Fix e7

[PriyaVerma06]

Issues #4

Summary

• Calculated extras (wides, no-balls, leg-byes, byes) for each team
• Compared performance across 1st and 2nd innings
• Visualized using stacked bar charts

Key Observations

• Some teams concede more extras in the 2nd innings, indicating pressure impact
• Wides are the most common type of extra across most teams
• Certain teams show higher discipline with consistently low extras

Insights

• Teams with fewer extras are more disciplined in bowling
• Extras tend to increase in 2nd innings due to scoreboard pressure

Most disciplined team:

-Most Disciplined Team: Kochi Tuskers Kerala
-The team with the lowest total/average extras across both innings (shortest stacked bars overall). From the chart, this appears to be one of the teams with consistently smaller bar heights across both innings (identify based on your plot).

Wide-dominant teams:

-Wide-dominant teams:
Mumbai Indians
Kolkata Knight Riders
Royal Challengers Bengaluru

-Teams where the largest portion of the stacked bar is wides → indicates poor line discipline (frequent wide deliveries).

Leg-bye dominant teams:
-Teams where leg-byes form a larger share → suggests better bowling line but runs conceded due to deflections, not errors.

Extras per Match Analysis:

-“extras per match” means how many free runs a team gives away in one match on average .
-These come from:

1. wides (most common)
2. no-balls (rare but costly)
3. leg-byes
4. byes

Teams that give a lot of extras (messy bowling)

Mumbai Indians
Chennai Super Kings
Kolkata Knight Riders

Teams that are okay-ish

Royal Challengers Bengaluru (after merging names)
Punjab Kings
Rajasthan Royals
Delhi Capitals

Teams that are disciplined (tight bowling)

Gujarat Titans
Lucknow Super Giants
Rising Pune Supergiant

VIsuals

Conclusion

• Discipline drops slightly in 2nd innings
• Wides contribute the most to extras across teams

Code snippet

df = df[df["inning"].isin([1, 2])]
df["wides"] = df.apply(lambda x: x["extra_runs"] if x["extras_type"] == "wides" else 0, axis=1)

extras_summary = df.groupby(["bowling_team", "inning"])[
    ["wides", "noballs", "legbyes", "byes"]
].sum()

[PriyaVerma06]

@Sanjana-chennu kindly review it

[Sanjana-chennu]

You must decide and conclude which team is most disciplined

[PriyaVerma06]

@Sanjana-chennu
required changes are made.

[Sanjana-chennu]

Please combine similar teams (Royal challengers Bangalore/Bengaluru)and also analyse extras per match

[PriyaVerma06]

@Sanjana-chennu
the required updates are made

[Sanjana-chennu]

You have to do per match extras comparison not total