by Michael Beuoy
In last week’s post, I showed how one can use the betting over/under in conjunction with the point spread to decompose team strength into an offensive and defensive Generic Points Favored (GPF = oGPF + dGPF). The post was essentially a redo of the Week 16 rankings, and unfortunately, I did not have enough time to apply the new method to the Wildcard Round of the playoffs. This week, I do have time, so here is a peek into the mind of the Betting Market for the Divisional Round of the Playoffs. In addition, I’ve laid out a table of the predicted lines and over/unders for each possible matchup in the Conference Finals and Superbowl. I’ll return to the predictions in the following weeks to see how the model did (testable predictions! science!).
For those of you that are interested, I’ve decided to start a blog for the purposes of publishing these rankings for various sports. I’ll start off with the NBA (see the first set of rankings here). After that, I’ll take a crack at NCAA Basketball, and then hopefully move on to Major League Baseball (which presents some interesting opportunities for decomposing team strength into offense, defense, and pitching, and creating a separate set of starting pitcher rankings). The blog will probably be pretty rough in the early going (i.e. ugly and confusing), but I hope to learn quickly.
Anyway, here are the Betting Market Power Rankings for the Divisional Round of the playoffs. I had to play things by ear with regard to weighting each week, given that each new playoff week only adds a handful of games, rather than the full 16 games. So rather than dropping the oldest week and adding the newest like I do during the regular season, I’m going to keep the old weeks frozen, and just add in the new weeks at progressively higher weights. Here are the weights I am using:
Week 13 – 1
Week 14 – 2
Week 15 – 3
Week 16 – 4
Week 17 – 0 <- final week, too many garbage games Week 18 – 6 <- Wildcard Round Week 19 – 7 <- Divisional Round Week 20 - 8 <- Conference Finals
Here is a glossary of terms (I had to drop the comparisons to the ANS model since the rankings aren’t updated during the playoffs):
LSTWK - The betting market rank as of the prior week (what I would have published for the Wildcard round if I had had the time)
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.
oGPF – Offensive Generic Points Favored. The component of a team’s total GPF attributable to its ability to score points.
dGPF – Defensive Generic Points Favored. The component of a team’s total GPF attributable to its ability to prevent the other team from scoring points
O RANK – The team’s oGPF ranking.
D RANK – The team’s dGPF ranking.
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)).
And here is the ranking table (the Rank column is relative to all 32 teams, but I’m only showing the 12 playoff teams):
• New Orleans has taken over the top spot, bypassing both Green Bay and New England.
• According to the betting market the 8 team playoff field breaks down as follows:
o 3 “elite” teams – New Orleans, New England, and Green Bay, all offensive powerhouses according to oGPF.
o 3 “good” teams – San Francisco, Baltimore, and New York (two defensive powerhouses and one good offense – which should tell you something about the relative importance of offenses and defenses)
o 1 “mediocre” team – Houston (with offsetting oGPF and dGPF)
o 1 “bad” team – Denver (negative on both oGPF and dGPF)
Here is how the model predicted the point spreads for each Divisional Round matchup. Thanks to the addition of oGPF and dGPF, I can also generate a prediction of the over/unders as well. “PRED LINE”, “ACT LINE”, and “LINE DIFF” show the line the model predicted, the actual line, and the difference. The next three columns do the same for the over/under.
|Game||Pred Line||Act Line||Line Diff||Pred OU||Act OU||OU Diff|
|DEN @ NE||13.5||13.5||0.0||49||51||2.0|
|NO @ SF||-2.0||-3.5||-1.5||50.5||47.5||-3.0|
|NYG @ GB||7.5||8.5||1.0||53.5||52||-1.5|
|Tex @ Bal||5.5||7.5||2.0||37.5||36||-1.5|
The model actually did a pretty good job (in my opinion) at predicting this week, which is unfortunate in a way. Assuming my model is a reasonable baseline for how the betting market normally reacts, a big miss is a reliable indicator that something unusual has happened, like a key injury, or a particularly illuminating on-field performance (there was a severe market correction prior to the Wild Card round in regards to Denver – the point spread was 4 additional points in favor of Pittsburgh than the baseline prediction, and it all came out of Denver’s point total – after that 7-3 final against KC, nobody was putting much confidence in Tebow’s ability to score points).
Here’s another view of the table above, but this time broken down by predicted score for each team (it’s a useful way to understand what’s driving a miss in the line prediction).
|Game||Away Pred||Away Act||Away Diff||Home Pred||Home Act||Home Diff|
|DEN @ NE||17.75||18.75||1.0||31.25||32.25||1.0|
|NO @ SF||26.25||25.5||-0.75||24.25||22||-2.3|
|NYG @ GB||23.0||21.75||-1.25||30.5||30.25||-0.3|
|Tex @ Bal||16.0||14.25||-1.75||21.5||21.75||0.3|
What surprised me most is the source of the miss on the NO @ SF line. New Orleans is favored by 1.5 points more than the model indicates. But the source of the miss is that SF is assumed to score fewer points, not that New Orleans is expected to score more.
Conference Finals and Superbowl Predictions
Here are predicted lines and over/unders for each possible Conference Finals matchup and Superbowl matchup (I’ll check back on these to see how I did).
Superbowl (positive line indicates NFC team is favored):
And finally, here is a brief tutorial on how to create point spreads and over/unders for any hypothetical matchup, using GPF, oGPF, and dGPF from the rankings:
How to Build a Point Spread:
Point Spread (in favor of home team) = 2.5 + GPFhome - GPFaway
How to Build a Over/Under
Over/Under = 2*22.0 + oGPFhome + oGPFaway - dGPFhome - dGPFaway
The 22.0 represents league average scoring and is determined dynamically each week with the ratings (but it tends to stay pretty consistent at 22).
How to Build Predicted Scores
Home Team Score = (22.0 + 1.25) + oGPFhome - dGPFaway
Away Team Score = (22.0 - 1.25) + oGPFaway - dGPFhome