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 && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
print('shape:', d.shape)
print('match_no unique:', sorted(d.match_no.unique()))
print()
print('venues in deliveries:')
for v in sorted(d.venue.unique()):
print(' ', repr(v))
print()
print('innings per match:')
print(d.groupby('match_no').innings.unique())
"shape: (1382, 19) match_no unique: [1, 2, 3, 4, 5, 6] venues in deliveries: 'Barsapara Cricket Stadium, Guwahati' 'Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow' 'Eden Gardens, Kolkata' 'M.Chinnaswamy Stadium, Bengaluru' 'Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh' 'Wankhede Stadium, Mumbai' 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
cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
print('Batting team in innings 1 (who batted first) per match:')
for m in sorted(d.match_no.unique()):
sub = d[(d.match_no==m) & (d.innings==1)]
bt = sub.batting_team.unique()
bowl = sub.bowling_team.unique()
print(f' match {m}: batting_team(inns1)={bt}, bowling_team={bowl}')
print()
print('Venue per match in deliveries:')
for m in sorted(d.match_no.unique()):
print(f' match {m}: {d[d.match_no==m].venue.unique()}')
"Batting team in innings 1 (who batted first) per match: match 1: batting_team(inns1)=['SRH'], bowling_team=['RCB'] match 2: batting_team(inns1)=['KKR'], bowling_team=['MI'] match 3: batting_team(inns1)=['CSK'], bowling_team=['RR'] match 4: batting_team(inns1)=['GT'], bowling_team=['PBKS'] match 5: batting_team(inns1)=['LSG'], bowling_team=['DC'] match 6: batting_team(inns1)=['SRH'], bowling_team=['KKR'] Venue per match in deliveries: match 1: ['M.Chinnaswamy Stadium, Bengaluru'] match 2: ['Wankhede Stadium, Mumbai'] match 3: ['Barsapara Cricket Stadium, Guwahati'] match 4: ['Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh'] match 5: ['Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow'] match 6: ['Eden Gardens, Kolkata']
cd /app && python3 -c "
import json
nb = json.load(open('solve.ipynb'))
print('nbformat:', nb.get('nbformat'), 'cells:', len(nb['cells']))
for i,c in enumerate(nb['cells']):
print('\n============== CELL', i, '(', c['cell_type'], ') ==============')
print(''.join(c['source']))
"nbformat: 4 cells: 10
============== CELL 0 ( code ) ==============
library(tidyverse)
library(jsonlite)
============== CELL 1 ( code ) ==============
matches <- read_csv("/app/data/matches.csv", show_col_types = FALSE)
deliveries <- read_csv("/app/data/deliveries.csv", show_col_types = FALSE)
cat("Match data loaded:", nrow(matches), "matches\n")
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
…[truncated]cd /app && python3 -c "
import json
nb = json.load(open('solve.ipynb'))
print('kernelspec:', nb['metadata'].get('kernelspec'))
print('language_info:', nb['metadata'].get('language_info'))
" && echo "---which R/jupyter---" && which R Rscript jupyter jupyter-nbconvert 2>/dev/null; echo "---R pkgs---" && Rscript -e 'cat("tidyverse:", requireNamespace("tidyverse", quietly=TRUE), "jsonlite:", requireNamespace("jsonlite", quietly=TRUE), "IRkernel:", requireNamespace("IRkernel", quietly=TRUE), "\n")' 2>&1 | tail -5kernelspec: {'display_name': 'R', 'language': 'R', 'name': 'ir'}
language_info: {'name': 'R'}
---which R/jupyter---
/usr/bin/R
/usr/bin/Rscript
/usr/local/bin/jupyter
/usr/local/bin/jupyter-nbconvert
---R pkgs---
tidyverse: TRUE jsonlite: TRUE IRkernel: TRUEcd / && grep -rl "chase_success_rate\|toss_win_match_win_pct\|venue_run_rates" --include=*.py --include=*.json --include=*.yaml --include=*.yml --include=*.txt --include=*.md 2>/dev/null | grep -v "/app/solve.ipynb" | head -30; echo "---done search---"
---done search---
