Week 2 Drive-Point Efficiency

As always, better is up and to the right.  Teams with the highest efficiency ratings in both offense and defense will be in the upper right quadrant.

Learn About Tableau

Visualizing Explosive Offenses

In his article "The five factors: College football's most important stats" Football Study Hall's Bill Connelly proposes that explosiveness was one of the most important statistics in college football.  Connelly defined explosiveness as Equivalent Points per Play and showed how winning this battle was highly correlated to winning the game.

I think there is another way to look at explosiveness.

Charting average points per drive and average drive starting position yields a chart that illustrates the relationship between the two.

On the vertical (Y) axis I plotted Average Starting Position per Drive.  This is the average distance from the opponent's goal line, so a smaller number is better.

On the horizontal (X) axis I plotted Average Points per Drive.  For this measure, larger is of course better.

The quadrants can be interpreted this way:  Teams in the lower right had, on average, further to go to score and averaged more points per drive than the rest of the FBS.  Essentially, those teams did more with less and did it more frequently.

Teams in the upper left quadrant had a more favorable starting position but failed to capitalize on that advantage, scoring on average less points per drive than the FBS.

Teams in the upper right had better than average field position AND turned it into points.  Your 70-80 point scoring explosions are probably located here.  Teams in the lower left had worse than average starting field position and didn't score much either.  Teams in these two quadrants essentially did what would be expected of them.  They scored or didn't score based on their field position.

Ideally, teams would want to be in the upper right...scoring frequently because you won the field position battle is always preferable, but the lower right is good too as it means a team has the ability to eat up large chunks of yards quickly.

Learn About Tableau

Week 1 Drive-Point Efficiency Report

Week 1 of the 2014 season is in the bag.  It's still early, and there will be a lot more regressing to the mean happening in the next weeks as teams' statistical performances begin to more closely portray their true strengths.  Until then, though, enjoy the wacky world of early season statistics.

Learn About Tableau

All FBS Recruits from 2002-2014

The map shows recruits for the last 4 years.  Use the filters to make the map more usable.

The size of the logo corresponds to the number of stars given the recruit by Scout.com.  I assigned 1 star to all unranked recruits...Scout.com does not identify recruits as less than 2 stars.

This visualization is copyrighted material.  You're welcome to use it as you'd like, but please credit me, Paul Dalen, as the author and provide a link back to this blog.

Learn About Tableau

HuskerMath's FBS Power Ranking Win Percentage Matrix

Use the filters at the bottom to make the matrix a bit easier to handle, as well as easier to load.

Learn About Tableau

Which Teams Overachieved and Which Teams Underachieved in 2012?

Tonight, while working putting together the final validation of the 2013 FBS Prediction Model, my analysis of the results highlighted some very interesting, and kind of disconcerting, things about Nebraska's 2012 season.

The model simulates every FBS game for the entire season.  

As validation, I used the methodology to simulate the 2012 season and prepared a comparison between between the model results and the real game results.  

This first chart shows some of those results of the predicted versus actual wins. 

A positive number of the vertical axis means the model predicted more wins than a team achieved.  Likewise, the negative numbers mean the model predicted fewer wins than the team achieved.

The average delta between the predicted win total and the actual win total is 1.15 games per team. 

21% of teams were predicted correctly.  20% were under-predicted by one win, and 29% were over-predicted by one win.  The model predicted more than 70% of seasons accurately to within one win.

The model under-predicted 12% of season by two wins and 5% by three wins.  It over-predicted 7% over season by two wins and 6% of season by three wins.

Can you guess which team was the most over-achieving?  

You betcha. Dear Old NU.  

The model predicted 6.91 wins but the Huskers finished with 10 wins, for a delta of -3.09 wins.  

While some may be inclined to see this as a positive, and it's certainly better than winning three fewer games than the model anticipated, the fact remains that Nebraska's scoring offense and scoring defense (the basis of the model) should have resulted in a 7-win season...not a 10-win season.

Remember the amazing streak of 4th quarter heroics in the Wisconsin, Northwestern, and Michigan State, and Penn State games?  Remember how Denard Robinson left the game before halftime?   

Sometimes, it's better to be lucky than good.

Interestingly, Ohio State is the #2 most overachieving team, with a predicted win total of 9.12.  Had they played in the Big Ten Championship Game or a bowl game, they would almost certainly have been the most overachieving team by a large margin.

The Top-10 overachievers and Bottom-10 underachievers are:

I put a table with the full results of the model at the end of this post.

Finally, the model provides some insight into the consistency of a team's on-field performance.  The standard deviation of the predicted wins can be used as a proxy for a team's consistency.

So, can you guess which team had the largest standard deviation in the model results, and by proxy, was the least consistent and predictable?  

Yup, Dear Old NU.  Again.

