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
This should be my last post on computing T20 Win Probability. In this post I compute Win Probability using Augmented Data with the help of Conditional Tabular Generative Adversarial Networks (CTGANs).
A.Introduction
I started the computation of T20 match Win Probability in my earlier post
This was lightweight and could be easily deployed in my Shiny GooglyPlusPlus app as opposed to the Tidymodel’s Random Forest, which was bulky and slow.
d) Finally I decided to try and improve the accuracy of my Deep Learning Model using Synthetic data. Towards this end, my explorations led me to Conditional Tabular Generative Adversarial Networks (CTGANs). CTGAN are GAN networks that can be used with Tabular data as GAN models are not useful with tabular data. However, the best performance I got for
DL Keras Model + Synthetic data : accuracy =0.77
The poorer accuracy was because CTGAN requires enormous computing power (GPUs) and RAM. The free version of Colab, Kaggle kept crashing when I tried with even 0.1 % of my 1.2 million dataset size. Finally, I tried with just 0.05% and was able to generate synthetic data. Most likely, it is the small sample size and the smaller number of epochs could be the reason for the poor result. In any case, it was worth trying and this approach would possibly work with sufficient computing resources.
B.Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs) was the brain child of Ian Goodfellow who demonstrated it in 2014. GANs are capable of generating synthetic text, tables, images, videos using available data. In Adversarial nets framework, the generative model is pitted against an adversary: a discriminative model that learns to determine whether a sample is from the model distribution or the data distribution.
GANs have 2 Deep Neural Networks , the Generator and Discriminator which compete against other
The Generator (Counterfeiter) takes random noise as input and generates fake images, tables, text. The generator learns to generate plausible data. The generated instances become negative training examples for the discriminator.
The Discriminator (Police) which tries to distinguish between the real and fake images, text. The discriminator learns to distinguish the generator’s fake data from real data. The discriminator penalises the generator for producing implausible results.
A pictorial representation of the GAN model can be shown below
Theoretically best performance of GANs are supposed to happen when the network reaches the ‘Nash equilibrium‘, i.e. when the Generator produces near fake images and the Discriminator’s loss is f ~0.5 i.e. the discriminator is unable to distinguish between real and fake images.
Note: Though I have mentioned T20 data in the above GAN model, the T20 tabular data is actually used in CTGAN which is slightly different from the above. See Reference 2) below.
C. Conditional Tabular Generative Adversial Networks (CTGANs)
“Modeling the probability distribution of rows in tabular data and generating realistic synthetic data is a non-trivial task. Tabular data usually contains a mix of discrete and continuous columns. Continuous columns may have multiple modes whereas discrete columns are sometimes imbalanced making the modeling difficult.” CTGANs handle these challenges.
I came upon CTGAN after spending some time exploring GANs via blogs, videos etc. For building the model I use real T20 match data. However, CTGAN requires immense raw computing power and a lot of RAM. My initial attempts on Colab, my Mac (12 core, 32GB RAM), took forever before eventually crashing, I switched to Kaggle and used GPUs. Still I was only able to use only a miniscule part of my T20 dataset. My match data has 1.2 million rows, hoanything > 0.05% resulted in Kaggle crashing. Since I was able to use only a fraction, I executed the CTGAN model over several iterations, each iteration with a random 0.05% sample of the dataset. At the end of each iterations I also generate synthetic dataset. Over 12 iterations, I generate close 360K of ‘synthetic‘ T20 match data.
I then augment the 1.2 million rows of ‘real‘ T20 match data with the generated ‘synthetic T20 match data to run my Deep Learning model
Here the quality of the synthetic data set is evaluated.
a) Statistical evaluation
Read the real T20 match data
Read the generated T20 synthetic match data
import pandas as pd
# Read the T20 match and synthetic match data
df = pd.read_csv('/kaggle/input/cricket1/t20.csv'). #1.2 million rows
synthetic=pd.read_csv('/kaggle/input/synthetic/synthetic.csv') #300K
# Randomly sample 1000 rows, and generate stats
df1=df.sample(n=1000)
real=df1.describe()
realData_stats=real.transpose
print(realData_stats)
synthetic1=synthetic.sample(n=1000)
synthetic=synthetic1.describe()
syntheticData_stats=synthetic.transpose
syntheticData_stats
import pandas as pd
# CTGAN prints out a new line for each epoch
epochs_output = str(output).split('\n')
# CTGAN separates the values with commas
raw_values = [line.split(',') for line in epochs_output]
loss_values = pd.DataFrame(raw_values)[:-1] # convert to df and delete last row (empty)
# Rename columns
loss_values.columns = ['Epoch', 'Generator Loss', 'Discriminator Loss']
# Extract the numbers from each column
loss_values['Epoch'] = loss_values['Epoch'].str.extract('(\d+)').astype(int)
loss_values['Generator Loss'] = loss_values['Generator Loss'].str.extract('([-+]?\d*\.\d+|\d+)').astype(float)
loss_values['Discriminator Loss'] = loss_values['Discriminator Loss'].str.extract('([-+]?\d*\.\d+|\d+)').astype(float)
# the result is a row for each epoch that contains the generator and discriminator loss
loss_values.head()
import plotly.graph_objects as go
# Plot loss function
fig = go.Figure(data=[go.Scatter(x=loss_values['Epoch'], y=loss_values['Generator Loss'], name='Generator Loss'),
go.Scatter(x=loss_values['Epoch'], y=loss_values['Discriminator Loss'], name='Discriminator Loss')])
# Update the layout for best viewing
fig.update_layout(template='plotly_white',
legend_orientation="h",
legend=dict(x=0, y=1.1))
title = 'CTGAN loss function for T20 dataset - '
fig.update_layout(title=title, xaxis_title='Epoch', yaxis_title='Loss')
fig.show()
G. Qualitative evaluation of Synthetic data
a) Quality of continuous columns in synthetic data
KSComplement -This metric computes the similarity of a real column vs. a synthetic column in terms of the column shapes.The KSComplement uses the Kolmogorov-Smirnov statistic. Closer to 1.0 is good and 0 is worst
The performance is decent but not excellent. I was unable to execute more epochs as it it required larger than the memory allowed
c) Correlation similarity
This metric measures the correlation between a pair of numerical columns and computes the similarity between the real and synthetic data – it compares the trends of 2D distributions. Best 1.0 and 0.0 is worst
In this final part I augment my T20 match data set with the generated synthetic T20 data set.
import pandas as pd
from numpy import savetxt
import tensorflow as tf
from tensorflow import keras
import pandas as pd
import numpy as np
from keras.layers import Input, Embedding, Flatten, Dense, Reshape, Concatenate, Dropout
from keras.models import Model
import matplotlib.pyplot as plt
# Read real and synthetic data
df = pd.read_csv('/kaggle/input/cricket1/t20.csv')
synthetic=pd.read_csv('/kaggle/input/synthetic/synthetic.csv')
# Augment the data. Concatenate real & synthetic data
df1=pd.concat([df,synthetic])
# Create training and test samples
print("Shape of dataframe=",df1.shape)
train_dataset = df1.sample(frac=0.8,random_state=0)
test_dataset = df1.drop(train_dataset.index)
train_dataset1 = train_dataset[['batsmanIdx','bowlerIdx','ballNum','ballsRemaining','runs','runRate','numWickets','runsMomentum','perfIndex']]
test_dataset1 = test_dataset[['batsmanIdx','bowlerIdx','ballNum','ballsRemaining','runs','runRate','numWickets','runsMomentum','perfIndex']]
train_dataset1
train_labels = train_dataset.pop('isWinner')
test_labels = test_dataset.pop('isWinner')
print(train_dataset1.shape)
a=train_dataset1.describe()
stats=a.transpose
print(a)
As can be seen the accuracy with augmented dataset is around 0.77, while without it I was getting 0.867 with just the real data. This degradation is probably due to the folllowing reasons
Only a fraction of the dataset was used for training. This was not representative of the data distribution for CTGAN to correctly synthesise data
The number of epochs had to be kept low to prevent Kaggle/Colab from crashing
I. Conclusion
This post shows how we can generate synthetic T20 match data to augment real T20 match data. Assuming we have sufficient processing power we should be able to generate synthetic data for augmenting our data set. This should improve the accuracy of the Win Probabily Deep Learning model.
