The final game really didn’t help Atlanta’s SOS much, but I’ll note that numbers are slowly beginning to look more normal. SRS isn’t a good stat at 3 games, and may not be a good stat at 4. As the season goes on, it will get better, and SOS, by the end of the season, is one component in a formula that predicts post season success.

Global Statistics:
Games  Home Wins Winning_Score Losing_Score Margin
48         26        27.85         17.83     10.02

Calculated Pythagorean Exponent:  3.30

Rank  Team    Median  GP   W   L   T  Pct   Pred   SRS    MOV   SOS
------------------------------------------------------------------------
1     PHI     19.0     3   3   0   0 100.0  98.3  19.99  21.67 -1.67
2     DEN     12.0     3   3   0   0 100.0  78.3  10.49   9.00  1.49
3     MIN      9.0     3   3   0   0 100.0  82.5   9.77   8.00  1.77
4     NE       7.0     3   3   0   0 100.0  87.5  17.34  12.00  5.34
5     BAL      5.0     3   3   0   0 100.0  70.2   3.68   4.33 -0.65
6     PIT      8.0     3   2   1   0  66.7  48.7   1.43  -0.33  1.77
7     ATL      7.0     3   2   1   0  66.7  60.9  -8.27   4.33 -12.60
8     HOU      7.0     3   2   1   0  66.7  31.7   3.07  -3.67  6.74
9     KC       6.0     3   2   1   0  66.7  75.6   8.96   6.67  2.29
10    LA       5.0     3   2   1   0  66.7  26.1 -11.22  -5.67 -5.56
11    DAL      4.0     3   2   1   0  66.7  69.5  -3.31   5.67 -8.97
12    GB       4.0     3   2   1   0  66.7  59.2   3.35   2.67  0.68
13    SEA      2.0     3   2   1   0  66.7  75.5   1.64   5.00 -3.36
14    OAK      1.0     3   2   1   0  66.7  51.0  -9.07   0.33 -9.40
15    NYG      1.0     3   2   1   0  66.7  52.7  -9.16   0.67 -9.83
16    DET     -1.0     3   1   2   0  33.3  46.0  -2.00  -1.33 -0.66
17    CAR     -1.0     3   1   2   0  33.3  56.8   7.40   2.00  5.40
18    NYJ     -1.0     3   1   2   0  33.3  31.9  -1.97  -5.33  3.37
19    ARI     -2.0     3   1   2   0  33.3  67.9   7.75   5.33  2.42
20    MIA     -2.0     3   1   2   0  33.3  46.2   5.20  -1.00  6.20
21    SD      -4.0     3   1   2   0  33.3  64.1   5.77   4.67  1.11
22    IND     -4.0     3   1   2   0  33.3  37.1   0.09  -4.67  4.76
23    WAS     -4.0     3   1   2   0  33.3  26.9 -11.68  -8.00 -3.68
24    TB      -5.0     3   1   2   0  33.3  22.9 -14.25 -10.33 -3.91
25    BUF     -6.0     3   1   2   0  33.3  53.6   4.16   1.00  3.16
26    TEN     -7.0     3   1   2   0  33.3  26.7  -5.43  -5.00 -0.43
27    CIN     -8.0     3   1   2   0  33.3  27.6  -3.01  -6.33  3.32
28    SF     -19.0     3   1   2   0  33.3  39.6  -4.06  -3.33 -0.73
29    NO      -3.0     3   0   3   0   0.0  34.4 -14.50  -5.67 -8.83
30    JAX     -4.0     3   0   3   0   0.0  18.8  -5.73 -10.00  4.27
31    CLE     -6.0     3   0   3   0   0.0  18.8  -0.37 -10.00  9.63
32    CHI    -14.0     3   0   3   0   0.0  11.7  -6.08 -12.67  6.59

Ok, all the games for week 3, but the Atlanta – New Orleans game have been played. It’s a little early to post data from the simple ranking system, as the SOS stat hasn’t stabilized yet, but hey, I can do this set today and in a day or two, add an update with the Atlanta stats.

