It is carnival time again as IPL 2023 is underway!! The new GooglyPlusPlus now includes AI/ML models for computing ball-by-ball Win Probability of matches and each individual player’s Win Probability Contribution (WPC). GooglyPlusPlus uses 2 ML models
Deep Learning (Tensorflow) – accuracy : 0.8584
Logistic Regression (glmnet-tidymodels) : 0.728
Besides, as before, GooglyPlusPlus will also include the usual near real-time analytics with the Shiny app being automatically updated with the previous day’s match data.
Note: The Win Probability Computation can also be done on a live feed of streaming data. Since, I don’t have access to live feeds, the app will show how Win Probability changed during the course of completed matches. For more details on Win Probability and Win Probability Contribution see my posts
GooglyPlusPlus has been also updated with all the latest T20 league’s match data. It includes data from BBL 2022, NTB 2022, CPL 2022, PSL 2023, ICC T20 2022 and now IPL 2023.
GooglyPlusPlus has the following functionality
Batsman tab: For detailed analysis of batsmen
Bowler tab: For detailed analysis of bowlers
Match tab: Analysis of individual matches, plot of Runs vs SR, Wickets vs ER in power play, middle and death overs, Win Probability Analysis of teams and Win Probability Contribution of players
Head-to-head tab: Detailed analysis of team-vs-team batting/bowling scorecard, batting, bowling performances, performances in power play, middle and death overs
Team performance tab: Analysis of team-vs-all other teams with batting /bowling scorecard, batting, bowling performances, performances in power play, middle and death overs
Optimisation tab: Allows one to pit batsmen vs bowlers and vice-versa. This tab also uses integer programming to optimise batting and bowling lineup
Batting analysis tab: Ranks batsmen using Runs or SR. Also plots performances of batsmen in power play, middle and death overs and plots them in a 4×4 grid
Bowling analysis tab: Ranks bowlers based on Wickets or ER. Also plots performances of bowlers in power play, middle and death overs and plots them in a 4×4 grid
Also note all these tabs and features are available for all T20 formats namely IPL, Intl. T20 (men, women), BBL, NTB, PSL, CPL, SSM.
Important note: It is possible, that at times, the Win Probability (Deep Learning) for some recent IPL matches will give an error. This is because I need to rebuild the models on a daily basis as the matches use player embeddings and there are new players. While I will definitely rebuild the models on weekends and whenever I find time, you may have to bear with this error occasionally.
Note: All charts are interactive, which means that you can hover, zoom-in, zoom-out, pan etc on the charts
The latest avatar of GooglyPlusPlus2023 is based on my R package yorkr with data from Cricsheet.
Follow me on twitter for daily highlights @tvganesh_85
GooglyPlusPlus can analyse players, matches, teams, rank, compute win probability and much more.
Included below are some random analyses of IPL 2023 matches so far
A) Chennai Super Kings vs Gujarat Titans – 31 Mar 2023
GT won by 5 wickets ( 4 balls remaining)
a) Worm Wicket Chart
b) Ball-by-ball Win Probability (Logistic Regression) (side-by-side)
This model shows that CSK had the upper hand in the 2nd last over, before it changed to GT. More details on Win Probability and Win Probability Contribution in the posts given by the links above.
c) b) Ball-by-ball Win Probability (Logistic Regression) (overlapping)
Here the ball-by-ball win probability is overlapped. CSK and GT both had nearly the same probability of winning in the 2nd last over before GT edges CSK out
B) Punjab Kings vs Rajasthan Royals – 05 Apr 2023
This was a another closely fought match. PBKS won by 5 runs
a) Worm wicket chart
b) Batting partnerships
Shikhar Dhawan scored 86 runs
c) Ball-by-ball Win Probability using Deep Learning (overlapping)
PBKS was generally ahead in the win probability race
d) Batsman Win Probability Contribution
This plot shows how the different batsmen contributed to the Win Probability. We can see that Shikhar Dhawan has a highest win probability. He played a very sensible innings. Also it appears that there is no difference between Prabhsimran Singh and others, though he score 60 runs. This computation is based on when they come to bat and how the win probability changes when they get dismissed, as seen in the 2nd chart
C) Delhi Capitals vs Gujarat Titans – 4 Apr 2023
GT won by 6 wickets (11 balls remaining)
a) Worm wicket chart
b) Runs scored across 20 overs
c) Runs vs SR plot
d) Batting scorecard (Gujarat Titans)
e) Batsman Win Probability Contribution (Gujarat Titans)
Miller has a higher percentage in the Win Contribution than Sai Sudershan who held the innings together.Strange are the ways of the ML models!!
D) Sunrisers Hyderabad vs Lucknow Supergiants ( 7 Apr 2023)
LSG won by 5 wickets (24 balls left). SRH were bamboozled by the pitch while LSG was able to cruise along
a) Worm wicket chart
b) Wickets vs ER plot
c) Wickets across 20 overs
d) Ball-by-ball win probability using Deep Learning (overlapping)
e) Bowler Win Probability Contribution (LSG)
Bishnoi has a higher win probability contribution than Krunal, though he just took 1 wicket to Krunal’s 3 wickets. This is based on how the Win Probability changed at that point in the game.
The above set of plots are just a random sample.
Note: There are 8 tabs each for 9 T20 leagues (BBL, CPL, T20 (men), T20 (women), IPL, PSL, NTB, SSM, WBB). So there are a lot more detailed charts/analses.
