# Boosting Win Probability accuracy with player embeddings

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

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

library(dplyr)
library(caret)
library(e1071)
library(ggplot2)
library(tidymodels)
library(embed)

# Helper packages
library(vip)

#Bind all dataframes together
df=rbind(df1,df2,df3,df4,df5,df6,df7,df8,df9)
glimpse(df)
Rows: 1,199,115
Columns: 10
$batsman <chr> "JD Smith", "M Klinger", "M Klinger", "M Klinger", "JD …$ bowler         <chr> "NM Hauritz", "NM Hauritz", "NM Hauritz", "NM Hauritz",…

$ballNum <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, …$ ballsRemaining <int> 125, 124, 123, 122, 121, 120, 119, 118, 117, 116, 115, …
$runs <int> 1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 7, 13, 14, 16, 18, 18,…$ runRate        <dbl> 1.0000000, 0.5000000, 0.6666667, 0.7500000, 0.6000000, …
$numWickets <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…$ runsMomentum   <dbl> 0.08800000, 0.08870968, 0.08943089, 0.09016393, 0.09090…
$perfIndex <dbl> 11.000000, 5.500000, 7.333333, 8.250000, 6.600000, 5.50…$ isWinner       <int> 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))
isWinner      n      prop
1
0 614237 0.5122419
2
1 584878 0.4877581


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

set.seed(123)
splits      <- initial_split(df,prop = 0.80)
splits
<Training/Testing/Total>
<959292/239823/1199115>
df_other <- training(splits)
df_test  <- testing(splits)

set.seed(234)
val_set <- validation_split(df_other,prop = 0.80)
val_set
# A tibble: 1 × 2
splits
id
<list>                  <chr>
1 <split [767433/191859]> validation



3) Create pre-processing recipe

a) Normalise the following predictors

• ballNum
• ballsRemaining
• runs
• runRate
• numWickets
• runsMomentum
• perfIndex

b) Create floating point embeddings for

• batsman
• bowler

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) %>%

# 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() %>%

# 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) %>%

#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.

runRate –

• runRate in 1st innings
• requiredRunRate in 2nd innings

12) Plot the ROC characteristics

last_lr_fit %>%
collect_predictions() %>%
roc_curve(isWinner, .pred_0) %>%
autoplot()

13) Generate a confusion matrix

14) Create a final Generalised Linear Model for Logistic Regression with the penalty of 0.000530

15) Save the model

# generate predictions from the test set
test_predictions <- last_lr_fit %>% collect_predictions()
test_predictions

# generate a confusion matrix
test_predictions %>%
conf_mat(truth = isWinner, estimate = .pred_class)

Truth
Prediction     0     1

0                  90105 32658

1                  32572 84488

final_lr_model <- fit(last_lr_workflow, df_other)

final_lr_model

obj_size(final_lr_model)
146.51 MB

butcher::weigh(final_lr_model)
A tibble: 305 × 2
object                                  size
<chr>                                  <dbl>
1 pre.actions.recipe.recipe.steps.terms1  57.9
2 pre.actions.recipe.recipe.steps.terms2  57.9
3 pre.actions.recipe.recipe.steps.terms3  57.9

cleaned_lm <- butcher::axe_env(final_lr_model, verbose = TRUE)
#✔ Memory released: "1.04 kB"
#✔ Memory released: "1.62 kB"

saveRDS(cleaned_lm, "cleanedLR.rds")


16) Compute Ball-by-ball Win Probability

• 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.

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(vip)
library(ranger)

# Read all the 9 T20 leagues

# 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_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() %>%

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

last_rf_mod <-
rand_forest(mtry = 4, min_n = 4, trees = 1000) %>%
set_engine("ranger", num.threads = cores, importance = "impurity") %>%
set_mode("classification")

# the last workflow
last_rf_workflow <-
rf_workflow %>%
update_model(last_rf_mod)

set.seed(345)
last_rf_fit <-
last_rf_workflow %>%
last_fit(splits)

last_rf_fit %>%
collect_metrics()

.metric  .estimator .estimate .config
<chr>    <chr>          <dbl> <chr>

1 accuracy binary         0.944 Preprocessor1_Model1
2 roc_auc  binary         0.988 Preprocessor1_Model1

last_rf_fit %>%
extract_fit_parsnip() %>%
vip(num_features = 9)

