🏈 College football with sportsdataverse-py

Saturdays in autumn, condensed into tidy DataFrames. 🍂 In a few lines of Python you're about to pull a decade of play-by-play, full rosters, schedules, team info, plus live ESPN scoreboards, standings, polls and recruiting boards — all as clean polars frames ready to model.

CFB has no single native premium API, so our premium path is two-pronged:

1. 🗄️ Release loaders (load_cfb_*) — pre-built, EPA/WPA-enriched datasets served straight from the cfbfastR-data GitHub release. Fast, reliable, no key needed.
2. 📡 ESPN families (espn_cfb_*) — live scoreboards, team pages, standings, rankings, recruiting and per-play participants.

R user? Every verb here has a twin in cfbfastR. Let's kick off! 🏈

🧰 The toolbox

Everything returns a tidy polars DataFrame by default — pass return_as_pandas=True for pandas, or (on the espn_cfb_* wrappers) return_parsed=False for the raw JSON. ⭐ marks the premium path.

Function	What it gives you	Source
load_cfb_pbp	Full play-by-play with EPA/WPA, since 2003	⭐ release
load_cfb_rosters	Season rosters (bio, position, hometown)	⭐ release
load_cfb_schedule	Season schedule + results + Elo	⭐ release
load_cfb_team_info	Team metadata: conference, colors, venue	⭐ release
load_cfb_betting_lines	Historical betting market lines (spread/total/ML)	⭐ release
espn_cfb_scoreboard	Live + recent scoreboard for a date/week	⭐ ESPN
espn_cfb_schedule	ESPN schedule frame for a date/week	⭐ ESPN
espn_cfb_teams	Every FBS/FCS team (grab team_ids)	⭐ ESPN
espn_cfb_team_roster	One team's roster	⭐ ESPN
espn_cfb_team_schedule	One team's schedule	⭐ ESPN
espn_cfb_standings	Conference / division standings	⭐ ESPN
espn_cfb_rankings	AP / Coaches / CFP polls	⭐ ESPN
espn_cfb_leaders	League stat leaders by category	⭐ ESPN
espn_cfb_recruits	Season recruiting class	⭐ ESPN
espn_cfb_play_participants	Per-play athletes (passer/rusher/tackler…)	⭐ ESPN
CFBPlayProcess	Full ESPN PBP pipeline (EPA/WPA + box)	⭐ ESPN
most_recent_cfb_season	The current season year helper	helper

🔌 Setup

pip install sportsdataverse

No API key required. The load_cfb_* loaders read public parquet from the cfbfastR-data release, and the espn_cfb_* wrappers hit ESPN's public endpoints.

import polars as pl
import sportsdataverse as sdv
from sportsdataverse.cfb import most_recent_cfb_season

SEASON = most_recent_cfb_season()
print('most recent CFB season:', SEASON)

most recent CFB season: 2025

ESPN's live endpoints are seasonal and occasionally rate-limited, so a tiny safe() helper runs the riskier calls defensively — you get the frame when the feed is up, and a friendly one-liner when it isn't (never a scary traceback). The release loaders are reliable, so we call those directly. 🛟

def safe(label, thunk):
    try:
        out = thunk()
        print(f'✅ {label}')
        return out
    except Exception as e:  # noqa: BLE001 -- demo resilience
        print(f'⏭️  {label}: unavailable right now ({type(e).__name__})')
        return None

🗄️ Premium loaders: a whole season in one call

The load_cfb_* family is the fastest way to get clean, complete season data. Each takes a seasons= int or list (≥ 2003) and returns one tidy frame. Let's start with the schedule — one row per game, with final scores, conference flags, and pre/post-game Elo ratings baked in.

Function	Grain	Highlights
load_cfb_schedule	one row / game	scores, Elo, neutral-site & conference flags

schedule = sdv.cfb.load_cfb_schedule(seasons=[2023])
print('schedule shape:', schedule.shape)
schedule.select([
    'game_id', 'week', 'home_team', 'away_team',
    'home_points', 'away_points', 'home_conference', 'neutral_site',
]).head()

schedule shape: (3734, 31)





shape: (5, 8)
┌───────────┬──────┬─────────────┬─────────────┬────────────┬────────────┬────────────┬────────────┐
│ game_id   ┆ week ┆ home_team   ┆ away_team   ┆ home_point ┆ away_point ┆ home_confe ┆ neutral_si │
│ ---       ┆ ---  ┆ ---         ┆ ---         ┆ s          ┆ s          ┆ rence      ┆ te         │
│ i32       ┆ i32  ┆ str         ┆ str         ┆ ---        ┆ ---        ┆ ---        ┆ ---        │
│           ┆      ┆             ┆             ┆ i32        ┆ i32        ┆ str        ┆ bool       │
╞═══════════╪══════╪═════════════╪═════════════╪════════════╪════════════╪════════════╪════════════╡
│ 401525434 ┆ 1    ┆ Notre Dame  ┆ Navy        ┆ 42         ┆ 3          ┆ FBS Indepe ┆ true       │
│           ┆      ┆             ┆             ┆            ┆            ┆ ndents     ┆            │
│ 401540199 ┆ 1    ┆ Mercer      ┆ North       ┆ 17         ┆ 7          ┆ Southern   ┆ true       │
│           ┆      ┆             ┆ Alabama     ┆            ┆            ┆            ┆            │
│ 401520145 ┆ 1    ┆ Jacksonvill ┆ UTEP        ┆ 17         ┆ 14         ┆ Conference ┆ false      │
│           ┆      ┆ e State     ┆             ┆            ┆            ┆ USA        ┆            │
│ 401532392 ┆ 1    ┆ San Diego   ┆ Ohio        ┆ 20         ┆ 13         ┆ Mountain   ┆ false      │
│           ┆      ┆ State       ┆             ┆            ┆            ┆ West       ┆            │
│ 401540628 ┆ 1    ┆ UAlbany     ┆ Fordham     ┆ 34         ┆ 13         ┆ CAA        ┆ false      │
└───────────┴──────┴─────────────┴─────────────┴────────────┴────────────┴────────────┴────────────┘

👥 Premium loaders: rosters

load_cfb_rosters gives you every listed player for a season — name, position, jersey, physicals and hometown. Perfect for joining onto play-by-play or building depth tables.

