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 =====" && head -1 data/deliveries.csv && echo "" && echo "===== first 5 rows =====" && head -6 data/deliveries.csv && echo "" && echo "===== total lines =====" && wc -l data/deliveries.csv
===== 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 5 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('Columns:', list(d.columns))
print()
print('Match numbers present:', sorted(d.match_no.unique()))
print()
print('Innings values:', sorted(d.innings.unique()))
print()
print('Rows per match:')
print(d.groupby('match_no').size())
"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'] Match numbers present: [1, 2, 3, 4, 5, 6] Innings values: [1, 2] Rows per match: match_no 1 228 2 247 3 199 4 250 5 228 6 230 dtype: int64
cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
print('First-innings batting team per match (from deliveries):')
for m in sorted(d.match_no.unique()):
sub = d[(d.match_no==m)]
i1 = sub[sub.innings==1]['batting_team'].iloc[0]
i2 = sub[sub.innings==2]['batting_team'].iloc[0]
ven = sub['venue'].iloc[0]
print(f' match {m}: innings1={i1:5s} innings2={i2:5s} | venue={ven}')
print()
print('Unique venues in deliveries:')
for v in d.venue.unique():
print(' ', repr(v))
"First-innings batting team per match (from deliveries): match 1: innings1=SRH innings2=RCB | venue=M.Chinnaswamy Stadium, Bengaluru match 2: innings1=KKR innings2=MI | venue=Wankhede Stadium, Mumbai match 3: innings1=CSK innings2=RR | venue=Barsapara Cricket Stadium, Guwahati match 4: innings1=GT innings2=PBKS | venue=Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh match 5: innings1=LSG innings2=DC | venue=Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow match 6: innings1=SRH innings2=KKR | venue=Eden Gardens, Kolkata Unique 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 && python3 -c "
import pandas as pd
m = pd.read_csv('data/matches.csv')
print('Toss decisions:', m.toss_decision.value_counts().to_dict())
print('Toss winner == match winner count:', (m.toss_winner==m.match_winner).sum(), 'of', len(m))
print()
print('first_ings avg:', m.first_ings_score.mean())
print('second_ings avg:', m.second_ings_score.mean())
print('max first:', m.first_ings_score.max(), '| max second:', m.second_ings_score.max())
print()
print('Per-match: toss_winner, toss_decision, winner, team1, team2')
print(m[['match_id','team1','team2','toss_winner','toss_decision','first_ings_score','second_ings_score','match_winner']].to_string(index=False))
"Toss decisions: {'Bowl': 7}
Toss winner == match winner count: 6 of 7
first_ings avg: 183.71428571428572
second_ings avg: 176.57142857142858
max first: 226 | max second: 224
Per-match: toss_winner, toss_decision, winner, team1, team2
match_id team1 team2 toss_winner toss_decision first_ings_score second_ings_score match_winner
1 RCB SRH RCB Bowl 201 203 RCB
2 MI KKR MI Bowl 220 224 MI
3 RR CSK RR Bowl 127 128 RR
4 PBKS GT PBKS Bowl 162 165 PBKS
5 LSG DC DC Bowl 141 145 DC
6 KKR SRH KKR Bowl 226 161 SRH
7 CSK PBKS PBKS Bowl 209 210 PBKScd /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 pandas as pd
d = pd.read_csv('data/deliveries.csv')
print('wide value counts:', d.wide.value_counts().to_dict())
print('noballs value counts:', d.noballs.value_counts().to_dict())
print('legbyes value counts:', d.legbyes.value_counts().to_dict())
print('byes value counts:', d.byes.value_counts().to_dict())
print()
# Check 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()
# Rows that are wides or noballs
print('rows with wide>0:', (d.wide>0).sum())
print('rows with noballs>0:', (d.noballs>0).sum())
print('rows with wide>0 AND noballs>0:', ((d.wide>0)&(d.noballs>0)).sum())
print()
print('Sample wide rows:')
print(d[d.wide>0].head(3)[['match_no','over','runs_of_bat','extras','wide','noballs']].to_string(index=False))
print('Sample noball rows:')
