Introduction & Data Loading

Introduction & Data Loading#

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This notebook introduces ODI win probability, the Cricsheet data format, and loads a famous match for exploration.

ODI scoring recap#

A standard ODI allots 50 overs (300 balls) and 10 wickets per innings. The team batting second must surpass the first-innings total to win.

Symbol

Meaning

R

Runs scored so far

W

Wickets lost

B

Balls bowled

RRR

Runs required per remaining ball

What is win probability?#

Win probability is P(batting team wins | current game state). Broadcasters show it as a live gauge; analysts use it to quantify how dramatically a chase shifted. Required run rate (RRR) is a related heuristic but does not map cleanly to probability.

import sys
from pathlib import Path
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
plt.style.use('seaborn-v0_8-whitegrid')

PROJ_DIR = Path('.').resolve()
if not (PROJ_DIR / 'cricsheet_utils.py').exists():
    PROJ_DIR = Path('projects/cricket-win-probability').resolve()
if str(PROJ_DIR) not in sys.path:
    sys.path.insert(0, str(PROJ_DIR))

from cricsheet_utils import (
    project_data_dir, load_cricsheet_json, match_metadata, balls_to_dataframe,
    build_game_states, cumulative_runs_by_innings, resource_remaining,
    dls_win_probability,
)
DATA_DIR = project_data_dir()
SAMPLE_DIR = DATA_DIR / 'sample_matches'

print('Ready:', DATA_DIR)

Ready: /Users/areeslindley/Documents/Git_repositories/projects-website/projects/cricket-win-probability/data

Load a case-study match#

We use bundled Cricsheet-format JSON from the 2019 World Cup Final.

match_path = SAMPLE_DIR / '2019_wc_final_eng_nz.json'
doc = load_cricsheet_json(match_path)
meta = match_metadata(doc)
balls = balls_to_dataframe(doc)
meta

{'match_id': 1,
 'teams': ['New Zealand', 'England'],
 'venue': 'Sample Ground',
 'city': 'Sample City',
 'event': 'ICC Cricket World Cup Final',
 'season': '2019',
 'date': '2019-07-14',
 'winner': 'England',
 'margin': {'wickets': 0},
 'outcome': {'winner': 'England', 'by': {'wickets': 0}},
 'match_type': 'ODI',
 'gender': 'male'}

Ball-by-ball table#

balls.head(10)
innings batting_team bowling_team over ball_in_over ball_number runs_total runs_batter is_wicket cumulative_runs ... venue city event season date winner margin outcome match_type gender
0 1 New Zealand England 1 1 1 2 2 0 2 ... Sample Ground Sample City ICC Cricket World Cup Final 2019 2019-07-14 England {'wickets': 0} {'winner': 'England', 'by': {'wickets': 0}} ODI male
1 1 New Zealand England 1 1 2 2 2 0 4 ... Sample Ground Sample City ICC Cricket World Cup Final 2019 2019-07-14 England {'wickets': 0} {'winner': 'England', 'by': {'wickets': 0}} ODI male
2 1 New Zealand England 1 1 3 0 0 0 4 ... Sample Ground Sample City ICC Cricket World Cup Final 2019 2019-07-14 England {'wickets': 0} {'winner': 'England', 'by': {'wickets': 0}} ODI male
3 1 New Zealand England 1 1 4 1 1 0 5 ... Sample Ground Sample City ICC Cricket World Cup Final 2019 2019-07-14 England {'wickets': 0} {'winner': 'England', 'by': {'wickets': 0}} ODI male
4 1 New Zealand England 1 1 5 0 0 0 5 ... Sample Ground Sample City ICC Cricket World Cup Final 2019 2019-07-14 England {'wickets': 0} {'winner': 'England', 'by': {'wickets': 0}} ODI male
5 1 New Zealand England 1 1 6 0 0 0 5 ... Sample Ground Sample City ICC Cricket World Cup Final 2019 2019-07-14 England {'wickets': 0} {'winner': 'England', 'by': {'wickets': 0}} ODI male
6 1 New Zealand England 2 1 7 1 1 0 6 ... Sample Ground Sample City ICC Cricket World Cup Final 2019 2019-07-14 England {'wickets': 0} {'winner': 'England', 'by': {'wickets': 0}} ODI male
7 1 New Zealand England 2 1 8 0 0 0 6 ... Sample Ground Sample City ICC Cricket World Cup Final 2019 2019-07-14 England {'wickets': 0} {'winner': 'England', 'by': {'wickets': 0}} ODI male
8 1 New Zealand England 2 1 9 1 1 0 7 ... Sample Ground Sample City ICC Cricket World Cup Final 2019 2019-07-14 England {'wickets': 0} {'winner': 'England', 'by': {'wickets': 0}} ODI male
9 1 New Zealand England 2 1 10 2 2 0 9 ... Sample Ground Sample City ICC Cricket World Cup Final 2019 2019-07-14 England {'wickets': 0} {'winner': 'England', 'by': {'wickets': 0}} ODI male

10 rows × 25 columns

Cumulative runs by innings#

cum = cumulative_runs_by_innings(balls)
fig, ax = plt.subplots(figsize=(10, 5))
for label, grp in cum.groupby('innings_label'):
    ax.plot(grp['x'], grp['cumulative_runs'], label=label, linewidth=2)
ax.set_xlabel('Overs')
ax.set_ylabel('Runs')
ax.set_title('2019 World Cup Final — cumulative runs')
ax.legend()
plt.tight_layout()
fig_path = PROJ_DIR / 'figures' / '01_innings_runs.png'
fig_path.parent.mkdir(exist_ok=True)
fig.savefig(fig_path, dpi=120)
plt.show()
print(f'Saved {fig_path}')
../../_images/c0c1740cac6584af196f70012ef10e80a70a35471cb3e5df5df25d44b0d82151.png 
Saved /Users/areeslindley/Documents/Git_repositories/projects-website/projects/cricket-win-probability/figures/01_innings_runs.png

Preview#

Each ball in the chase carries a binary outcome label: did the batting team eventually win? Later chapters model that probability from the live state.

states = build_game_states(doc)
print(f'Chase balls: {len(states)}')
print(f"Chase team won: {states['batting_team_won'].iloc[-1]}")
states[['ball_number', 'runs_scored', 'wickets_lost', 'runs_required', 'runs_required_per_ball']].tail()

Chase balls: 300
Chase team won: 1
ball_number runs_scored wickets_lost runs_required runs_required_per_ball
295 296 294 7 0 0.0
296 297 296 7 0 0.0
297 298 298 7 0 0.0
298 299 298 7 0 0.0
299 300 298 7 0 0.0

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