13) Plot the ROC curve for the best fit

# Plot the ROC for the final model
last_rf_fit %>%
collect_predictions() %>%
roc_curve(isWinner, .pred_0) %>%
autoplot()


14) Create a confusion matrix

We can see that the number of false positives and false negatives is very low

15) Create the final fit with the Random Forest Model

# generate predictions from the test set
test_predictions <- last_rf_fit %>% collect_predictions()
test_predictions

id               .pred_0 .pred_1  .row .pred_class isWinner .config
<chr>              <dbl>   <dbl> <int> <fct>       <fct>    <chr>
1 train/test split   0.838  0.162      1 0           0       Preprocessor1_Mo…
2
train/test split   0.463  0.537     11 1           0        Preprocessor1_Mo…
3
train/test split   0.846  0.154     14 0           0        Preprocessor1_Mo…
4
train/test split   0.839  0.161     22 0           0        Preprocessor1_Mo…
5
train/test split   0.846  0.154     36 0           0        Preprocessor1_Mo…
6
train/test split   0.848  0.152     37 0           0        Preprocessor1_Mo…
7
train/test split   0.731  0.269     39 0           0        Preprocessor1_Mo…
8
train/test split   0.972  0.0281    40 0           0        Preprocessor1_Mo…
9
train/test split   0.655  0.345     42 0           0        Preprocessor1_Mo…
10
train/test split   0.662  0.338     43 0           0        Preprocessor1_Mo…

# generate a confusion matrix
test_predictions %>%
conf_mat(truth = isWinner, estimate = .pred_class)

Truth
Prediction      0      1

0 116576   7096

1   6391 109760

# Create the final model
final_model <- fit(last_rf_workflow, df_other)



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

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(embed)

# Helper packages
library(vip)
library(ranger)

df=rbind(df1,df2,df3,df4,df5,df6,df7,df8,df9)

set.seed(123)
df$isWinner = as.factor(df$isWinner)
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



3) Create a Deep Neural Network recipe

• Normalize parameters
• Add embeddings for batsman, bowler

4) Set the MLP-DNN hyperparameters

• epochs=100
• hidden units =5
• dropout regularization =0.1

5) Fit on Training data

cores <- parallel::detectCores()
cores

nn_recipe <-
recipe(isWinner ~ ., data = df_other) %>%
step_normalize(ballNum,ballsRemaining,runs,runRate,numWickets,runsMomentum,perfIndex) %>%
step_embed(batsman,bowler, outcome = vars(isWinner)) %>%
prep(training = df_other, retain = TRUE)

# For validation:
test_normalized <- bake(nn_recipe, new_data = df_test)

set.seed(57974)
# Set the hyper parameters for DNN
# Use Keras
# Fit on training data
nnet_fit <-
mlp(epochs = 100, hidden_units = 5, dropout = 0.1) %>%
set_mode("classification") %>%
# Also set engine-specific verbose argument to prevent logging the results:
set_engine("keras", verbose = 0) %>%
fit(isWinner ~ ., data = bake(nn_recipe, new_data = df_other))

nnet_fit
parsnip model object
Model:"sequential"

____________________________________________________________________________

Layer (type)                                           Output Shape                                    Param #
============================================================================
dense (Dense)                                           (None, 5)                                          60
____________________________________________________________________________

dense_1 (Dense)                                         (None, 5)                                          30
____________________________________________________________________________
dropout (Dropout)                                       (None, 5)                                          0
____________________________________________________________________________
dense_2 (Dense)                                         (None, 2)                                          12
============================================================================
Total params: 102
Trainable params: 102
Non-trainable params: 0


6) Test on Test data

• Check ROC_AUC. It is 0.854
• Check accuracy. The MLP-DNN gives a decent performance with an acuracy of 0.76
• Compute the Confusion Matrix
# Validate on test data
val_results <-
df_test %>%
bind_cols(
predict(nnet_fit, new_data = test_normalized),
predict(nnet_fit, new_data = test_normalized, type = "prob")
)
val_results

# Check roc_auc
val_results %>% roc_auc(truth = isWinner, .pred_0)
.metric .estimator .estimate

<chr>   <chr>          <dbl>
1 roc_auc binary         0.854

# Check accuracy
val_results %>% accuracy(truth = isWinner, .pred_class)
.metric  .estimator .estimate
<chr>    <chr>          <dbl>
1 accuracy binary         0.762

# Display confusion matrix
val_results %>% conf_mat(truth = isWinner, .pred_class)
Truth
Prediction
0     1
0 97419 31564
1 25548 85292

Conclusion

1. Of the 3 ML models, glmnet, random forest and Multi-layer Perceptron DNN, random forest had the best performance
2. 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
3. The glmnet and NN models are fairly light weight. Random Forest is computationally very intensive.

