wlaxr is an R package built to make acquiring NCAA Women’s Lacrosse
data simple.
You can install the package via:
library(devtools)
devtools::install_github("BillPetti/wlaxr")Currently, the package contains three functions:
get_ncaa_wlax_team_schedules(): used to acquire game schedule and
results for a team in a given year.
get_ncaaa_wlax_team_stats(): used to acquire individual- and
team-level stats for a team in a given year.
Both of these functions should work on seasons back to 2001-2002.
school_lu(): used to quickly lookup school IDs and other information.
The package contains a few internal data sets to make the user’s life easier.
master_wlax_ncaa_team_lu: A data frame that includes school name, ID,
conference, conference ID, and division by year. Goes back to 2010.
Please note that there are likely some errors where the NCAA has teams
listed as either in two divisions or, due to being an Indepedent team,
they will appear to be in all three divisions in a single season. I’ve
tried to minimize this by restricting the data to 2010-present, but
there are still some duplicates.
ncaa_stats_year_lu_table: A helper data set that contains the season
codes used by the NCAA
First, let’s say you are interested in The University of Maryland. You
can use the school_lu function to find their NCAA stats ID:
school_lu(school_name = 'Maryland',
school_division = 1)
#> school school_id year division conference_id conference
#> 1 Loyola Maryland 369 2010 1 823 AAC
#> 2 Maryland 392 2010 1 821 ACC
#> 3 Loyola Maryland 369 2011 1 823 AAC
#> 4 Maryland 392 2011 1 821 ACC
#> 5 Loyola Maryland 369 2012 1 823 AAC
#> 6 Maryland 392 2012 1 821 ACC
#> 7 Loyola Maryland 369 2013 1 823 AAC
#> 8 Maryland 392 2013 1 821 ACC
#> 9 Maryland 392 2014 1 821 ACC
#> 10 Loyola Maryland 369 2014 1 838 Patriot
#> 11 Maryland 392 2015 1 827 Big Ten
#> 12 Loyola Maryland 369 2015 1 838 Patriot
#> 13 Maryland 392 2016 1 827 Big Ten
#> 14 Loyola Maryland 369 2016 1 838 Patriot
#> 15 Maryland 392 2017 1 827 Big Ten
#> 16 Loyola Maryland 369 2017 1 838 Patriot
#> 17 Maryland 392 2018 1 827 Big Ten
#> 18 Loyola Maryland 369 2018 1 838 Patriot
#> 19 Maryland 392 2019 1 827 Big Ten
#> 20 Loyola Maryland 369 2019 1 838 Patriot
#> 21 Maryland 392 2020 1 827 Big Ten
#> 22 Loyola Maryland 369 2020 1 838 Patriot
#> 23 Maryland 392 2021 1 827 Big Ten
#> 24 Loyola Maryland 369 2021 1 838 PatriotThis returns all records in the master_wlax_ncaa_team_lu data set
where ‘Mayrland’ is in the school name and the teams are listed in
division 1. You can see that Maryland has switched conferences over the
years from the ACC to the Big Ten. We also see that Maryland’s
school_id is 392. We’ll need that school_id for the rest of the
functions.
