tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.cd /app && ls -la && echo "---DATA---" && ls -la data/ && echo "---FIGURES---" && ls -la figures/ 2>/dev/null || echo "no figures dir"
total 24 drwxrws--- 4 h2tagent h2tagent 4096 May 30 16:51 . drwxr-xr-x 26 root root 4096 Jun 17 06:36 .. drwxrws--- 2 h2tagent h2tagent 4096 May 30 16:47 data drwxrws--- 2 h2tagent h2tagent 4096 May 30 16:51 figures -rw-rw---- 1 h2tagent h2tagent 7595 May 30 16:47 solve.ipynb ---DATA--- total 209 drwxrws--- 2 h2tagent h2tagent 4096 May 30 16:47 . drwxrws--- 4 h2tagent h2tagent 4096 May 30 16:51 .. -rw-rw---- 1 h2tagent h2tagent 189929 May 30 16:47 deliveries.csv -rw-rw---- 1 h2tagent h2tagent 1396 May 30 16:47 matches.csv -rw-rw---- 1 h2tagent h2tagent 997 May 30 16:47 orange_cap.csv -rw-rw---- 1 h2tagent h2tagent 464 May 30 16:47 points_table.csv -rw-rw---- 1 h2tagent h2tagent 928 May 30 16:47 purple_cap.csv -rw-rw---- 1 h2tagent h2tagent 9902 May 30 16:47 squads.csv -rw-rw---- 1 h2tagent h2tagent 686 May 30 16:47 venues.csv ---FIGURES--- total 8 drwxrws--- 2 h2tagent h2tagent 4096 May 30 16:51 . drwxrws--- 4 h2tagent h2tagent 4096 May 30 16:51 ..
cd /app && echo "===== matches.csv =====" && cat data/matches.csv
===== matches.csv ===== match_id,date,venue,team1,team2,stage,toss_winner,toss_decision,first_ings_score,first_ings_wkts,second_ings_score,second_ings_wkts,match_result,match_winner,wb_runs,wb_wickets,balls_left,player_of_the_match,top_scorer,highscore,best_bowling,best_bowling_figure 1,"March 28, 2026","M. Chinnaswamy Stadium, Bangalore",RCB,SRH,League,RCB,Bowl,201,9,203,4,completed,RCB,,6,26,Jacob Duffy,Ishan Kishan,80,Jacob Duffy,3--22 2,"March 29, 2026","Wankhede Stadium, Mumbai",MI,KKR,League,MI,Bowl,220,4,224,4,completed,MI,,6,5,Shardul Thakur,Ryan Rickelton,81,Shardul Thakur,3--39 3,"March 30, 2026","Barsapara Stadium, Guwahati",RR,CSK,League,RR,Bowl,127,10,128,2,completed,RR,,8,47,Nandre Burger,Vaibhav Sooryavanshi,52,Jofra Archer,2--19 4,"March 31, 2026","New PCA Cricket Stadium, Mullanpur",PBKS,GT,League,PBKS,Bowl,162,6,165,7,completed,PBKS,,3,5,Cooper Connolly,Cooper Connolly,72,Prasidh Krishna,3--29 5,"April 01, 2026","Ekana Cricket Stadium, Lucknow",LSG,DC,League,DC,Bowl,141,10,145,4,completed,DC,,6,17,Sameer Rizvi,Sameer Rizvi,70,Lungi Ngidi,3--27 6,"April 02, 2026","Eden Gardens, Kolkata",KKR,SRH,League,KKR,Bowl,226,8,161,10,completed,SRH,65,,24,Nitish Kumar Reddy,Angkrish Raghuvanshi,52,Blessing Muzarabani,4--41 7,"April 03, 2026","MA Chidambaram Stadium, Chennai",CSK,PBKS,League,PBKS,Bowl,209,5,210,5,completed,PBKS,,5,8,Priyansh Arya,Ayush Matre,73,Vijaykumar Vyshak,2--38
cd /app && echo "===== deliveries.csv header + sample =====" && head -5 data/deliveries.csv && echo "..." && echo "===== columns =====" && head -1 data/deliveries.csv | tr ',' '\n' | nl
===== deliveries.csv header + sample =====
match_no,date,stage,venue,batting_team,bowling_team,innings,over,striker,bowler,runs_of_bat,extras,wide,legbyes,byes,noballs,wicket_type,player_dismissed,fielder
1,"Mar 28, 2026",League stage,"M.Chinnaswamy Stadium, Bengaluru",SRH,RCB,1,0.1,Travis Head,Jacob Duffy,0,0,0,0,0,0,,,
1,"Mar 28, 2026",League stage,"M.Chinnaswamy Stadium, Bengaluru",SRH,RCB,1,0.2,Travis Head,Jacob Duffy,1,0,0,0,0,0,,,
1,"Mar 28, 2026",League stage,"M.Chinnaswamy Stadium, Bengaluru",SRH,RCB,1,0.3,Abhishek Sharma,Jacob Duffy,0,0,0,0,0,0,,,
1,"Mar 28, 2026",League stage,"M.Chinnaswamy Stadium, Bengaluru",SRH,RCB,1,0.4,Abhishek Sharma,Jacob Duffy,6,0,0,0,0,0,,,
...
