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 "===== header =====" && head -1 data/deliveries.csv && echo "===== first rows =====" && head -6 data/deliveries.csv && echo "..." && echo "total lines:" && wc -l data/deliveries.csv
===== header ===== 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 ===== first rows ===== 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,,, 1,"Mar 28, 2026",League stage,"M.Chinnaswamy Stadium, Bengaluru",SRH,RCB,1,0.5,Abhishek Sharma,Jacob Duffy,0,0,0,0,0,0,,, ... total lines: 1383 data/deliveries.csv
cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
print('match_no present:', sorted(d.match_no.unique()))
print('innings present:', sorted(d.innings.unique()))
print()
print('--- venues in deliveries (unique) ---')
for v in d.venue.unique(): print(repr(v))
print()
print('--- innings-1 batting_team per match (deliveries) ---')
for m in sorted(d.match_no.unique()):
sub = d[(d.match_no==m)&(d.innings==1)]
print(m, '->', sub.batting_team.unique(), '| venue:', sub.venue.unique())
"match_no present: [1, 2, 3, 4, 5, 6] innings present: [1, 2] --- venues in deliveries (unique) --- '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' --- innings-1 batting_team per match (deliveries) --- 1 -> ['SRH'] | venue: ['M.Chinnaswamy Stadium, Bengaluru'] 2 -> ['KKR'] | venue: ['Wankhede Stadium, Mumbai'] 3 -> ['CSK'] | venue: ['Barsapara Cricket Stadium, Guwahati'] 4 -> ['GT'] | venue: ['Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh'] 5 -> ['LSG'] | venue: ['Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow'] 6 -> ['SRH'] | venue: ['Eden Gardens, Kolkata']
cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
# check extras composition
d['sum_parts'] = d[['wide','legbyes','byes','noballs']].sum(axis=1)
print('rows where extras != wide+legbyes+byes+noballs:', (d.extras != d.sum_parts).sum())
print('extras unique vs parts mismatch sample:')
print(d[d.extras != d.sum_parts][['extras','wide','legbyes','byes','noballs']].head())
print()
# legal delivery = not wide and not noball
d['is_legal'] = ((d.wide==0) & (d.noballs==0)).astype(int)
d['total_runs'] = d.runs_of_bat + d.extras
print('=== Venue run rates (total_runs*6/legal_balls) ===')
rows=[]
for v, sub in d.groupby('venue'):
tr = sub.total_runs.sum()
legal = sub.is_legal.sum()
rr = tr*6/legal
rows.append((v, tr, legal, rr))
print(f'{rr:7.4f} runs={tr:4d} legal={legal:4d} {v}')
print()
rows.sort(key=lambda r:-r[3])
print('MOST favorable:', rows[0][0], round(rows[0][3],4))
print('LEAST favorable:', rows[-1][0], round(rows[-1][3],4))
"rows where extras != wide+legbyes+byes+noballs: 12
extras unique vs parts mismatch sample:
extras wide legbyes byes noballs
154 4 0 0 1 0
173 4 0 0 1 0
483 4 0 1 0 0
630 4 0 0 1 0
799 2 1 0 0 0
=== Venue run rates (total_runs*6/legal_balls) ===
8.0419 runs= 256 legal= 191 Barsapara Cricket Stadium, Guwahati
7.9535 runs= 285 legal= 215 Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow
10.7222 runs= 386 legal= 216 Eden Gardens, Kolkata
11.2778 runs= 406 legal= 216 M.Chinnaswamy Stadium, Bengaluru
8.3489 runs= 327 legal= 235 Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh
11.3362 runs= 444 legal= 235 Wankhede Stadium, Mumbai
MOST favorable: Wankhede Stadium, Mumbai 11.3362
LEAST favorable: Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow 7.9535cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
d['sum_parts'] = d[['wide','legbyes','byes','noballs']].sum(axis=1)
mm = d[d.extras != d.sum_parts]
print('count:', len(mm))