Next, let’s acquire Maryland’s schedule for the 2019 season when they won the National Championship:
md_2019 <- get_ncaa_wlax_team_schedules(team_id = 392,
year = 2019)
md_2019
#> team conference conference_id division date opponent result
#> 1 Maryland Big Ten 827 1 2019-02-09 George Mason W
#> 2 Maryland Big Ten 827 1 2019-02-14 Florida W
#> 3 Maryland Big Ten 827 1 2019-02-24 North Carolina W
#> 4 Maryland Big Ten 827 1 2019-02-27 UMBC W
#> 5 Maryland Big Ten 827 1 2019-03-02 Hofstra W
#> 6 Maryland Big Ten 827 1 2019-03-09 Syracuse W
#> 7 Maryland Big Ten 827 1 2019-03-13 Penn W
#> 8 Maryland Big Ten 827 1 2019-03-16 Ohio St. W
#> 9 Maryland Big Ten 827 1 2019-03-21 Penn St. W
#> 10 Maryland Big Ten 827 1 2019-03-24 James Madison W
#> 11 Maryland Big Ten 827 1 2019-03-27 Princeton W
#> 12 Maryland Big Ten 827 1 2019-03-30 Rutgers W
#> 13 Maryland Big Ten 827 1 2019-04-03 Virginia W
#> 14 Maryland Big Ten 827 1 2019-04-06 Michigan W
#> 15 Maryland Big Ten 827 1 2019-04-11 Northwestern W
#> 16 Maryland Big Ten 827 1 2019-04-17 Georgetown W
#> 17 Maryland Big Ten 827 1 2019-04-27 Johns Hopkins W
#> 18 Maryland Big Ten 827 1 2019-05-03 Penn St. W
#> 19 Maryland Big Ten 827 1 2019-05-05 Northwestern L
#> 20 Maryland Big Ten 827 1 2019-05-12 Stony Brook W
#> 21 Maryland Big Ten 827 1 2019-05-18 Denver W
#> 22 Maryland Big Ten 827 1 2019-05-24 Northwestern W
#> 23 Maryland Big Ten 827 1 2019-05-26 Boston College W
#> attendance goals_for goals_against location
#> 1 2105 16 5 home
#> 2 574 17 12 away
#> 3 3696 13 12 home
#> 4 1128 18 1 away
#> 5 1208 17 9 away
#> 6 1734 12 11 home
#> 7 981 14 9 home
#> 8 1216 16 1 home
#> 9 318 17 6 away
#> 10 804 18 5 away
#> 11 1058 15 7 home
#> 12 1294 20 3 away
#> 13 529 16 6 away
#> 14 6540 14 3 home
#> 15 926 17 13 away
#> 16 577 17 8 away
#> 17 1968 19 12 home
#> 18 0 15 8 neutral
#> 19 0 11 16 neutral
#> 20 311 17 8 home
#> 21 1049 17 8 home
#> 22 8508 25 13 neutral
#> 23 9433 12 10 neutralThe function returns a data frame with information about each game played in that season, including opponents, goals for and against, and whether the game was played at home, away, or a neutral site.
Let’s say we are interested in Maryland’s player stats for that season:
md_stats_2019 <- get_ncaa_wlax_team_stats(team_id = 392,
year = 2019)
md_stats_2019
#> team conference conference_id division jersey player yr
#> 1 Maryland Big Ten 827 1 28 Warther, Hannah Jr
#> 2 Maryland Big Ten 827 1 22 Griffin, Grace So
#> 3 Maryland Big Ten 827 1 25 Colson, Lizzie Jr
#> 4 Maryland Big Ten 827 1 16 Hartshorn, Kali Jr
#> 5 Maryland Big Ten 827 1 1 Griffin, Brindi Jr
#> 6 Maryland Big Ten 827 1 34 Taylor, Megan Sr
#> 7 Maryland Big Ten 827 1 24 Braig, Julia Sr
#> 8 Maryland Big Ten 827 1 6 Doherty, Meghan Jr
