IPL Analysis
Ajeet Singh Chaudhary
December 31, 2019
In this document, we are going to analyze IPL data till 2017.
We have 2 datasets available as “matches” and “Deliveries”
We joined both datasets,cleaned and grouped it for analysis.
TOSS KA BOSS
table(toss_r$toss_decision)##
## bat field
## 273 363- Teams elect to field first 57% of times after winning the toss.
toss_r%>%filter(city=="Mumbai"|city=="Bangalore"|city=="Delhi"|city=="Chennai"|city=="Kolkata"|city=="Jaipur"|city=="Hyderabad"|city=="Chandigarh"|city=="Pune")%>%ggplot()+geom_bar(aes(x=toss_decision,fill=toss_winner))+facet_wrap(~city)
CSK loves to bat first on their home ground.
Teams chose to bowl first in Bangalore as its a high scoring ground with small boundaries makes chasing scores easy.
How many times a team won the toss won the match?:-
winner=toss_r%>%filter(toss_winner==winner)
match_winner_after_winning_toss=nrow(winner)
loser=toss_r%>%filter(toss_winner!=winner)
loosing_match_after_wining_toss=nrow(loser)
data.frame(match_winner_after_winning_toss,loosing_match_after_wining_toss)## match_winner_after_winning_toss loosing_match_after_wining_toss
## 1 325 311- Team winning toss won 325 matches and lost 310 matches means 51% team winning the toss won the match. This shows approx 50-50 chances to win a game independent on toss result.
Who won more matches, batting 1st or batting 2nd:-
data.frame(batting_1st,batting_2nd)## batting_1st batting_2nd
## 1 284 349- Team batting 2nd won 349 matches compared to 284 for batting first. 55% of times team batting 2nd are winning the game. This is maybe due to dew in 8 PM games which cause difficulty in bowling under lights.
RUNS KI GAME
Maximum runs in a season:-
highest_runs=by_players%>%arrange(desc(batsman_run))
head(highest_runs)## # A tibble: 6 x 5
## # Groups: season [3]
## season batsman batsman_run balls_played srr
## <int> <fct> <int> <int> <dbl>
## 1 2016 V Kohli 973 655 149.
## 2 2016 DA Warner 848 579 146.
## 3 2012 CH Gayle 733 472 155.
## 4 2013 MEK Hussey 733 580 126.
## 5 2013 CH Gayle 720 484 149.
## 6 2016 AB de Villiers 687 415 166.- Virat Kohli is leading the list with 973 runs in 2016 followed by Warner in same season.
Seasonwise highest run scorers in IPL history:-
head(max_scorer)## # A tibble: 6 x 6
## season.x top season.y batsman balls_played srr
## <int> <int> <int> <fct> <int> <dbl>
## 1 2008 616 2008 SE Marsh 452 136.
## 2 2009 572 2009 ML Hayden 409 140.
## 3 2010 618 2010 SR Tendulkar 489 126.
## 4 2011 608 2011 CH Gayle 342 178.
## 5 2012 733 2012 CH Gayle 472 155.
## 6 2013 733 2013 MEK Hussey 580 126.ggplot(max_scorer,aes(y=top,x=factor(season.x),color=batsman,size=srr))+geom_count()+geom_text(aes(label=batsman),size=5,nudge_y = 12)+
xlab("season")+ylab("Total Runs")+coord_cartesian()
Now we will plot top 10 batsman with maximum strike rate (Atleat played 100 balls):
ggplot(sttr[1:10,],aes(x=run,y=srrt,color=batsman,size=srrt))+geom_count()+
geom_text(aes(label=batsman),size=4,nudge_y = 0.5)
Average change in batsman’s strike rate with balls faced in his IPL career:
ggplot(sttr,aes(x=balls,y=srrt,size=srrt))+geom_point()+geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Maximum run scorers in IPL history:
highest_runs=str%>%arrange(desc(run))
ggplot(highest_runs[1:10,],aes(x=balls,y=run,color=batsman,size=srrt))+geom_count()+
geom_text(aes(label=batsman),size=5,nudge_y = 15)
Runs scored by batsmen with balls played in their career
Black line represents strike rate of 100 or 1 run per ball equation.
ggplot(sttr,aes(x=balls,y=run,color=srrt))+geom_point()+geom_smooth()+geom_abline()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
- We can clearly see the player who play more balls has chances of scoring more runs and with better strike rate.
