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NFL Draft 2014
It can’t be easy being a Jacksonville Jaguars fan. It seems that management has learned nothing from the Blaine Gabbert debacle. By that I mean I’m not impressed with their first round pick Blake Bortles. Bucky Brooks of NFL.com called him a “developmental prospect”. Developmental? Is that what you want from the third overall pick? I hate to say it but I have to agree with Skip Bayless that Bortles is the new Gabbert in J-Ville. We shall see soon enough if I will eat these words!
At any rate, I’ve downloaded draft data from the web and you can explore the draft at your own leisure. I scrapped it off of wikipedia at this link: http://en.wikipedia.org/wiki/2014_NFL_Draft
I put together some code to get you on your way to exploring
attach(nfldraft) str(nfldraft) ## 'data.frame': 256 obs. of 8 variables: ## $ round : int 1 1 1 1 1 1 1 1 1 1 ... ## $ pick : int 1 2 3 4 5 6 7 8 9 10 ... ## $ team : Factor w/ 32 levels "Arizona Cardinals",..: 13 29 15 4 23 2 30 8 18 11 ... ## $ player : Factor w/ 256 levels "Aaron Colvin ",..: 96 89 25 204 147 97 178 131 12 84 ... ## $ position : Factor w/ 18 levels "C","CB","DE",..: 3 11 13 18 9 11 18 2 9 17 ... ## $ college : Factor w/ 113 levels "Alabama","Arizona",..: 86 6 99 15 12 95 95 71 100 62 ... ## $ conference: Factor w/ 24 levels "ACC","Big 12",..: 21 21 24 1 11 21 21 2 17 1 ... ## $ notes : Factor w/ 88 levels "","from Arizona[R1 - 5]",..: 1 86 1 20 1 1 1 51 9 1 ... names(nfldraft) ## [1] "round" "pick" "team" "player" "position" ## [6] "college" "conference" "notes" mytable = table(position, round) #create a table of player position and round selected mytable ## round ## position 1 2 3 4 5 6 7 ## C 0 1 2 2 2 2 1 ## CB 5 1 2 9 4 7 5 ## DE 2 3 3 2 5 3 4 ## DT 2 3 4 3 3 1 4 ## FB 0 0 0 0 0 1 1 ## FS 0 0 0 0 0 0 2 ## G 0 1 6 1 4 2 0 ## K 0 0 0 0 0 0 2 ## LB 5 3 3 5 6 2 7 ## OLB 0 0 0 0 2 1 0 ## OT 5 4 3 1 1 3 4 ## P 0 0 0 0 0 1 0 ## QB 3 2 0 2 2 5 0 ## RB 0 3 5 5 0 4 2 ## S 4 1 2 4 3 1 0 ## SS 0 0 0 0 0 1 2 ## TE 1 3 3 0 1 0 2 ## WR 5 7 3 6 3 5 5 margin.table(mytable) #generic table with sum...not very useful ## [1] 256 margin.table(mytable, 1) #sum of positions selected ## position ## C CB DE DT FB FS G K LB OLB OT P QB RB S SS TE WR ## 10 33 22 20 2 2 14 2 31 3 21 1 14 19 15 3 10 34 margin.table(mytable, 2) #sum of players selected by round ## round ## 1 2 3 4 5 6 7 ## 32 32 36 40 36 39 41 mosaicplot(mytable, main = "Position by Round", xlab = "Position", ylab = "Round", cex.axis = 1) #you can use mosaicplot to graphically represent the data in the table
What if you want to drill-down on a specific team or the like? Here is some simple code to examine a specific team, in this case the Colts:
colts = nfldraft[which(nfldraft$team == "Indianapolis Colts"), ] #select the rows of data unique to the Colts colts.table = table(colts$round, colts$position) margin.table(colts.table, 2) ## ## C CB DE DT FB FS G K LB OLB OT P QB RB S SS TE WR ## 0 0 1 0 0 0 0 0 1 0 2 0 0 0 0 0 0 1 barplot(colts.table)
There you have it. With some quick modifications to the above, you could produce tables examining players selected by conference, college etc.
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