#4 E7 done

[kpflow369]

Closes #4

Visualization

Code Core Logic

team_mapping = {
"Royal Challengers Bangalore": "Royal Challengers Bengaluru",
"Rising Pune Supergiant": "Rising Pune Supergiants",
"RPS": "Rising Pune Supergiants"
}

deliveries_df["bowling_team"] = deliveries_df["bowling_team"].replace(team_mapping)
deliveries_df["batting_team"] =
deliveries_df["batting_team"].replace(team_mapping)

extras_total = deliveries_df_filtered.groupby(
['bowling_team', 'inning']
)['extra_runs'].sum().reset_index()

matches_count = deliveries_df_filtered.groupby(
['bowling_team', 'inning']
)['match_id'].nunique().reset_index()

matches_count = matches_count.rename(columns={'match_id': 'matches_played'})

avg_extras = extras_total.merge(matches_count, on=['bowling_team', 'inning'])

avg_extras['avg_extras_per_match'] = (
avg_extras['extra_runs'] / avg_extras['matches_played']
)

extras_for_viz = deliveries_df_filtered[deliveries_df_filtered['extra_runs'] > 0].copy()

viz_data_grouped = extras_for_viz.groupby(['bowling_team', 'inning', 'extras_type'])['extra_runs'].sum().reset_index()

viz_data_grouped = viz_data_grouped.rename(columns={'extra_runs': 'total_runs'})

viz_data_melted_filtered = viz_data_grouped[viz_data_grouped['extras_type'].isin(['wides', 'noballs', 'legbyes', 'byes'])]

g = sns.catplot(data=viz_data_melted_filtered, x='bowling_team',y='total_runs',hue='extras_type',col='inning',kind='bar',palette='viridis',errorbar=None,height=6, aspect=1.5)

g.set_axis_labels('Bowling Team', 'Total Runs Conceded')
g.set_titles('Inning {col_name}')
g.set_xticklabels(rotation=45, ha='right')
plt.suptitle('Breakdown of Extra Types Conceded by Bowling Team (1st vs 2nd Innings)', y=1.02, fontsize=16)
plt.tight_layout(rect=[0, 0, 1, 0.98])
plt.show()

Summary

Wides dominate: Most of the extra runs given by teams come from wides in both innings.
More extras in 2nd innings: Teams usually give more extras in the second innings, probably due to pressure, fatigue, or aggressive bowling.
Byes & no-balls are less: Compared to wides and leg-byes, byes and no-balls contribute much less to extras.
Teams with fewer extras: Gujarat Lions give the least extras on average, followed by teams like Pune Warriors.
Teams needing improvement: Teams like Mumbai Indians, RCB, KKR, and SRH give a lot of wides, so controlling wides is important for them.

[Sanjana-chennu]

To find the truly disciplined teams, we must calculate the average extras conceded per match for each team

[kpflow369]

@Sanjana-chennu I had done that, forgot to update

[Sanjana-chennu]

The avg_extras variable doesn't exist yet. The code is missing the pandas logic that actually counts the number of matches played by each team and divides the total runs to get the averag