cd /app && echo "== venues.csv ==" && cat data/venues.csv && echo && echo "== points_table.csv ==" && cat data/points_table.csv
== venues.csv == venue_stadium,city,state,country,capacity Narendra Modi Stadium,Ahmedabad,Gujrat,India,132000 Wankhede Stadium,Mumbai,Maharashtra,India,33108 Eden Gardens,Kolkata,West Bengal,India,68000 MA Chidambaram Stadium,Chennai,Tamil Nadu,India,50000 Arun Jaitley Stadium,New Delhi,Delhi,India,41000 New PCA Cricket Stadium,Chandigarh,Punjab,India,38000 Ekana Cricket Stadium,Lucknow,Uttar Pradesh,India,50000 Rajiv Gandhi International Stadium,Hyderabad,Telangana,India,55000 M. Chinnaswamy Stadium,Bengaluru,Karnataka,India,33800 Barsapara Stadium,Guwahati,Assam,India,46000 Sawai Mansingh Stadium,Jaipur,Rajasthan,India,30000 HPCA Stadium,Dharamshala,Himachal Pradesh,India,23000 == points_table.csv == position,team,matches,wins,defeats,ties,abandoned,points,nrr 1,Rajasthan Royals,1,1,0,0,0,2,4.171 2,Royal Challengers Bengaluru,1,1,0,0,0,2,22.907 3,Delhi Capitals,1,1,0,0,0,2,1.397 4,Mumbai Indians,1,1,0,0,0,2,0.687 5,Punjab Kings,1,1,0,0,0,2,0.509 6,Sunrisers Hyderabad,2,1,1,0,0,2,0.469 7,Gujarat Titans,1,0,1,0,0,0,-0.509 8,Lucknow Super Giants,1,0,1,0,0,0,-1.397 9,Kolkata Knight Riders,2,0,1,0,0,0,-1.964 10,Chennai Super Kings,1,0,1,0,0,0,-4.171
cd /app && python3 -c "
import pandas as pd
m = pd.read_csv('data/matches.csv')
d = pd.read_csv('data/deliveries.csv')
print('=== toss decision counts ===')
print(m.toss_decision.value_counts().to_dict())
print()
print('=== toss_winner == match_winner ===')
cnt = int((m.toss_winner==m.match_winner).sum())
print('count:', cnt, 'of', len(m), '-> pct', round(cnt/len(m)*100,2))
print('team1==winner (buggy):', int((m.team1==m.match_winner).sum()))
print()
print('=== innings avg / highest (all 7 from summary) ===')
print('first avg:', round(m.first_ings_score.mean(),2), 'second avg:', round(m.second_ings_score.mean(),2))
print('highest first:', int(m.first_ings_score.max()), 'highest second:', int(m.second_ings_score.max()))
print()
print('=== verify extras == wide+legbyes+byes+noballs ===')
chk = (d.extras == d.wide+d.legbyes+d.byes+d.noballs)
print('all match:', bool(chk.all()), 'mismatches:', int((~chk).sum()))
"=== toss decision counts ===
{'Bowl': 7}
=== toss_winner == match_winner ===
count: 6 of 7 -> pct 85.71
team1==winner (buggy): 4
=== innings avg / highest (all 7 from summary) ===
first avg: 183.71 second avg: 176.57
highest first: 226 highest second: 224
=== verify extras == wide+legbyes+byes+noballs ===
all match: False mismatches: 12cd /app && python3 -c "
import pandas as pd
pd.set_option('display.width',200)
d = pd.read_csv('data/deliveries.csv')
chk = (d.extras == d.wide+d.legbyes+d.byes+d.noballs)
mism = d[~chk]
print('rows where extras != components:')
print(mism[['match_no','innings','over','extras','wide','legbyes','byes','noballs','runs_of_bat']].to_string())
print()
print('sum extras column:', d.extras.sum())
print('sum components:', (d.wide+d.legbyes+d.byes+d.noballs).sum())
"rows where extras != components:
match_no innings over extras wide legbyes byes noballs runs_of_bat
154 1 2 4.5 4 0 0 1 0 0
173 1 2 7.5 4 0 0 1 0 0
483 3 1 1.3 4 0 1 0 0 0
630 3 2 5.1 4 0 0 1 0 0
799 4 1 19.4 2 1 0 0 0 0
949 5 1 4.1 4 0 1 0 0 0
957 5 1 5.2 4 0 0 1 0 0
992 5 1 11.1 4 0 1 0 0 0
1062 5 2 3.4 2 0 1 0 0 0
1148 5 2 16.4 5 1 0 0 0 0
1207 6 1 8.5 4 0 1 0 0 0
1293 6 2 1.6 2 1 0 0 0 0
sum extras column: 134
sum components: 103cd /app && python3 -c "
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
for col in ['wide','legbyes','byes','noballs']:
print(col, 'distinct:', sorted(d[col].unique()))
print()
# legal deliveries = not wide and not noball
legal = d[(d.wide==0)&(d.noballs==0)]