Check out my other posts

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# GooglyPlusPlus gets ready for ICC Men’s T20 World Cup

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.

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

1. 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

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)

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.

Go ahead give GooglyPlusPlus a spin !!!

Also see

To see all posts click Index of posts

# Then, Now(IPL 2022), Beyond : Insights from GooglyPlusPlus

IPL 2022 has just concluded and yet again, it is has thrown a lot of promising and potential youngsters in its wake, while established players have fallen! With IPL 2022, we realise that “Sceptre and Crown must tumble down” and that ‘the glories‘ of form and class like everything else are “shadows not substantial things” (Death the Leveller by James Shirley).

So King Kohli had to kneel, and hitman’ himself got hit. Rishabh Pant, Jadeja also had a poor season. On the contrary there were several youngsters who shone like Abhishek Sharma, Tilak Verma, Umran Malik or a Mohsin Khan

This post is about my potential T20 Indian players for the World Cup 2022 and beyond.

The post below includes my own analysis and thoughts. Feel free to try out my Shiny app GooglyPlusPlus and draw your own conclusions.

You can also view the analyais as a youtube video at Insights from GooglyPlusPlus

How often we hear that data by itself is useless, unless we can draw insights from it? This is a prevailing theme in the corporate world and everybody uses all sorts of tools to analyse and subsequently draw insights. Data analysis can be done in many ways as data can be sliced, diced, chopped in a zillion ways. There are many facets and perspectives to analysing data. Creating insights is easy, but arriving at actionable insights is anything but. So, the problem of selecting the best 11 is difficult as there are so many ways to look at the analysis. My Shiny app GooglyPlusPlus based on my R package yorkr can analyse data in several ways namely

1. Batsman analysis
2. Bowler analysis
3. Match analysis
4. Team vs team analysis
5. Team vs all teams analysis
6. Batsman vs bowler and vice versa
7. Analysis of in 3,4,5 in power play, middle and death overs

GooglyPlusPlus uses my R package yorkr which has ~ 160 functions some which have several options. So, we can say roughly there are ~500 different ways that analysis can be done or in other words we can gather almost roughly 500+ different insights, not to mention that there are so many combinations of head-on matches and one-vs-all matches.

So generating insights or different ways of analysis data alone is not enough. The question is whether we can get a consolidated view from the different insights. In this post, I try to identify the best contenders for the Indian T20 team. This is far more difficult than it looks. Do you select players on past historical performance or do you choose from the newer crop of players, who have excelled in the recent IPL season. I think this boils down the typical situation in any domain. In engineering, we have tradeoffs – processing power vs memory tradeoff, throughput vs latency tradeoff or in the financial domain it is cost vs benefit or risk vs reward tradeoff. For team selection, the quandary is, whether to choose seasoned players with good historical performance but a poor performances in recent times or go with youngsters who have played with great courage and flair in this latest episode of IPL 2022. Hence there is a tradeoff between reliable but below average performance or risky but superlative performances of new players.

For this I base my potential list from

• Then (past history of batsmen & bowlers) – I have chosen the performance of batsmen and bowlers in the last 3 years. With we can arrive at those who have had reasonably reliable performance for the last 3 years
• Now (IPL 2022) – Performance in the current season IPL 2022

A. Then (Jan 2020 – May 2022) – Batsmen analysis

In this section I analyse the performances of batsmen and bowlers from Jan 2022 – May 2022. This is done based on ranking, and plots of Runs vs Strike Rate in Power Play, Middle and Death overs

