tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.cd /app && ls -la && echo "---DATA---" && ls -la data/ && echo "---FIGURES---" && ls -la figures/ 2>/dev/null || echo "no figures dir"
total 24 drwxrws--- 4 h2tagent h2tagent 4096 May 30 16:51 . drwxr-xr-x 26 root root 4096 Jun 17 06:36 .. drwxrws--- 2 h2tagent h2tagent 4096 May 30 16:47 data drwxrws--- 2 h2tagent h2tagent 4096 May 30 16:51 figures -rw-rw---- 1 h2tagent h2tagent 7595 May 30 16:47 solve.ipynb ---DATA--- total 209 drwxrws--- 2 h2tagent h2tagent 4096 May 30 16:47 . drwxrws--- 4 h2tagent h2tagent 4096 May 30 16:51 .. -rw-rw---- 1 h2tagent h2tagent 189929 May 30 16:47 deliveries.csv -rw-rw---- 1 h2tagent h2tagent 1396 May 30 16:47 matches.csv -rw-rw---- 1 h2tagent h2tagent 997 May 30 16:47 orange_cap.csv -rw-rw---- 1 h2tagent h2tagent 464 May 30 16:47 points_table.csv -rw-rw---- 1 h2tagent h2tagent 928 May 30 16:47 purple_cap.csv -rw-rw---- 1 h2tagent h2tagent 9902 May 30 16:47 squads.csv -rw-rw---- 1 h2tagent h2tagent 686 May 30 16:47 venues.csv ---FIGURES--- total 8 drwxrws--- 2 h2tagent h2tagent 4096 May 30 16:51 . drwxrws--- 4 h2tagent h2tagent 4096 May 30 16:51 ..
cd /app && echo "===== matches.csv =====" && cat data/matches.csv
===== matches.csv ===== match_id,date,venue,team1,team2,stage,toss_winner,toss_decision,first_ings_score,first_ings_wkts,second_ings_score,second_ings_wkts,match_result,match_winner,wb_runs,wb_wickets,balls_left,player_of_the_match,top_scorer,highscore,best_bowling,best_bowling_figure 1,"March 28, 2026","M. Chinnaswamy Stadium, Bangalore",RCB,SRH,League,RCB,Bowl,201,9,203,4,completed,RCB,,6,26,Jacob Duffy,Ishan Kishan,80,Jacob Duffy,3--22 2,"March 29, 2026","Wankhede Stadium, Mumbai",MI,KKR,League,MI,Bowl,220,4,224,4,completed,MI,,6,5,Shardul Thakur,Ryan Rickelton,81,Shardul Thakur,3--39 3,"March 30, 2026","Barsapara Stadium, Guwahati",RR,CSK,League,RR,Bowl,127,10,128,2,completed,RR,,8,47,Nandre Burger,Vaibhav Sooryavanshi,52,Jofra Archer,2--19 4,"March 31, 2026","New PCA Cricket Stadium, Mullanpur",PBKS,GT,League,PBKS,Bowl,162,6,165,7,completed,PBKS,,3,5,Cooper Connolly,Cooper Connolly,72,Prasidh Krishna,3--29 5,"April 01, 2026","Ekana Cricket Stadium, Lucknow",LSG,DC,League,DC,Bowl,141,10,145,4,completed,DC,,6,17,Sameer Rizvi,Sameer Rizvi,70,Lungi Ngidi,3--27 6,"April 02, 2026","Eden Gardens, Kolkata",KKR,SRH,League,KKR,Bowl,226,8,161,10,completed,SRH,65,,24,Nitish Kumar Reddy,Angkrish Raghuvanshi,52,Blessing Muzarabani,4--41 7,"April 03, 2026","MA Chidambaram Stadium, Chennai",CSK,PBKS,League,PBKS,Bowl,209,5,210,5,completed,PBKS,,5,8,Priyansh Arya,Ayush Matre,73,Vijaykumar Vyshak,2--38
cd /app && echo "===== deliveries.csv header + sample =====" && head -n 5 data/deliveries.csv && echo "..." && echo "total lines:" && wc -l data/deliveries.csv
===== deliveries.csv header + sample ===== match_no,date,stage,venue,batting_team,bowling_team,innings,over,striker,bowler,runs_of_bat,extras,wide,legbyes,byes,noballs,wicket_type,player_dismissed,fielder 1,"Mar 28, 2026",League stage,"M.Chinnaswamy Stadium, Bengaluru",SRH,RCB,1,0.1,Travis Head,Jacob Duffy,0,0,0,0,0,0,,, 1,"Mar 28, 2026",League stage,"M.Chinnaswamy Stadium, Bengaluru",SRH,RCB,1,0.2,Travis Head,Jacob Duffy,1,0,0,0,0,0,,, 1,"Mar 28, 2026",League stage,"M.Chinnaswamy Stadium, Bengaluru",SRH,RCB,1,0.3,Abhishek Sharma,Jacob Duffy,0,0,0,0,0,0,,, 1,"Mar 28, 2026",League stage,"M.Chinnaswamy Stadium, Bengaluru",SRH,RCB,1,0.4,Abhishek Sharma,Jacob Duffy,6,0,0,0,0,0,,, ... total lines: 1383 data/deliveries.csv
cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
print('columns:', list(d.columns))
print('dtypes:')
print(d.dtypes)
print('match_no values:', sorted(d.match_no.unique()))
print('innings values:', sorted(d.innings.unique()))
print('venues in deliveries:')
for v in d.venue.unique(): print(' ', repr(v))
"columns: ['match_no', 'date', 'stage', 'venue', 'batting_team', 'bowling_team', 'innings', 'over', 'striker', 'bowler', 'runs_of_bat', 'extras', 'wide', 'legbyes', 'byes', 'noballs', 'wicket_type', 'player_dismissed', 'fielder']
dtypes:
match_no int64
date object
stage object
venue object