rosters = sdv.cfb.load_cfb_rosters(seasons=[2023])
print('rosters shape:', rosters.shape)
rosters.select([
    'athlete_id', 'first_name', 'last_name', 'team',
    'position', 'jersey', 'home_state',
]).head()

rosters shape: (22467, 18)





shape: (5, 7)
┌────────────┬────────────┬───────────┬───────────────────┬──────────┬────────┬────────────┐
│ athlete_id ┆ first_name ┆ last_name ┆ team              ┆ position ┆ jersey ┆ home_state │
│ ---        ┆ ---        ┆ ---       ┆ ---               ┆ ---      ┆ ---    ┆ ---        │
│ str        ┆ str        ┆ str       ┆ str               ┆ str      ┆ i32    ┆ str        │
╞════════════╪════════════╪═══════════╪═══════════════════╪══════════╪════════╪════════════╡
│ 102597     ┆ Will       ┆ Rogers    ┆ Mississippi State ┆ QB       ┆ 7      ┆ MS         │
│ 107494     ┆ Trey       ┆ Sanders   ┆ TCU               ┆ RB       ┆ 2      ┆ FL         │
│ 146583     ┆ John       ┆ Adams     ┆ Temple            ┆ WR       ┆ 17     ┆ NJ         │
│ 160900     ┆ Will       ┆ Johnson   ┆ Michigan          ┆ null     ┆ null   ┆ null       │
│ 169499     ┆ Ryan       ┆ Johnson   ┆ Akron             ┆ DL       ┆ 4      ┆ MS         │
└────────────┴────────────┴───────────┴───────────────────┴──────────┴────────┴────────────┘

🏟️ Premium loaders: team info

load_cfb_team_info carries the reference metadata you'll want to label every chart: school name, conference, classification (FBS/FCS), team colors, and venue.

team_info = sdv.cfb.load_cfb_team_info(seasons=[2023])
print('team_info shape:', team_info.shape)
team_info.select([
    'team_id', 'school', 'conference', 'classification',
    'venue_name', 'city', 'state', 'dome',
]).head()

team_info shape: (1840, 28)





shape: (5, 8)
┌─────────┬───────────────┬───────────────┬──────────────┬──────────────┬──────────┬───────┬───────┐
│ team_id ┆ school        ┆ conference    ┆ classificati ┆ venue_name   ┆ city     ┆ state ┆ dome  │
│ ---     ┆ ---           ┆ ---           ┆ on           ┆ ---          ┆ ---      ┆ ---   ┆ ---   │
│ i32     ┆ str           ┆ str           ┆ ---          ┆ str          ┆ str      ┆ str   ┆ bool  │
│         ┆               ┆               ┆ str          ┆              ┆          ┆       ┆       │
╞═════════╪═══════════════╪═══════════════╪══════════════╪══════════════╪══════════╪═══════╪═══════╡
│ 2000    ┆ Abilene       ┆ UAC           ┆ fcs          ┆ Wildcat      ┆ Abilene  ┆ TX    ┆ false │
│         ┆ Christian     ┆               ┆              ┆ Stadium (TX) ┆          ┆       ┆       │
│ 2001    ┆ Adams State   ┆ Rocky         ┆ ii           ┆ Rex Stadium  ┆ Alamosa  ┆ CO    ┆ false │
│         ┆               ┆ Mountain      ┆              ┆              ┆          ┆       ┆       │
│ 2003    ┆ Adrian        ┆ Michigan      ┆ iii          ┆ Docking      ┆ Adrian   ┆ MI    ┆ false │
│         ┆               ┆               ┆              ┆ Stadium      ┆          ┆       ┆       │
│ 2005    ┆ Air Force     ┆ Mountain West ┆ fbs          ┆ Falcon       ┆ Colorado ┆ CO    ┆ false │
│         ┆               ┆               ┆              ┆ Stadium      ┆ Springs  ┆       ┆       │
│ 2006    ┆ Akron         ┆ Mid-American  ┆ fbs          ┆ Summa Field  ┆ Akron    ┆ OH    ┆ false │
│         ┆               ┆               ┆              ┆ at           ┆          ┆       ┆       │
│         ┆               ┆               ┆              ┆ InfoCision   ┆          ┆       ┆       │
│         ┆               ┆               ┆              ┆ Stad…        ┆          ┆       ┆       │
└─────────┴───────────────┴───────────────┴──────────────┴──────────────┴──────────┴───────┴───────┘

🎬 Premium loaders: play-by-play with EPA

The crown jewel. load_cfb_pbp returns every play of a season with hundreds of engineered columns — down & distance, win probability, and Expected Points Added (EPA) already computed. (It's a big pull, so we grab a single season and peek.) 📊

# The release serves PBP for whichever seasons are currently published.
# Try a few recent-ish seasons and keep the first one that comes back full,
# so the EPA recipes below always have real plays to chew on.
pbp = pl.DataFrame()
for yr in (2023, 2022, 2021, 2020):
    cand = safe(f'load_cfb_pbp {yr}', lambda yr=yr: sdv.cfb.load_cfb_pbp(seasons=[yr]))
    if cand is not None and cand.width > 0 and cand.height > 0:
        pbp, PBP_SEASON = cand, yr
        break
else:
    PBP_SEASON = None

print('pbp season:', PBP_SEASON, '| pbp shape:', pbp.shape)
cols = ['game_id', 'start.pos_team.name', 'down', 'distance',
        'play_type', 'EPA', 'wpa']
have = [c for c in cols if c in pbp.columns]
pbp.select(have).head() if have else 'pbp not published for these seasons right now'

