pitchRx-dependent functions, updated retrosheet function and documentationedge_scrape() and edge_scrape_split() from package. See data-raw/archived/ for the last updated script. These may be re-visited at a later date with improved data repository maturity.| name | file |
|---|---|
mlb_all_star_ballots() |
R/mlb_all_star_ballots.R |
mlb_all_star_final_vote() |
R/mlb_all_star_final_vote.R |
mlb_all_star_write_ins() |
R/mlb_all_star_write_ins.R |
mlb_attendance() |
R/mlb_attendance.R |
mlb_awards() |
R/mlb_awards.R |
mlb_awards_recipient() |
R/mlb_awards_recipient.R |
mlb_baseball_stats() |
R/mlb_baseball_stats.R |
mlb_conferences() |
R/mlb_conferences.R |
mlb_divisions() |
R/mlb_divisions.R |
mlb_draft_latest() |
R/mlb_draft_latest.R |
mlb_draft_prospects() |
R/mlb_draft_prospects.R |
mlb_event_types() |
R/mlb_event_types.R |
mlb_fielder_detail_types() |
R/mlb_fielder_detail_types.R |
mlb_game_changes() |
R/mlb_game_changes.R |
mlb_game_content() |
R/mlb_game_content.R |
mlb_game_context_metrics() |
R/mlb_game_context_metrics.R |
mlb_game_linescore() |
R/mlb_game_linescore.R |
mlb_game_pace() |
R/mlb_game_pace.R |
mlb_game_status_codes() |
R/mlb_game_status_codes.R |
mlb_game_timecodes() |
R/mlb_game_timecodes.R |
mlb_game_types() |
R/mlb_game_types.R |
mlb_game_wp() |
R/mlb_game_wp.R |
mlb_high_low_stats() |
R/mlb_high_low_stats.R |
mlb_high_low_types() |
R/mlb_high_low_types.R |
mlb_hit_trajectories() |
R/mlb_hit_trajectories.R |
mlb_homerun_derby() |
R/mlb_homerun_derby.R |
mlb_homerun_derby_bracket() |
R/mlb_homerun_derby.R |
mlb_homerun_derby_players() |
R/mlb_homerun_derby.R |
mlb_job_types() |
R/mlb_job_types.R |
mlb_jobs() |
R/mlb_jobs.R |
mlb_jobs_datacasters() |
R/mlb_jobs_datacasters.R |
mlb_jobs_official_scorers() |
R/mlb_jobs_official_scorers.R |
mlb_jobs_umpires() |
R/mlb_jobs_umpires.R |
mlb_languages() |
R/mlb_languages.R |
mlb_league() |
R/mlb_league.R |
mlb_league_leader_types() |
R/mlb_league_leader_types.R |
mlb_logical_events() |
R/mlb_logical_events.R |
mlb_metrics() |
R/mlb_metrics.R |
mlb_pbp_diff() |
R/mlb_pbp_diff.R |
mlb_people() |
R/mlb_people.R |
mlb_people_free_agents() |
R/mlb_people_free_agents.R |
mlb_pitch_codes() |
R/mlb_pitch_codes.R |
mlb_pitch_types() |
R/mlb_pitch_types.R |
mlb_player_game_stats() |
R/mlb_player_game_stats.R |
mlb_player_game_stats_current() |
R/mlb_player_game_stats_current.R |
mlb_player_status_codes() |
R/mlb_player_status_codes.R |
mlb_positions() |
R/mlb_positions.R |
mlb_probables() |
R/mlb_probables.R |
mlb_review_reasons() |
R/mlb_review_reasons.R |
mlb_roster_types() |
R/mlb_roster_types.R |
mlb_rosters() |
R/mlb_rosters.R |
mlb_runner_detail_types() |
R/mlb_runner_detail_types.R |
mlb_schedule_event_types() |
R/mlb_schedule_event_types.R |
mlb_schedule_games_tied() |
R/mlb_schedule_games_tied.R |
mlb_schedule_postseason() |
R/mlb_schedule_postseason.R |
mlb_schedule_postseason_series() |
R/mlb_schedule_postseason_series.R |
mlb_seasons() |
R/mlb_seasons.R |
mlb_seasons_all() |
R/mlb_seasons_all.R |
mlb_situation_codes() |
R/mlb_situation_codes.R |
mlb_sky() |
R/mlb_sky.R |
mlb_sports() |
R/mlb_sports.R |
mlb_sports_info() |
R/mlb_sports_info.R |
mlb_sports_players() |
R/mlb_sports_players.R |
mlb_standings() |
R/mlb_standings.R |
mlb_standings_types() |
R/mlb_standings_types.R |
mlb_stat_groups() |
R/mlb_stat_groups.R |
mlb_stat_types() |
R/mlb_stat_types.R |
mlb_stats() |
R/mlb_stats.R |
mlb_stats_leaders() |
R/mlb_stats_leaders.R |
mlb_team_affiliates() |
R/mlb_team_affiliates.R |
mlb_team_alumni() |
R/mlb_team_alumni.R |
mlb_team_coaches() |
R/mlb_team_coaches.R |
mlb_team_history() |
R/mlb_team_history.R |
mlb_team_info() |
R/mlb_team_info.R |
mlb_team_leaders() |
R/mlb_team_leaders.R |
mlb_team_personnel() |
R/mlb_team_personnel.R |
mlb_team_stats() |
R/mlb_team_stats.R |
mlb_teams() |
R/mlb_teams.R |
mlb_teams_stats() |
R/mlb_teams_stats.R |
mlb_teams_stats_leaders() |
R/mlb_teams_stats_leaders.R |
mlb_venues() |
R/mlb_venues.R |
mlb_wind_direction_codes() |
R/mlb_wind_direction_codes.R |
ncaa_season_id_lu for 2022 season.| legacy_name | new_name | file |
|---|---|---|
| daily_batter_bref | bref_daily_batter() |
R/bref_daily_batter.R |
| daily_pitcher_bref | bref_daily_pitcher() |
R/bref_daily_pitcher.R |
| standings_on_date_bref | bref_standings_on_date() |
R/bref_standings_on_date.R |
| team_results_bref | bref_team_results() |
R/bref_team_results.R |
| get_chadwick_lu | chadwick_player_lu() |
R/chadwick_player_lu.R |
| batter_game_logs_fg | fg_batter_game_logs() |
R/fg_batter_game_logs.R |
| fg_bat_leaders | fg_batter_leaders() |
R/fg_batter_players.R |
| milb_batter_game_logs_fg | fg_milb_batter_game_logs() |
R/fg_milb_batter_game_logs.R |
| milb_pitcher_game_logs_fg | fg_milb_pitcher_game_logs() |
R/fg_milb_pitcher_game_logs.R |
| pitcher_game_logs_fg | fg_pitcher_game_logs() |
R/fg_pitcher_game_logs.R |
| fg_pitch_leaders | fg_pitcher_leaders() |
R/fg_pitcher_leaders.R |
| get_batting_orders | mlb_batting_orders() |
R/mlb_batting_orders.R |
| get_draft_mlb | mlb_draft() |
R/mlb_draft.R |
| get_game_info_mlb | mlb_game_info() |
R/mlb_game_info.R |
| get_game_pks_mlb | mlb_game_pks() |
R/mlb_game_pks.R |
| get_pbp_mlb | mlb_pbp() |
R/mlb_pbp.R |
| get_probables_mlb | mlb_probables() |
R/mlb_probables.R |
| get_ncaa_baseball_pbp | ncaa_baseball_pbp() |
R/ncaa_baseball_pbp.R |
| get_ncaa_baseball_roster | ncaa_baseball_roster() |
R/ncaa_baseball_roster.R |
| get_ncaa_game_logs | ncaa_game_logs() |
R/ncaa_game_logs.R |
| get_ncaa_lineups | ncaa_lineups() |
R/ncaa_lineups.R |
| get_ncaa_park_factor | ncaa_park_factor() |
R/ncaa_park_factor.R |
| get_ncaa_schedule_info | ncaa_schedule_info() |
R/ncaa_schedule_info.R |
| school_id_lu | ncaa_school_id_lu() |
R/ncaa_school_id_lu.R |
| get_retrosheet_data | retrosheet_data() |
R/retrosheet_data.R |
| get_game_info_sup_petti | load_game_info_sup() |
R/load_game_info_sup.R |
| get_umpire_ids_petti | load_umpire_ids() |
R/load_umpire_ids.R |
| scrape_savant_leaderboards | statcast_leaderboards() |
R/sc_scrape_statcast_leaderboards.R |
| scrape_statcast_savant | statcast_search() |
R/sc_scrape_statcast.R |
| scrape_statcast_savant_batter | statcast_search_batters() |
R/sc_scrape_statcast.R |
| scrape_statcast_savant_batter_all | statcast_search_batters() |
R/sc_scrape_statcast.R |
| scrape_statcast_savant_pitcher | statcast_search_pitchers() |
R/sc_scrape_statcast.R |
| scrape_statcast_savant_pitcher_all | statcast_search_pitchers() |
R/sc_scrape_statcast.R |
`*mlb_schedules()function added * Removedviz_gb_on_period()from package. Seedata-raw/archived/for the last updated script. * Removed GameDay2 MLB API functions,batter_boxscore()andpitcher_boxscore(), from package. Seedata-raw/archived/` for the last updated script.