print(d[d.noballs>0].head(3)[['match_no','over','runs_of_bat','extras','wide','noballs']].to_string(index=False))
"wide value counts: {0: 1312, 1: 70}
noballs value counts: {0: 1378, 1: 4}
legbyes value counts: {0: 1358, 1: 24}
byes value counts: {0: 1377, 1: 5}
extras == wide+legbyes+byes+noballs for all rows: False
rows with wide>0: 70
rows with noballs>0: 4
rows with wide>0 AND noballs>0: 0
Sample wide rows:
match_no over runs_of_bat extras wide noballs
1 1.4 0 1 1 0
1 1.5 0 1 1 0
1 2.1 0 1 1 0
Sample noball rows:
match_no over runs_of_bat extras wide noballs
2 19.1 0 1 0 1
2 9.1 1 1 0 1
3 18.2 1 1 0 1cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
mism = d[d.extras != (d.wide + d.legbyes + d.byes + d.noballs)]
print('Rows where extras != flag sum:', len(mism))
print(mism[['match_no','over','runs_of_bat','extras','wide','legbyes','byes','noballs']].to_string(index=False))
print()
print('extras value counts:', d.extras.value_counts().to_dict())
print('Total extras sum:', d.extras.sum())
print('Total runs_of_bat sum:', d.runs_of_bat.sum())
"Rows where extras != flag sum: 12
match_no over runs_of_bat extras wide legbyes byes noballs
1 4.5 0 4 0 0 1 0
1 7.5 0 4 0 0 1 0
3 1.3 0 4 0 1 0 0
3 5.1 0 4 0 0 1 0
4 19.4 0 2 1 0 0 0
5 4.1 0 4 0 1 0 0
5 5.2 0 4 0 0 1 0
5 11.1 0 4 0 1 0 0
5 3.4 0 2 0 1 0 0
5 16.4 0 5 1 0 0 0
6 8.5 0 4 0 1 0 0
6 1.6 0 2 1 0 0 0
extras value counts: {0: 1279, 1: 91, 4: 8, 2: 3, 5: 1}
Total extras sum: 134
Total runs_of_bat sum: 1970cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
d['total_runs_ball'] = 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_ball','sum'), legal_balls=('legal','sum'), all_rows=('legal','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)
pd.set_option('display.width',200); pd.set_option('display.max_colwidth',100)
print(g.to_string(index=False))
print()
print('MOST favorable (highest rr):', g.iloc[0].venue, round(g.iloc[0].run_rate,2))
print('LEAST favorable (lowest rr):', g.iloc[-1].venue, round(g.iloc[-1].run_rate,2))
"venue total_runs legal_balls all_rows 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 7.95cd /app && python3 -c "
import json
nb = json.load(open('solve.ipynb'))
print('nbformat:', nb.get('nbformat'))
print('kernelspec:', nb.get('metadata',{}).get('kernelspec'))
print('language_info:', nb.get('metadata',{}).get('language_info'))
print('num cells:', len(nb['cells']))
for i,c in enumerate(nb['cells']):
print(f'--- cell {i} ({c[\"cell_type\"]}) ---')
print(''.join(c['source'])[:120])
"nbformat: 4
kernelspec: {'display_name': 'R', 'language': 'R', 'name': 'ir'}
language_info: {'name': 'R'}
num 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",
--- cell 2 (code) ---
total_matches <- length(unique(deliveries$match_no))
matches_with_deliveries <- nrow(matches)
toss_decisions <- matches
--- cell 3 (code) ---
toss_winner_match_winner_count <- sum(matches$team1 == matches$match_winner)
toss_win_match_win_pct <- round(toss_winner
--- cell 4 (code) ---
batting_first_info <- deliveries %>%
filter(innings == 1) %>%
group_by(match_no) %>%
summarise(batting_first_team
--- cell 5 (code) ---
matches_with_del <- matches %>%
filter(match_id %in% unique(deliveries$match_no))
first_innings_avg_score <- round(me
--- cell 6 (code) ---
venue_stats <- deliveries %>%
group_by(venue) %>%
summarise(
total_runs = sum(runs_of_bat),
total_balls = n(
--- cell 7 (code) ---
venue_stats <- venue_stats %>% arrange(run_rate)
most_favorable_venue <- venue_stats$venue[1]
most_favorable_venue_rr <
--- cell 8 (code) ---
venue_abbrev <- c("WAK", "CHN", "EDN", "MUL", "BAR", "EKA")
chart_data <- venue_stats %>% arrange(desc(run_rate))
dir.c
--- cell 9 (code) ---
results <- list(
toss_win_match_win_pct = toss_win_match_win_pct,
toss_decision_counts = toss_decision_counts,