print(mm[['match_no','over','runs_of_bat','extras','wide','legbyes','byes','noballs']].to_string())
print()
print('wide value counts:', dict(d.wide.value_counts()))
print('noballs value counts:', dict(d.noballs.value_counts()))
"count: 12
match_no over runs_of_bat extras wide legbyes byes noballs
154 1 4.5 0 4 0 0 1 0
173 1 7.5 0 4 0 0 1 0
483 3 1.3 0 4 0 1 0 0
630 3 5.1 0 4 0 0 1 0
799 4 19.4 0 2 1 0 0 0
949 5 4.1 0 4 0 1 0 0
957 5 5.2 0 4 0 0 1 0
992 5 11.1 0 4 0 1 0 0
1062 5 3.4 0 2 0 1 0 0
1148 5 16.4 0 5 1 0 0 0
1207 6 8.5 0 4 0 1 0 0
1293 6 1.6 0 2 1 0 0 0
wide value counts: {0: 1312, 1: 70}
noballs value counts: {0: 1378, 1: 4}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 && which R Rscript jupyter 2>/dev/null; echo "---"; Rscript -e 'suppressMessages({library(tidyverse); library(jsonlite)}); cat("R packages OK\n")' 2>&1 | tail -3/usr/bin/R /usr/bin/Rscript /usr/local/bin/jupyter --- R packages OK
cd /app && python3 -c "
import pandas as pd, numpy as np
m = pd.read_csv('data/matches.csv')
d = pd.read_csv('data/deliveries.csv')
print('NA in key delivery cols:', d[['runs_of_bat','extras','wide','noballs','batting_team','venue']].isna().sum().to_dict())
print('NA in key match cols:', m[['toss_winner','match_winner','toss_decision','first_ings_score','second_ings_score','team1','team2']].isna().sum().to_dict())
print()
total_matches = len(m) # 7
mwd = d.match_no.nunique() # 6
print('total_matches:', total_matches, '| matches_with_deliveries:', mwd)
# toss decision counts
print('toss_decision_counts:', m.toss_decision.value_counts().to_dict())
# toss winner == match winner
tw = int((m.toss_winner==m.match_winner).sum())
print('toss_winner_match_winner_count:', tw, '-> pct', round(tw/total_matches*100,2))
# batting first from deliveries, fill match7 via toss (all chose Bowl => toss winner bats 2nd)
bf = d[d.innings==1].groupby('match_no').batting_team.first()
def batfirst(r):
if r.match_id in bf.index: return bf[r.match_id]
return r.team2 if r.team1==r.toss_winner else r.team1
m['bf'] = m.apply(batfirst, axis=1)
chase_wins = int((m.match_winner!=m.bf).sum())
print('batting_first per match:', dict(zip(m.match_id, m.bf)))
print('chase_wins:', chase_wins, '| defend_wins:', total_matches-chase_wins)
print('chase_success_rate fraction:', round(chase_wins/total_matches,2), '| as pct:', round(chase_wins/total_matches*100,2))
print()
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()), '| highest_second:', int(m.second_ings_score.max()))
"NA in key delivery cols: {'runs_of_bat': 0, 'extras': 0, 'wide': 0, 'noballs': 0, 'batting_team': 0, 'venue': 0}
NA in key match cols: {'toss_winner': 0, 'match_winner': 0, 'toss_decision': 0, 'first_ings_score': 0, 'second_ings_score': 0, 'team1': 0, 'team2': 0}
total_matches: 7 | matches_with_deliveries: 6
toss_decision_counts: {'Bowl': 7}
toss_winner_match_winner_count: 6 -> pct 85.71
batting_first per match: {1: 'SRH', 2: 'KKR', 3: 'CSK', 4: 'GT', 5: 'LSG', 6: 'SRH', 7: 'CSK'}
chase_wins: 6 | defend_wins: 1
chase_success_rate fraction: 0.86 | as pct: 85.71
first_innings_avg: 183.71
second_innings_avg: 176.57
highest_first: 226 | highest_second: 224cd /app && python3 -c "
import json
nb = json.load(open('solve.ipynb'))
print('nbformat:', nb.get('nbformat'), '| kernel:', nb.get('metadata',{}).get('kernelspec',{}).get('name'))
print('language:', nb.get('metadata',{}).get('language_info',{}).get('name'))
print('num cells:', len(nb['cells']))
for i,c in enumerate(nb['cells']):
src=''.join(c['source'])
first = src.strip().split(chr(10))[0][:70] if src.strip() else '(empty)'