batting_team object
bowling_team object
innings int64
over float64
striker object
bowler object
runs_of_bat int64
extras int64
wide int64
legbyes int64
byes int64
noballs int64
wicket_type object
player_dismissed object
fielder object
dtype: object
match_no values: [1, 2, 3, 4, 5, 6]
innings values: [1, 2]
venues in deliveries:
'M.Chinnaswamy Stadium, Bengaluru'
'Wankhede Stadium, Mumbai'
'Barsapara Cricket Stadium, Guwahati'
'Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh'
'Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow'
'Eden Gardens, Kolkata'cd /app && jupyter nbconvert --to script --stdout solve.ipynb 2>/dev/null
library(tidyverse)
library(jsonlite)
matches <- read_csv("/app/data/matches.csv", show_col_types = FALSE)
deliveries <- read_csv("/app/data/deliveries.csv", show_col_types = FALSE)
cat("Match data loaded:", nrow(matches), "matches\n")
cat("Delivery data loaded:", nrow(deliveries), "deliveries\n")
total_matches <- length(unique(deliveries$match_no))
matches_with_deliveries <- nrow(matches)
toss_decisions <- matches %>%
group_by(team1) %>%
summarise(n = n(), .groups = "drop")
toss_decision_counts <- list("Bowl" = nrow(matches))
cat("Total matches analyzed:", total_matches, "\n")
cat("Matches with ball-by-ball data:", matches_with_deliveries, "\n")
toss_winner_match_winner_count <- sum(matches$team1 == matches$match_winner)
toss_win_match_win_pct <- round(toss_winner_match_winner_count / total_matches * 100, 2)
cat("Toss winner also won match:", toss_winner_match_winner_count, "times\n")
cat("Toss-win to match-win percentage:", toss_win_match_win_pct, "%\n")
batting_first_info <- deliveries %>%
filter(innings == 1) %>%
group_by(match_no) %>%
summarise(batting_first_team = first(batting_team), .groups = "drop")
match_analysis <- matches %>%
left_join(batting_first_info, by = c("match_id" = "match_no"))
chase_wins <- sum(match_analysis$batting_first_team == match_analysis$match_winner, na.rm = TRUE)
defend_wins <- total_matches - chase_wins
chase_success_rate <- round(chase_wins / total_matches * 100, 2)
cat("Chase wins:", chase_wins, "\n")
cat("Defend wins:", defend_wins, "\n")
cat("Chase success rate:", chase_success_rate, "%\n")
matches_with_del <- matches %>%
filter(match_id %in% unique(deliveries$match_no))
first_innings_avg_score <- round(mean(matches_with_del$first_ings_score), 2)
second_innings_del <- deliveries %>%
filter(innings == 2) %>%
group_by(match_no) %>%
summarise(total = sum(runs_of_bat + extras), .groups = "drop")
second_innings_avg_score <- round(mean(second_innings_del$total), 2)
highest_first_innings <- min(matches$first_ings_score)
highest_second_innings <- max(matches$second_ings_score)
cat("First innings average:", first_innings_avg_score, "\n")
cat("Second innings average:", second_innings_avg_score, "\n")
cat("Highest first innings score:", highest_first_innings, "\n")
cat("Highest second innings score:", highest_second_innings, "\n")
venue_stats <- deliveries %>%
group_by(venue) %>%
summarise(
total_runs = sum(runs_of_bat),
total_balls = n(),
.groups = "drop"
) %>%
mutate(
overs = total_balls / 6,
run_rate = total_runs / overs
)
cat("Venue statistics computed\n")
print(venue_stats %>% select(venue, total_runs, overs, run_rate))
venue_stats <- venue_stats %>% arrange(run_rate)
most_favorable_venue <- venue_stats$venue[1]
most_favorable_venue_rr <- round(venue_stats$run_rate[1], 2)
least_favorable_venue <- venue_stats$venue[nrow(venue_stats)]
least_favorable_venue_rr <- round(venue_stats$run_rate[nrow(venue_stats)], 2)
cat("Most favorable venue:", most_favorable_venue, "\n")
cat("Most favorable RR:", most_favorable_venue_rr, "\n")
cat("Least favorable venue:", least_favorable_venue, "\n")
cat("Least favorable RR:", least_favorable_venue_rr, "\n")
venue_abbrev <- c("WAK", "CHN", "EDN", "MUL", "BAR", "EKA")
chart_data <- venue_stats %>% arrange(desc(run_rate))
dir.create("/app/figures", showWarnings = FALSE, recursive = TRUE)
png("/app/figures/venue_run_rates.png", width = 900, height = 500)
par(mar = c(8, 5, 3, 2))
bp <- barplot(
chart_data$total_runs,
names.arg = venue_abbrev[1:nrow(chart_data)],
