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 -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 && echo "=== match_no present in deliveries ===" && cut -d',' -f1 data/deliveries.csv | tail -n +2 | sort -n | uniq -c && echo "=== innings per match ===" && awk -F',' 'NR>1{print $1"-ings"$7}' data/deliveries.csv | sort | uniq -c=== match_no present in deliveries ===
228 1
247 2
199 3
250 4
228 5
230 6
=== innings per match ===
102 1-ingsRCB
126 1-ingsSRH
128 2-ingsKKR
119 2-ingsMI
125 3-ingsCSK
74 3-ingsRR
250 4-ings New Chandigarh"
114 5-ingsDC
114 5-ingsLSG
101 6-ingsKKR
129 6-ingsSRHcd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
print('columns:', list(d.columns))
print()
print('=== innings-1 batting_team per match (who batted first) ===')
for m in sorted(d.match_no.unique()):
sub = d[(d.match_no==m)&(d.innings==1)]
print(m, '->', sub.batting_team.unique())
print()
print('=== venues in deliveries (raw) ===')
for m in sorted(d.match_no.unique()):
print(m, repr(d[d.match_no==m].venue.unique()))
"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']
=== innings-1 batting_team per match (who batted first) ===
1 -> ['SRH']
2 -> ['KKR']
3 -> ['CSK']
4 -> ['GT']
5 -> ['LSG']
6 -> ['SRH']
=== venues in deliveries (raw) ===
1 array(['M.Chinnaswamy Stadium, Bengaluru'], dtype=object)
2 array(['Wankhede Stadium, Mumbai'], dtype=object)
3 array(['Barsapara Cricket Stadium, Guwahati'], dtype=object)
4 array(['Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh'],
dtype=object)
5 array(['Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow'],
dtype=object)
6 array(['Eden Gardens, Kolkata'], dtype=object)cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
# verify extras = wide+legbyes+byes+noballs
chk = (d.extras == d.wide+d.legbyes+d.byes+d.noballs).all()
print('extras == wide+legbyes+byes+noballs for all rows:', chk)
print()
print('=== venue run rates (runs off bat + extras) / (legal_deliveries/6) ===')
rows=[]
for v in d.venue.unique():
sub = d[d.venue==v]
total_runs = sub.runs_of_bat.sum() + sub.extras.sum()
legal = ((sub.wide==0)&(sub.noballs==0)).sum()
rr = total_runs*6/legal
rows.append((v, int(total_runs), int(legal), round(rr,4)))
for r in sorted(rows, key=lambda x:-x[3]):
print(r)
"extras == wide+legbyes+byes+noballs for all rows: False
=== venue run rates (runs off bat + extras) / (legal_deliveries/6) ===
('Wankhede Stadium, Mumbai', 444, 235, 11.3362)
('M.Chinnaswamy Stadium, Bengaluru', 406, 216, 11.2778)
('Eden Gardens, Kolkata', 386, 216, 10.7222)
('Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh', 327, 235, 8.3489)
('Barsapara Cricket Stadium, Guwahati', 256, 191, 8.0419)
('Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow', 285, 215, 7.9535)cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
d['calc'] = d.wide+d.legbyes+d.byes+d.noballs
diff = d[d.extras != d.calc]
print('num rows where extras != sum of components:', len(diff))
print(diff[['match_no','over','runs_of_bat','extras','wide','legbyes','byes','noballs']].to_string())
print()
print('total extras col sum:', d.extras.sum())
print('total component sum:', d.calc.sum())
"num rows where extras != sum of components: 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
total extras col sum: 134
total component sum: 103cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
m = pd.read_csv('data/matches.csv')
print('Validate innings totals: deliveries(runs_of_bat+extras) vs matches.csv official score')
print(f'{\"match\":>5} {\"ings\":>4} {\"bat+extras\":>11} {\"bat+components\":>15} {\"official\":>9}')
for mno in sorted(d.match_no.unique()):