Also I analyse bowlers based on the overall rank from Jan 2022- May 2022. Further more analysis is done on Wickets vs Economy Rate overall and in Power Play, Middle and Death overs

a. Ranks of batsmen (Runs over Strike Rate) : Jan 2020 – May 2022

The top batsmen consistency wise

[KL Rahul, Shikhar Dhawan, Ruturaj Gaikwad, Ishan Kishan, Shubman Gill, Suryakumar Yadav, Sanju Samson, Mayank Agarwal, Prithvi Shaw, Devdutt Padikkal, Nitish Rana, Virat Kohli, Shreyas Iyer, Ambati Rayadu, Rahul Tripathi, Rishabh Pant, Rohit Sharma, Hardik Pandya]

b. Ranks of batsmen (Strike Rate over Runs) : Jan 2020 – May 2022

The most consistent players from the Strike Rate perspective are

The batsmen with best Strike Rate in the last 3 years are

[Dinesh Karthik, Prithvi Shaw, Hardik Pandya, Rishabh Pant, Sanju Samson, Rahul Tripathi, Suryakumar Yadav, Nitish Rana, Mayank Agarwal, Krunal Pandya, MS Dhoni, Shikhar Dhawan, Ishan Kishan, KL Rahul]

c.Best Batsmen Runs vs SR : Jan 2020 – May 2022

The best batsmen should have a reasonable combination of Runs and SR. The best batsmen are

[KL Rahul, Shikhar Dhawan, Ruturaj Gaikwad, Ishan Kishan, Shubman Gill , Sanju Samson, Suryakumar Yadav, Shubman Gill, Mayank Agarwal, Prithvi Shaw, Nitish Rana, Hardik Pandya, Rishabh Pant, Rahul Tripathi,

d. Best batsmen Runs vs SR in Powerplay: Jan 2020 – May 2022

The best players in Power play

The best players in Power play in the last 3 years are

[KL Rahul, Prithvi Shaw, Rohit Sharma, Devdutt Padikkal, Mayank Agarwal, Virat Kohli, Ishan Kishan, Yashashvi Jaiswal, Wriddhiman Saha, Rahul Tripathi, Sanju Samson, Robin Uthappa, Venkatesh Iyer, Nitish Rana,Suryakumar Yadav, Abhishek Sharma Shreyas Iyer ]

e. Best batsmen Runs vs SR in Middleovers: Jan 2020 – May 2022

The most consistent players in the last 3 years in the middle overs are

[KL Rahul, Sanju Samson, Shikhar Dhawan, Rishabh Pant, Nitish Rana, Shreyas Iyer, Shubman Gill, Ishan Kishan, Devdutt Padikkal, Rahul Tripathi, Ruturaj Gaikwad, Shivam Dube, Hardik Pandya]

f. Best batsmen Runs vs SR in Death overs: Jan 2020 – May 2022

The best batsmen in death overs are

[Dinesh Karthik, Ravindra Jadeja, Hardik Pandya, Rahul Tewatia, MS Dhoni, KL Rahul, Rishabh Pant, Suryakumar Yadav, Ambati Rayadu, Virat Kohli, Nitish Rana, Shikhar Dhawan, Ruturaj Gaikwad, Ishan Kishan]

B) Now (IPL 2022) – Batsmen analysis

IPL 2022 just finished and clearly brings out the batsmen who are in great nick. It is always going to be a judgment call of whether to go for ‘old reliable’ or ‘new and awesome’.

a. Ranks of batsmen (Runs over Strike Rate) : IPL 2022

The best batsmen this season in Runs over Strike rate are

The best batsmen are

[KL Rahul, Shikhar Dhawan, Hardik Pandya, Deepak Hooda, Shubman Gill, Rahul Tripathi, Abhishek Sharma, Ishan Kishan, Wriddhiman Saha, Shreyas Iyer, Tilak Verma, Ruturaj Gaikwad, Sanju Samson, Shivam Dube]

b. Ranks of batsmen (Strike Rate over Runs) : IPL 2022

The batsmen with the best strike rate are

[Dinesh Karthik, Rishabh Pant, Rahul Tewathia, Rahul Tripathi, Sanju Samson, R Ashwin, Deepak Hooda, MS Dhoni, Nitish Rana, Riyan Parag, Shreya Iyer]

c.Best Batsmen Runs vs SR :IPL 2022

From an overall performance the following batsmen shone this season

[KL Rahul, Shikhar Dhawan, Shubman Gill, Hardik Pandya, Abhishel Sharma, Deepak Hooda, Rahul Tripathi, Tilak Verma, Shreya Iyer, Nitish Rana, Sanju Samson, Rishabh Pant]