===== 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 - <<'PY'
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
print("match_no present:", sorted(d.match_no.unique()))
print("rows:", len(d))
print("\n--- venue values in deliveries ---")
print(d.venue.unique())
print("\n--- innings per match ---")
print(d.groupby('match_no')['innings'].unique())
print("\n--- batting_team in innings 1 per match (who batted first) ---")
print(d[d.innings==1].groupby('match_no')['batting_team'].first())
PYmatch_no present: [1, 2, 3, 4, 5, 6] rows: 1382 --- venue values 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'] --- 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 in innings 1 per match (who batted first) --- match_no 1 SRH 2 KKR 3 CSK 4 GT 5 LSG 6 SRH Name: batting_team, dtype: object
cd /app && jupyter nbconvert --to script --stdout solve.ipynb 2>/dev/null | sed -n '1,400p'
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 - <<'PY'
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
# verify extras = wide+legbyes+byes+noballs
chk = (d.extras == d.wide+d.legbyes+d.byes+d.noballs).all()
print("extras == wide+legbyes+byes+noballs for all rows:", chk)
print("any noballs?", d.noballs.sum(), "any wides?", d.wide.sum())
# legal delivery = wide==0 and noball==0
d['legal'] = ((d.wide==0) & (d.noballs==0)).astype(int)
g = d.groupby('venue').apply(lambda x: pd.Series({
'total_runs': (x.runs_of_bat + x.extras).sum(),
'legal_balls': x.legal.sum(),
'all_balls': len(x),
}), include_groups=False)
g['overs'] = g.legal_balls/6
g['run_rate'] = g.total_runs/g.overs
g = g.sort_values('run_rate', ascending=False)
import pandas as pd
pd.set_option('display.width',200); pd.set_option('display.max_colwidth',80)
print(g)
print("\nMost favorable (highest rr):", g.index[0], round(g.run_rate.iloc[0],2))
print("Least favorable (lowest rr):", g.index[-1], round(g.run_rate.iloc[-1],2))
PYextras == wide+legbyes+byes+noballs for all rows: False
any noballs? 4 any wides? 70
[stderr]
Traceback (most recent call last):
File "/usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 1770, in apply
result = self._python_apply_general(f, self._selected_obj)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 1819, in _python_apply_general
values, mutated = self.grouper.apply_groupwise(f, data, self.axis)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/pandas/core/groupby/ops.py", line 911, in apply_groupwise
res = f(group)
^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 1758, in f
return func(g, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: <lambda>() got an unexpected keyword argument 'include_groups'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 10, in <module>
File "/usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 1780, in apply
return self._python_apply_general(f, self._obj_with_exclusions)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 1819, in _python_apply_general
values, mutated = self.grouper.apply_groupwise(f, data, self.axis)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/pandas/core/groupby/ops.py", line 911, in apply_groupwise
res = f(group)
^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 1758, in f
return func(g, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: <lambda>() got an unexpected keyword argument 'include_groups'cd /app && python3 - <<'PY'
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
comp = d.wide+d.legbyes+d.byes+d.noballs
mism = d[d.extras != comp]
print("num mismatched rows:", len(mism))
print(mism[['match_no','over','runs_of_bat','extras','wide','legbyes','byes','noballs']].to_string())
PYnum mismatched rows: 12
match_no over runs_of_bat extras wide legbyes byes noballs
154 1 4.5 0 4 0 0 1 0
173 1 7.5 0 4 0 0 1 0
483 3 1.3 0 4 0 1 0 0
630 3 5.1 0 4 0 0 1 0
799 4 19.4 0 2 1 0 0 0
949 5 4.1 0 4 0 1 0 0