ncaa_game_info() functionncaa_game_log() functionncaa_park_factors() functionbatter_game_logs_fg(), pitcher_game_logs_fg(), milb_batter_game_logs_fg(), milb_pitcher_game_logs_fg()Thanks to Robert Frey, we’ve added a new function that allows the user to calculate park factors at the NCAA level.
get_ncaa_park_factors takes two arguments; teamid, years, and type. Users can submit a single year or multiple years (recommended) and uses a team’s schedule with results to calculate their home park’s factors. Also, if a user selects conference as the type, the park factor will be adjusted based on the number of teams in a conference. This has the practical effect of suggesting more regression should be applied to players who play against fewer teams in fewer parks.
The function will return two versions of the park factor: the base_pf and the final_pf, which simply takes the base_pf and applies an adjustment that is based on FanGraphs’ method:
get_ncaa_park_factor(736, c(2017:2019),type = "conference")
school home_game away_game runs_scored_home runs_allowed_home runs_scored_away
1 Vanderbilt 104 91 782 416 591
runs_allowed_away base_pf home_game_adj final_pf
1 426 1.028 1.013 1.011
get_ncaa_park_factor(736, c(2017:2019),type = "division")
school home_game away_game runs_scored_home runs_allowed_home runs_scored_away
1 Vanderbilt 104 91 782 416 591
runs_allowed_away base_pf home_game_adj final_pf
1 426 1.031 1.014 1.011
get_ncaa_park_factor(736, c(2015:2019),type = "division")
school home_game away_game runs_scored_home runs_allowed_home runs_scored_away
1 Vanderbilt 175 154 1314 658 951
runs_allowed_away base_pf home_game_adj final_pf
1 680 1.209 1.098 1.093Shane Piesik made some changes to the scrape_statcast_savant function. The function should now be easier to use in a loop or map when combining payloads, and more importantly the data should read in faster thanks to swapping in vroom.
The master_ncaa_team_lu was updated by Robert Frey. For some reason, the NCAA website that had the 2019 Division 1 information has disappeared, and when I rebuilt the table at the beginning of 2020 it led to tons of NA values for those teams that year. Thanks to Robert for manually fixing.
Messages have been added to all functions that pull data from FanGraphs.com and Baseball-Reference.com. The messages ask users to support both sites through their paid subscription services. Please consider supporting both, especially if you are using baseballr to pull data from their sites.
This release of baseballr includes functions that allow a user to query data through MLB’s stats api at the minor league level.
You can view a look up table with ?get_game_pks_mlb to get the appropriate IDs for each level, but I’ll post them here as well (this is not comprehensive):
| 1 | MLB |
|---|---|
| 11 | Triple-A |
| 12 | Double-A |
| 13 | Class A Advanced |
| 14 | Class A |
| 15 | Class A Short Season |
| 5442 | Rookie Advanced |
| 16 | Rookie |
| 17 | Winter League |
Game information can be acquired using the get_game_pks_mlb function, along with the level for which you want games:
games <- get_game_pks_mlb(date = '2019-05-01',
level_ids = c(11, 12))
games %>%
select(game_pk, gameDate, teams.away.team.name, teams.home.team.name) %>%
slice(1:10)
game_pk gameDate teams.away.team.name teams.home.team.name
1 579921 2019-05-01T16:05:00Z Round Rock Express Oklahoma City Dodgers
2 579919 2019-05-01T03:33:00Z Round Rock Express Oklahoma City Dodgers
3 584340 2019-05-01T21:30:00Z Midland RockHounds Tulsa Drillers
4 584269 2019-05-01T03:33:00Z Midland RockHounds Tulsa Drillers
5 579571 2019-05-01T21:38:00Z San Antonio Missions Iowa Cubs
6 579570 2019-05-01T03:33:00Z San Antonio Missions Iowa Cubs
7 571587 2019-05-01T14:30:00Z Erie SeaWolves Altoona Curve
8 572288 2019-05-01T14:30:00Z New Hampshire Fisher Cats Trenton Thunder
9 575163 2019-05-01T14:35:00Z Norfolk Tides Durham Bulls
10 575655 2019-05-01T14:35:00Z Louisville Bats Toledo Mud HensYou can also use the get_game_info_mlb function to grab additional info on each game, such as weather and (in some cases) attendance:
map_df(.x = games$game_pk[1:10],
~get_game_info_mlb(.x)) %>%
select(game_date, venue_name, temperature, other_weather, wind)
# A tibble: 10 x 5
game_date venue_name temperature other_weather wind
<chr> <chr> <chr> <chr> <chr>
1 2019-05-01 Chickasaw Bricktown Ballp… 63 Cloudy 4 mph, R To L
2 2019-05-01 Chickasaw Bricktown Ballp… 72 Cloudy 13 mph, R To L
3 2019-05-01 ONEOK Field 77 Overcast 14 mph, In Fro…
4 2019-05-01 ONEOK Field 74 clear 14 mph, In Fro…
5 2019-05-01 Principal Park 54 Overcast 3 mph, In From…
6 2019-05-01 Principal Park 53 Overcast 1 mph, Calm
7 2019-05-01 Peoples Natural Gas Field 58 Overcast 7 mph, In From…
8 2019-05-01 ARM & HAMMER Park 55 Overcast 5 mph, L To R
9 2019-05-01 Durham Bulls Athletic Park 70 Partly Cloudy 7 mph, In From…
10 2019-05-01 Fifth Third Field 59 Cloudy 7 mph, R To L Once you have the game_pk IDs grabbing the pbp data is very simple. All you need to do is pass the game_pk of interest to the get_pbp_mlb function.