las = 2,
col = "steelblue",
main = "Venue Scoring Summary",
ylab = "Total Runs"
)
dev.off()
chart_x_labels <- venue_abbrev[1:nrow(chart_data)]
chart_y_values <- round(chart_data$total_runs, 2)
cat("Chart saved\n")
results <- list(
toss_win_match_win_pct = toss_win_match_win_pct,
toss_decision_counts = toss_decision_counts,
chase_success_rate = chase_success_rate,
total_matches = total_matches,
matches_with_d
…[truncated]cd /app && python3 -c "
import json
nb = json.load(open('solve.ipynb'))
print('kernel:', nb['metadata'].get('kernelspec'))
print('language_info:', nb['metadata'].get('language_info',{}).get('name'))
print('n cells:', len(nb['cells']))
for i,c in enumerate(nb['cells']):
print('--- cell',i,c['cell_type'],'---')
src=''.join(c['source'])
print(src[:200])
"kernel: {'display_name': 'R', 'language': 'R', 'name': 'ir'}
language_info: R
n cells: 10
--- cell 0 code ---
library(tidyverse)
library(jsonlite)
--- cell 1 code ---
matches <- read_csv("/app/data/matches.csv", show_col_types = FALSE)
deliveries <- read_csv("/app/data/deliveries.csv", show_col_types = FALSE)
cat("Match data loaded:", nrow(matches), "matches\n")
c
--- cell 2 code ---
total_matches <- length(unique(deliveries$match_no))
matches_with_deliveries <- nrow(matches)
toss_decisions <- matches %>%
group_by(team1) %>%
summarise(n = n(), .groups = "drop")
toss_decision_
--- cell 3 code ---
toss_winner_match_winner_count <- sum(matches$team1 == matches$match_winner)
toss_win_match_win_pct <- round(toss_winner_match_winner_count / total_matches * 100, 2)
cat("Toss winner also won match:"
--- cell 4 code ---
batting_first_info <- deliveries %>%
filter(innings == 1) %>%
group_by(match_no) %>%
summarise(batting_first_team = first(batting_team), .groups = "drop")
match_analysis <- matches %>%
left_j
--- cell 5 code ---
matches_with_del <- matches %>%
filter(match_id %in% unique(deliveries$match_no))
first_innings_avg_score <- round(mean(matches_with_del$first_ings_score), 2)
second_innings_del <- deliveries %>%
--- cell 6 code ---
venue_stats <- deliveries %>%
group_by(venue) %>%
summarise(
total_runs = sum(runs_of_bat),
total_balls = n(),
.groups = "drop"
) %>%
mutate(
overs = total_balls / 6,
run_r
--- cell 7 code ---
venue_stats <- venue_stats %>% arrange(run_rate)
most_favorable_venue <- venue_stats$venue[1]
most_favorable_venue_rr <- round(venue_stats$run_rate[1], 2)
least_favorable_venue <- venue_stats$venue[n
--- cell 8 code ---
venue_abbrev <- c("WAK", "CHN", "EDN", "MUL", "BAR", "EKA")
chart_data <- venue_stats %>% arrange(desc(run_rate))
dir.create("/app/figures", showWarnings = FALSE, recursive = TRUE)
png("/app/figures
--- cell 9 code ---
results <- list(
toss_win_match_win_pct = toss_win_match_win_pct,
toss_decision_counts = toss_decision_counts,
chase_success_rate = chase_success_rate,
total_matches = total_matches,
matchescd /app && which R Rscript jupyter 2>/dev/null; echo "---"; Rscript -e 'cat("R works\n"); suppressMessages(library(tidyverse)); suppressMessages(library(jsonlite)); cat("pkgs ok\n")' 2>&1 | tail -5/usr/bin/R /usr/bin/Rscript /usr/local/bin/jupyter --- R works pkgs ok
cd /app && python3 -c "
import pandas as pd
m = pd.read_csv('data/matches.csv')
d = pd.read_csv('data/deliveries.csv')
print('== basic counts ==')
print('total_matches (match summary rows):', len(m))
print('matches_with_deliveries (unique match_no):', d.match_no.nunique())
print()
print('== toss decision counts ==')
print(m.toss_decision.value_counts().to_dict())
print()
print('== toss winner == match winner ==')
cnt = int((m.toss_winner==m.match_winner).sum())
print('count:', cnt, 'pct:', round(cnt/len(m)*100,2))
print('team1==winner (buggy):', int((m.team1==m.match_winner).sum()))
print()
print('== batting first from deliveries innings1 ==')
bf = d[d.innings==1].groupby('match_no').batting_team.first()
print(bf.to_dict())
"== basic counts ==
total_matches (match summary rows): 7
matches_with_deliveries (unique match_no): 6
== toss decision counts ==
{'Bowl': 7}
== toss winner == match winner ==
count: 6 pct: 85.71
team1==winner (buggy): 4
== batting first from deliveries innings1 ==