✅ load_cfb_pbp 2023
pbp season: 2023 | pbp shape: (153625, 460)





shape: (5, 6)
┌───────────┬─────────────────────┬──────┬──────────┬───────────┬───────────┐
│ game_id   ┆ start.pos_team.name ┆ down ┆ distance ┆ EPA       ┆ wpa       │
│ ---       ┆ ---                 ┆ ---  ┆ ---      ┆ ---       ┆ ---       │
│ i64       ┆ str                 ┆ i64  ┆ i64      ┆ f64       ┆ f64       │
╞═══════════╪═════════════════════╪══════╪══════════╪═══════════╪═══════════╡
│ 401523986 ┆ San José State      ┆ 1    ┆ 10       ┆ 0.0       ┆ 0.0       │
│ 401523986 ┆ San José State      ┆ 1    ┆ 10       ┆ -0.687781 ┆ -0.003872 │
│ 401523986 ┆ San José State      ┆ 2    ┆ 10       ┆ 2.339749  ┆ 0.019609  │
│ 401523986 ┆ San José State      ┆ 1    ┆ 10       ┆ 0.121639  ┆ 0.002179  │
│ 401523986 ┆ San José State      ┆ 2    ┆ 5        ┆ -1.149794 ┆ -0.006335 │
└───────────┴─────────────────────┴──────┴──────────┴───────────┴───────────┘

📡 Live from ESPN: the scoreboard

When you need today's slate (or a specific date), the ESPN wrappers shine. espn_cfb_scoreboard takes a dates=YYYYMMDD (or season year) and returns the games on the board. We wrap it in safe() since live endpoints can be quiet in the offseason.

board = safe(
    'ESPN scoreboard',
    lambda: sdv.cfb.espn_cfb_scoreboard(dates=20231125),  # rivalry Saturday
)
if board is not None and getattr(board, 'height', 0):
    keep = [c for c in board.columns
            if c in ('game_id', 'name', 'short_name', 'status_type_description',
                     'home_team_abbreviation', 'away_team_abbreviation')]
    out = board.select(keep).head() if keep else board.head()
else:
    out = 'no games on the board for that date'
out

✅ ESPN scoreboard





shape: (5, 4)
┌───────────┬─────────────────────────────────┬────────────┬─────────────────────────┐
│ game_id   ┆ name                            ┆ short_name ┆ status_type_description │
│ ---       ┆ ---                             ┆ ---        ┆ ---                     │
│ str       ┆ str                             ┆ str        ┆ str                     │
╞═══════════╪═════════════════════════════════╪════════════╪═════════════════════════╡
│ 401520430 ┆ Georgia Bulldogs at Georgia Te… ┆ UGA @ GT   ┆ Final                   │
│ 401520434 ┆ Ohio State Buckeyes at Michiga… ┆ OSU @ MICH ┆ Final                   │
│ 401524068 ┆ Washington State Cougars at Wa… ┆ WSU @ WASH ┆ Final                   │
│ 401520429 ┆ Florida State Seminoles at Flo… ┆ FSU @ FLA  ┆ Final                   │
│ 401520427 ┆ Alabama Crimson Tide at Auburn… ┆ ALA @ AUB  ┆ Final                   │
└───────────┴─────────────────────────────────┴────────────┴─────────────────────────┘

🏫 Live from ESPN: teams (and their team_ids)

espn_cfb_teams lists every team in a division (groups=80 FBS, groups=81 FCS). The team_id column is the key you feed into every team-scoped ESPN call below.

teams = safe('ESPN teams', sdv.cfb.espn_cfb_teams)
if teams is not None and teams.height:
    cols = [c for c in ('team_id', 'team_location', 'team_name',
                        'team_abbreviation') if c in teams.columns]
    out = teams.select(cols).head(8)
else:
    out = 'teams unavailable right now'
out

✅ ESPN teams





shape: (8, 4)
┌─────────┬───────────────────┬──────────────┬───────────────────┐
│ team_id ┆ team_location     ┆ team_name    ┆ team_abbreviation │
│ ---     ┆ ---               ┆ ---          ┆ ---               │
│ str     ┆ str               ┆ str          ┆ str               │
╞═════════╪═══════════════════╪══════════════╪═══════════════════╡
│ 2000    ┆ Abilene Christian ┆ Wildcats     ┆ ACU               │
│ 2001    ┆ Adams State       ┆ Grizzlies    ┆ ADSU              │
│ 2003    ┆ Adrian            ┆ Bulldogs     ┆ ADR               │
│ 2005    ┆ Air Force         ┆ Falcons      ┆ AF                │
│ 2006    ┆ Akron             ┆ Zips         ┆ AKR               │
│ 2010    ┆ Alabama A&M       ┆ Bulldogs     ┆ AAMU              │
│ 333     ┆ Alabama           ┆ Crimson Tide ┆ ALA               │
│ 2011    ┆ Alabama State     ┆ Hornets      ┆ ALST              │
└─────────┴───────────────────┴──────────────┴───────────────────┘

🍳 Cookbook: common CFB tasks

Now the fun part — real questions, answered with a few expressions. The loaders are reliable so these recipes lean on them, reaching for ESPN where it adds something live.

Recipe 1 — Highest-scoring games of the season 🔥

Straight from the loaded schedule: add the two scores and sort. No casting needed — the release frame already stores points as integers.

(schedule
    .with_columns(
        (pl.col('home_points') + pl.col('away_points')).alias('total_points')
    )
    .sort('total_points', descending=True)
    .select(['week', 'home_team', 'away_team',
             'home_points', 'away_points', 'total_points'])
    .head(10))

shape: (10, 6)
┌──────┬──────────────────────┬────────────────────┬─────────────┬─────────────┬──────────────┐
│ week ┆ home_team            ┆ away_team          ┆ home_points ┆ away_points ┆ total_points │
│ ---  ┆ ---                  ┆ ---                ┆ ---         ┆ ---         ┆ ---          │
│ i32  ┆ str                  ┆ str                ┆ i32         ┆ i32         ┆ i32          │
╞══════╪══════════════════════╪════════════════════╪═════════════╪═════════════╪══════════════╡
│ 9    ┆ Colby College        ┆ Middlebury         ┆ null        ┆ null        ┆ null         │
│ 9    ┆ Bowdoin              ┆ Trinity (CT)       ┆ null        ┆ null        ┆ null         │
│ 9    ┆ Bates                ┆ Williams           ┆ null        ┆ null        ┆ null         │
│ 11   ┆ Worcester St         ┆ Framingham State   ┆ null        ┆ null        ┆ null         │
│ 10   ┆ Defiance College     ┆ Rose-Hulman        ┆ 54          ┆ 78          ┆ 132          │
│ 10   ┆ Muskingum University ┆ Wilmington (OH)    ┆ 64          ┆ 63          ┆ 127          │
│ 2    ┆ Coast Guard          ┆ Anna Maria College ┆ 93          ┆ 24          ┆ 117          │
│ 13   ┆ Oklahoma             ┆ TCU                ┆ 69          ┆ 45          ┆ 114          │
│ 3    ┆ Texas State          ┆ Jackson State      ┆ 77          ┆ 34          ┆ 111          │
│ 10   ┆ Cornell College (IA) ┆ Illinois College   ┆ 34          ┆ 76          ┆ 110          │
└──────┴──────────────────────┴────────────────────┴─────────────┴─────────────┴──────────────┘