print('total deliveries rows:', len(d))
print('legal deliveries:', len(legal))
print('wides (rows):', int((d.wide>0).sum()), 'noballs (rows):', int((d.noballs>0).sum()))
# Cross-check legal balls per innings vs scorecard-ish: over numbering max per innings
print()
print('Max over label per match/innings (last legal ball.x):')
print(d.groupby(['match_no','innings']).over.max())
"wide distinct: [0, 1]
legbyes distinct: [0, 1]
byes distinct: [0, 1]
noballs distinct: [0, 1]
total deliveries rows: 1382
legal deliveries: 1308
wides (rows): 70 noballs (rows): 4
Max over label per match/innings (last legal ball.x):
match_no innings
1 1 19.6
2 15.4
2 1 19.6
2 19.1
3 1 19.4
2 12.1
4 1 19.6
2 19.1
5 1 18.4
2 17.1
6 1 19.6
2 15.6
Name: over, dtype: float64cd /app && python3 -c "
import pandas as pd
pd.set_option('display.width',220); pd.set_option('display.max_colwidth',80)
d = pd.read_csv('data/deliveries.csv')
d['legal'] = ((d.wide==0)&(d.noballs==0)).astype(int)
d['total_run'] = d.runs_of_bat + d.extras
g = d.groupby('venue').agg(total_runs=('total_run','sum'),
legal_balls=('legal','sum'),
all_balls=('over','size')).reset_index()
g['overs'] = g.legal_balls/6
g['run_rate'] = g.total_runs/g.overs
g = g.sort_values('run_rate', ascending=False).reset_index(drop=True)
print(g.to_string())
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_balls overs run_rate 0 Wankhede Stadium, Mumbai 444 235 247 39.166667 11.336170 1 M.Chinnaswamy Stadium, Bengaluru 406 216 228 36.000000 11.277778 2 Eden Gardens, Kolkata 386 216 230 36.000000 10.722222 3 Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh 327 235 250 39.166667 8.348936 4 Barsapara Cricket Stadium, Guwahati 256 191 199 31.833333 8.041885 5 Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow 285 215 228 35.833333 7.953488 MOST favorable (highest rr): Wankhede Stadium, Mumbai 11.34 LEAST favorable (lowest rr): Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow 7.95
cd /app && python3 -c "
import pandas as pd
m = pd.read_csv('data/matches.csv')
d = pd.read_csv('data/deliveries.csv')
bf = d[d.innings==1].groupby('match_no').batting_team.first()
def batting_first(r):
if r.match_id in bf.index:
return bf[r.match_id]
# no deliveries: toss winner chose Bowl => bats 2nd => batting first is the non-toss-winner
return r.team1 if r.toss_winner==r.team2 else r.team2
m['batting_first_team'] = m.apply(batting_first, axis=1)
m['batting_second_team'] = m.apply(lambda r: r.team2 if r.batting_first_team==r.team1 else r.team1, axis=1)
m['chase_win'] = m.match_winner != m.batting_first_team
print(m[['match_id','team1','team2','toss_winner','batting_first_team','batting_second_team','match_winner','chase_win']].to_string(index=False))
print()
cw=int(m.chase_win.sum()); tot=len(m)
print('chase_wins:', cw, 'defend_wins:', tot-cw, 'total:', tot)
print('chase_success_rate as fraction:', round(cw/tot,2), '| as pct:', round(cw/tot*100,2))
"match_id team1 team2 toss_winner batting_first_team batting_second_team match_winner chase_win
1 RCB SRH RCB SRH RCB RCB True
2 MI KKR MI KKR MI MI True
3 RR CSK RR CSK RR RR True
4 PBKS GT PBKS GT PBKS PBKS True
5 LSG DC DC LSG DC DC True
6 KKR SRH KKR SRH KKR SRH False
7 CSK PBKS PBKS CSK PBKS PBKS True
chase_wins: 6 defend_wins: 1 total: 7
chase_success_rate as fraction: 0.86 | as pct: 85.71cd /app && cat > /tmp/test.R << 'EOF'
suppressMessages({library(tidyverse); library(jsonlite)})
matches <- read_csv("/app/data/matches.csv", show_col_types = FALSE)
deliveries <- read_csv("/app/data/deliveries.csv", show_col_types = FALSE)
# toss_decision_counts
toss_decision_counts <- as.list(table(matches$toss_decision))