Now, guess which team had the lowest standard deviation in model results.  

Hint:  they won the National Championship.

I'll let my readers draw their own conclusions about this one.

By the way, I'm now a writer over at Football Study Hall.  Stop by and check it out.  I'll be writing about more than just the Huskers over there.

GBR!
@HuskerMath

Rank	Team	Conf	Pred. Wins	Actual Wins	Delta (rnd)	Pred. Delta	Abs( Pred Delta)
1	Northern Illinois	MAC	13.1	12	1	1.1	1.1
2	Alabama	SEC	12.7	13	0	-0.3	0.3
3	Florida State	ACC	12.5	12	1	0.5	0.5
4	Utah State	WAC	11.2	11	0	0.2	0.2
5	Oregon	Pac-12	11.0	12	-1	-1.0	1.0
6	UCF	C-USA	11.0	10	1	1.0	1.0
7	Georgia	SEC	10.9	12	-1	-1.1	1.1
8	Boise State	MWC	10.8	11	0	-0.2	0.2
9	Arizona State	Pac-12	10.7	8	3	2.7	2.7
10	Cincinnati	Big East	10.6	10	1	0.6	0.6
11	Tulsa	C-USA	10.5	11	0	-0.5	0.5
12	BYU	Ind	10.5	8	2	2.5	2.5
13	Texas A&M	SEC	10.4	11	-1	-0.6	0.6
14	Kansas State	Big 12	9.9	11	-1	-1.1	1.1
15	North Carolina	ACC	9.8	8	2	1.8	1.8
16	Rutgers	Big East	9.8	9	1	0.8	0.8
17	Clemson	ACC	9.8	11	-1	-1.2	1.2
18	Oklahoma State	Big 12	9.8	8	2	1.8	1.8
19	Bowling Green	MAC	9.7	8	2	1.7	1.7
20	Ohio	MAC	9.7	9	1	0.7	0.7
21	Vanderbilt	SEC	9.7	9	1	0.7	0.7
22	San Jose State	WAC	9.6	11	-1	-1.4	1.4
23	Fresno State	MWC	9.6	9	1	0.6	0.6
24	Stanford	Pac-12	9.6	12	-2	-2.4	2.4
25	Kent State	MAC	9.5	11	-1	-1.5	1.5
26	Wisconsin	Big Ten	9.4	8	1	1.4	1.4
27	South Carolina	SEC	9.2	11	-2	-1.8	1.8
28	Notre Dame	Ind	9.2	12	-3	-2.8	2.8
29	Arkansas State	Sun Belt	9.2	10	-1	-0.8	0.8
30	Ohio State	Big Ten	9.1	12	-3	-2.9	2.9
31	San Diego State	MWC	9.1	9	0	0.1	0.1
32	Penn State	Big Ten	8.8	8	1	0.8	0.8
33	Florida	SEC	8.7	11	-2	-2.3	2.3
34	LSU	SEC	8.7	10	-1	-1.3	1.3
35	Oregon State	Pac-12	8.7	9	0	-0.3	0.3
36	Louisiana-Lafayette	Sun Belt	8.6	9	0	-0.4	0.4
37	Northwestern	Big Ten	8.6	10	-1	-1.4	1.4
38	Oklahoma	Big 12	8.5	10	-2	-1.5	1.5
39	Michigan	Big Ten	8.3	8	0	0.3	0.3
40	UCLA	Pac-12	8.2	9	-1	-0.8	0.8
41	Louisiana-Monroe	Sun Belt	8.2	8	0	0.2	0.2
42	Pittsburgh	Big East	8.1	6	2	2.1	2.1
43	Western Kentucky	Sun Belt	8.1	7	1	1.1	1.1
44	Louisville	Big East	8.1	11	-3	-2.9	2.9
45	Mississippi State	SEC	8.1	8	0	0.1	0.1
46	Rice	C-USA	7.9	7	1	0.9	0.9
47	TCU	Big 12	7.8	7	1	0.8	0.8
48	SMU	C-USA	7.8	7	1	0.8	0.8
49	Louisiana Tech	WAC	7.7	9	-1	-1.3	1.3
50	USC	Pac-12	7.7	7	1	0.7	0.7
51	Nevada	MWC	7.6	7	1	0.6	0.6
52	Michigan State	Big Ten	7.5	7	1	0.5	0.5
53	Syracuse	Big East	7.4	8	-1	-0.6	0.6
54	North Carolina State	ACC	7.4	7	0	0.4	0.4
55	Toledo	MAC	7.2	9	-2	-1.8	1.8
