In my last post GooglyPlusPlus gets ready for ICC Men’s T20 World Cup, I had mentioned that GooglyPlusPlus was preparing for the big event the ICC Men’s T20 World cup. Now that the T20 World cup is underway, my Shiny app in R, GooglyPlusPlus ,will be generating near real-time analytics of matches completed the previous day. Besides the app can also do historical analysis of players, teams and matches.
The whole process is automated. A cron job will execute every day, in the morning, which will automatically download the matches of the previous day from Cricsheet, unzip them, start a pipeline which will transform and process the match data into necessary folders and finally upload the newly acquired data into my Shiny app. Hence, you will be able to access all the breathless, pulsating cricketing action in timeless, interactive plots and tables which will capture all aspects of Men’s T20 matches, namely batsman, bowler performance, match analysis, team-vs-team, team-vs-all teams besides ranking of batsmen & bowlers. Since the data is cumulative, all the analytics are historical and current.
The data for GooglyPlusPlus is taken from Cricsheet
Interest in cricket, has mushroomed in recent times around the world, with the addition of new formats which started with ODI, T20, T10, 100 ball and so on. There are leagues which host these matches at different levels around the world. While GooglyPlusPlus, provides near real-time analytics of Men’s T20 World cup, we can clearly envision a big data platform which ingests matches daily from multiple cricket formats, leagues around the world generating real-time and near real-time analytics which are essential these days to selection of teams at different levels through auctions. For more discussion on this see my posts
We could imagine a Data Lake, into which are ingested data from the different cricket formats, leagues through appropriate technology connectors. Once the data is ingested, we could have data pipelines, based on Azure ADF, Apache NiFi, Apache Airflow or Amazon EMR etc., to transform, process and enhance the data, generating real-time analytics on the fly. Recent formats like T20, T10 require more urgency in strategic thinking based on scoring within limited overs, or containing batsmen from going on a rampage within the set of overs, the analytics on a fly may help the coach to modify the batting or bowling lineup at points in match. In this context see my earlier post Using Linear Programming (LP) for optimizing bowling change or batting lineup in T20 cricket
All of these are not just possible, but are likely to become reality as more and more formats, leagues and cricket data proliferate around the world.
The opening match between Namibia vs Sri Lanka resulted in an upset. We can see this in the match worm-wicket graph below
2. Scotland vs West Indies – 17 Oct 2022: Batsmen vs Bowlers
George Munsey was the top scorer for Scotland and was instrumental in the win against WI. His performance against West Indies bowlers is shown below. Note, the charts are interactive
3. Zimbabwe vs Ireland – 17 Oct 2022 : Team Runs vs SR
Sikander Raza of Zimbabwe with 82 runs with the strike rate ~ 170
4.United Arab Emirates vs Netherlands – 16 Oct 2022: Team runs across 20 overs
UAE pipped Netherlands in the middle overs and were able to win by 1 ball and 3 wickets
5.Scotland vs Ireland – 19 Oct 2022 : Team Runs vs SR Middle overs plot
Curtis Campher snatched the game away from Scotland with his stellar performance in middle and death overs
6. UAE vs Namibia : 20 Oct 2022 : Team Wickets vs ER plot
Basoor Hameed and Zahoor Khan got 2 wickets apiece with an economy rate of ~5.00 but still they were not able to stop UAE from stealing a win
7. Overall Runs vs SR in T20 World Cup 2022
It is too early to rank the players, nevertheless in the current T20 World Cup, MP O’Dowd (Netherlands), BKG Mendis (Sri Lanka) and JN Frylinck(Namibia) are the top 3 batsmen with good runs and Strike Rate
8. Overall Wickets over ER in T20 World Cup 2022
The top 3 bowlers so far in T20 World Cup 2022 are a) BFW de Leede (Netherlands) b) PWH De Silva (Sri Lanka) c) KP Meiyappan (UAE) with a total of 7,7, and 6 wickets respectively
Note: Besides the match analysis GooglyPlusPlus also provides detailed analysis of batsmen, bowlers, matches as above, team-vs-team, team-vs-all teams, ranking of batsmen & bowlers etc. For more details see my post GooglyPlusPlus gets ready for ICC Men’s T20 World Cup
Do visit GooglyPlusPlus everyday to check out the cricketing actions of matches gone by. You can also follow me on twitter @tvganesh_85 for daily highlights.
It is time!! So last weekend, I turned the wheels, moved the levers and listened to the hiss of steam, as I cranked up my Shiny app GooglyPlusPlus. The ICC Men’s T20 World Cup is just around the corner, and it was time to prepare for this event. This latest GooglyPlusPlus is current with the latest Intl. men’s T20 match data, give or take a few. GooglyPlusPlus can analyze batsmen, bowlers, matches, team-vs-team, team-vs-all teams, besides also ranking batsmen, bowlers and plot performances in Powerplay, middle and death overs.
