The standard deviation and interquartile range are two measures of the spread of a distribution. It is potentially misleading to rely on standard deviation to assess player consistency in fantasy football for a couple of reasons.

Standard deviation is the square root of variance, which is the sum of squares of the deviation from the mean. In small sample sizes, such as a NFL season, a single outlier game can distort the mean. There is a related problem that the reader may assume that 68% of the values are within one standard deviation of the mean, which does not hold if the values are not normally distributed.

Consider the following player, who has a random score between 10-15 in 15 games and then a 40-point explosion in week 17. This player is extremely consistent, and the "inconsistent" game is not problematic from a fantasy perspective, as there is no downside from a player having an occasional big game.

In [28]:

```
gamelog1 <- c(sample(10:15, 15, replace = TRUE), 40)
gamelog1
```

In [29]:

```
library(psych)
describe(gamelog1)
```

In [30]:

```
summary(gamelog1)
```

In [35]:

```
gamelog2 <- sample(7:17, 16, replace = TRUE)
gamelog2
```

In [36]:

```
describe(gamelog2)
```

In [37]:

```
summary(gamelog2)
```

In [ ]:

```
```