Using data from NCAA Div I lacrosse teams to explore the importance of winning faceoffs
Authors
Affiliation
Jack Fay
St. Lawrence University
Ivan Ramler
St. Lawrence University
A.J. Dykstra
St. Lawrence University
Published
February 5, 2024
Introduction
In this engaging activity, we explore the exciting sport of NCAA Division I Lacrosse, with a special focus on faceoff percentages—a critical aspect of the game. A faceoff occurs at the start of each quarter and after every goal, where two players compete to gain possession of the ball, setting the stage for their team’s offensive play. Winning a high percentage of faceoffs is often key to controlling the game and can significantly impact a team’s overall performance.
Video Demonstrating a Faceoff
Our primary goal is to compare a specific team’s faceoff performance with overall league statistics for the 2022-2023 season. Through this exploration, we’ll introduce you to the concept of one-sample proportion hypothesis testing, a powerful statistical tool widely used in sports analytics. By the end of this exercise, you’ll gain a fundamental understanding of hypothesis testing and how it can be practically applied to evaluate team performance in lacrosse and beyond.
Activity Length
This activity would be suitable for an in-class example (of approximately 10 - 20 minutes) or can be modified to be a quiz or part of an exam.
Learning Objectives
Comprehend the concept of one sample proportion hypothesis testing and its relevance in sports statistics.
Analyze and interpret dataset variables related to faceoff percentages in NCAA Division I Lacrosse.
Evaluate a specific team’s faceoff performance by comparing it with league-wide statistics using hypothesis testing.
Methods
Students are expected to have been exposed to the following concepts and use the activity to reinforce their understanding of these methods.
Basic probability and percentages.
Null and alternative hypotheses.
Sample size and sample proportion calculations.
Success-failure condition for hypothesis testing.
Calculation of test statistics (Z-score).
Understanding significance levels (⍺) and p-values.
Drawing conclusions and implications from hypothesis test results.
Data
Note that because the activity only uses results from one team, students do not necessarily need to directly access this data. However, the activity can easily be adapted to use other teams. Instructors are encouraged to personalize the activity if they so choose.
The data set where the activities statistics come from contains 72 rows and 22 columns. Each row represents the season results for a lacrosse team at the NCAA Division 1 level from the 2022-2023 season.
Select Men’s Lacrosse, season of choice, Division I, Final Statistics
In the “Teams”, download each of the data tables.
Read in each file, join the tables, and do some light cleaning. The code below shows an example used for the 2022-2023 season.
Show the code
library(tidyverse)# reading# the files listed here are what# you will download from the siteassists<-read_csv("assists_l.csv", col_select =1:2)caused_turnovers<-read_csv("caused_turnovers_l.csv", col_select =1:2)clearing<-read_csv("clearing_pctg_l.csv", col_select =1:2)fo <-read_csv("fo_win_pctg.csv", col_select =1:4)goals_against<-read_csv("goals_against.csv", col_select =1:2)goals<-read_csv("goals_l.csv", col_select =1:2)groundballs<-read_csv("ground_balls_l.csv", col_select =1:2)man_down <-read_csv("man_down_defense_l.csv", col_select =1:2)man_up <-read_csv("man_up__offense_l.csv", col_select =1:2)margin <-read_csv("margin_l.csv", col_select =1:2)opp_clear <-read_csv("opp_clear_l.csv", col_select =1:2)points <-read_csv("points_l.csv", col_select =1:2)saves <-read_csv("saves_l.csv", col_select =1:2)shot <-read_csv("shot_pctg_l.csv", col_select =1:2)turnovers<-read_csv("turnovers_l.csv", col_select =1:2)shots_per_game <-read_csv("shots_per_game.csv", col_select =1:3)win_loss <-read_csv("win_loss_l.csv")# joining# students familiar with the purrr package could# use the reduce function to reduce the amount of codelax_2022_2023 <-left_join(assists, caused_turnovers, by ="Team") %>%left_join(clearing, by ="Team") %>%left_join(fo, by ="Team") %>%left_join(goals, by ="Team") %>%left_join(goals_against, by ="Team") %>%left_join(groundballs, by ="Team") %>%left_join(man_down, by ="Team") %>%left_join(man_up, by ="Team") %>%left_join(margin, by ="Team") %>%left_join(opp_clear, by ="Team") %>%left_join(points, by ="Team") %>%left_join(saves, by ="Team") %>%left_join(shot, by ="Team") %>%left_join(turnovers, by ="Team") %>%left_join(shots_per_game, by ="Team") %>%left_join(win_loss, by ="Team")# cleaninglax_2022_2023 <- lax_2022_2023 %>%separate(Team, into =c("Team","Conference"), sep ="\\(", extra ="merge")%>%mutate(Conference =str_remove_all(Conference,"\\)"),Team =str_trim(Team))%>%mutate(shots_per_game = Shots/Games)%>%select(-20, -21)# savingwrite_csv(x = lax_2022_2023, file ="lax_2022_2023.csv")
In this insightful exploration of NCAA Division I Lacrosse faceoff percentages, we have embarked on a statistical journey to evaluate a specific team’s performance in comparison to league-wide statistics. Through the application of one sample proportion hypothesis testing, we gained valuable insights into the team’s faceoff win percentage, unveiling strong evidence that their performance exceeded what we would expect by random chance alone. As we consider the broader implications of faceoffs in Division I Lacrosse, it becomes evident that faceoff wins play a pivotal role in team rankings and outcomes. The fact that Duke, the second-best team in the country, exhibited a faceoff win percentage above the league average highlights the significance of excelling in this aspect of the game. Winning faceoffs likely translates to higher goal-scoring opportunities, ultimately leading to more successful game outcomes.