d. Best batsmen Runs vs SR in Powerplay: IPL 2022

Top batsmen in Power play in IPL 2022

[Abhishek Sharma, Shikhar Dhawan, Rohit Sharma, Ishan Kishan, Shubman Gill, Prithvi Shaw, Wriddhiman Saha, Ishan Kishan, KL Rahul, Ruturaj Gaikwad, Virat Kohli, Yashasvi Jaiswal, Mayank Agarwal, Robin Uthappa, Sanju Samson, Nitish Rana]

e. Best batsmen Runs vs SR in Middleovers: IPL 2022

Best batsmen in middle overs in IPL 2022

[Deepak Hooda, Hardik Pandya, Tilak Verma, KL Rahul, Sanju Samson, Rishabh Pant, Shubman Gill, Ambati Rayudu, Suryaprakash Yadav, Shikhar Dhawan, Ruturaj Gaikwad]

f. Best batsmen Runs vs SR in Death overs: IPL 2022

Top batsmen in death overs in IPL 2022

[Dinesh Karthik, Rahul Tewatia, MS Dhoni, KL Rahul, Azar Patel, Washington Sundar, R Ashwin, Hardik Pandya, Ayush Badoni, Shivam Dube, Suryakumar Yadav, Ravindra Jadeja, Sanju Samson]

Overall Batting Performance in season

Kohli peaked in 2016 and from then on it has been a downward slide (see below)

Taking a look at Kohli’s moving average it is clear that he is past his prime and it will take a herculean effort to regain his lost glory

Similarly, Rohit Sharma’s moving average is constantly around ~30 as seen below

The cumulative average of Rohit Sharma is shown below

Comparing KL Rahul, Shikhar Dhawan, Rohit Sharma and V Kohli we see that KL Rahul and Shikhar Dhawan have had a much superior performance in the last 2-3 years. Rohit has averaged about ~25 runs every season.

Comparing the 4 wicket-keeper batsmen Sanju Samson, Rishabh Pant, Ishan Kishan and Dinesh Karthik from 2016

i) Runs over Strike Rate

We see that Pant peaked in 2018 but has not performed as well since. In the last 2 years Sanju Samson and Ishan Kishan have done well

ii) Strike Rate over Runs

For the last couple of seasons Rishabh Pant and Dinesh Kartik top the strike rate over the other 2

Similar analysis can be done other combinations of batsmen

Choosing the best batsmen from the above, my top 5 batsmen would be

1. KL Rahul
2. Shikhar Dhawan
3. Prithvi Shaw, Ruturaj Gaikwad, Ishan Kishan
4. Sanju Samson, Shreyas Iyer, Shubman Gill, Shivam Dube,
5. Abhishek Sharma, Tilak Verma, Rahul Tripathi, Suryakumar Yadav, Deepak Hooda
6. Rishabh Pant, Dinesh Karthik

Personally, I feel Ishan Kishan and Shreyas Iyer are a little tardy while playing express speeds, as compared to Sanju Samson or Rishabh Pant.

If you notice, I have not included both Virat Kohli or Rohit Sharma who have been below par for some time

C. Then (Jan 2020 – May 2022) – Bowler analysis

This section I analyse the performances of bowlers from Jan 2022 – May 2022. This is done based on ranking, and plots of Wickets vs Economy Rate in Power Play, Middle and Death overs

a. Ranks of bowlers (Wickets over Economy Rate) : Jan 2020 – May 2022

The most consistent bowlers Wickets over Economy Rate for the last 3 years are

[YS Chahal, Jasprit Bumrah, Mohammed Dhami, Harshal Patel, Shardul Thakur, Arshdeep Singh, Rahul Chahar, Varun Chakravarthy, Ravi Bishnoi, Prasidh Krishna, R Ashwon, Axar Patel, Mohammed Siraj, Ravindra Jadeja, Krunal Pandya, Rahul Tewatia]

b. Ranks of bowlers (Economy Rate over Wickets) : Jan 2020 – May 2022

The most economical bowlers since 2020 are

[Axar Patel, Krunal Pandya, Jasprit Bumrah, CV Varun, R Ashwin, Ravi Bishnoi, Rahul Chahar, YS Chahal, Ravindra Jadeja, Harshal Patel, Mohammed Shami, Mohammed Siraj, Rahul Tewatia, Arshdeep Singh, Prasidh Krishna, Shardul Thakur]

c.Best Bowlers Wickets vs ER : Jan 2020 – May 2022

The best bowlers Wickets vs ER will be in the bottom right quadrant. The most consistent and reliable bowlers are