chacd /app && which R Rscript jupyter 2>&1; echo "---"; jupyter kernelspec list 2>&1; echo "--- R packages ---"; Rscript -e 'for(p in c("tidyverse","jsonlite","readr","dplyr","IRkernel")) cat(p, as.character(requireNamespace(p, quietly=TRUE)), "\n")' 2>&1 | tail -20/usr/bin/R /usr/bin/Rscript /usr/local/bin/jupyter --- Available kernels: ir /usr/local/share/jupyter/kernels/ir python3 /usr/local/share/jupyter/kernels/python3 --- R packages --- tidyverse TRUE jsonlite TRUE readr TRUE dplyr TRUE IRkernel TRUE
cd /app && cp solve.ipynb /tmp/solve_backup.ipynb && jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=300 --output /tmp/baseline_exec.ipynb solve.ipynb 2>&1 | tail -5; echo "=== baseline results.json ==="; cat results.json 2>/dev/null
[NbConvertApp] Converting notebook solve.ipynb to notebook
[NbConvertApp] Writing 16232 bytes to /tmp/baseline_exec.ipynb
=== baseline results.json ===
{
"toss_win_match_win_pct": 66.67,
"toss_decision_counts": {
"Bowl": 7
},
"chase_success_rate": 16.67,
"total_matches": 6,
"matches_with_deliveries": 7,
"first_innings_avg_score": 179.5,
"second_innings_avg_score": 170.67,
"highest_first_innings": 127,
"highest_second_innings": 224,
"toss_winner_match_winner_count": 4,
"chase_wins": 1,
"defend_wins": 5,
"most_favorable_venue": "Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow",
"most_favorable_venue_rr": 6.63,
"least_favorable_venue": "Wankhede Stadium, Mumbai",
"least_favorable_venue_rr": 10.4,
"chart_x_labels": [
"WAK",
"CHN",
"EDN",
"MUL",
"BAR",
"EKA"
],
"chart_y_values": [
428,
381,
366,
309,
234,
252
]
}cd /app && python3 -c "
import json
nb = json.load(open('solve.ipynb'))
for i,c in enumerate(nb['cells']):
print(f'############### CELL {i} ###############')
print(''.join(c['source']))
print()
"############### CELL 0 ###############
library(tidyverse)
library(jsonlite)
############### CELL 1 ###############
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 ###############
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
…[truncated]cd /tmp && cat > 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)
# toss_decision_counts robust
tc <- table(matches$toss_decision)
toss_decision_counts <- as.list(setNames(as.integer(tc), names(tc)))
cat("toss_decision_counts JSON: ", toJSON(toss_decision_counts, auto_unbox=TRUE), "\n")
# batting order fill for all 7
batting_first_info <- deliveries %>% filter(innings == 1) %>%
group_by(match_no) %>% summarise(batting_first_team = first(batting_team), .groups="drop")
match_analysis <- matches %>%
left_join(batting_first_info, by = c("match_id" = "match_no")) %>%
mutate(
batting_first_team = if_else(is.na(batting_first_team),
if_else(toss_decision == "Bowl",
if_else(toss_winner == team1, team2, team1), toss_winner),
batting_first_team),
batting_second_team = if_else(batting_first_team == team1, team2, team1))
print(match_analysis %>% select(match_id, team1, team2, toss_winner, batting_first_team, batting_second_team, match_winner))
chase_wins <- sum(match_analysis$batting_second_team == match_analysis$match_winner)
cat("chase_wins:", chase_wins, " defend_wins:", nrow(matches)-chase_wins, "\n")
EOF
Rscript test.R 2>&1toss_decision_counts JSON: {"Bowl":7}
# A tibble: 7 × 7
match_id team1 team2 toss_winner batting_first_team batting_second_team
<dbl> <chr> <chr> <chr> <chr> <chr>
1 1 RCB SRH RCB SRH RCB
2 2 MI KKR MI KKR MI
3 3 RR CSK RR CSK RR
4 4 PBKS GT PBKS GT PBKS
5 5 LSG DC DC LSG DC
6 6 KKR SRH KKR SRH KKR
7 7 CSK PBKS PBKS CSK PBKS
# ℹ 1 more variable: match_winner <chr>
chase_wins: 6 defend_wins: 1cd /app && python3 << 'PYEOF'
import json
with open('solve.ipynb') as f:
nb = json.load(f)
def setcell(i, code):
# store as list of lines each ending with \n except last
lines = code.split('\n')
src = [l + '\n' for l in lines[:-1]] + ([lines[-1]] if lines[-1] != '' else [])
nb['cells'][i]['source'] = src
nb['cells'][i]['outputs'] = []
nb['cells'][i]['execution_count'] = None
# ---- Cell 2: fix swapped counts; data-driven toss_decision_counts ----
setcell(2, '''# Total matches come from the match summary (all seven games);
