Betting Market Power Rankings – Week 14

by Michael Beuoy

Here are the week 14 betting market power rankings. Here is a link to the first set of rankings I generated last week.

First off, thanks to Brian for making the Community site available as well as calling out last week’s post on the main blog. There was a lot of good feedback in the comments section. Most of it I’m still chewing over, but I have decided to incorporate a suggestion from Jim A, who has been creating a very similar set of rankings over at Nutshellsports.com. More on that later.

Methodology

The original post has more detail, but here’s an overview:

The goal I had was to generate a set of point-based rankings that would best predict how the betting market would set the point spread for the coming week’s matchups. The better I was able to match the point spread, the better the rankings were a reflection of the market’s estimate of team by team strength. Through trial and error experimentation, I found that using point spreads for the most recent five weeks (with higher weighting given to more recent weeks), combined with an adjustment that accounted for actual game outcomes, generated the best predictive accuracy. More detail here.

The Rankings

Here are the Betting Market Power Rankings for Week 14:

Glossary:

LSTWK - The betting market rank as of the prior week (using the same methodology). It’s interesting to see who the big movers are.
GPF - Stands for Generic Points Favored. It’s what you would expect a team to be favored by against a league average opponent at a neutral site.
GWP - Stands for Generic Win Probability. I converted the GPF into a generic win probability using the following formula: GWP = 1/(1+exp(-GPF/7)). This gives a more direct comparison to the ANS rankings.
ANS RNK - The Advanced NFL Stats Team Efficiency rankings for the same week.
ANS GWP - The Advanced NFL Stats Generic Win Probability for the same week.

RANK	Team	LSTWK	GPF	GWP	ANS RNK	ANS GWP
1	GB	1	8.5	0.77	3	0.71
2	NE	2	8.5	0.77	5	0.67
3	PIT	4	7.5	0.75	2	0.74
4	NO	3	7	0.73	4	0.69
5	BAL	5	5.5	0.69	7	0.62
6	SF	8	4	0.64	11	0.54
7	ATL	6	3.5	0.62	12	0.54
8	NYJ	9	3.5	0.62	13	0.54
9	DAL	7	3	0.61	8	0.61
10	TEX	10	1	0.54	1	0.8
11	NYG	12	1	0.53	6	0.65
12	MIA	15	1	0.53	17	0.5
13	SD	18	0.5	0.53	16	0.5
14	CIN	16	0.5	0.52	18	0.48
15	DET	13	0.5	0.52	9	0.61
16	PHI	11	0.5	0.51	10	0.59
17	TEN	19	-0.5	0.49	20	0.44
18	RAI	17	-0.5	0.48	14	0.53
19	CHI	14	-1	0.47	15	0.52
20	CAR	20	-1.5	0.45	22	0.39
21	DEN	22	-1.5	0.45	23	0.39
22	SEA	25	-2.5	0.41	25	0.37
23	WAS	23	-3	0.39	21	0.44
24	BUF	21	-3	0.39	19	0.48
25	ARZ	27	-3.5	0.38	24	0.37
26	MIN	24	-4	0.36	28	0.33
27	KC	30	-4	0.36	31	0.3
28	TB	26	-4.5	0.34	29	0.33
29	CLE	29	-5	0.33	26	0.36
30	JAC	28	-6.5	0.28	27	0.36
31	STL	31	-6.5	0.28	30	0.32
32	IND	32	-8.5	0.23	32	0.27

Some observations:

• San Francisco moved up a couple spots on the strength of their convincing victory over St. Louis.
• Philadelphia continued their nosedive, dropping from 11th to 16th.
• San Diego moved in the opposite direction, climbing 5 spots to 13th.
• If you throw out Houston (the ANS model doesn’t know Matt Schaub is injured), the ANS top 4 are in line with the betting market top 4: Green Bay, New England, Pittsburgh, and New Orleans.
• Count the nation’s gamblers among the Doubting Thomases to the miracles of Tebow. Denver only moved up one spot to 21.
• My Colts are now only buried under 25 feet of crap (2 points instead of last week’s 4). Progress!