In my last post ‘GooglyPlusPlus now with Win Probability Analysis for all T20 matches‘ I had discussed the performance of my ML models, created with and without player embeddings, in computing the Win Probability of T20 matches. With batsman & bowler embeddings I got much better performance than without the embeddings
glmnet – Accuracy – 0.73
Random Forest (RF) – Accuracy – 0.92
While the Random Forest gave excellent accuracy, it was bulky and also took an unusually long time to predict the Win Probability of a single T20 match. The above 2 ML models were built using R’s Tidymodels. glmnet was fast, but I wanted to see if I could create a ML model that was better, lighter and faster. I had initially tried to use Tensorflow, Keras in Python but then abandoned it, since I did not know how to port the Deep Learning model to R and use in my app GooglyPlusPlus.
But later, since I was stuck with a bulky Random Forest model, I decided to again explore options for saving the Keras Deep Learning model and loading it in R. I found out that saving the model as .h5, we can load it in R and use it for predictions. Hence, I rebuilt a Deep Learning model using Keras, Python with player embeddings and I got excellent performance. The DL model was light and had an accuracy 0.8639 with an ROC_AUC of 0.964 which was great!
GooglyPlusPlus uses data from Cricsheet and is based on my R package yorkr
You can try out this latest version of GooglyPlusPlus at gpp2023-1
Here are the steps
A. Build a Keras Deep Learning model
a. Import necessary packages
import pandas as pd
import numpy as np
from zipfile import ZipFile
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import regularizers
from pathlib import Path
import matplotlib.pyplot as plt
b, Upload the data of all 9 T20 leagues (BBL, CPL, IPL, T20 (men) , T20(women), NTB, CPL, SSM, WBB)
# Read all T20 leagues
df1=pd.read_csv('t20.csv')
print("Shape of dataframe=",df1.shape)
# Create training and test data set
train_dataset = df1.sample(frac=0.8,random_state=0)
test_dataset = df1.drop(train_dataset.index)
train_dataset1 = train_dataset[['batsmanIdx','bowlerIdx','ballNum','ballsRemaining','runs','runRate','numWickets','runsMomentum','perfIndex']]
test_dataset1 = test_dataset[['batsmanIdx','bowlerIdx','ballNum','ballsRemaining','runs','runRate','numWickets','runsMomentum','perfIndex']]
train_dataset1
# Set the target data
train_labels = train_dataset.pop('isWinner')
test_labels = test_dataset.pop('isWinner')
train_dataset1
a=train_dataset1.describe()
stats=a.transpose
a
c. Create a Deep Learning ML model using batsman & bowler embeddings
This was a huge success for me to be able to create the Deep Learning model in Python and use it in my Shiny app GooglyPlusPlus. The Deep Learning Keras model is light-weight and extremely fast.
The Deep Learning model has now been integrated into GooglyPlusPlus. Now you can check the Win Probability using both a) glmnet (Logistic Regression with lasso regularisation) b) Keras Deep Learning model with dropouts as regularisation
In addition I have created 2 features based on Win Probability (WP)
i) Win Probability (Side-by-side– Plot(interactive) : With this functionality the 1st and 2nd innings will be side-by-side. When the 1st innings is played by team 1, the Win Probability of team 2 = 100 – WP (team1). Similarly, when the 2nd innings is being played by team 2, the Win Probability of team1 = 100 – WP (team 2)
ii) Win Probability (Overlapping) – Plot (static): With this functionality the Win Probabilities of both team1(1st innings) & team 2 (2nd innings) are displayed overlapping, so that we can see how the probabilities vary ball-by-ball.
Note: Since the same UI is used for all match functions I had to re-use the Plot(interactive) and Plot(static) radio buttons for Win Probability (Side-by-side) and Win Probability(Overlapping) respectively
Here are screenshots using both ML models with both functionality for some random matches
B) ICC T20 Men World Cup – Netherland-South Africa- 2022-11-06
i) Match Worm wicket chart
ii) Win Probability with LR (Side-by-Side- Plot(interactive))
iii) Win Probability LR (Overlapping- Plot(static))
iv) Win Probability Deep Learning (Side-by-side – Plot(interactive)
In the 213th ball of the innings South Africa was slightly ahead of Netherlands. After that they crashed and burned!
v) Win Probability Deep Learning (Overlapping – Plot (static)
It can be seen that in the 94th ball of both innings South Africa was ahead of Netherlands before the eventual slump.
C) Intl. T20 (Women) India – New Zealand – 2020 – 02 – 27
Here is an interesting match between India and New Zealand T20 Women’s teams. NZ successfully chased the India’s total in a wildly swinging fortunes. See the charts below
i) Match Worm Wicket chart
ii) Win Probability with LR (Side-by-side – Plot (interactive)
iii) Win Probability with LR (Overlapping – Plot (static)
iv) Win Probability with DL model (Side-by-side – Plot (interactive))
v) Win Probability with DL model (Overlapping – Plot (static))
The above functionality in plotting the Win Probability using LR or DL with both options (Side-by-side or Overlapping) is available for all 9 T20 leagues currently supported by GooglyPlusPlus.
glmnet – Logistic Regression(LR) with lasso regularization and penalty – Accuracy – 0.73
Random Forest (RF) – Accuracy – 0.92
Incidentally, both these models can be used on live streaming ball-by-ball data if available
I have now integrated the trained ML Logistic Regression model with penalty into my Shiny app GooglyPlusPlus. Unfortunately, the Random Forest model, besides being computationally intensive is also heavy-weight (1.29GB) when compared to LR model which is just 91.2 MB. So, I was not able to upload the Random Forest model to Shiny as the memory allowed exceeded that allowed in my paid subscription.
However, I will demonstrate the performance of both models, LR ( in my Web app) and RF (in my local machine). Incidentally the Random Forest model takes a long time to load and even longer (~90 secs) to compute the Win Probability of a T20 match, while the LR model computes in a few seconds. Interestingly, I find the LR model’s Win Probability more intuitive and explainable than the Random Forest. Possibly, the RF model overfits. I need to explore this more. Anyway, take a look at some interesting Win Probability Charts (fortune swings of teams!!!) over the course of the T20 match.
You can try out this latest version here at GooglyPlusPlus !!
Some major upsets in the ICC T20 World Cup, 2022
A) Netherlands vs South Africa – 2022-11-06
B) Zimbabwe vs Pakistan – 2022-10-27
1a) Netherlands vs South Africa – ICC 2022-11-06 (Worm-wicket chart)
Netherlands shocked South Africa and ended South Africa’s hopes for a place in the semi-finals. The match worm-wicket chart for this match is shown below
The 2 circled areas are where the South Africa lost the plot around the 8th over (~120+48=168) and 15th over (~120+90=210)
Around 205-215 ball of the innings South Africa started to lose
1b) Netherlands vs South Africa – ICC 2022-11-06 – Logistic Regression with regularisation (Shiny)
1c) 1b) Netherlands vs South Africa – ICC 2022-11-06 – Random Forest (not in Web app, local)
If you notice, for some reason, Random Forest model decided that Netherland was on the winning side, right from the start. Why would this happen? Possibly overfitting, I presume…
2a) Zimbabwe vs Pakistan – ICC 2022-10-27 Worm-wicket chart
Pakistan seemed to be cruising along with finally 11 runs in the last over, and for some reason they panicked and lost.
2a) Zimbabwe vs Pakistan -ICC 2022 – 2022-10-27 – Logistic Regression with regularisation (Shiny)
It can be seen that Pakistan did seem to have the upper hand , save the last over.