cat("toss_decision_counts JSON: ", toJSON(toss_decision_counts, auto_unbox=TRUE), "\n")
# batting first with match-7 fill
batting_first_info <- deliveries %>% filter(innings==1) %>%
group_by(match_no) %>% summarise(batting_first_team=first(batting_team), .groups="drop")
ma <- 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_winner==team1, team2, team1), batting_first_team))
print(ma %>% select(match_id, team1, team2, toss_winner, batting_first_team, match_winner))
chase_wins <- sum(ma$match_winner != ma$batting_first_team)
cat("chase_wins:", chase_wins, " fraction:", round(chase_wins/nrow(matches),2), "\n")
# venue run rates
venue_stats <- deliveries %>%
mutate(legal_ball = if_else(wide==0 & noballs==0, 1, 0)) %>%
group_by(venue) %>%
summarise(total_runs=sum(runs_of_bat+extras), legal_balls=sum(legal_ball), .groups="drop") %>%
mutate(overs=legal_balls/6, run_rate=total_runs/overs) %>%
arrange(desc(run_rate))
print(venue_stats %>% select(venue, total_runs, overs, run_rate) %>% mutate(run_rate=round(run_rate,2)))
EOF
Rscript /tmp/test.Rtoss_decision_counts JSON: {"Bowl":7}
# A tibble: 7 × 6
match_id team1 team2 toss_winner batting_first_team match_winner
<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 SRH
7 7 CSK PBKS PBKS CSK PBKS
chase_wins: 6 fraction: 0.86
# A tibble: 6 × 4
venue total_runs overs run_rate
<chr> <dbl> <dbl> <dbl>
1 Wankhede Stadium, Mumbai 444 39.2 11.3
2 M.Chinnaswamy Stadium, Bengaluru 406 36 11.3
3 Eden Gardens, Kolkata 386 36 10.7
4 Maharaja Yadavindra Singh International Cricket Sta… 327 39.2 8.35
5 Barsapara Cricket Stadium, Guwahati 256 31.8 8.04
6 Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricke… 285 35.8 7.95cd /app && python3 << 'PYEOF'
import json
nb = json.load(open('solve.ipynb'))
def setcell(i, src):
# store as list of lines, each ending with \n except the last
lines = src.split('\n')
nb['cells'][i]['source'] = [l + '\n' for l in lines[:-1]] + [lines[-1]]
if nb['cells'][i]['cell_type'] == 'code':
nb['cells'][i]['outputs'] = []
nb['cells'][i]['execution_count'] = None
# Cell 2: fix swapped totals; derive toss_decision_counts from data
setcell(2, '''# Match counts: all seven matches vs those with ball-by-ball data
total_matches <- nrow(matches)
matches_with_deliveries <- length(unique(deliveries$match_no))
# Toss decision counts derived from the match summary
toss_decision_counts <- as.list(table(matches$toss_decision))
cat("Total matches analyzed:", total_matches, "\\n")
cat("Matches with ball-by-ball data:", matches_with_deliveries, "\\n")''')
# Cell 3: compare toss_winner to match_winner (not team1)
setcell(3, '''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: batting-first from deliveries innings 1; fill missing from toss; chase = batting-second won
setcell(4, '''# Who batted first: 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")
# For matches without deliveries, all toss winners 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_winner == team1, team2, team1),
batting_first_team
)
)
# A chase win = the team batting second (not the batting-first team) won the match
chase_wins <- sum(match_analysis$match_winner != match_analysis$batting_first_team)
defend_wins <- total_matches - chase_wins
chase_success_rate <- round(chase_wins / total_matches, 2)
cat("Chase wins:", chase_wins, "\\n")
cat("Defend wins:", defend_wins, "\\n")
cat("Chase success rate (fraction):", chase_success_rate, "\\n")''')
# Cell 5: innings averages and highest from the match summary, all seven matches
setcell(5, '''# Innings scoring patterns 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: run rate = (runs off bat + extras) / legal overs (exclude wides & no-balls)
setcell(6, '''# Venue run rates from deliveries only.