957 5 5.2 0 4 0 0 1 0
992 5 11.1 0 4 0 1 0 0
1062 5 3.4 0 2 0 1 0 0
1148 5 16.4 0 5 1 0 0 0
1207 6 8.5 0 4 0 1 0 0
1293 6 1.6 0 2 1 0 0 0cd /app && python3 - <<'PY'
import pandas as pd
d = pd.read_csv('data/deliveries.csv')
d['legal'] = ((d.wide==0) & (d.noballs==0)).astype(int)
d['runs'] = d.runs_of_bat + d.extras
rows=[]
for v,x in d.groupby('venue'):
tr=x.runs.sum(); lb=x.legal.sum(); overs=lb/6; rr=tr/overs
rows.append((v,tr,lb,round(overs,4),round(rr,6)))
g=pd.DataFrame(rows,columns=['venue','total_runs','legal_balls','overs','run_rate']).sort_values('run_rate',ascending=False)
pd.set_option('display.width',220); pd.set_option('display.max_colwidth',85)
print(g.to_string(index=False))
print("\nMost favorable:", g.venue.iloc[0], round(g.run_rate.iloc[0],2))
print("Least favorable:", g.venue.iloc[-1], round(g.run_rate.iloc[-1],2))
# match-summary-based stats (all 7)
m=pd.read_csv('data/matches.csv')
print("\n--- match summary stats (all 7) ---")
print("first avg:", round(m.first_ings_score.mean(),2), "second avg:", round(m.second_ings_score.mean(),2))
print("highest first:", m.first_ings_score.max(), "highest second:", m.second_ings_score.max())
print("toss==winner:", int((m.toss_winner==m.match_winner).sum()), "team1==winner:", int((m.team1==m.match_winner).sum()))
print("toss decisions:", m.toss_decision.value_counts().to_dict())
PYvenue total_runs legal_balls overs run_rate
Wankhede Stadium, Mumbai 444 235 39.1667 11.336170
M.Chinnaswamy Stadium, Bengaluru 406 216 36.0000 11.277778
Eden Gardens, Kolkata 386 216 36.0000 10.722222
Maharaja Yadavindra Singh International Cricket Stadium, Mullanpur, New Chandigarh 327 235 39.1667 8.348936
Barsapara Cricket Stadium, Guwahati 256 191 31.8333 8.041885
Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow 285 215 35.8333 7.953488
Most favorable: Wankhede Stadium, Mumbai 11.34
Least favorable: Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow 7.95
--- match summary stats (all 7) ---
first avg: 183.71 second avg: 176.57
highest first: 226 highest second: 224
toss==winner: 6 team1==winner: 4
toss decisions: {'Bowl': 7}cd /app && python3 - <<'PY'
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 batted_first(r):
if r.match_id in bf.index:
return bf[r.match_id]
# no deliveries: all toss winners chose Bowl -> toss winner bats 2nd -> other team bats first
return r.team1 if r.toss_winner==r.team2 else r.team2
m['bf']=m.apply(batted_first,axis=1)
m['bs']=m.apply(lambda r: r.team2 if r.bf==r.team1 else r.team1, axis=1) # batted second
m['chase']=m.match_winner==m.bs
print(m[['match_id','team1','team2','toss_winner','match_winner','bf','bs','chase']].to_string(index=False))
cw=int(m.chase.sum()); dw=7-cw
print("\nchase_wins:",cw,"defend_wins:",dw,"chase_success_rate:",round(cw/7*100,2))
PYmatch_id team1 team2 toss_winner match_winner bf bs chase
1 RCB SRH RCB RCB SRH RCB True
2 MI KKR MI MI KKR MI True
3 RR CSK RR RR CSK RR True
4 PBKS GT PBKS PBKS GT PBKS True
5 LSG DC DC DC LSG DC True
6 KKR SRH KKR SRH SRH KKR False
7 CSK PBKS PBKS PBKS CSK PBKS True
chase_wins: 6 defend_wins: 1 chase_success_rate: 85.71cd /app && which R Rscript jupyter 2>/dev/null; echo "---kernels---"; jupyter kernelspec list 2>/dev/null; echo "---R pkgs---"; Rscript -e 'cat(rownames(installed.packages()),sep="\n")' 2>/dev/null | grep -iE 'tidyverse|jsonlite|IRkernel|ggplot2|readr|dplyr'
/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 pkgs--- IRkernel dplyr ggplot2 jsonlite readr tidyverse
cd /app && python3 - <<'PY'
import json
nb=json.load(open('solve.ipynb'))
print("kernel:", nb['metadata'].get('kernelspec',{}))
print("ncells:", len(nb['cells']))
for i,c in enumerate(nb['cells']):
src=''.join(c['source'])
first=src.strip().splitlines()[0] if src.strip() else '(empty)'
print(f"\n=== cell {i} [{c['cell_type']}] first line: {first!r}")