for ing in [1,2]:
sub = d[(d.match_no==mno)&(d.innings==ing)]
bat_extras = sub.runs_of_bat.sum()+sub.extras.sum()
bat_comp = sub.runs_of_bat.sum()+(sub.wide+sub.legbyes+sub.byes+sub.noballs).sum()
official = m.loc[m.match_id==mno, 'first_ings_score' if ing==1 else 'second_ings_score'].iloc[0]
flag = '' if bat_extras==official else ' <-- extras mismatch'
print(f'{mno:>5} {ing:>4} {bat_extras:>11} {bat_comp:>15} {official:>9}{flag}')
"Validate innings totals: deliveries(runs_of_bat+extras) vs matches.csv official score
match ings bat+extras bat+components official
1 1 203 203 201 <-- extras mismatch
1 2 203 197 203
2 1 220 220 220
2 2 224 224 224
3 1 128 125 127 <-- extras mismatch
3 2 128 125 128
4 1 162 161 162
4 2 165 165 165
5 1 141 132 141
5 2 144 139 145 <-- extras mismatch
6 1 226 223 226
6 2 160 159 161 <-- extras mismatchcd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
print('Per-innings: legal balls, overs, and wkts context')
for mno in sorted(d.match_no.unique()):
for ing in [1,2]:
sub = d[(d.match_no==mno)&(d.innings==ing)]
legal = ((sub.wide==0)&(sub.noballs==0)).sum()
wides = (sub.wide>0).sum(); nb = (sub.noballs>0).sum()
print(f'match {mno} ings {ing}: rows={len(sub)}, legal={legal} ({legal/6:.2f} overs), wides={wides}, noballs={nb}, max_over={sub.over.max()}')
"Per-innings: legal balls, overs, and wkts context match 1 ings 1: rows=126, legal=121 (20.17 overs), wides=5, noballs=0, max_over=19.6 match 1 ings 2: rows=102, legal=95 (15.83 overs), wides=7, noballs=0, max_over=15.4 match 2 ings 1: rows=128, legal=120 (20.00 overs), wides=7, noballs=1, max_over=19.6 match 2 ings 2: rows=119, legal=115 (19.17 overs), wides=3, noballs=1, max_over=19.1 match 3 ings 1: rows=125, legal=118 (19.67 overs), wides=6, noballs=1, max_over=19.4 match 3 ings 2: rows=74, legal=73 (12.17 overs), wides=1, noballs=0, max_over=12.1 match 4 ings 1: rows=131, legal=120 (20.00 overs), wides=10, noballs=1, max_over=19.6 match 4 ings 2: rows=119, legal=115 (19.17 overs), wides=4, noballs=0, max_over=19.1 match 5 ings 1: rows=114, legal=112 (18.67 overs), wides=2, noballs=0, max_over=18.4 match 5 ings 2: rows=114, legal=103 (17.17 overs), wides=11, noballs=0, max_over=17.1 match 6 ings 1: rows=129, legal=120 (20.00 overs), wides=9, noballs=0, max_over=19.6 match 6 ings 2: rows=101, legal=96 (16.00 overs), wides=5, noballs=0, max_over=15.6
cd /app && python3 -c "
import pandas as pd
pd.set_option('display.max_rows',200)
d = pd.read_csv('data/deliveries.csv')
sub = d[(d.match_no==1)&(d.innings==1)].copy()
# Count legal balls per over (integer part of over)
sub['ov'] = sub.over.astype(str).str.split('.').str[0].astype(int)
sub['legal'] = ((sub.wide==0)&(sub.noballs==0)).astype(int)
g = sub.groupby('ov').agg(rows=('over','size'), legal=('legal','sum'), wides=('wide', lambda x:(x>0).sum()), nb=('noballs', lambda x:(x>0).sum()))
print(g)
print('total legal:', sub.legal.sum())
"rows legal wides nb ov 0 6 6 0 0 1 8 6 2 0 2 8 6 2 0 3 6 6 0 0 4 6 6 0 0 5 6 6 0 0 6 6 6 0 0 7 6 6 0 0 8 6 6 0 0 9 6 6 0 0 10 7 7 0 0 11 6 6 0 0 12 6 6 0 0 13 6 6 0 0 14 6 6 0 0 15 6 6 0 0 16 7 6 1 0 17 6 6 0 0 18 6 6 0 0 19 6 6 0 0 total legal: 121
cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
sub = d[(d.match_no==1)&(d.innings==1)].copy()
sub['ov'] = sub.over.astype(str).str.split('.').str[0].astype(int)
print(sub[sub.ov==10][['over','striker','bowler','runs_of_bat','extras','wide','legbyes','byes','noballs','wicket_type','player_dismissed']].to_string())