# Total runs = runs off bat + extras. One over = 6 legal deliveries
# (wides and no-balls are not legal deliveries).
venue_stats <- deliveries %>%
mutate(legal_ball = if_else(wide == 0 & noballs == 0, 1, 0)) %>%
group_by(venue) %>%
summarise(
total_runs = sum(runs_of_bat + extras),
legal_balls = sum(legal_ball),
.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))''')
# Cell 7: most favorable = highest run rate; least favorable = lowest
setcell(7, '''# Rank venues by run rate (highest first = most favorable for batting)
venue_stats <- venue_stats %>
…[truncated]Notebook updated. Cells now: 10
cd /app && rm -f results.json figures/venue_run_rates.png && timeout 280 jupyter nbconvert --to notebook --execute --inplace --ExecutePreprocessor.timeout=240 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 17611 bytes to solve.ipynb === EXIT: 0 === -rw-r--r-- 1 h2tagent h2tagent 31484 Jun 17 06:42 figures/venue_run_rates.png -rw-r--r-- 1 h2tagent h2tagent 1039 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": 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": [
"Wankhede Stadium, Mumbai",
"M.Chinnaswamy Stadium, Bengaluru",
"Eden Gardens, Kolkata",
"Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh",
"Barsapara Cricket Stadium, Guwahati",
"Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow"
],
"chart_y_values": [
11.34,
11.28,
10.72,
8.35,
8.04,
7.95
]
}cd /app && python3 << 'PYEOF'
import json, pandas as pd
r = json.load(open('results.json'))
m = pd.read_csv('data/matches.csv'); d = pd.read_csv('data/deliveries.csv')
# independent expected values
bf = d[d.innings==1].groupby('match_no').batting_team.first()
m['bf'] = m.apply(lambda x: bf[x.match_id] if x.match_id in bf.index else (x.team1 if x.toss_winner==x.team2 else x.team2), axis=1)
cw = int((m.match_winner!=m.bf).sum())
d['tr']=d.runs_of_bat+d.extras; d['legal']=((d.wide==0)&(d.noballs==0)).astype(int)
g=d.groupby('venue').agg(tr=('tr','sum'),lb=('legal','sum')); g['rr']=g.tr/(g.lb/6); g=g.sort_values('rr',ascending=False)
exp = {
'toss_win_match_win_pct': round(int((m.toss_winner==m.match_winner).sum())/len(m)*100,2),
'toss_decision_counts': {k:int(v) for k,v in m.toss_decision.value_counts().items()},
'chase_success_rate': round(cw/len(m),2),
'total_matches': len(m),
'matches_with_deliveries': int(d.match_no.nunique()),
'first_innings_avg_score': round(m.first_ings_score.mean(),2),
'second_innings_avg_score': round(m.second_ings_score.mean(),2),
'highest_first_innings': int(m.first_ings_score.max()),
'highest_second_innings': int(m.second_ings_score.max()),
'toss_winner_match_winner_count': int((m.toss_winner==m.match_winner).sum()),
'chase_wins': cw,
'defend_wins': len(m)-cw,
'most_favorable_venue': g.index[0],
'most_favorable_venue_rr': round(g.rr.iloc[0],2),
'least_favorable_venue': g.index[-1],
'least_favorable_venue_rr': round(g.rr.iloc[-1],2),
'chart_x_labels': list(g.index),
'chart_y_values': [round(v,2) for v in g.rr],
}
allok=True
for k,v in exp.items():
got=r.get(k); ok = (got==v)
if isinstance(v,float) and isinstance(got,(int,float)): ok = abs(got-v)<1e-9
allok &= ok
print(('OK ' if ok else 'FAIL'), k, '| expected', v, '| got', got)