Let’s say you interested in the Gwinnett Stripers versus the Charlotte Knights:
payload <- get_pbp_mlb(575589)The function will return a data frame with 131 columns. Data availability will vary depending on the park and the league level, as most sensor data is not available in minor league parks via this API. Also note that the column names have mostly been left as-is and there are likely duplicate columns in terms of the information they provide. I plan to clean the output up down the road, but for now I am leaving the majority as-is.
Some of the columns of interest at the minor league level are:
pitchNumber and atBatIndex: the pitch number within a given plate appearance and the plate appearance within a given game.pitchData.coordinates.x and pitchData.coordinates.y: the x,z coordinates of the pitch as it crosses the plate. As far as I can tell, these are the pixel coordinates for a location that a stringer manually plots and likely need to be transformed and rotated to get a view of the pitch as it crosses the plate. I am working on figuring out an easy transformation to get them on the same scale as the MLB coordinates, but they appear different by park. I do believe you can multiple both by -1 and that will at least allow you to orient the coordinates correctly (i.e. catcher’s view)details.call.code, details.call.description, result.event, result.eventType, and result.description: these are similar to what we find with Statcast data–codes and detailed descriptions for what happened on a pitch or at the end of a plate appearance.count. variables that tell you how many balls, strikes, and outs before and after the pitch.batter.id and pitcher.idmatchup.batSide.code and matchup.pitchHand.code: handedness of the batter and pitcher.batted.ball.result, hitData.coordinates.coordX, hitData.coordinates.coordY, hitData.trajectory: various information about the batted ball. Of most interest will be the coordinate columns.The latest release of the baseballr package for R includes a number of enhancements and bug fixes.
fg_pitch_leaders()
This function is the compliment to the fg_bat_leaders() function, returning leaderboard information for pitchers from FanGraphs.com.
In addition to noting the start and end years, along with qualified/IP requirements, etc., you can note whether you want all pitchers, starters, or just relievers using the pitcher_type argument.
pit will give you all pitchers that meet your requirementssta gives you starters onlyrel gives you relievers onlyfg_pitch_leaders(2018,2018, pitcher_type = "sta") %>% slice (1:10) %>% .[,1:10]
playerid Seasons # Name Team Age W L ERA G
1 10954 2018 1 Jacob deGrom Mets 30 10 9 1.70 32
2 13543 2018 2 Blake Snell Rays 25 21 5 1.89 31
3 12703 2018 3 Trevor Bauer Indians 27 12 6 2.26 27
4 16149 2018 4 Aaron Nola Phillies 25 17 6 2.37 33
5 8700 2018 5 Justin Verlander Astros 35 16 9 2.52 34
6 3137 2018 6 Max Scherzer Nationals 33 18 7 2.53 33
7 9803 2018 7 Miles Mikolas Cardinals 29 18 4 2.83 32
8 16256 2018 8 Kyle Freeland Rockies 25 17 7 2.85 33
9 10811 2018 9 Mike Foltynewicz Braves 26 13 10 2.85 31
10 13125 2018 10 Gerrit Cole Astros 27 15 5 2.88 32scrape_statcast_leaderboards()
The scrape_statcast_leaderboards() function can be used to access all of the leaderboards available from BaseballSavant as csv downloads. The function isn’t doing anything too sophisticated; it simply builds the appropriate url for the csv download based on a series of parameters and then reads the csv into R.
Users specify which leaderboard they want to download using the leaderboard argument. The following are currently available:
exit_velocity_barrelsexpected_statisticspitch_arsenalouts_above_averagedirectional_oaacatch_probabilitypop_timesprint_speedrunning_splits_90_ftEach leaderboard has different parameters that can be specific to alter the content of the downloads, but not all parameters work for every leaderboard. (I would check the leaderboard interface on BaseballSavant directly if you are not sure which ones to use.) Some of the leaderboards do not include a variable for the year selected, so the function will check if it exists and, if not, it will add a column based on your parameter setting.
Here is an example of the expected_statistics leaderboard for pitchers who faced at least 250 batters in 2018:
payload <- scrape_savant_leaderboards(leaderboard = "expected_statistics",
year = 2018,
player_type = "pitcher",
min_pa = 250)
payload %>%
arrange(est_woba) %>%
select(year:last_name, pa, woba:est_woba_minus_woba_diff)
year last_name pa woba est_woba est_woba_minus_woba_diff
<int> <chr> <int> <dbl> <dbl> <dbl>
1 2018 Diaz 280 0.214 0.212 0.002
2 2018 Hader 306 0.219 0.229 -0.01
3 2018 Treinen 315 0.187 0.23 -0.043
4 2018 Ottavino 309 0.231 0.23 0.001
5 2018 Sale 617 0.237 0.232 0.005
6 2018 Verlander 833 0.26 0.236 0.024
7 2018 Betances 272 0.259 0.236 0.023
8 2018 Pressly 292 0.267 0.241 0.026
9 2018 deGrom 835 0.23 0.243 -0.013
10 2018 Scherzer 866 0.252 0.246 0.006
# ... with 264 more rowsmilb_batter_game_logs_fg() milb_pitcher_game_logs_fg()
These functions were contributed by Mat Adams, and they are similar to the game log functions included in an earlier release except that they will return minor league game logs for the player specified. The functions take only two arguments: playerid and year. The playerid is the minor league ID assigned by FanGraphs. This can be found in the url slug for a minor league player’s page.
For example, here is the url for Vladimir Guerrero Jr.’s minor league game logs: https://www.fangraphs.com/statsd.aspx?playerid=sa920245&position=3B&gds=&gde=
You will see the playerid= portion of the url, and the actual ID follows (i.e. sa920245).
milb_batter_game_logs_fg(playerid = "sa920245", year = 2017) %>% slice(1:10)
name minor_playerid Date Team Level Opp AVG G AB PA H
1 Vladimir Guerrero Jr. sa920245 2017-04-07 TOR (A) @LAD .000 1 1 3 0
2 Vladimir Guerrero Jr. sa920245 2017-04-07 TOR (A) @LAD .500 1 4 4 2
3 Vladimir Guerrero Jr. sa920245 2017-04-08 TOR (A) LAD .500 1 4 5 2
4 Vladimir Guerrero Jr. sa920245 2017-04-09 TOR (A) LAD .333 1 3 5 1
5 Vladimir Guerrero Jr. sa920245 2017-04-10 TOR (A) @TBR .250 1 4 4 1
6 Vladimir Guerrero Jr. sa920245 2017-04-12 TOR (A) @TBR .000 1 3 4 0
7 Vladimir Guerrero Jr. sa920245 2017-04-12 TOR (A) @TBR .667 1 3 3 2
8 Vladimir Guerrero Jr. sa920245 2017-04-13 TOR (A) CLE .000 1 3 4 0
9 Vladimir Guerrero Jr. sa920245 2017-04-14 TOR (A) CLE .000 1 3 4 0
10 Vladimir Guerrero Jr. sa920245 2017-04-15 TOR (A) CLE .333 1 3 4 1ncaa_scrape() - Updated to allow scraping of data for the 2019 season.
edge_code() - Changes made to make function more compatible with BaseballSavant data. ## Bug Fixes
fg_bat_leaders() - playerid now returned as part of the data returned.
run_expectancy_code() - Removed filter for final pitch==1 when not grouping by plate appearances.