2a) Zimbabwe vs Pakistan ICC 2022-10-27 – Random Forest (not in Web app, local)
Again the Random Forest model implies that Zimbabwe was on a winning foot except in brief stretches for e.g ball 248 of the innings
So while the accuracy of Random Forest model is better by about ~20% I feel it is the Logistic Regression with penalty has generalised better and is more intuitive. Meanwhile, I will see if I can improve LR or try another model which can provide better accuracy besides generalising well
Henceforth, I will only be using the LR model that is in the Shiny app
3a) England vs New Zealand T20 Women – 2021-09-04
Another close match till the 15th over. After that England’s seems to have had a slower strike rate and lost
3b) England vs New Zealand T20 Women – 2021-09-04 – Logistic Regression
4a) Chennai Super Kings vs Gujarat Titans (IPL 2022) – Worm wicket chart
4a) Chennai Super Kings vs Gujarat Titans (IPL 2022) – Logistic Regression
5a) Islamabad United vs Peshawar Zalmi -2021-06-17 – Worm wicket chart
This match seems to be close, with both worms inter-twined almost all the way
5b) Islamabad United vs Peshawar Zalmi -2021-06-17 – Logistic Regression
According to the model Peshawar Zalmi lost the game around 14-15th over
In my previous post Computing Win Probability of T20 matches I had discussed various approaches on computing Win Probability of T20 matches. I had created ML models with glmnet and random forest using TidyModels. This was what I had achieved
glmnet : accuracy – 0.67 and sensitivity/specificity – 0.68/0.65
random forest : accuracy – 0.737 and roc_auc- 0.834
DL model with Keras in Python : accuracy – 0.73
I wanted to see if the performance of the models could be further improved. I got a suggestion from a AI/DL whizkid, who is close to me, to include embeddings for batsmen and bowlers. He felt that win percentage is influenced by which batsman faces which bowler.
So, I started to explore this idea. Embeddings can be used to convert categorical variables to a vector of continuous floating point numbers.Fortunately R’s Tidymodels, has a convenient functionality to create embeddings. By including embeddings for batsman, bowler the performance of my ML models improved vastly. Now the performance is
glmnet : accuracy – 0.728 and roc_auc – 0.81
random forest : accuracy – 0.927 and roc_auc – 0.98
mlp-dnn :accuracy – 0.762 and roc_auc – 0.854
As can be seem there is almost a 20% increase in accuracy with random forests with embeddings over the model without embeddings. Moreover, the feature importance which is plotted below shows that the bowler and batsman embeddings have a significant influence on the Win Probability
Note: The data for this analysis is taken from Cricsheet and has been processed with my R package yorkr.
A. Win Probability using GLM with penalty and player embeddings
Here Generalised Linear Model (GLMNET) for Logistic Regression is used. In the GLMNET the regularisation path is computed for the lasso or elastic net penalty at a grid of values for the regularisation parameter lambda. glmnet is extremely fast and gave an accuracy of 0.72 for an roc_auc of 0.81 with batsman, bowler embeddings. This was good improvement over my earlier implementation with glmnet without the batsman & bowler embeddings which had a
Read the data
a) Read the data from 9 T20 leagues (BBL, CPL, IPL, NTB, PSL, SSM, T20 Men, T20 Women, WBB) and create a single data frame of ball-by-ball data. Display the data frame
b) Split to training, validation and test sets. The dataset is initially split into training and test in the ratio 80%:20%. The training data is again split into training and validation in the ratio 80:20
4) Create a Logistic Regression Workflow by adding the GLM model and the recipe
5) Create grid of elastic penalty values for regularisation
6) Train all 30 models
7) Plot the ROC of the model against the penalty
# Use all 12 cores
cores <- parallel::detectCores()
cores
# Create a Logistic Regression model with penalty
lr_mod <-
logistic_reg(penalty = tune(), mixture = 1) %>%
set_engine("glmnet",num.threads = cores)
# Create pre-processing recipe
lr_recipe <-
recipe(isWinner ~ ., data = df_other) %>%
step_embed(batsman,bowler, outcome = vars(isWinner)) %>% step_normalize(ballNum,ballsRemaining,runs,runRate,numWickets,runsMomentum,perfIndex)
# Set the workflow by adding the GLM model with the recipe
lr_workflow <-
workflow() %>%
add_model(lr_mod) %>%
add_recipe(lr_recipe)
# Create a grid for the elastic net penalty
lr_reg_grid <- tibble(penalty = 10^seq(-4, -1, length.out = 30))
lr_reg_grid %>% top_n(-5)
# A tibble: 5 × 1
penalty
<dbl>
1 0.0001
2 0.000127
3 0.000161
4 0.000204
5 0.000259
lr_reg_grid %>% top_n(5) # highest penalty values
# A tibble: 5 × 1
penalty
<dbl>
1 0.0386
2 0.0489
3 0.0621
4 0.0788
5 0.1
# Train 30 penalized models
lr_res <-
lr_workflow %>%
tune_grid(val_set,
grid = lr_reg_grid,
control = control_grid(save_pred = TRUE),
metrics = metric_set(accuracy,roc_auc))
# Plot the penalty versus ROC
lr_plot <-
lr_res %>%
collect_metrics() %>%
ggplot(aes(x = penalty, y = mean)) +
geom_point() +
geom_line() +
ylab("Area under the ROC Curve") +
scale_x_log10(labels = scales::label_number())
lr_plot
The Penalty vs ROC plot is shown below
8) Display the ROC_AUC of the top models with the penalty
9) Select the model with the best ROC_AUC and the associated penalty. It can be seen the best mean ROC_AUC is 0.81 and the associated penalty is 0.000530
top_models <-
lr_res %>%
show_best("roc_auc", n = 15) %>%
arrange(penalty)
top_models
# A tibble: 15 × 7
penalty .metric .estimator mean n std_err .config
<dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
1 0.0001 roc_auc binary 0.810 1 NA Preprocessor1_Model01
2 0.000127 roc_auc binary 0.810 1 NA Preprocessor1_Model02
3 0.000161 roc_auc binary 0.810 1 NA Preprocessor1_Model03
4 0.000204 roc_auc binary 0.810 1 NA Preprocessor1_Model04
5 0.000259 roc_auc binary 0.810 1 NA Preprocessor1_Model05
6 0.000329 roc_auc binary 0.810 1 NA Preprocessor1_Model06
7 0.000418 roc_auc binary 0.810 1 NA Preprocessor1_Model07
8 0.000530 roc_auc binary 0.810 1 NA Preprocessor1_Model08
9 0.000672 roc_auc binary 0.810 1 NA Preprocessor1_Model09
10 0.000853 roc_auc binary 0.810 1 NA Preprocessor1_Model10
11 0.00108 roc_auc binary 0.810 1 NA Preprocessor1_Model11
12 0.00137 roc_auc binary 0.810 1 NA Preprocessor1_Model12
13 0.00174 roc_auc binary 0.809 1 NA Preprocessor1_Model13
14 0.00221 roc_auc binary 0.809 1 NA Preprocessor1_Model14
15 0.00281 roc_auc binary 0.809 1 NA Preprocessor1_Model15
#Picking the best model and the corresponding penalty
lr_best <-
lr_res %>%
collect_metrics() %>%
arrange(penalty) %>%
slice(8)
lr_best
# A tibble: 1 × 7
penalty .metric .estimator mean n std_err .config
<dbl> <chr> <chr> <dbl> <int> <dbl> <chr>
1 0.000530 roc_auc binary 0.810 1 NA Preprocessor1_Model08
# Collect predictions and generate the AUC curve
lr_auc <-
lr_res %>%
collect_predictions(parameters = lr_best) %>%
roc_curve(isWinner, .pred_0) %>%
mutate(model = "Logistic Regression")
autoplot(lr_auc)
7) Plot the Area under the Curve (AUC).