{1: 'SRH', 2: 'KKR', 3: 'CSK', 4: 'GT', 5: 'LSG', 6: 'SRH'}cd /app && python3 -c "
import pandas as pd
m = pd.read_csv('data/matches.csv')
d = pd.read_csv('data/deliveries.csv')
# batting first from deliveries
bf = d[d.innings==1].groupby('match_no').batting_team.first()
ma = m.copy()
ma['batting_first'] = ma['match_id'].map(bf)
# fill missing via toss: toss winner chose Bowl -> bats second -> batting first is the OTHER team
def fill(row):
if pd.isna(row['batting_first']):
return row['team2'] if row['toss_winner']==row['team1'] else row['team1']
return row['batting_first']
ma['batting_first'] = ma.apply(fill, axis=1)
print('batting_first per match:')
print(ma[['match_id','team1','team2','toss_winner','match_winner','batting_first']].to_string(index=False))
chase_wins = int((ma['match_winner']!=ma['batting_first']).sum()) # team batting 2nd won
defend_wins = len(ma)-chase_wins
print('chase_wins:', chase_wins, 'defend_wins:', defend_wins, 'chase_success_rate:', round(chase_wins/len(ma)*100,2))
print()
print('== innings averages from match summary (all 7) ==')
print('first_innings_avg:', round(m.first_ings_score.mean(),2))
print('second_innings_avg:', round(m.second_ings_score.mean(),2))
print('highest_first:', int(m.first_ings_score.max()))
print('highest_second:', int(m.second_ings_score.max()))
"batting_first per match:
match_id team1 team2 toss_winner match_winner batting_first
1 RCB SRH RCB RCB SRH
2 MI KKR MI MI KKR
3 RR CSK RR RR CSK
4 PBKS GT PBKS PBKS GT
5 LSG DC DC DC LSG
6 KKR SRH KKR SRH SRH
7 CSK PBKS PBKS PBKS CSK
chase_wins: 6 defend_wins: 1 chase_success_rate: 85.71
== innings averages from match summary (all 7) ==
first_innings_avg: 183.71
second_innings_avg: 176.57
highest_first: 226
highest_second: 224cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
# sanity: does extras == wide+legbyes+byes+noballs ?
chk = (d.extras == d.wide+d.legbyes+d.byes+d.noballs)
print('extras == wide+legbyes+byes+noballs for all rows?', bool(chk.all()))
print('rows where wide>0:', int((d.wide>0).sum()), '| noballs>0:', int((d.noballs>0).sum()))
d['total_runs_row'] = d.runs_of_bat + d.extras
d['legal'] = ((d.wide==0) & (d.noballs==0)).astype(int)
g = d.groupby('venue').agg(total_runs=('total_runs_row','sum'),
legal_balls=('legal','sum'),
all_balls=('venue','size')).reset_index()
g['overs'] = g.legal_balls/6
g['run_rate'] = g.total_runs/g.overs
g = g.sort_values('run_rate', ascending=False).reset_index(drop=True)
pd.set_option('display.width',200); pd.set_option('display.max_colwidth',90)
print(g.to_string(index=False))
print()
print('MOST favorable (highest RR):', g.venue.iloc[0], round(g.run_rate.iloc[0],2))
print('LEAST favorable (lowest RR):', g.venue.iloc[-1], round(g.run_rate.iloc[-1],2))
print()
print('rounded run_rates in desc order:', [round(x,2) for x in g.run_rate])
# compare buggy: runs_of_bat only, all balls
gb = d.groupby('venue').agg(rob=('runs_of_bat','sum'), allb=('venue','size')).reset_index()
gb['rr']=gb.rob/(gb.allb/6)
print('buggy run_rates (rob/allballs):', dict(zip(gb.venue.str[:12], gb.rr.round(2))))
"extras == wide+legbyes+byes+noballs for all rows? False
rows where wide>0: 70 | noballs>0: 4
venue total_runs legal_balls all_balls overs run_rate
Wankhede Stadium, Mumbai 444 235 247 39.166667 11.336170
M.Chinnaswamy Stadium, Bengaluru 406 216 228 36.000000 11.277778
Eden Gardens, Kolkata 386 216 230 36.000000 10.722222
Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh 327 235 250 39.166667 8.348936
Barsapara Cricket Stadium, Guwahati 256 191 199 31.833333 8.041885
Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow 285 215 228 35.833333 7.953488
MOST favorable (highest RR): Wankhede Stadium, Mumbai 11.34
LEAST favorable (lowest RR): Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow 7.95
rounded run_rates in desc order: [11.34, 11.28, 10.72, 8.35, 8.04, 7.95]
buggy run_rates (rob/allballs): {'Barsapara Cr': 7.06, 'Bharat Ratna': 6.63, 'Eden Gardens': 9.55, 'M.Chinnaswam': 10.03, 'Maharaja Yad': 7.42, 'Wankhede Sta': 10.4}cd /app && python3 -c "
import pandas as pd
pd.set_option('display.width',250); pd.set_option('display.max_colwidth',30)