Global Statistics:
Games  Home Wins Winning_Score Losing_Score Margin
47         26        27.49         17.53      9.96

Calculated Pythagorean Exponent:  3.21


Rank  Team    Median  GP   W   L   T  Pct   Pred   SRS    MOV   SOS
------------------------------------------------------------------------
1     PHI     19.0     3   3   0   0 100.0  98.1  20.56  21.67 -1.11
2     DEN     12.0     3   3   0   0 100.0  77.6  10.41   9.00  1.41
3     MIN      9.0     3   3   0   0 100.0  81.9   9.56   8.00  1.56
4     NE       7.0     3   3   0   0 100.0  86.8  16.86  12.00  4.86
5     BAL      5.0     3   3   0   0 100.0  69.6   3.46   4.33 -0.87
6     PIT      8.0     3   2   1   0  66.7  48.8   2.33  -0.33  2.67
7     HOU      7.0     3   2   1   0  66.7  32.2   3.19  -3.67  6.86
8     KC       6.0     3   2   1   0  66.7  75.0   8.94   6.67  2.28
9     LA       5.0     3   2   1   0  66.7  26.7 -12.60  -5.67 -6.94
10    DAL      4.0     3   2   1   0  66.7  69.0  -1.44   5.67 -7.10
11    GB       4.0     3   2   1   0  66.7  58.9   3.19   2.67  0.52
12    SEA      2.0     3   2   1   0  66.7  74.9   0.72   5.00 -4.28
13    OAK      1.0     3   2   1   0  66.7  51.0  -8.97   0.33 -9.30
14    NYG      1.0     3   2   1   0  66.7  52.6  -6.29   0.67 -6.95
15    ATL      0.0     2   1   1   0  50.0  50.0 -12.77   0.00 -12.77
16    DET     -1.0     3   1   2   0  33.3  46.1  -2.11  -1.33 -0.77
17    CAR     -1.0     3   1   2   0  33.3  56.6   7.00   2.00  5.00
18    NYJ     -1.0     3   1   2   0  33.3  32.4  -2.04  -5.33  3.29
19    ARI     -2.0     3   1   2   0  33.3  67.4   6.66   5.33  1.33
20    MIA     -2.0     3   1   2   0  33.3  46.3   4.72  -1.00  5.72
21    SD      -4.0     3   1   2   0  33.3  63.7   5.68   4.67  1.02
22    IND     -4.0     3   1   2   0  33.3  37.5  -0.00  -4.67  4.66
23    WAS     -4.0     3   1   2   0  33.3  27.5  -9.80  -8.00 -1.80
24    TB      -5.0     3   1   2   0  33.3  23.6 -16.57 -10.33 -6.24
25    BUF     -6.0     3   1   2   0  33.3  53.5   3.69   1.00  2.69
26    TEN     -7.0     3   1   2   0  33.3  27.3  -5.50  -5.00 -0.50
27    CIN     -8.0     3   1   2   0  33.3  28.2  -2.77  -6.33  3.57
28    SF     -19.0     3   1   2   0  33.3  39.8  -4.96  -3.33 -1.63
29    NO      -2.0     2   0   2   0   0.0  43.5  -9.63  -2.00 -7.63
30    JAX     -4.0     3   0   3   0   0.0  19.5  -5.89 -10.00  4.11
31    CLE     -6.0     3   0   3   0   0.0  19.5  -0.42 -10.00  9.58
32    CHI    -14.0     3   0   3   0   0.0  12.3  -5.23 -12.67  7.44

I think Atlanta suffers the most here. The SOS close to -13 will almost certainly stabilize after the game tomorrow. That said, I’m really impressed by the Eagles so far this season and for now, they’re the top ranked team on this table, via a variety of metrics.

I didn’t expect another trade of this magnitude, and so quickly. But let’s crunch the numbers on this trade, and compare them to the 2016 Titans-Rams trade.

The Browns received from the Eagles, the #8, #77 and #100 picks in this draft. In 2017 they receive the Eagles first round pick. In 2018 they receive the Eagles 2nd round pick. The Eagles have received the #2 pick in this draft, and the Browns 4th round pick in 2017.

For the purposes of this calculation, we assume the Eagles will pick 20th in 2017 and 2018, and that the Brown in 2017 will rise from 2nd to 10th.