Recipe 2 — Team offensive EPA/play leaderboard 📈

This is what premium EPA-tagged play-by-play unlocks. Filter to real scrimmage plays, group by the offense, and average the EPA per play — a clean efficiency ranking in five lines.

team_col = 'start.pos_team.name'  # human-readable offense on each play
epa_cols = {team_col, 'EPA', 'play'}
if epa_cols.issubset(pbp.columns):
    leaderboard = (
        pbp
        .filter(pl.col('play') & pl.col('EPA').is_not_null())
        .group_by(team_col)
        .agg(
            pl.len().alias('plays'),
            pl.col('EPA').mean().round(3).alias('epa_per_play'),
        )
        .filter(pl.col('plays') >= 500)
        .sort('epa_per_play', descending=True)
        .rename({team_col: 'offense'})
        .head(15)
    )
    out = leaderboard
else:
    out = 'expected EPA columns not present in this pbp build'
out

shape: (15, 3)
┌────────────┬───────┬──────────────┐
│ offense    ┆ plays ┆ epa_per_play │
│ ---        ┆ ---   ┆ ---          │
│ str        ┆ u32   ┆ f64          │
╞════════════╪═══════╪══════════════╡
│ LSU        ┆ 945   ┆ 0.374        │
│ Oregon     ┆ 1052  ┆ 0.343        │
│ Georgia    ┆ 1064  ┆ 0.268        │
│ USC        ┆ 956   ┆ 0.213        │
│ Liberty    ┆ 1079  ┆ 0.205        │
│ …          ┆ …     ┆ …            │
│ Missouri   ┆ 1003  ┆ 0.153        │
│ Alabama    ┆ 1030  ┆ 0.153        │
│ Ohio State ┆ 931   ┆ 0.151        │
│ Miami      ┆ 1005  ┆ 0.148        │
│ Ole Miss   ┆ 989   ┆ 0.147        │
└────────────┴───────┴──────────────┘

Recipe 3 — A team's roster, sorted by position 🧩

Join the loaded roster against team_info to resolve a school name to its players, then count the depth at each position group.

team_name = 'Michigan'
squad = (
    rosters
    .filter(pl.col('team') == team_name)
    .select(['first_name', 'last_name', 'position', 'jersey',
             'height', 'weight', 'home_state'])
)
if squad.height:
    depth = (squad.group_by('position')
                  .agg(pl.len().alias('players'))
                  .sort('players', descending=True))
    print(f'{team_name}: {squad.height} players')
    out = depth.head(10)
else:
    out = f'no roster rows for {team_name} (try another school string)'
out

Michigan: 144 players





shape: (10, 2)
┌──────────┬─────────┐
│ position ┆ players │
│ ---      ┆ ---     │
│ str      ┆ u32     │
╞══════════╪═════════╡
│ DB       ┆ 23      │
│ OL       ┆ 21      │
│ WR       ┆ 19      │
│ LB       ┆ 18      │
│ DL       ┆ 12      │
│ DE       ┆ 12      │
│ TE       ┆ 11      │
│ RB       ┆ 11      │
│ PK       ┆ 6       │
│ QB       ┆ 6       │
└──────────┴─────────┘

Recipe 4 — Who was on the field? Per-play participants 🕵️

espn_cfb_play_participants resolves the athletes involved in each play (passer, rusher, receiver, tackler…) straight from ESPN's authoritative participants[] array — far more reliable than regex-parsing the play text. Set resolve_missing=False to skip the per-athlete $ref fan-out and keep it snappy.

gid = 401628334  # 2024 CFP National Championship
participants = safe(
    f'play participants {gid}',
    lambda: sdv.cfb.espn_cfb_play_participants(
        game_id=gid, resolve_missing=False,
    ),
)
if participants is not None and getattr(participants, 'height', 0):
    name_cols = [c for c in participants.columns if c.endswith('_player_name')]
    show = ['play_id'] + name_cols[:4] if 'play_id' in participants.columns else name_cols[:5]
    out = participants.select([c for c in show if c in participants.columns]).head()
else:
    out = 'participants feed quiet right now (offseason / rate limit)'
out

✅ play participants 401628334





shape: (5, 5)
┌───────────────────┬───────────────────┬───────────────────┬───────────────────┬──────────────────┐
│ play_id           ┆ kicker_player_nam ┆ returner_player_n ┆ passer_player_nam ┆ receiver_player_ │
│ ---               ┆ e                 ┆ ame               ┆ e                 ┆ name             │
│ i64               ┆ ---               ┆ ---               ┆ ---               ┆ ---              │
│                   ┆ str               ┆ str               ┆ str               ┆ str              │
╞═══════════════════╪═══════════════════╪═══════════════════╪═══════════════════╪══════════════════╡
│ 40162833410184990 ┆ Michael Lantz     ┆ Zavion Thomas     ┆ null              ┆ null             │
│ 2                 ┆                   ┆                   ┆                   ┆                  │
│ 40162833410185440 ┆ null              ┆ null              ┆ Garrett Nussmeier ┆ Kyren Lacy       │
│ 1                 ┆                   ┆                   ┆                   ┆                  │
│ 40162833410185750 ┆ null              ┆ null              ┆ Garrett Nussmeier ┆ Kyren Lacy       │
│ 1                 ┆                   ┆                   ┆                   ┆                  │
│ 40162833410185960 ┆ null              ┆ null              ┆ null              ┆ null             │
│ 1                 ┆                   ┆                   ┆                   ┆                  │
│ 40162833410186700 ┆ null              ┆ null              ┆ Garrett Nussmeier ┆ CJ Daniels       │
│ 1                 ┆                   ┆                   ┆                   ┆                  │
└───────────────────┴───────────────────┴───────────────────┴───────────────────┴──────────────────┘

