at the conference & divisional levels
I’m going to be performing an analysis on the 2022-2023 NHL season data. This data contains information about shoot outs, games played, games won/loss, etc. Each row is an team and each column is a statistic that team. Learn more about this data at this link: https://data.scorenetwork.org/hockey/nhl_2223.html
hockey<- read_csv("https://myxavier-my.sharepoint.com/:x:/g/personal/meyerratkeni_xavier_edu/Ecko0LcMGjJCsN1RZOK4RsoBmlTCgqaaHw_Yy1ehLWqZxQ?download=1")
hist(hockey$GA,
main = "Historgram of Goals Against",
xlab = "Goals Against",
ylab= "Count")
hist(hockey$GF,
main = "Historgram of Goals Scored",
xlab = "Goals Scored",
ylab= "Count")
Do teams that score more goals tend to allow more or less goals against at the conference level?
The information presented in this visual is important because it can be used when planning game strategy, building a roster, to have better informed sports bets, predicting game outcomes, looking at overall conference trends, etc. Allows a team to see if they should focus on getting a better goalie or if they have strong enough shooters to offset their low save rate.
In order to answer this question, data from the 2022-2023 NHL season will be used. Specifically the variables: GA, GF, & Conference (mutated using data from ESPN). A scatter plot with a smoothing line will be used since both variables are continuous. Mutate function will be used to create a new variable called conference which will allow us to see if there are any significant differences at the conference level (will be used to color code). Lastly, facet_wrap will be used to have a side by side comparison by conference.
hockey %>%
mutate(Conference = ifelse(Team %in% c("BOS","CAR","NJ","TOR","NYR","TB",
"NYI","FLA","PIT","BUF","OTT","DET",
"WSH","PHI","MTL","CB"),
"Eastern", "Western")) # A tibble: 32 × 19
Team GP SOG W L OTL PTS Reg_PTS PPG SO_PTS Pct_SO RW
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 BOS 82 7 65 12 5 135 131 1.75 4 0.571 54
2 CAR 82 7 52 21 9 113 109 1.45 4 0.571 39
3 NJ 82 6 52 22 8 112 110 1.45 2 0.333 39
4 TOR 82 3 50 21 11 111 110 1.39 1 0.333 42
5 VGK 82 9 51 22 9 111 106 1.45 5 0.556 38
6 EDM 82 4 50 23 9 109 109 1.40 0 0 45
7 COL 82 9 51 24 7 109 103 1.41 6 0.667 36
8 DAL 82 7 47 21 14 108 104 1.39 4 0.571 39
9 NYR 82 7 47 22 13 107 103 1.37 4 0.571 37
10 LA 82 9 47 25 10 104 98 1.34 6 0.667 37
# ℹ 22 more rows
# ℹ 7 more variables: ROW <dbl>, SOW <dbl>, SOL <dbl>, GF <dbl>, GA <dbl>,
# DIFF <dbl>, Conference <chr>hockey %>%
mutate(Conference = ifelse(Team %in% c("BOS","CAR","NJ","TOR","NYR","TB",
"NYI","FLA","PIT","BUF","OTT","DET",
"WSH","PHI","MTL","CB"),
"Eastern", "Western")) %>%
ggplot(aes(x= GA, y=GF, color=Conference))+
geom_point()+
labs(title = "Goals Against vs Goals Scored by Conference",
subtitle = "2022-2023 Season",
x= "Goals Against",
y= "Goals Scored")+
facet_wrap(~Conference)+
geom_smooth(method=lm)
From this visual, we can see that the eastern and western conferences have similar trend lines with the western conf. being stepper due to have more teams with goals against above 300 and goals scored below 240. The western conference has an outlier around 340 goals scored and 260 goals against.