#> 9 Maryland Big Ten 827 1 5 Giles, Jen Sr
#> 10 Maryland Big Ten 827 1 33 Evans, Erica Sr
#> 11 Maryland Big Ten 827 1 2 Mercer, Shelby Sr
#> 12 Maryland Big Ten 827 1 7 May, Catie So
#> 13 Maryland Big Ten 827 1 37 Siverson, Meghan Sr
#> 14 Maryland Big Ten 827 1 9 Donoghue, Marissa Sr
#> 15 Maryland Big Ten 827 1 11 Steele, Caroline Sr
#> 16 Maryland Big Ten 827 1 23 Glaros, Hannah Fr
#> 17 Maryland Big Ten 827 1 12 Cummings, Kelsey Sr
#> 18 Maryland Big Ten 827 1 20 Hoffman, Julia Fr
#> 19 Maryland Big Ten 827 1 10 Renehan, Courtney So
#> 20 Maryland Big Ten 827 1 36 McSally, Maddie Fr
#> 21 Maryland Big Ten 827 1 14 Barretta, Tori So
#> 22 Maryland Big Ten 827 1 4 Golladay, Katherine Sr
#> 23 Maryland Big Ten 827 1 19 Welsh, Darby Fr
#> 24 Maryland Big Ten 827 1 15 Bracey, Laurie So
#> 25 Maryland Big Ten 827 1 17 Robbins, Brooke So
#> 26 Maryland Big Ten 827 1 13 Sliwak, Nikki Jr
#> 27 Maryland Big Ten 827 1 30 Davis, Kylie So
#> 28 Maryland Big Ten 827 1 3 Sanchez, Maddie Fr
#> 29 Maryland Big Ten 827 1 27 Miller, Natalie Jr
#> 30 Maryland Big Ten 827 1 32 Hine, Madison So
#> 31 Maryland Big Ten 827 1 18 McTaggart, Andrea Jr
#> 32 Maryland Big Ten 827 1 8 Salandra, Julia Jr
#> 33 Maryland Big Ten 827 1 35 Ayer, Sarah Fr
#> 34 Maryland Big Ten 827 1 44 Lynch, Kennedy Fr
#> 35 Maryland Big Ten 827 1 29 L'Insalata, Victoria Jr
#> 36 Maryland Big Ten 827 1 - Totals -
#> 37 Maryland Big Ten 827 1 - Opponent Totals -
#> pos gp gs games fouls gs_1 goals assists points shots shot_pct sog sog_pct
#> 1 23 0 23 12 0 12 3 15 44 0.273 32 0.727
#> 2 23 22 23 20 22 45 12 57 81 0.556 64 0.790
#> 3 23 22 23 59 22 1 1 2 2 0.500 2 1.000
#> 4 23 23 23 37 23 48 19 67 110 0.436 80 0.727
#> 5 23 22 23 13 22 44 24 68 83 0.530 73 0.880
#> 6 23 23 23 0 23 0 0 0 0 0.000 0 0.000
#> 7 23 23 23 25 23 1 0 1 1 1.000 1 1.000
#> 8 23 23 23 17 23 0 0 0 0 0.000 0 0.000
#> 9 23 23 23 26 23 59 23 82 127 0.465 92 0.724
#> 10 23 23 23 24 23 59 15 74 104 0.567 89 0.856
#> 11 23 23 23 23 23 0 1 1 0 0.000 0 0.000
#> 12 22 11 22 11 11 13 14 27 27 0.481 21 0.778
#> 13 21 16 21 14 16 18 4 22 47 0.383 36 0.766
#> 14 20 1 20 5 1 0 0 0 0 0.000 0 0.000
#> 15 20 19 20 14 19 53 17 70 119 0.445 88 0.739
#> 16 12 0 12 3 0 4 1 5 7 0.571 7 1.000
#> 17 10 1 10 2 1 3 1 4 8 0.375 7 0.875
#> 18 9 0 9 1 0 6 3 9 12 0.500 10 0.833
#> 19 7 0 7 0 0 0 0 0 2 0.000 0 0.000
#> 20 7 0 7 0 0 0 0 0 0 0.000 0 0.000
#> 21 7 0 7 3 0 0 0 0 0 0.000 0 0.000
#> 22 6 1 6 0 1 0 0 0 0 0.000 0 0.000
#> 23 6 0 6 0 0 3 0 3 3 1.000 3 1.000
#> 24 6 0 6 0 0 0 0 0 0 0.000 0 0.000
#> 25 5 0 5 1 0 0 0 0 1 0.000 0 0.000
#> 26 4 0 4 0 0 1 0 1 2 0.500 2 1.000
#> 27 4 0 4 1 0 3 0 3 6 0.500 3 0.500
#> 28 3 0 3 0 0 0 0 0 0 0.000 0 0.000
#> 29 3 0 3 1 0 0 0 0 0 0.000 0 0.000
#> 30 3 0 3 0 0 0 0 0 0 0.000 0 0.000
