Monday, March 31, 2008

Observations on playoff seeding

Starting with the 1975 season, the NFL has used a seeding system to determine how home field is awarded in playoff games leading up to the Super Bowl. In that time there have been 33 Super Bowls played. Of the Super Bowls played under the seeding system:

  • Every Super Bowl has had at least one team that was a 1 or 2 seed.

  • Only 4 Super Bowls have had a 2-seed vs a lower seed (1979: Rams (3) vs Steelers (2); 1980: Raiders (4) vs Eagles (2); 1992: Bills (4) vs Cowboys (2), and 1997: Packers (2) vs Broncos (4))

  • 20 Super Bowls were either 1 vs 1 seed or 1 vs 2 seed.

  • Only 7 Super Bowls were won by a team not a 1 or 2 seed (1980: Raiders (4), 1987: Redskins (3), 1997: Broncos (4), 2000: Ravens (4), 2005 Steelers (6), 2006: Colts (3), and 2007: Giants (6))

  • In spite of the above, 5 of the last 11 Super Bowls have been won by lower-seeded teams.

The final point is particularly notable, and (to my way of thinking, anyway), suggests there is no need to give low-seeded teams any additional special advantage.

Saturday, March 29, 2008

Historical performance of Isaacson-Tarbell

In the column he describes it in, TMQ claimed Isaacson-Tarbell finished the 2007 season 183-84, which is a pretty impressive 68.5% accuracy. However, in my analysis, I was only able to account for 176 correct picks (65.9%). I have triple-checked my results, so I'm going to blog them.

Here's a graph of how the Isaacson-Tarbell Predictor performs on historical data, from 1960 to 2007 (along with the same "Home Team" data as posted below).

Click on the image to open a full-sized version

Note that ITP seems to have its best years in the 60's and early 70's; about the same time the "Home Team" strategy gives its worst results. Then, during the late 70's, they close together, giving what looks to be much more correlated results.

ITP looks to be an interesting algorithm, but not quite the home-run TMQ makes it out to be (you can see why he didn't mention its performance on the 2006 season).

A few notes on methodology

  • There are a number of tie games in the historical data, particularly before the advent of the overtime period for regular season games in 1974. Tie games are counted as a "push" for pick algorithms. As with the NFL, for the purposes of computing a winning percentage, push results are counted as half wins, half losses.

  • TMQ's description of Isaacson-Tarbell reads
    Best Record Wins; If Records Equal, Home Team Wins.
    Instead of "best record", I used the easier-to-deal-with "best winning percentage". The only time this will matter is when two undefeated (or two winless) teams match up, and one has had their bye, and the other hasn't. Coincidentally, this situation came up in the 2007 season, when the 8-0 Patriots met the 7-0 Colts. They both have the same winning percentage (100%), but the Patriots have the better record (one more win).

Thursday, March 27, 2008

Historical results of picking the home team

Here's a graph of the home team winning percentage, for NFL games (and AFL, prior to 1970), from 1960 to 2007.

Click on the image to open a full-sized version

A few things stand out:

  • From 1960 to 1976, only 4 years (1963, 1969, 1970, and 1973) are above the mean. There were 4 really bad years (1962, 1965, 1968, 1972). There were only two relatively good years (1969 and 1973). From 1974-2007, the worst year was 2006 (53.9%).

  • Since 1986, the year-to-year difference has been in the range -5% < δ < +5%.

    . From 1960 to 1986, there were 11 swings of 5% or wider, including three (1968 to 1969: +13.3%, 1972 to 1973: +12.1%, and 1985 to 1986: -9.8%) of 9.8% or wider.

  • It is surprising to me that the home-team winning percentage varies so much, even in the modern era. For a 267 game season (256 regular season and 11 playoff games), a 10% swing represents nearly 27 games.

    The disadvantages of being a road team seem pretty static: they still have to spend time travelling, they still have to spend time away from home, away from familiar comforts, away from team facilities...

Wednesday, March 26, 2008

Some more game-picking algorithms

In his February 15, 2008 column, Gregg Easterbrook (aka the Tuesday Morning Quarterback) described a couple of objective ways to pick football games.

The first method is the model of simplicity: always pick the home team. This yielded a correct prediction 152 times out of 267 games (56.9%, including the playoffs). Some historical analysis shows this can be surprisingly good (63.9% correct in 1985) or surprisingly bad (47.5% NFL and AFL combined, in 1968). Still, at the very least, the "Pick the Home Team" method provides a base-line point of comparison for other techniques.