"over striker bowler runs_of_bat extras wide legbyes byes noballs wicket_type player_dismissed 64 10.1 Heinrich Klaasen Krunal Pandya 0 0 0 0 0 0 NaN NaN 65 10.2 Heinrich Klaasen Krunal Pandya 1 0 0 0 0 0 NaN NaN 66 10.3 Ishan Kishan Krunal Pandya 1 0 0 0 0 0 bowled NaN 67 10.3 Ishan Kishan Krunal Pandya 1 0 0 0 0 0 NaN NaN 68 10.4 Heinrich Klaasen Krunal Pandya 1 0 0 0 0 0 NaN NaN 69 10.5 Ishan Kishan Krunal Pandya 1 0 0 0 0 0 NaN NaN 70 10.6 Heinrich Klaasen Krunal Pandya 1 0 0 0 0 0 NaN NaN
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$total_runs, 2)
cat("Chart saved\n")
results <- list(
toss_win_match_win_pct = toss_win_match_win_pct,
toss_decision_counts = toss_decision_counts,
chase_success_rate = chase_success_rate,
total_matches = total_matches,
matches_with_d
…[truncated]cd /app && which R Rscript jupyter 2>/dev/null; echo "---"; Rscript -e 'cat("R ok\n"); suppressMessages(library(tidyverse)); cat("tidyverse ok\n"); suppressMessages(library(jsonlite)); cat("jsonlite ok\n"); cat(as.character(getRversion()),"\n")' 2>&1 | tail -20/usr/bin/R /usr/bin/Rscript /usr/local/bin/jupyter --- R ok tidyverse ok jsonlite ok 4.5.0
cd /app && jupyter kernelspec list 2>/dev/null; echo "--- notebook metadata ---"; python3 -c "
import json
nb=json.load(open('solve.ipynb'))
print('kernel:', nb['metadata'].get('kernelspec'))
print('language_info:', nb['metadata'].get('language_info',{}).get('name'))
print('num cells:', len(nb['cells']))
for i,c in enumerate(nb['cells']):
src=''.join(c['source'])
first=src.strip().split(chr(10))[0][:70] if src.strip() else '(empty)'
print(i, c['cell_type'], '|', first)
"Available kernels:
ir /usr/local/share/jupyter/kernels/ir
python3 /usr/local/share/jupyter/kernels/python3
--- notebook metadata ---
kernel: {'display_name': 'R', 'language': 'R', 'name': 'ir'}
language_info: R
num cells: 10
0 code | library(tidyverse)
1 code | matches <- read_csv("/app/data/matches.csv", show_col_types = FALSE)
2 code | total_matches <- length(unique(deliveries$match_no))
3 code | toss_winner_match_winner_count <- sum(matches$team1 == matches$match_w
4 code | batting_first_info <- deliveries %>%
5 code | matches_with_del <- matches %>%
6 code | venue_stats <- deliveries %>%
7 code | venue_stats <- venue_stats %>% arrange(run_rate)
8 code | venue_abbrev <- c("WAK", "CHN", "EDN", "MUL", "BAR", "EKA")
9 code | results <- list(cd /app && python3 -c "
import json
nb=json.load(open('solve.ipynb'))
for i,c in enumerate(nb['cells']):
print(f'===== CELL {i} ({c[\"cell_type\"]}) =====')
print(''.join(c['source']))
print()
"===== 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")
cat("Delivery data loaded:", nrow(deliveries), "deliveries\n")
===== CELL 2 (code) =====
total_matches <- length(unique(deliveries$match_no))
matches_with_deliveries <- nrow(matches)
toss_decisions <- matches %>%
group_by(team1) %>%
summarise(n = n(), .groups = "drop")
toss_decision_counts <- list("Bowl" = nrow(matches))
cat("Total matches analyzed:", total_matches, "\n")
cat("Matches with ball-by-ball data:", matches_with_deliveries, "\n")
===== CELL 3 (code) =====
toss_winner_match_winner_count <- sum(matches$team1 == matches$match_winner)
toss_win_match_win_pct <- round(toss_winner_match_winner_count / total_matches * 100, 2)
cat("Toss winner also won match:", toss_winner_match_winner_count, "times\n")
cat("Toss-win to match-win percentage:", toss_win_match_win_pct, "%\n")
===== CELL 4 (code) =====
batting_first_info <- deliveries %>%
filter(innings == 1) %>%
group_by(match_no) %>%
summarise(batting_first_team = first(batting_team), .groups = "drop")
match_analysis <- matches %>%
left_join(batting_first_info, by = c("match_id" = "match_no"))
chase_wins <- sum(match_analysis$batting_first_team == match_analysis$match_winner, na.rm = TRUE)