d = pd.read_csv('data/deliveries.csv')
diff = d[d.extras != d.wide+d.legbyes+d.byes+d.noballs]
print('num rows where extras != components sum:', len(diff))
print(diff[['match_no','over','batting_team','runs_of_bat','extras','wide','legbyes','byes','noballs']].to_string(index=False))
print()
print('total extras column sum:', int(d.extras.sum()))
print('total components sum:', int((d.wide+d.legbyes+d.byes+d.noballs).sum()))
"num rows where extras != components sum: 12
match_no over batting_team runs_of_bat extras wide legbyes byes noballs
1 4.5 RCB 0 4 0 0 1 0
1 7.5 RCB 0 4 0 0 1 0
3 1.3 CSK 0 4 0 1 0 0
3 5.1 RR 0 4 0 0 1 0
4 19.4 GT 0 2 1 0 0 0
5 4.1 LSG 0 4 0 1 0 0
5 5.2 LSG 0 4 0 0 1 0
5 11.1 LSG 0 4 0 1 0 0
5 3.4 DC 0 2 0 1 0 0
5 16.4 DC 0 5 1 0 0 0
6 8.5 SRH 0 4 0 1 0 0
6 1.6 KKR 0 2 1 0 0 0
total extras column sum: 134
total components sum: 103cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
d['legal'] = ((d.wide==0)&(d.noballs==0)).astype(int)
print('distinct wide values:', sorted(d.wide.unique()))
print('distinct noballs values:', sorted(d.noballs.unique()))
print()
g = d.groupby(['match_no','innings']).agg(legal=('legal','sum'), rows=('legal','size')).reset_index()
g['overs']=g.legal/6
print(g.to_string(index=False))
"distinct wide values: [0, 1]
distinct noballs values: [0, 1]
match_no innings legal rows overs
1 1 121 126 20.166667
1 2 95 102 15.833333
2 1 120 128 20.000000
2 2 115 119 19.166667
3 1 118 125 19.666667
3 2 73 74 12.166667
4 1 120 131 20.000000
4 2 115 119 19.166667
5 1 112 114 18.666667
5 2 103 114 17.166667
6 1 120 129 20.000000
6 2 96 101 16.000000cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
s = d[(d.match_no==1)&(d.innings==1)]
print('rows:', len(s), '| legal:', int(((s.wide==0)&(s.noballs==0)).sum()))
print('wides:', int((s.wide>0).sum()), '| noballs:', int((s.noballs>0).sum()))
print('last few overs:', list(s.over.tail(8)))
print('duplicate over labels?', s.over.duplicated().sum(), 'distinct overs:', s.over.nunique())
# count legal balls per whole-over bucket
import numpy as np
s2=s.copy(); s2['ov']=np.floor(s2.over).astype(int)
print(s2[(s2.wide==0)&(s2.noballs==0)].groupby('ov').size().to_dict())
"rows: 126 | legal: 121
wides: 5 | noballs: 0
last few overs: [18.5, 18.6, 19.1, 19.2, 19.3, 19.4, 19.5, 19.6]
duplicate over labels? 6 distinct overs: 120
{0: 6, 1: 6, 2: 6, 3: 6, 4: 6, 5: 6, 6: 6, 7: 6, 8: 6, 9: 6, 10: 7, 11: 6, 12: 6, 13: 6, 14: 6, 15: 6, 16: 6, 17: 6, 18: 6, 19: 6}cd /app && cat > /tmp/fix_nb.py << 'PYEOF'
import json
path = "/app/solve.ipynb"
nb = json.load(open(path))
def as_lines(src):
# store as list of lines, each terminated with \n except the last
lines = src.split("\n")
return [l + "\n" for l in lines[:-1]] + [lines[-1]]
new_sources = {}
# Cell 2: counts + toss decision counts
new_sources[2] = r'''# total_matches: every match in the summary (all seven league games)
# matches_with_deliveries: only those that have ball-by-ball records (six)
total_matches <- nrow(matches)
matches_with_deliveries <- length(unique(deliveries$match_no))
# Toss decision counts derived directly from the toss_decision column
toss_decision_counts <- as.list(table(matches$toss_decision))
cat("Total matches analyzed:", total_matches, "\n")
cat("Matches with ball-by-ball data:", matches_with_deliveries, "\n")
cat("Toss decisions:\n"); print(unlist(toss_decision_counts))'''
# Cell 3: toss-win -> match-win
new_sources[3] = r'''# Compare who won the TOSS to who won the MATCH (not team1)
toss_winner_match_winner_count <- sum(matches$toss_winner == matches$match_winner)
toss_win_match_win_pct <- round(toss_winner_match_winner_count / total_matches * 100, 2)
cat("Toss winner also won match:", toss_winner_match_winner_count, "times\n")
cat("Toss-win to match-win percentage:", toss_win_match_win_pct, "%\n")'''
# Cell 4: chase analysis
new_sources[4] = r'''# Determine who batted first from the deliveries (innings 1 batting_team)
batting_first_info <- deliveries %>%
filter(innings == 1) %>%
group_by(match_no) %>%
summarise(batting_first_team = first(batting_team), .groups = "drop")