PYkernel: {'display_name': 'R', 'language': 'R', 'name': 'ir'}
ncells: 10
=== cell 0 [code] first line: 'library(tidyverse)'
=== cell 1 [code] first line: 'matches <- read_csv("/app/data/matches.csv", show_col_types = FALSE)'
=== cell 2 [code] first line: 'total_matches <- length(unique(deliveries$match_no))'
=== cell 3 [code] first line: 'toss_winner_match_winner_count <- sum(matches$team1 == matches$match_winner)'
=== cell 4 [code] first line: 'batting_first_info <- deliveries %>%'
=== cell 5 [code] first line: 'matches_with_del <- matches %>%'
=== cell 6 [code] first line: 'venue_stats <- deliveries %>%'
=== cell 7 [code] first line: 'venue_stats <- venue_stats %>% arrange(run_rate)'
=== cell 8 [code] first line: 'venue_abbrev <- c("WAK", "CHN", "EDN", "MUL", "BAR", "EKA")'
=== cell 9 [code] first line: 'results <- list('cd /app && python3 - <<'PY'
import json
nb=json.load(open('solve.ipynb'))
for i in [2,3,4,5,6,7,8]:
print(f"################## CELL {i} ##################")
print(''.join(nb['cells'][i]['source']))
print()
PY################## CELL 2 ##################
total_matches <- length(unique(deliveries$match_no))
matches_with_deliveries <- nrow(matches)
toss_decisions <- matches %>%
group_by(team1) %>%
summarise(n = n(), .groups = "drop")
toss_decision_counts <- list("Bowl" = nrow(matches))
cat("Total matches analyzed:", total_matches, "\n")
cat("Matches with ball-by-ball data:", matches_with_deliveries, "\n")
################## CELL 3 ##################
toss_winner_match_winner_count <- sum(matches$team1 == matches$match_winner)
toss_win_match_win_pct <- round(toss_winner_match_winner_count / total_matches * 100, 2)
cat("Toss winner also won match:", toss_winner_match_winner_count, "times\n")
cat("Toss-win to match-win percentage:", toss_win_match_win_pct, "%\n")
################## CELL 4 ##################
batting_first_info <- deliveries %>%
filter(innings == 1) %>%
group_by(match_no) %>%
summarise(batting_first_team = first(batting_team), .groups = "drop")
match_analysis <- matches %>%
left_join(batting_first_info, by = c("match_id" = "match_no"))
chase_wins <- sum(match_analysis$batting_first_team == match_analysis$match_winner, na.rm = TRUE)
defend_wins <- total_matches - chase_wins
chase_success_rate <- round(chase_wins / total_matches * 100, 2)
cat("Chase wins:", chase_wins, "\n")
cat("Defend wins:", defend_wins, "\n")
cat("Chase success rate:", chase_success_rate, "%\n")
################## CELL 5 ##################
matches_with_del <- matches %>%
filter(match_id %in% unique(deliveries$match_no))
first_innings_avg_score <- round(mean(matches_with_del$first_ings_score), 2)
second_innings_del <- deliveries %>%
filter(innings == 2) %>%
group_by(match_no) %>%
summarise(total = sum(runs_of_bat + extras), .groups = "drop")
second_innings_avg_score <- round(mean(second_innings_del$total), 2)
highest_first_innings <- min(matches$first_ings_score)
highest_second_innings <- max(matches$second_ings_score)
cat("First innings average:", first_innings_avg_score, "\n")
cat("Second innings average:", second_innings_avg_score, "\n")
cat("Highest first innings score:", highest_first_innings, "\n")
cat("Highest second innings score:", highest_second_innings, "\n")
################## CELL 6 ##################
venue_stats <- deliveries %>%
group_by(venue) %>%
summarise(
total_runs = sum(runs_of_bat),
total_balls = n(),
.groups = "drop"
) %>%
mutate(
overs = total_balls / 6,
run_rate = total_runs / overs
)
cat("Venue statistics computed\n")
print(venue_stats %>% select(venue, total_runs, overs, run_rate))
################## CELL 7 ##################
venue_stats <- venue_stats %>% arrange(run_rate)
most_favorable_venue <- venue_stats$venue[1]
most_favorable_venue_rr <- round(venue_stats$run_rate[1], 2)
least_favorable_venue <- venue_stats$venue[nrow(venue_stats)]
least_favorable_venue_rr <- round(venue_stats$run_rate[nrow(venue_stats)], 2)