print(f'--- cell {i} [{c[\"cell_type\"]}] lines={len(c[\"source\"])} :: {first}')
"nbformat: 4 | kernel: ir
language: R
num cells: 10
--- cell 0 [code] lines=2 :: library(tidyverse)
--- cell 1 [code] lines=5 :: matches <- read_csv("/app/data/matches.csv", show_col_types = FALSE)
--- cell 2 [code] lines=10 :: total_matches <- length(unique(deliveries$match_no))
--- cell 3 [code] lines=5 :: toss_winner_match_winner_count <- sum(matches$team1 == matches$match_w
--- cell 4 [code] lines=15 :: batting_first_info <- deliveries %>%
--- cell 5 [code] lines=18 :: matches_with_del <- matches %>%
--- cell 6 [code] lines=14 :: venue_stats <- deliveries %>%
--- cell 7 [code] lines=11 :: venue_stats <- venue_stats %>% arrange(run_rate)
--- cell 8 [code] lines=21 :: venue_abbrev <- c("WAK", "CHN", "EDN", "MUL", "BAR", "EKA")
--- cell 9 [code] lines=23 :: results <- list(cd /app && python3 -c "
import json
nb = json.load(open('solve.ipynb'))
for i in [2,3,4,5,6,7,8]:
print('='*25, 'CELL', i, '='*25)
print(''.join(nb['cells'][i]['source']))
print()
"========================= CELL 2 =========================
total_matches <- length(unique(deliveries$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")
========================= CELL 3 =========================
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")
========================= CELL 4 =========================
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")
========================= CELL 5 =========================
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$second_ings_score)
cat("First innings average:", first_innings_avg_score, "\n")
cat("Second innings average:", second_innings_avg_score, "\n")
cat("Highest first innings score:", highest_first_innings, "\n")
cat("Highest second innings score:", highest_second_innings, "\n")
========================= CELL 6 =========================
venue_stats <- deliveries %>%
group_by(venue) %>%
summarise(
total_runs = sum(runs_of_bat),
total_balls = n(),
.groups = "drop"
) %>%
mutate(
overs = total_balls / 6,
run_rate = total_runs / overs
)
cat("Venue statistics computed\n")
print(venue_stats %>% select(venue, total_runs, overs, run_rate))
========================= CELL 7 =========================
venue_stats <- venue_stats %>% arrange(run_rate)
most_favorable_venue <- venue_stats$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$run_rate[nrow(venue_stats)], 2)
cat("Most favorable venue:", most_favorable_venue, "\n")
cat("Most favorable RR:", most_favorable_venue_rr, "\n")
cat("Least favorable venue:", least_favorable_venue, "\n")
cat("Least favorable RR:", least_favorable_venue_rr, "\n")
========================= CELL 8 =========================
venue_abbrev <- c("WAK", "CHN", "EDN", "MUL", "BAR", "EKA")
chart_data <- venue_stats %>% arrange(desc(run_rate))
dir.create("/app/figures", showWarnings = FALSE, recursive = TRUE)
png("/app/figures/venue_run_rates.png", width = 900, height = 500)
par(mar = c(8, 5, 3, 2))
bp <- barplot(
chart_data$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$total_runs, 2)
cat("Chart saved\n")cd /app && cat > /tmp/test.R << 'EOF'
suppressMessages({library(tidyverse); library(jsonlite)})
matches <- read_csv("/app/data/matches.csv", show_col_types = FALSE)
deliveries <- read_csv("/app/data/deliveries.csv", show_col_types = FALSE)
total_matches <- nrow(matches)
matches_with_deliveries <- length(unique(deliveries$match_no))
td <- table(matches$toss_decision)
toss_decision_counts <- as.list(setNames(as.integer(td), names(td)))