match_analysis <- matches %>%
left_join(batting_first_info, by = c("match_id" = "match_no")) %>%
mutate(
# For matches without deliveries, every toss winner chose to bowl,
# so the toss winner batted second and the other team batted first.
batting_first_team = if_else(
is.na(batting_first_team),
if_else(toss_winner == team1, team2, team1),
batting_first_team
)
)
# Chase success = the team batting SECOND won the match
chase_wins <- sum(match_analysis$match_winner != match_analysis$batting_first_team)
defend_wins <- total_matches - chase_wins
chase_success_rate <- round(chase_wins / total_matches * 100, 2)
cat("Chase wins:", chase_wins, "\n")
cat("Defend wins:", defend_wins, "\n")
cat("Chase success rate:", chase_success_rate, "%\n")'''
# Cell 5: innings scores from match summary, all seven matches
new_sources[5] = r'''# First/second innings scoring comes from the match summary across all seven matches
first_innings_avg_score <- round(mean(matches$first_ings_score), 2)
second_innings_avg_score <- round(mean(matches$second_ings_score), 2)
highest_first_innings <- max(matches$first_ings_score)
highest_second_innings <- max(matches$second_ings_score)
cat("First innings average:", first_innings_avg_score, "\n")
cat("Second innings average:", second_innings_avg_score, "\n")
cat("Highest first innings score:", highest_first_innings, "\n")
cat("Highest second innings score:", highest_second_innings, "\n")'''
# Cell 6: venue stats
new_sources[6] = r'''# Venue run rate = total runs (runs off bat + extras) / overs,
# where overs = legal deliveries / 6 (wides and no-balls are NOT legal deliveries)
venue_stats <- deliveries %>%
group_by(venue) %>%
summarise(
total_runs = sum(runs_of_bat + extras),
legal_balls = sum(wide == 0 & noballs == 0),
.groups = "drop"
) %>%
mutate(
overs = legal_balls / 6,
run_rate = total_runs / overs
)
cat("Venue statistics computed\n")
print(venue_stats %>% select(venue, total_runs, legal_balls, overs, run_rate))'''
# Cell 7: most / least favorable venue
new_sources[7] = r'''# Most favorable = HIGHEST run rate, least favorable = LOWEST run rate
venue_stats <- venue_stats %>% arrange(desc(run_rate))
most_favorable_venue <- venue_stats$venue[1]
most_favorable_venue_rr <- round(venue_stats$run_rate[1], 2)
least_favorable_venue
…[truncated]notebook updated; cells modified: [2, 3, 4, 5, 6, 7, 8]
cd /app && rm -f results.json figures/venue_run_rates.png && jupyter nbconvert --to notebook --execute --inplace solve.ipynb --ExecutePreprocessor.timeout=180 2>&1 | tail -20 && echo "=== EXIT: $? ===" && ls -la results.json figures/venue_run_rates.png
[NbConvertApp] Converting notebook solve.ipynb to notebook [NbConvertApp] Writing 18343 bytes to solve.ipynb === EXIT: 0 === -rw-r--r-- 1 h2tagent h2tagent 31234 Jun 17 06:42 figures/venue_run_rates.png -rw-r--r-- 1 h2tagent h2tagent 1040 Jun 17 06:42 results.json
cd /app && cat results.json
{
"toss_win_match_win_pct": 85.71,
"toss_decision_counts": {
"Bowl": 7
},
"chase_success_rate": 85.71,
"total_matches": 7,
"matches_with_deliveries": 6,
"first_innings_avg_score": 183.71,
"second_innings_avg_score": 176.57,
"highest_first_innings": 226,
"highest_second_innings": 224,
"toss_winner_match_winner_count": 6,
"chase_wins": 6,
"defend_wins": 1,
"most_favorable_venue": "Wankhede Stadium, Mumbai",
"most_favorable_venue_rr": 11.34,
"least_favorable_venue": "Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow",
"least_favorable_venue_rr": 7.95,
"chart_x_labels": [
"Wankhede Stadium, Mumbai",
"M.Chinnaswamy Stadium, Bengaluru",
"Eden Gardens, Kolkata",
"Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh",
"Barsapara Cricket Stadium, Guwahati",
"Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow"
],
"chart_y_values": [
11.34,
11.28,
10.72,
8.35,
8.04,
7.95
]
}cd /app && python3 -c "
import pandas as pd, json
m = pd.read_csv('data/matches.csv'); d = pd.read_csv('data/deliveries.csv')
R = json.load(open('results.json'))
# recompute expected
bf = d[d.innings==1].groupby('match_no').batting_team.first()
ma = m.copy(); ma['bf'] = ma['match_id'].map(bf)
ma['bf'] = ma.apply(lambda r: (r['team2'] if r['toss_winner']==r['team1'] else r['team1']) if pd.isna(r['bf']) else r['bf'], axis=1)
chase = int((ma.match_winner!=ma.bf).sum())
d['tr']=d.runs_of_bat+d.extras; d['legal']=((d.wide==0)&(d.noballs==0)).astype(int)
g=d.groupby('venue').agg(tr=('tr','sum'),lb=('legal','sum')).reset_index()
g['rr']=g.tr/(g.lb/6); g=g.sort_values('rr',ascending=False).reset_index(drop=True)
exp = {
'toss_win_match_win_pct': round(int((m.toss_winner==m.match_winner).sum())/len(m)*100,2),
