Saturday, December 1, 2012

ND vs Alabama

I thought it would be interesting to compare ND and Alabama performance against top opponents.  I don't yet have the Alabama/Georgia game data, but currently, this compares:

ND vs Mich, Ok, USV, and Stanford
Alabama vs LSU, Mich, and A&M

I then created a "virtual game", combining the outcomes of these three games into one.

I then compared the performance in these games relative to season averages, to see how strong or week offenses and defenses are relatively speaking.

Wednesday, February 8, 2012

Football fan commandments

Through our recent coaching changes – Davie to Ty to Weis to Kelly – there have been fan favorites of sorts that I thought would suddenly get more PT once the new coach came in – or that the reason they weren’t playing more was due to some sort of short sightedness of the prior coach.

In each regime change, very few (if any) players have come out of the woodwork.   I have now come to believe that coaches, almost always, will play the person they think will win games… as their incentive as coaches is to win, so they have no reason to keep an ace in the hole.

I’d go so far as to say this is one of my personal rules of football fandom – if a guy isn’t playing it’s because the other guy is likely better.  This rule takes some fun out of being fan – as it is always great to speculate that the next guy is really better than the first… and if that you had the next guy your team would be better.  But the reality is, this rule is more often true than not.

That said, I have noticed a few exceptions to this rule, or where a ‘better’ player is on the bench.  These exceptions are:
1. Consistency
2. Character flaws (on the player’s part)
3. Seniority (specifically, lack thereof)

Put differently, for a coach to not play the best player, they either don’t like the best player, want to respect a more senior player, or don’t know when they’ll get Jekyll or Hyde.

Saturday, February 4, 2012

Kelly vs other top coaches

I thought it would be fun to compare the first two years of coaches vs the two years (averaging the two years) before they showed up across a variety of programs. 
A popular notion on this board is that great coaches make it to NC in yr 3, so I thought I'd take a few NC coaches (Saban, Meyer), and a few good coaches (Dantonio, Harbargh), and our coach (Kelly) to see how 2 year improvements would look.  I took 3 very basic metrics - Wins, Pts differential, and my favorite yards per play differential (adjusted for SOS).
Here are their performance, ranked by pts differential improvement:
#1:
Wins 1.5
Pt diff  111
YPPD  0.25
#2:
Wins 1.5
Pt diff  54
YPPD  0.95
#3:
Wins 3.5
Pt diff  47
YPPD  0
#4:
Wins 3.5
Pt diff  42.5
YPPD  0.35
#5:
Wins 1.5
Pt diff  26
YPPD  0.05
1: Harbaugh
2: Kelly
3: Dantonio
4: Meyer
5: Saban
It is worth noting that each had a different starting point for pts differential, here's what each coach inherited:
1: Harbaugh -165
2: Kelly +44
3: Dantonio +10
4: Meyer +101
5: Saban +96
You can make a number of conclusions from this data - but it's hard not to think that Kelly is on the right path, at least compared to what these other coaches were able to accomplish.

Friday, January 20, 2012

Why teams win

Wanted to find why teams win games
Took data from 2002-2010
Looked for non-scoring stat that predicts winning, yards per play is #1... Accounting for 79% of games
Then looked at all wins where yards per play was wrong – turnover margin is #1 reason... Accounting for another 14% (93% total)
Then looked at all wins where yppd and turnover margin is wrong # of rushes is # 1 reason... Accounting for another 5% (98% total)
# passes completed it then then 1%, leaving 1%

Tuesday, January 10, 2012

Yppd goes bowling

So, I decided a good test for my yppd (yards per play differential) prediction model would be to use it for confidence picks for the bowl games. 
<i>For some background, I’ve been playing with trying to find a way to predict winners to college football games – the idea being that a team’s ability is best measured by how many yards per play they can get relative to their opposition... so it’s not as important if they are a running team or a passing team or a defensive team or an offensive team, but if they can manage to get 1 yard per play  more than their opposition, then they are more likely to win.  If they get 1 yard per play less than their opposition, they are more likely to lose.

I don’t use straight yppd – I do an adjustment for strength of schedule when using it to predict games – but it’s pretty much just yards per play differential.

Looking at game data for 2009-2010, 81% of winners won the yppd margin – more than any other non-scoring stat (more than passing yards, rushing yards, first downs, etc).  In 93% of games, the winner was either even on yppd and turnovers or ‘won’ one or both of yppd /turnovers.  Using the yppd model I’ve made, I have been able to ‘predict’ winners to ~75-77% of games.

So, what I’m trying to say – I think this model is a pretty good representation of a team’s ability.</i>

Back to bowling - I entered into yahoo and scored a 509 – or in the 96th percentile.  Not bad, right?  Makes me thing that this model is actually pretty good.  Granted, I only got 68% of the games right, but that’s because there were more close games than in a typical season.  The reason I was able to get in the 96% is because the yppd model gives me a view of what games are of higher confidence... which is reflected in how my picks faired.