In this post, I compute each batsman’s or bowler’s Win Probability Contribution (WPC) in a T20 match. This metric captures by how much the player (batsman or bowler) changed/impacted the Win Probability of the T20 match. For this computation I use my machine learning models, I had created earlier, which predicts the ball-by-ball win probability as the T20 match progresses through the 2 innings of the match.
In the picture snippet below, you can see how the win probability changes ball-by-ball for each batsman for a T20 match between CSK vs LSG- 31 Mar 2022
In my previous posts I had created several Machine Learning models. In order to compute the player’s Win Probability contribution in this post, I have used the following ML models
The batsman’s or bowler’s win probability contribution changes ball-by=ball. The player’s contribution is calculated as the difference in win probability when the batsman faces the 1st ball in his innings and the last ball either when is out or the innings comes to an end. If the difference is +ve the the player has had a positive impact, and likewise for negative contribution. Similarly, for a bowler, it is the win probability when he/she comes into bowl till, the last delivery he/she bowls
Note: The Win Probability Contribution does not have any relation to the how much runs or at what strike rate the batsman scored the runs. Rather the model computes different win probability for each player, based on his/her embedding, the ball in the innings and six other feature vectors like runs, run rate, runsMomentum etc. These values change for every ball as seen in the table above. Also, this is not continuous. The 2 ML models determine the Win Probability for a specific player, ball and the context in the match.
This metric is similar to Win Probability Added (WPA) used in Sabermetrics for baseball. Here is the definition of WPA from Fangraphs “Win Probability Added (WPA) captures the change in Win Expectancy from one plate appearance to the next and credits or debits the player based on how much their action increased their team’s odds of winning.” This article in Fangraphs explains in detail how this computation is done.
In this post I have added 4 new function to my R package yorkr.
batsmanWinProbLR – batsman’s win probability contribution based on glmnet (Logistic Regression)
bowlerWinProbLR – bowler’s win probability contribution based on glmnet (Logistic Regression)
batsmanWinProbDL – batsman’s win probability contribution based on Deep Learning Model
bowlerWinProbDL – bowlerWinProbLR – bowler’s win probability contribution based on Deep Learning
Hence there are 4 additional features in GooglyPlusPlus based on the above 4 functions. In addition I have also updated
-winProbLR (overLap) function to include the names of batsman when they come to bat and when they get out or the innings comes to an end, based on Logistic Regression
-winProbDL(overLap) function to include the names of batsman when they come to bat and when they get out based on Deep Learning
Hence there are 6 new features in this version of GooglyPlusPlus.
Note: All these new 6 features are available for all 9 formats of T20 in GooglyPlusPlus namely
a) IPL b) BBL c) NTB d) PSL e) Intl, T20 (men) f) Intl. T20 (women) g) WBB h) CSL i) SSM
Check out the latest version of GooglyPlusPlus at gpp2023-2
Note: The data for GooglyPlusPlus comes from Cricsheet and the Shiny app is based on my R package yorkr
A) Chennai SuperKings vs Delhi Capitals – 04 Oct 2021
To understand Win Probability Contribution better let us look at Chennai Super Kings vs Delhi Capitals match on 04 Oct 2021
This was closely fought match with fortunes swinging wildly. If we take a look at the Worm wicket chart of this match
a) Worm Wicket chart – CSK vs DC – 04 Oct 2021
Delhi Capitals finally win the match
b) Win Probability Logistic Regression (side-by-side) – CSK vs DC – 4 Oct 2021
Plotting how win probability changes over the course of the match using Logistic Regression Model
In this match Delhi Capitals won. The batting scorecard of Delhi Capitals
c) Batting Scorecard of Delhi Capitals – CSK vs DC – 4 Oct 2021
d) Win Probability Logistic Regression (Overlapping) – CSK vs DC – 4 Oct 2021
The Win Probability LR (overlapping) shows the probability function of both teams superimposed over one another. The plot includes when a batsman came into to play and when he got out. This is for both teams. This looks a little noisy, but there is a way to selectively display the change in Win Probability for each team. This can be done , by clicking the 3 arrows (orange or blue) from top to bottom. First double-click the team CSK or DC, then click the next 2 items (blue,red or black,grey) Sorry the legends don’t match the colors! 😦
Below we can see how the win probability changed for Delhi Capitals during their innings, as batsmen came into to play. See below
e)Batsman Win Probability contribution:DC – CSK vs DC – 4 Oct 2021
Computing the individual batsman’s Win Contribution and plotting we have. Hetmeyer has a higher Win Probability contribution than Shikhar Dhawan depsite scoring fewer runs
f) Bowler’s Win Probability contribution :CSK – CSK vs DC – 4 Oct 2021
We can also check the Win Probability of the bowlers. So for e.g the CSK bowlers and which bowlers had the most impact. Moeen Ali has the least impact in this match
B) Intl. T20 (men) Australia vs India – 25 Sep 2022
a) Worm wicket chart – Australia vs India – 25 Sep 2022
This was another close match in which India won with the penultimate ball
b) Win Probability based on Deep Learning model (side-by-side) –Australia vs India – 25 Sep 2022
c) Win Probability based on Deep Learning model (overlapping) –Australia vs India – 25 Sep 2022
The plot below shows how the Win Probability of the teams varied across the 20 overs. The 2 Win Probability distributions are superimposed over each other
d) Batsman Win Probability Contribution : India – Australia vs India – 25 Sep 2022
Selectively choosing the India Win Probability plot by double-clicking legend ‘India’ on the right , followed by single click of black, grey legend we have
We see that Kohli, Suryakumar Yadav have good contribution to the Win Probability
e) Plotting the Runs vs Strike Rate:India – Australia vs India – 25 Sep 2022
f) Batsman’s Win Probability Contribution-Australia vs India – 25 Sep 2022
Finally plotting the Batsman’s Win Probability Contribution
Interestingly, Kohli has a greater Win Probability Contribution than SKY, though SKY scored more runs at a better strike rate. As mentioned above, the Win Probability is context dependent and also depends on past performances of the player (batsman, bowler)
Finally let us look at
C) India vs England Intll T20 Women (11 July 2021)
a) Worm wicket chart – India vs England Intl. T20 Women (11 July 2021)
India won this T20 match by 8 runs
b) Win Probability using the Logistic Regression Model –India vs England Intl. T20 Women (11 July 2021)
c) Win Probability with the DL model –India vs England Intl. T20 Women (11 July 2021)
d) Bowler Win Probability Contribution with the LR model–India vs England Intl. T20 Women (11 July 2021)
e) Bowler Win Contribution with the DL model–India vs England Intl. T20 Women (11 July 2021)
Go ahead and try out the latest version of GooglyPlusPlus
IPL 2022 has just concluded and yet again, it is has thrown a lot of promising and potential youngsters in its wake, while established players have fallen! With IPL 2022, we realise that “Sceptre and Crownmusttumble down” and that ‘theglories‘ of form and class like everything else are “shadows not substantial things” (Death the Leveller by James Shirley).