defend_wins <- total_matches - chase_wins
chase_success_rate <- round(chase_wins / total_matches * 100, 2)
cat("Chase wins:", chase_wins, "\n")
cat("Defend wins:", defend_wins, "\n")
cat("Chase success rate:", chase_success_rate, "%\n")
===== CELL 5 (code) =====
matches_with_del <- matches %>%
filter(match_id %in% unique(deliveries$match_no))
first_innings_avg_score <- round(mean(matches_with_del$first_ings_score), 2)
second_innings_del <- deliveries %>%
filter(innings == 2) %>%
group_by(match_no) %>%
summarise(total = sum(runs_of_bat + extras), .groups = "drop")
second_innings_avg_score <- round(mean(second_innings_del$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 (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_rate = total_runs / overs
)
cat("Venue statistics computed\n")
print(venue_stats %>% select(venue, total_runs, overs, run_rate))
===== 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$venue[nrow(venue_stats)]
least_favorable_venue_rr <- round(venue_stats$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 (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/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,
…[truncated]cd /app && Rscript -e '
suppressMessages({library(tidyverse); library(jsonlite)})
matches <- read_csv("/app/data/matches.csv", show_col_types = FALSE)
toss_tab <- table(matches$toss_decision)
toss_decision_counts <- as.list(setNames(as.integer(toss_tab), names(toss_tab)))
cat("toss_decision_counts JSON: ", toJSON(toss_decision_counts, auto_unbox=TRUE), "\n")
# test abbrev map lookup alignment
map <- c("Wankhede Stadium, Mumbai"="WAK","M.Chinnaswamy Stadium, Bengaluru"="CHN",
"Eden Gardens, Kolkata"="EDN",
"Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh"="MUL",
"Barsapara Cricket Stadium, Guwahati"="BAR",
"Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow"="EKA")
d <- read_csv("/app/data/deliveries.csv", show_col_types=FALSE)
venues_desc <- c("Wankhede Stadium, Mumbai","M.Chinnaswamy Stadium, Bengaluru","Eden Gardens, Kolkata",
"Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh",
"Barsapara Cricket Stadium, Guwahati","Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow")
cat("mapped labels:", unname(map[venues_desc]), "\n")
cat("any NA in delivery-venue lookup:", any(is.na(map[unique(d$venue)])), "\n")
'toss_decision_counts JSON: {"Bowl":7}
mapped labels: WAK CHN EDN MUL BAR EKA
any NA in delivery-venue lookup: FALSEcd /app && cat > /tmp/fix_nb.py << 'PYEOF'
import json
nb = json.load(open('/app/solve.ipynb'))
def setcell(i, text):
# store as list of lines preserving trailing newlines (nbformat style)
lines = text.splitlines(keepends=True)
nb['cells'][i]['source'] = lines
nb['cells'][i]['outputs'] = []
nb['cells'][i]['execution_count'] = None
# ---- Cell 2: total_matches vs matches_with_deliveries were swapped; compute toss decisions from data
setcell(2, '''# Total matches comes from the match summary (all seven games);
# matches_with_deliveries is how many of them have ball-by-ball records.
total_matches <- nrow(matches)
matches_with_deliveries <- length(unique(deliveries$match_no))
# Count toss decisions directly from the toss_decision column.
toss_tab <- table(matches$toss_decision)
toss_decision_counts <- as.list(setNames(as.integer(toss_tab), names(toss_tab)))
cat("Total matches analyzed:", total_matches, "\\n")
cat("Matches with ball-by-ball data:", matches_with_deliveries, "\\n")
''')
# ---- Cell 3: compare toss_winner (not team1) to match_winner
setcell(3, '''# Toss advantage: compare the toss winner to the match winner.