cat("toss_decision_counts JSON:", toJSON(toss_decision_counts, auto_unbox=TRUE), "\n")
toss_winner_match_winner_count <- sum(matches$toss_winner == matches$match_winner)
toss_win_match_win_pct <- round(toss_winner_match_winner_count / total_matches * 100, 2)
batting_first_info <- deliveries %>% filter(innings==1) %>% group_by(match_no) %>%
summarise(batting_first_team = first(batting_team), .groups="drop")
match_analysis <- matches %>%
left_join(batting_first_info, by=c("match_id"="match_no")) %>%
mutate(batting_first_team = ifelse(is.na(batting_first_team),
ifelse(team1==toss_winner, team2, team1), batting_first_team))
chase_wins <- sum(match_analysis$match_winner != match_analysis$batting_first_team)
defend_wins <- total_matches - chase_wins
chase_success_rate <- round(chase_wins / total_matches, 2)
first_innings_avg_score <- round(mean(matches$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$second_ings_score)
venue_stats <- deliveries %>% group_by(venue) %>%
summarise(total_runs=sum(runs_of_bat+extras),
legal_balls=sum(wide==0 & noballs==0), .groups="drop") %>%
mutate(overs=legal_balls/6, run_rate=total_runs/overs) %>%
arrange(desc(run_rate))
cat(sprintf("tm=%d mwd=%d pct=%.2f tw=%d chase=%d defend=%d csr=%.2f\n",
total_matches, matches_with_deliveries, toss_win_match_win_pct,
toss_winner_match_winner_count, chase_wins, defend_wins, chase_success_rate))
cat(sprintf("f_avg=%.2f s_avg=%.2f hi1=%d hi2=%d\n",
first_innings_avg_score, second_innings_avg_score, highest_first_innings, highest_second_innings))
print(venue_stats %>% mutate(rr=round(run_rate,2)) %>% select(venue, total_runs, legal_balls, rr))
cat("most:", venue_stats$venue[1], round(venue_stats$run_rate[1],2), "\n")
cat("least:", venue_stats$venue[nrow(venue_stats)], round(venue_stats$run_rate[nrow(venue_stats)],2), "\n")
cat("chart_y:", paste(round(venue_stats$run_rate,2), collapse=", "), "\n")
EOF
Rscript /tmp/test.Rtoss_decision_counts JSON: {"Bowl":7}
tm=7 mwd=6 pct=85.71 tw=6 chase=6 defend=1 csr=0.86
f_avg=183.71 s_avg=176.57 hi1=226 hi2=224
# A tibble: 6 × 4
venue total_runs legal_balls rr
<chr> <dbl> <int> <dbl>
1 Wankhede Stadium, Mumbai 444 235 11.3
2 M.Chinnaswamy Stadium, Bengaluru 406 216 11.3
3 Eden Gardens, Kolkata 386 216 10.7
4 Maharaja Yadavindra Singh International Cricket … 327 235 8.35
5 Barsapara Cricket Stadium, Guwahati 256 191 8.04
6 Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cri… 285 215 7.95
most: Wankhede Stadium, Mumbai 11.34
least: Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow 7.95
chart_y: 11.34, 11.28, 10.72, 8.35, 8.04, 7.95cd /app && cp solve.ipynb solve.ipynb.bak && python3 << 'PYEOF'
import json
nb = json.load(open('solve.ipynb'))
def lines(s):
# Split into ipynb-style source list: each line keeps its trailing newline
# except the final line (no trailing newline).
parts = s.split('\n')
out = [p + '\n' for p in parts[:-1]]
if parts[-1] != '':
out.append(parts[-1])
return out
cells = {}
cells[2] = '''total_matches <- nrow(matches)
matches_with_deliveries <- length(unique(deliveries$match_no))
# All toss decisions come straight from the match summary.
td <- table(matches$toss_decision)
toss_decision_counts <- as.list(setNames(as.integer(td), names(td)))
cat("Total matches analyzed:", total_matches, "\\n")
cat("Matches with ball-by-ball data:", matches_with_deliveries, "\\n")'''
cells[3] = '''# Compare the toss winner to the match winner (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")'''
cells[4] = '''# Who batted first? Use innings-1 batting_team from the deliveries data.