cat("Most favorable venue:", most_favorable_venue, "\n")
cat("Most favorable RR:", most_favorable_venue_rr, "\n")
cat("Least favorable venue:", least_favorable_venue, "\n")
cat("Least favorable RR:", least_favorable_venue_rr, "\n")
################## CELL 8 ##################
venue_abbrev <- c("WAK", "CHN", "EDN", "MUL", "BAR", "EKA")
chart_data <- venue_stats %>% arrange(desc(run_rate))
dir.create("/app/figures", showWarnings = FALSE, recursive = TRUE)
png("/app/figures/venue_run_rates.png", width = 900, height = 500)
par(mar = c(8, 5, 3, 2))
bp <- barplot(
chart_data$total_runs,
names.arg = venue_abbrev[1:nrow(chart_data)],
las = 2,
col = "steelblue",
main = "Venue Scoring Summary",
ylab = "Total Runs"
)
dev.off()
chart_x_labels <- venue_abbrev[1:nrow(chart_data)]
chart_y_values <- round(chart_data$total_runs, 2)
cat("Chart saved\n")cd /app && python3 - <<'PY'
import json
nb=json.load(open('solve.ipynb'))
def setcell(i, text):
lines = text.split('\n')
src = [l + '\n' for l in lines[:-1]] + ([lines[-1]] if lines[-1] != '' else [])
nb['cells'][i]['source'] = src
if nb['cells'][i].get('cell_type')=='code':
nb['cells'][i]['outputs']=[]
nb['cells'][i]['execution_count']=None
setcell(2, '''# Match counts: the summary file covers all seven matches; the deliveries
# file only records six of them.
total_matches <- nrow(matches)
matches_with_deliveries <- length(unique(deliveries$match_no))
# Every toss winner in this dataset made the same decision; count them directly
# from the toss_decision column so the tally is data-driven.
toss_decision_counts <- as.list(table(matches$toss_decision))
cat("Total matches analyzed:", total_matches, "\\n")
cat("Matches with ball-by-ball data:", matches_with_deliveries, "\\n")
''')
setcell(3, '''# Toss advantage: compare who WON THE TOSS to who won the match (not team1).
toss_winner_match_winner_count <- sum(matches$toss_winner == matches$match_winner)
toss_win_match_win_pct <- round(toss_winner_match_winner_count / total_matches * 100, 2)
cat("Toss winner also won match:", toss_winner_match_winner_count, "times\\n")
cat("Toss-win to match-win percentage:", toss_win_match_win_pct, "%\\n")
''')
setcell(4, '''# Determine who actually batted first from the deliveries (innings 1 batting_team).
batting_first_info <- deliveries %>%
filter(innings == 1) %>%
group_by(match_no) %>%
summarise(batting_first_team = first(batting_team), .groups = "drop")
match_analysis <- matches %>%
left_join(batting_first_info, by = c("match_id" = "match_no")) %>%
mutate(
# For matches without delivery data, infer the batting order from the toss:
# every toss winner here chose to bowl, so the toss winner batted second and
# the other side batted first.
batting_first_team = if_else(
is.na(batting_first_team),
if_else(toss_winner == team1, team2, team1),
batting_first_team
),
batting_second_team = if_else(batting_first_team == team1, team2, team1)
)
# A chase win = the team batting second won the match.
chase_wins <- sum(match_analysis$match_winner == match_analysis$batting_second_team)
defend_wins <- total_matches - chase_wins
chase_success_rate <- round(chase_wins / total_matches * 100, 2)
cat("Chase wins:", chase_wins, "\\n")
cat("Defend wins:", defend_wins, "\\n")
cat("Chase success rate:", chase_success_rate, "%\\n")
''')
json.dump(nb, open('solve.ipynb','w'), indent=1)
print("cells 2-4 updated")
PYcells 2-4 updated
cd /app && python3 - <<'PY'
import json
nb=json.load(open('solve.ipynb'))
def setcell(i, text):
lines = text.split('\n')
src = [l + '\n' for l in lines[:-1]] + ([lines[-1]] if lines[-1] != '' else [])
nb['cells'][i]['source'] = src
if nb['cells'][i].get('cell_type')=='code':
nb['cells'][i]['outputs']=[]
nb['cells'][i]['execution_count']=None
setcell(5, '''# Innings scoring patterns come from the match summary across ALL seven matches.