So King Kohli had to kneel, and hitman’ himself got hit. Rishabh Pant, Jadeja also had a poor season. On the contrary there were several youngsters who shone like Abhishek Sharma, Tilak Verma, Umran Malik or a Mohsin Khan
This post is about my potential T20 Indian players for the World Cup 2022 and beyond.
The post below includes my own analysis and thoughts. Feel free to try out my Shiny app GooglyPlusPlus and draw your own conclusions.
How often we hear that data by itself is useless, unless we can draw insights from it? This is a prevailing theme in the corporate world and everybody uses all sorts of tools to analyse and subsequently draw insights. Data analysis can be done in many ways as data can be sliced, diced, chopped in a zillion ways. There are many facets and perspectives to analysing data. Creating insights is easy, but arriving at actionable insights is anything but. So, the problem of selecting the best 11 is difficult as there are so many ways to look at the analysis. My Shiny app GooglyPlusPlus based on my R package yorkr can analyse data in several ways namely
Batsman analysis
Bowler analysis
Match analysis
Team vs team analysis
Team vs all teams analysis
Batsman vs bowler and vice versa
Analysis of in 3,4,5 in power play, middle and death overs
GooglyPlusPlus uses my R package yorkr which has ~ 160 functions some which have several options. So, we can say roughly there are ~500 different ways that analysis can be done or in other words we can gather almost roughly 500+ different insights, not to mention that there are so many combinations of head-on matches and one-vs-all matches.
So generating insights or different ways of analysis data alone is not enough. The question is whether we can get a consolidated view from the different insights. In this post, I try to identify the best contenders for the Indian T20 team. This is far more difficult than it looks. Do you select players on past historical performance or do you choose from the newer crop of players, who have excelled in the recent IPL season. I think this boils down the typical situation in any domain. In engineering, we have tradeoffs – processing power vs memory tradeoff, throughput vs latency tradeoff or in the financial domain it is cost vs benefit or risk vs reward tradeoff. For team selection, the quandary is, whether to choose seasoned players with good historical performance but a poor performances in recent times or go with youngsters who have played with great courage and flair in this latest episode of IPL 2022. Hence there is a tradeoff between reliable but below average performance or risky but superlative performances of new players.
For this I base my potential list from
Then (past history of batsmen & bowlers) – I have chosen the performance of batsmen and bowlers in the last 3 years. With we can arrive at those who have had reasonably reliable performance for the last 3 years
Now (IPL 2022) – Performance in the current season IPL 2022
A. Then (Jan 2020 – May 2022) – Batsmen analysis
In this section I analyse the performances of batsmen and bowlers from Jan 2022 – May 2022. This is done based on ranking, and plots of Runs vs Strike Rate in Power Play, Middle and Death overs
Also I analyse bowlers based on the overall rank from Jan 2022- May 2022. Further more analysis is done on Wickets vs Economy Rate overall and in Power Play, Middle and Death overs
a. Ranks of batsmen (Runs over Strike Rate) : Jan 2020 – May 2022
IPL 2022 just finished and clearly brings out the batsmen who are in great nick. It is always going to be a judgment call of whether to go for ‘old reliable’ or ‘new and awesome’.
a. Ranks of batsmen (Runs over Strike Rate) : IPL 2022
The best batsmen this season in Runs over Strike rate are
f. Best batsmen Runs vs SR in Death overs:IPL 2022
Top batsmen in death overs in IPL 2022
[Dinesh Karthik, Rahul Tewatia, MS Dhoni, KL Rahul, Azar Patel, Washington Sundar, R Ashwin, Hardik Pandya, Ayush Badoni, Shivam Dube, Suryakumar Yadav, Ravindra Jadeja, Sanju Samson]
OverallBattingPerformance in season
Kohli peaked in 2016 and from then on it has been a downward slide (see below)
Taking a look at Kohli’s moving average it is clear that he is past his prime and it will take a herculean effort to regain his lost glory
Similarly, Rohit Sharma’s moving average is constantly around ~30 as seen below
The cumulative average of Rohit Sharma is shown below
Comparing KL Rahul, Shikhar Dhawan, Rohit Sharma and V Kohli we see that KL Rahul and Shikhar Dhawan have had a much superior performance in the last 2-3 years. Rohit has averaged about ~25 runs every season.