10) Build the final model with the best LR parameters value as found in lr_best
a) The best performance was for a penalty of 0.000530
b) The accuracy achieved is 0.72. Clearly using the embeddings for batsman, bowlers improves on the performance of the GLM model without the embeddings. The accuracy achieved was 0.72 whereas previously it was 0.67 see (Computing Win Probability of T20 Matches)
c) Create a fit with the best parameters
d) The accuracy is 72.8% and the ROC_AUC is 0.813
# Create a model with the penalty for best ROC_AUC
last_lr_mod <-
logistic_reg(penalty = 0.000530, mixture = 1) %>%
set_engine("glmnet",num.threads = cores,importance = "impurity")
#Update the workflow with this model
last_lr_workflow <-
lr_workflow %>%
update_model(last_lr_mod)
#Create a fit
set.seed(345)
last_lr_fit <-
last_lr_workflow %>%
last_fit(splits)
#Generate accuracy, roc_auc
last_lr_fit %>%
collect_metrics()
# A tibble: 2 × 4
.metric .estimator .estimate .config
<chr> <chr> <dbl> <chr>
1 accuracy binary 0.728 Preprocessor1_Model1
2 roc_auc binary 0.813 Preprocessor1_Model1
11) Plot the feature importance
It can be seen that bowler and batsman embeddings are the most significant for the prediction followed by runRate.
Chennai Super Kings-Lucknow Super Giants-2022-03-31
16a) The corresponding Worm-wicket graph for this match is as below
Chennai Super Kings-Lucknow Super Giants-2022-03-31
B) Win Probability using Random Forest with player embeddings
In the 2nd approach I use Random Forest with batsman and bowler embeddings. The performance of the model with embeddings is quantum jump from the earlier performance without embeddings. However, the random forest is also computationally intensive.
1) Read the data
a) Read the data from 9 T20 leagues (BBL, CPL, IPL, NTB, PSL, SSM, T20 Men, T20 Women, WBB) and create a single data frame of ball-by-ball data. Display the data frame
2) Create training.validation and test sets
b) Split to training, validation and test sets. The dataset is initially split into training and test in the ratio 80%:20%. The training data is again split into training and validation in the ratio 80:20
library(dplyr)
library(caret)
library(e1071)
library(ggplot2)
library(tidymodels)
library(tidymodels)
library(embed)
# Helper packages
library(readr) # for importing data
library(vip)
library(ranger)
# Read all the 9 T20 leagues
df1=read.csv("output3/matchesBBL3.csv")
df2=read.csv("output3/matchesCPL3.csv")
df3=read.csv("output3/matchesIPL3.csv")
df4=read.csv("output3/matchesNTB3.csv")
df5=read.csv("output3/matchesPSL3.csv")
df6=read.csv("output3/matchesSSM3.csv")
df7=read.csv("output3/matchesT20M3.csv")
df8=read.csv("output3/matchesT20W3.csv")
df9=read.csv("output3/matchesWBB3.csv")
# Bind into a single dataframe
df=rbind(df1,df2,df3,df4,df5,df6,df7,df8,df9)
set.seed(123)
df$isWinner = as.factor(df$isWinner)
#Split data into training, validation and test sets
splits <- initial_split(df,prop = 0.80)
df_other <- training(splits)
df_test <- testing(splits)
set.seed(234)
val_set <- validation_split(df_other, prop = 0.80)
val_set
2) Create a Random Forest model tuning for number of predictor nodes at each decision node (mtry) and minimum number of predictor nodes (min_n)
3) Use the ranger engine and set up for classification
4) Set up the recipe and include batsman and bowler embeddings
5) Create a workflow and add the recipe and the random forest model with the tuning parameters
# Use all 12 cores parallely
cores <- parallel::detectCores()
cores
[1] 12
# Create the random forest model with mtry and min as tuning parameters
rf_mod <-
rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>%
set_engine("ranger", num.threads = cores) %>%
set_mode("classification")
# Setup the recipe with batsman and bowler embeddings
rf_recipe <-
recipe(isWinner ~ ., data = df_other) %>%
step_embed(batsman,bowler, outcome = vars(isWinner))
# Create the random forest workflow
rf_workflow <-
workflow() %>%
add_model(rf_mod) %>%
add_recipe(rf_recipe)
rf_mod
# show what will be tuned
extract_parameter_set_dials(rf_mod)
set.seed(345)
# specify which values meant to tune
# Build the model
rf_res <-
rf_workflow %>%
tune_grid(val_set,
grid = 10,
control = control_grid(save_pred = TRUE),
metrics = metric_set(accuracy,roc_auc))
# Pick the best roc_auc and the associated tuning parameters
rf_res %>%
show_best(metric = "roc_auc")
# A tibble: 5 × 8
mtry min_n .metric .estimator mean n std_err .config
<int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
1 4 4 roc_auc binary 0.980 1 NA Preprocessor1_Model08
2 9 8 roc_auc binary 0.979 1 NA Preprocessor1_Model03
3 8 16 roc_auc binary 0.974 1 NA Preprocessor1_Model10
4 7 22 roc_auc binary 0.969 1 NA Preprocessor1_Model09
5 5 19 roc_auc binary 0.969 1 NA Preprocessor1_Model06
rf_res %>%
show_best(metric = "accuracy")
# A tibble: 5 × 8
mtry min_n .metric .estimator mean n std_err .config
<int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
1 4 4 accuracy binary 0.927 1 NA Preprocessor1_Model08
2 9 8 accuracy binary 0.926 1 NA Preprocessor1_Model03
3 8 16 accuracy binary 0.915 1 NA Preprocessor1_Model10
4 7 22 accuracy binary 0.906 1 NA Preprocessor1_Model09
5 5 19 accuracy binary 0.904 1 NA Preprocessor1_Model0
6) Select all models with the best roc_auc. It can be seen that the best roc_auc is 0.980 for mtry=4 and min_n=4
7) Get the model with the highest accuracy. The highest accuracy achieved is 0.927 or 92.7. This accuracy is also for mtry=4 and min_n=4
# Pick the best roc_auc and the associated tuning parameters
rf_res %>%
show_best(metric = "roc_auc")
# A tibble: 5 × 8
mtry min_n .metric .estimator mean n std_err .config
<int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
1 4 4 roc_auc binary 0.980 1 NA Preprocessor1_Model08
2 9 8 roc_auc binary 0.979 1 NA Preprocessor1_Model03
3 8 16 roc_auc binary 0.974 1 NA Preprocessor1_Model10
4 7 22 roc_auc binary 0.969 1 NA Preprocessor1_Model09
5 5 19 roc_auc binary 0.969 1 NA Preprocessor1_Model06
# Display the accuracy of the models in descending order and the parameters
rf_res %>%
show_best(metric = "accuracy")
# A tibble: 5 × 8
mtry min_n .metric .estimator mean n std_err .config
<int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
1 4 4 accuracy binary 0.927 1 NA Preprocessor1_Model08
2 9 8 accuracy binary 0.926 1 NA Preprocessor1_Model03
3 8 16 accuracy binary 0.915 1 NA Preprocessor1_Model10
4 7 22 accuracy binary 0.906 1 NA Preprocessor1_Model09
5 5 19 accuracy binary 0.904 1 NA Preprocessor1_Model0
8) Select the model with the best parameters for accuracy mtry=4 and min_n=4. For this the accuracy is 0.927. For this configuration the roc_auc is also the best at 0.980
9) Plot the Area Under the Curve (AUC). It can be seen that this model performs really well and it hugs the top left.
