Calculate FIP and related metrics for any set of data

This function allows you to calculate FIP and related metrics for any given set of data, provided the right variables are in the data set. The function currently returns both FIP per inning pitched, wOBA against (based on batters faced), and wOBA against per instance of fair contact.

Usage

fip_plus(df)

Arguments

df

A data frame of statistics that includes, at a minimum, the following columns: IP (innings pitched), BF (batters faced), uBB (unintentional walks), HBP (Hit By Pitch), x1B (singles), x2B (doubles), x3B (triples), HR (home runs), AB (at-bats), SH (sacrifice hits), SO (strike outs), and season.

Value

Returns a tibble with the following columns:

col_name	types
bbref_id	character
season	integer
Name	character
Age	numeric
Level	character
Team	character
G	numeric
GS	numeric
W	numeric
L	numeric
SV	numeric
IP	numeric
H	numeric
R	numeric
ER	numeric
uBB	numeric
BB	numeric
SO	numeric
HR	numeric
HBP	numeric
ERA	numeric
AB	numeric
X1B	numeric
X2B	numeric
X3B	numeric
IBB	numeric
GDP	numeric
SF	numeric
SB	numeric
CS	numeric
PO	numeric
BF	numeric
Pit	numeric
Str	numeric
StL	numeric
StS	numeric
GB.FB	numeric
LD	numeric
PU	numeric
WHIP	numeric
BAbip	numeric
SO9	numeric
SO.W	numeric
SO_perc	numeric
uBB_perc	numeric
SO_uBB	numeric
FIP	numeric
wOBA_against	numeric
wOBA_CON_against	numeric

Examples

# \donttest{
  df <- bref_daily_pitcher("2015-04-05", "2015-04-30")
  try(fip_plus(df))
#> ── MLB Daily Pitcher data from baseball-reference.com ──────────────────
#> ℹ Data updated: 2022-04-30 07:15:22 UTC
#> # A tibble: 453 × 49
#>    bbref_id season Name    Age Level Team      G    GS     W     L    SV
#>    <chr>     <int> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 453265     2015 Heat…    26 Maj-… Bost…     1     0    NA    NA    NA
#>  2 592879     2015 Dere…    28 Maj-… Texas     1     1    NA     1    NA
#>  3 519455     2015 Jon …    27 Maj-… Texas     1     0    NA    NA    NA
#>  4 453264     2015 Cory…    25 Maj-… San …     2     0    NA    NA    NA
#>  5 501957     2015 Seve…    22 Maj-… Phil…     1     1    NA     1    NA
#>  6 573244     2015 John…    26 Maj-… Atla…     1     0    NA    NA    NA
#>  7 445612     2015 Chri…    27 Maj-… San …     5     0    NA    NA    NA
#>  8 458677     2015 Donn…    30 Maj-… Atla…     1     0    NA    NA    NA
#>  9 425532     2015 Jorg…    27 Maj-… Colo…     1     0    NA    NA    NA
#> 10 425794     2015 Anth…    25 Maj-… Texas     1     1    NA     1    NA
#> # … with 443 more rows, and 38 more variables: IP <dbl>, H <dbl>,
#> #   R <dbl>, ER <dbl>, uBB <dbl>, BB <dbl>, SO <dbl>, HR <dbl>,
#> #   HBP <dbl>, ERA <dbl>, AB <dbl>, X1B <dbl>, X2B <dbl>, X3B <dbl>,
#> #   IBB <dbl>, GDP <dbl>, SF <dbl>, SB <dbl>, CS <dbl>, PO <dbl>,
#> #   BF <dbl>, Pit <dbl>, Str <dbl>, StL <dbl>, StS <dbl>, GB.FB <dbl>,
#> #   LD <dbl>, PU <dbl>, WHIP <dbl>, BAbip <dbl>, SO9 <dbl>, SO.W <dbl>,
#> #   SO_perc <dbl>, uBB_perc <dbl>, SO_uBB <dbl>, FIP <dbl>, …
# }