Comparing the 4 wicket-keeper batsmen Sanju Samson, Rishabh Pant, Ishan Kishan and Dinesh Karthik from 2016
i) Runs over Strike Rate
We see that Pant peaked in 2018 but has not performed as well since. In the last 2 years Sanju Samson and Ishan Kishan have done well
ii) Strike Rate over Runs
For the last couple of seasons Rishabh Pant and Dinesh Kartik top the strike rate over the other 2
Similar analysis can be done other combinations of batsmen
Choosing the best batsmen from the above, my top 5 batsmen would be
Personally, I feel Ishan Kishan and Shreyas Iyer are a little tardy while playing express speeds, as compared to Sanju Samson or Rishabh Pant.
If you notice, I have not included both Virat Kohli or Rohit Sharma who have been below par for some time
C.Then (Jan 2020 – May 2022) – Bowler analysis
This section I analyse the performances of bowlers from Jan 2022 – May 2022. This is done based on ranking, and plots of Wickets vs Economy Rate in Power Play, Middle and Death overs
a. Ranks of bowlers (Wickets over Economy Rate) : Jan 2020 – May 2022
The most consistent bowlers Wickets over Economy Rate for the last 3 years are
[YS Chahal, Jasprit Bumrah, Mohammed Dhami, Harshal Patel, Shardul Thakur, Arshdeep Singh, Rahul Chahar, Varun Chakravarthy, Ravi Bishnoi, Prasidh Krishna, R Ashwon, Axar Patel, Mohammed Siraj, Ravindra Jadeja, Krunal Pandya, Rahul Tewatia]
b. Ranks of bowlers (Economy Rate over Wickets) : Jan 2020 – May 2022
The most economical bowlers since 2020 are
[Axar Patel, Krunal Pandya, Jasprit Bumrah, CV Varun, R Ashwin, Ravi Bishnoi, Rahul Chahar, YS Chahal, Ravindra Jadeja, Harshal Patel, Mohammed Shami, Mohammed Siraj, Rahul Tewatia, Arshdeep Singh, Prasidh Krishna, Shardul Thakur]
c.BestBowlers Wickets vs ER :Jan 2020 – May 2022
The best bowlers Wickets vs ER will be in the bottom right quadrant. The most consistent and reliable bowlers are
[YS Chahal, Jasprit Bumrah, Mohammed Shami, Harshal Patel, CV Arun, Ravi Bishnoi, Rahul Chahar, R Ashwin, Axar Patel]
d. Best bowlers Wickets vs ER in Powerplay:Jan 2020 – May 2022
The best bowlers in Powerplay are
[Mohammed Shami, Deepak Chahar, Mohammed Siraj, Arshdeep Singh, Jasprit Bumrah, Avesh Khan, Mukesh Choudhary, Shardul Thakur, T Natarajan, Bhuvaneshwar Kumar, WashingtonSundar, Shivam Mavi]
e. Best bowlers Wickets vs ER in Middle overs : Jan 2020 – May 2022
The most reliable performers in middle overs from 2020-2022 are
[YS Chahal, Rahul Chahr, Ravi Bishnoi, Harshal Patel, Axar Patel, Jasprit Bumrah, Umran Malik, R Ashwin, Avesh Khan, Shardul Thakur, Kuldeep Yadav]
f. Best bowlers Wickets vs ER in Death overs : Jan 2020 – May 2022
The most reliable bowlers are
[Harshal Patel, Mohammed Shami, Jasprit Bumrah, Arshdeep Singh, T Natarajan, Avesh Khan, Shardul Thakur, Bhuvaneshwar Kumar, Shivam Mavi, YS Chahal, Prasidh Krishna, Mohammed Siraj, Chetan Sakariya]
B) Now (IPL 2022)– Bowler analysis
a. Ranks of bowlers (Wickets over Economy Rate) : IPL 2022
The best bowlers in IPL 2022 when considering Wickets over Economy Rate
[YS Chahal, Umran Malik, Prasidh Krishna, Mohammed Shami, Kuldeep Yadav, Harshal Patel, T Natarajan, Avesh Khan, Shardul Thakur, Mukesh Choudhary, Jasprit Bumrah, Ravi Bishnoi]
a. Ranks of bowlers (Economy Rate over Wickets) : IPL 2022
The most economical bowlers in IPL 2022 are
[Axar Patel, Jasprit Bumrah, Krunal Pandya, Umesh Yadav, Bhuvaneshwar Kumar, Rahul Chahr, Harshal Patel, Arshdeep Singh, R Ashwion, Umran Malik, Kuldeep Yadav, YS Chahal, Mohammed Shami, Avesh Khan, Prasidh Krishna]
c.BestBowlers Wickets vs ER :IPL 2022
The overall best bowlers in IPL 2022 are
[YS Chahal, Umran Malik, Harshal Patel, Prasidh Krishna, Mohammed Shami, Kuldeep Yadav, Avesh Khan, Jasprit Bumrah, Umesh Yadav, Bhuvaneshwar Kumar, Arshdeep Singh, R Ashwin, Rahul Chahar, Krunal Pandya]
d. Best bowlers Wickets vs ER in Powerplay: IPL 2022
The best bowlers in IPL 2022 in Power play are
[Mukesh Choudhary, Mohammed Shami, Prasidh Krishna, Umesh Yadav, Avesh Khan, Mohsin Khan, T Natarajan, Jasprit Bumrah, Yash Dayal, Mohammed Siraj]
d. Best bowlers Wickets vs ER in Middle overs: IPL 2022
The best bowlers in IPL 2022 during middle overs
The best bowlers are
[YS Chahal, Umran Malik, Kuldeep Yadav, Harshal Patel, Ravi Bishnoi, R Ashwin]
e. Best bowlers Wickets vs ER in Death overs: IPL 2022
The best bowlers in death overs in IPL 2022 are
[T Natarajan, Harshal Patel, Bhuvaneshwar Kumar, Mohammed Shami, Jasprit Bumrah, Shardul Thakur, YS Chahal, Prasidh Krishna, Avesh Khan, Mohsin Khan, Yash Dayal, Umran Malik, Arshdeep Singh]
Typically in a team we would need a combination of 4 bowlers (2 fast & 2 spinner or 3 fast and 1 spinner) with an additional player who is all rounder.
