Statistics


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.

Perhaps the most important new thing I note is that Pro Football Reference now has play by play data, and ways to display those data as a CSV format. Creating parsers for the data would be work, but that means that advanced stats are now accessible to the average fan.

In Ubuntu 16.04, PDL::Stats is now a standard Ubuntu package and so the standard PDL installation can be used with my scripts. About the only thing you need to use CPAN for, at this point, is installing Sport::Analytics::SimpleRanking.

At work I use a lot of Python these days. I have not had time to rethink all this into Pythonese. But I’m curious, as the curve fitting tools in Python are better/different than those in Perl.

Football diagrams: Although the Perl module Graphics::Magick isn’t a part of CPAN, graphicsmagick and libgraphics-magick-perl are part of the Ubuntu repositories.

Odds for the 2015 NFL playoff final, presented from the AFC team’s point of view:

SuperBowl Playoff Odds
Prediction Method AFC Team NFC Team Score Diff Win Prob Est. Point Spread
C&F Playoff Model Denver Broncos Carolina Panthers 2.097 0.891 15.5
Pythagorean Expectations Denver Broncos Carolina Panthers -0.173 0.295 -6.4
Simple Ranking Denver Broncos Carolina Panthers -2.3 0.423 -2.3
Median Point Spread Denver Broncos Carolina Panthers -5.0 0.337 -5.0

 

Last week the system went 1-1, for a total record of 6-4. The system favors Denver more than any other team, and does not like Carolina at all. Understand, when a team makes it to the Super Bowl easily, and a predictive system gave them about a 3% chance to get there in the first place, it’s reasonable to assume that in that instance, the system really isn’t working.

So we’re going to modify our table a little bit and give some other predictions and predictive methods. The first is the good old Pythagorean formula. We best fit the Pythagorean exponent to the data for the year, so there is good reason to believe that it is more accurate than the old 2.37. It favors Carolina by a little more than six points. SRS directly gives point spread, which can be back calculated into a 57.7% chance of Carolina winning. Likewise, using median point spreads to predict the Denver-Carolina game gives Carolina a 66.3% chance of winning.

Note that none of these systems predicted the outcome of the Carolina – Arizona game. Arizona played a tougher schedule and was more of a regular season statistical powerhouse than Carolina. Arizona, however, began to lose poise as it worked its way through the playoffs. And it lost a lot of poise in the NFC championship game.

Odds for the third week of the 2015 playoffs, presented from the home team’s point of view:

Conference Championship Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Carolina Panthers Arizona Cardinals -1.40 0.198 -10.4
Denver Broncos New England Patriots 1.972 0.879 14.6

 

Last week the system went 2-2, for a total record of 5-3. The system favors Arizona markedly, and Denver by an even larger margin. That said, the teams my system does not like have already won one game. There have been years when a team my system didn’t like much won anyway. That was the case in 2009, when my system favored the Colts over the Saints. The system isn’t perfect, and the system is static. It does not take into account critical injuries, morale, better coaching, etc.

Odds for the second week of the 2015 playoffs, presented from the home team’s point of view:

Second Round Playoff Odds
Home Team Visiting Team Score Diff Win Prob Est. Point Spread
Carolina Panthers Seattle Seahawks -1.713 0.153 -12.7
Arizona Cardinals Green Bay Packers -0.001 0.500 0.0
Denver Broncos Pittsburgh Steelers 0.437 0.608 3.2
New England Patriots Kansas City Chiefs -0.563 0.363 -4.2

 

Last week the system went 3-1 and perhaps would have gone 4-0 if after the Burflict interception, Cincinnati had just killed three plays and kicked a field goal.

The system currently gives Seattle a massive advantage in the playoffs. It says that Green Bay/Arizona is effectively an even match up, and that both the AFC games are pretty close. It favors Denver in their matchup, and the Chiefs in theirs.

One last comment about last week’s games. The Cincinnati-Pitt game was the most depressing playoff game I’ve seen in a long time, both for the dirty play on both sides of the ball, and the end being decided by stupid play on Cincinnati’s part.  It took away from the good parts of the game, the tough defense when people weren’t pushing the edges of the rules, and the gritty play on the part of McCarron and Roethlisberger. There was some heroic play on both their parts, in pouring rain.

But for me, watching Ryan Shazier leading with the crown of his helmet and then listening to officials explain away what is obvious on video more or less took the cake. If in any way shape or form, this kind of hit is legal, then the NFL rules system is busted.

The cumulative stats for the 2015 regular season are:

2015-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:

2015 NFL Playoff Teams, C&F Worksheet.
NFC
Rank Name Home Field Adv Playoff Experience SOS Total Score
1 Carolina Panthers 0.406 0.434 -1.35 -0.51
2 Arizona Cardinals 0.406 0.434 0.456 1.296
3 Minnesota Vikings 0.406 0.0 0.654 1.06
4 Washington Redskins 0.406 0.0 -0.866 -0.46
5 Green Bay Packers 0.0 0.434 0.863 1.297
6 Seattle Seahawks 0.0 0.434 0.769 1.203
AFC
1 Denver Broncos 0.406 0.434 0.727 1.567
2 NE Patriots 0.406 0.434 -0.839 0.001
3 Cinncinnati Bengals 0.406 0.434 0.661 1.501
4 Houston Texans 0.406 0.0 -0.828 -0.422
5 Kansas City Chiefs 0.0 0.0 0.564 0.564
6 Pittsburgh Steelers 0.0 0.434 0.696 1.130

 

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 2014 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
Minnesota Vikings Seattle Seahawks -0.143 0.464 -1.05
Washington Redskins Green Bay Packers -1.757 0.147 -13.0
Cinncinnati Bengals Pittsburgh Steelers 0.371 0.591 2.75
Houston Texans Kansas City Chiefs -0.986 0.271 -7.30

 

So the system suggests that Minnesota – Seattle should be close, perhaps unbettable. Cinncinnati-Pittsburgh is an even match, with Cinncinnati’s factors amounting to a typical home field advantage. Houston-Kansas City and Washington-GB are predicted to be easy wins for the visiting team.

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