METHODOLOGY
The primary sources of data for this study were Pro-Football-Reference.com's
list of "Single-Season Rushing Attempts Leaders." This list contains the Top 250 single-season records for attempts. When accessed for this study, the number of attempts ranged 286 to 416, with a mean of 326 and SD of 30. To test the potential effects of overuse theory, two groups were identified; Group 1 consisted of players who were one or more SD above the mean (356 or more carries). Group 1 was compared to Group 2, which consisted of running backs who were more than one SD below the mean (295 carries). This break conveniently resulted in two groups with 43 members each. It should be noted that several running backs' data were discarded because they: a.) didn't play the following year (e.g. Ricky Williams) or made the list in 2008 (e.g. Michael Turner). Also, Quarterbacks that made the list, were discarded.
For each RB, the data from individual player "cards" at Pro-Football-Reference.com was utilized. Data was gathered for the relevant season (i.e. the year they rushed either 356 or more carries, or less than 295 carries = "Year") and the following season ("Year+1"). This design allowed for testing of the hypotheses via Repeated Measures 2x2 factorial designs.
Two sets of statistical analyses were conducted. The first looked at the issue of overuse utilizing readily available standard measures of running back performance utilizing data from the above described data source. The second set of analyses, utilized Football Outsiders measures to assess
running back. This second analysis required creating a subset of data from the first because Football Outsider's data extends only back to the 1995 season. Therefore, all running back performances prior to 1995 were discarded from these analyses (sorry Jim Brown fans). This conveniently left two groups of 20 each after one of Jamaal Anderson's season was
discarded because he did not have enough carries in Year+1 to generate Football Outsider statistics. Football Outsider data for each RB was "Year"
and "Year+1" included the following measures: individual DVOA and DYAR.
DATA ANALYSIS
Standard Statistics
# CARRIES: The first analyses simply addressed whether or not the two
groups being analyzed differed in the number of carries.
Group Means
Group Year Mean Year+1 Mean
One 377.16 282
Two 290.18 240.74
Note that the average number of carries for Group 1 is actually greater than
370.
ANOVA results yielded the following
Between subjects F =39.63, df (1,84), 0.0001
Within subjects F=46.28, df (1,84), 0.0001
AxB F=4.63, df(1,84), 0.05
These results indicate that in terms of number of carries, there is a statistical difference between the Groups, between years and a year by group interaction. These results suggest that not only is there a difference from "Year" to "Year +1," but that the magnitude of the decrease is significantly different between the groups.
# of GAMES: A central component of "The Curse" proposes that more carries (specifically greater than 370) will result in a decline in the number of games a running back will play the following year (Year+1). The following analysis compared the two groups' number of games played in Year and Year +1.
Group Year Mean Year+1 Mean
One 15.674 13.534
Two 14.953 13.116
ANOVA results yielded the following
Between subjects F =1.95, df (1,84), p <ns.
Within subjects F=27.99, df (1,84), p<0.0001
AxB F=, df(1,84), p<ns.
These results indicate that high use, whether above or below 370 carries results in a decline. This is most consistent with Mr. Burke's findings. There is no statistical difference between the groups, nor is the magnitude of decline between the groups different. If a "Curse" exists, the bar is much lower than 370.
Y/A: Next, yards per attempt were assessed. Yards per attempt provide an indicator of a RB's effectiveness on a carry per carry basis.
Group Year Mean Year+1 Mean
One 4.3419 4.086
Two 4.3163 4.093
As pointless as it seems, here are the results of the analysis.
Between subjects F =0 df (1,84).
Within subjects F=10.74, df (1,84),0.01
AxB F=0.04, df(1,84),
In sum, a running back that carries a lot, on average, will experience a decline the following year - again, there is nothing magic about 370.
"DVOA Era Statistics"
Perhaps the measures utilized above are simply too crude to properly detect the effects of "370." To address this possibility, similar analyses were conducted utilizing Football Outsiders stats. First a series of analyses utilizing "Standard Statistics" were conducted to ensure that the sub-groups being analyzed were relatively the same as the original groups.
Standard Statistics
#Carries: Group Year Mean Year+1 Mean
One 375.5 291.4
Two 291.65 242.7
ANOVA results yielded the following
Between subjects F =19.83, df (1,38), p <0.0001
Within subjects F=17.11, df (1,38), p<0.001
AxB F=, df(1,38), p<ns.
These results suggest the groups are different in the number of carries. The nonsignificance of the interaction effect is the only difference and suggests that the previously detected difference may be due to a larger sample size.
#Games
Group Year Mean Year+1 Mean
One 15.5 13.85
Two 15.4 14.2
ANOVA results yielded the following
Between subjects F =.04, df (1,38).
Within subjects F=6.89, df (1,38), 0.01
AxB F=.17, df(1,38), p<ns.
These results are similar to those found above.
Y/A (Yards/Attempt)
Group Year Mean Year+1 Mean
One 4.355 3.94
Two 4.165 3.89
ANOVA results yielded the following
Between subjects F =.52, df (1,38),.
Within subjects F=11.33, df (1,38), p<0.01
AxB F=0.48, df(1,38), p<ns.
Results are again similar to above. These results then suggest that the sub-group of players, post 1995, are statistically, fairly similar to the group as a whole.
DVOA Statistics
To test validity of Football Outsider's "Curse of 370," it seems appropriate to utilize some of Football Outsider metrics. For an explanation of these metrics - http://www.footballoutsiders.com/info/methods.
Individual DVOA: This analysis tests the difference in DVOA between running backs in Group 1 and Group 2 based on Year and Year+1. DVOA is usually expressed as percent, in the following, it is expressed in decimal fashion.
Group Year Mean Year+1 Mean
One 0.0755 -0.026
Two -0.000 -0.062
ANOVA results yielded the following
Between subjects F =6, df (38,1), p <.05.
Within subjects F=13, df (38,1), p<0.001
AxB F=1.0, df(38,1), p<ns
These results suggest that there is a difference in Year and Year+1 (wear and tear or regression) and a difference between Group 1 and Group 2; however, the ns interaction effect indicates that the magnitude of the wear and tear is nonsignificant between groups. That is, the 0.1015 drop between Year and Year+1 observed in Group 1 is not significantly greater in magnitude than the approximately 0.062 drop observed in Group 2.
DYAR
Group Year Mean Year+1 Mean
One 260.1 93.05
Two 105.95 55.45
ANOVA results yielded the following
Between subjects F =7.06, df (38,1), p <.01.
Within subjects F=10.46, df (38,1), p<0.01
AxB F=3, df(38,1), p<ns
These results are similar to those for DVOA.
DISCUSSION
This study analyzed "the Curse of 370" by comparing two groups comprised of RBs who accumulated enough carries to be ranked in the top 250 single-season attempts list. The first group consisted of 43 running backs that were at least one SD above the mean in number of carries. The second group consisted of 43 running backs who were one SD below the mean. This process yielded two groups that had a high likelihood of being significantly different regarding the number of carries in a season, and if a high-number of carries is the cause for decline in subsequent seasons, then there should be significant detectable differences in the magnitude of decline. None of the results suggested this to be the case. Simply stated, this study did not detect any evidence to support "the Curse of 370." The study did find that a large number of carries are associated with decline the following year; however, that decline does not become magnified as the number of carries increases - at least not in this data set.
Mr. Burke has asserted that the decline is probably due to regression to the mean; however, further analyses utilizing this data set suggest other factors may be associated with the decline and will be examined in future posts.
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