[YS Chahal, Jasprit Bumrah, Mohammed Shami, Harshal Patel, CV Arun, Ravi Bishnoi, Rahul Chahar, R Ashwin, Axar Patel]

d. Best bowlers Wickets vs ER in Powerplay: Jan 2020 – May 2022

The best bowlers in Powerplay are

[Mohammed Shami, Deepak Chahar, Mohammed Siraj, Arshdeep Singh, Jasprit Bumrah, Avesh Khan, Mukesh Choudhary, Shardul Thakur, T Natarajan, Bhuvaneshwar Kumar, WashingtonSundar, Shivam Mavi]

e. Best bowlers Wickets vs ER in Middle overs : Jan 2020 – May 2022

The most reliable performers in middle overs from 2020-2022 are

[YS Chahal, Rahul Chahr, Ravi Bishnoi, Harshal Patel, Axar Patel, Jasprit Bumrah, Umran Malik, R Ashwin, Avesh Khan, Shardul Thakur, Kuldeep Yadav]

f. Best bowlers Wickets vs ER in Death overs : Jan 2020 – May 2022

The most reliable bowlers are

[Harshal Patel, Mohammed Shami, Jasprit Bumrah, Arshdeep Singh, T Natarajan, Avesh Khan, Shardul Thakur, Bhuvaneshwar Kumar, Shivam Mavi, YS Chahal, Prasidh Krishna, Mohammed Siraj, Chetan Sakariya]

B) Now (IPL 2022) – Bowler analysis

a. Ranks of bowlers (Wickets over Economy Rate) : IPL 2022

The best bowlers in IPL 2022 when considering Wickets over Economy Rate

[YS Chahal, Umran Malik, Prasidh Krishna, Mohammed Shami, Kuldeep Yadav, Harshal Patel, T Natarajan, Avesh Khan, Shardul Thakur, Mukesh Choudhary, Jasprit Bumrah, Ravi Bishnoi]

a. Ranks of bowlers (Economy Rate over Wickets) : IPL 2022

The most economical bowlers in IPL 2022 are

[Axar Patel, Jasprit Bumrah, Krunal Pandya, Umesh Yadav, Bhuvaneshwar Kumar, Rahul Chahr, Harshal Patel, Arshdeep Singh, R Ashwion, Umran Malik, Kuldeep Yadav, YS Chahal, Mohammed Shami, Avesh Khan, Prasidh Krishna]

c.Best Bowlers Wickets vs ER : IPL 2022

The overall best bowlers in IPL 2022 are

[YS Chahal, Umran Malik, Harshal Patel, Prasidh Krishna, Mohammed Shami, Kuldeep Yadav, Avesh Khan, Jasprit Bumrah, Umesh Yadav, Bhuvaneshwar Kumar, Arshdeep Singh, R Ashwin, Rahul Chahar, Krunal Pandya]

d. Best bowlers Wickets vs ER in Powerplay: IPL 2022

The best bowlers in IPL 2022 in Power play are

[Mukesh Choudhary, Mohammed Shami, Prasidh Krishna, Umesh Yadav, Avesh Khan, Mohsin Khan, T Natarajan, Jasprit Bumrah, Yash Dayal, Mohammed Siraj]

d. Best bowlers Wickets vs ER in Middle overs: IPL 2022

The best bowlers in IPL 2022 during middle overs

The best bowlers are

[YS Chahal, Umran Malik, Kuldeep Yadav, Harshal Patel, Ravi Bishnoi, R Ashwin]

e. Best bowlers Wickets vs ER in Death overs: IPL 2022

The best bowlers in death overs in IPL 2022 are

[T Natarajan, Harshal Patel, Bhuvaneshwar Kumar, Mohammed Shami, Jasprit Bumrah, Shardul Thakur, YS Chahal, Prasidh Krishna, Avesh Khan, Mohsin Khan, Yash Dayal, Umran Malik, Arshdeep Singh]

Typically in a team we would need a combination of 4 bowlers (2 fast & 2 spinner or 3 fast and 1 spinner) with an additional player who is all rounder.

