This analysis is going to look at how offense has taken over the NFL in the last decade and what that means for the future of the league
draft <- read_csv("/Users/brendandioli/Desktop/archive (13)/draft.csv")
player <- read_csv("/Users/brendandioli/Desktop/archive (13)/players.csv")
pen <- read_csv("/Users/brendandioli/Desktop/archive (13)/penalties.csv")
games <- read_csv("/Users/brendandioli/Desktop/archive (13)/games.csv")
combine <- read_csv("/Users/brendandioli/Desktop/archive (13)/combine.csv")
play <- read_csv("/Users/brendandioli/Desktop/archive (13)/plays.csv")
bowl <- read_csv("/Users/brendandioli/Desktop/superbowl.csv")
In 2019, the NFL had one of their largest draft classes yet with 254 players chosen in a 7-round draft. The Arizona Cardinals had the #1 pick in the draft and chose quarterback, Kyler Murray.
The last three NFL drafts a quarterback has been selected as the number one pick in the draft. Most people aren’t surprised when they hear that a quarterback was the number one overall pick. But what might surprise people is how disproportionally NFL teams spend on offensive players compared to defensive players.
# Checking position categories
draft %>%
count(position,sort=TRUE)
## # A tibble: 45 x 2
## position n
## <chr> <int>
## 1 LB 1549
## 2 DB 1489
## 3 WR 1481
## 4 RB 1219
## 5 DE 980
## 6 OT 873
## 7 DT 771
## 8 OG 766
## 9 TE 689
## 10 QB 570
## # … with 35 more rows
draft_df <- draft %>%
mutate(pos =case_when(position %in% c("QB","RB","WR")~"Offense",
position %in% c("LB","DB","CB")~"Defense",
TRUE ~ "others")) %>%
relocate(pos) %>%
filter(draft %in% c(2000:2018))
#Count year & round wise Offense & defense players
p0 <- draft_df %>%
filter(pos!="others",
round==1) %>%
count(pos,draft,round,wt=draftTradeValue, name = "Amount") %>%
mutate(draft=as.factor(draft)) %>%
ggplot(aes(x=draft,y=Amount,fill=pos))+
geom_col(alpha=0.5, position = "dodge")+
theme_minimal()+
labs(title="Money spent on players with their respective position in Round 1",
x="",
y="Dollars spent (USD)",
fill="Position",
caption = "Caption")+
scale_y_continuous(labels = dollar_format())+
scale_fill_manual(values = c("red", "green"))+
theme(axis.text.x = element_text(angle=90))
ggplotly(p0)
As you can see the amount of money each year spent on offensive players is significantly higher than what is spent on defensive players. While every draft year is different one thing remains the same and that is the increasing emphasis on offensive strength. If we continue to look at previous drafts we continuously see NFL teams using their most valuable draft picks on offensive players.
p1 <- draft_df %>%
filter(pos!="others",
round==1,
draft==2018) %>%
count(position,draft,round) %>%
mutate(position=as.factor(position)) %>%
ggplot(aes(x=position,y=n,fill=position))+
geom_col(show.legend = FALSE)+
# facet_wrap(~round)+
theme_minimal()+
labs(title="Position selected in Round 1 of 2018 draft",
x="Positions",
y="Count of players")+
theme(legend.position='none')
ggplotly(p1)
You may be wondering what the reasoning for this disproportion is, let’s take a look back into the history of the NFL. One of the most controversial calls in the league is “Defensive Pass Interference.” In 2003 the New England Patriots defense exploited the Indianapolis Colts offense with an overly aggressive secondary defense. At the start of the 2004 NFL season the leagues Officials stated they would be making Defensive Pass Interference calls a point of emphasis. Since then, there was an addition to the rule after a miss call during a post season game making all Pass Interreference calls or no calls, reviewable.
p2 <- pen %>%
filter(penaltyDescrip=="Defensive Pass Interference") %>%
inner_join(play %>% select(playId,gameId)) %>%
relocate(gameId) %>%
inner_join(games %>% select(gameId,season)) %>%
relocate(season) %>%
count(season,name="Total") %>%
mutate(season=as.factor(season)) %>%
ggplot(aes(x=season,y=Total,fill=season))+
geom_col()+
theme_minimal()+
labs(title="Change in Def Pass Interference penalties over the years",
x="",
y="Count of penalties")+
theme(legend.position = "none")
ggplotly(p2)
So is this why the offense has become some dominant in the NFL?
League rule changes and teams spending more money on a high caliber offense are a contributing factor. But whatever happened to “Defense wins championships.”
p3 <- bowl %>%
mutate(Date=mdy(Date)) %>%
mutate(total_points=`Winner Pts`+`Loser Pts`,
year=year(Date))%>%
filter(year %in% c(2008:2020)) %>%
mutate( year=as.factor(year)) %>%
ggplot(aes(x=year,y=total_points,fill=year))+
geom_col()+
theme_minimal()+
labs(title="Total number of points in Superbowl over the years",
x="",
y="Total Points scored")+
theme(legend.position = "none",
axis.text.x = element_text(angle=90))
ggplotly(p3)
As you can see from the visual above, year after year the Super Bowl is becoming a more higher scoring game. We have seen how rule changes have resulted in more penalties, of which favoring offensive players. NFL teams spending more money on offensive players and drafting more offensive positions than defensive. Finally, we looked at total points in a championship game and how they have continued to increase. Given these factors it is no surprise offensive players get so much media attention and high valued contracts. The NFL is known for being one of the most cut-throat and bottom-line businesses there is. At the end of the day whoever has the most points on the board is the champion and whoever has the least is not. With the way the rules, NFL team direction, and Super Bowl history has been headed it is no surprise why high powered offenses have taken over the National Football League.