This data frame gives the number of fumbles by each NCAA FBS team for the first three weeks in November, 2010.
Format
A data frame with 120 observations on the following 7 variables.
- team
NCAA football team
- rank
rank based on fumbles per game through games on November 26, 2010
- W
number of wins through games on November 26, 2010
- L
number of losses through games on November 26, 2010
- week1
number of fumbles on November 6, 2010
- week2
number of fumbles on November 13, 2010
- week3
number of fumbles on November 20, 2010
Details
The fumble counts listed here are total fumbles, not fumbles lost. Some of these fumbles were recovered by the team that fumbled.
Examples
data(Fumbles)
#> Warning: data set ‘Fumbles’ not found
m <- max(Fumbles$week1)
table(factor(Fumbles$week1,levels = 0:m))
#>
#> 0 1 2 3 4 5 6 7
#> 22 36 29 23 5 4 0 1
favstats( ~ week1, data = Fumbles)
#> min Q1 median Q3 max mean sd n missing
#> 0 1 2 3 7 1.75 1.361228 120 0
# compare with Poisson distribution
cbind(
fumbles = 0:m,
observedCount = table(factor(Fumbles$week1,levels = 0:m)),
modelCount= 120* dpois(0:m,mean(Fumbles$week1)),
observedPct = table(factor(Fumbles$week1,levels = 0:m))/120,
modelPct= dpois(0:m,mean(Fumbles$week1))
) %>% signif(3)
#> fumbles observedCount modelCount observedPct modelPct
#> 0 0 22 20.900 0.18300 0.17400
#> 1 1 36 36.500 0.30000 0.30400
#> 2 2 29 31.900 0.24200 0.26600
#> 3 3 23 18.600 0.19200 0.15500
#> 4 4 5 8.150 0.04170 0.06790
#> 5 5 4 2.850 0.03330 0.02380
#> 6 6 0 0.832 0.00000 0.00693
#> 7 7 1 0.208 0.00833 0.00173
showFumbles <- function(x, lambda = mean(x),...) {
result <-
gf_dhistogram( ~ week1, data = Fumbles, binwidth = 1, alpha = 0.3) %>%
gf_dist("pois", lambda = mean( ~ week1, data = Fumbles) )
print(result)
return(result)
}
showFumbles(Fumbles$week1)
showFumbles(Fumbles$week2)
showFumbles(Fumbles$week3)