For 4 bowlers we could have

1. JJ Bumrah
2. Mohammed Shami, Umran Malik, Bhuvaneshwar Kumar, Umesh Yadav
3. Arshdeep Singh, Avesh Khan, Mohsin Khan, Harshal Patel
4. YS Chahal, Ravi Bishnoi, Rahul Chahar, Axar Patel
5. Ravindra Jadeja, Hardik Pandya, Rahul Tewathia, R Ashwin

i) Performance comparison (Wickets over Economy Rate)

Bumrah had the best season in 2020. He has been doing quite well and has been among the wickets

ii) Performance comparison (Economy Rate over Wickets)

Bumrah has the best Economy Rate

We can do a wicket prediction of bowlers. So for example for Bumrah it is

iii) Performance evaluation (Wickets over Economy Rate)

Harshal Patel followed by Avesh Khan had a good season last year, but Umran Malik pipped them this year (see below)

iv) Performance analysis of spinners

a. Wickets over Economy Rate: 2022

Chahal has the best season followed by Bishnoi and Chahar this season

b) Economy Rate over WIckets

Axar Patel has the best economy rate followed by Rahul Chahar

Conclusion

The above post identified the best candidates for the Indian team in the future and beyond. In my T20 list, I have neither included Virat Kohli or Rohit Sharma. The data in T20 clearly indicates that they have had their days. There is a lot more talent around. The tradeoff is a little risk for a greater potential performance. My list would be

1. KL Rahul
2. Shikhar Dhawan
3. Ruturaj Gaikwad, Prithvi Shaw, Rahul Tripathi
4. Suryakumar Yadav, Shreyas Iyer, Abhishek Sharma, Deepak Hooda
5. Sanju Samson (Wicket keeper/captain)/ Rishabh Pant/Dinesh Karthik
6. Hardik Pandya, Ravindra Jadeja, Rahul Tewathia
7. Jasprit Bumrah
8. Mohammed Shami, Bhuvaneshwar Kumar, Umran Malik
9. Arshdeep Singh, Avesh Khan, Harshal Patel
10. YS Chahal
11. Axar Patel, Ravi Bishnoi, Rahul Chahar

You may agree/ disagree with my list. Feel free to do your analysis with GooglyPlusPlus and come to your own conclusions

This analysis is also available on youtube Insights from GooglyPlusPlus

You may also like

To see all posts click Index of posts

# Player Performance Estimation using AI Collaborative Filtering

## 1. Introduction

Often times before crucial matches, or in general, we would like to know the performance of a batsman against a bowler or vice-versa, but we may not have the data. We generally have data where different batsmen would have faced different sets of bowlers with certain performance data like ballsFaced, totalRuns, fours, sixes, strike rate and timesOut. Similarly different bowlers would have performance figures(deliveries, runsConceded, economyRate and wicketTaken) against different sets of batsmen. We will never have the data for all batsmen against all bowlers. However, it would be good estimate the performance of batsmen against a bowler, even though we do not have the performance data. This could be done using collaborative filtering which identifies and computes based on the similarity between batsmen vs bowlers & bowlers vs batsmen.

This post shows an approach whereby we can estimate a batsman’s performance against bowlers even though the batsman may not have faced those bowlers, based on his/her performance against other bowlers. It also estimates the performance of bowlers against batsmen using the same approach. This is based on the recommender algorithm which is used to recommend products to customers based on their rating on other products.

This idea came to me while generating the performance of batsmen vs bowlers & vice-versa for 2 IPL teams in this IPL 2022 with my Shiny app GooglyPlusPlus in the optimization tab, I found that there were some batsmen for which there was no data against certain bowlers, probably because they are playing for the first time in their team or because they were new (see picture below)

In the picture above there is no data for Dewald Brevis against Jasprit Bumrah and YS Chahal. Wouldn’t be great to estimate the performance of Brevis against Bumrah or vice-versa? Can we estimate this performance?

While pondering on this problem, I realized that this problem formulation is similar to the problem formulation for the famous Netflix movie recommendation problem, in which user’s ratings for certain movies are known and based on these ratings, the recommender engine can generate ratings for movies not yet seen.

This post estimates a player’s (batsman/bowler) using the recommender engine This post is based on R package recommenderlab

“Michael Hahsler (2021). recommenderlab: Lab for Developing and Testing Recommender Algorithms. R package version 0.2-7. https://github.com/mhahsler/recommenderlab

Note 1: Thw data for this analysis is taken from Cricsheet after being processed by my R package yorkr.