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 = team batting SECOND won; fill missing match from toss decision
setcell(4, '''# Who batted first in each match, taken from innings 1 of the deliveries data.
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 with no deliveries, infer the batting order from the toss.
# Every toss winner in this dataset chose to bowl, so the toss winner
# batted second and the other team batted first.
batting_first_team = ifelse(
is.na(batting_first_team),
ifelse(toss_decision == "Bowl",
ifelse(toss_winner == team1, team2, team1),
toss_winner),
batting_first_team
),
batting_second_team = ifelse(team1 == batting_first_team, team2, team1)
)
# A chase win is when the team batting second wins the match.
chase_wins <- sum(match_analysis$batting_second_team == match_analysis$match_winner)
defend_wins <- sum(match_analysis$batting_first_team == match_analysis$match_winner)
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: averages/highs from match summary across ALL seven matches; highest uses max
setcell(5, '''# Innings scoring patterns 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")
''')
# ---- Cell 6: total runs include extras; overs use legal deliveries only
setcell(6, '''# Venue run rates come only from deliveries:
# total runs = runs off bat + extras; one over = six legal deliveries
# (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),
…[truncated]notebook updated; cells: 10
cd /app && python3 -c "
import json
nb=json.load(open('solve.ipynb'))
print('valid JSON, cells:', len(nb['cells']))
" && echo "=== converted script preview ===" && jupyter nbconvert --to script --stdout solve.ipynb 2>/dev/null | sed -n '1,80p'valid JSON, cells: 10
=== converted script preview ===
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 comes from the match summary (all seven games);
# matches_with_deliveries is how many of them have ball-by-ball records.
total_matches <- nrow(matches)
matches_with_deliveries <- length(unique(deliveries$match_no))
# Count toss decisions directly from the toss_decision column.
toss_tab <- table(matches$toss_decision)
toss_decision_counts <- as.list(setNames(as.integer(toss_tab), names(toss_tab)))
cat("Total matches analyzed:", total_matches, "\n")
cat("Matches with ball-by-ball data:", matches_with_deliveries, "\n")
# Toss advantage: compare the toss winner to the match winner.
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")
# Who batted first in each match, taken from innings 1 of the deliveries data.
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 with no deliveries, infer the batting order from the toss.
# Every toss winner in this dataset chose to bowl, so the toss winner
# batted second and the other team batted first.
batting_first_team = ifelse(
is.na(batting_first_team),
ifelse(toss_decision == "Bowl",
ifelse(toss_winner == team1, team2, team1),
toss_winner),
batting_first_team
),
batting_second_team = ifelse(team1 == batting_first_team, team2, team1)
)
# A chase win is when the team batting second wins the match.
chase_wins <- sum(match_analysis$batting_second_team == match_analysis$match_winner)
defend_wins <- sum(match_analysis$batting_first_team == match_analysis$match_winner)
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")
# Innings scoring patterns 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")
# Venue run rates come only from deliveries:
# total runs = runs off bat + extras; one over = six legal deliveries
# (wides and no-balls are not legal deliveries).cd /app && rm -f results.json figures/venue_run_rates.png && jupyter nbconvert --to notebook --execute --inplace --ExecutePreprocessor.timeout=180 solve.ipynb 2>&1 | tail -20 && echo "=== EXIT: $? ===" && ls -la results.json figures/venue_run_rates.png
[NbConvertApp] Converting notebook solve.ipynb to notebook [NbConvertApp] Writing 17994 bytes to solve.ipynb === EXIT: 0 === -rw-r--r-- 1 h2tagent h2tagent 12343 Jun 17 06:42 figures/venue_run_rates.png -rw-r--r-- 1 h2tagent h2tagent 795 Jun 17 06:42 results.json