NEWS.md file to track changes to the package.The latest release of the baseballr package for R (0.3.4) includes a number of enhancements and bug fixes.
run_expectancy_code()
This function formats Baseball Savant data so that users can generate the run expectancy for different base-out or count-base-out states. It will also append the data frame with new variables necessary for generating linear weights (see new function below). The only argument is a data frame downloaded from Baseball Savant
Columns created and appended to Baseball Savant data:
final_pitch_game: whether a pitch was the final one thrown in a gamefinal_pitch_inning: whether a pitch is the final one thrown in an inningfinal_pitch_at_bat: whether a pitch is the final one thrown in an at batruns_scored_on_pitch: how many runs scored as a result of the pitchbat_score_start_inning: the score for the batting team at the beginning of the inningbat_score_end_inning: the score for the batting team at the end of the inningbat_score_after: the score for the batting team after the pitch is throwncum_runs_in_inning: how many cumulative runs have been scored from the beginning of the inning through the pitchruns_to_end_inning: how many runs were scored as a result of the pitch through the end of the inningbase_out_state or count_base_out_state: the specific combination of base-outs or count-base-outs when the pitch was thrownavg_re: the average run expectancy of that base-out or count-base-out statenext_avg_re: the average run expectancy of the base-out or count-base-out state that results from the pitchchange_re: the change in run expectancy as a result of the pitchre24: the total change in run expectancy through the end of the inning resulting from the pitch based on the change in base-out or count-base-out state plus the number of runs scored as a result of the pitch/at batExample:
> x2016_statcast_re <- run_expectancy_code(x2016_statcast)
> sample_n(x2016_statcast_re, 10) %>%
select(final_pitch_inning:re24) %>%
glimpse()
Observations: 10
Variables: 11
$ final_pitch_inning <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0
$ bat_score_start_inning <dbl> 1, 0, 5, 0, 3, 2, 1, 0, 0, 0
$ bat_score_end_inning <dbl> 2, 0, 5, 1, 3, 2, 5, 0, 0, 2
$ cum_runs_in_inning <dbl> 1, 0, 0, 0, 0, 0, 2, 0, 0, 1
$ runs_to_end_inning <dbl> 0, 0, 0, 1, 0, 0, 2, 0, 0, 1
$ base_out_state <chr> "2 outs, 1b _ _", "0 outs, _ _ _", "0 outs...
$ avg_re <dbl> 0.2149885, 0.5057877, 0.5057877, 0.5057877, 0.5...
$ next_base_out_state <chr> "2 outs, 1b 2b _", "1 outs, _ _ _", "1 out...
$ next_avg_re <dbl> 0.4063525, 0.2718802, 0.2718802, 0.8629357, 0.2...
$ change_re <dbl> 0.1913640, -0.2339075, -0.2339075, 0.3571479, -...
$ re24 <dbl> 0.1913640, -0.2339075, -0.2339075, 0.3571479, -...run_expectancy_table()
This functions works with the run_expectancy_code function and does the work of generating the run expectancy tables that are automatically exported into the Global Environment
Example:
> x2016_statcast_re %>%
run_expectancy_table() %>%
print(n=Inf)
base_out_state avg_re
<chr> <dbl>
1 0 outs, 1b 2b 3b 2.13
2 0 outs, _ 2b 3b 1.95
3 0 outs, 1b _ 3b 1.76
4 1 outs, 1b 2b 3b 1.55
5 0 outs, 1b 2b _ 1.42
6 1 outs, _ 2b 3b 1.36
7 0 outs, _ _ 3b 1.36
8 1 outs, 1b _ 3b 1.18
9 0 outs, _ 2b _ 1.14
10 1 outs, _ _ 3b 0.951
11 1 outs, 1b 2b _ 0.906
12 0 outs, 1b _ _ 0.863
13 2 outs, 1b 2b 3b 0.689
14 1 outs, _ 2b _ 0.669
15 2 outs, _ 2b 3b 0.525
16 1 outs, 1b _ _ 0.520
17 0 outs, _ _ _ 0.506
18 2 outs, 1b _ 3b 0.456
19 2 outs, 1b 2b _ 0.406
20 2 outs, _ _ 3b 0.366
21 2 outs, _ 2b _ 0.299
22 1 outs, _ _ _ 0.272
23 2 outs, 1b _ _ 0.215
24 2 outs, _ _ _ 0.106linear_weights_savant()
This function works in tandem with run_expectancy_code() to generate linear weights for offensive events after the Baseball Savant data has been properly formatted. Currently, the function will return linear weights above average and linear weights above outs. It does not apply any scaling to align with league wOBA. Users can do that themselves if they like, or it may be added to a future version of the function.
Example:
> x2016_statcast_re %>%
linear_weights_savant() %>%
print(n=Inf)
A tibble: 7 x 3
events linear_weights_above_average linear_weights_above_outs
<chr> <dbl> <dbl>
1 home_run 1.38 1.63
2 triple 1.00 1.25
3 double 0.730 0.980
4 single 0.440 0.690
5 hit_by_pitch 0.320 0.570
6 walk 0.290 0.540
7 outs -0.250 0. I used Baseball Savant data from 2010-2015 and compared the linear weights generated by baseballr to those by Tom Tango using retrosheet data. baseballr’s weights are generally a little lower than what Tango generated, but that could be due to a number of things, such as the data source, code, etc., but the values appear reasonable enough to be reliable:
| base_out_state | baseballr_2010_2015 | tango_2010_2015 | diff | %_diff |
|---|---|---|---|---|
| 0 outs, 1b 2b 3b | 2.27 | 2.29 | -0.02 | -1% |
| 0 outs, _ 2b 3b | 1.96 | 1.96 | 0 | 0% |
| 0 outs, 1b _ 3b | 1.76 | 1.78 | -0.03 | -1% |
| 1 outs, 1b 2b 3b | 1.51 | 1.54 | -0.03 | -2% |
| 0 outs, 1b 2b _ | 1.42 | 1.44 | -0.02 | -1% |
| 0 outs, _ _ 3b | 1.38 | 1.38 | 0 | 0% |
| 1 outs, _ 2b 3b | 1.35 | 1.35 | 0 | 0% |
| 1 outs, 1b _ 3b | 1.1 | 1.13 | -0.03 | -2% |
| 0 outs, _ 2b _ | 1.09 | 1.1 | -0.01 | -1% |
| 1 outs, _ _ 3b | 0.93 | 0.95 | -0.02 | -2% |
| 1 outs, 1b 2b _ | 0.86 | 0.88 | -0.02 | -3% |
| 0 outs, 1b _ _ | 0.84 | 0.86 | -0.02 | -2% |
| 2 outs, 1b 2b 3b | 0.71 | 0.75 | -0.04 | -5% |
| 1 outs, _ 2b _ | 0.65 | 0.66 | -0.01 | -2% |
| 2 outs, _ 2b 3b | 0.54 | 0.58 | -0.04 | -7% |
| 1 outs, 1b _ _ | 0.5 | 0.51 | -0.01 | -2% |
| 0 outs, _ _ _ | 0.48 | 0.48 | 0 | -1% |
| 2 outs, 1b _ 3b | 0.45 | 0.48 | -0.03 | -7% |
| 2 outs, 1b 2b _ | 0.41 | 0.43 | -0.02 | -4% |
| 2 outs, _ _ 3b | 0.33 | 0.35 | -0.02 | -6% |
| 2 outs, _ 2b _ | 0.31 | 0.32 | -0.01 | -3% |
| 1 outs, _ _ _ | 0.25 | 0.25 | 0 | -1% |
| 2 outs, 1b _ _ | 0.21 | 0.22 | -0.01 | -6% |
| 2 outs, _ _ _ | 0.1 | 0.1 | 0 | -2% |
We also had some great contributions by others that I’ve added into this release:
label_statcast_imputed_data()
Ben Dilday again contributes with a cool experimental function meant to tag batted ball cases where significant imputation may have been used to generate some of the Statcast values by MLBAM, i.e. launch_speed and launch_angle. You can read more about Ben’s function here.
fg_park()
Sam Boysel updated the park factors function so that it now includes the new columns added by FanGraphs (5-year, 3-year, 1-year park factors) and ensures the column names are correct
fg_bat_leaders()
playerid now returned as part of the data returned.process_statcast_payload() - hc_x, hc_y are now converted to numeric
The latest release of the baseballr package for R includes a number of enhancement to acquiring data from Baseball Savant as well as minor grammatical clean up in the documentation.
