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 HEAD =====" && head -5 data/deliveries.csv && echo "..." && echo "===== columns =====" && head -1 data/deliveries.csv | tr ',' '\n' | nl
===== deliveries.csv HEAD =====
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,,,
...
===== columns =====
1 match_no
2 date
3 stage
4 venue
5 batting_team
6 bowling_team
7 innings
8 over
9 striker
10 bowler
11 runs_of_bat
12 extras
13 wide
14 legbyes
15 byes
16 noballs
17 wicket_type
18 player_dismissed
19 fieldercd /app && echo "match_no values present:" && cut -d',' -f1 data/deliveries.csv | tail -n +2 | sort -n | uniq -c && echo "---venues in deliveries---" && cut -d',' -f4 data/deliveries.csv | tail -n +2 | sort -u && echo "---innings values---" && cut -d',' -f7 data/deliveries.csv | tail -n +2 | sort -u
match_no values present:
228 1
247 2
199 3
250 4
228 5
230 6
---venues in deliveries---
League stage
---innings values---
New Chandigarh"
CSK
DC
KKR
LSG
MI
RCB
RR
SRHcd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
print('shape', d.shape)
print('match_no present:', sorted(d.match_no.unique()))
print()
print('venues per match_no:')
print(d.groupby('match_no')['venue'].unique())
print()
print('innings per match:')
print(d.groupby('match_no')['innings'].unique())
print()
print('batting_team innings1 per match:')
print(d[d.innings==1].groupby('match_no')['batting_team'].first())
"shape (1382, 19) match_no present: [1, 2, 3, 4, 5, 6] venues per match_no: match_no 1 [M.Chinnaswamy Stadium, Bengaluru] 2 [Wankhede Stadium, Mumbai] 3 [Barsapara Cricket Stadium, Guwahati] 4 [Maharaja Yadavindra Singh International Crick... 5 [Bharat Ratna Shri Atal Bihari Vajpayee Ekana ... 6 [Eden Gardens, Kolkata] Name: venue, dtype: object innings per match: match_no 1 [1, 2] 2 [1, 2] 3 [1, 2] 4 [1, 2] 5 [1, 2] 6 [1, 2] Name: innings, dtype: object batting_team innings1 per match: match_no 1 SRH 2 KKR 3 CSK 4 GT 5 LSG 6 SRH Name: batting_team, dtype: object
cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
# legal delivery = not wide and not no-ball
d['legal'] = ((d['wide']==0) & (d['noballs']==0)).astype(int)
d['total_runs'] = d['runs_of_bat'] + d['extras']
g = d.groupby('venue').agg(total_runs=('total_runs','sum'), legal=('legal','sum')).reset_index()
g['overs'] = g['legal']/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:', g.iloc[0]['venue'], round(g.iloc[0]['run_rate'],4))
print('least favorable:', g.iloc[-1]['venue'], round(g.iloc[-1]['run_rate'],4))
"venue total_runs legal overs run_rate
Wankhede Stadium, Mumbai 444 235 39.166667 11.336170
M.Chinnaswamy Stadium, Bengaluru 406 216 36.000000 11.277778
Eden Gardens, Kolkata 386 216 36.000000 10.722222
Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh 327 235 39.166667 8.348936
Barsapara Cricket Stadium, Guwahati 256 191 31.833333 8.041885
Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow 285 215 35.833333 7.953488
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['total_runs']=d['runs_of_bat']+d['extras']
print('per match/innings totals from deliveries:')
print(d.groupby(['match_no','innings'])['total_runs'].sum())
" && echo "=== matches summary scores ===" && python3 -c "
import pandas as pd
m=pd.read_csv('data/matches.csv')
print(m[['match_id','first_ings_score','second_ings_score','toss_winner','toss_decision','match_winner']].to_string(index=False))
"per match/innings totals from deliveries:
match_no innings
1 1 203
2 203
2 1 220
2 224
3 1 128
2 128
4 1 162
2 165
5 1 141
2 144
6 1 226
2 160
Name: total_runs, dtype: int64
=== matches summary scores ===
match_id first_ings_score second_ings_score toss_winner toss_decision match_winner
1 201 203 RCB Bowl RCB
2 220 224 MI Bowl MI
3 127 128 RR Bowl RR
4 162 165 PBKS Bowl PBKS
5 141 145 DC Bowl DC
6 226 161 KKR Bowl SRH