For 4 bowlers we could have
JJ Bumrah
Mohammed Shami, Umran Malik, Bhuvaneshwar Kumar, Umesh Yadav
Arshdeep Singh, Avesh Khan, Mohsin Khan, Harshal Patel
YS Chahal, Ravi Bishnoi, Rahul Chahar, Axar Patel
Ravindra Jadeja, Hardik Pandya, Rahul Tewathia, R Ashwin
i) Performance comparison (Wickets over Economy Rate)
Bumrah had the best season in 2020. He has been doing quite well and has been among the wickets
ii) Performance comparison (Economy Rate over Wickets)
Bumrah has the best Economy Rate
We can do a wicket prediction of bowlers. So for example for Bumrah it is
iii) Performance evaluation(Wickets over Economy Rate)
Harshal Patel followed by Avesh Khan had a good season last year, but Umran Malik pipped them this year (see below)
iv) Performance analysis of spinners
a. Wickets over Economy Rate: 2022
Chahal has the best season followed by Bishnoi and Chahar this season
b) Economy Rate over WIckets
Axar Patel has the best economy rate followed by Rahul Chahar
Conclusion
The above post identified the best candidates for the Indian team in the future and beyond. In my T20 list, I have neither included Virat Kohli or Rohit Sharma. The data in T20 clearly indicates that they have had their days. There is a lot more talent around. The tradeoff is a little risk for a greater potential performance. My list would be
Analytics for e.g. sports analytics, business analytics or analytics in e-commerce or in other domain has 2 main requirements namely a) What kind of analytics (set of parameters,function) will squeeze out the most intelligence from the data b) How to represent the analytics so that an expert can garner maximum insight?
While it may appear that the former is more important, the latter is also equally, if not, more vital to the problem. Indeed, a picture is worth a thousand words, and often times is more insightful than a large table of numbers. However, in the case of sports analytics, for e.g. in cricket a batting or bowling scorecard captures more information and can never be represented in chart.
So, my Shiny app GooglyPlusPlus includes both charts and tables for different aspects of the analysis. In this post, a newer type of chart, popular among senior management experts, namely the 4 quadrant graph is introduced, which helps in categorising batsmen and bowlers into 4 categories as shown below
a) Batting Performances – Top right quadrant (High runs, High Strike rate)
b) Bowling Performances – Bottom right quadrant( High wickets, Low Economy Rate)
I have added the following 32 functions in this latest version of GooglyPlusPlus
A. Match Tab
All the functions below are at match level
Team Runs vs SR Plot
Team Wickets vs ER Plot
Team Runs vs SR Power play plot
Team Runs vs SR Middle overs plot
Team Runs vs SR Death overs plot
Team Wickets vs ER Power Play
Team Wickets vs ER Middle overs
Team Wickets vs ER Death overs
B. Head-to-head Tab
The below functions are based on all matches between 2 teams’
Team Runs vs SR Plot all Matches
Team Wickets vs ER Plot all Matches
Team Runs vs SR Power play plot all Matches
Team Runs vs SR Middle overs plot all Matches
Team Runs vs SR Death overs plot all Matches
Team Wickets vs ER Power Play plot all Matches
Team Wickets vs ER Middle overs plot all Matches
Team Wickets vs ER Death overs plot all Matches
C. Team Performance tab
The below functions are based on a team’s performance against all other teams
Team Runs vs SR Plot overall
Team Wickets vs ER Plot overall
Team Runs vs SR Power play plot overall
Team Runs vs SR Middle overs plot overall
Team Runs vs SR Death overs plot overall
Team Wickets vs ER Power Play overall
Team Wickets vs ER Middle overs overall
Team Wickets vs ER Death overs overall
D. T20 format Batting Analysis
This analysis is at T20 format level (IPL, Intl. T20(men), Intl. T20 (women), PSL, CPL etc.)
Overall Runs vs SR plot
Overall Runs vs SR Power play plot
Overall Runs vs SR Middle overs plot
Overall Runs vs SR Death overs plot
E. T20 Bowling Analysis
This analysis is at T20 format level (IPL, Intl. T20(men), Intl. T20 (women), PSL, CPL etc.)
