The former Stanford Cardinal would rank 24th in AY/A the rest of the season while Buffalo finished the year with a 2-8 record.
The Bills won just ten games the next two seasons, but made some noise at the start of the 2011 season. Going back to the nerd well at quarterback, Buffalo raced out to a 5-2 start. Fitzpatrick led two fourth-quarter comebacks/game-winning drives and ranked 11th in AY/A after eight weeks. Unfortunately, the former Harvard star ranked 32nd in that metric over the last nine weeks of the season, and the Bills finished the season 1-8.How unusual is it for a team to have such a hot start and cold finish? It’s simple enough to look at first-half/second-half splits, but I prefer a more nuanced approach by weighing each team game based on when it occurred; e.g., game 1 counts 16 times as much as game 16, game 2 counts 15 times as much as game 16, game 3 counts 14 times as much as game 16, and so on. I looked at each team since 1990 and calculated their actual winning percentage and their “weighted” winning percentage.
The 2011 Bills had a 0.375 winning percentage last year, but by placing greater weight on games earlier in the season, Buffalo had a 0.507 weighted winning percentage. In 2008, the Bills had a 0.438 actual winning percentage and a 0.566 weighted winning percentage. As it turns out, those were two of the five “strongest-starting” teams of the last five years. The table below lists the “strongest-starting teams” since 1990, along with their actual and weighted winning percentages. The last column represents the difference between the two winning percentages.
Tm | Year | Act. Win% | Wt. Win% | DIFF |
---|---|---|---|---|
DEN | 2009 | 0.500 | 0.676 | 0.176 |
SDG | 2001 | 0.313 | 0.485 | 0.173 |
SDG | 2002 | 0.500 | 0.669 | 0.169 |
OAK | 1995 | 0.500 | 0.669 | 0.169 |
PHI | 1994 | 0.438 | 0.596 | 0.158 |
MIA | 1993 | 0.563 | 0.713 | 0.151 |
STL | 1995 | 0.438 | 0.588 | 0.151 |
NOR | 2009 | 0.813 | 0.956 | 0.143 |
MIN | 2000 | 0.688 | 0.831 | 0.143 |
MIA | 1999 | 0.563 | 0.706 | 0.143 |
PIT | 1998 | 0.438 | 0.581 | 0.143 |
TAM | 2011 | 0.250 | 0.390 | 0.140 |
NOR | 2002 | 0.563 | 0.699 | 0.136 |
BUF | 2011 | 0.375 | 0.507 | 0.132 |
NOR | 1993 | 0.500 | 0.632 | 0.132 |
PHO | 1991 | 0.250 | 0.382 | 0.132 |
BUF | 2008 | 0.438 | 0.566 | 0.129 |
NYJ | 2000 | 0.563 | 0.691 | 0.129 |
NYG | 2009 | 0.500 | 0.625 | 0.125 |
DET | 2007 | 0.438 | 0.559 | 0.121 |
MIN | 2003 | 0.563 | 0.684 | 0.121 |
KAN | 1996 | 0.563 | 0.684 | 0.121 |
IND | 2006 | 0.750 | 0.868 | 0.118 |
NYJ | 2004 | 0.625 | 0.743 | 0.118 |
STL | 2000 | 0.625 | 0.743 | 0.118 |
We can also look at the “fastest finishers.” These teams all had significantly higher actual winning percentages than weighted winning percentages, because of how well they played at the end of the season:
Tm | Year | Act. Win% | Wt. Win% | DIFF |
---|---|---|---|---|
CIN | 2008 | 0.281 | 0.129 | -0.153 |
CLE | 2009 | 0.313 | 0.162 | -0.151 |
TEN | 2006 | 0.500 | 0.353 | -0.147 |
TEN | 2009 | 0.500 | 0.353 | -0.147 |
DET | 2010 | 0.375 | 0.235 | -0.140 |
BUF | 2004 | 0.563 | 0.426 | -0.136 |
MIA | 2011 | 0.375 | 0.243 | -0.132 |
TEN | 2002 | 0.688 | 0.559 | -0.129 |
MIA | 2008 | 0.688 | 0.559 | -0.129 |
IND | 2008 | 0.750 | 0.625 | -0.125 |
SEA | 2002 | 0.438 | 0.316 | -0.121 |
MIN | 2002 | 0.375 | 0.257 | -0.118 |
HOU | 2008 | 0.500 | 0.382 | -0.118 |
ARI | 2011 | 0.500 | 0.382 | -0.118 |
MIA | 2005 | 0.563 | 0.449 | -0.114 |
SDG | 2007 | 0.688 | 0.574 | -0.114 |
GNB | 2006 | 0.500 | 0.390 | -0.110 |
SDG | 2009 | 0.813 | 0.706 | -0.107 |
CAR | 2009 | 0.500 | 0.397 | -0.103 |
PHI | 2011 | 0.500 | 0.397 | -0.103 |
GNB | 2003 | 0.625 | 0.529 | -0.096 |
MIA | 2004 | 0.250 | 0.154 | -0.096 |
DAL | 2010 | 0.375 | 0.279 | -0.096 |
NYJ | 2002 | 0.563 | 0.471 | -0.092 |
CAR | 2004 | 0.438 | 0.346 | -0.092 |
In case you were wondering, there doesn’t appear to be much of a correlation between whether a team is a “strong starter” or a “fast finisher” and how well the team does the following season. I ran a regression using each team’s winning percentage and the differential column in the tables above. As it turns out, the differential variable was not statistical significant at the 10% level (p-value = 0.13), and even if it was, it was not practically significant (but with a very slight tendency towards the ‘strong starters’ playing better the next year). My guess is that the data gets clouded by teams who suffer injuries versus teams that simply see their play dropoff, and also by teams resting opponents late in the season and/or playing teams resting starers.
I looked at the top 24 “strongest starters” and top 24 “fastest finishers” and found similar results. On average, both groups won 53% of their games in Year N. The “strong starters” had a weighted winning percentage of 0.665 while the “fast finishers” had a weighted winning percentage of 0.383. In Year N+1, the strong starters won 54% of their games, while the fast finishers won 49% of their games.
Previous “Random Perspective On” Articles:
AFC East: Buffalo Bills, Miami Dolphins, New England Patriots, New York Jets
AFC North: Baltimore Ravens, Cincinnati Bengals, Cleveland Browns, Pittsburgh Steelers
AFC South: Houston Texans, Indianapolis Colts, Jacksonville Jaguars, Tennessee Titans
AFC West: Denver Broncos, Kansas City Chiefs, Oakland Raiders, San Diego Chargers
NFC East: Dallas Cowboys, New York Giants, Philadelphia Eagles, Washington Redskins
NFC North: Chicago Bears, Detroit Lions, Minnesota Vikings, Atlanta Falcons
NFC South: Atlanta Falcons, Carolina Panthers, New Orleans Saints, Tampa Bay Buccaneers
NFC West: Arizona Cardinals, San Francisco 49ers, Seattle Seahawks, St. Louis Rams