7 209 210 PBKS Bowl 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 && which R Rscript jupyter 2>/dev/null; echo "---kernels---"; jupyter kernelspec list 2>/dev/null; echo "---R packages---"; Rscript -e 'cat("tidyverse:", requireNamespace("tidyverse", quietly=TRUE), "\n"); cat("jsonlite:", requireNamespace("jsonlite", quietly=TRUE), "\n"); cat("IRkernel:", requireNamespace("IRkernel", quietly=TRUE), "\n")' 2>&1 | tail -5/usr/bin/R /usr/bin/Rscript /usr/local/bin/jupyter ---kernels--- Available kernels: ir /usr/local/share/jupyter/kernels/ir python3 /usr/local/share/jupyter/kernels/python3 ---R packages--- tidyverse: TRUE jsonlite: TRUE IRkernel: TRUE
cd /app && python3 -c "
import json
nb=json.load(open('solve.ipynb'))
print('nbformat', nb.get('nbformat'), nb.get('nbformat_minor'))
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] if src.strip() else '(empty)'
print(f'--- cell {i} [{c[\"cell_type\"]}] first line: {first!r} lines={len(c[\"source\"])}')
"nbformat 4 5
kernel {'display_name': 'R', 'language': 'R', 'name': 'ir'}
language_info R
num cells 10
--- cell 0 [code] first line: 'library(tidyverse)' lines=2
--- cell 1 [code] first line: 'matches <- read_csv("/app/data/matches.csv", show_col_types = FALSE)' lines=5
--- cell 2 [code] first line: 'total_matches <- length(unique(deliveries$match_no))' lines=10
--- cell 3 [code] first line: 'toss_winner_match_winner_count <- sum(matches$team1 == matches$match_winner)' lines=5
--- cell 4 [code] first line: 'batting_first_info <- deliveries %>%' lines=15
--- cell 5 [code] first line: 'matches_with_del <- matches %>%' lines=18
--- cell 6 [code] first line: 'venue_stats <- deliveries %>%' lines=14
--- cell 7 [code] first line: 'venue_stats <- venue_stats %>% arrange(run_rate)' lines=11
--- cell 8 [code] first line: 'venue_abbrev <- c("WAK", "CHN", "EDN", "MUL", "BAR", "EKA")' lines=21
--- cell 9 [code] first line: 'results <- list(' lines=23cd /app && python3 -c "
import json
nb=json.load(open('solve.ipynb'))
for i,c in enumerate(nb['cells']):
print(f'===================== CELL {i} =====================')
print(''.join(c['source']))
"===================== CELL 0 =====================
library(tidyverse)
library(jsonlite)
===================== CELL 1 =====================
matches <- read_csv("/app/data/matches.csv", show_col_types = FALSE)
deliveries <- read_csv("/app/data/deliveries.csv", show_col_types = FALSE)
cat("Match data loaded:", nrow(matches), "matches\n")
cat("Delivery data loaded:", nrow(deliveries), "deliveries\n")
===================== CELL 2 =====================
total_matches <- length(unique(deliveries$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:n
…[truncated]cd /app && cat > /tmp/proto.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$match_no))
cat("total_matches:", total_matches, " matches_with_deliveries:", matches_with_deliveries, "\n")
toss_decision_counts <- as.list(table(matches$toss_decision))
print(toss_decision_counts)
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 count:", toss_winner_match_winner_count, " pct:", 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")) %>%
mutate(
batting_first_team = ifelse(is.na(batting_first_team),
ifelse(team1 == toss_winner, team2, team1),
batting_first_team),
batting_second_team = ifelse(team1 == batting_first_team, 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$match_winner == match_analysis$batting_second_team)
defend_wins <- total_matches - chase_wins
chase_success_rate <- round(chase_wins / total_matches, 2)
cat("chase_wins:", chase_wins, " defend_wins:", defend_wins, " chase_success_rate:", chase_success_rate, "\n")
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("1st avg:", first_innings_avg_score, " 2nd avg:", second_innings_avg_score,
" hi1:", highest_first_innings, " hi2:", highest_second_innings, "\n")
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))
print(venue_stats)
cat("most fav:", venue_stats$venue[1], round(venue_stats$run_rate[1],2), "\n")
cat("least fav:", venue_stats$venue[nrow(venue_stats)], round(venue_stats$run_rate[nrow(venue_stats)],2), "\n")
EOF
Rscript /tmp/proto.Rtotal_matches: 7 matches_with_deliveries: 6
$Bowl
[1] 7
toss count: 6 pct: 85.71
# A tibble: 7 × 7
match_id team1 team2 toss_winner batting_first_team batting_second_team
<dbl> <chr> <chr> <chr> <chr> <chr>
1 1 RCB SRH RCB SRH RCB