Previous functions scrape_statcast_savant_batter and scrape_statcast_savant_pitcher allowed for the acquistion of data from baseballsavant.com for a given player over a user-determined time frame. However, this is somewhat inefficient if you want to acquire data on all players over a given time frame.
Two new functions have been added, scrape_statcast_savant_batter_all and scrape_statcast_savant_pitcher_all, that allow a user to acquire data for either all pitchers or all hitters over a given time frame.
Both functions take only two arguments:
start_date: the first date for which the user wants records returned end_date: the final date for which the user wants records returned
Remember, baseballsavant.com’s csv download option allows for about 50,000 records in a single query. That works out to roughly 10-12 days of games. Longer time frames will take longer to download.
Example: acquire data for all batters from 2017-04-03 through 2017-04-10
> head(scrape_statcast_savant_batter_all('2017-04-03', '2017-04-10'))
[1] "These data are from BaseballSevant and are property of MLB Advanced Media, L.P. All rights reserved."
[1] "Grabbing data, this may take a minute..."
URL read and payload aquired successfully.
pitch_type game_date release_speed release_pos_x release_pos_z player_name
1 FF 2017-04-10 92.7 -1.0367 5.7934 Eric Fryer
2 FF 2017-04-10 93.2 -0.9753 5.6007 Eric Fryer
3 FF 2017-04-10 93.0 -1.1196 5.6958 Eric Fryer
4 FF 2017-04-10 93.1 -0.9952 5.7978 Eric Fryer
5 SL 2017-04-10 83.4 -1.2385 5.8164 Eric Fryer
6 FF 2017-04-10 93.7 -1.0307 5.8740 Aledmys Diaz
batter pitcher events description spin_dir spin_rate_deprecated
1 518700 518875 strikeout swinging_strike NA NA
2 518700 518875 <NA> ball NA NA
3 518700 518875 <NA> ball NA NA
4 518700 518875 <NA> swinging_strike NA NA
5 518700 518875 <NA> called_strike NA NA
6 649557 518875 field_out hit_into_play NA NA
break_angle_deprecated break_length_deprecated zone
1 NA NA 5
2 NA NA 12
3 NA NA 12
4 NA NA 3
5 NA NA 6
6 NA NA 6
des game_type stand
1 Eric Fryer strikes out swinging. R R
2 <NA> R R
3 <NA> R R
4 <NA> R R
5 <NA> R R
6 Aledmys Diaz flies out to right fielder Bryce Harper. R R
p_throws home_team away_team type hit_location bb_type balls strikes
1 R WSH STL S <NA> <NA> 2 2
2 R WSH STL B <NA> <NA> 1 2
3 R WSH STL B <NA> <NA> 0 2
4 R WSH STL S <NA> <NA> 0 1
5 R WSH STL S <NA> <NA> 0 0
6 R WSH STL X 9 fly_ball 0 1
game_year pfx_x pfx_z plate_x plate_z on_3b on_2b on_1b outs_when_up
1 2017 -0.4262 1.7261 -0.0042 2.9680 NA NA 594824 2
2 2017 0.2420 1.3633 1.3747 3.5269 NA NA 594824 2
3 2017 0.4912 1.6758 0.5389 4.3795 NA NA 594824 2
4 2017 0.1924 1.7964 0.6868 3.5700 NA NA 594824 2
5 2017 -0.1604 0.3532 0.6048 2.6308 NA NA 594824 2
6 2017 0.5956 1.8068 0.4993 3.1386 NA NA 594824 1
inning inning_topbot hc_x hc_y tfs_deprecated tfs_zulu_deprecated
1 9 Top <NA> <NA> NA NA
2 9 Top <NA> <NA> NA NA
3 9 Top <NA> <NA> NA NA
4 9 Top <NA> <NA> NA NA
5 9 Top <NA> <NA> NA NA
6 9 Top 186.56 105.27 NA NA
pos2_person_id umpire sv_id vx0 vy0 vz0 ax ay az sz_top sz_bot
1 446308 NA 170411_025210 NA NA NA NA NA NA 3.8420 1.5890
2 446308 NA 170411_025153 NA NA NA NA NA NA 3.5602 1.7127
3 446308 NA 170411_025133 NA NA NA NA NA NA 3.6761 1.6780
4 446308 NA 170411_025117 NA NA NA NA NA NA 3.6760 1.5040
5 446308 NA 170411_025104 NA NA NA NA NA NA 3.5139 1.6548
6 446308 NA 170411_025018 NA NA NA NA NA NA 3.9500 1.6810
hit_distance_sc launch_speed launch_angle effective_speed release_spin_rate
1 NA NA NA 93.033 2285
2 NA NA NA 93.301 2323
3 NA NA NA 92.892 2322
4 NA NA NA 92.906 2324
5 NA NA NA 83.371 NA
6 266 87.5 47.444 93.529 2406
release_extension game_pk pos1_person_id pos2_person_id.1 pos3_person_id
1 6.248 490201 518875 446308 475582
2 6.265 490201 518875 446308 475582
3 6.281 490201 518875 446308 475582
4 6.187 490201 518875 446308 475582
5 6.155 490201 518875 446308 475582
6 6.269 490201 518875 446308 475582
pos4_person_id pos5_person_id pos6_person_id pos7_person_id pos8_person_id
1 502517 543685 452220 594809 572191
2 502517 543685 452220 594809 572191
3 502517 543685 452220 594809 572191
4 502517 543685 452220 594809 572191
5 502517 543685 452220 594809 572191
6 502517 543685 452220 594809 572191
pos9_person_id release_pos_y estimated_ba_using_speedangle
1 547180 54.2491 0.000
2 547180 54.2319 0.000
3 547180 54.2163 0.000
4 547180 54.3096 0.000
5 547180 54.3420 0.000
6 547180 54.2282 0.007
estimated_woba_using_speedangle woba_value woba_denom babip_value iso_value
1 0.000 0.00 1 0 0
2 0.000 <NA> <NA> <NA> <NA>
3 0.000 <NA> <NA> <NA> <NA>
4 0.000 <NA> <NA> <NA> <NA>
5 0.000 <NA> <NA> <NA> <NA>
6 0.008 0.00 1 0 0
barrel
1 NA
2 NA
3 NA
4 NA
5 NA
6 0The latest release of the baseballr package for R includes a number of enhancements and bug fixes.
In terms of new functions, statline_from_statcast allows users to take raw pitch-by-pitch data from Statcast/PITCHf/x and calculate aggregated, stat line-like output. Examples include count data such as number of singles, doubles, etc., as well as rate metrics like Slugging and wOBA on swings or contact.