Recipe 5 — Build a standings table from the schedule 🏆

No standings endpoint needed: stack each team's home and away results, count wins and losses, and you've got a win-percentage table for any season the loader serves.

completed = schedule.filter(pl.col('completed') == True)
home = completed.select(
    pl.col('home_team').alias('team'),
    (pl.col('home_points') > pl.col('away_points')).alias('win'),
)
away = completed.select(
    pl.col('away_team').alias('team'),
    (pl.col('away_points') > pl.col('home_points')).alias('win'),
)
standings_tbl = (
    pl.concat([home, away])
    .group_by('team')
    .agg(
        pl.col('win').sum().alias('wins'),
        (~pl.col('win')).sum().alias('losses'),
    )
    .with_columns(
        (pl.col('wins') / (pl.col('wins') + pl.col('losses')))
        .round(3).alias('win_pct')
    )
    .sort(['wins', 'win_pct'], descending=True)
)
standings_tbl.head(10)

shape: (10, 4)
┌──────────────────────────┬──────┬────────┬─────────┐
│ team                     ┆ wins ┆ losses ┆ win_pct │
│ ---                      ┆ ---  ┆ ---    ┆ ---     │
│ str                      ┆ u32  ┆ u32    ┆ f64     │
╞══════════════════════════╪══════╪════════╪═════════╡
│ Michigan                 ┆ 15   ┆ 0      ┆ 1.0     │
│ Harding University       ┆ 15   ┆ 0      ┆ 1.0     │
│ South Dakota State       ┆ 15   ┆ 0      ┆ 1.0     │
│ Washington               ┆ 14   ┆ 1      ┆ 0.933   │
│ Cortland                 ┆ 14   ┆ 1      ┆ 0.933   │
│ Colorado School Of Mines ┆ 14   ┆ 1      ┆ 0.933   │
│ North Central College    ┆ 14   ┆ 1      ┆ 0.933   │
│ Florida State            ┆ 13   ┆ 1      ┆ 0.929   │
│ Georgia                  ┆ 13   ┆ 1      ┆ 0.929   │
│ Wartburg                 ┆ 13   ┆ 1      ┆ 0.929   │
└──────────────────────────┴──────┴────────┴─────────┘

Recipe 6 — End-of-season Elo power ratings ⚡

Every schedule row ships pre- and post-game Elo ratings. Grab each team's most recent post-game Elo (sort by week, take the first) for a tidy, ready-to-rank power table — no model to fit.

elo = (
    pl.concat([
        schedule.select(
            pl.col('home_team').alias('team'),
            pl.col('week'),
            pl.col('home_postgame_elo').alias('elo'),
        ),
        schedule.select(
            pl.col('away_team').alias('team'),
            pl.col('week'),
            pl.col('away_postgame_elo').alias('elo'),
        ),
    ])
    .filter(pl.col('elo').is_not_null())
    .sort('week', descending=True)
    .group_by('team', maintain_order=True)
    .agg(pl.first('elo').alias('final_elo'))
    .sort('final_elo', descending=True)
)
elo.head(15)

shape: (15, 2)
┌───────────────┬───────────┐
│ team          ┆ final_elo │
│ ---           ┆ ---       │
│ str           ┆ i32       │
╞═══════════════╪═══════════╡
│ Michigan      ┆ 2174      │
│ Georgia       ┆ 2111      │
│ Ohio State    ┆ 2108      │
│ Penn State    ┆ 2061      │
│ Texas         ┆ 2050      │
│ …             ┆ …         │
│ Florida State ┆ 1951      │
│ Kansas State  ┆ 1942      │
│ Washington    ┆ 1883      │
│ SMU           ┆ 1861      │
│ James Madison ┆ 1835      │
└───────────────┴───────────┘

Recipe 7 — One team's full game log 📜

Filter the schedule to a single program, then flip the home/away columns so every row reads from that team's perspective — opponent, points for, points against, and the margin. Swap team to scout anyone.

team = 'Michigan'
gamelog = (
    schedule
    .filter((pl.col('home_team') == team) | (pl.col('away_team') == team))
    .unique(subset=['game_id'])
    .with_columns(
        pl.when(pl.col('home_team') == team)
          .then(pl.col('away_team')).otherwise(pl.col('home_team'))
          .alias('opponent'),
        pl.when(pl.col('home_team') == team)
          .then(pl.col('home_points')).otherwise(pl.col('away_points'))
          .alias('pts_for'),
        pl.when(pl.col('home_team') == team)
          .then(pl.col('away_points')).otherwise(pl.col('home_points'))
          .alias('pts_against'),
    )
    .with_columns(
        (pl.col('pts_for') - pl.col('pts_against')).alias('margin')
    )
    .select(['week', 'opponent', 'pts_for', 'pts_against', 'margin',
             'neutral_site'])
    .sort('week')
)
gamelog.head(16) if gamelog.height else f'no games found for {team}'

shape: (15, 6)
┌──────┬───────────────┬─────────┬─────────────┬────────┬──────────────┐
│ week ┆ opponent      ┆ pts_for ┆ pts_against ┆ margin ┆ neutral_site │
│ ---  ┆ ---           ┆ ---     ┆ ---         ┆ ---    ┆ ---          │
│ i32  ┆ str           ┆ i32     ┆ i32         ┆ i32    ┆ bool         │
╞══════╪═══════════════╪═════════╪═════════════╪════════╪══════════════╡
│ 1    ┆ East Carolina ┆ 30      ┆ 3           ┆ 27     ┆ false        │
│ 1    ┆ Alabama       ┆ 27      ┆ 20          ┆ 7      ┆ true         │
│ 1    ┆ Washington    ┆ 34      ┆ 13          ┆ 21     ┆ true         │
│ 2    ┆ UNLV          ┆ 35      ┆ 7           ┆ 28     ┆ false        │
│ 3    ┆ Bowling Green ┆ 31      ┆ 6           ┆ 25     ┆ false        │
│ …    ┆ …             ┆ …       ┆ …           ┆ …      ┆ …            │
│ 10   ┆ Purdue        ┆ 41      ┆ 13          ┆ 28     ┆ false        │
│ 11   ┆ Penn State    ┆ 24      ┆ 15          ┆ 9      ┆ false        │
│ 12   ┆ Maryland      ┆ 31      ┆ 24          ┆ 7      ┆ false        │
│ 13   ┆ Ohio State    ┆ 30      ┆ 24          ┆ 6      ┆ false        │
│ 14   ┆ Iowa          ┆ 26      ┆ 0           ┆ 26     ┆ true         │
└──────┴───────────────┴─────────┴─────────────┴────────┴──────────────┘