# Pick the best model
rf_best <-
rf_res %>%
select_best(metric = "accuracy")
# The best model has mtry=4 and min=4
rf_best
mtry min_n .config
<int> <int> <chr>
1 4 4 Preprocessor1_Model08
#Plot AUC
rf_auc <-
rf_res %>%
collect_predictions(parameters = rf_best) %>%
roc_curve(isWinner, .pred_0) %>%
mutate(model = "Random Forest")
autoplot(rf_auc)
10) Create the final model with the best parameters
11) Execute the final fit
12) Plot feature importance, The bowler and batsman embedding followed by perfIndex and runRate are features that contribute the most to the Win Probability
16) Computing Win Probability with Random Forest Model for match
Pakistan-India-2022-10-23
17) Worm -wicket graph of match
Pakistan-India-2022-10-23
C) Win Probability using MLP – Deep Neural Network (DNN) with player embeddings
In this approach the MLP package of Tidymodels was used. Multi-layer perceptron (MLP) with Deep Neural Network (DNN) was used to compute the Win Probability using player embeddings. An accuracy of 0.76 was obtained
1) Read the data
a) Read the data from 9 T20 leagues (BBL, CPL, IPL, NTB, PSL, SSM, T20 Men, T20 Women, WBB) and create a single data frame of ball-by-ball data. Display the data frame
2) Create training.validation and test sets
b) Split to training, validation and test sets. The dataset is initially split into training and test in the ratio 80%:20%. The training data is again split into training and validation in the ratio 80:20
Of the 3 ML models, glmnet, random forest and Multi-layer Perceptron DNN, random forest had the best performance
Random Forest ML model with batsman, bowler embeddings was able to achieve an accuracy of 92.4% and a ROC_AUC of 0.98 with very low false positives, negatives. This was a quantum jump from my earlier random forest model without embeddings which had an accuracy of 73.7% and an ROC_AUC of 0.834
The glmnet and NN models are fairly light weight. Random Forest is computationally very intensive.
I am late to the ‘Win probability’ computation for T20 matches, but managed to jump on to this bus with this post. Win Probability analysis and computation have been around for some time and are used in baseball, NFL, soccer hockey and others. On T20 cricket, the following posts from White Ball Analytics & Sports Data Science were good pointers to the general approach. The data for the Win Probability computation is taken from Cricsheet.
My initial Machine Learning models could not do better than 62% accuracy. I created a data set of ~830 IPL matches which roughly came to about 280,000 rows of ball-by-ball match data but I could not move beyond 62%. Addition of T20 men moved the needle to 64% accuracy. I spent time tuning Deep Learning networks using Tensorflow and Keras. Finally, I added T20 data from 9 T20 leagues – IPL, T20 men, T20 women, BBL, CPL, NTB, PSL, WBB, SSM. I had one large data set of 1.2 million rows of ball by ball data. The data frame looks like
I created a data frame for each match from ball Num 1 to ballNum ~240 for the 1st and 2nd innings of the match. My initial set of features were ballNum, runs, runRate, numWickets. The target variable isWinner= {0,1} depending on whether the team has won or lost the match.
The features
ballNum – ball number for 1 ~ 240+ in data frame. 1 – 120+ for 1st innings and 120+ – 240+ in 2nd innings including noballs, wides etc.
runs = cumulative runs scored at the ball count
runRate = cumulative runs scored/ ballNum (for 1st innings) and runs= required runs/ball Num for 2nd innings
numWickets = wickets lost
The target variable isWinner can take values {0,1} depending whether the team won or lost
With this initial dataframe, even though I had close to 1.2 million rows of ball by ball data of T20 matches my best performance with vanilla Logistic regression & SVM in Python was about 64% accuracy.
# Read all the data from 9 T20 leagues
# BBL,CPL, IPL, NTB, PSL, SSM, T20 Men, T20 Women, WBB
df1=pd.read_csv('matchesT20M.csv')
df2=pd.read_csv('matchesIPL.csv')
df3=pd.read_csv('matchesBBL.csv')
df4=pd.read_csv('matchesCPL.csv')
df5=pd.read_csv('matchesNTB.csv')
df6=pd.read_csv('matchesPSL.csv')
df7=pd.read_csv('matchesSSM.csv')
df8=pd.read_csv('matchesT20W.csv')
df9=pd.read_csv('matchesWBB.csv')
# Create one large dataframe
df10=pd.concat([df1,df2,df3,df4,df5,df6,df7,df8,df9])
print("Shape of dataframe=",df10.shape)
print("#####################################")
stats=check_values(df10)
print("#####################################")
model_fit(df10)
#norm_model_fit(df,stats)
svm_model_fit(df10)
Shape of dataframe= (1206901, 6)
#####################################
Null values: False
It contains 0 infinite values
Accuracy of Logistic regression classifier on training set: 0.63
Accuracy of Logistic regression classifier on test set: 0.64
Accuracy: 0.64
Precision: 0.62
Recall: 0.65
F1: 0.64
Accuracy of Linear SVC classifier on training set: 0.52
Accuracy of Linear SVC classifier on test set: 0.52
With Tensorflow/Keras the performance was about 67%. I tried several things
Normalisation
Tried different learning rates
Different optimisers – SGD, RMSProp, Adam
Changed depth and width of Neural Network
However I did not get much improvement. Finally I decided to do some Feature engineering. I added 2 new features
a) Runs Momentum : This feature is based on the fact that more the wickets in hand, the more freely the batsmen can make risky strokes, hence increasing the momentum of the runs, This is calculated as
runsMomentum = (11 – numWickets)/balls remaining
b) Performance Index: This feature is the product of the run rate x wickets in hand. In other words, if the strike rate is good and fewer wickets lost at the point in the match, then the performance index is higher at that point in the match will be higher
The final set of features chosen were as below
I had also included the balls Remaining in the innings. Now with this set of features I decided to execute Tensorflow/Keras and do a GridSearch with different learning rates, optimisers. After a couple of hours of computation I got an accuracy of 0.73. I needed to be able to read the ML model in R which required installation of Tensorflow, reticulate and Keras in RStudio and I had several issues. Since I hit a roadblock I moved to regular R models
I performed WIn Probability computation in the following ways
A) Win Probability with Vanilla Logistic Regression (R)
With vanilla Logistic Regression in R using the ‘glm’ package I got an accuracy of 0.67, sensitivity of 0.68 and specificity of 0.65 as shown below
library(dplyr)
library(caret)
library(e1071)
library(ggplot2)
# Read all the data from 9 T20 leagues
# BBL,CPL, IPL, NTB, PSL, SSM, T20 Men, T20 Women, WBB
df1=read.csv("output2/matchesBBL2.csv")
df2=read.csv("output2/matchesCPL2.csv")
df3=read.csv("output2/matchesIPL2.csv")
df4=read.csv("output2/matchesNTB2.csv")
df5=read.csv("output2/matchesPSL2.csv")
df6=read.csv("output2/matchesSSM2.csv")
df7=read.csv("output2/matchesT20M2.csv")
df8=read.csv("output2/matchesT20W2.csv")
df9=read.csv("output2/matchesWBB2.csv")
# Create one large dataframe
df=rbind(df1,df2,df3,df4,df5,df6,df7,df8,df9)
# Helper function to split into training/test
trainTestSplit <- function(df,trainPercent,seed1){
## Sample size percent
samp_size <- floor(trainPercent/100 * nrow(df))
## set the seed
set.seed(seed1)
idx <- sample(seq_len(nrow(df)), size = samp_size)
idx
}
train_idx <- trainTestSplit(df,trainPercent=80,seed=5)
train <- df[train_idx, ]
test <- df[-train_idx, ]
# Fit a generalized linear logistic model,
fit=glm(isWinner~.,family=binomial,data=train,control = list(maxit = 50))
a=predict(fit,newdata=train,type="response")
# Set response >0.5 as 1 and <=0.5 as 0
b=as.factor(ifelse(a>0.5,1,0))
# Compute the confusion matrix for training data
confusionMatrix(
factor(b, levels = 0:1),
factor(train$isWinner, levels = 0:1)
)
Confusion Matrix and Statistics
Reference
Prediction
0 1
0 339938 160336
1 154236 310217
Accuracy : 0.6739
95% CI : (0.673, 0.6749)
No Information Rate : 0.5122
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.3473
Mcnemar's Test P-Value : < 2.2e-16
Sensitivity : 0.6879
Specificity : 0.6593
Pos Pred Value : 0.6795
Neg Pred Value : 0.6679
Prevalence : 0.5122
Detection Rate : 0.3524
Detection Prevalence : 0.5186
Balanced Accuracy : 0.6736
'Positive' Class : 0
# This can be saved and loaded as
saveRDS(fit, "glm.rds")
ml_model <- readRDS("glm.rds")
Using the above ML model on Deccan Chargers vs Chennai Super on 27-04-2009 the Win Probability as the match progresses is as below
The Worm wicket graph of this match shows it was a closely fought match
B) Win Probability using Random Forests with Tidy Models – R
Initially I tried Tidy models with tuning for glmnet. The best I got was 0.67. However, I got an excellent performance using TidyModels with Random Forests. I am using Tidy Models for the first time and I have been blown away with how logically it is constructed, much like dplyr & ggplot2.