batting_first_info <- deliveries %>%
filter(innings == 1) %>%
group_by(match_no) %>%
summarise(batting_first_team = first(batting_team), .groups = "drop")
# For matches without deliveries, every toss winner chose to bowl, so the
# toss winner batted second and the other side batted first.
match_analysis <- matches %>%
left_join(batting_first_info, by = c("match_id" = "match_no")) %>%
mutate(
batting_first_team = ifelse(
is.na(batting_first_team),
ifelse(team1 == toss_winner, team2, team1),
batting_first_team
)
)
# A chase win is when the team batting second (not the batting-first team) wins.
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, 2)
cat("Chase wins:", chase_wins, "\\n")
cat("Defend wins:", defend_wins, "\\n")
cat("Chase success rate:", chase_success_rate, "\\n")'''
cells[5] = '''# Innings scores come 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")'''
cells[6] = '''# Venue run rate = (runs off bat + extras) / overs, where overs use only
# legal deliveries (wides and no-balls are excluded).
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, overs, run_rate))'''
cells[7] = '''# 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 <- 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")'''
…[truncated]Notebook updated. Cells edited: [2, 3, 4, 5, 6, 7, 8]
cd /app && python3 -c "import json; json.load(open('solve.ipynb')); print('valid JSON ipynb')" && echo "--- executing notebook ---" && rm -f results.json figures/venue_run_rates.png && jupyter nbconvert --to notebook --execute --inplace solve.ipynb --ExecutePreprocessor.timeout=180 2>&1 | tail -5valid JSON ipynb --- executing notebook --- [NbConvertApp] Converting notebook solve.ipynb to notebook [NbConvertApp] Writing 16859 bytes to solve.ipynb
cd /app && echo "=== results.json ===" && cat results.json && echo && echo "=== figure ===" && ls -la figures/venue_run_rates.png && file figures/venue_run_rates.png
=== results.json ===
{
"toss_win_match_win_pct": 85.71,
"toss_decision_counts": {
"Bowl": 7
},
"chase_success_rate": 0.86,
"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": [
"WAK",
"CHN",
"EDN",
"MUL",
"BAR",
"EKA"
],
"chart_y_values": [
11.34,
11.28,
10.72,
8.35,
8.04,
7.95
]
}
=== figure ===
-rw-r--r-- 1 h2tagent h2tagent 12343 Jun 17 06:41 figures/venue_run_rates.png
[stderr]
bash: file: command not foundcd /app && python3 -c "
sig = open('figures/venue_run_rates.png','rb').read(8)
print('PNG signature OK:', sig == b'\x89PNG\r\n\x1a\n')
from struct import unpack
d = open('figures/venue_run_rates.png','rb').read()
w,h = unpack('>II', d[16:24])
print('dimensions:', w, 'x', h)
"PNG signature OK: True dimensions: 900 x 500
command: view path: /app/figures/venue_run_rates.png
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cd /app && python3 -c "
import json
nb = json.load(open('solve.ipynb'))
errs = 0
for i,c in enumerate(nb['cells']):
for o in c.get('outputs',[]):
if o.get('output_type')=='error':
errs+=1; print('ERROR in cell',i, o.get('ename'))
print('notebook executed with', errs, 'error outputs')
" && echo "--- independent cross-check ---" && python3 -c "
import json, pandas as pd
r = json.load(open('results.json'))
m = pd.read_csv('data/deliveries.csv'); M = pd.read_csv('data/matches.csv')
exp = {}
exp['total_matches']=len(M)
exp['matches_with_deliveries']=m.match_no.nunique()
exp['toss_decision_counts']={k:int(v) for k,v in M.toss_decision.value_counts().items()}
exp['toss_winner_match_winner_count']=int((M.toss_winner==M.match_winner).sum())