 

The AV costs of the 2016 Eagles Browns trade.
Eagles Browns Results
Pick Average AV Pick Average AV Delta AV Risk Ratio
2 46 8 40
(138) 8 77 12
100 17
(20) 29
(52) 22
Total 54 Total 120
66 2.22

 

The Delta AV for both trades are the same, but since the Eagles received a lot less AV, the relative ratio of AV given to AV received is higher. The trade cost is the same, but the purchase is more highly leveraged.

To start, a summary of the 2014 regular season data:

2014-regular-season-stats

This gives us the basis to generate playoff values based on my playoff formula. Playoff Odds are calculated according to this model:

logit P = 0.668 + 0.348*(delta SOS) + 0.434*(delta Playoff Experience)

and the results are:

2014 NFL Playoff Teams, C&F Playoff Model Worksheet.
NFC
Rank Name Home Field Advantage Prev. Playoff Experience Strength of Schedule Total Score
1 Seattle Seahawks 0.406 0.434 0.275 1.115
2 Green Bay Packers 0.406 0.434 -0.118 0.722
3 Dallas Cowboys 0.406 0.0 -0.630 -0.224
4 Carolina Panthers 0.406 0.434 -0.292 0.548
5 Arizona Cardinals 0.0 0.0 0.449 0.449
6 Detroit Lions 0.0 0.0 -0.132 -0.132
AFC
1 NE Patriots 0.406 0.434 0.438 1.278
2 Denver Broncos 0.406 0.434 0.550 1.390
3 Pittsburgh Steelers 0.406 0.0 -0.703 -0.297
4 Indianapolis Colts 0.406 0.434 -0.393 0.447
5 Cinncinnati Bengals 0.0 0.434 -0.202 -0.602
6 Baltimore Ravens 0.0 0.0 -0.724 -0.724

 

The total score of a particular team is used as a base. Subtract the score of the opponent and the result is the logit of the win probability for that game. You can use the inverse logit (see Wolfram Alpha to do this easily) to get the probability, and you can multiply the logit of the win probability by 7.4 to get the estimated point spread.

For the second week of the 2014 playoffs, I’ve done all this for you, in the table below. Odds are presented from the home team’s point of view.

Second Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Seattle Seahawks Carolina Panthers 0.567 0.638 4.2
Green Bay Packers Dallas Cowboys 1.352 0.795 10.0
New England Patriots Baltimore Ravens 2.002 0.881 14.8
Denver Broncos Indianapolis Colts 1.349 0.794 10.0

 

Baltimore is not given much of a chance by these techniques, but an interesting analysis by Benjamin Morris of Skeptical Sports (featured now on fivethirtyeight.com) is worth paying attention to. Though the divisional round is hard on teams without a bye, those that survive appear to have a superior chance to go forward in the playoffs. Benjamin has always struck me as an incisive analyst, so he’s absolutely worth paying attention to.

My system went 3-0-1 last weekend (Or 3-1 if you consider my prediction in the Bengals – Chargers game a loss, as opposed to “too close to pick”), so time to present playoff odds for the second round of the playoffs.

Divisional Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Seattle Seahawks New Orleans Saints 0.676 0.663 5.0
Carolina Panthers SF 49ers -0.157 0.461 -1.2
Denver Broncos San Diego Chargers 0.411 0.601 3.0
New England Patriots Indianapolis Colts -0.060 0.485 -0.4

 

Odds that differ by less than a point in estimated point spread are probably not significant, and from my POV, a suggestion that you don’t bet that particular game.

The regular season has ended and the playoffs have begun. It would be useful to have a set of playoff grade data to do playoff probabilities, and though I’ve been down and out this season (no job at times, foot stress fracture at times, and a bad right shoulder), I currently have some time off my new job, a new laptop, and enough time to grind through some playoff numbers.

NFL stats at the end of the regular season:

week_17_2013_stats

To explain the columns above, Median is a median point spread, and can be used to get a feel for how good a team is without overly weighting a blowout win or blowout loss. HS is Brian Burke’s Homemade Sagarin, as implemented in Maggie Xiong’s PDL::Stats. Pred is the predicted Pythagorean expectation. The exponent for this measure is fitted to the data set itself. SOS, SRS, and MOV are the simple ranking components, analyzed via this Perl implementation. MOV is margin of victory, or point spread divided by games played. SOS is strength of schedule. SRS is the simple ranking.