In this post, I include a quick refresher of some of features of my app GooglyPlusPlus. Note: This is a random sampling of the functions available. There are more than 120+ features available in the app.
Check out your favourite players and your country’s team with GooglyPlusPlus
Note 1: All charts are interactive
Note 2: You can choose a date range for your analysis
Note 3: The data for this app is taken from Cricsheet
This tab includes functions pertaining to individual batsmen. Functions include Runs vs Deliveries, moving average runs, cumulative average run, cumulative average strike rate, runs against opposition, runs at venue etc.
a) Suryakumar Yadav’s (India) cumulative strike rate
b) Mohammed Rizwan’s (Pakistan) performance against opposition
2. T20 Bowler’s Tab
The bowlers tab has functions for computing mean economy rate, moving average wickets, cumulative average wicks, cumulative economy rate, bowlers performance against opposition, bowlers performance in venue, predict wickets and others
A random function is shown below
a) Predict wickets for Wanindu Hasaranga of Sri Lanka
3. T20 Match tab
The match tab has functions that can compute match batting & bowling scorecard, batting partnerships, batsmen performance vs bowlers, bowler’s wicket kind, bowler’s wicket match, match worm graph, match worm wicket graph, team runs across 20 overs, team wickets in 20 overs, teams runs or wickets in powerplay, middle and death overs
Here are a couple of functions from this tab
a) Afghanistan vs Ireland – 2022-08-15
b) Australia vs Sri Lanka – 2019-11-01 – Runs across 20 overs
4. T20 Head-to-head tab
This tab provides the analysis of all combination of T20 teams (countries) in different aspects. This tab can compute the overall batting, bowling scorecard in all matches between 2 countries, batsmen partnerships, performances against bowlers, bowlers vs batsmen, runs, strike rate, wickets, economy rate across 20 overs, runs vs SR plot and wicket vs ER plot in all matches between team and so on. Here are a couple of examples from this tab
a) Bangladesh vs West Indies – Batting scorecard from 2019-01-01 to 2022-07-07
b) Wickets vs ER plot – England vs New Zealand – 2019-01-01 to 2021-11-10
5. T20 Team performance overalltab
This tab provides detailed analysis of the team’s performance against all other teams. As in the previous tab there are functions to compute the overall batting, bowling scorecard of a team against all other teams for any specific interval of time. This can help in picking out the most consistent batsmen, bowlers. Besides there are functions to compute overall batting partnerships, bowler vs batsmen, runs, wickets across 20 overs, run vs SR and wickets vs ER etc.
a) Batsmen vs Bowlers (Rank 1- V Kohli 2019-01-01 to 2022-09-25)
b) team Runs vs SR in Death overs (India) (2019-01-01 to 2022-09-25)
6) Optimisation tab
In the optimisation tab we can check the performance of a specific batsmen against specific bowlers or bowlers against batsmen
a) Batsmen vs Bowlers
b) Bowlers vs batsmen
7) T20 Batting Performancetab
This tab performs various analytics like ranking batsmen based on Run over SR and SR over Runs. Also you can plot overall Runs vs SR, and more specifically Runs vs SR in Powerplay, Middle and Death overs. All of this can be done for a specific date range. Here are some examples. The data includes all of T20 (all countries all matches)
a) Rank batsmen (Runs over SR, minimum matches played=33, date range=2019-01-01 to 2022-09-27)
The top 3 batsmen are Mohamen Rizwan, V Kohli and Babar Azam
b) Overall runs vs SR plot (2019-01-01 to 2022-09-27)
c) Overall Runs vs SR in Powerplay (all teams- 2019-01-01-2022-09-27)
This plot will be crowded. However, we can zoom into an area of interest. The controls for interacting with the plot are in the top of the plot as shown
Zooming in and panning to the area we can see the best performers in powerplay are as below
8) T20 Bowling Performancetab
This tab computes and ranks bowlers on Wickets over Economy and Economy rate over wickets. We can also compute and plot the Wickets vs ER in all matches , besides the Wickets vs ER in powerplay, middle and death overs with data from all countries
a) Rank Bowlers (Wickets over ER, minimum matches=28, 2019-01-01 to 2022-09-27)
b) Wickets vs ER plot
S Lamichhane (NEP), Hasaranga (SL) and Shamsi (SA) are excellent bowlers with high wickets and low ER as seen in the plot below
c) Wickets vs ER in death overs (2019-01-01 to 2022-09-27, min matches=24)
Zooming in and panning we see the best performers in death overs are MR Adair (IRE), Haris Rauf(PAK) and Chris Jordan (ENG)
With the excitement building up, it is time you checked out how your country will perform and the players who will do well.