Recipe 8 — Rushing leaders, EPA included 🏃

Premium play-by-play means leaderboards aren't just totals — they carry efficiency. Filter to designed runs, sum the yards, and average the EPA per carry to separate the bell-cows from the truly explosive backs.

rush_cols = {'rush', 'rusher_player_name', 'statYardage', 'EPA'}
if rush_cols.issubset(pbp.columns):
    rushers = (
        pbp
        .filter((pl.col('rush') == True)
                & pl.col('rusher_player_name').is_not_null())
        .group_by('rusher_player_name')
        .agg(
            pl.len().alias('carries'),
            pl.col('statYardage').sum().alias('rush_yds'),
            pl.col('EPA').mean().round(3).alias('epa_per_rush'),
        )
        .filter(pl.col('carries') >= 100)
        .sort('rush_yds', descending=True)
        .head(15)
    )
    out = rushers
else:
    out = 'rushing columns not present in this pbp build'
out

shape: (15, 4)
┌────────────────────┬─────────┬──────────┬──────────────┐
│ rusher_player_name ┆ carries ┆ rush_yds ┆ epa_per_rush │
│ ---                ┆ ---     ┆ ---      ┆ ---          │
│ str                ┆ u32     ┆ i64      ┆ f64          │
╞════════════════════╪═════════╪══════════╪══════════════╡
│ Ollie Gordon II    ┆ 280     ┆ 1762     ┆ 0.076        │
│ Kimani Vidal       ┆ 288     ┆ 1600     ┆ 0.044        │
│ Cody Schrader      ┆ 274     ┆ 1585     ┆ 0.14         │
│ Tahj Brooks        ┆ 289     ┆ 1570     ┆ 0.196        │
│ Omarion Hampton    ┆ 243     ┆ 1503     ┆ 0.121        │
│ …                  ┆ …       ┆ …        ┆ …            │
│ Makhi Hughes       ┆ 255     ┆ 1365     ┆ 0.073        │
│ Ismail Mahdi       ┆ 217     ┆ 1362     ┆ 0.139        │
│ Jaydn Ott          ┆ 244     ┆ 1349     ┆ -0.007       │
│ Ashton Jeanty      ┆ 212     ┆ 1340     ┆ 0.112        │
│ Marcus Carroll     ┆ 272     ┆ 1336     ┆ 0.026        │
└────────────────────┴─────────┴──────────┴──────────────┘

Recipe 9 — The most thrilling games of the year 🎢

cfbfastR's schedule ships an excitement_index (a win-probability swinginess score). Sort it descending and you've ranked the season's white-knuckle finishes in one line.

thrillers = (
    schedule
    .filter(pl.col('excitement_index').is_not_null())
    .sort('excitement_index', descending=True)
    .select(['week', 'home_team', 'away_team',
             'home_points', 'away_points', 'excitement_index'])
    .head(10)
)
thrillers

shape: (10, 6)
┌──────┬──────────────────┬────────────────┬─────────────┬─────────────┬──────────────────┐
│ week ┆ home_team        ┆ away_team      ┆ home_points ┆ away_points ┆ excitement_index │
│ ---  ┆ ---              ┆ ---            ┆ ---         ┆ ---         ┆ ---              │
│ i32  ┆ str              ┆ str            ┆ i32         ┆ i32         ┆ f64              │
╞══════╪══════════════════╪════════════════╪═════════════╪═════════════╪══════════════════╡
│ 7    ┆ Southern         ┆ Lincoln (CA)   ┆ 45          ┆ 18          ┆ 14.267416        │
│ 9    ┆ Western Carolina ┆ Mercer         ┆ 38          ┆ 45          ┆ 13.938438        │
│ 11   ┆ Bucknell         ┆ Georgetown     ┆ 47          ┆ 50          ┆ 12.731991        │
│ 3    ┆ Tennessee State  ┆ Gardner-Webb   ┆ 27          ┆ 25          ┆ 12.041674        │
│ 6    ┆ Brown            ┆ Rhode Island   ┆ 30          ┆ 34          ┆ 11.825262        │
│ 3    ┆ Eastern Illinois ┆ Illinois State ┆ 14          ┆ 13          ┆ 11.431072        │
│ 5    ┆ Robert Morris    ┆ Howard         ┆ 10          ┆ 35          ┆ 11.33141         │
│ 6    ┆ Lindenwood       ┆ Tennessee Tech ┆ 23          ┆ 0           ┆ 11.198615        │
│ 10   ┆ New Hampshire    ┆ Villanova      ┆ 33          ┆ 45          ┆ 11.10256         │
│ 4    ┆ Eastern Illinois ┆ McNeese        ┆ 31          ┆ 28          ┆ 10.873191        │
└──────┴──────────────────┴────────────────┴─────────────┴─────────────┴──────────────────┘

Recipe 10 — Where does the talent come from? 🗺️

Roll the season roster up by home_state to map the recruiting footprint of college football — a quick reminder of just how much of the sport flows out of a handful of states.

talent_map = (
    rosters
    .filter(pl.col('home_state').is_not_null())
    .group_by('home_state')
    .agg(pl.len().alias('players'))
    .sort('players', descending=True)
    .head(15)
)
talent_map

shape: (15, 2)
┌────────────┬─────────┐
│ home_state ┆ players │
│ ---        ┆ ---     │
│ str        ┆ u32     │
╞════════════╪═════════╡
│ TX         ┆ 2526    │
│ FL         ┆ 1853    │
│ CA         ┆ 1748    │
│ GA         ┆ 1584    │
│ OH         ┆ 825     │
│ …          ┆ …       │
│ PA         ┆ 574     │
│ TN         ┆ 559     │
│ NJ         ┆ 534     │
│ MD         ┆ 512     │
│ SC         ┆ 472     │
└────────────┴─────────┘