library(dplyr)
library(caret)
library(e1071)
library(ggplot2)
library(tidymodels)
# Helper packages
library(readr) # for importing data
library(vip)
library(ranger)
# Read all the data from 9 T20 leagues
# BBL,CPL, IPL, NTB, PSL, SSM, T20 Men, T20 Women, WBB
df1=read.csv("output2/matchesBBL2.csv")
df2=read.csv("output2/matchesCPL2.csv")
df3=read.csv("output2/matchesIPL2.csv")
df4=read.csv("output2/matchesNTB2.csv")
df5=read.csv("output2/matchesPSL2.csv")
df6=read.csv("output2/matchesSSM2.csv")
df7=read.csv("output2/matchesT20M2.csv")
df8=read.csv("output2/matchesT20W2.csv")
df9=read.csv("output2/matchesWBB2.csv")
# Create one large dataframe
df=rbind(df1,df2,df3,df4,df5,df6,df7,df8,df9)
dim(df)
[1]
1205909 8
# Take a peek at the dataset
glimpse(df)
$ ballNum <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28…
$ ballsRemaining <int> 125, 124, 123, 122, 121, 120, 119, 118, 117, 116, 115, 114, 113, 112, 111, 110, 109, 108, 107, 106, 1…
$ runs <int> 1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 7, 13, 14, 16, 18, 18, 18, 24, 24, 24, 26, 26, 32, 32, 33, 34, 34, 3…
$ runRate <dbl> 1.0000000, 0.5000000, 0.6666667, 0.7500000, 0.6000000, 0.5000000, 0.5714286, 0.5000000, 0.5555556, 0.…
$ numWickets <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3,…
$ runsMomentum <dbl> 0.08800000, 0.08870968, 0.08943089, 0.09016393, 0.09090909, 0.09166667, 0.09243697, 0.09322034, 0.094…
$ perfIndex <dbl> 11.000000, 5.500000, 7.333333, 8.250000, 6.600000, 5.500000, 6.285714, 5.500000, 6.111111, 5.000000, …
$ isWinner <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
df %>%
count(isWinner) %>%
mutate(prop = n/sum(n))
set.seed(123)
df$isWinner = as.factor(df$isWinner)
# Split the data into training and test set in 80%:20%
splits <- initial_split(df,prop = 0.80)
df_other <- training(splits)
df_test <- testing(splits)
# Create a validation set from training set in 80%:20%
set.seed(234)
val_set <- validation_split(df_other,
prop = 0.80)
val_set
# Setup for Random forest using Ranger for classification
# Set up cores for parallel execution
cores <- parallel::detectCores()
cores
#Set up Random Forest engine
rf_mod <-
rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>%
set_engine("ranger", num.threads = cores) %>%
set_mode("classification")
rf_mod
# The Random Forest engine includes mtry which is number of predictor
# variables required at each decision tree with min_n the minimum number # of
Random Forest Model Specification (classification)
Main Arguments:
mtry = tune()
trees = 1000
min_n = tune()
Engine-Specific Arguments:
num.threads = cores
Computational engine: ranger
# Setup the predictors and target variable
# Normalise all predictors. Random Forest don't need normalization but
# I have done it anyway
rf_recipe <-
recipe(isWinner ~ ., data = df_other) %>%
step_normalize(all_predictors())
# Create workflow adding the ML model and recipe
rf_workflow <-
workflow() %>%
add_model(rf_mod) %>%
add_recipe(rf_recipe)
# The tune is done for 5 different values of the tuning parameters.
# Metrics include accuracy and roc_auc
rf_res <-
rf_workflow %>%
tune_grid(val_set,
grid = 5,
control = control_grid(save_pred = TRUE),
metrics = metric_set(accuracy,roc_auc))
$ Pick the best of ROC/AUC
rf_res %>%
show_best(metric = "roc_auc")
We can see that when mtry (number of predictors) is 5 or 7 the ROC_AUC is 0.834 which is quite good
# A tibble: 5 × 8
mtry min_n .metric .estimator mean n std_err .config
<int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
1 5 26 roc_auc binary 0.834 1 NA Preprocessor1_Model5
2 7 36 roc_auc binary 0.834 1 NA Preprocessor1_Model3
3 2 17 roc_auc binary 0.833 1 NA Preprocessor1_Model4
4 1 20 roc_auc binary 0.832 1 NA Preprocessor1_Model2
5 5 6 roc_auc binary 0.825 1 NA Preprocessor1_Model1
# Select the model with highest accuracy
rf_res %>%
show_best(metric = "accuracy")
mtry min_n .metric .estimator mean n std_err .config
<int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
1 7 36 accuracy binary 0.737 1 NA Preprocessor1_Model3
2 5 26 accuracy binary 0.736 1 NA Preprocessor1_Model5
3 1 20 accuracy binary 0.736 1 NA Preprocessor1_Model2
4 2 17 accuracy binary 0.735 1 NA Preprocessor1_Model4
5 5 6 accuracy binary 0.731 1 NA Preprocessor1_Model1
# The model with mtry (number of predictors) is 7 has the best accuracy.
# Hence the best model has mtry=7 and min_n=36
rf_best <-
rf_res %>%
select_best(metric = "accuracy")
# Display the best model
rf_best
# A tibble: 1 × 3
mtry min_n .config
<int> <int> <chr>
1 7 36 Preprocessor1_Model3
rf_res %>%
collect_predictions()
id .pred_class .row mtry min_n .pred_0 .pred_1 isWinner .config
<chr> <fct> <int> <int> <int> <dbl> <dbl> <fct> <chr>
1 validation 1 1 5 6 0.497 0.503 0 Preprocessor1_Model1
2 validation 1 9 5 6 0.00753 0.992 1 Preprocessor1_Model1
3 validation 0 10 5 6 0.627 0.373 0 Preprocessor1_Model1
4 validation 0 16 5 6 0.998 0.002 0 Preprocessor1_Model1
5 validation 1 18 5 6 0.270 0.730 1 Preprocessor1_Model1
6 validation 0 23 5 6 0.899 0.101 0 Preprocessor1_Model1
7 validation 1 26 5 6 0.452 0.548 1 Preprocessor1_Model1
8 validation 0 30 5 6 0.657 0.343 1 Preprocessor1_Model1
9 validation 0 34 5 6 0.576 0.424 0 Preprocessor1_Model1
10 validation 0 35 5 6 1.00 0.000167 0 Preprocessor1_Model1
rf_auc <-
rf_res %>%
collect_predictions(parameters = rf_best) %>%
roc_curve(isWinner, .pred_0) %>%
mutate(model = "Random Forest")
autoplot(rf_auc)
I
The Final Model
# Create the final Random Forest model with mtry=7 and min_n=36
# engine as "ranger" for classification
last_rf_mod <-
rand_forest(mtry = 7, min_n = 36, trees = 1000) %>%
set_engine("ranger", num.threads = cores, importance = "impurity") %>%
set_mode("classification")
# the last workflow is updated with the final model
last_rf_workflow <-
rf_workflow %>%
update_model(last_rf_mod)
set.seed(345)
last_rf_fit <-
last_rf_workflow %>%
last_fit(splits)
# Collect metrics
last_rf_fit %>%
collect_metrics()
.metric .estimator .estimate .config
<chr> <chr> <dbl> <chr>
1 accuracy binary 0.739 Preprocessor1_Model1
2 roc_auc binary 0.837 Preprocessor1_Model1
The Random Forest model gives an accuracy of 0.739 and ROC_AUC of .837 which I think is quite good. This is roughly what I got with Tensorflow/Keras
# Get the feature importance
last_rf_fit %>%
extract_fit_parsnip() %>%
vip(num_features = 7)
Interestingly the feature that I engineered seems to have the maximum importancce namely Performance Index which is a product of Run rate x Wicket in Hand. I would have thought numWickets would be important but in T20 match probably is is not.