cd /app && echo "===== results.json =====" && cat results.json
===== 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": [
"WAK",
"CHN",
"EDN",
"MUL",
"BAR",
"EKA"
],
"chart_y_values": [
11.34,
11.28,
10.72,
8.35,
8.04,
7.95
]
}cd /app && python3 << 'PYEOF'
import pandas as pd, json
m = pd.read_csv('data/matches.csv')
d = pd.read_csv('data/deliveries.csv')
res = json.load(open('results.json'))
exp = {}
# toss
exp['toss_winner_match_winner_count'] = int((m.toss_winner==m.match_winner).sum())
exp['total_matches'] = int(len(m))
exp['matches_with_deliveries'] = int(d.match_no.nunique())
exp['toss_win_match_win_pct'] = round(exp['toss_winner_match_winner_count']/exp['total_matches']*100,2)
exp['toss_decision_counts'] = m.toss_decision.value_counts().to_dict()
# batting first from deliveries; fill missing via toss (all chose Bowl)
bf = d[d.innings==1].groupby('match_no').batting_team.first().to_dict()
chase=defend=0
for _,r in m.iterrows():
first = bf.get(r.match_id)
if first is None:
first = r.team2 if r.toss_winner==r.team1 else r.team1 # toss winner bowled -> bats 2nd
second = r.team2 if first==r.team1 else r.team1
if second==r.match_winner: chase+=1
elif first==r.match_winner: defend+=1
exp['chase_wins']=chase; exp['defend_wins']=defend
exp['chase_success_rate']=round(chase/len(m)*100,2)
# innings scores from summary (all 7)
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())
# venue run rates from deliveries
vs=[]
for v in d.venue.unique():
s=d[d.venue==v]
tr=s.runs_of_bat.sum()+s.extras.sum()
legal=((s.wide==0)&(s.noballs==0)).sum()
vs.append((v, tr*6/legal))
vs.sort(key=lambda x:-x[1])
exp['most_favorable_venue']=vs[0][0]; exp['most_favorable_venue_rr']=round(vs[0][1],2)
exp['least_favorable_venue']=vs[-1][0]; exp['least_favorable_venue_rr']=round(vs[-1][1],2)
exp['chart_y_values']=[round(x[1],2) for x in vs]
# compare
keys=['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_y_values']
allok=True
for k in keys:
got=res[k]; want=exp[k]
ok = (got==want)
if not ok: allok=False
print(f"{'OK ' if ok else 'BAD'} {k}: notebook={got} | expected={want}")
print("\nchart_x_labels:", res['chart_x_labels'])
print("lengths -> x:", len(res['chart_x_labels']), "y:", len(res['chart_y_values']))
print("\nALL MATCH" if allok else "\nMISMATCH FOUND")
PYEOFOK toss_win_match_win_pct: notebook=85.71 | expected=85.71
OK toss_decision_counts: notebook={'Bowl': 7} | expected={'Bowl': 7}
OK chase_success_rate: notebook=85.71 | expected=85.71
OK total_matches: notebook=7 | expected=7
OK matches_with_deliveries: notebook=6 | expected=6
OK first_innings_avg_score: notebook=183.71 | expected=183.71
OK second_innings_avg_score: notebook=176.57 | expected=176.57
OK highest_first_innings: notebook=226 | expected=226
OK highest_second_innings: notebook=224 | expected=224
OK toss_winner_match_winner_count: notebook=6 | expected=6
OK chase_wins: notebook=6 | expected=6
OK defend_wins: notebook=1 | expected=1
OK most_favorable_venue: notebook=Wankhede Stadium, Mumbai | expected=Wankhede Stadium, Mumbai
OK most_favorable_venue_rr: notebook=11.34 | expected=11.34
OK least_favorable_venue: notebook=Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow | expected=Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow
OK least_favorable_venue_rr: notebook=7.95 | expected=7.95
OK chart_y_values: notebook=[11.34, 11.28, 10.72, 8.35, 8.04, 7.95] | expected=[11.34, 11.28, 10.72, 8.35, 8.04, 7.95]
chart_x_labels: ['WAK', 'CHN', 'EDN', 'MUL', 'BAR', 'EKA']
lengths -> x: 6 y: 6
ALL MATCHcommand: view path: /app/figures/venue_run_rates.png