2 2 MI KKR MI KKR MI
3 3 RR CSK RR CSK RR
4 4 PBKS GT PBKS GT PBKS
5 5 LSG DC DC LSG DC
6 6 KKR SRH KKR SRH KKR
7 7 CSK PBKS PBKS CSK PBKS
# ℹ 1 more variable: match_winner <chr>
chase_wins: 6 defend_wins: 1 chase_success_rate: 0.86
1st avg: 183.71 2nd avg: 176.57 hi1: 226 hi2: 224
# A tibble: 6 × 5
venue total_runs legal_balls overs run_rate
<chr> <dbl> <int> <dbl> <dbl>
1 Wankhede Stadium, Mumbai 444 235 39.2 11.3
2 M.Chinnaswamy Stadium, Bengaluru 406 216 36 11.3
3 Eden Gardens, Kolkata 386 216 36 10.7
4 Maharaja Yadavindra Singh International… 327 235 39.2 8.35
5 Barsapara Cricket Stadium, Guwahati 256 191 31.8 8.04
6 Bharat Ratna Shri Atal Bihari Vajpayee … 285 215 35.8 7.95
most fav: Wankhede Stadium, Mumbai 11.34
least fav: Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow 7.95cd /app && Rscript -e '
suppressMessages({library(tidyverse)})
deliveries <- read_csv("/app/data/deliveries.csv", show_col_types = FALSE)
v <- deliveries %>% group_by(venue) %>%
summarise(total_runs=sum(runs_of_bat+extras), legal=sum(wide==0 & noballs==0), .groups="drop") %>%
mutate(run_rate=total_runs/(legal/6)) %>% arrange(desc(run_rate))
print(round(v$run_rate,2))
' 2>&1 | tail -3[1] 11.34 11.28 10.72 8.35 8.04 7.95
cd /app && cp solve.ipynb /tmp/solve.ipynb.bak && python3 << 'PYEOF'
import json
nb = json.load(open('/app/solve.ipynb'))
def lines(s):
# split keeping newlines, nbformat style (each element ends with \n except last)
out = s.split('\n')
return [l + '\n' for l in out[:-1]] + ([out[-1]] if out[-1] != '' else [])
cells = {}
cells[2] = '''# total_matches: every league-stage game in the summary (all seven)
# matches_with_deliveries: only the games that have ball-by-ball records (six)
total_matches <- nrow(matches)
matches_with_deliveries <- length(unique(deliveries$match_no))
# Toss-decision counts come straight from the summary's toss_decision column.
toss_decision_counts <- as.list(table(matches$toss_decision))
cat("Total matches analyzed:", total_matches, "\\n")
cat("Matches with ball-by-ball data:", matches_with_deliveries, "\\n")
print(toss_decision_counts)'''
cells[3] = '''# Toss advantage: compare who won the toss with who won the match.
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 comes from the deliveries (innings 1 batting_team).
batting_first_info <- deliveries %>%
filter(innings == 1) %>%
group_by(match_no) %>%
summarise(batting_first_team = first(batting_team), .groups = "drop")
match_analysis <- matches %>%
left_join(batting_first_info, by = c("match_id" = "match_no")) %>%
mutate(
# For matches without deliveries, infer the batting order from the toss:
# every toss winner in this dataset chose to bowl, so the toss winner
# fields first and the other side bats first.
batting_first_team = ifelse(
is.na(batting_first_team),
ifelse(team1 == toss_winner, team2, team1),
batting_first_team
),
# The chasing side is whichever team did not bat first.
batting_second_team = ifelse(team1 == batting_first_team, team2, team1)
)
# Chase success = matches won by the team batting second (a fraction of all seven).
chase_wins <- sum(match_analysis$match_winner == match_analysis$batting_second_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 scoring comes from the match summary across all seven matches.
first_innings_avg_score <- round(mean(matches$first_ings_score), 2)
second_innings_avg_score <- round(mean(matches$second_ings_score), 2)
highest_first_innings <- max(matches$first_ings_score)
highest_second_innings <- max(matches$second_ings_score)
cat("First innings average:", first_innings_avg_score, "\\n")
cat("Second innings average:", second_innings_avg_score, "\\n")
cat("Highest first innings score:", highest_first_innings, "\\n")
cat("Highest second innings score:", highest_second_innings, "\\n")'''
cells[6] = '''# Venue run rates come only from deliveries:
# total runs = runs off bat + extras
# overs bowled = legal deliveries / 6 (wides and no-balls are NOT legal)
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] = '''# Highest run rate = most favorable venue; lowest run rate = least favorable.
