# 2015.2: Did the New England Patriots experience a decrease in fumbles starting in 2007?

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Here’s a timely guest entry from Jeffrey Witmer (Oberlin College).

As the “Deflate Gate” saga was unfolding, Warren Sharp analyzed “touches per fumble” for NFL teams before and after 2006, when a rule was changed so that teams playing on the road could provide their own footballs (http://www.sharpfootballanalysis.com/blog/). Sharp noted that, for whatever reason, the Patriots went from being a typical team, as regards fumbling, to a team with a very low fumble rate. Rather than rely on the data the Sharp provides at his website, I choose to collect and analyze some data on my own. I took a random sample of 30 games played by New England and 30 other games. For each game, I recorded all rushing and passing plays (except for QB kneels), but excluded kicking plays (the NFL, rather than the teams, provides special footballs for those plays). (Data source: http://www.pro-football-reference.com/play-index/play_finder.cgi.) I also recorded the weather for the game. (Data source: http://www.wunderground.com/history/.) Once I had the data (in a file that I called AllBig, which can be downloaded from http://www.amherst.edu/~nhorton/AllBig.csv), I noted whether or not there was a fumble on each play, aided by the `grep()` command:

grep("Fumb", AllBig$Detail, ignore.case=TRUE)

I labeled each play as Late or not according to whether it happened after the rule change:

AllBig$Late 2006, 1, 0)

Now for the analysis. My data set has 7558 plays including 145 fumbles (1.9%). I used the mosaic package and the `tally()` command to see how often teams other than the Patriots fumble:

require(mosaic) tally(~Fumble+Late, data=filter(AllBig,Pats==0)) Late Fumble 0 1 0 2588 2919 1 54 65

Then I asked for the data in proportion terms:

tally(Fumble~Late, data=filter(AllBig,Pats==0))

and got

Late Fumble 0 1 0 0.9796 0.9782 1 0.0204 0.0218

For non-Pats there is a tiny increase in fumbles. This can be displayed graphically using a mosaiplot (though it’s not a particularly compelling figure). `mosaicplot(Fumble~Late, data=filter(AllBig,Pats==0))` Repeating this for the Patriots shows a different picture:

tally(~Fumble+Late, data=filter(AllBig,Pats==1)) Late Fumble 0 1 0 996 910 1 19 7 tally(Fumble~Late, data=filter(AllBig,Pats==1)) Late Fumble 0 1 0 0.98128 0.99237 1 0.01872 0.00763

I fit a logistic regression model with the glm() command: `glm(Fumble~Late*Pats, family=binomial, data=AllBig)`

Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -3.8697 0.1375 -28.14

I wanted to control for any weather effect, so I coded the weather as Bad if it was raining or snowing and good if not. This led to a model that includes BadWeather and Temperature – which turn out not to make much of a difference:

AllBig$BadWeather |z|) (Intercept) -4.23344 0.43164 -9.81

Because there was suspicion that something changed starting in 2007 I added a three-way interaction:

glm(formula = Fumble ~ BadWeather + Temp + IsAway * Late * Pats, family = binomial, data = AllBig) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -4.51110 0.47707 -9.46

There is some evidence here that the Patriots fumble less than the rest of the NFL and that things changed in 2007. The p-values above are based on asymptotic normality, but there is a cleaner and easier way to think about the Patriots’ fumble rate. I wrote a short simulation that mimics something I do in my statistics classes, where I use a physical deck of cards to show what each step in the R simulation is doing.

#Simulation of deflategate data null hypothesis Late = rep(1,72) #creates 72 late fumbles Early = rep(0,73) #creates 73 early fumbles alldata = append(Late,Early) #puts the two groups together table(alldata) #check to see that we have what we want cards =1:length(alldata) # creates 145 cards, one "ID number" per fumble FumbleLate = NULL # initializes a vector to hold the results for (i in 1:10000){# starts a loop that will be executed 10,000 times cardsgroup1 = sample(cards,119, replace=FALSE) # takes a sample of 119 cards cardsgroup2 = cards[-cardsgroup1] # puts the remaining cards in group 2 NEPats = (alldata[cardsgroup2]) #reads the values of the cards in group 2 FumbleLate[i] = sum(NEPats) # counts NEPats late fumbles (the only stat we need) } table(FumbleLate) #look at the results hist(FumbleLate, breaks=seq(2.5,23.5)) #graph the results sum(FumbleLate

Additional note: kudos to Steve Taylor for the following graphical depiction of the interaction.

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