The second method Easterbrook discusses is a method I will call the Isaacson-Tarbell Predictor: always pick the team with the better record (in case of a tie, choose the home team). TMQ claims this yielded a correct prediction 183 times out of 267 games (68.5%, including the playoffs). This is pretty spectacular, considering (as TMQ points out), none of the 8 experts featured on ESPN's "Expert Picks" did as well (Jaws came closest, at 68.3%).

Some questions for further investigation: how good is ITB (the Isaacson-Tarbell Predictor)? Was 2007 a fluke season? Can some variant of SRS, or some other power index algorithm do better?

Stay tuned for further details.

Welcome to FSPI

FSPI stands for Final Score Power Index. Generically, it's any method for assigning a numerical power value to teams in a league, based on the final score(s) of the games each team has played. One example is the "Simple Ranking System", described in this blog post on

Once each team has a power index, teams can be sorted from most to least power to yield a power ranking. If the power index is predictive, it can also be used to predict winners of upcoming games.

This blog will use various power index algorithms (including some variant of SRS) to follow teams over the course of the NFL season. That means most of the traffic will be from August to February. But I didn't want to wait until August to figure out how to drive blogger, and this will give me an incentive to get my software ready to go.

So welcome, and thanks for reading.

Power Rankings: 2007 Season, Post-week 17

This is a test post I am using to figure out how I want to present power ranking information.
1  (LW)New England
1.5662 (LW * 0.9973)Beat Giants by 3
2  (LW)Indianapolis
1.4840 (LW * 0.9804)Lost to Titans by 6
3  (LW)Dallas
1.4581 (LW * 0.9690)Lost to Redskins by 21
4  (LW-4)Washington
1.4480 (LW * 1.0313)Beat Cowboys by 21
5  (LW-2)San Diego
1.4432 (LW * 1.0145)Beat Raiders by 13
6  (LW+1)Green Bay
1.4395 (LW * 1.0085)Beat Lions by 21
7  (LW+3)Jacksonville
1.4187 (LW * 0.9693)Lost to Texans by 14
8  (LW+2)Philadelphia
1.4137 (LW * 0.9914)Beat Bills by 8
9  (LW-1)New York
1.3726 (LW * 0.9949)Lost to Patriots by 3
10  (LW+1)Minnesota
1.3643 (LW * 0.9837)Lost to Broncos by 3
11  (LW-5)Houston
1.3447 (LW * 1.0551)Beat Jaguars by 14
12  (LW-5)Tennessee
1.3310 (LW * 1.0553)Beat Colts by 6
13  (LW)Chicago
1.3296 (LW * 1.0133)Beat Saints by 8
14  (LW+3)Seattle
1.3137 (LW * 0.9808)Lost to Falcons by 3
15  (LW+3)Pittsburgh
1.2956 (LW * 0.9766)Lost to Ravens by 6
16  (LW+2)Cleveland
1.2877 (LW * 1.0067)Beat 49ers by 13
17  (LW-1)New Orleans
1.2532 (LW * 0.9979)Lost to Bears by 8
18  (LW+3)Tampa Bay
1.2520 (LW * 0.9817)Lost to Panthers by 8
19  (LW-2)Denver
1.2326 (LW * 1.0329)Beat Vikings by 3
20  (LW-5)Carolina
1.2237 (LW * 1.0422)Beat Buccaneers by 8
21  (LW+2)Detroit
1.2140 (LW * 0.9864)Lost to Packers by 21
22  (LW-1)Cincinnati
1.2137 (LW * 1.0239)Beat Dolphins by 13
23  (LW+3)Buffalo
1.2126 (LW * 0.9988)Lost to Eagles by 8
24  (LW)Arizona
1.2039 (LW * 1.0205)Beat Rams by 29
25  (LW+3)New York
1.1922 (LW * 1.0025)Beat Chiefs by 3
26  (LW)Oakland
1.1597 (LW * 0.9959)Lost to Chargers by 13
27  (LW)Kansas City
1.1323 (LW * 1.0071)Lost to Jets by 3
28  (LW-1)Baltimore
1.1239 (LW * 1.0269)Beat Steelers by 6
29  (LW-3)Atlanta
1.1065 (LW * 1.0409)Beat Seahawks by 3
30  (LW+2)San Fransico
1.1006 (LW * 1.0031)Lost to Browns by 13
31  (LW+1)Miami
1.0683 (LW * 0.9830)Lost to Bengals by 13
32  (LW+1)St. Louis
1.0493 (LW * 0.9857)Lost to Cardinals by 29