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
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
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
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
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
It is that time of the year when there is “a song in the air, the lark’s on the wing, and the snail’s on the the thorn“. Yes, it is the that time of year when the grand gala event of IPL 2022 is underway. So, I managed to wake myself from my Covid-induced slumber, worked up my ‘creaking bones‘ and cranked up the GooglyPlusPlus machinery.
So now, every morning, a scheduled CRON tab entry will automatically download the previous night’s match data from Cricsheet, unzip, process and transform it into the necessary format required by my R package yorkr, and make it available to my Shiny app GooglyPlusPlus. Hence the data is current and you have access to ‘analytics-in-the-now’!.
As you know in 2021, I added a lot of new features to GooglyPlusPlus, new tabs to do even more. analytics – or in other words there is “more GooglyPlusPlus per click!!”. So now, you have the following
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
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.
Note: All charts are interactive, which means that you can hover, zoom-in, zoom-out, pan etc on the charts
The latest avatar of GooglyPlusPlus2022 is based on my R package yorkr with data from Cricsheet.
GooglyPlusPlus2022 is the new avatar of last year’s GooglyPlusPlus2021. Roughly, about 5 years back I had written a post on Using linear programming to optimize T20 batting and bowling line up. This post has been on the back of my mind for a long time and I decided to pay this post a revisit. This requires computing performance of individual batsmen vs bowlers and vice-versa for performing the optimization. So in this latest incarnation, there are 4 new functions
batsmanVsBowlerPerf – Performance of batsmen against chosen bowlers
bowlerVsBatsmanPerf – Performance of bowlers versus specific batsmen
battingOptimization – Optimizing batting line up based on strike rates ad remaining overs
bowlingOptimization – Optimizing bowling line up based on economy rates and remaining overs
These 4 functions have been incorporated in all the supported 9 T20 formats namely a. IPL b. Intl. T20(men) c. Intl. T20 (women) d. BBL e. NTB f. PSL g. WBB h. CPL i. SSM
You can clone/fork the code for GooglyPlusPlus2022 from Github from gpp2022-1
With this latest update you can do a myriad of analyses of batsmen, bowlers, teams, matches. This is just-in-time for the IPL Mega-auction!! Do check out these other posts of GooglyPlusPlus for other detailed analysis
A) Batsman Vs Bowlers – This option computes the performance of individual batsman against individual bowlers
a) IPL Batsmen vs Bowlers
Included below are the performances of Dhoni, Raina and Kohli against Malinga, Ashwin and Bumrah. Note: The last 2 text box input are not required for this.
b) Intl. T20 (men) Batsmen vs Bowlers
Note: You can type the name and choose from the drop down list
B) Bowler vs Batsmen – You can check the performance of specific bowlers against specific batsmen
a) Intl. T20 (women) India vs Australia
b) PSL Bowlers vs Batsmen
C) Strategy for optimizing batting and bowling line up
From the above 2 tabs, it is obvious, that different bowlers have different ER and wicket rate against different batsmen. In other words, the effectiveness of the bowlers varies by batsmen. Conversely, batsmen are more comfortable with certain bowlers versus others and this shows up in different strike rates.
Hence during the death overs, when trying to restrict batsmen to a certain score or on the flip side when the batting side needs to score a target within certain overs, we need to take advantage of the relative effectiveness of bowlers vs batsmen for optimising bowling and aggressiveness of batsmen versus bowlers to quickly reach the target.
This is the approach that is used for bowling and batting optimisation. For optimising bowling, we need to formulate a minimisation problem based on ER rates and for optimising batting, a maximisation strategy is chosen based on SR. ‘Integer programming’ is used to compute during the last set of overs
This latest version includes optimization using “integer programming” based on R package lpSolve.
Here are the 2 formulations
Assume there are 3 bowlers – and there are 3 batsmen –
I) LP Formulation for bowling order
Let the economy rate be the Economy Rate of the jth bowler to the ith batsman. Also if remaining overs for the bowlers are and the total number of overs left to be bowled are
Let the economy rate be the Economy Rate of the jth bowler to the ith batsman. Objective function : Minimize –
Constraints Where is the number of overs remaining for the jth bowler against ‘k’ batsmen
and if the total number of overs remaining to be bowled is N then or
The overs that any bowler can bowl is
II) LP Formulation for batting lineup
Let the strike rate be the Strike Rate of the ith batsman to the jth bowler Objective function : Maximize –
Constraints Where is the number of overs remaining for the jth bowler against ‘k’ batsmen
and the total number of overs remaining to be bowled is N then or
The overs that any bowler can bowl is
C) Optimized bowling lineup
a) IPL – Optimizing bowling line up
Note: For computing the Optimal bowling lineup, the total number of overs remaining and the number of overs for each bowler have to be entered.
b) PSL – Optimizing batting line up
d) Optimized batting lineup
a) Intl. T20 (men) India vs England
b) Carribean Premier League – Optimizing batting line up
Analytics is by definition, the science (& art) of identifying, discovering and interpreting patterns in data. There are different ways of capturing these patterns through charts (bar, pie, cumulative data, moving average etc.). One such way is the motion or animated chart which captures the changes in data across different time periods. This was made famous by Hans Rosling in his Gapminder charts.