56	East Carolina	C-USA	7.2	8	-1	-0.8	0.8
57	Navy	Ind	7.2	8	-1	-0.8	0.8
58	Air Force	MWC	7.2	6	1	1.2	1.2
59	Georgia Tech	ACC	7.1	7	0	0.1	0.1
60	Texas	Big 12	7.1	9	-2	-1.9	1.9
61	Texas Tech	Big 12	7.0	8	-1	-1.0	1.0
62	Nebraska	Big Ten	6.9	10	-3	-3.1	3.1
63	Western Michigan	MAC	6.8	4	3	2.8	2.8
64	UTSA	WAC	6.6	8	-1	-1.4	1.4
65	Central Michigan	MAC	6.6	7	0	-0.4	0.4
66	New Mexico	MWC	6.5	4	3	2.5	2.5
67	Virginia Tech	ACC	6.4	7	-1	-0.6	0.6
68	Connecticut	Big East	6.4	5	1	1.4	1.4
69	Iowa State	Big 12	6.4	6	0	0.4	0.4
70	Washington	Pac-12	6.3	7	-1	-0.7	0.7
71	Middle Tennessee	Sun Belt	6.3	8	-2	-1.7	1.7
72	Ball State	MAC	6.3	9	-3	-2.7	2.7
73	Baylor	Big 12	6.2	8	-2	-1.8	1.8
74	Mississippi	SEC	6.2	7	-1	-0.8	0.8
75	Minnesota	Big Ten	6.2	6	0	0.2	0.2
76	Troy	Sun Belt	6.1	5	1	1.1	1.1
77	Utah	Pac-12	6.1	5	1	1.1	1.1
78	Memphis	C-USA	5.9	4	2	1.9	1.9
79	Iowa	Big Ten	5.6	4	2	1.6	1.6
80	Miami (Florida)	ACC	5.4	7	-2	-1.6	1.6
81	West Virginia	Big 12	5.3	7	-2	-1.7	1.7
82	Purdue	Big Ten	5.2	6	-1	-0.8	0.8
83	Arizona	Pac-12	5.2	8	-3	-2.8	2.8
84	Texas State	WAC	5.1	4	1	1.1	1.1
85	Houston	C-USA	5.1	5	0	0.1	0.1
86	Wyoming	MWC	4.9	4	1	0.9	0.9
87	South Alabama	Sun Belt	4.8	2	3	2.8	2.8
88	Marshall	C-USA	4.8	5	0	-0.2	0.2
89	UNLV	MWC	4.8	2	3	2.8	2.8
90	Virginia	ACC	4.8	4	1	0.8	0.8
91	Maryland	ACC	4.8	4	1	0.8	0.8
92	North Texas	Sun Belt	4.7	4	1	0.7	0.7
93	Colorado State	MWC	4.7	4	1	0.7	0.7
94	Buffalo	MAC	4.6	4	1	0.6	0.6
95	Indiana	Big Ten	4.5	4	0	0.5	0.5
96	Tennessee	SEC	4.4	5	-1	-0.6	0.6
97	UTEP	C-USA	4.4	3	1	1.4	1.4
98	Florida International	Sun Belt	4.3	3	1	1.3	1.3
99	UAB	C-USA	4.3	3	1	1.3	1.3
100	Duke	ACC	4.2	6	-2	-1.8	1.8
101	Akron	MAC	4.0	1	3	3.0	3.0
102	Army	Ind	4.0	2	2	2.0	2.0
103	South Florida	Big East	3.9	3	1	0.9	0.9
104	Temple	Big East	3.9	4	0	-0.1	0.1
105	Boston College	ACC	3.8	2	2	1.8	1.8
106	Florida Atlantic	Sun Belt	3.8	3	1	0.8	0.8
107	Missouri	SEC	3.5	5	-2	-1.5	1.5
108	Arkansas	SEC	3.5	4	-1	-0.5	0.5
109	Auburn	SEC	3.4	3	0	0.4	0.4
110	Wake Forest	ACC	3.3	5	-2	-1.7	1.7
111	Miami (Ohio)	MAC	3.3	4	-1	-0.8	0.8
112	Washington State	Pac-12	3.1	3	0	0.1	0.1
113	California	Pac-12	2.8	3	0	-0.2	0.2
114	Hawaii	MWC	2.7	3	0	-0.3	0.3
115	Kentucky	SEC	2.5	2	1	0.5	0.5
116	New Mexico State	WAC	2.4	1	1	1.4	1.4
117	Illinois	Big Ten	2.4	2	0	0.4	0.4
118	Eastern Michigan	MAC	2.3	2	0	0.3	0.3
119	Tulane	C-USA	1.8	2	0	-0.2	0.2
120	Kansas	Big 12	1.8	1	1	0.8	0.8
121	Southern Mississippi	C-USA	1.7	0	2	1.7	1.7
122	Idaho	WAC	1.3	1	0	0.3	0.3
123	Colorado	Pac-12	0.9	1	0	-0.1	0.1