 | 1 | 31 | 80 | 2.58 |
55 | Connecticut | 27 | 4 | 19 | 10 | 52 | 133 | 2.56 |
56 | Texas Tech | 18 | 3 | 14 | 11 | 40 | 101 | 2.53 |
57 | Troy | 28 | 4 | 19 | 11 | 54 | 136 | 2.52 |
58 | Duke | 21 | 4 | 18 | 14 | 49 | 123 | 2.51 |
59 | Navy | 14 | 3 | 11 | 4 | 26 | 64 | 2.46 |
60 | Mississippi St. | 17 | 4 | 16 | 10 | 39 | 94 | 2.41 |
61 | Baylor | 22 | 4 | 20 | 7 | 45 | 108 | 2.40 |
62 | Georgia | 20 | 4 | 19 | 7 | 42 | 97 | 2.31 |
63 | Middle Tenn. | 24 | 4 | 20 | 14 | 54 | 124 | 2.30 |
64 | UAB | 17 | 4 | 19 | 12 | 44 | 101 | 2.30 |
65 | Arkansas St. | 21 | 4 | 22 | 10 | 49 | 112 | 2.29 |
66 | SMU | 22 | 4 | 21 | 11 | 50 | 114 | 2.28 |
67 | Penn St. | 22 | 4 | 11 | 12 | 41 | 93 | 2.27 |
68 | LSU | 24 | 4 | 18 | 9 | 47 | 106 | 2.26 |
69 | UCF | 20 | 4 | 18 | 9 | 43 | 96 | 2.23 |
70 | Kansas | 17 | 4 | 18 | 9 | 40 | 89 | 2.23 |
71 | Northern Ill. | 19 | 4 | 17 | 8 | 40 | 89 | 2.23 |
72 | Bowling Green | 23 | 4 | 20 | 12 | 51 | 112 | 2.20 |
73 | Illinois | 15 | 3 | 15 | 8 | 35 | 76 | 2.17 |
74 | Texas | 23 | 4 | 16 | 13 | 48 | 104 | 2.17 |
75 | West Virginia | 20 | 4 | 20 | 12 | 48 | 100 | 2.08 |
76 | Tennessee | 22 | 4 | 26 | 10 | 54 | 112 | 2.07 |
77 | Cincinnati | 19 | 4 | 25 | 10 | 50 | 102 | 2.04 |
78 | Southern Miss. | 21 | 4 | 19 | 9 | 45 | 91 | 2.02 |
79 | Fla. Atlantic | 14 | 3 | 17 | 5 | 33 | 66 | 2.00 |
80 | Iowa St. | 19 | 4 | 16 | 10 | 41 | 81 | 1.98 |
81 | Washington | 16 | 3 | 21 | 6 | 40 | 79 | 1.98 |
82 | Miami (OH) | 20 | 4 | 15 | 12 | 43 | 84 | 1.95 |
83 | Boston College | 14 | 3 | 14 | 8 | 33 | 64 | 1.94 |
84 | Utah St. | 19 | 4 | 23 | 10 | 48 | 93 | 1.94 |
85 | Western Mich. | 16 | 3 | 16 | 16 | 45 | 87 | 1.93 |
86 | Toledo | 17 | 4 | 25 | 9 | 47 | 90 | 1.91 |
87 | Louisville | 14 | 3 | 15 | 9 | 35 | 67 | 1.91 |
88 | North Carolina | 14 | 3 | 11 | 12 | 34 | 65 | 1.91 |
89 | Pittsburgh | 14 | 3 | 15 | 8 | 34 | 65 | 1.91 |
90 | Purdue | 19 | 4 | 20 | 11 | 46 | 87 | 1.89 |
91 | Temple | 19 | 4 | 22 | 9 | 46 | 87 | 1.89 |
92 | Notre Dame | 21 | 4 | 21 | 11 | 49 | 92 | 1.88 |
93 | Rutgers | 15 | 3 | 14 | 8 | 34 | 63 | 1.85 |
94 | UCLA | 19 | 4 | 18 | 14 | 47 | 87 | 1.85 |
95 | Colorado | 12 | 3 | 17 | 8 | 34 | 62 | 1.82 |
96 | Eastern Mich. | 16 | 4 | 23 | 13 | 48 | 82 | 1.71 |
97 | Tulane | 15 | 3 | 17 | 8 | 37 | 63 | 1.70 |
98 | Western Ky. | 15 | 4 | 24 | 7 | 42 | 71 | 1.69 |
99 | Marshall | 16 | 4 | 25 | 11 | 48 | 80 | 1.67 |
100 | UNLV | 17 | 4 | 30 | 7 | 50 | 83 | 1.66 |
101 | Akron | 17 | 4 | 26 | 4 | 43 | 70 | 1.63 |