Playoff Odds are calculated according to this model:

logit P  =  0.668 + 0.348*(delta SOS) + 0.434*(delta Playoff Experience)

The results are given below, as a “score” in logits:

2013 NFL Playoff Teams, C&F Playoff Model Worksheet.
NFC
Rank Name Home Field Advantage Prev. Playoff Experience Strength of Schedule Total Score
1 Seattle Seahawks 0.406 0.434 0.494 1.334
2 Carolina Panthers 0.406 0.0 0.484 0.889
3 Philadelphia Eagles 0.406 0.0 -0.661 -0.256
4 Green Bay Packers 0.406 0.434 -0.842 -0.003
5 San Francisco 49ers 0.0 0.434 0.612 1.046
6 New Orleans Saints 0.0 0.0 0.658 0.658
AFC
1 Denver Broncos 0.406 0.434 -0.546 0.293
2 NE Patriots 0.406 0.434 -0.258 0.582
3 Cancinnati Bengals 0.406 0.434 -0.856 -0.017
4 Indianapolis Colts 0.406 0.434 0.209 1.048
5 Kansas City Chiefs 0.0 0.0 -0.602 -0.602
6 San Diego Chargers 0.0 0.0 -0.118 -0.118

 

The total score of a particular team is used as a base. Subtract the score of the opponent and the result is the logit of the win probability for that game. You can use the inverse logit (see Wolfram Alpha to do this easily) to get the probability, and you can multiply the logit of the win probability by 7.4 to get the estimated point spread.

For the first week of the playoffs, I’ve done all this for you, in the table below. Odds are presented from the home team’s point of view.

First Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Philadelphia Eagles New Orleans Saints -0.914 0.286 -6.8
Green Bay Packers SF 49ers -1.049 0.259 -7.8
Cincinnati Bengals San Diego Chargers 0.101 0.525 0.7
Indianapolis Colts Kansas City Chiefs 1.650 0.839 12.2

 

Some general conclusions from the data above: the teams my model favors most are the Seattle Seahawks, the Indianapolis Colts, the 49ers, the Carolina Panthers, and then the New Orleans Saints, mostly NFC teams. Since the Super Bowl itself does not have a home team, the odds change once you actually reach the Super Bowl. The sum of the SOS column and the Previous Playoff Experience column can be used to estimate odds of winning “the big one”. The strongest team in a Super Bowl setting would be the San Francisco 49ers, with a total score, less HFA, of 1.049. The Indianapolis Colts, with a total score of 0.643 less HFA, would be the strongest possible AFC contender.

A point I’d like the reader to consider is this question: should the New Orleans Saints be granted an exception to the previous playoff experience rule of “last year only counts” and given the 0.434 advantage of a playoff team? 2012 was an aberration as the coach was suspended. I’m not calculating this variation into the formula at this point, but I’ll note that this is an issue that you, the reader, need to resolve for yourself.

The road to the playoffs is not easy, a topic that can be studied by trying to calculate the path to the playoffs of the Indianapolis colts, a team that would be favored in every matchup along the way. Let’s calculate the odds of Indianapolis actually winning all three games.

Odds of Indianapolis reaching the Super Bowl
WP versus Kansas City WP versus Denver Broncos WP versus NE Pats Cume Probability
0.839 0.586 0.515 0.253

 

Three teams from the NFC would be favored over any possible AFC contender. Those are San Francisco, Seattle, and the New Orleans Saints. Carolina would be favored over any AFC contender except the Indianapolis Colts.

Sorry about any delays in publication. I was between jobs at the time.

Week 13 NFL Stats:

2013_stats_week_13

To explain the columns above, Median is a median point spread, and can be used to get a feel for how good a team is without overly weighting a blowout win or blowout loss. HS is Brian Burke’s Homemade Sagarin, as implemented in Maggie Xiong’s PDL::Stats. Pred is the predicted Pythagorean expectation. The exponent for this measure is fitted to the data set itself. SOS, SRS, and MOV are the simple ranking components, analyzed via this Perl implementation. MOV is margin of victory, or point spread divided by games played. SOS is strength of schedule. SRS is the simple ranking.

OSRS and DSRS stats look like this:

2013_stats_week_13_srs

The two most impressive teams so far, IMO, are Seattle and Carolina. New Orleans may win the division but right now Carolina is something of a statistical darling.