Now to my statistical analysis of why this injury bug just can't be luck. Football Outsiders tracks a statistic called adjusted games lost (AGL) for each team. The reason it is

__adjusted__games lost and not just total games lost is that they adjust the number on basis of which player was lost. They value loss of a starter or regular rotation player more than a bench player, rendering the statistic a little more meaningful. The Giants finished last of all 32 teams in the league in this AGL statistic for the last 3 years, something that's very hard to do. But it's too easy to simply say, it's unlikely and that this could happen by chance without putting some hard measurements around it. Here goes.

I looked at the AGL statistics for the last two years, 2015 and 2014. I made the fairly safe assumption that the AGL sample for each year was normally distributed. I then calculated the average and standard deviation for each year. The standard deviation is a measure for how widely spread the measurements are around the mean. For example, if a student's average score on two tests that he took is 80, he may have gotten 75 and 85 on his two tests or may have gotten scores of 100 and 60. In both cases the average is 80, but in the second case the standard deviation is greater because the scores are more widely spread around the mean. For a "normal distribution" 99.7% of all the possible measurements are within 3 standard deviations of the mean. In 2015 the Giants' AGL was more than 3 standard deviations away from the mean and in 2014 it was slightly less than that, about 2.59 standard deviations away. Using these statistics and the normal distribution, you can calculate the probability that a team will get a sample score less than (or greater than) a particular number.

OK ---- for those of you whose eyes are glazed over with these boring statistics, let me turn this into English. The probability that by pure chance a number would be

__less than or equal__to the Giants AGL for year 2015 is 99.88%. The same probability for year 2014 is 99.5%.

Looking at it another way, the probability that a random score would be

**than the Giants for 2015 and 2014 are respectively: .119% and .482%**

*greater*Putting these together - the probability that any team could do as badly as the Giants in both years together is .000574%

Understanding this better - that is 5.7 chances out of a million.

(With apologies to D&D, quoting Lloyd Christmas... " so you're saying there's a chance")

Looking at the second worst team in the two year period, the Washington Redskins were .38%

In other words, Giants were roughly 1,000 times worse than the second worst team in the league over the 2 year period.

Here's hoping the new S&C coach does a little better than the last one.

## 1 comment:

Spot on. While I wont give Jerry the credit for punching the numbers quite like you did, there's obviously been an emphasis this offseason on getting guys that will simply be able to show up every week. What remains to be seen is whether that's just lazy. Is Janoris Jenkins a much better player than prince? Maybe maybe not but the Giants strategy seems to be lets just get guys with some talent but more importantly guys that will be on the field every week. Again I hope that's not just lazy.

Post a Comment