The function only has two arguments:
df: a data frame that includes pitch-by-pitch information. The function assumes the following columns are present: events, description, game_date, and type.base: base indicates what the denominator should be for the rate stats that are calculated. The function defaults to “swings”, but you can also choose to use “contact”Here is an example using all data from the week of 2017-09-04. Here, we want to see a statline for all hitters based on swings:
test <- scrape_statcast_savant_batter_all("2017-09-04", "2017-09-10")
statline_from_statcast(test)
year swings batted_balls X1B X2B X3B HR swing_and_miss swinging_strike_percent ba
1 2017 13790 10663 1129 352 37 259 3127 0.227 0.129
obp slg ops woba
1 0.129 0.216 0.345 0.144You can also combine the statline_from_statcast function with a loop to create statlines for multiple players at once.
Example: calculate statlines for batters on contact for all games played 2017-09-04 through 2017-09-10:
test <- scrape_statcast_savant_batter_all("2017-09-04", "2017-09-10")
output <- data.frame()
for (i in c("Jose Ramirez", "J.D. Martinez", "Francisco Lindor", "Gary Sanchez", "Rhys Hoskins")) {
reduced_test <- test %>%
filter(player_name == i)
x <- statline_from_statcast(reduced_test, base = "contact")
x$player <- i
x <- x %>%
select(player, everything())
output <- rbind(output, x) %>%
arrange(desc(woba))
}
print(output, width = Inf)
# A tibble: 5 x 12
player year batted_balls X1B X2B X3B HR ba obp slg ops woba
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 J.D. Martinez 2017 17 4 1 0 7 0.706 0.706 2.000 2.706 1.092
2 Gary Sanchez 2017 11 3 1 0 2 0.545 0.545 1.182 1.727 0.710
3 Francisco Lindor 2017 27 4 2 1 3 0.370 0.370 0.852 1.222 0.498
4 Rhys Hoskins 2017 14 2 1 0 2 0.357 0.357 0.857 1.214 0.495
5 Jose Ramirez 2017 16 0 0 0 3 0.188 0.188 0.750 0.938 0.370The latest release of the baseballr includes a function for acquiring player statistics from the NCAA’s website for baseball teams across the three major divisions (I, II, III).
The function, ncaa_scrape, requires the user to pass values for three parameters for the function to work:
school_id: numerical code used by the NCAA for each school year: a four-digit year type: whether to pull data for batters or pitchers
If you want to pull batting statistics for Vanderbilt for the 2013 season, you would use the following:
> baseballr::ncaa_scrape(736, 2013, "batting") %>%
+ select(year:OBPct)
year school conference division Jersey Player Yr Pos GP GS BA OBPct
1 2013 Vanderbilt Southeastern 1 18 Yastrzemski, Mike Sr OF 66 66 0.312 0.411
2 2013 Vanderbilt Southeastern 1 20 Harrell, Connor Sr OF 66 66 0.312 0.418
3 2013 Vanderbilt Southeastern 1 3 Conde, Vince So INF 66 65 0.307 0.380
4 2013 Vanderbilt Southeastern 1 6 Kemp, Tony Jr OF 66 66 0.391 0.471
5 2013 Vanderbilt Southeastern 1 55 Gregor, Conrad Jr OF 65 65 0.308 0.440
6 2013 Vanderbilt Southeastern 1 9 Turner, Xavier Fr INF 59 51 0.324 0.387
7 2013 Vanderbilt Southeastern 1 5 Navin, Spencer Jr C 57 56 0.302 0.430
8 2013 Vanderbilt Southeastern 1 51 Lupo, Jack Sr OF 57 51 0.297 0.352
9 2013 Vanderbilt Southeastern 1 8 Wiseman, Rhett Fr OF 54 11 0.289 0.360
10 2013 Vanderbilt Southeastern 1 10 Norwood, John So OF 33 9 0.328 0.388
11 2013 Vanderbilt Southeastern 1 43 Wiel, Zander So INF 33 15 0.305 0.406
12 2013 Vanderbilt Southeastern 1 44 Harvey, Chris So C 29 13 0.250 0.328
13 2013 Vanderbilt Southeastern 1 42 McKeithan, Joel Jr INF 25 12 0.220 0.267
14 2013 Vanderbilt Southeastern 1 39 Smith, Kyle Fr INF 23 7 0.250 0.455
15 2013 Vanderbilt Southeastern 1 17 Harris, Andrew Sr INF 21 0 0.125 0.222
16 2013 Vanderbilt Southeastern 1 2 Campbell, Tyler Fr INF 12 2 0.312 0.389
17 2013 Vanderbilt Southeastern 1 7 Swanson, Dansby Fr INF 11 4 0.188 0.435
18 2013 Vanderbilt Southeastern 1 25 Luna, D.J. Jr INF 8 0 0.000 0.333
19 2013 Vanderbilt Southeastern 1 23 Cooper, Will So OF 4 0 1.000 1.000
20 2013 Vanderbilt Southeastern 1 - Totals - - - - 0.313 0.407
21 2013 Vanderbilt Southeastern 1 - Opponent Totals - - - - 0.220 0.320The same can be done for pitching, just by changing the type parameter:
> baseballr::ncaa_scrape(736, 2013, "pitching") %>%
+ select(year:ERA)
year school conference division Jersey Player Yr Pos GP App GS ERA
1 2013 Vanderbilt Southeastern 1 11 Beede, Tyler So P 37 17 17 2.32
2 2013 Vanderbilt Southeastern 1 33 Miller, Brian So P 32 32 NA 1.58
3 2013 Vanderbilt Southeastern 1 35 Ziomek, Kevin Jr P 32 17 17 2.12
4 2013 Vanderbilt Southeastern 1 15 Fulmer, Carson Fr P 26 26 NA 2.39
5 2013 Vanderbilt Southeastern 1 39 Smith, Kyle Fr INF 23 1 NA 0.00
6 2013 Vanderbilt Southeastern 1 28 Miller, Jared So P 22 22 NA 2.31
7 2013 Vanderbilt Southeastern 1 19 Rice, Steven Jr P 21 21 NA 2.57
8 2013 Vanderbilt Southeastern 1 13 Buehler, Walker Fr P 16 16 9 3.14
9 2013 Vanderbilt Southeastern 1 22 Pfeifer, Philip So P 15 15 12 3.68
10 2013 Vanderbilt Southeastern 1 12 Ravenelle, Adam So P 11 11 NA 3.18
11 2013 Vanderbilt Southeastern 1 40 Pecoraro, T.J. Jr P 10 10 7 5.97
12 2013 Vanderbilt Southeastern 1 45 Ferguson, Tyler Fr P 8 8 4 4.21
13 2013 Vanderbilt Southeastern 1 27 Kolinsky, Keenan Jr P 2 2 NA 0.00
14 2013 Vanderbilt Southeastern 1 24 Wilson, Nevin So P 1 1 NA 0.00
15 2013 Vanderbilt Southeastern 1 - Totals - - - NA NA 2.76
16 2013 Vanderbilt Southeastern 1 - Opponent Totals - - - NA NA 6.19Now, the function is dependent on the user knowing the school_id used by the NCAA website. Given that, I’ve included a school_id_lu function so that users can find the school_id they need.
