COLLEGE_BASEBALL — additional Python functions

Hand-written wrappers, loaders, and helpers in sportsdataverse.college_baseball not covered by the generated API-endpoint reference above.

stats.ncaa.org

college_baseball_re24

college_baseball_re24(seasons: 'Union[int, List[int], None]' = None, *, state: 'Optional[pl.DataFrame]' = None, return_as_pandas: 'bool' = False) -> "Union[pl.DataFrame, 'pd.DataFrame']"

sportsdataverse.baseball.college_run_expectancy.college_baseball_re24 fixed to league="college_baseball".

Parameters

Parameter	Type	Default	Description
seasons	Union[int, List[int], None]	None	See the core function.
state	Optional[DataFrame]	None	See the core function.
return_as_pandas	bool	False	Return pandas.DataFrame instead of polars.

Returns

see the core function's Returns table.

col_name	type	description
base_state	character	3-char base occupancy code ("_" = empty, "1"/"2"/"3" = occupied), e.g. "1_3" for runners on first and third.
outs	integer	Outs at the start of the base-out state (0-2).
run_expectancy	double	Empirical mean runs scored from this state through the end of the half-inning (RE24).
n	integer	Number of plate appearances observed starting in this base-out state.

Example

from sportsdataverse.baseball.college_baseball.college_baseball_re import college_baseball_state, college_baseball_re24
state = college_baseball_state(raw)
matrix = college_baseball_re24(state=state)

college_baseball_state

college_baseball_state(plays: 'Dict[str, Any]') -> 'pl.DataFrame'

sportsdataverse.baseball.college_run_expectancy.college_baseball_state fixed to league="college_baseball".

Parameters

Parameter	Type	Default	Description
plays	Dict[str, Any]		Raw payload from espn_college_baseball_game_plays(event_id, return_parsed=False).

Returns

see the core function's Returns table.

col_name	type	description
game_id	character
inning	integer
half	character
base_state	character
outs	integer
runs_before	integer
runs_after	integer
batting_team_id	character
play_seq	integer
score_diff	integer

Example

from sportsdataverse.baseball.college_baseball.college_baseball_re import college_baseball_state
state = college_baseball_state(raw)

college_baseball_wpa

college_baseball_wpa(seasons: 'Union[int, List[int], None]' = None, *, state: 'Optional[pl.DataFrame]' = None, results: 'Optional[pl.DataFrame]' = None, return_as_pandas: 'bool' = False) -> "Union[pl.DataFrame, 'pd.DataFrame']"

sportsdataverse.baseball.college_run_expectancy.college_baseball_wpa fixed to league="college_baseball".

Parameters

Parameter	Type	Default	Description
seasons	Union[int, List[int], None]	None	See the core function.
state	Optional[DataFrame]	None	See the core function.
results	Optional[DataFrame]	None	See the core function.
return_as_pandas	bool	False	Return pandas.DataFrame instead of polars.

Returns

see the core function's Returns table.

col_name	type	description
game_id	character	ESPN event id for the game (join key to the schedule).
play_seq	integer	Game-global sequential plate-appearance order.
re_before	double	RE24 of the base-out state before the PA.
re_after	double	RE24 of the base-out state after the PA.
run_value	double	re_after minus re_before, plus runs scored on the play.
wpa	double	Home-perspective win-probability added.

Example

from sportsdataverse.baseball.college_baseball.college_baseball_re import college_baseball_wpa
wpa = college_baseball_wpa(state=state, results=results)

Play-by-play processing

decompose_college_baseball_plays

decompose_college_baseball_plays(rows: "'list[dict]'", *, return_as_pandas: 'bool' = False) -> "'Union[pl.DataFrame, pd.DataFrame]'"

Decompose pre-extracted play rows into the full PBP_SCHEMA frame.

The row-level half of parse_college_baseball_ncaa_pbp -- the play-text decomposition engine without the HTML extraction. This is the entry point for sources that already hold the base play fields, e.g. the legacy R-era baseballr-data trees (2012-2023: description/inning/ inning_top_bot/batting/fielding/score), so legacy and freshly captured games resolve into IDENTICAL pbp columns.

Parameters

Parameter	Type	Default	Description
rows	list[dict]		One dict per play. Recognized keys (all optional except description): contest_id, inning (int), inning_top_bot ("top"/"bot"), batting, fielding, play_number, score_away/score_home (ints) or a combined score string ("3-2", away-home), and description. Unrecognized keys are ignored; play_number defaults to the 1-based position in rows.
return_as_pandas	bool	False	Return a pandas.DataFrame instead of polars.

Returns

One row per input play with every text-derivable PBP_SCHEMA column populated (play_type, hit/out flags, rbi, pitch_sequence, runner movement, ...). Empty input returns a zero-row frame with the documented schema.

col_name	type	description
contest_id	character
inning	integer
inning_top_bot	character
batting	character
fielding	character
play_number	integer
score_away	integer
score_home	integer
batter	character	MLBAM player id of the batter.
play_type	character
hit_trajectory	character
fielded_position	character
is_hit	logical
is_out	logical
strikeout_type	character
is_sacrifice	logical
sac_type	character
is_double_play	logical
rbi	integer
count_balls	integer
count_strikes	integer
pitch_sequence	character
error_position	character
unearned	logical
runs_scored	integer
scoring_runners	character
runners_advanced	character
outs_on_play	integer
is_scoring_play	logical	Flag indicating that the play put points on the board (1 = scoring play, 0 = not).
description	character

Example

from sportsdataverse.baseball.college_baseball import decompose_college_baseball_plays
df = decompose_college_baseball_plays(
    [{"inning": 1, "inning_top_bot": "top", "score": "0-0",
      "description": "Jack Moss singled to left field (1-2 KBFX)."}]
)
print(df.select("play_type", "is_hit", "pitch_sequence").row(0))