Splitting my picks into 3 buckets – 1-10, 11-20, 21-35, my record was:
1-10: 3-7
11-20: 7-3
21-35: 14-1

There are some picks where the model predicted a winner that, after watching the game, I was completely lucky (it picked UM over VT... confidence 26).  Other games showed how the model works pretty well in seeing things that others don’t (like WV destroying Clemson... confidence 28).

Overall, I’m pretty psyched as the yppd predictor seems to have legs.  I’m going to try to fix it up a bit this offseason and create a second scoring metric – we’ll call it the “big play” factor – so things like turnovers, defensive scores, TD:FG ratio.. I’m going to figure out a way to measure them. 

So – to bring this back to ND.

ND finished with a 1.3 yppd score, good for #13. 
We beat #6-MSU, 59-Pitt, 66-MD, 70-Navy, 71-AF, 85-Wake, 93-PU, 94-BC. 
We lost to #8-Stanford, #14-USC, #15-UM, #17-FSU, #47-USF.

I see good news and bad news.
The bad news (which is largely obvious)...
We went 1-4 against relatively even opponents.  The yppd scores aren’t perfect, but I think it’s pretty clear that we are in the same ballpark (if you believe the yppd score means anything).  An as-expected performance would have us at 2-3 or 3-2.  We also should not have lost to USF... we are significantly better and if we played a conservative game, we should have won (I know, not anything new)... but it is very common for a team that is not great to lose a game it shouldn’t. So... an expected outcome to our season would have been 10-3-ish.  8-5 is 2 games off, so I think it is disappointing that we didn’t fair better and I think Kelly clearly needs to do a better job avoiding the collapses (maybe it is a qb thing...)

The good news (which is not as obvious)...
We had a 1.3 yppd score.  That is very good.  Last year, we were 0.9, the year before 0.3, the year before 0.0.  Check out our yppd scores since 2000:
<pre>
2000       -0.2
2001       0
2002       0.7
2003       0.3
2004       0.2
2005       0.8
2006       0.3
2007       -0.9
2008       0
2009       0.3
2010       0.9
2011       1.3
</pre>

Not bad, right?  Our 2 year average of 1.1 is better than any other two years by a significant amount (0.55 next best in 05-06).  Maybe, just maybe Kelly is putting the whole thing together.

One other interesting thing worth noting – Post Maryland, our YPPD score was much worse this year.  We were at 1.7 through Maryland and then 0.4 afterwards (also 1.6 pre-USC, 0.8 USC and later).  This is very different than last year, where our final 5 games were at 1.1 vs our first 8 at 0.8.

 <pre>
Rank      Institution           yppd score
1              Alabama              3.0
2              LSU        2.4
3              Oregon 2.2
4              Oklahoma St.     1.8
5              Houston               1.8
6              Michigan St.       1.5
7              Wisconsin            1.5
8              Stanford              1.4
9              South Carolina   1.3
10           Arkansas              1.3
11           Florida  1.3
12           West Virginia     1.3
13           Notre Dame       1.3
14           Southern California         1.3
15           Michigan              1.2
16           Oklahoma           1.2
17           Florida St.            1.2
18           Georgia                1.1
19           Tulsa      1.1
20           Texas A&M         1.1
21           Penn St.               1.0
22           Southern Miss. 1.0
23           Boise St.               1.0
24           SMU      1.0
25           Baylor   0.8
26           Virginia Tech      0.8
27           Missouri               0.8
28           Northern Ill.       0.8
29           North Carolina   0.7
30           Utah St.                0.7
31           Texas    0.7
32           TCU        0.6
33           Nebraska             0.6
34           Tennessee          0.6
35           Toledo  0.6
36           Clemson              0.6
37           Vanderbilt           0.6
38           Auburn 0.5
39           Miami (FL)           0.5
40           California             0.5
41           UCLA     0.5
42           Louisiana Tech   0.5
43           Arkansas St.       0.5
44           Georgia Tech     0.5
45           Mississippi St.    0.4
46           Nevada                0.4
47           South Fla.            0.3
48           Ohio      0.3
49           UCF        0.3
50           Iowa      0.2
51           Washington        0.2
52           BYU        0.2
53           Temple 0.2
54           Illinois   0.2
55           La.-Lafayette     0.1
56           Arizona St.          0.1
57           Cincinnati            0.1
58           Ohio St.                0.1
59           Pittsburgh           0.0
60           Oregon St.          0.0
61           La.-Monroe        (0.0)
62           Arizona (0.1)
63           Virginia (0.1)
64           Utah      (0.1)
65           FIU         (0.1)
66           Maryland             (0.1)
67           Marshall               (0.2)
68           Minnesota          (0.2)
69           Louisville              (0.2)
70           Navy      (0.2)
71           Air Force              (0.3)
72           East Carolina      (0.3)
73           Bowling Green  (0.3)
74           Kansas St.            (0.3)
75           Miami (OH)         (0.3)
76           North Carolina St.            (0.4)
77           Kent St.                (0.4)
78           Rutgers                (0.4)
79           UTEP     (0.4)
80           North Texas       (0.4)
81           San Diego St.      (0.4)
82           Syracuse              (0.5)
83           Iowa St.                (0.5)
84           Western Mich.  (0.5)
85           Wake Forest      (0.5)
86           Fresno St.            (0.6)
87           Central Mich.     (0.6)
88           Northwestern   (0.6)
89           San Jose St.        (0.7)
90           Washington St. (0.7)
91           Ole Miss               (0.7)
92           Hawaii   (0.8)
93           Purdue (0.8)
94           Boston College  (0.8)
95           Eastern Mich.    (0.8)
96           New Mexico St.                (0.8)
97           Duke     (0.9)
98           Texas Tech          (0.9)
99           Colorado              (0.9)
100         Army     (0.9)
101         Troy       (1.0)
102         Western Ky.       (1.1)
103         Buffalo (1.1)
104         Connecticut        (1.1)
105         Rice        (1.1)
106         Ball St.   (1.2)
107         Kentucky             (1.2)
108         Kansas  (1.2)
109         Fla. Atlantic         (1.2)
110         UAB       (1.3)
111         Middle Tenn.     (1.3)
112         Wyoming             (1.3)
113         Idaho    (1.3)
114         Colorado St.       (1.5)
115         Indiana (1.5)
116         Tulane  (1.8)
117         Akron    (1.8)
118         UNLV    (1.9)
119         New Mexico      (1.9)
120         Memphis             (2.3)
121         Texas St.              (3.7)