exp['toss_win_match_win_pct']=round(exp['toss_winner_match_winner_count']/len(M)*100,2)
bf=m[m.innings==1].groupby('match_no').batting_team.first()
bff=[bf[mid] if mid in bf.index else (r2.team2 if r2.team1==r2.toss_winner else r2.team1) for mid,r2 in zip(M.match_id,M.itertuples())]
# build properly
import numpy as np
bff=[]
for _,row in M.iterrows():
mid=row.match_id
bff.append(bf[mid] if mid in bf.index else (row.team2 if row.team1==row.toss_winner else row.team1))
M['bf']=bff
exp['chase_wins']=int((M.match_winner!=M.bf).sum())
exp['defend_wins']=len(M)-exp['chase_wins']
exp['chase_success_rate']=round(exp['chase_wins']/len(M),2)
exp['first_innings_avg_score']=round(M.first_ings_score.mean(),2)
exp['second_innings_avg_score']=round(M.second_ings_score.mean(),2)
exp['highest_first_innings']=int(M.first_ings_score.max())
exp['highest_second_innings']=int(M.second_ings_score.max())
m['tr']=m.runs_of_bat+m.extras; m['legal']=((m.wide==0)&(m.noballs==0)).astype(int)
g=m.groupby('venue').apply(lambda s: s.tr.sum()*6/s.legal.sum()).sort_values(ascending=False)
exp['most_favorable_venue']=g.index[0]; exp['most_favorable_venue_rr']=round(g.iloc[0],2)
exp['least_favorable_venue']=g.index[-1]; exp['least_favorable_venue_rr']=round(g.iloc[-1],2)
exp['chart_y_values']=[round(x,2) for x in g.values]
exp['chart_x_labels']=['WAK','CHN','EDN','MUL','BAR','EKA']
ok=True
for k,v in exp.items():
got=r.get(k)
match = (got==v)
if isinstance(v,list): match = list(got)==list(v)
if not match: ok=False; print('MISMATCH',k,'expected',v,'got',got)
print('ALL KEYS MATCH INDEPENDENT CHECK:' , ok)
print('keys in results.json:', len(r))
"notebook executed with 0 error outputs --- independent cross-check --- ALL KEYS MATCH INDEPENDENT CHECK: True keys in results.json: 18
cd /app && rm -f solve.ipynb.bak /tmp/test.R && echo "cleaned up" && echo "--- final /app contents ---" && ls -la && echo "--- figures ---" && ls -la figures/
cleaned up --- final /app contents --- total 34 drwxrws--- 4 h2tagent h2tagent 4096 Jun 17 06:41 . 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 Jun 17 06:41 figures -rw-r--r-- 1 h2tagent h2tagent 794 Jun 17 06:41 results.json -rw-rw---- 1 h2tagent h2tagent 17044 Jun 17 06:41 solve.ipynb --- figures --- total 21 drwxrws--- 2 h2tagent h2tagent 4096 Jun 17 06:41 . drwxrws--- 4 h2tagent h2tagent 4096 Jun 17 06:41 .. -rw-r--r-- 1 h2tagent h2tagent 12343 Jun 17 06:41 venue_run_rates.png
No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary # task: ipl-toss-impact-analysis-r # attempt: 10 status: completed raw_score: 0.0 # reward rule: binary: score 0.0 != 1.0 # => reward = 0.0 (FAIL) # # 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). ---------------------------------------------------------------------- cleaned up --- final /app contents --- total 34 drwxrws--- 4 h2tagent h2tagent 4096 Jun 17 06:41 . 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 Jun 17 06:41 figures -rw-r--r-- 1 h2tagent h2tagent 794 Jun 17 06:41 results.json -rw-rw---- 1 h2tagent h2tagent 17044 Jun 17 06:41 solve.ipynb --- figures --- total 21 drwxrws--- 2 h2tagent h2tagent 4096 Jun 17 06:41 . drwxrws--- 4 h2tagent h2tagent 4096 Jun 17 06:41 .. -rw-r--r-- 1 h2tagent h2tagent 12343 Jun 17 06:41 venue_run_rates.png
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_99793a71c4724845. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_99793a71c4724845 · verifier authoritative; classifier explanatory.