Recipe 11 — Conference vs. non-conference, by margin 🔀

The schedule's conference_game flag lets you split the slate. Restrict to FBS, then compare the average final margin in league play versus the out-of-conference cupcakes — group games are (predictably) tighter.

fbs = schedule.filter(
    (pl.col('home_division') == 'fbs') & (pl.col('completed') == True)
)
splits = (
    fbs
    .with_columns(
        (pl.col('home_points') - pl.col('away_points')).abs().alias('margin')
    )
    .group_by('conference_game')
    .agg(
        pl.len().alias('games'),
        pl.col('margin').mean().round(1).alias('avg_margin'),
        pl.col('home_points').add(pl.col('away_points'))
          .mean().round(1).alias('avg_total_points'),
    )
    .sort('conference_game')
)
splits

shape: (2, 4)
┌─────────────────┬───────┬────────────┬──────────────────┐
│ conference_game ┆ games ┆ avg_margin ┆ avg_total_points │
│ ---             ┆ ---   ┆ ---        ┆ ---              │
│ bool            ┆ u32   ┆ f64        ┆ f64              │
╞═════════════════╪═══════╪════════════╪══════════════════╡
│ false           ┆ 362   ┆ 22.2       ┆ 54.0             │
│ true            ┆ 548   ┆ 15.2       ┆ 53.5             │
└─────────────────┴───────┴────────────┴──────────────────┘

Recipe 12 — Biggest betting favorites in history 💸

load_cfb_betting_lines is a premium release frame of historical sportsbook lines. Average the spread across books per game and sort to surface the most lopsided favorites — the mismatches Vegas saw coming a mile away.

lines = safe('load_cfb_betting_lines', sdv.cfb.load_cfb_betting_lines)
if lines is not None and {'season', 'market_type', 'lines',
                          'game_desc', 'abbr'}.issubset(lines.columns):
    target = sorted(lines['season'].drop_nulls().unique().to_list())[-1]
    favorites = (
        lines
        .filter((pl.col('season') == target)
                & (pl.col('market_type') == 'spread')
                & pl.col('lines').is_not_null())
        .group_by(['game_desc', 'abbr'])
        .agg(pl.col('lines').mean().round(1).alias('avg_spread'))
        .filter(pl.col('avg_spread') < 0)  # negative spread = favorite
        .sort('avg_spread')
        .head(10)
    )
    print(f'biggest favorites, {int(target)} season:')
    out = favorites
else:
    out = 'betting-lines frame unavailable right now'
out

✅ load_cfb_betting_lines
biggest favorites, 2025 season:





shape: (10, 3)
┌────────────────────────────────┬────────────┬────────────┐
│ game_desc                      ┆ abbr       ┆ avg_spread │
│ ---                            ┆ ---        ┆ ---        │
│ str                            ┆ str        ┆ f64        │
╞════════════════════════════════╪════════════╪════════════╡
│ Arkansas-Pine Bluff@Texas Tech ┆ Texas Tech ┆ -54.5      │
│ Bethune-Cookman@Miami          ┆ Miami      ┆ -54.5      │
│ Samford@Texas A&M              ┆ Texas A&M  ┆ -54.2      │
│ Grambling@Ohio State           ┆ Ohio State ┆ -53.5      │
│ Samford@Baylor                 ┆ Baylor     ┆ -52.0      │
│ The Citadel@Ole Miss           ┆ Ole Miss   ┆ -51.8      │
│ Eastern Illinois@Alabama       ┆ Alabama    ┆ -51.3      │
│ East Texas A&M@SMU             ┆ SMU        ┆ -50.8      │
│ SE Louisiana@LSU               ┆ LSU        ┆ -48.8      │
│ Northwestern State@Cincinnati  ┆ Cincinnati ┆ -48.5      │
└────────────────────────────────┴────────────┴────────────┘

Recipe 13 — Hand it to pandas 🐼

Every loader takes return_as_pandas=True, and any polars frame converts with .to_pandas(). Once it's a pandas DataFrame the whole pandas/numpy/scikit-learn world opens up — here, a one-call .describe() of scoring across the season.

score_pd = (
    schedule
    .select(['home_points', 'away_points'])
    .to_pandas()
)
score_pd['total_points'] = score_pd['home_points'] + score_pd['away_points']
print(type(score_pd).__module__)
score_pd.describe().round(1)

pandas





       home_points  away_points  total_points
count       3730.0       3730.0        3730.0
mean          28.9         24.3          53.1
std           16.0         15.0          17.8
min            0.0          0.0           0.0
25%           17.0         14.0          41.0
50%           28.0         23.0          52.0
75%           38.0         34.0          65.0
max           96.0         91.0         132.0

🗞️ Live tour: standings, polls, leaders & recruits

A quick lap through the rest of the live ESPN surface. Each is wrapped in safe() so the page renders cleanly whatever the feed is doing today.

Function	Use it for
espn_cfb_standings	conference / division standings
espn_cfb_rankings	AP / Coaches / CFP polls
espn_cfb_leaders	league stat leaders by category
espn_cfb_recruits	a season's recruiting class

standings = safe('ESPN standings', sdv.cfb.espn_cfb_standings)
rankings = safe('ESPN rankings (polls)', sdv.cfb.espn_cfb_rankings)
(standings.head()
 if standings is not None and getattr(standings, 'height', 0)
 else (rankings.head()
       if rankings is not None and getattr(rankings, 'height', 0)
       else 'standings & rankings unavailable right now'))