102 | Ohio | 17 | 4 | 21 | 14 | 48 | 76 | 1.58 |
103 | Ball St. | 16 | 4 | 20 | 9 | 41 | 63 | 1.54 |
104 | Rice | 19 | 4 | 23 | 11 | 49 | 75 | 1.53 |
105 | La.-Monroe | 9 | 3 | 17 | 9 | 32 | 48 | 1.50 |
106 | Washington St. | 17 | 4 | 26 | 14 | 53 | 77 | 1.45 |
107 | Memphis | 16 | 4 | 28 | 9 | 49 | 71 | 1.45 |
108 | Vanderbilt | 11 | 3 | 25 | 3 | 36 | 52 | 1.44 |
109 | BYU | 15 | 4 | 21 | 11 | 43 | 60 | 1.40 |
110 | Kent St. | 13 | 3 | 20 | 9 | 39 | 54 | 1.38 |
111 | New Mexico St. | 11 | 3 | 22 | 4 | 34 | 47 | 1.38 |
112 | Louisiana Tech | 18 | 4 | 23 | 15 | 52 | 71 | 1.37 |
113 | Colorado St. | 14 | 4 | 15 | 16 | 41 | 55 | 1.34 |
114 | Wyoming | 12 | 4 | 22 | 11 | 41 | 55 | 1.34 |
115 | La.-Lafayette | 11 | 3 | 24 | 7 | 39 | 52 | 1.33 |
116 | FIU | 13 | 3 | 24 | 13 | 47 | 62 | 1.32 |
117 | North Texas | 14 | 4 | 24 | 13 | 47 | 62 | 1.32 |
118 | Buffalo | 16 | 4 | 30 | 15 | 57 | 68 | 1.19 |
119 | San Jose St. | 12 | 4 | 29 | 8 | 45 | 36 | 0.80 |
120 | New Mexico | 11 | 4 | 32 | 19 | 58 | 41 | 0.71 |
Michigan is near the top of the list (6th); this is no surprise. Also near(er) the top and of interest to some folks around these parts is Stanford (2nd), along with this week's opponent, Indiana (3rd), and the Buckeyes (4th).
I would love it if this sort of statistic would eventually make its way into the "mainstream." Again, it seems like basketball is leading the charge for tempo-free stats, but there's no reason that we can't look at it for football as well. Perhaps we could lay this up against the dreaded time of possession stat and look for correlation -- or lack thereof. I also think it would be an interesting metric to use along with the work that The Mathlete has done -- we could start to replace some of the assumptions (Top 20 offense, average defense, etc) with data.
As I said above, please feel free to rip this apart and tell me that I'm a flaming idiot, or offer suggestions, critiques, ways to improve.
Note: All original data for the above was collected here:
http://www.ncaa.com/sports/m-footbl/stats/ncaa-m-footbl-stats-index.html
September 28th, 2010 at 4:24 PM ^
This is a great start to what I think is a killer metric. Thinking ahead, it would also be cool to weight these against strength of schedule and/or defensive ratings. Having just read a diary post on the Big Ten's strength of schedule, it seems that Michigan's offensive efficiency is somewhat more impressive than OSU's, for example, because on average it took place against better teams.
Three cheers for new metrics!