first_innings_avg_score <- round(mean(matches$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$second_ings_score)
cat("First innings average:", first_innings_avg_score, "\\n")
cat("Second innings average:", second_innings_avg_score, "\\n")
cat("Highest first innings score:", highest_first_innings, "\\n")
cat("Highest second innings score:", highest_second_innings, "\\n")
''')
setcell(6, '''# Venue run rates from deliveries only:
# total runs = runs off bat + extras
# overs = legal deliveries / 6 (wides and no-balls are NOT legal)
# run rate = total runs / overs
venue_stats <- deliveries %>%
group_by(venue) %>%
summarise(
total_runs = sum(runs_of_bat + extras),
legal_balls = sum(wide == 0 & noballs == 0),
.groups = "drop"
) %>%
mutate(
overs = legal_balls / 6,
run_rate = total_runs / overs
)
cat("Venue statistics computed\\n")
print(venue_stats %>% select(venue, total_runs, overs, run_rate))
''')
setcell(7, '''# Most favorable venue = HIGHEST run rate; least favorable = lowest.
venue_stats <- venue_stats %>% arrange(desc(run_rate))
most_favorable_venue <- venue_stats$venue[1]
most_favorable_venue_rr <- round(venue_stats$run_rate[1], 2)
least_favorable_venue <- venue_stats$venue[nrow(venue_stats)]
least_favorable_venue_rr <- round(venue_stats$run_rate[nrow(venue_stats)], 2)
cat("Most favorable venue:", most_favorable_venue, "\\n")
cat("Most favorable RR:", most_favorable_venue_rr, "\\n")
cat("Least favorable venue:", least_favorable_venue, "\\n")
cat("Least favorable RR:", least_favorable_venue_rr, "\\n")
''')
setcell(8, '''# Bar chart of venue RUN RATES (highest -> lowest), labelled by venue.
chart_data <- venue_stats %>% arrange(desc(run_rate))
dir.create("/app/figures", showWarnings = FALSE, recursive = TRUE)
png("/app/figures/venue_run_rates.png", width = 1000, height = 650)
par(mar = c(16, 5, 3, 2))
bp <- barplot(
chart_data$run_rate,
names.arg = chart_data$venue,
las = 2,
col = "steelblue",
main = "Venue Run Rates (IPL 2026, opening week)",
ylab = "Run rate (runs per over)",
cex.names = 0.7
)
text(bp, chart_data$run_rate, labels = round(chart_data$run_rate, 2), pos = 3, cex = 0.8, xpd = NA)
dev.off()
chart_x_labels <- chart_data$venue
chart_y_values <- round(chart_data$run_rate, 2)
cat("Chart saved\\n")
''')
json.dump(nb, open('solve.ipynb','w'), indent=1)
print("cells 5-8 updated")
PYcells 5-8 updated
cd /app && python3 -c "import json; nb=json.load(open('solve.ipynb')); print('valid JSON,', len(nb['cells']), 'cells')" && echo "=== CELL 9 ===" && python3 -c "import json; nb=json.load(open('solve.ipynb')); print(''.join(nb['cells'][9]['source']))"valid JSON, 10 cells
=== CELL 9 ===
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,
highest_second_innings = highest_second_innings,
toss_winner_match_winner_count = toss_winner_match_winner_count,
chase_wins = chase_wins,
defend_wins = defend_wins,
most_favorable_venue = most_favorable_venue,
most_favorable_venue_rr = most_favorable_venue_rr,
least_favorable_venue = least_favorable_venue,
least_favorable_venue_rr = least_favorable_venue_rr,
chart_x_labels = as.list(chart_x_labels),
chart_y_values = as.list(chart_y_values)
)
write_json(results, "/app/results.json", auto_unbox = TRUE, pretty = TRUE)
cat("Results written to /app/results.json\n")cd /app && rm -f results.json figures/venue_run_rates.png && jupyter nbconvert --to notebook --execute --inplace --ExecutePreprocessor.timeout=180 solve.ipynb 2>&1 | tail -20 && echo "=== EXIT: $? ===" && ls -la results.json figures/venue_run_rates.png
[NbConvertApp] Converting notebook solve.ipynb to notebook [NbConvertApp] Writing 17431 bytes to solve.ipynb === EXIT: 0 === -rw-r--r-- 1 h2tagent h2tagent 31658 Jun 17 06:40 figures/venue_run_rates.png -rw-r--r-- 1 h2tagent h2tagent 1040 Jun 17 06:40 results.json
cd /app && cat 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
]
}cd /app && python3 - <<'PY'
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'))
# independent expected values
bf=d[d.innings==1].groupby('match_no')['batting_team'].first()