In this post, I use animated charts, based on gganimate(), to display the rise and fall of batsmen and bowlers in IPL and Intl. T20 (men). I only did this for these 2 formats as they have sufficient data over at least 10+ years.
To construct these animated charts, I use a ‘sliding window’ of 3 years, so that we get a clearer view of batsman and bowler consistency. The animated charts show the performance of players for this moving window for e.g. Jan 2008- Dec 2010, Jan 2009-Dec 2011, Jan 2010- Dec 2012 and so on till Jan 2019- Dec 2021. This is done for both batting( total runs) and bowling (total wickets). If you would like to analyse the performance of particular batsmen, bowler during specific periods or for a team vs another team or in the overall T20 format, check out my post GooglyPlusPlus2021: Towards more picturesque analytics!
You clone/fork the code from Github here animation.
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!!!
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
b) Head-to-head tab
c) Overall performance tab
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
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
This latest update to GooglyPlusPlus2021 includes the following changes
a) All the functions in the ‘Batsman’ and ‘Bowler ‘tabs now include a date range, which allows you specify a period of interest.
b) The ‘Rank Batsman’ and ‘Rank Bowler’ tabs also include a date range selector, against the earlier version which had a ‘Since year’ slider see GooglyPlusPlus2021 bubbles up top T20 players in all formats!. The earlier ‘Since year’ slider option could only rank for the latest year or for all years up to the current year. Now with the new ‘date range’ picker we can check the batsman and bowler ranks in any IPL season or (any T20 format) or for a range of years.
c) Note: The Head-to-head and Overall performance tabs already include a date range selector.
There are 10 batsman functions and 9 bowler function that have changed for the following T20 and ODI formats and Rank batsman and bowler includes the ‘date range’ and has changed for all T20 formats.
GooglyPlusPlus2021 supports all the following T20 formats
I have included some random screen shots of some of using these tabs and options in GooglyPlusPlus2021.
A) KL Rahul’s Cumulative average in IPL 2021 vs IPL 2020
a) KL Rahul in IPL 2021
b) KL Rahul in IPL 2020
B) Performance of Babar Azam in Intl. T20 (men)
a) Babar Azam’s cumulative average from 2019
b) Babar Azam’s Runs against opposition since 2019
Note: Intl. T20 (women) data available upto Mar 2020 from Cricsheet
a) A J Healy performance between 2010 – 2015
b) A J Healy performance between 2015 – 2020
D) M S Dhoni’s performance with the bat pre-2020 and post 2020
There has been a significant decline in Dhoni’s performance in the last couple of years
I) Dhoni’s performance from Jan 2010 to Dec 2019
a) Moving average at 25+(Dhoni before)
The moving average actually moves up…
b) Cumulative average at 25+(Dhoni before)
c) Cumulative Strike rate 140+ (Dhoni before)
d) Dhoni’s moving average is ~10-12 (post 2020)
e) Dhoni’s cumulative average (post 2020)
f) Dhoni’s strike rate ~80 (post 2020)
E) Bumrah’s performance in IPL
a) Bumrah’s performance in IPL 2020
b) Bumrah’s performance in IPL 2021
F) Moving average wickets for A. Shrubsole in ODI (women)
G) Chris Jordan’s cumulative economy rate
We can see that Jordan has become more expensive over the years
G) Ranking players
In this latest version the ‘Since year slider’ has been replaced with a Date Range selector. With this we can identify the player ranks in any IPL, CPL, PSL or BBL season. We can also check the performance over the last couple of years. Note: The matches played and Runs over Strike rate or Strike rate over runs can be computed. Similarly for bowlers we have Wickets over Economy rate and Economy rate over wickets options.
a) Ranking IPL batsman in IPL season 2020
b) Ranking Intl. T20 (batsmen) from Jan 2019 to Jul 2021
c) Ranking Intl. T20 bowlers (women) from Jan 2019 – Jul 2021
d) Best IPL bowlers over the last 3 seasons (Wickets over Economy rate)
e) Best IPL bowlers over the last 3 seasons (Economy rate over wickets)
You can clone/download this latest version of GooglyPlusPlus2021 from Github at gpp2021-7