GooglyPlusPlus – Giga thoughts …

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.

Check out the latest version of GooglyPlusPlus

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.

Do take GooglyPlusPlus for a test drive!!!

Follow me on twitter @tvganesh_85 for daily highlights of previous day matches

Take a look at some of my other posts

To see all posts click Index of posts

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

T20 Batsman tab

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.

For e.g.

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 overall tab

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 Performance tab

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 Performance tab

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)