Overall Wickets vs ER plot
Team Wickets vs ER Power Play
Team Wickets vs ER Middle overs
Team Wickets vs ER Death overs
These 32 functions have been added to my yorkr package and so all these functions become plug-n-play in my Shiny app GooglyPlusPlus2021 which means that the 32 functions apply across all the nine T20 formats that the app supports i.e. IPL, Intl. T20 (men), Intl. T20 (women), BBL, NTB, PSL, CPL, SSM, WBB.
Hence the multiplicative factor of the new addition is 32 x 9 = 288 additional ways of exploring match, team and player data
You can clone/fork GooglyPlusPlus from Github at gpp2021-10
Check out my app GooglyPlusPlus2021 and analyze batsmen, bowlers, teams, overall performance. The data for all the nine T20 formats have been updated to include the latest data.
Hence, the app is just in time for the IPL mega auction. You should be able to analyse players in IPL, Intl. T20 or in any of the other formats from where they could be drawn and check out their relative standings
I am including some random plots to demonstrate the newly minted functions
Note 1: All plots are interactive. The controls are on the top right. You can hover over data, zoom-in, zoom-out, compare data etc by choosing the appropriate control. To know more about how to use the interactive charts see GooglyPlusPlus2021 is now fully interactive!!!
New Zealand batting, except K Williamson, the rest did not fire as much
For Australia, Warner, Maxwell and Marsh played good knocks to wrest control
II) Head-to-head
a) Wickets vs ER during Power play of Mumbai Indians in all matches against Chennai Super Kings (IPL)
b) Karachi Kings Runs vs SR during middle overs against Multan Sultans (PSL)
c) Wickets vs ER during death overs of Barbados Tridents in all matches against Jamaica Tallawahs (CPL)
III) Teams overall batting performance
India’s best T20 performers in Power play since 2018 (Intl. T20)
e) Australia’s best performers in Death overs since Mar 2017 (Intl. T20)
f) India’s Intl. T20 (women) best Runs vs SR since 2018
g) England’s Intl. T20 (women) best bowlers in Death overs
IV) Overall Batting Performanceacross T20
This tab gives the batsmen’s rank and overall batting performance across the T20 format.
a) Why was Hardik Pandya chosen, and why this was in error?
Of course, it provides an insight into why Hardik Pandya was chosen in India’s World cup team despite poor performances recently. Here are the best Intl. T20 death over batsmen
Of course, we can zoom in to get a better look
This is further substantiated when we performances in IPL
However, if you move the needle forward a year at a time, you see Hardik Pandya’s performance drops significantly
and further down
Rather, Dinesh Karthik, Sanju Samson or Ruturaj Gaikwad would have been better options
b) Best batsmen Intl. T20 (women) in Power play since 2018
V) Overall bowling performance
This tab gives the bowler’s rank and overall bowling performance in Power play, middle and death overs across all T20 formats
a) Intl. T20 (men) best bowlers in Power Play from 2019 (zoomed in)
b) Intl. T20(men) best bowlers in Death overs since 2019
c) Was B. Kumar a good choice for India team in World cup?
Bhuvi was one of India’s best bowler in Power play only if we go back to the beginning of time
i) From 2008
But if we move forward to 2020 onwards we see Arshdeep Singh or D Chahar would have been a better choice
ii) From 2020 onwards
iii) 2021 onwards
Hence D Chahar & Arshdeep Singh are the natural choice moving forwards for India
iv) T20 Best batsman
If we look at Intl. T20 performances since 2017, Babar Azam leads the pack, however his Strike rate needs to move up.
This latest edition of GooglyPlusPlus2021 now includes detailed analysis of teams, batsmen and bowlers in power play, middle and death overs. The T20 format is based on 3 phases as each side faces 20 overs.
Power play: Overs: 0 – 6 – No more than 2 players can be outside the 30 yard circle
Middle overs: Overs: 7- 16 – During these overs the batting side tries to consolidate their innings
Death overs: Overs: 16 -20 – During these 5 overs the batting side tries to accelerate the scoring rate, while the bowling side will try to restrict the batsmen against going for big hits
This is shown below
This latest update of GooglyPlusPlus2021 includes the following functions
a) Match tab
teamRunsAcrossOvers
teamSRAcrossOvers
teamWicketsAcrossOvers
teamERAcrossOvers
matchWormWickets
b) Head-to-head tab
teamRunsAcrossOversOppnAllMatches
teamSRAcrossOversOppnAllMatches
teamWicketsAcrossOversOppnAllMatches
teamERAcrossOversOppnAllMatches
topRunsBatsmenAcrossOversOppnAllMatches
topSRBatsmenAcrossOversOppnAllMatches
topWicketsBowlersAcrossOversOppnAllMatches
topERBowlerAcrossOverOppnAllMatches
c) Overall performance tab
teamRunsAcrossOversAllOppnAllMatches
teamSRAcrossOversAllOppnAllMatches
teamWicketsAcrossOversAllOppnAllMatches
teamERAcrossOversAllOppnAllMatches
topRunsBatsmenAcrossOversAllOppnAllMatches
topSRBatsmenAcrossOversAllOppnAllMatches
topWicketsBowlersAcrossOversAllOppnAllMatches
topERBowlerAcrossOverAllOppnAllMatches
Hence a total of 8 + 8 + 5 = 21 functions have been added. These functions can be utilized across all the 9 T20 formats that are supported in GooglyPlusPlus2021 namely
You can clone/fork the code for the Shiny app from Github – gpp2021-9
Included below is a random selection of options from the 189 possibilities mentioned above. Feel free to try out for yourself
A) IPL – CSK vs KKR 2018-04-10
a) Team Runs in power play, middle and death overs
b) Team Strike rate in power play, middle and death overs
B) Intl. T20 (men) – India vs Afghanistan (2021-11-03)
a) Team wickets in power play, middle and death overs
b) Team Economy rate in power play, middle and death overs
C) Intl. T20 (women) Head-to-head : India vs Australia since 2018
a) Team Runs in all matches in power play, middle and death overs
D) PSL Head-to-head strike rate since 2019
a) Team vs team Strike rate : Karachi Kings vs Lahore Qalanders since 2019 in power play, middle and death overs
E) Team overall performance in all matches against all opposition
a) BBL : Brisbane Heats : Team Wickets between 2015 – 2018 in power play, middle and death overs
F) Top Runs and Strike rate Batsman of Mumbai Indians vs Royal Challengers Bangalore since 2018
a) Top runs scorers for Mumbai Indians (MI) in power play, middle and death overs
b) Top strike rate for RCB in power play, middle and death overs
F) Intl. T20 (women) India vs England since 2018
a) Top wicket takers for England in power play, middle and death overs since 2018
b) Top wicket takers for India in power play, middle and death overs since 2018
G) Intl. T20 (men) All time best batsmen and bowlers for India
a) Most runs in power play, middle and death overs
b) Highest strike rate in power play, middle and death overs
H) Match worm wicket chart
In addition to the usual Match worm chart, I have also added a Match Wicket worm chart in the latest version
Note: You can zoom to the area where you would like to focus more
The option of looking at the Match worm chart (without wickets) also exists.