Just pass a string to the function and it will return possible matches based on the school’s name:
> school_id_lu("Vand")
# A tibble: 4 × 6
school conference school_id year division conference_id
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Vanderbilt Southeastern 736 2013 1 911
2 Vanderbilt Southeastern 736 2014 1 911
3 Vanderbilt Southeastern 736 2015 1 911
4 Vanderbilt Southeastern 736 2016 1 911Updates to functions in this release:
scrape_statcast_savant_batter
scrape_statcast_savant_pitcher
New functions in this release:
code_barrel
The research team at Major League Baseball Advanced Media have developed a way to categorize batted balls that on average having a batting average over .500 and slugging over 1.500. The specific coding criteria can be found in comment #2 [here] (http://tangotiger.com/index.php/site/comments/statcast-lab-barrels#2).
Now, whenever a user scrapes Statcast data using either the scrape_statcast_savant_batter or scrape_statcast_savant_pitcher functions the results will include a column barrel, where if the batted ball matches the barrel criteria it will code as 1, otherwise 0.
Example:
> scrape_statcast_savant_batter(start_date = "2016-04-06", end_date = "2016-04-15", batterid = 621043) %>%
+ filter(type == "X") %>%
+ filter(!is.na(barrel)) %>%
+ select(player_name, game_date, hit_angle, hit_speed, barrel) %>%
+ tail()
[1] "Be patient, this may take a few seconds..."
[1] "Data courtesy of Baseball Savant and MLBAM (baseballsavant.mlb.com)"
player_name game_date hit_angle hit_speed barrel
25 Carlos Correa 2016-04-07 31.10 103.33 1
26 Carlos Correa 2016-04-07 27.77 87.25 0
27 Carlos Correa 2016-04-06 29.62 103.97 1
28 Carlos Correa 2016-04-06 0.11 105.20 0
29 Carlos Correa 2016-04-06 23.76 113.55 1
30 Carlos Correa 2016-04-06 -2.18 113.39 0If you already have Statcast data–say, in a database that you’ve been collecting–I’ve also included a simple function that will take a dataframe and code whether each row contains a barrel or not. All you need to do is pass your dataframe to code_barrel.
Functions added to this release:
scrape_statcast_savant_batter
scrape_statcast_savant_pitcher
playerid_lookup
The two savant functions allow a user to retrieve PITCHf/x and Statcast data for either a specific batter or pitcher from [Baseball Savants’ Statcast Search] (https://baseballsavant.mlb.com/statcast_search). The user needs to provide a start date, end date, and the batter or pitcher’s MLBAMID.
Example:
> scrape_statcast_savant_batter(start_date = "2016-04-06", end_date = "2016-04-15", batterid = 621043) %>%
filter(type == "X") %>%
select(3,7,54:56) %>%
tail()
[1] "Be patient, this may take a few seconds..."
[1] "Data courtesy of Baseball Savant and MLBAM (baseballsavant.mlb.com)"
game_date player_name hit_distance_sc hit_speed hit_angle
26 2016-04-07 Carlos Correa 385 103.33 31.10
27 2016-04-07 Carlos Correa 288 87.25 27.77
28 2016-04-06 Carlos Correa 392 103.97 29.62
29 2016-04-06 Carlos Correa 189 105.20 0.11
30 2016-04-06 Carlos Correa 462 113.55 23.76
31 2016-04-06 Carlos Correa 228 113.39 -2.18Since the savant functions require users to pass a valid MLBAMID, a lookup function is included that leverages the Chadwick public register. Users provide a text string and only those players with that string present in their last name will be returned.
Here is an example where the user is looking for players with the last name “Seager”:
> playerid_lookup("Seager")
[1] "Be patient, this may take a few seconds..."
[1] "Data courtesy of the Chadwick Bureau Register (https://github.com/chadwickbureau/register)"
first_name last_name given_name name_suffix nick_name birth_year mlb_played_first mlbam_id retrosheet_id bbref_id fangraphs_id
1 Ben Seager Ben NA NA NA NA
2 Corey Seager Corey Drew 1994 2015 608369 seagc001 seageco01 13624
3 Justin Seager Justin Ryan 1992 NA 643529 NA
4 Kyle Seager Kyle Duerr 1987 2011 572122 seagk001 seageky01 9785Functions added to this release:
pitcher_boxscore: This function allows a user to retrieve a boxscore of pitcher statistics for any game played in the PITCHf/x era (2008-current). The function takes a boxscore.xml url as it’s only argument and returns boxscore data for both the home and away pitchers.
Example:
> pitcher_boxscore("http://gd2.mlb.com/components/game/mlb/year_2016/month_05/day_21/gid_2016_05_21_milmlb_nynmlb_1/boxscore.xml") %>% select(id:so)
Source: local data frame [9 x 10]
id name name_display_first_last pos out bf er r h so
(chr) (chr) (chr) (chr) (chr) (chr) (chr) (chr) (chr) (chr)
1 605200 Davies Zach Davies P 16 22 4 4 5 5
2 430641 Boyer Blaine Boyer P 2 4 0 0 2 0
3 448614 Torres, C Carlos Torres P 3 4 0 0 0 1
4 592804 Thornburg Tyler Thornburg P 3 3 0 0 0 1
5 518468 Blazek Michael Blazek P 1 5 1 1 2 0
6 594798 deGrom Jacob deGrom P 15 23 4 4 5 7
7 570663 Robles Hansel Robles P 6 7 0 0 0 3
8 592665 Reed, A Addison Reed P 3 5 0 0 1 2
9 544727 Familia Jeurys Familia P 3 4 0 0 1 1batter_boxscore: This function does the same thing as pitcher_boxscore, but for batters.
Example:
> batter_boxscore("http://gd2.mlb.com/components/game/mlb/year_2016/month_05/day_21/gid_2016_05_21_milmlb_nynmlb_1/boxscore.xml") %>% select(id:bb)
Source: local data frame [29 x 10]
id name name_display_first_last pos bo ab po r a bb
(chr) (chr) (chr) (chr) (chr) (chr) (chr) (chr) (chr) (chr)
1 542340 Villar Jonathan Villar SS 100 5 1 0 0 0
2 571697 Gennett Scooter Gennett 2B 200 4 2 0 3 1
3 518960 Lucroy Jonathan Lucroy C 300 5 8 0 1 0
4 474892 Carter Chris Carter 1B 400 4 10 0 2 0
5 543590 Nieuwenhuis Kirk Nieuwenhuis CF 500 4 0 0 0 0
6 431094 Hill, A Aaron Hill 3B 600 1 1 2 4 3
7 502100 Presley Alex Presley LF 700 3 0 1 0 1
8 570717 Flores, R Ramon Flores RF 800 3 2 1 0 0
9 605200 Davies Zach Davies P 900 2 1 0 1 0
10 430641 Boyer Blaine Boyer P 901 0 0 0 0 0Functions added to this release:
edge_code: This function allows a user to pass their own dataframe and have individual pitches coded according to the scheme provided by Edge%. The dataframe must contain at least three columns of data: b_height, stand, px, and pz.
Example (based on data from “2015-04-05”):
> edge_code(df) %>% .[, c(6:7, 27:28, 82)] %>% head(10)
stand b_height px pz location
1 L 6-3 0.416 2.963 Inside Edge
2 L 6-3 -0.191 2.347 Heart
3 L 6-3 -0.518 3.284 Upper Edge
4 L 6-3 -0.641 1.221 Out of Zone
5 L 6-3 -1.821 2.083 Out of Zone
6 L 6-3 0.627 2.397 Inside Edge
7 L 6-5 -1.088 1.610 Out of Zone
8 L 6-5 -0.257 2.047 Lower Edge
9 L 6-5 NA NA <NA>
10 L 6-3 -1.539 1.525 Out of ZoneFunctions updated for this release:
standings_on_date_bref: Updated this function to allow for records to be returned for the given date or from that date forward. Also, users can input a full date string instead of three separate arguments for the day, month, and year. Users can also choose to pull records for the AL and NL overall, not just for a given division.