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
…[truncated]Notebook updated. Cells edited: [2, 3, 4, 5, 6, 7, 8]
cd /app && python3 -c "
import json
nb=json.load(open('solve.ipynb'))
print('valid JSON, cells:', len(nb['cells']))
import nbformat
nbformat.read('solve.ipynb', as_version=4)
print('nbformat validation: OK')
" 2>&1 | tail -5valid JSON, cells: 10 nbformat validation: OK
cd /app && rm -f results.json figures/venue_run_rates.png && timeout 280 jupyter nbconvert --to notebook --execute --inplace --ExecutePreprocessor.kernel_name=ir --ExecutePreprocessor.timeout=240 solve.ipynb 2>&1 | tail -20; echo "EXIT: $?"
[NbConvertApp] Converting notebook solve.ipynb to notebook [NbConvertApp] Writing 18117 bytes to solve.ipynb EXIT: 0
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 06:42 figures/venue_run_rates.png
[stderr]
bash: file: command not foundcd /app && python3 -c "
with open('figures/venue_run_rates.png','rb') as f:
sig=f.read(8)
print('PNG signature valid:', sig==b'\x89PNG\r\n\x1a\n')
from struct import unpack
import os
print('size bytes:', os.path.getsize('figures/venue_run_rates.png'))
"PNG signature valid: True size bytes: 12343
command: view path: /app/figures/venue_run_rates.png
<output_image> …[truncated]
cd /app && python3 -c "
import json
nb=json.load(open('solve.ipynb'))
errs=0
for i,c in enumerate(nb['cells']):
if c.get('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'), o.get('evalue'))
elif o.get('output_type')=='stream':
txt=''.join(o.get('text',[]))
print(f'[cell {i} {o.get(\"name\")}]'); print(txt.rstrip())
print('--- total error outputs:', errs)
"[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 2 stdout]
$Bowl
[1] 7
[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: 0.86
[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.json
--- total error outputs: 0cd /app && python3 << 'PYEOF'
import pandas as pd, json
m = pd.read_csv('data/matches.csv')
d = pd.read_csv('data/deliveries.csv')
exp = {}
exp['toss_win_match_win_pct'] = round((m.toss_winner==m.match_winner).sum()/len(m)*100, 2)
exp['toss_decision_counts'] = m.toss_decision.value_counts().to_dict()
# batting second per match: deliveries innings1 -> other team; match7 -> toss winner bats 2nd (all chose Bowl)
bf = d[d.innings==1].groupby('match_no').batting_team.first()
chase=0
for _,r in m.iterrows():
if r.match_id in bf.index:
first = bf[r.match_id]
else:
first = r.team2 if r.team1==r.toss_winner else r.team1
second = r.team2 if r.team1==first else r.team1
if r.match_winner==second: chase+=1
exp['chase_wins']=chase
exp['defend_wins']=len(m)-chase
exp['chase_success_rate']=round(chase/len(m),2)
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())
d['tr']=d.runs_of_bat+d.extras
d['legal']=((d.wide==0)&(d.noballs==0)).astype(int)
v=d.groupby('venue').agg(tr=('tr','sum'),legal=('legal','sum'))
v['rr']=v.tr/(v.legal/6)
v=v.sort_values('rr',ascending=False)
exp['most_favorable_venue']=v.index[0]
exp['most_favorable_venue_rr']=round(v.rr.iloc[0],2)
exp['least_favorable_venue']=v.index[-1]
exp['least_favorable_venue_rr']=round(v.rr.iloc[-1],2)
exp['chart_y_values']=[round(x,2) for x in v.rr.tolist()]
res=json.load(open('results.json'))
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_x_labels','chart_y_values']
print("All 18 keys present in results.json:", all(k in res for k in keys))
ok=True
for k,ev in exp.items():
rv=res[k]
match = (rv==ev)
if not match: ok=False
print(f"{'OK ' if match else 'MISMATCH'} {k}: results={rv!r} expected={ev!r}")
print("chart_x_labels:", res['chart_x_labels'], "(len", len(res['chart_x_labels']),")")
print("y-values align with x-labels length:", len(res['chart_x_labels'])==len(res['chart_y_values']))
print("\nALL INDEPENDENT CHECKS PASS:", ok)
PYEOFAll 18 keys present in results.json: True
OK toss_win_match_win_pct: results=85.71 expected=85.71
OK toss_decision_counts: results={'Bowl': 7} expected={'Bowl': 7}
OK chase_wins: results=6 expected=6