def first_team(r):
return bf[r.match_id] if r.match_id in bf.index else (r.team1 if r.toss_winner==r.team2 else r.team2)
m['bf']=m.apply(first_team,axis=1)
m['bs']=m.apply(lambda r:r.team2 if r.bf==r.team1 else r.team1,axis=1)
cw=int((m.match_winner==m.bs).sum())
d['legal']=((d.wide==0)&(d.noballs==0)).astype(int); d['runs']=d.runs_of_bat+d.extras
vs=d.groupby('venue').apply(lambda x:pd.Series({'tr':x.runs.sum(),'lb':x.legal.sum()}),include_groups=False)
vs['rr']=vs.tr/(vs.lb/6); vs=vs.sort_values('rr',ascending=False)
exp={
'toss_win_match_win_pct':round(int((m.toss_winner==m.match_winner).sum())/7*100,2),
'toss_decision_counts':m.toss_decision.value_counts().to_dict(),
'chase_success_rate':round(cw/7*100,2),
'total_matches':7,'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':7-cw,
'most_favorable_venue':vs.index[0],'most_favorable_venue_rr':round(vs.rr.iloc[0],2),
'least_favorable_venue':vs.index[-1],'least_favorable_venue_rr':round(vs.rr.iloc[-1],2),
'chart_x_labels':list(vs.index),'chart_y_values':[round(v,2) for v in vs.rr],
}
allok=True
for k,v in exp.items():
got=R.get(k)
ok = (got==v) or (isinstance(v,float) and isinstance(got,(int,float)) and abs(got-v)<1e-9)
if not ok: allok=False
print(("OK " if ok else "FAIL")+f" {k}: got={got!r} exp={v!r}")
# keys present check
missing=set(exp)-set(R); extra=set(R)-set(exp)
print("\nmissing keys:",missing or "none","| extra keys:",extra or "none")
print("\nALL MATCH:" , allok and not missing)
PY[stderr]
Traceback (most recent call last):
File "/usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 1770, in apply
result = self._python_apply_general(f, self._selected_obj)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 1819, in _python_apply_general
values, mutated = self.grouper.apply_groupwise(f, data, self.axis)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/pandas/core/groupby/ops.py", line 911, in apply_groupwise
res = f(group)
^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 1758, in f
return func(g, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: <lambda>() got an unexpected keyword argument 'include_groups'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 13, in <module>
File "/usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 1780, in apply
return self._python_apply_general(f, self._obj_with_exclusions)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 1819, in _python_apply_general
values, mutated = self.grouper.apply_groupwise(f, data, self.axis)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/pandas/core/groupby/ops.py", line 911, in apply_groupwise
res = f(group)
^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 1758, in f
return func(g, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: <lambda>() got an unexpected keyword argument 'include_groups'cd /app && python3 - <<'PY'
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'))
bf=d[d.innings==1].groupby('match_no')['batting_team'].first()
def first_team(r):
return bf[r.match_id] if r.match_id in bf.index else (r.team1 if r.toss_winner==r.team2 else r.team2)
m['bf']=m.apply(first_team,axis=1)
m['bs']=m.apply(lambda r:r.team2 if r.bf==r.team1 else r.team1,axis=1)
cw=int((m.match_winner==m.bs).sum())
d['legal']=((d.wide==0)&(d.noballs==0)).astype(int); d['runs']=d.runs_of_bat+d.extras
vs=d.groupby('venue').agg(tr=('runs','sum'), lb=('legal','sum'))
vs['rr']=vs.tr/(vs.lb/6); vs=vs.sort_values('rr',ascending=False)
exp={
'toss_win_match_win_pct':round(int((m.toss_winner==m.match_winner).sum())/7*100,2),
'toss_decision_counts':m.toss_decision.value_counts().to_dict(),
'chase_success_rate':round(cw/7*100,2),
'total_matches':7,'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':7-cw,
'most_favorable_venue':vs.index[0],'most_favorable_venue_rr':round(float(vs.rr.iloc[0]),2),
'least_favorable_venue':vs.index[-1],'least_favorable_venue_rr':round(float(vs.rr.iloc[-1]),2),
'chart_x_labels':list(vs.index),'chart_y_values':[round(float(v),2) for v in vs.rr],