Runs scored by batsmen per innings:
by_match=ipl%>%group_by(season,match_id,inning,batsman)%>%summarise(bat_run=sum(batsman_runs),balls=n())
head(by_match)## # A tibble: 6 x 6
## # Groups: season, match_id, inning [2]
## season match_id inning batsman bat_run balls
## <int> <int> <int> <fct> <int> <int>
## 1 2008 60 1 BB McCullum 158 77
## 2 2008 60 1 DJ Hussey 12 12
## 3 2008 60 1 Mohammad Hafeez 5 3
## 4 2008 60 1 RT Ponting 20 20
## 5 2008 60 1 SC Ganguly 10 12
## 6 2008 60 2 AA Noffke 9 12by_match%>%filter(inning<=2)%>%ggplot(aes(x=balls,y=bat_run,color=factor(inning),size=bat_run))+geom_point(alpha=1/2)+geom_smooth()## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Highest individual scores are mostly coming in first inning,because of low pressure in first innings and in second innings many times chaseable target is not enough for a big individual score.
In second innings players are starting with better strike rate.
For less than 50 balls 2nd innings players have higher scores,maybe these are finishers.
Now lets see comaparison between runs scored by teams batting 1st and 2nd in every over of a T20 game:-
by_over=ipl%>%group_by(season,match_id,inning,bowler,over)%>%summarise(bat_run=sum(batsman_runs),balls=n(),runs=sum(total_runs))
head(by_over)## # A tibble: 6 x 8
## # Groups: season, match_id, inning, bowler [3]
## season match_id inning bowler over bat_run balls runs
## <int> <int> <int> <fct> <int> <int> <int> <int>
## 1 2008 60 1 AA Noffke 4 17 7 23
## 2 2008 60 1 AA Noffke 7 7 6 7
## 3 2008 60 1 AA Noffke 16 4 6 4
## 4 2008 60 1 AA Noffke 18 7 6 7
## 5 2008 60 1 CL White 15 22 7 24
## 6 2008 60 1 JH Kallis 9 4 6 4by_over%>%filter(inning<=2)%>%ggplot(aes(x=over,y=runs,color=factor(inning)))+geom_boxplot(aes(cut_width(over,1)))
1st innings batting team always start slower than 2nd team and scores less runs in powerplay.
Both teams score approx equal number of runs in middle overs.
1st innings batting team score more runs in death overs.
Now we will compare india’s two best white ball cricketers in IPL through years:
rohit_kohli=by_players%>%filter(batsman=="RG Sharma"|batsman=="V Kohli")
head(rohit_kohli)## # A tibble: 6 x 5
## # Groups: season [3]
## season batsman batsman_run balls_played srr
## <int> <fct> <int> <int> <dbl>
## 1 2008 RG Sharma 404 276 146.
## 2 2008 V Kohli 165 168 98.2
## 3 2009 RG Sharma 362 323 112.
## 4 2009 V Kohli 246 225 109.
## 5 2010 RG Sharma 404 310 130.
## 6 2010 V Kohli 307 216 142.ggplot(rohit_kohli,aes(y=batsman_run,x=season))+geom_point()+xlim(c("2008","2017"))+geom_path(aes(size=batsman_run,color=batsman))+
geom_text(aes(label=season),nudge_x = .2)
WHY BOWLERS ARE SO IMPORTANT
Most Expensive overs in IPL history:
most_expensive=by_over%>%arrange(desc(runs))
most_expensive[1:15,]%>%ggplot(aes(x=over,y=runs,color=factor(season)))+geom_count()+geom_text(aes(label=bowler),nudge_y = 0.5)+
ylim(c(25,40))
Now lets have a look at bowlers economy (bowled Min. 50 overs):
economy_history=by_over%>%group_by(bowler)%>%summarise(overs_bowled=n(),balls_bowled=sum(balls),runs_conceded=sum(runs),eco=(runs_conceded/overs_bowled))
economy_overall=economy_history%>%filter(overs_bowled>=50)%>%arrange(eco)
head(economy_overall)## # A tibble: 6 x 5
## bowler overs_bowled balls_bowled runs_conceded eco
## <fct> <int> <int> <int> <dbl>
## 1 SP Narine 323 1956 2085 6.46
## 2 R Ashwin 383 2359 2552 6.66
## 3 A Kumble 163 983 1089 6.68
## 4 GD McGrath 54 329 366 6.78
## 5 Rashid Khan 54 328 368 6.81
## 6 DL Vettori 131 785 894 6.82ggplot(economy_overall[1:15,],aes(y=eco,x=factor(overs_bowled),color=eco))+geom_count()+geom_text(aes(label=bowler),nudge_y=0.02)+
xlab("Overs Bowled ")+ylab("Economy")
- Sunil Narine has best best economy in IPL history followed by R. Ashwin and Anil KUmble.
- Spinners are leading the list of best economies,maybe because they bowl in middle overs.