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The time has come!! So last weekend, I spun the wheels, swung the lever and listened to steam as I cranked up my Shiny app GooglyPlusPlus. The ICC Men’s T20 World Cup is about to arrive, and it was time to prepare for the event. This latest GooglyPlusPlus is current with latest Intl. Men’s T20 match statistics, give or take. GooglyPlus can analyze batsmen, bowlers, matches, team-vs-team, team-vs-all teams, as well as performance of batsmen, bowlers and plot in powerplay, middle and death overs.
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In this post, I include a quick refresher of some of the features of my app GooglyPlusPlus. Note: This is a random sample of the available functions. More than 120+ features are available in the app.
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GooglyPlusPlus. Watch your favorite players and team of your country with
Note 1: All charts are interactive
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Note 2: You can choose a date range for your analysis
Note 3: The data of this app is taken from Cricsheet
t20 batsman tab
This tab contains tasks related to individual batsmen. Functions include runs vs deliveries, moving average runs, cumulative average runs, cumulative average strike rate, runs against the opposition, runs at the venue, etc.
For example
a) Cumulative strike rate of Suryakumar Yadav (India)
b) Demonstration of Mohammad Rizwan (Pakistan) against the opposition
2. T20 Bowlers Tab
The bowlers tab has functions for calculating average economy rate, moving average wickets, cumulative average wickets, cumulative economy rate, bowlers’ performance against opposition, bowlers’ performance at the venue, prediction of wickets and others.
A random function is shown below
a) Predicting wickets for Wanindu Hasaranga of Sri Lanka
3. T20 Matches Tab
Match tab contains functions which are like match batting and bowling scorecard, batting partnership, performance of batsmen vs bowlers, bowlers wicket, bowler wicket match, match worm graph, match worm wicket graph, team runs in 20 overs, team Wickets can count in 20 overs. Teams take runs or wickets in powerplay, middle and death overs
Here are some of the functions of this tab
a) Afghanistan vs Ireland – 2022-08-15
b) Australia vs Sri Lanka – 2019-11-01 – Runs in 20 overs
4. T20 Face-to-Face Tab
This tab provides the analysis of all the combinations of T20 teams (countries) in various aspects. This tab can calculate overall batting, bowling scorecard in all matches between 2 countries, partnership of batsmen, performance against bowlers, bowler vs batsman, runs, strike rate, wickets, economy rate in 20 overs, runs vs sr plot And Wicket vs ER Plot in all matches between team and so on. Here are some examples from this tab
a) Bangladesh vs West Indies – Batting Scorecard from 2019-01-01 to 2022-07-07
b) Wicket vs ER Plot – England vs New Zealand – 2019-01-01 to 2021-11-10
5. T20 Team Performance Overall Tab
This tab provides a detailed analysis of the team’s performance against all other teams. Like the previous tab, there are functions to calculate the overall batting, bowling scorecard of a team against all other teams for any specific time interval. It can help in picking the most consistent batsmen, bowlers. Also there are functions to calculate overall batting partnership, bowler vs batsman, runs, wickets in 20 overs, runs vs SR and wickets vs ER etc.
a) Batsman Vs Bowler (Rank 1- V Kohli from 2019-01-01 to 2022-09-25)
b) Team Runs in Death Over (India) vs SR (2019-01-01 to 2022-09-25)
6) Customization tab
In Optimization tab we can check bowlers performance against specific batsmen or batsmen against specific bowlers
a) batsman vs bowler
b) bowler vs batsman
7) T20 Batting Performance Tab
This tab performs various analysis such as ranking batsmen based on runs over SR and SR over runs. Also you can plot the powerplay, overall runs vs SR in middle and death overs, and more specifically runs vs SR. All this can be done for a specific date range. Here are some examples. The data includes all T20s (all countries all matches)
a) Rank Batsman (Run More Than SR, Minimum Matches Played = 33, Date Range = 2019-01-01 to 2022-09-27)
Top 3 batsmen are Mohman Rizwan, V Kohli and Babar Azam
b) Overall Run 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 in on the area of interest. Controls to interact with the plot are at the top of the plot as shown
Zooming in and panning in that area we can see the best performers in Powerplay below
8) T20 Bowling Performance Tab
This tab calculates and ranks bowlers on wickets over economy and economy rate over wickets. We can also calculate and plot Wicket vs ER in all matches, except Wicket vs ER in Powerplay, Middle and Death overs with data for all countries
a) Rank bowler (Wickets more than ER, Minimum matches = 28, 2019-01-01 to 2022-09-27)
b) Wicket vs ER Plot
S Lamichhane (NEP), Hasaranga (SL) and Shamsi (South Africa) 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 Match = 24)
Zooming in and panning we see that the best performers in the death overs are MR Adair (IRE), Haris Rauf (PAK) and Chris Jordan (Eng).
With the excitement building up, it is time to take a look at how your country will perform and the players who will do well.
Give GooglyPlusPlus a spin!!!
See also
Deep Learning from First Principles in Python, R and Octave – Part 5 Big Data-5: kNiFi-ing through Cricket Data with Yorkpy Understanding Neural Style Transfer with Tensorflow and Keras Re-introducing OpenCV Wiener using Cricketer Blur again with filters! : An R Package for Analyzing the Performance of Cricpy in an Android Presentation on “Intelligent Networks, CAMEL Protocols, Services and Applications” Practical Machine Learning with R and Python – Part 2 Cricpy adds team analytics to its arsenal!! Benford’s law derives from IPL, Intl. T20 and ODI cricket
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