# matches with ball-by-ball data come from the deliveries file (six games).
total_matches <- nrow(matches)
matches_with_deliveries <- length(unique(deliveries$match_no))
# Toss decision breakdown, derived directly from the match summary.
toss_tbl <- table(matches$toss_decision)
toss_decision_counts <- as.list(setNames(as.integer(toss_tbl), names(toss_tbl)))
cat("Total matches analyzed:", total_matches, "\\n")
cat("Matches with ball-by-ball data:", matches_with_deliveries, "\\n")''')
# ---- Cell 3: compare toss_winner to match_winner (not team1) ----
setcell(3, '''# Toss advantage: how often the toss winner also won the match (all seven matches).
toss_winner_match_winner_count <- sum(matches$toss_winner == matches$match_winner)
toss_win_match_win_pct <- round(toss_winner_match_winner_count / total_matches * 100, 2)
cat("Toss winner also won match:", toss_winner_match_winner_count, "times\\n")
cat("Toss-win to match-win percentage:", toss_win_match_win_pct, "%\\n")''')
# ---- Cell 4: chase = team batting SECOND won; fill batting order for match w/o deliveries ----
setcell(4, '''# Determine who batted first in each match from the deliveries (innings 1 batting_team).
batting_first_info <- deliveries %>%
filter(innings == 1) %>%
group_by(match_no) %>%
summarise(batting_first_team = first(batting_team), .groups = "drop")
# For matches without delivery records, infer batting order from the toss.
# Every toss winner in this dataset chose to bowl, so the toss winner batted
# second and the other team batted first.
match_analysis <- matches %>%
left_join(batting_first_info, by = c("match_id" = "match_no")) %>%
mutate(
batting_first_team = if_else(
is.na(batting_first_team),
if_else(toss_decision == "Bowl",
if_else(toss_winner == team1, team2, team1),
toss_winner),
batting_first_team
),
batting_second_team = if_else(batting_first_team == team1, team2, team1)
)
# A chase win is a match won by the team batting second.
chase_wins <- sum(match_analysis$batting_second_team == match_analysis$match_winner)
defend_wins <- total_matches - chase_wins
chase_success_rate <- round(chase_wins / total_matches * 100, 2)
cat("Chase wins:", chase_wins, "\\n")
cat("Defend wins:", defend_wins, "\\n")
cat("Chase success rate:", chase_success_rate, "%\\n")''')
# ---- Cell 5: averages & highs from match summary across all seven matches ----
setcell(5, '''# Innings scoring patterns come from the match summary across all seven matches.
first_innings_avg_score <- round(mean(matches$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: venue run rates = (runs off bat + extras) / legal overs ----
setcell(6, '''# Venue run rates come exclusively from the deliveries.