Go ahead take GooglyPlusPlus2021 for a test drive and check out how your favourite players perform in power play, middle and death overs. Click GooglyPlusPlus2021
You can fork/download the app code from Github at gpp2021-9
This year 2021, we are witnessing a rare spectacle in the cricketing universe, where IPL playoffs are immediately followed by ICC World Cup T20. Cricket pundits have claimed such a phenomenon occurs once in 127 years! Jokes apart, the World cup T20 is underway and as usual GooglyPlusPlus is ready for the action.
GooglyPlusPlus will provide near-real time analytics, by automatically downloading the latest match data daily, processing and organising the match data into appropriate folders so that my R package yorkr can slice and dice the data to provide the pavilion-view analytics.
The charts capture all the breathless, heart-pounding, and nail-biting action in great details in the many tables and plots. Every table and chart tell a story. You just have to ‘read between the lines!’
GooglyPlusPlus2021 will update itself automatically every day, so the data will be current and you can analyse all matches upto the previous day, along with the historical performances of the teams. So make sure you check it everyday.
The are 5 tabs for each of the formats supported by GooglyPlusPlus2021 which now supports IPL, Intl. T20(men), Intl. T20(women), BBL, NTB, PSL, CPL, SSM, WBB. Besides, it also supports ODI (men) and ODI (women)
Each of the formats have 5 tabs – Batsman, Bowler, Match, Head-to-head and Overall Performace.
All T20 formats also include a ranking functionality for the batsmen and bowlers
You can now perform drill-down analytics for batsmen, bowlers, head-to-head and overall performance based on date-range selector functionality. The ranking tabs also include date range selector granular analysis. For more details see GooglyPlusPlus2021 enhanced with drill-down batsman, bowler analytics
This latest update of GooglyPlusPlus2021 includes new controls which allow for granular analysis of teams and matches. This version includes a new ‘Date Range’ widget which will allow you to choose a specific interval between which you would like to analyze data. The Date Range widget has been added to 2 tabs namely
a) Head-to-Head
b) Overall Performance
Important note:
This change is applicable to all T20 formats and ODI formats that GooglyPlusPlus2021 handles. This means you can do fine-grained analysis of the following formats
a. IPL b. Intl. T20 (men) c. Intl. T20 (women)
d. BBL e. NTB f. PSL
g. WBB h. CPL i. SSM
j. ODI (men) k. ODI (women)
Important note 1: Also note that all charts in GooglyPlusPlus2021 are interactive. You ca hover over the charts to get details of the data below. You can also selectively filter in bar charts using double-click and click. To know more about how to use GooglyPlusPlus2021 interactively, please see my post GooglyPlusPlus2021 is now fully interactive!!
You can clone/download the code for GooglyPlusPlus2021 from Github at GooglyPlusPlus2021
GooglyPlusPlus2021 is now fully interactive. Please read the below post carefully to see the different ways you can interact with the data in the plots.
There are 2 main updates in this latest version of GooglyPlusPlus2021
a) GooglyPlusPlus gets all ‘touchy, feely‘ with the data and now you can interact with the plot/chart to get more details of the underlying data. There are many ways you can slice’n dice the data in the charts. The examples below illustrate a few of this. You can interact with plots by hover’ing, ‘click’ing and ‘double-click’ing curves, plots, barplots to get details of the data.
b) GooglyPlusPlus also includes the ‘Super Smash T20’ league from New Zealand. You can analyze batsmen, bowlers, matches, teams and rank Super Smash (SSM) also
Note: GooglyPlusPlus2021 can handle a total of 11 formats including T20 and ODI. They are
Within these 11 x 7 = 77 tabs you can analyze batsmen, bowlers, matches, head-to-head, team vs all other teams and rank players for T20 and ODI. In addition all plots have been made interactive so there is a lot more information that you can get from these charts
Below I have randomly included some charts for different formats to show how you can interact with them
a) Batsman Analysis – Runs vs Deliveries (IPL)
Mouse-over/Hover
The plot below gives the number of runs scored by David Warner vs Deliveries faced.