September 28th, 2010 at 4:33 PM ^
This info would really benefit from a strength of opposing defense adjustment (i.e how much more PPP did M put up against UConn versus other teams that have played UConn. Indiana's offense is good and so is Michigan's, but its reasonable to wonder just how good
The Mathlete's database probably already contains the necessary info, but I'll try and run the scanner myself to see if I can come up with something.
Nice job.
September 28th, 2010 at 5:07 PM ^
for instance, Indiana hasn't played anyone in Sagarin's top 100 iirc.
September 28th, 2010 at 5:15 PM ^
I looked today. The two FBS teams are 105 and 109 in scoring defense and the FCS team is 75 in scoring defense.
September 28th, 2010 at 4:43 PM ^
I would be interested to see if you could weight field goals based on how likely an average kicker is to make a kick of that distance (something along the lines of probability of success times 3 to get the value). This idea would probably require evidence that field goal kicking percentage is a random variable, and might be completely off your topic, but it could prove to be a better predictor of future offensive performance.
September 28th, 2010 at 4:45 PM ^
You do realize that this has been done. By the Mathlete. Here.
http://mgoblog.com/diaries/never-punt-denard-fourth-down-strategy-revis…
September 28th, 2010 at 4:42 PM ^
Boy looking at these numbers it appears that the tempo-free metric is informative only up to a point (ain't that always the way), since one thing that shoves you WAY down on the list is having a higher-than-average number of possessions.
Number of possessions is, presumably, positively affected by the strength of one's defense (its ability to get the opposition off the field efficiently), and negatively affected strength of schedule (the ability of an opposition to hold onto the ball for long drives). So, PPP isn't a 'pure' offensive metric, although it's a pretty friggin awesome analysis just the same.
Look at Indiana - a low number of possessions per game. Not very good at getting the ball back perhaps? I'm just sayin.
September 28th, 2010 at 4:52 PM ^
Number of possessions does not affect the number, because it's accounted for explicitly in the equation. The whole question is points per possession, not total points, which, as you suggest, will vary wildly with total possessions.
That said, your general point that one can only learn so much from one metric is certainly true. If it weren't, we wouldn't get to spend so much time on mgoblog....
September 28th, 2010 at 5:09 PM ^
having a high number of total possessions would pull the team toward the mean. so perhaps some kind of regression should be thrown in to account for that.
September 28th, 2010 at 10:21 PM ^
So you might try plotting points vs. number of possessions and taking the regression. Perhaps the residual plot would be helpful. You could create a histogram of the binned residuals and then see where a team fits on the residual bell curve. A z-score could then give you an idea about how good the offense is.
September 28th, 2010 at 4:52 PM ^
Nice work. Looking at the leaderboard and the bottom few, you see teams you kind of expect to occupy those spaces. That is always a nice confirmation of sorts.
I do have a question regarding the metrics:
Does it consider an INT-to-fumble recovery by the offense a possession? I just remember that happening twice so far this season (maybe more?), and it seemed like a special case.
September 28th, 2010 at 5:03 PM ^
After my original posting, I edited the flaws section to include the Gordon/Vinopal special as another thing that isn't currently accounted for.
September 28th, 2010 at 4:54 PM ^
Could those somehow be included? No punt or kickoff results, and a missed FG is not considered a turnover.
That would still be difficult to account for, as "missed FG" is probably not an easily sortable stat. You may have to go team by team and check their kicking statistics...
Interesting analysis!
September 28th, 2010 at 5:01 PM ^
I completely missed that - thanks. The data is available on an individual basis, so we'd just need to map the individual kickers to their teams.
http://www.ncaa.com/sports/m-footbl/stats/ncaa-m-footbl-fbs-ind-field-g…
I really wish I had access to an actual database with this information rather than a bunch of web pages that I copy-paste into excel to dick around with for entirely too long. If I actually had access to the database behind all of this, it could be done pretty easily with a query.
