The 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, statline-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 dataframe that includes pitch-by-pitch information. The function assumes the following columns are present:events,description,game_date, andtype.base: base indicates what the denomincator 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 0.144
You can also combine the statline_from_statcast function with a for loop to create statlines for multiple players at once. Say you wanted to calculate stat lines based on contact for batters that saw at least 125 pitches over the past week (i.e. games played 2017-09-04 through 2017-09-10):
test <- scrape_statcast_savant_batter_all("2017-09-04", "2017-09-10")
output <- data.frameI()
players_to_loop <- test %>%
group_by(player_name) %>%
count() %>%
arrange(desc(n)) %>%
filter(n >= 125) %>%
ungroup()
for (i in players_to_loop$player_name) {
reduced_test <- test %>%
filter(player_name == i)
x <- statline_from_statcast(reduced_test, base = "contact")
x$player <- i
x <- x %>%
select(player, everything())
output <- rbind(output, x) %>%
arrange(desc(woba))
}
print(output, n = 40, width = Inf)
# A tibble: 21 x 12
player year batted_balls X1B X2B X3B HR ba obp slg ops woba
<chr> <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Wil Myers 2017 18 6 1 0 3 0.556 0.556 1.111 1.667 0.690
2 Aaron Judge 2017 13 2 1 0 3 0.462 0.462 1.231 1.693 0.685
3 Matt Chapman 2017 17 5 1 1 2 0.529 0.529 1.059 1.588 0.654
4 Nolan Arenado 2017 23 8 2 0 2 0.522 0.522 0.870 1.392 0.584
5 Brandon Nimmo 2017 19 6 1 0 2 0.474 0.474 0.842 1.316 0.550
6 Charlie Blackmon 2017 21 4 3 0 2 0.429 0.429 0.857 1.286 0.531
7 Francisco Lindor 2017 27 4 2 1 3 0.370 0.370 0.852 1.222 0.498
8 Jose Martinez 2017 21 6 1 0 2 0.429 0.429 0.762 1.191 0.497
9 Rhys Hoskins 2017 14 2 1 0 2 0.357 0.357 0.857 1.214 0.495
10 Christian Yelich 2017 22 6 4 0 0 0.455 0.455 0.636 1.091 0.463
11 Freddie Freeman 2017 24 7 2 0 1 0.417 0.417 0.625 1.042 0.441
12 J.T. Realmuto 2017 24 8 1 1 0 0.417 0.417 0.542 0.959 0.408
13 Cesar Hernandez 2017 22 7 2 0 0 0.409 0.409 0.500 0.909 0.391
14 DJ LeMahieu 2017 25 6 3 0 0 0.360 0.360 0.480 0.840 0.358
15 Ender Inciarte 2017 30 8 0 1 1 0.333 0.333 0.500 0.833 0.351
16 Cody Bellinger 2017 17 2 3 0 0 0.294 0.294 0.471 0.765 0.320
17 Brett Gardner 2017 23 5 1 1 0 0.304 0.304 0.435 0.739 0.312
18 Byron Buxton 2017 17 2 0 1 1 0.235 0.235 0.529 0.764 0.311
19 Eugenio Suarez 2017 19 5 1 0 0 0.316 0.316 0.368 0.684 0.296
20 Andrew Benintendi 2017 21 4 2 0 0 0.286 0.286 0.381 0.667 0.284
21 Brian Dozier 2017 23 2 0 0 2 0.174 0.174 0.435 0.609 0.248
From here, you can cut the data in infinite ways. You could take all pitches that induced a swing by J.D. Martinez over the same time period and see how well he performed on swings by pitch type:
jd_martinez <- test %>%
filter(player_name == "J.D. Martinez")
jd_pitches <- jd_martinez %>%
group_by(pitch_type) %>%
count() %>%
arrange(desc(n)) %>%
ungroup()
output <- data.frame()
for (i in jd_pitches$pitch_type) {
reduced <- jd_martinez %>%
filter(pitch_type == i)
x <- statline_from_statcast(reduced)
x$player <- "J.D. Martinez"
x$pitch_type <- i
x <- x %>%
select(player, pitch_type, everything())
output <- rbind(output, x) %>%
arrange(desc(woba))
}
print(output, n = 40, width = Inf)
player pitch_type year swings batted_balls X1B X2B X3B HR swing_and_miss
1 J.D. Martinez FF 2017 19 12 0 1 0 4 7
2 J.D. Martinez FT 2017 10 8 3 0 0 1 2
3 J.D. Martinez SL 2017 12 6 0 0 0 2 6
4 J.D. Martinez CU 2017 6 3 1 0 0 0 3
5 J.D. Martinez CH 2017 8 3 0 0 0 0 5
swinging_strike_percent ba obp slg ops woba
1 0.368 0.263 0.263 0.947 1.210 0.481
2 0.200 0.400 0.400 0.700 1.100 0.461
3 0.500 0.167 0.167 0.667 0.834 0.329
4 0.500 0.167 0.167 0.167 0.334 0.146
5 0.625 0.000 0.000 0.000 0.000 0.000
Now we can see that J.D. Martinez’s monster week was driven largely by 4- and 2-seam fastballs and sliders. When swinging at curveballs and change ups, Martinex whiffed over 50% of the time and only generated one hit–a single–when putting bat to ball.