b) Batsman Analysis – Runs vs Deliveries (IPL) (prediction)
Since a 2nd order regression line,with confidence intervals(shaded area), has been fitted in the above plot, we can predict the runs given the ‘balls faced’ as below
Click ‘Toggle Spike lines’ (use palette on top-right)
By using hover(mouse-over) on the curve we can determine the predicted number of runs Warner will score given a certain number of deliveries
c) Bowler Analysis – Wickets against opposition – Intl. T20 (women)
Jhulan Goswami’s wickets against opposition countries in Intl. T20 (women)
Note: Some plots are non-interactive, like the one below which predicts the number of wickets Bumrah will take based on number of deliveries bowled
e) Match Analysis – Batsmen Partnership -Intl. T20 (men)
India vs England batting partnership between Virat Kohli & Shikhar Dhawan in all matches between England and India
f) Match Analysis – Worm chart (Super Smash T20) SSM
i) Worm chart of Auckland vs Northern Districts (29 Jan 2021).
ii) The final cross-over happens around the 2nd delivery of the 19th over (18.2) as Northern Districts over-takes Auckland to win the match.
g) Head-to-head – Team batsmen vs bowlers (Bangladesh batsmen against Afghanistan bowlers) Intl. T20 (men)
Batting performance of Shakib-al-Hasan (Bangladesh) against Afghanistan bowlers in Intl. T20 (men)
h) Head-to-head – Team batsmen vs bowlers (Bangladesh batsmen against Afghanistan bowlers) Intl. T20 (men) –Filter
Double click on Shakib-al-Hasan on the legend to get the performance of Shakib-al-Hasan against Afghanistan bowlers
Avoiding the clutter
i) Head-to-head – Team bowler vs batsmen (Chennai Super Kings bowlers vs Mumbai Indians batsmen) – IPL
If you choose the above option the resulting plot is very crowded as shown below
To get the performance of Mumbai Indian (MI) batsmen (Rohit Sharma & Kieron Pollard) against Chennai Super Kings (CSK) bowlers in all matches do as told below
Steps to avoid clutter in stacked bar plots
1) This can be avoided by selectively choosing to filter out the batsmen we are interested in. say RG Sharma and Kieron Pollard. Then double-ciick RG Sharma, this is will bring up the chart with only RG Sharma as below
2) Now add additional batsmen you are interested in by single-clicking. In the example below Kieron Pollard is added
You can continue to add additional players that you are interested by single clicking.
j) Head-to-head (Performance of Indian batsmen vs Australian bowlers)- ODI
In the plot V Kohli, MS Dhoni and SC Ganguly have been selected for their performance against Australian bowlers (use toggle spike lines)
k) Overall Performance – PSL batting partnership against all teams (Fakhar Zaman)
The plot below shows Fakhar Zaman (Lahore Qalanders) partnerships with other teammates in all matches in PSL.
l) Win-loss against all teams (CPL)
Win-loss chart of Jamaica Talawallahs (CPL) in all matches against all opposition
m) Team batting partnerships against all teams for India (ODI Women)
Batting partnerships of Indian ODI women against all other teams
n) Ranking of batsmen (IPL 2021)
Finally here is the latest ranking of IPL batsmen for IPL 2021 (can be done for all other T20 formats)
“Would you tell me, please, which way I ought to go from here?” “That depends a good deal on where you want to get to.” “I don’t much care where –” “Then it doesn’t matter which way you go.” Lewis Carroll, Alice in Wonderland
Every time I think that I have my R packages or Shiny apps all wrapped up, I find another idea trots up and knocks at my door. Since I intend to keep GooglyPlusPlus current with the latest data, I decided to include the ranking functions in my Shiny app GooglyPlusPlus.
Fortunately, since GooglyPlusPlus is based on my R package ‘yorkr‘ (see Introducing cricket package yorkr: Beaten by sheer pace!), I could make the necessary changes to the ranking functions in the package, so that it could be incorporated into my latest Shiny app GooglyPlusPlus2021!! To know how to use GooglyPlusPlus see my post Introducing GooglyPlusPlus
Note: GooglyPlusPlus can analyze batsmen, bowlers, matches and teams.
Ranking IPL batsmen (minMatches = 80) – The following table shows the ranking of IPL players who have played 80 matches or more
B) Identifying batsmen of potential and promise
Ranking IPL batsmen (minMatches =70) – If we reduce the minimum number of matches played to 70, then we see it pushes up KL Rahul above Kohli.
Ranking IPL batsmen (minMatches =60) – When the slider is moved to 60, we see that Rishabh Pant has a better mean average and mean strike rate and is also ranked above Kohli. We can identify promising players this way. However, it is also likely that some players may be just a bright flash in the pan
C) Ranking T20 bowlers (men)
D) Ranking NTB Batsmen
GooglyPlusPlus2021 can rank all T20 formats (IPL, BBL, Intl. T20 (men), Intl. T20 (women), NTB, PSL and WBB. Do give it a try!
Also remember that GooglyPlusPlus2021 includes close to 100+ functions which enable it to perform analysis of batsmen, bowlers, T20 matches, head-to-head confrontation of T20 teams and overall performance of T20 teams . To know more about GooglyPlusPlus2021 see Introducing GooglyPlusPlus
You can download the code for this app from Github at GooglyPlusPlus2021
Giga thoughts ... 