September 28th, 2010 at 4:57 PM ^
I think you shouldn't focus on how a possion ends, but rather how it begins. I think the solution to the end of quarter dilemma and a more accurate measure of the number of possessions for a team is simply using the times the opponent kicks off, punts, or turns the ball over. That is if you want to know how many times TEAM X had the ball, you need to calculate how many times its opponent, TEAM Y, kicked off, turned the ball over, and punted.
September 28th, 2010 at 5:03 PM ^
I know this is more labor intensive (i.e., meaning you'd have to go back and look at the stats from each game), but I think it is the right way to calculate your measure.
September 28th, 2010 at 5:07 PM ^
That would definitely be another way to look at it and I think you're right, but you're also right that it's much more difficult. Again, if I had access to the database of this info, it would be much more doable. As it is, I'm dealing with team-centric data without an easy way to flip the lens.
September 28th, 2010 at 6:09 PM ^
Here is an easy way to figure this all out (still somewhat labor intensive, but really not so bad). SI.com has drive charts for all games. You can simply count the number of "drives" for each team. This would obviously take a while given that we are 4 games into the season, but for each week after that I bet it woud only take 30 minutes to update each week. This way you wouldn't even need to account for turnovers, kickoffs, or punts. It would solve the end of half issue. It would take care of any other issue that would result from calculating it from end of drive outcomes.
September 28th, 2010 at 9:39 PM ^
...when you mentioned focusing on how drives start. To properly sum the number of drives a team has:
#of punt returns + #of kick returns + #of opponent's turnovers = #times (Michigan) drove the field in an offensive manner in a game.
Considering that most punt/kick returns are made by offensive skill players, I have no problem with punt/kick return points being awarded to the offense. I agree that defensive touchdowns should be removed from a measure of offensive efficiency.
September 29th, 2010 at 9:40 AM ^
I didn't read your post, but based on your nick, I expect there is some flawed logic in there somewhere ;)
September 28th, 2010 at 5:08 PM ^
You accounted for turnovers in looking at number of possessions, but did you consider points off interception, fumble and kick returns? That would increase your points per possession metric but really would have no bearing on your offense's ability.
September 28th, 2010 at 5:12 PM ^
This is another one of the things that I added in after the initial posting, so it's possible that it wasn't there when you read it. Sorry about that.
Points scored without the offense's involvement (pick-six, punt/kick return etc) should not count towards offensive efficiency. I am not sure whether or not the NCAA's "Team Scoring Offense -- Total Points" adjusts for this or not.
September 28th, 2010 at 6:36 PM ^
I used the drive charts from SI.com and came up with the following. I only calculated points per possession (PPP) for UofM, OSU, and IU.
Number of possessions / PPP:
OSU - 57 / 3.46
UofM - 47 / 3.51
Indiana - 33 / 3.76
Because of limitation with the NCAA data you are using the number of possessions for each team is being underestimated. The problem however is that this is not constant across teams. While you underestimate the number of possessions for IU and UofM equally (-4), you underestimate the number of OSU possession by 6. What these results show is that this is affecting your calculations and the rankings of the teams. If we use the drive chart from SI.com UofM is more efficient offensively than OSU.
Granted this is a very small sample, but I think you'd find small, but potentially significant fluctuations in the rankings if you changed your calcuation methods.
I think that this measure is a wonderful idea and I apologize for being overtly critical. Just want to help make it as good as can be.
September 28th, 2010 at 7:38 PM ^
I appreciate the feedback. My method is far from perfect and I'm absolutely willing to acknowledge that. Anything we can do to improve accuracy is good. There's got to be a better way to get actual number of possessions than counting drive charts, though. I mean, the fact that SI.com has those drive charts means that the data is out there; we just need a way to get at it. I'm not overly excited about counting drive charts for 120 teams!
September 28th, 2010 at 6:59 PM ^
PPR adjusted based on competition. Pomeroy has a formula that takes account into the strength of defense. Your data is based on raw numbers so I don't know how you can come up with a formula that adjust for the strength of defense. I'm not real good in math so if any math major, engineer, etc. can come up with it then by all means, great!
Rash, I would love to see you include this as part of NCAA football tempo-free stats simliar to Ken Pomeroy(if no one hasn't started the website that it is.)