generate predictions from the test set
test_predictions <- last_rf_fit %>% collect_predictions()
> test_predictions
# A tibble: 241,182 × 7
id .pred_0 .pred_1 .row .pred_class isWinner .config
<chr> <dbl> <dbl> <int> <fct> <fct> <chr>
1 train/test split 0.496 0.504 1 1 0 Preprocessor1_Model1
2 train/test split 0.640 0.360 11 0 0 Preprocessor1_Model1
3 train/test split 0.596 0.404 14 0 0 Preprocessor1_Model1
4 train/test split 0.287 0.713 22 1 0 Preprocessor1_Model1
5 train/test split 0.616 0.384 28 0 0 Preprocessor1_Model1
6 train/test split 0.516 0.484 36 0 0 Preprocessor1_Model1
7 train/test split 0.754 0.246 37 0 0 Preprocessor1_Model1
8 train/test split 0.641 0.359 39 0 0 Preprocessor1_Model1
9 train/test split 0.811 0.189 40 0 0 Preprocessor1_Model1
10 train/test split 0.618 0.382 42 0 0 Preprocessor1_Model1
# generate a confusion matrix
test_predictions %>%
conf_mat(truth = isWinner, estimate = .pred_class)
Truth
Prediction 0 1
0 92173 31623
1 31320 86066
# Create the final model on the train/test data
final_model <- fit(last_rf_workflow, df_other)
# Final model
final_model
══ Workflow [trained] ════════════════════════════════════════════════════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: rand_forest()
── Preprocessor ──────────────────────────────────────────────────────────────────────────────────────────────────────────────
1 Recipe Step
• step_normalize()
── Model ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Ranger result
Call:
ranger::ranger(x = maybe_data_frame(x), y = y, mtry = min_cols(~7, x), num.trees = ~1000, min.node.size = min_rows(~36, x), num.threads = ~cores, importance = ~"impurity", verbose = FALSE, seed = sample.int(10^5, 1), probability = TRUE)
Type: Probability estimation
Number of trees: 1000
Sample size: 964727
Number of independent variables: 7
Mtry: 7
Target node size: 36
Variable importance mode: impurity
Splitrule: gini
OOB prediction error (Brier s.): 0.1631303
The Random Forest Model’s performance has been quite impressive and probably requires further exploration.
# Saving and loading the model
save(final_model, file = "fit.rda")
load("fit.rda")
#Predicting the Win Probability of CSK vs DD match on 12 May 2012
Comparing this with the Worm wicket graph of this match we see that DD had no chance at all
C) Win Probability with Tensorflow/Keras with Grid Search – Python
I spent a fair amount of time tuning the hyper parameters of the Keras Deep Learning Network. Finally did go for the Grid Search. Incidentally I did ask ChatGPT to suggest code snippets for GridSearch which it promptly did!!!
import pandas as pd
import numpy as np
from zipfile import ZipFile
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import regularizers
from sklearn.model_selection import GridSearchCV
# Define the model
def create_model(optimizer='adam'):
tf.random.set_seed(4)
model = tf.keras.Sequential([
keras.layers.Dense(32, activation=tf.nn.relu, input_shape=[len(train_dataset1.keys())]),
keras.layers.Dense(16, activation=tf.nn.relu),
keras.layers.Dense(8, activation=tf.nn.relu),
keras.layers.Dense(1,activation=tf.nn.sigmoid)
])
# Since this is binary classification use binary_crossentropy
model.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics='accuracy')
return(model)
# Create a KerasClassifier object
model = keras.wrappers.scikit_learn.KerasClassifier(build_fn=create_model)
# Define the grid of hyperparameters to search over
batch_size = [1024]
epochs = [40]
learning_rate = [0.01, 0.001, 0.0001]
optimizer = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']
param_grid = dict(dict(optimizer=optimizer,batch_size=batch_size, epochs=epochs) )
# Create the grid search object
grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=3)
# Fit the grid search object to the training data
grid_search.fit(normalized_train_data, train_labels)
# Print the best hyperparameters
print('Best hyperparameters:', grid_search.best_params_)
# summarize results
print("Best: %f using %s" % (grid_search.best_score_, grid_search.best_params_))
means = grid_search.cv_results_['mean_test_score']
stds = grid_search.cv_results_['std_test_score']
params = grid_search.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print("%f (%f) with: %r" % (mean, stdev, param))
The best worked out to be the optimiser ‘Nadam’ with a learning rate of 0.001
So, the Keras Deep Learning Network gives about the same performance of Random Forest in Tidy Models. But I went with R Random Forest as it was easier to save and load the model for use with my data. Also, I am not sure whether the performance of the ML model can be improved beyond a point. However, I will continue to explore.
There is a school of thought which considers that total runs scored and strike rate for a batsman, or total wickets taken and economy rate for a bowler, do not tell the whole story. This is true to a fair extent. The runs scored or the wickets taken could have been against weaker teams and hence the runs, strike rate or the wickets and economy rate alone do not capture all the performance details of the batsman or bowler. A technique to determine the performance of batsmen against different bowlers and identify the batsman’s possible performance even against bowlers he/she has not yet faced could be done with collaborative filtering. Collaborative filtering, with embeddings can also be used to group players with similar characteristics. Similarly, we could also identify the performance of bowlers versus different batsmen. Hence we need to look at average runs, SR and total wickets, ER with the lens of batsmen, bowlers against similar opposition. This is where collaborative filtering is useful.
The table below shows the performance of all batsman against all bowlers in the table below. The row in the table below is the batsman and the column is the bowler, with the value in the cell is the total Runs scored by the batsman against the bowler in all matches. Note the values are 0 for batsmen who have not yet faced specific bowlers. The table is fairly sparse.
Table A
Similarly, we can compute the performance of all bowlers against all batsmen as in the table below. Here the row is the bowler, the column batsman and the value in the cell is the number of times the bowler got the batsman’s wicket. As before the data is sparsely populated
This problem of computing batsman’s performance against bowlers or vice versa, is identical to the user vs movie rating problem used in collaborative filtering. For e.g we could consider
This above problem depicted could be computed using collaborative filtering with embeddings. We could assign sequential numbers for the batsmen from 1 to M, and for the bowlers from 1 to N. The total runs scored could be represented only for the rows where there are values. One way to solve this problem in Machine Learning is to use One Hot Encoding (OHE), where we assign values for each row and each column and map the values of the table with values of the cell for each combination. But this would take a enormous computation time and memory. The solution to this is use vector embeddings. Here embeddings could be used for capturing the sparse tensors between the batsmen, bowlers, runs scored or vice versa between bowlers against batsmen and the wickets taken. We only need to consider the cells for which values exist. An embedding is a relatively low-dimensional space, into which you can translate high-dimensional vectors. An embedding captures some of the semantics of the input by placing semantically similar inputs close together in the embedding space.
a) To compute bowler performances and identify similarities between bowlers the following embedding in the Deep Learning Network was used
To compute batsmen similarities a similar Deep Learning network for bowler vs batsmen is used
I had earlier created another post Player Performance Estimation using AI Collaborative Filtering for batsman and bowler recommendation, using R package Recommender Lab. However, I was not too happy with the results I got with this R package. When I searched the net for material on using embeddings for collaborative filtering, most of material on the web on movie lens or word2vec are repetitive and have no new material. Finally, this short video lecture from Developer Google on Embeddings provided the most clarity.