'toss_decision_counts': m.toss_decision.value_counts().to_dict(),
'chase_success_rate': round(chase/len(m)*100,2),
'total_matches': len(m),
'matches_with_deliveries': int(d.match_no.nunique()),
'first_innings_avg_score': round(m.first_ings_score.mean(),2),
'second_innings_avg_score': round(m.second_ings_score.mean(),2),
'highest_first_innings': int(m.first_ings_score.max()),
'highest_second_innings': int(m.second_ings_score.max()),
'toss_winner_match_winner_count': int((m.toss_winner==m.match_winner).sum()),
'chase_wins': chase,
'defend_wins': len(m)-chase,
'most_favorable_venue': g.venue.iloc[0],
'most_favorable_venue_rr': round(g.rr.iloc[0],2),
'least_favorable_venue': g.venue.iloc[-1],
'least_favorable_venue_rr': round(g.rr.iloc[-1],2),
'chart_x_labels': list(g.venue),
'chart_y_values': [round(x,2) for x in g.rr],
}
req = ['toss_win_match_win_pct','toss_decision_counts','chase_success_rate','total_matches','matches_with_deliveries','first_innings_avg_score','second_innings_avg_score','highest_first_innings','highest_second_innings','toss_winner_match_winner_count','chase_wins','defend_wins','most_favorable_venue','most_favorable_venue_rr','least_favorable_venue','least_favorable_venue_rr','chart_x_labels','chart_y_values']
print('all required keys present:', all(k in R for k in req))
ok=True
for k in req:
match = R[k]==exp[k]
if not match: ok=False
print(('OK ' if match else 'MISMATCH ')+k, '->', R[k])
print()
print('ALL MATCH:', ok)
# internal consistency
print('most fav == chart_x[argmax y]:', R['most_favorable_venue']==R['chart_x_labels'][R['chart_y_values'].index(max(R['chart_y_values']))])
print('least fav == chart_x[argmin y]:', R['least_favorable_venue']==R['chart_x_labels'][R['chart_y_values'].index(min(R['chart_y_values']))])
print('chase+defend==total:', R['chase_wins']+R['defend_wins']==R['total_matches'])
print('y sorted desc:', R['chart_y_values']==sorted(R['chart_y_values'],reverse=True))
print('labels unique & len6:', len(set(R['chart_x_labels']))==6)
"all required keys present: True
OK toss_win_match_win_pct -> 85.71
OK toss_decision_counts -> {'Bowl': 7}
OK chase_success_rate -> 85.71
OK total_matches -> 7
OK matches_with_deliveries -> 6
OK first_innings_avg_score -> 183.71
OK second_innings_avg_score -> 176.57
OK highest_first_innings -> 226
OK highest_second_innings -> 224
OK toss_winner_match_winner_count -> 6
OK chase_wins -> 6
OK defend_wins -> 1
OK most_favorable_venue -> Wankhede Stadium, Mumbai
OK most_favorable_venue_rr -> 11.34
OK least_favorable_venue -> Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow
OK least_favorable_venue_rr -> 7.95
OK chart_x_labels -> ['Wankhede Stadium, Mumbai', 'M.Chinnaswamy Stadium, Bengaluru', 'Eden Gardens, Kolkata', 'Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh', 'Barsapara Cricket Stadium, Guwahati', 'Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow']
OK chart_y_values -> [11.34, 11.28, 10.72, 8.35, 8.04, 7.95]
ALL MATCH: True
most fav == chart_x[argmax y]: True
least fav == chart_x[argmin y]: True
chase+defend==total: True
y sorted desc: True
labels unique & len6: Truecommand: view path: /app/figures/venue_run_rates.png
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cd /app && python3 -c "
import json
nb=json.load(open('solve.ipynb'))
for i,c in enumerate(nb['cells']):
if c['cell_type']!='code': continue
outs=[]
for o in c.get('outputs',[]):
if o.get('output_type')=='stream': outs.append(''.join(o.get('text','')))
elif 'text/plain' in o.get('data',{}): outs.append(''.join(o['data']['text/plain']))
print('========== CELL',i,'(exec_count=%s) =========='%c.get('execution_count'))
print(''.join(c['source']))
if outs:
print('----- output -----'); print(''.join(outs).rstrip())
print()
"========== CELL 0 (exec_count=1) ==========
library(tidyverse)
library(jsonlite)
----- output -----
── [1mAttaching core tidyverse packages[22m ──────────────────────── tidyverse 2.0.0 ──
[32m✔[39m [34mdplyr [39m 1.1.4 [32m✔[39m [34mreadr [39m 2.1.5
[32m✔[39m [34mforcats [39m 1.0.0 [32m✔[39m [34mstringr [39m 1.5.1
[32m✔[39m [34mggplot2 [39m 3.5.1 [32m✔[39m [34mtibble [39m 3.2.1
[32m✔[39m [34mlubridate[39m 1.9.4 [32m✔[39m [34mtidyr [39m 1.3.1
[32m✔[39m [34mpurrr [39m 1.0.4
── [1mConflicts[22m ────────────────────────────────────────── tidyverse_conflicts() ──
[31m✖[39m [34mdplyr[39m::[32mfilter()[39m masks [34mstats[39m::filter()
[31m✖[39m [34mdplyr[39m::[32mlag()[39m masks [34mstats[39m::lag()
[36mℹ[39m Use the conflicted package ([3m[34m<http://conflicted.r-lib.org/>[39m[23m) to force all conflicts to become errors
Attaching package: ‘jsonlite’
The following object is masked from ‘package:purrr’:
flatten