Predicted of This Week’s Point Spreads

See below for how well the ranking methodology predicted this week’s point spreads (that SF/ARZ spread puzzles me):

Game	Pred Line	ActLine	Diff
ATL @ CAR	-1	-2.5	-1.5
BUF @ SD	5.5	7	1.5
CHI @ DEN	2.5	3.5	1
CLE @ PIT	15	14	-1
IND @ BAL	17	16.5	-0.5
KC @ NYJ	10.5	9	-1.5
MIN @ DET	7	7.5	0.5
NE @ WAS	-10	-8	2
NO @ TEN	-6	-3.5	2.5
NYG @ DAL	5	3.5	-1.5
PHI @ MIA	4	3	-1
RAI @ GB	12.5	11	-1.5
SF @ ARZ	-6.5	-3.5	3
STL @ SEA	5	7	2
TB @ JAC	0	0	0
TEX @ CIN	1.5	3	1.5

Proposed Methodology Change

In the comments, Jim A mentioned that he uses point spreads for future weeks as well (since these are available more than a week in advance in a lot of cases). I think this makes sense and have recalculated the rankings using this approach. Under the new approach, I gave both this week and the next a weight of 4, and prior weeks descending weights of 3, 2, and 1. The model seemed to work best with 5 weeks of “interconnectedness”, so the new approach keeps 5 weeks of data, but now has the advantage of being able to give more weight to the latest betting market information, as opposed to stale information from prior weeks. Here is how the new rankings compare to the rankings above:

Rank	Team	GPF	GPF New	Diff
1	GB	8.5	9	0.5
2	NE	8.5	7.5	-1
3	PIT	7.5	7	-0.5
4	NO	7	7.5	0.5
5	BAL	5.5	6	0.5
6	SF	4	5	1
7	ATL	3.5	3.5	0
8	NYJ	3.5	2.5	-1
9	DAL	3	3	0
10	TEX	1	0.5	-0.5
11	NYG	1	1.5	0.5
12	MIA	1	1	0
13	SD	0.5	0.5	0
14	CIN	0.5	0	-0.5
15	DET	0.5	1	0.5
16	PHI	0.5	0	-0.5
17	TEN	-0.5	0	0.5
18	RAI	-0.5	-0.5	0
19	CHI	-1	-1.5	-0.5
20	CAR	-1.5	-1	0.5
21	DEN	-1.5	-1	0.5
22	SEA	-2.5	-3	-0.5
23	WAS	-3	-4	-1
24	BUF	-3	-3	0
25	ARZ	-3.5	-2.5	1
26	MIN	-4	-3.5	0.5
27	KC	-4	-4.5	-0.5
28	TB	-4.5	-4.5	0
29	CLE	-5	-5	0
30	JAC	-6.5	-7	-0.5
31	STL	-6.5	-7	-0.5
32	IND	-8.5	-8.5	0

The biggest change is clear separation of Green Bay and New England, although the ranks remain the same at 1 and 2. For future weeks, I plan on using this approach, so the “Last Week” column for next week’s rankings will reflect this new approach, rather than the ranks in the first table above.

Future Investigations

Another source of insight into the betting market is the Superbowl futures (also pointed out by Jim A). Unfortunately, these are not only just a function of inherent team strength, but also a reflection of how difficult a team’s path may be to get to the Superbowl. If they’re in a strong conference, or if they’ve underachieved thus far in regards to wins and losses, this will reflect poorly on their Superbowl odds.

To get around this difficulty, I was thinking I could use Chris Cox's ridiculously awesome NFL Forecast tool. I could plug in the betting market GWP’s into the tool (I believe it allows for that type of customization), and then use those to generate Superbowl probabilities for each team. These could then be compared to the probabilities implied by the Superbowl futures odds.