I have created 4 Colab notebooks to identify player similarities (recommendations)
a) Batsman similarities IPL
b) Batsman similarities T20
c) Bowler similarities IPL
d) Bowler similarities T20
For creating the model I have used all the data for T20 and IPL from so that I get the best results. The data is from Cricsheet. I have also used Google’s Embeddings Projector to display batsman and bowler embedding to and to group similar players
All the Colab notebooks and the data associated with the code are available in Github. Feel free to download and execute them. See if you get better performance. I tried a wide variety of hyperparameters – learning rate, width and depth of nodes per layer, number of layers, gradient methods etc.
You can download all the code & data from Github at embeddings
b) Create integer dictionaries for batsmen & bowlers
bowlers = df3["bowler1"].unique().tolist()
bowlers
# Create dictionary of bowler to index
bowlers2index = {x: i for i, x in enumerate(bowlers)}
bowlers2index
#Create dictionary of index tp bowler
index2bowlers = {i: x for i, x in enumerate(bowlers)}
index2bowlers
batsmen = df3["batsman1"].unique().tolist()
batsmen
# Create dictionary of batsman to index
batsmen2index = {x: i for i, x in enumerate(batsmen)}
batsmen2index
# Create dictionary of index to batsman
index2batsmen = {i: x for i, x in enumerate(batsmen)}
index2batsmen
#Map bowler, batsman to respective indices
df3["bowler"] = df3["bowler1"].map(bowlers2index)
df3["batsman"] = df3["batsman1"].map(batsmen2index)
df3
num_bowlers =len(bowlers2index)
num_batsmen = len(batsmen2index)
df3["wicketTaken"] = df3["wicketTaken"].values.astype(np.float32)
df3
# min and max ratings will be used to normalize the ratings later
min_wicketTaken = min(df3["wicketTaken"])
max_wicketTaken = max(df3["wicketTaken"])
print(
"Number of bowlers: {}, Number of batsmen: {}, Min wicketsTaken: {}, Max wicketsTaken: {}".format(
num_bowlers, num_batsmen, min_wicketTaken, max_wicketTaken
)
)
c) Concatenate additional features
df3
df6
df31=pd.concat([df3,df6],axis=1)
df31
d) Create a Tensorflow/Keras deep learning mode. Minimise using Mean Squared Error using Stochastic Gradient Descent. I used ‘dropouts’ to regularise the model to keep validation loss within limits
tf.random.set_seed(4)
vector_size=len(batsmen2index)
df4=df31[['bowler','batsman','wicketTaken','balls','runsConceded','ER']]
df4
train_dataset = df4.sample(frac=0.9,random_state=0)
test_dataset = df4.drop(train_dataset.index)
train_dataset1 = train_dataset[['bowler','batsman','balls','runsConceded','ER']]
test_dataset1 = test_dataset[['bowler','batsman','balls','runsConceded','ER']]
train_stats = train_dataset1.describe()
train_stats = train_stats.transpose()
#print(train_stats)
train_labels = train_dataset.pop('wicketTaken')
test_labels = test_dataset.pop('wicketTaken')
# Create a Deep Learning model with keras
model = tf.keras.Sequential([
tf.keras.layers.Embedding(vector_size,16,input_length=5),
tf.keras.layers.Flatten(),
keras.layers.Dropout(.2),
keras.layers.Dense(16),
keras.layers.Dense(8,activation=tf.nn.relu),
keras.layers.Dense(4,activation=tf.nn.relu),
keras.layers.Dense(1)
])
# Print the model summary
#model.summary()
# Use the Adam optimizer with a learning rate of 0.01
#optimizer=keras.optimizers.Adam(learning_rate=.0009, beta_1=0.5, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=True)
#optimizer=keras.optimizers.RMSprop(learning_rate=0.01, rho=0.2, momentum=0.2, epsilon=1e-07)
#optimizer=keras.optimizers.SGD(learning_rate=.009,momentum=0.1) - Works without dropout
optimizer=keras.optimizers.SGD(learning_rate=.01,momentum=0.1)
model.compile(loss='mean_squared_error',
optimizer=optimizer,
)
# Setup the training parameters
#model.compile(loss='binary_crossentropy',optimizer='rmsprop',metrics=['accuracy'])
# Create a model
history=model.fit(
train_dataset1, train_labels,batch_size=32,
epochs=40, validation_data = (test_dataset1,test_labels), verbose=1)
e) Plot losses
f) Predict wickets that will be taken by bowlers against random batsmen
df5= df4[['bowler','batsman','balls','runsConceded','ER']]
test1 = df5.sample(n=10)
test1.shape
for i in range(test1.shape[0]):
print('Bowler :', index2bowlers.get(test1.iloc[i,0]), ", Batsman : ",index2batsmen.get(test1.iloc[i,1]), '- Times wicket Prediction:',model.predict(test1.iloc[[i]]))
1/1 [==============================] - 0s 90ms/step
Bowler : Harbhajan Singh , Batsman : AM Nayar - Times wicket Prediction: [[1.0114906]]
1/1 [==============================] - 0s 18ms/step
Bowler : T Natarajan , Batsman : Arshdeep Singh - Times wicket Prediction: [[0.98656166]]
1/1 [==============================] - 0s 19ms/step
Bowler : KK Ahmed , Batsman : A Mishra - Times wicket Prediction: [[1.0504484]]
1/1 [==============================] - 0s 24ms/step
Bowler : M Muralitharan , Batsman : F du Plessis - Times wicket Prediction: [[1.0941994]]
1/1 [==============================] - 0s 25ms/step
Bowler : SK Warne , Batsman : DR Smith - Times wicket Prediction: [[1.0679393]]
1/1 [==============================] - 0s 28ms/step
Bowler : Mohammad Nabi , Batsman : Ishan Kishan - Times wicket Prediction: [[1.403399]]
1/1 [==============================] - 0s 32ms/step
Bowler : R Bhatia , Batsman : DJ Thornely - Times wicket Prediction: [[0.89399755]]
1/1 [==============================] - 0s 26ms/step
Bowler : SP Narine , Batsman : MC Henriques - Times wicket Prediction: [[1.1997008]]
1/1 [==============================] - 0s 19ms/step
Bowler : AS Rajpoot , Batsman : K Gowtham - Times wicket Prediction: [[0.9911405]]
1/1 [==============================] - 0s 21ms/step
Bowler : K Rabada , Batsman : P Simran Singh - Times wicket Prediction: [[1.0064855]]
g) The embedding can be visualised using Google’s Embedding Projector, which identifies other batsmen who have similar characteristics. Here Cosine Similarity is used for grouping similar batsmen of IPL
The closest neighbor for AB De Villiers in IPL is SK Raina, then Rohit Sharma as seen in the visualisation below
The Tensorboard Pmbeddings projector is also interesting. There are multiple ways the data can be visualised namely UMAP, T-SNE, PCA(included). You could play with it.
As mentioned above the Colab notebooks and data are available at Github embeddings
The ability to identify batsmen & bowlers who would perform similarly against specific bowling attacks coupled with the average runs & strike rate should give a good measure of a player’s performance.
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.
This post, focuses on generating near-real time analytics for ICC Men’s T20 World Cup using GooglyPlusPlus. Included below, is a sampling of the analytics that you can perform for analysing the matches. In addition you can do all the analysis included in my post GooglyPlusPlus gets ready for ICC Men’s T20 World Cup
Namibia-Sri Lanka-16 Oct 2022 : Match Worm graph
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
T20Batsman 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 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.
Giga thoughts ... 