<output_image> …[truncated]
cd /app && python3 -c "
import json
nb=json.load(open('solve.ipynb'))
for i,c in enumerate(nb['cells']):
for o in c.get('outputs',[]):
if o.get('output_type')=='error':
print(f'CELL {i} ERROR:', o.get('ename'), o.get('evalue'))
elif o.get('output_type')=='stream':
tag='STDERR' if o.get('name')=='stderr' else 'stdout'
txt=''.join(o.get('text',[]))
if tag=='STDERR':
print(f'--- CELL {i} {tag} ---'); print(txt)
else:
print(f'--- CELL {i} stdout ---'); print(txt)
" --- CELL 0 STDERR ---
── [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
--- CELL 0 STDERR ---
── [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
--- CELL 0 STDERR ---
Attaching package: ‘jsonlite’
--- CELL 0 STDERR ---
The following object is masked from ‘package:purrr’:
flatten
--- CELL 1 stdout ---
Match data loaded: 7 matches
--- CELL 1 stdout ---
Delivery data loaded: 1382 deliveries
--- CELL 2 stdout ---
Total matches analyzed: 7
--- CELL 2 stdout ---
Matches with ball-by-ball data: 6
--- CELL 3 stdout ---
Toss winner also won match: 6 times
--- CELL 3 stdout ---
Toss-win to match-win percentage: 85.71 %
--- CELL 4 stdout ---
Chase wins: 6
--- CELL 4 stdout ---
Defend wins: 1
--- CELL 4 stdout ---
Chase success rate: 85.71 %
--- CELL 5 stdout ---
First innings average: 183.71
--- CELL 5 stdout ---
Second innings average: 176.57
--- CELL 5 stdout ---
Highest first innings score: 226
--- CELL 5 stdout ---
Highest second innings score: 224
--- CELL 6 stdout ---
Venue statistics computed
--- CELL 6 stdout ---
[90m# A tibble: 6 × 4[39m
venue total_runs overs run_rate
[3m[90m<chr>[39m[23m [3m[90m<dbl>[39m[23m [3m[90m<dbl>[39m[23m [3m[90m<dbl>[39m[23m
[90m1[39m Barsapara Cricket Stadium, Guwahati 256 31.8 8.04
[90m2[39m Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricke… 285 35.8 7.95
[90m3[39m Eden Gardens, Kolkata 386 36 10.7
[90m4[39m M.Chinnaswamy Stadium, Bengaluru 406 36 11.3
[90m5[39m Maharaja Yadavindra Singh International Cricket Sta… 327 39.2 8.35
[90m6[39m Wankhede Stadium, Mumbai 444 39.2 11.3
--- CELL 7 stdout ---
Most favorable venue: Wankhede Stadium, Mumbai
--- CELL 7 stdout ---
Most favorable RR: 11.34
--- CELL 7 stdout ---
Least favorable venue: Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow
--- CELL 7 stdout ---
Least favorable RR: 7.95
--- CELL 8 stdout ---
Chart saved
--- CELL 9 stdout ---
Results written to /app/results.jsoncd /app && echo "=== deliverables ===" && ls -la /app/results.json /app/figures/venue_run_rates.png /app/solve.ipynb && python3 -c "import json; d=json.load(open('/app/results.json')); print('\nresults.json keys present:', len(d)); 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']; missing=[k for k in req if k not in d]; print('missing keys:', missing if missing else 'NONE')"=== deliverables === -rw-r--r-- 1 h2tagent h2tagent 12343 Jun 17 06:42 /app/figures/venue_run_rates.png -rw-r--r-- 1 h2tagent h2tagent 795 Jun 17 06:42 /app/results.json -rw-rw---- 1 h2tagent h2tagent 18179 Jun 17 06:42 /app/solve.ipynb results.json keys present: 18 missing keys: NONE
No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary # task: ipl-toss-impact-analysis-r # attempt: 3 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). ---------------------------------------------------------------------- === deliverables === -rw-r--r-- 1 h2tagent h2tagent 12343 Jun 17 06:42 /app/figures/venue_run_rates.png -rw-r--r-- 1 h2tagent h2tagent 795 Jun 17 06:42 /app/results.json -rw-rw---- 1 h2tagent h2tagent 18179 Jun 17 06:42 /app/solve.ipynb results.json keys present: 18 missing keys: NONE
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_a53e6054e1c34aed. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_a53e6054e1c34aed · verifier authoritative; classifier explanatory.