DLS & Resource Methods

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A simplified Duckworth–Lewis–Stern resource model provides an interpretable baseline.

Note: ICC uses proprietary DLS tables with protected overs. This chapter implements an educational approximation to teach the resource logic.

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'

Resource remaining surface

overs_grid = np.linspace(0, 50, 51)
wickets_grid = range(10)
surface = np.array([[resource_remaining(o, w) for o in overs_grid] for w in wickets_grid])
fig, ax = plt.subplots(figsize=(9, 5))
im = ax.imshow(surface, aspect='auto', origin='lower', cmap='YlGnBu')
ax.set_xlabel('Overs completed')
ax.set_ylabel('Wickets lost')
ax.set_title('Simplified ODI resource remaining (%)')
plt.colorbar(im, ax=ax, label='Resource %')
plt.tight_layout()
plt.show()

Ball-by-ball DLS probability for WC Final

doc = load_cricsheet_json(SAMPLE_DIR / '2019_wc_final_eng_nz.json')
states = build_game_states(doc)
first_runs = int(states['target'].iloc[0] - 1)
states = states.copy()
states['dls_prob'] = dls_win_probability(states, first_runs)
fig, ax = plt.subplots(figsize=(10, 4))
ax.plot(states['overs_completed'], states['dls_prob'], color='#2e7d32', linewidth=2)
ax.set_xlabel('Overs (chase)')
ax.set_ylabel('P(win)')
ax.set_ylim(0, 1)
ax.set_title('DLS-style win probability — 2019 World Cup Final chase')
plt.tight_layout()
plt.show()
states[['over', 'runs_scored', 'wickets_lost', 'dls_prob']].tail(8)

	over	runs_scored	wickets_lost	dls_prob
292	49	294	7	1.0
293	49	294	7	1.0
294	50	294	7	1.0
295	50	294	7	1.0
296	50	296	7	1.0
297	50	298	7	1.0
298	50	298	7	1.0
299	50	298	7	1.0

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