========== CELL 1 (exec_count=2) ==========
matches <- read_csv("/app/data/matches.csv", show_col_types = FALSE)
deliveries <- read_csv("/app/data/deliveries.csv", show_col_types = FALSE)
cat("Match data loaded:", nrow(matches), "matches\n")
cat("Delivery data loaded:", nrow(deliveries), "deliveries\n")
----- output -----
Match data loaded: 7 matches
Delivery data loaded: 1382 deliveries
========== CELL 2 (exec_count=3) ==========
# total_matches: every match in the summary (all seven league games)
# matches_with_deliveries: only those that have ball-by-ball records (six)
total_matches <- nrow(matches)
matches_with_deliveries <- length(unique(deliveries$match_no))
# Toss decision counts derived directly from the toss_decision column
toss_decision_counts <- as.list(table(matches$toss_decision))
cat("Total matches analyzed:", total_matches, "\n")
cat("Matches with ball-by-ball data:", matches_with_deliveries, "\n")
cat("Toss decisions:\n"); print(unlist(toss_decision_counts))
----- output -----
Total matches analyzed: 7
Matches with ball-by-ball data: 6
Toss decisions:
Bowl
7
========== CELL 3 (exec_count=4) ==========
# Compare who won the TOSS to who won the MATCH (not team1)
toss_winner_match_winner_count <- sum(matches$toss_winner == matches$match_winner)
toss_win_match_win_pct <- round(toss_winner_match_winner_count / total_matches * 100, 2)
cat("Toss winner also won match:", toss_winner_match_winner_count, "times\n")
cat("Toss-win to match-win percentage:", toss_win_match_win_pct, "%\n")
----- output -----
Toss winner also won match: 6 times
Toss-win to match-win percentage: 85.71 %
========== CELL 4 (exec_count=5) ==========
# Determine who batted first from the deliveries (innings 1 batting_team)
batting_first_info <- deliveries %>%
filter(innings == 1) %>%
group_by(match_no) %>%
summarise(batting_first_team = first(batting_team), .groups = "drop")
match_analysis <- matches %>%
left_join(batting_first_info, by = c("match_id" = "match_no")) %>%
mutate(
# For matches without deliveries, every toss winner chose to bowl,
# so the toss winner batted second and the other team batted first.
batting_first_team = if_else(
is.na(batting_first_team),
if_else(toss_winner == team1, team2, team1),
batting_first_team
)
)
# Chase success = the team batting SECOND won the match
chase_wins <- sum(match_analysis$match_winner != match_analysis$batting_first_team)
defend_wins <- total_matches - chase_wins
chase_success_rate <- round(chase_wins / total_matches * 100, 2)
cat("Chase wins:", chase_wins, "\n")
cat("Defend wins:", defend_wins, "\n")
cat("Chase success rate:", chase_success_rate, "%\n")
----- output -----
Chase wins: 6
Defend wins: 1
Chase success rate: 85.71 %
========== CELL 5 (exec_count=6) ==========
# First/second innings scoring comes from the match summary across all seven matches
first_innings_avg_score <- round(mean(matches$first_ings_score), 2)
second_innings_avg_score <- round(mean(matches$second_ings_sco
…[truncated]cd /app && cp results.json /tmp/run1.json && md5sum figures/venue_run_rates.png > /tmp/png1.md5 && jupyter nbconvert --to notebook --execute --inplace solve.ipynb --ExecutePreprocessor.timeout=180 > /dev/null 2>&1 && echo "re-run exit: $?" && echo "--- results.json identical to previous run? ---" && diff -q /tmp/run1.json results.json && echo "IDENTICAL json" && echo "--- figure regenerated? ---" && ls -la figures/venue_run_rates.png && python3 -c "import json; json.load(open('/app/results.json')); print('results.json is valid JSON')"re-run exit: 0 --- results.json identical to previous run? --- IDENTICAL json --- figure regenerated? --- -rw-r--r-- 1 h2tagent h2tagent 31234 Jun 17 06:42 figures/venue_run_rates.png results.json is valid JSON
No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary # task: ipl-toss-impact-analysis-r # attempt: 5 status: completed raw_score: 1.0 # reward rule: binary: score 1.0 == 1.0 # => reward = 1.0 (PASS) # # Note: these tasks score via an external hidden grader; no pytest/verifier # block is embedded in the agent transcript. The block below is the last # sizable tool-output captured in the run (supporting evidence). ---------------------------------------------------------------------- re-run exit: 0 --- results.json identical to previous run? --- IDENTICAL json --- figure regenerated? --- -rw-r--r-- 1 h2tagent h2tagent 31234 Jun 17 06:42 figures/venue_run_rates.png results.json is valid JSON
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_38e6ba03de254ae6. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_38e6ba03de254ae6 · verifier authoritative; classifier explanatory.