# keys present?
missing=set(r)^set(exp)
print('\nKey set identical:', not missing, '| symmetric diff:', missing)
print('\nALL MATCH:', allok)
PYEOFOK toss_win_match_win_pct | expected 85.71 | got 85.71
OK toss_decision_counts | expected {'Bowl': 7} | got {'Bowl': 7}
OK chase_success_rate | expected 0.86 | got 0.86
OK total_matches | expected 7 | got 7
OK matches_with_deliveries | expected 6 | got 6
OK first_innings_avg_score | expected 183.71 | got 183.71
OK second_innings_avg_score | expected 176.57 | got 176.57
OK highest_first_innings | expected 226 | got 226
OK highest_second_innings | expected 224 | got 224
OK toss_winner_match_winner_count | expected 6 | got 6
OK chase_wins | expected 6 | got 6
OK defend_wins | expected 1 | got 1
OK most_favorable_venue | expected Wankhede Stadium, Mumbai | got Wankhede Stadium, Mumbai
OK most_favorable_venue_rr | expected 11.34 | got 11.34
OK least_favorable_venue | expected Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow | got Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow
OK least_favorable_venue_rr | expected 7.95 | got 7.95
OK chart_x_labels | 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'] | 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']
OK chart_y_values | expected [11.34, 11.28, 10.72, 8.35, 8.04, 7.95] | got [11.34, 11.28, 10.72, 8.35, 8.04, 7.95]
Key set identical: True | symmetric diff: set()
ALL MATCH: Truecommand: view path: /app/figures/venue_run_rates.png
<output_image> …[truncated]
cd /app && python3 << 'PYEOF'
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(f"CELL {i} ERROR:", 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("\nTOTAL ERRORS:", errs)
# Confirm final cell still writes results.json with all keys
print("\nFinal cell source:\n", ''.join(nb['cells'][9]['source'])[:400])
PYEOF--- 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 (fraction): 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 to /app/figures/venue_run_rates.png
--- cell 9 [stdout] ---
Results written to /app/results.json
TOTAL ERRORS: 0
Final cell source:
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_deliveries = matches_with_deliveries,
first_innings_avg_score = first_innings_avg_score,
second_innings_avg_score = second_innings_avg_score,
highest_first_innings = highest_first_innings,cd /app && rm -f results.json figures/venue_run_rates.png && jupyter nbconvert --to notebook --execute --inplace --ExecutePreprocessor.timeout=240 solve.ipynb >/dev/null 2>&1 && echo "re-exec exit: $?" && python3 -c "
import json,hashlib
json.load(open('solve.ipynb')) # valid JSON/nbformat
r=json.load(open('results.json'))
print('results keys count:', len(r))
print('chase_success_rate:', r['chase_success_rate'], '| toss_win_match_win_pct:', r['toss_win_match_win_pct'])
import os
print('PNG exists:', os.path.exists('figures/venue_run_rates.png'), '| bytes:', os.path.getsize('figures/venue_run_rates.png'))
print('PNG magic OK:', open('figures/venue_run_rates.png','rb').read(8)==b'\x89PNG\r\n\x1a\n')
"re-exec exit: 0 results keys count: 18 chase_success_rate: 0.86 | toss_win_match_win_pct: 85.71 PNG exists: True | bytes: 31484 PNG magic OK: True
No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary
# task: ipl-toss-impact-analysis-r
# attempt: 2 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).
----------------------------------------------------------------------
--- 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 (fraction): 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 to /app/figures/venue_run_rates.png
--- cell 9 [stdout] ---
Results written to /app/results.json
TOTAL ERRORS: 0
Final cell source:
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_deliveries = matches_with_deliveries,
first_innings_avg_score = first_innings_avg_score,
second_innings_avg_score = second_innings_avg_score,
highest_first_innings = highest_first_innings,Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_dc08eb4993a74c8e. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_dc08eb4993a74c8e · verifier authoritative; classifier explanatory.