Betting Market Power Rankings

by Michael Beuoy

The purpose of this post is to use the point spreads from recent weeks of the season to derive an implied power ranking. Basically, the point is to try to figure out what the betting market thinks are the best and worst teams in the NFL. From a broader perspective, I hope to provide insight into how the betting market “thinks” in general. One result that emerged from this analysis was a measure of how much the betting market reacts to the result of a particular game.

The challenge in deriving a power ranking from the point spreads is that the point spread only tells you the relative strength of the two teams. For example, Green Bay is favored by 7.0 points on the road against the NY Giants this week. We know that home teams are favored on average by 2.5 points, so after removing the home team bias, the betting market appears to think that Green Bay is 9.5 points better than the Giants. New England is favored by 21(!) points at home against Indianapolis So the betting market thinks that New England is 18.5 points better than Indianapolis.

The question is, does the betting market think New England or Green Bay is the better team? It’s impossible to answer just using the spreads from this week (you have 32 unknowns and only 16 equations). My approach below is to look back over the past five weeks of point spreads and results to come up with a best fit ranking, where the ranking is calibrated such that it best predicts the point spread according to the following formula:

Point Spread = Home Team Rank - Visiting Team Rank + 2.5

I figured I would cut to the chase and provide the rankings themselves, and save the methodology explanation for the end. I followed the format of the Advanced NFL Stats (ANS) Team Efficiency rankings, and also provided the actual ANS rankings as a point of comparison.

Here is a glossary of terms:

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 (week 12 in this case)
ANS GWP - The Advanced NFL Stats Generic Win Probability for the same week.

Here are the rankings:

RANK	TEAM	LSTWK	GPF	GWP	ANS RNK	ANS GWP
1	GB	2	9.5	0.80	2	0.74
2	NE	1	9.0	0.78	4	0.69
3	NO	3	7.0	0.73	5	0.69
4	PIT	4	5.5	0.69	3	0.72
5	BAL	6	5.0	0.66	10	0.59
6	ATL	12	4.5	0.66	14	0.53
7	DAL	7	4.0	0.64	6	0.65
8	SF	8	3.5	0.63	13	0.53
9	NYJ	10	3.5	0.62	15	0.52
10	TEX	5	3.0	0.61	1	0.82
11	PHI	9	1.5	0.55	7	0.62
12	NYG	11	1.0	0.54	8	0.62
13	CHI	13	0.5	0.52	11	0.57
14	DET	15	0.5	0.51	9	0.61
15	MIA	19	0.5	0.51	18	0.48
16	CIN	14	0.0	0.51	16	0.50
17	RAI	17	0.0	0.50	12	0.55
18	SD	16	0.0	0.50	19	0.45
19	TEN	18	-0.5	0.47	21	0.44
20	DEN	25	-2.5	0.42	23	0.38
21	BUF	22	-2.5	0.42	17	0.48
22	TB	20	-2.5	0.40	26	0.36
23	WAS	28	-3.0	0.39	20	0.44
24	SEA	23	-3.5	0.37	28	0.34
25	MIN	26	-3.5	0.37	29	0.34
26	CAR	27	-4.0	0.36	25	0.38
27	ARZ	29	-4.5	0.34	30	0.33
28	JAC	21	-4.5	0.34	22	0.40
29	CLE	24	-5.0	0.33	24	0.38
30	KC	31	-5.5	0.31	31	0.27
31	STL	30	-6.0	0.30	27	0.35
32	IND	32	-10.0	0.19	32	0.23

Some observations:
The top team and bottom team shouldn’t come as any surprise. In addition, there is the proverbial “50 feet of crap” (or 4 points) between the Colts and the next worse team.
Despite San Francisco’s place near the top of the “conventional” power rankings (ESPN, CBS, etc.), the market has them ranked much lower at number 8; not as low as the ANS rank of 13, but in the ballpark.
I was surprised to see New England ranked so closely to Green Bay (they were actually a half point ahead of them last week).


Methodology

The first step was to see how many prior weeks of point spreads I had to feed into the model in order to get an optimized estimate of the point spreads for future games. The drawback of using prior weeks is that you’re using stale information. The point spread from a few weeks ago will not accurately reflect the market’s latest assessment of their strength. I attempted to address this somewhat by using a recency weighted average. If I was using 7 weeks of spreads, the most recent week would get a weight of 7, the week before a weight of 6, and so on. This allowed me to arrive at an answer while still giving preferential treatment to the more recent market estimates. Through trial and error optimization, I found that using the most recent five weeks of point spreads produced the lowest mean squared estimate error of the point spread for the coming week. The calculation itself is equivalent to a weighted linear regression with 32 dummy variables, 1 for each team.

How the Betting Market Reacts to Game Results

Although the approach above generated a set of rankings, it ignores some potentially useful information that could be used to better match the coming week’s point spreads. For example, the week 13 rankings used the point spreads from weeks 9 through 13. In week 9, New England was favored by 9.5 points over the New York Giants. However, the Giants ended up winning by 4 in that game. So, the outcome of the game deviated from the market’s expectation by 13.5 points. One would expect that the market would factor that result into future estimates of both New England’s and New York’s strength. I assumed that the betting market would recallibrate itself according to the following formula:

revised “best estimate” spread = original spread + (credibility coefficient) x (deviation from expected)

I then determined what that credibility coefficient (CC) was by trial and error optimization. I found that a coefficient of 15% generated the most accurate prediction of the coming week’s spreads. In other words, the betting market appears to treat the outcome of each game with 15% credibility when revising its estimates of each team’s strength. So, in the New England/ New York example above, if those two teams had been scheduled to play each other at New England again, the new spread would have been revised down from 9.5 points to 7.5 points ( = 9.5 + 0.15 * (-9.5 - 4).

Prediction of This Week’s Point Spreads

See below for a comparison if how well the ranking methodology predicted this week’s point spreads. Note that this uses rankings that factor in the results from last week’s games, but does not factor in the spreads of this week’s games into the rolling 5 week average (this keeps the estimate independent):

GAME	PRED LINE	ACT LINE	DIFF
ATL @ TEX	6.0	-2.0	-8.0
BAL @ CLE	-6.0	-6.5	-0.5
CAR @ TB	5.0	3.5	-1.5
CIN @ PIT	8.5	6.5	-2.0
DAL @ ARZ	-6.5	-4.5	2.0
DEN @ MIN	2.0	0.0	-2.0
DET @ NO	10.0	8.5	-1.5
GB @ NYG	-4.5	-7.0	-2.5
IND @ NE	21.5	21.0	-0.5
KC @ CHI	8.5	8.0	-0.5
NYJ @ WAS	-5.0	-3.0	2.0
PHI @ SEA	-4.0	-3.0	1.0
RAI @ MIA	2.5	3.0	0.5
SD @ JAC	1.0	-2.5	-3.5
STL @ SF	11.0	13.5	2.5
TEN @ BUF	0.5	1.5	1.0

The biggest miss in the line prediction is on the ATL/TEX game where it appears that the market values Matt Schaub’s talents over his replacement by a significant margin. I think this may also be inflating ATL’s overall ranking somewhat. Its favorable point spread over Houston is being compared (on a recency weighted basis) against point spreads when Matt Schaub was playing.

If there’s interest, I can produce these weekly. I’ve got this boiled down to a quick piece of R code (which anyone is welcome to if they’re curious about the details of the methodology).