OK defend_wins: results=1 expected=1
OK chase_success_rate: results=0.86 expected=0.86
OK total_matches: results=7 expected=7
OK matches_with_deliveries: results=6 expected=6
OK first_innings_avg_score: results=183.71 expected=183.71
OK second_innings_avg_score: results=176.57 expected=176.57
OK highest_first_innings: results=226 expected=226
OK highest_second_innings: results=224 expected=224
OK toss_winner_match_winner_count: results=6 expected=6
OK most_favorable_venue: results='Wankhede Stadium, Mumbai' expected='Wankhede Stadium, Mumbai'
OK most_favorable_venue_rr: results=11.34 expected=11.34
OK least_favorable_venue: results='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: results=7.95 expected=7.95
OK chart_y_values: results=[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'] (len 6 )
y-values align with x-labels length: True
ALL INDEPENDENT CHECKS PASS: Truecd /app && jupyter nbconvert --to script --stdout solve.ipynb 2>/dev/null > /tmp/final.R echo "--- references to removed/obsolete vars (should be none) ---" grep -nE 'matches_with_del\b|second_innings_del|toss_decisions\b|total_balls|runs_of_bat\)' /tmp/final.R || echo "none found (good)" echo echo "--- confirm key corrected lines ---" grep -nE 'total_matches <-|matches_with_deliveries <-|toss_winner == |batting_second_team|chase_success_rate <-|mean\(matches\$|max\(matches\$first|sum\(runs_of_bat \+ extras\)|wide == 0 & noballs == 0|arrange\(desc\(run_rate\)\)|chart_data\$run_rate' /tmp/final.R
--- references to removed/obsolete vars (should be none) --- none found (good) --- confirm key corrected lines --- 14:total_matches <- nrow(matches) 15:matches_with_deliveries <- length(unique(deliveries$match_no)) 25:toss_winner_match_winner_count <- sum(matches$toss_winner == matches$match_winner) 49: batting_second_team = ifelse(team1 == batting_first_team, team2, team1) 53:chase_wins <- sum(match_analysis$match_winner == match_analysis$batting_second_team) 55:chase_success_rate <- round(chase_wins / total_matches, 2) 62:first_innings_avg_score <- round(mean(matches$first_ings_score), 2) 63:second_innings_avg_score <- round(mean(matches$second_ings_score), 2) 65:highest_first_innings <- max(matches$first_ings_score) 79: total_runs = sum(runs_of_bat + extras), 80: legal_balls = sum(wide == 0 & noballs == 0), 92:venue_stats <- venue_stats %>% arrange(desc(run_rate)) 115:chart_data <- venue_stats %>% arrange(desc(run_rate)) 124: chart_data$run_rate, 134:chart_y_values <- round(chart_data$run_rate, 2)
No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary # task: ipl-toss-impact-analysis-r # attempt: 6 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). ---------------------------------------------------------------------- --- references to removed/obsolete vars (should be none) --- none found (good) --- confirm key corrected lines --- 14:total_matches <- nrow(matches) 15:matches_with_deliveries <- length(unique(deliveries$match_no)) 25:toss_winner_match_winner_count <- sum(matches$toss_winner == matches$match_winner) 49: batting_second_team = ifelse(team1 == batting_first_team, team2, team1) 53:chase_wins <- sum(match_analysis$match_winner == match_analysis$batting_second_team) 55:chase_success_rate <- round(chase_wins / total_matches, 2) 62:first_innings_avg_score <- round(mean(matches$first_ings_score), 2) 63:second_innings_avg_score <- round(mean(matches$second_ings_score), 2) 65:highest_first_innings <- max(matches$first_ings_score) 79: total_runs = sum(runs_of_bat + extras), 80: legal_balls = sum(wide == 0 & noballs == 0), 92:venue_stats <- venue_stats %>% arrange(desc(run_rate)) 115:chart_data <- venue_stats %>% arrange(desc(run_rate)) 124: chart_data$run_rate, 134:chart_y_values <- round(chart_data$run_rate, 2)
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_774260eaa514468b. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_774260eaa514468b · verifier authoritative; classifier explanatory.