</pre>

Monday, December 12, 2011

Oversigning meets yppd

So, I can’t help my love for yppd (yards per per play differential).  I think it’s a great indicator of relative team strength.  I thought it would be interesting to compare a team’s yppd vs the size of their classes – so does oversigning help or hurt a team’s performance?

On the one hand, the data is clearly all over the place.  There are bad teams that sign a lot of players and there are good teams that sign a lot of players. 

That said, check this out – here are yppd scores (x-axis) mapped against the 4-year average size signing class (y-axis, using data from oversigning.com).  This is data for BCS schools only for 2006-2011 seasons.  The dots are specific “YPPD scores” (actual yppd + a SOS adjustment), the red line is a 5 data-point rolling average.



Interestingly, there is a very straight line here – the better the team is from a yppd perspective, the more players they sign on year on average.   Although, it trends up by only about 1.5 signees/yr (23 is at 0 yppd score [average] and about 24.5 as you move towards the top end).  

Also interestingly, is how, for the most part, top teams sign a high number of players.  Consider the list below – showing all teams with a 2 or greater yppd score – and how large their signing classes are.

So, the intuition that oversigning helps teams looks to be true, at least at a high level.



(the teams below are the top 27 yppd teams & avg # of signees over a 4 year period... the average class size is 24.7, the average class size of the bottom 27 is 22.8)
2006 West Virginia  29
2011 Alabama  28
2009 Alabama  28
2010 Auburn  28
2010 Arkansas  28
2010 Alabama  27
2007 Texas Tech  27
2011 LSU  26
2006 Arkansas  26
2008 Oregon  26
2011 Oregon  25
2010 Oregon  25
2007 Oklahoma  24
2007 West Virginia  24
2006 Texas Tech  24
2007 Mizzu  24
2008 Florida  24
2009 Florida  24
2007 Florida  24
2008 Oklahoma  24
2006 LSU  23
2007 LSU  23
2007 USC  23
2006 Florida  23
2011 Wisconsin  22
2009 Texas  21
2007 Ohio State  20

Wednesday, December 7, 2011

Does running = winning?

Here's how much a team rushes relative to what % of their games they win (2008-2011 data)

1st column = % of yards gained through running (for the whole season)
2nd column = % of games won

10% 32%
20% 42%
30% 49%
40% 51%
50% 55%
60% 56%
70% 54%
80% 42%

Note: the first column is actually a range, so 10%, really equals 10-19.9%

Then distribution of teams across the rushing categories by number of wins.

For teams that have won 10 or more games:

1st column = % of yards gained through running (for the whole season)
2nd column = % of teams in that bucket

10% 0%
20% 7%
30% 30%
40% 27%
50% 28%
60% 6%
70% 1%
80% 0%


Then teams with 7-9 wins

10% 1%
20% 7%
30% 31%
40% 37%
50% 16%
60% 4%
70% 3%
80% 1%


Then teams with 6 or fewer
10% 1%
20% 13%
30% 39%
40% 26%
50% 15%
60% 3%
70% 1%
80% 1%


A quick look at this tells me that you can be a very successful team with running or throwing the ball. Although, a higher proportion of very winning teams run the ball more.

ND runs the ball more with Kelly than it did with Weis, but it in the mid-to-high 30%'s - within the range in which successful teams commonly are.

Also - one more cut of data, teams winning 12 or more games over the last 4 years:
10% 0%
20% 8%
30% 32%
40% 20%
50% 36%
60% 4%
70% 0%
80% 0%