}
allok=True
for k,v in exp.items():
got=R.get(k)
ok=(got==v) or (isinstance(v,float) and isinstance(got,(int,float)) and abs(got-v)<1e-9)
allok&=ok
print(("OK " if ok else "FAIL")+f" {k}")
missing=set(exp)-set(R); extra=set(R)-set(exp)
print("\nmissing:",missing or "none","| extra:",extra or "none")
print("ALL MATCH:",allok and not missing and not extra)
PYOK toss_win_match_win_pct OK toss_decision_counts OK chase_success_rate OK total_matches OK matches_with_deliveries OK first_innings_avg_score OK second_innings_avg_score OK highest_first_innings OK highest_second_innings OK toss_winner_match_winner_count OK chase_wins OK defend_wins OK most_favorable_venue OK most_favorable_venue_rr OK least_favorable_venue OK least_favorable_venue_rr OK chart_x_labels OK chart_y_values missing: none | extra: none ALL MATCH: True
command: view path: /app/figures/venue_run_rates.png
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cd /app && python3 - <<'PY'
import json
nb=json.load(open('solve.ipynb'))
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':
print(f"CELL {i} ERROR:",o.get('ename'),o.get('evalue'))
elif o.get('output_type')=='stream':
tag='STDERR' if o.get('name')=='stderr' else 'out'
if tag=='STDERR':
print(f"CELL {i} STDERR:",''.join(o['text'])[:300])
print("--- stdout of cells 2-8 ---")
for i in [2,3,4,5,7]:
for o in nb['cells'][i].get('outputs',[]):
if o.get('output_type')=='stream' and o.get('name')=='stdout':
print(f"[cell {i}]"); print(''.join(o['text']).rstrip())
PYCELL 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✔[
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<htt
CELL 0 STDERR:
Attaching package: ‘jsonlite’
CELL 0 STDERR: The following object is masked from ‘package:purrr’:
flatten
--- stdout of cells 2-8 ---
[cell 2]
Total matches analyzed: 7
[cell 2]
Matches with ball-by-ball data: 6
[cell 3]
Toss winner also won match: 6 times
[cell 3]
Toss-win to match-win percentage: 85.71 %
[cell 4]
Chase wins: 6
[cell 4]
Defend wins: 1
[cell 4]
Chase success rate: 85.71 %
[cell 5]
First innings average: 183.71
[cell 5]
Second innings average: 176.57
[cell 5]
Highest first innings score: 226
[cell 5]
Highest second innings score: 224
[cell 7]
Most favorable venue: Wankhede Stadium, Mumbai
[cell 7]
Most favorable RR: 11.34
[cell 7]
Least favorable venue: Bharat Ratna Shri Atal Bihari Vajpayee Ekana Cricket Stadium, Lucknow
[cell 7]
Least favorable RR: 7.95cd /app && rm -f results.json figures/venue_run_rates.png && jupyter nbconvert --to notebook --execute --inplace --ExecutePreprocessor.timeout=180 solve.ipynb >/dev/null 2>&1 && echo "re-run exit: $?" && ls -la results.json figures/venue_run_rates.png && echo "--- quick value check ---" && python3 -c "import json;r=json.load(open('results.json'));print('keys:',len(r));print('total_matches',r['total_matches'],'| with_deliveries',r['matches_with_deliveries'],'| chase',r['chase_wins'],'defend',r['defend_wins'],'| most',r['most_favorable_venue_rr'],'least',r['least_favorable_venue_rr'])"re-run exit: 0 -rw-r--r-- 1 h2tagent h2tagent 31658 Jun 17 06:41 figures/venue_run_rates.png -rw-r--r-- 1 h2tagent h2tagent 1040 Jun 17 06:41 results.json --- quick value check --- keys: 18 total_matches 7 | with_deliveries 6 | chase 6 defend 1 | most 11.34 least 7.95
No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary # task: ipl-toss-impact-analysis-r # attempt: 1 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). ---------------------------------------------------------------------- re-run exit: 0 -rw-r--r-- 1 h2tagent h2tagent 31658 Jun 17 06:41 figures/venue_run_rates.png -rw-r--r-- 1 h2tagent h2tagent 1040 Jun 17 06:41 results.json --- quick value check --- keys: 18 total_matches 7 | with_deliveries 6 | chase 6 defend 1 | most 11.34 least 7.95
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_4d8581de43ee43c1. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_4d8581de43ee43c1 · verifier authoritative; classifier explanatory.