Example:
> standings_on_date_bref("2015-08-01", "NL East", from = FALSE)
$`NL East`
Tm W L W-L% GB RS RA pythW-L%
1 WSN 54 48 0.529 -- 422 391 0.535
2 NYM 54 50 0.519 1.0 368 373 0.494
3 ATL 46 58 0.442 9.0 379 449 0.423
4 MIA 42 62 0.404 13.0 370 408 0.455
5 PHI 41 64 0.390 14.5 386 511 0.374
> standings_on_date_bref("2015-08-01", "NL East", from = TRUE)
$`NL East`
Tm W L W-L% GB RS RA pythW-L%
1 NYM 36 22 0.621 -- 315 240 0.622
2 MIA 29 29 0.500 7.0 243 270 0.452
3 WSN 29 31 0.483 8.0 281 244 0.564
4 PHI 22 35 0.386 13.5 240 298 0.402
5 ATL 21 37 0.362 15.0 194 311 0.297Functions added to this release:
edge_scrape_split(): This function builds of off edge_scrape() and adds the ability to view the data split by batter and pitcher handedness. As with edge_scrape(), the function returns a tibble grouped by either pitchers or batters and the percentage of pitches in each of the various Edge zones, but adds in handedness.
Example (Edge% splits by batters with handedness):
> edge_scrape_split("2015-04-05", "2015-04-05", "batter") %>% .[,c(1:5,9:14)]
batter_name batter p_throws stand All_pitches Upper_Edge Lower_Edge Inside_Edge Outside_Edge Heart Out_of_Zone
(chr) (dbl) (chr) (chr) (int) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
1 Matt Holliday 407812 L R 11 0.000 0.182 0.000 0.182 0.182 0.455
2 Matt Holliday 407812 R R 10 0.000 0.000 0.000 0.200 0.300 0.500
3 David Ross 424325 R R 8 0.000 0.000 0.000 0.125 0.625 0.250
4 Jhonny Peralta 425509 L R 9 0.000 0.111 0.444 0.000 0.111 0.333
5 Jhonny Peralta 425509 R R 6 0.167 0.000 0.000 0.167 0.167 0.500
6 Adam Wainwright 425794 L R 8 0.000 0.125 0.000 0.000 0.125 0.750
7 Adam Wainwright 425794 R R 3 0.000 0.000 0.000 0.333 0.667 0.000
8 Yadier Molina 425877 L R 13 0.077 0.077 0.000 0.000 0.077 0.769
9 Yadier Molina 425877 R R 7 0.143 0.000 0.143 0.143 0.143 0.429
10 Jonathan Jay 445055 L L 9 0.000 0.000 0.222 0.000 0.556 0.222
.. ... ... ... ... ... ... ... ... ... ... ...Functions added to this release:
fip_plus(): This function mimics the functionality in the woba_plus() function, except that the unit of analysis is pitchers. The function will generate Fielding Indepedent Pitching (FIP) for each pitcher in the data set that is passed to the function, along with wOBA against and wOBA against on contact.
Example:
> daily_pitcher_bref("2015-04-05", "2015-04-30") %>% fip_plus() %>% select(season, Name, IP, ERA, SO, uBB, HBP, HR, FIP, wOBA_against, wOBA_CON_against) %>% arrange(desc(IP)) %>% head(10)
season Name IP ERA SO uBB HBP HR FIP wOBA_against wOBA_CON_against
1 2015 Johnny Cueto 37.0 1.95 38 4 2 3 2.62 0.210 0.276
2 2015 Dallas Keuchel 37.0 0.73 22 11 0 0 2.84 0.169 0.151
3 2015 Sonny Gray 36.1 1.98 25 6 1 1 2.69 0.218 0.239
4 2015 Mike Leake 35.2 3.03 25 7 0 5 4.16 0.240 0.281
5 2015 Felix Hernandez 34.2 1.82 36 6 3 1 2.20 0.225 0.272
6 2015 Corey Kluber 34.0 4.24 36 5 2 2 2.40 0.295 0.391
7 2015 Jake Odorizzi 33.2 2.41 26 8 1 0 2.38 0.213 0.228
8 2015 Josh Collmenter 32.2 2.76 16 3 0 1 2.82 0.290 0.330
9 2015 Bartolo Colon 32.2 3.31 25 1 0 4 3.29 0.280 0.357
10 2015 Zack Greinke 32.2 1.93 27 7 1 2 3.01 0.240 0.274edge_scrape(): This function allows the user to scrape PITCHf/x data from the GameDay application using Carson Sievert’s pitchRx package and to calculate metrics associated with Edge%. The function returns a data.frame grouped by either pitchers or batters and the percentage of pitches in each of the various Edge zones.
Example (pitchers):
> edge_scrape("2015-04-06", "2015-04-07", "pitcher") %>% .[, c(1:3,7:12)] %>% head(10)
pitcher_name pitcher All_pitches Upper_Edge Lower_Edge Inside_Edge Outside_Edge Heart Out_of_Zone
(chr) (dbl) (int) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
1 Bartolo Colon 112526 86 0.035 0.081 0.058 0.151 0.209 0.465
2 LaTroy Hawkins 115629 12 0.000 0.333 0.000 0.000 0.083 0.583
3 Joe Nathan 150274 4 0.000 0.000 0.000 0.000 0.000 1.000
4 Buddy Carlyle 234194 9 0.000 0.222 0.000 0.000 0.333 0.444
5 Jason Grilli 276351 14 0.000 0.000 0.214 0.000 0.286 0.500
6 Kevin Gregg 276514 17 0.000 0.000 0.118 0.176 0.235 0.471
7 Joaquin Benoit 276542 19 0.053 0.053 0.105 0.000 0.158 0.632
8 Ryan Vogelsong 285064 99 0.010 0.051 0.141 0.061 0.182 0.556
9 Jeremy Affeldt 346793 5 0.000 0.000 0.200 0.000 0.000 0.800
10 Grant Balfour 346797 21 0.095 0.000 0.000 0.048 0.333 0.524Example (batters):
> edge_scrape("2015-04-06", "2015-04-07", "batter") %>% .[, c(1:3,7:12)] %>% head(10)
batter_name batter All_pitches Upper_Edge Lower_Edge Inside_Edge Outside_Edge Heart Out_of_Zone
(chr) (dbl) (int) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
1 Bartolo Colon 112526 7 0.000 0.000 0.429 0.000 0.143 0.429
2 Torii Hunter 116338 19 0.000 0.105 0.105 0.105 0.000 0.684
3 David Ortiz 120074 18 0.056 0.000 0.111 0.056 0.222 0.556
4 Alex Rodriguez 121347 17 0.000 0.000 0.353 0.000 0.118 0.529
5 Aramis Ramirez 133380 23 0.000 0.087 0.261 0.000 0.261 0.391
6 Adrian Beltre 134181 26 0.000 0.038 0.154 0.115 0.231 0.462
7 Carlos Beltran 136860 22 0.136 0.045 0.136 0.000 0.136 0.545
8 Michael Cuddyer 150212 14 0.000 0.214 0.214 0.000 0.214 0.357
9 Jimmy Rollins 276519 41 0.024 0.122 0.049 0.049 0.220 0.537
10 Ryan Vogelsong 285064 10 0.000 0.200 0.300 0.000 0.200 0.300