# Total runs = runs off the bat + extras.
# 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
…[truncated]Notebook updated. Cells modified: 2,3,4,5,6,7,8
cd /app && python3 -c "import json; json.load(open('solve.ipynb')); print('Valid JSON')" && rm -f results.json figures/venue_run_rates.png && jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=300 --output solve.ipynb solve.ipynb 2>&1 | tail -4Valid JSON [NbConvertApp] Converting notebook solve.ipynb to notebook [NbConvertApp] Writing 17407 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": 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
]
}
=== figure ===
-rw-r--r-- 1 h2tagent h2tagent 30898 Jun 17 06:41 figures/venue_run_rates.png
[stderr]
bash: file: command not foundcd /app && python3 << 'PYEOF'
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'))
# Expected (independent) computations
exp = {}
exp['toss_win_match_win_pct'] = round((m.toss_winner==m.match_winner).mean()*100,2)
exp['toss_decision_counts'] = m.toss_decision.value_counts().to_dict()
exp['total_matches'] = len(m)
exp['matches_with_deliveries'] = d.match_no.nunique()
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())
exp['toss_winner_match_winner_count'] = int((m.toss_winner==m.match_winner).sum())
# batting order: deliveries innings1, else toss (all Bowl -> toss winner bats 2nd)
bf = d[d.innings==1].groupby('match_no').batting_team.first().to_dict()
chase=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 bowls -> bats 2nd
second = r.team2 if first==r.team1 else r.team1
if second==r.match_winner: chase+=1
exp['chase_wins']=chase
exp['defend_wins']=len(m)-chase
exp['chase_success_rate']=round(chase/len(m)*100,2)
# venue run rates
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['most_favorable_venue']=g.venue.iloc[0]
exp['most_favorable_venue_rr']=round(g.rr.iloc[0],2)
exp['least_favorable_venue']=g.venue.iloc[-1]
exp['least_favorable_venue_rr']=round(g.rr.iloc[-1],2)
exp['chart_x_labels']=g.venue.tolist()
exp['chart_y_values']=[round(x,2) for x in g.rr.tolist()]
all_ok=True
for k,v in exp.items():
got=R.get(k)
ok = (got==v)
if not ok: all_ok=False
print(f"{'OK ' if ok else 'XX '}{k}: got={got!r} expected={v!r}")
# keys present check
missing=[k for k in exp if k not in R]
extra=[k for k in R if k not in exp]
print("\nMissing keys:",missing," Extra keys:",extra)
print("\nALL MATCH:", all_ok and not missing)
PYEOFOK toss_win_match_win_pct: got=85.71 expected=85.71
OK toss_decision_counts: got={'Bowl': 7} expected={'Bowl': 7}
OK total_matches: got=7 expected=7
OK matches_with_deliveries: got=6 expected=6
OK first_innings_avg_score: got=183.71 expected=183.71
OK second_innings_avg_score: got=176.57 expected=176.57
OK highest_first_innings: got=226 expected=226
OK highest_second_innings: got=224 expected=224
OK toss_winner_match_winner_count: got=6 expected=6
OK chase_wins: got=6 expected=6
OK defend_wins: got=1 expected=1
OK chase_success_rate: got=85.71 expected=85.71
OK most_favorable_venue: got='Wankhede Stadium, Mumbai' expected='Wankhede Stadium, Mumbai'
OK most_favorable_venue_rr: got=11.34 expected=11.34
OK least_favorable_venue: got='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: got=7.95 expected=7.95
OK chart_x_labels: got=['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'] expected=['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: got=[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]
Missing keys: [] Extra keys: []
ALL MATCH: 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'))
errs=0
for i,c in enumerate(nb['cells']):
if c['cell_type']!='code': continue
for o in c.get('outputs',[]):
if o.get('output_type')=='error':
errs+=1; print('ERROR in cell',i,o.get('ename'))
print('Total error outputs:', errs)
print('Executed OK, all cells have execution_count:', all(c.get('execution_count') is not None for c in nb['cells'] if c['cell_type']=='code'))
" && echo "--- Clean re-run reproducibility check ---" && rm -f /tmp/rerun.ipynb results.json figures/venue_run_rates.png && jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=300 --output /tmp/rerun.ipynb solve.ipynb >/dev/null 2>&1 && python3 -c "import json;print('results.json keys:',len(json.load(open('results.json'))));print('figure exists:', __import__('os').path.exists('figures/venue_run_rates.png'))"Total error outputs: 0 Executed OK, all cells have execution_count: True --- Clean re-run reproducibility check --- results.json keys: 18 figure exists: True
cd /app && cat results.json | python3 -c "import json,sys; r=json.load(sys.stdin); print('\n'.join(f'{k}: {v}' for k,v in r.items()))"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]No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary
# task: ipl-toss-impact-analysis-r
# attempt: 8 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).
----------------------------------------------------------------------
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]Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_01c6d244e72a4c58. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_01c6d244e72a4c58 · verifier authoritative; classifier explanatory.