September 28th, 2010 at 9:03 PM ^
In basketball, each offensive possession is very nearly independent of the defensive possession that preceded it (with the exception of a fast break due to a steal). That's nowhere near true in football. If your defense is forcing turnovers deep in enemy territory, your offense is going to look a lot better than if your drives are always starting in the shadow of your own goalposts.
Perhaps a better approach would be adjusting Mathlete's offense and defense grades (which take into account starting field position and also, I think, credits the offense appropriately when a drive does not end in points but alters the field position battle significantly) based on number of possessions.
September 28th, 2010 at 10:19 PM ^
are perfect but they do tell a story. Yards per drive, PPP, and average starting field position would all tell us the story of how efficient an offense really is.
September 28th, 2010 at 10:26 PM ^
Good point.
September 28th, 2010 at 10:28 PM ^
This seems like data that the Mathlete would have in hand (or be able to obtain). He's always talking about his way cool database. He had to get it somehow!
I'm not sure he does anything other than get data from websites and then d*ck around with Excel too...FWIW However, his database would make looking at data from the past few seasons trivial.
September 29th, 2010 at 5:39 AM ^
As always, it would be interesting to see where the Michigan offenses have ranked vis a vis the best and worst in the country over the last decade. We could have even more charts!
September 29th, 2010 at 7:36 AM ^
I think this analysis is better served as a team scoring efficiency metric based on how many times the offense touches the ball. If you have a defense that's excellent at forcing turnovers, and even adding to the team's point output, that should definitely be captured (and it seems it is in your analysis). Same with an excellent return game. Points from returns/defense, or even short field scores offered by an opportunistic defense, are worth just as much as a 20-play, 80 yard drive. It might not be the best measure of pure offensive efficiency, but if my offense only needs to be responsible for X number of points while my defense and return game score Y, that shouldn't mean I'm a less dangerous team than one that gets Z number of points on offense and 0 via defense/returns, assuming X+Y=Z.
September 29th, 2010 at 11:11 AM ^
Your formula includes punts, turnovers, and kickoffs. Your logic was to subtract the number of games since once a game you kick off to start a half. This is true but you forgot to account for the other half where you receive a kickoff. The two factors cancel each other out and you are left with punts + turnovers + kickoffs.
Thanks for the read!
September 30th, 2010 at 8:38 PM ^
I went back and looked at the box scores for U/M. I subracted any possession that was just running out the half or end of game. M has had 46 possessions for the first 4 games.
So, your numbers for M are pretty close.
BTW, the Fremeau Efficiency Index (FEI) looks like it does what folks are looking for. I've added to my weekly diary on National Rankings here:
http://mgoblog.com/diaries/week-4-national-rankings-update-fremeau-effi…
October 18th, 2010 at 10:57 PM ^
i'm not much of a college football fan, i focus primarily on the nfl. when i was doing research on yards per possession i came across this blog post. i found a formula for calculating possessions from the pro football reference blog. it is as follows
offensive points per drive = (7*(rushTD+passTD) + 3*FGM) / (rushTD + passTD + turnovers + punts + FGA)
no idea how to adjust for strength of schedule though. hope this helps.
October 29th, 2010 at 3:30 PM ^
Stumbled on this thread during a search for 'possessions resulting in points.' This accomplishes almost the same thing. The chief benefit in this is to give some means of comparing offensive effectiveness between teams with dissimilar philosophies: for example a team like Oregon who relies on offense to win games, vs a team like mine that relies on defense and special teams to set the offense up with the short field and control the clock.
You should start a website on this alone, and in time the stats you calculate will be adopted by more and more media outlets.
You are correct in that the NCAA scoring offense stats include defensive and special teams scoring.
November 5th, 2010 at 4:58 PM ^
I have been using this metric for a little over a year. I look at it from how an offense can end its possession.
= TD+ Field Goal Attempt (FGA) + Turnover (Int thrown or fumble lost) + Punt + Fail 4th down conversion + Safety
This will usually not account for possessions at the end of the half or at the end of the game.
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