by Delta Whiskey
INTRODUCTION
In the previous post, the notion that running backs are subject to "wear and
tear" as popularized by Football Outsiders "Curse of 370" was explored.
Brian Burke's critique of "the Curse" suggests that regression to the mean
better explains "The Curse." The following examines factors that may
explain or at least describe the processes associated with said regression.
Specifically, the study examines win differences (decline possibly due to
the fact that teams that are winning run more than those that are losing),
overall team offensive decline (running backs' performance may decline if
their own team in general declines) or tougher opposition in subsequent
years.
METHODOLOGY
As in the previous study, the primary sources of data for this study were
Pro-Football-Reference.com's list of "Single-Season Rushing Attempts
Leaders"
(http://www.pro-football-reference.com/leaders/rush_att_single_season.htm).
The data sets for testing the hypothesis were created in the same manner as
before. Again, two sets of statistical analyses were conducted. The first
utilized the full data set, while the second utilized Football Outsiders
measures to assess issues related to offensive strength and opponents'
strength. Again, 2x2 repeated measure factorial design was utilized to
analyze the data.
DATA ANALYSIS
WINs: The number of wins for each running back's team was examined. The
rationale for this analysis is that teams that are winning run the ball more
and therefore a difference in Year and Year+1 wins might indirectly explain
the differences noted in number of carries.
Wins were expressed as a percentage rather than raw totals due to the
progression from 12 to 14 and finally 16 games per year within the data set.
Group Year Mean Year+1 Mean
One 0.6114 0.4973
Two 0.5422 0.5536
Between subjects F =0 df (84,1), p <ns.
Within subjects F=2.2, df (84,1), p<ns
AxB F=3.4, df(84,1), p<ns
These results certainly do not support the hypothesis that running back
decline may be due to his team winning less.
DVOA Era Statistics
As in the previous study, this data set only includes players from 1996 and
beyond.
Wins: Again, analysis was conducted to assess for differences in Team Wins.
Group Year Mean Year+1 Mean
One 10.55 8
Two 8.35 7.55
ANOVA results yielded the following
Between subjects F =6, df (38,1), p < 0.05
Within subjects F=6.78, df (38,1), p < 0.01
AxB F=1.85, df(38,1), p
In contrast to the earlier analysis of wins, the within subjects results
appear to support the idea that a decrease in wins may explain in part of a
running backs decline, most likely in number of carries. Furthermore, the
between groups difference indirectly supports the notion that teams that are
winning run more. It should be noted the direction of this relationship, if
it exists is not determinate by this analysis. This also suggests that the
role of running the ball seems to have changed in the last twenty or so
years (i.e. DVOA data only goes back to 1996).
DVOA Statistics
Offensive DVOA: This analysis looked at the differences in offensive
performance, as measured by Offensive DVOA.
Group Year Mean Year+1 Mean
One .0943 .0131
Two -0.000 -0.03
ANOVA results yielded the following
Between subjects F =10, df (38,1), p < 0.01.
Within subjects F=6, df (38,1), p < 0.05
AxB F=1, df(38,1), p<ns
The within subjects results indicated that there is a significant offensive
decline from Year to Year+1. It is entirely possible that this decline is
mostly due to the decline in RB performance as the analysis does not control
for the RB's contribution to Offensive DVOA.
Opponent DVOA (x100): Next, the average overall DVOA of opponents was
examined. In order to facilitate analysis, the DVOA was returned to a
percentage.
Group Year Mean Year+1 Mean
One .9 1.365
Two -1.48 1.205
ANOVA results yielded the following
Between subjects F =1.5, df (38,1), p <ns.
Within subjects F=3.4, df (38,1), p<ns
AxB F=1.1, df(38,1), p<ns
There were no statistically differences in the quality of opponents between
groups or from year to year.
DISCUSSION
Brian Burke has suggested that the decline in running back performance is primarily due to regression to the mean. This study examined whether other measurable group differences could be associated with and/or explain decline
in performance. The results indicated that at least since 1996 there is a difference in the number of wins from Year to Year+1. Additionally, there
is a decline in offensive performance (as measured by Offensive DVOA) from
Year to Year+1; however, as noted, the RB's individual contribution to
Offensive DVOA was not controlled for, and therefore it is difficult to
ascertain the robustness of overall offensive decline.
Next, regarding the first study, Derek asked "Question about the sampling
method:
A running back had 356+ carries in year Y and less than 290 in Y+1. Years Y
and Y+1 will show up in Group 1, but would Y+1 and Y+2 be in Group 2 as
well?" The answer is yes. He then asks "A thought stemming from that
question: Maybe it's not one year of overuse but multiple years of overuse
that kills a RB. Is there some threshold for the number of carries over 2 or
3 years that will cause a total break down the following year?" Football
Outsider's may thinks so, but I think the answer to this question is no.
Recall that the data analyzed is essentially the upper and lower bounds of
the "Top 250 Single-Season Rushing Attempts Leaders." The complete data set
of "Top 250 Single-Season Rushing Attempts Leaders," contains a total of 259
total data points, that is 259 RB seasons qualified. If overuse and decline
over one to two seasons is a real phenomena, then it is expected that there
would be somewhere between 129 (or 130) and 259 individual players on the
list. Instead, 103 individual players are on the list, meaning that each
player who makes the list, does so an average of 2.51 times. Moreover, of
those 103, 36 (35%) make the list (not necessarily consecutively) 3 or more
times. Wear and tear may be a real phenomena; however, it does not appear
as robust as Football Outsiders would like you to believe.
Follow on studies will attempt to isolate or model predictors of decline.
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