#> 31 3 0 3 0 0 0 0 0 0 0.000 0 0.000
#> 32 2 0 2 0 0 0 0 0 0 0.000 0 0.000
#> 33 2 0 2 0 0 0 0 0 0 0.000 0 0.000
#> 34 1 0 1 0 0 0 0 0 0 0.000 0 0.000
#> 35 1 0 1 0 0 0 0 0 0 0.000 0 0.000
#> 36 - 0 0 23 372 0 373 138 511 786 0.475 610 0.776
#> 37 - 0 0 23 525 0 186 67 253 590 0.315 422 0.715
#> gwg ppg shg ground_balls ct ggp ggs g_min goals_allowed gaa saves
#> 1 0 1 0 6 3 0 0 0 0.000 0
#> 2 4 3 0 27 14 0 0 0 0.000 0
#> 3 0 0 0 46 25 0 0 0 0.000 0
#> 4 1 5 0 11 8 0 0 0 0.000 0
#> 5 3 3 0 19 2 0 0 0 0.000 0
#> 6 0 0 0 25 3 23 23 1257:47 177 8.443 217
#> 7 0 0 0 30 27 0 0 0 0.000 0
#> 8 0 0 0 22 17 0 0 0 0.000 0
#> 9 2 5 0 37 14 0 0 0 0.000 0
#> 10 5 5 0 23 5 0 0 0 0.000 0
#> 11 0 0 0 31 18 0 0 0 0.000 0
#> 12 0 0 0 14 4 0 0 0 0.000 0
#> 13 2 4 0 10 4 0 0 0 0.000 0
#> 14 0 0 0 5 4 0 0 0 0.000 0
#> 15 5 7 0 20 6 0 0 0 0.000 0
#> 16 0 0 0 2 1 0 0 0 0.000 0
#> 17 0 0 0 2 1 0 0 0 0.000 0
#> 18 0 0 0 4 2 0 0 0 0.000 0
#> 19 0 0 0 0 0 0 0 0 0.000 0
#> 20 0 0 0 1 0 7 0 115:23 8 4.160 16
#> 21 0 0 0 0 0 0 0 0 0.000 0
#> 22 0 0 0 0 0 0 0 0 0.000 0
#> 23 0 0 0 0 0 0 0 0 0.000 0
#> 24 0 0 0 3 1 0 0 0 0.000 0
#> 25 0 0 0 2 2 0 0 0 0.000 0
#> 26 0 0 0 1 0 0 0 0 0.000 0
#> 27 0 0 0 1 0 0 0 0 0.000 0
#> 28 0 0 0 0 0 0 0 0 0.000 0
#> 29 0 0 0 1 0 0 0 0 0.000 0
#> 30 0 0 0 1 0 2 0 17:15 0 0.000 3
#> 31 0 0 0 0 0 0 0 0 0.000 0
#> 32 0 0 0 0 0 0 0 0 0.000 0
#> 33 0 0 0 0 0 0 0 0 0.000 0
#> 34 0 0 0 0 0 0 0 0 0.000 0
#> 35 0 0 0 1 1 0 0 0 0.000 0
#> 36 22 33 0 345 162 0 0 1391:30 186 8.020 236
#> 37 1 5 4 299 132 0 0 1391:30 373 16.083 237
#> save_pct t rc yc draw_controls clears clr_att
#> 1 0.000 NA NA 1 5 0 0
#> 2 0.000 NA NA 1 3 0 0
#> 3 0.000 NA NA 3 127 0 0
#> 4 0.000 NA NA 4 137 0 0
#> 5 0.000 NA NA 1 1 0 0
#> 6 0.551 NA NA 0 0 0 0
#> 7 0.000 NA NA 5 2 0 0
#> 8 0.000 NA NA 2 1 0 0
#> 9 0.000 NA NA 2 25 0 0
#> 10 0.000 NA NA 0 12 0 0
#> 11 0.000 NA NA 2 0 0 0
#> 12 0.000 NA NA 0 2 0 0
#> 13 0.000 NA NA 1 15 0 0
#> 14 0.000 NA NA 0 0 0 0
#> 15 0.000 NA NA 2 3 0 0
#> 16 0.000 NA NA 0 3 0 0
#> 17 0.000 NA NA 0 2 0 0
#> 18 0.000 NA NA 0 0 0 0
#> 19 0.000 NA NA 0 0 0 0
#> 20 0.667 NA NA 0 0 0 0
#> 21 0.000 NA NA 0 0 0 0
#> 22 0.000 NA NA 0 0 0 0
#> 23 0.000 NA NA 0 2 0 0
#> 24 0.000 NA NA 0 0 0 0
#> 25 0.000 NA NA 0 1 0 0
#> 26 0.000 NA NA 0 0 0 0
#> 27 0.000 NA NA 0 0 0 0
#> 28 0.000 NA NA 0 0 0 0
#> 29 0.000 NA NA 0 0 0 0
#> 30 1.000 NA NA 0 0 0 0
#> 31 0.000 NA NA 0 0 0 0
#> 32 0.000 NA NA 0 0 0 0
#> 33 0.000 NA NA 0 0 0 0
#> 34 0.000 NA NA 0 0 0 0
#> 35 0.000 NA NA 0 0 0 0
#> 36 0.559 NA NA 24 343 407 435
#> 37 0.389 NA NA 64 259 350 403The function returns a data frame with each individual player, their class, number, etc., and their individual statistics tracked by the NCAA. There are also team and opponent totals.