✅ ESPN standings
✅ ESPN rankings (polls)





shape: (5, 26)
┌────────────┬────────────┬─────────┬────────────┬───┬────────────┬───────────┬───────────┬────────┐
│ group_name ┆ group_abbr ┆ team_id ┆ team_name  ┆ … ┆ vs         ┆ vs. conf. ┆ vs ap top ┆ vs usa │
│ ---        ┆ eviation   ┆ ---     ┆ ---        ┆   ┆ division   ┆ ---       ┆ 25        ┆ ranked │
│ str        ┆ ---        ┆ str     ┆ str        ┆   ┆ ---        ┆ str       ┆ ---       ┆ teams  │
│            ┆ str        ┆         ┆            ┆   ┆ str        ┆           ┆ str       ┆ ---    │
│            ┆            ┆         ┆            ┆   ┆            ┆           ┆           ┆ str    │
╞════════════╪════════════╪═════════╪════════════╪═══╪════════════╪═══════════╪═══════════╪════════╡
│ American   ┆ American   ┆ 5       ┆ Blazers    ┆ … ┆ null       ┆ null      ┆ null      ┆ null   │
│ Conference ┆            ┆         ┆            ┆   ┆            ┆           ┆           ┆        │
│ American   ┆ American   ┆ 58      ┆ Bulls      ┆ … ┆ null       ┆ null      ┆ null      ┆ null   │
│ Conference ┆            ┆         ┆            ┆   ┆            ┆           ┆           ┆        │
│ American   ┆ American   ┆ 151     ┆ Pirates    ┆ … ┆ null       ┆ null      ┆ null      ┆ null   │
│ Conference ┆            ┆         ┆            ┆   ┆            ┆           ┆           ┆        │
│ American   ┆ American   ┆ 202     ┆ Golden     ┆ … ┆ null       ┆ null      ┆ null      ┆ null   │
│ Conference ┆            ┆         ┆ Hurricane  ┆   ┆            ┆           ┆           ┆        │
│ American   ┆ American   ┆ 218     ┆ Owls       ┆ … ┆ null       ┆ null      ┆ null      ┆ null   │
│ Conference ┆            ┆         ┆            ┆   ┆            ┆           ┆           ┆        │
└────────────┴────────────┴─────────┴────────────┴───┴────────────┴───────────┴───────────┴────────┘

leaders = safe(
    'ESPN passing leaders',
    lambda: sdv.cfb.espn_cfb_leaders(category='passingYards', season=2023, limit=15),
)
recruits = safe(
    'ESPN recruiting class',
    lambda: sdv.cfb.espn_cfb_recruits(season=2024, limit=25),
)
(leaders.head()
 if leaders is not None and getattr(leaders, 'height', 0)
 else (recruits.head()
       if recruits is not None and getattr(recruits, 'height', 0)
       else 'leaders & recruits unavailable right now'))

✅ ESPN passing leaders
✅ ESPN recruiting class





shape: (1, 2)
┌──────┬─────────────────────────────────┐
│ code ┆ message                         │
│ ---  ┆ ---                             │
│ i64  ┆ str                             │
╞══════╪═════════════════════════════════╡
│ 400  ┆ http://sports.core.api.espn.pv… │
└──────┴─────────────────────────────────┘

🧪 Bonus: process one game from scratch with CFBPlayProcess

Want EPA/WPA on a single live game without loading a whole season? CFBPlayProcess drives the full ESPN pipeline: .espn_cfb_pbp() fetches the raw summary, then .run_processing_pipeline() returns a dict whose plays key is the fully-featured play list (alongside an advanced box score and metadata).

from sportsdataverse.cfb import CFBPlayProcess

def process_game(game_id):
    game = CFBPlayProcess(gameId=game_id)
    game.espn_cfb_pbp()
    processed = game.run_processing_pipeline()
    return pl.DataFrame(processed['plays'], infer_schema_length=None)

plays = safe('CFBPlayProcess 401628334', lambda: process_game(401628334))
if plays is not None and plays.height:
    cols = [c for c in ('period', 'pos_team', 'down', 'distance',
                        'play_type', 'EPA') if c in plays.columns]
    out = plays.select(cols).head()
else:
    out = 'live PBP pipeline quiet right now'
out

✅ CFBPlayProcess 401628334





shape: (5, 5)
┌────────┬──────────┬──────┬──────────┬───────────┐
│ period ┆ pos_team ┆ down ┆ distance ┆ EPA       │
│ ---    ┆ ---      ┆ ---  ┆ ---      ┆ ---       │
│ i64    ┆ i64      ┆ i64  ┆ i64      ┆ f64       │
╞════════╪══════════╪══════╪══════════╪═══════════╡
│ 1      ┆ 99       ┆ 1    ┆ 10       ┆ -1.056855 │
│ 1      ┆ 99       ┆ 1    ┆ 10       ┆ 1.160273  │
│ 1      ┆ 99       ┆ 1    ┆ 10       ┆ 1.005587  │
│ 1      ┆ 99       ┆ 1    ┆ 10       ┆ -0.563137 │
│ 1      ┆ 99       ┆ 2    ┆ 8        ┆ 0.052125  │
└────────┴──────────┴──────┴──────────┴───────────┘

🏛️ stats.ncaa.org football — parse_cfb_ncaa_pbp + box parsers

New in 0.0.72: parsers for the stats.ncaa.org football surface. parse_cfb_ncaa_pbp takes the raw HTML of a /contests/{id}/play_by_play page and emits one tidy row per play, cfbfastR-style — drive context, down/distance/yard line, a classified play_type, extracted players/yards/kick details, and boolean flags — while cfb_ncaa_box adds the box-score / drives / officials tables:

from sportsdataverse.cfb import parse_cfb_ncaa_pbp

pbp = parse_cfb_ncaa_pbp(html, contest_id=6081276)   # html = captured page
pbp.filter(pl.col("touchdown") == True).head()

stats.ncaa.org is rate-limit unfriendly, so this family is parser-first: feed it pages captured by your own fetch pipeline (the shared proxy-bound NCAA fetch layer used by the ncaa_mbb_* family) rather than scraping live in a notebook.

🎉 Where to next

• 🗄️ Loaders are your premium fast-path — full reference on the Loaders page (load_cfb_pbp, load_cfb_rosters, load_cfb_schedule, load_cfb_team_info).
• 📡 ESPN families live across the Site, Web, Core and Additional reference pages.
• 🐼 Pass return_as_pandas=True for pandas, or return_parsed=False on the espn_cfb_* wrappers for the raw JSON.
• 🟥 R user? The same verbs live in cfbfastR.
• Part of the SportsDataverse ecosystem.

Now go chart some chunk plays — and may your EPA always be positive! 📈🏈