You can also read this post in RPubs at Player Performance Estimation using AI Collaborative Filtering

A PDF copy of this post is available at Player Performance Estimation using AI Collaborative Filtering.pdf

You can download this R Markdown file and the associated data and perform the analysis yourself using any other recommender engine from Github at playerPerformanceEstimation

## Problem statement

In the table below we see a set of bowlers vs a set of batsmen and the number of times the bowlers got these batsmen out.
By knowing the performance of the bowlers against some of the batsmen we can use collaborative filter to determine the missing values. This is done using the recommender engine.

The Recommender Engine works as follows. Let us say that there are feature vectors $x^1$, $x^2$ and $x^3$ for the 3 bowlers which identify the characteristics of these bowlers (“fast”, “lateral drift through the air”, “movement off the pitch”). Let each batsman be identified by parameter vectors $\theta^1$, $\theta^2$ and so on

For e.g. consider the following table

Then by assuming an initial estimate for the parameter vector $\theta$ and the feature vector xx we can formulate this as an optimization problem which tries to minimize the error for $\theta^T*x$ This can work very well as the algorithm can determine features which cannot be captured. So for e.g. some particular bowler may have very impressive figures. This could be due to some aspect of the bowling which cannot be captured by the data for e.g. let’s say the bowler uses the ‘scrambled seam’ when he is most effective, with a slightly different arc to the flight. Though the algorithm cannot identify the feature as we know it, but the ML algorithm should pick up intricacies which cannot be captured in data.

Hence the algorithm can be quite effective.

Note: The recommender lab performance is not very good and the Mean Square Error is quite high. Also, the ROC and AUC curves show that not in aLL cases the algorithm is doing a clean job of separating the True positives (TPR) from the False Positives (FPR)

Note: This is similar to the recommendation problem

The collaborative optimization object can be considered as a minimization of both $\theta$ and the features x and can be written as

J($x^{(1)},x^{(2)},..x^{(n_{u})}$, $\theta^{(1)},\theta^{(2)},..,\theta^{(n_{m})}$}= 1/2$\sum(\theta^{j})^{T}x^{i}- y^{(i,j)})^{2} + \lambda\sum\sum (x_{k}^{i})^{2} + \lambda\sum\sum (_\theta{k}^{j})^{2}$

The collaborative filtering algorithm can be summarized as follows

1. Initialize $\theta^1$, $\theta^2$$\theta^{n_{u}}$ and the set of features be $x^1$,$x^2$, … ,$x^{n_{m}}$ to small random values
2. Minimize J($\theta^1$, $\theta^2$$\theta^{n_{u}}$,$x^1$, $x^2$, … ,$x^{n_{m}}$) using gradient descent. For every
j=1,2, …$n_{u}$, i= 1,2,.., $n_{m}$
3. $x_{k}^{i}$ := $x_{k}^{i}$$\alpha$ ( $\sigma$ $(\theta^j)^T$)$x^i$$y^(i,j)\theta_{k}^{j} + \lambda x_{k}^i$

&

$\theta_{k}^{i}$ := $\theta_{k}^{i}$$\alpha$ ( $\sigma$ $(\theta^j)^T)x^i - y^(i,j)\theta_{k}^{j} + \lambda x_{k}^i$
4. Hence for a batsman with parameters $\theta$ and a bowler with (learned) features x, predict the “times out” for the player where the value is not known using $\theta^Tx$

The above derivation for the recommender problem is taken from Machine Learning by Prof Andrew Ng at Coursera from the lecture Collaborative filtering

There are 2 main types of Collaborative Filtering(CF) approaches

1. User based Collaborative Filtering User-based CF is a memory-based algorithm which tries to mimics word-of-mouth by analyzing rating data from many individuals. The assumption is that users with similar preferences will rate items similarly.
2. Item based Collaborative Filtering Item-based CF is a model-based approach which produces recommendations based on the relationship between items inferred from the rating matrix. The assumption behind this approach is that users will prefer items that are similar to other items they like.

## 1a. A note on ROC and Precision-Recall curves

A small note on interpreting ROC & Precision-Recall curves in the post below

ROC Curve: The ROC curve plots the True Positive Rate (TPR) against the False Positive Rate (FPR). Ideally the TPR should increase faster than the FPR and the AUC (area under the curve) should be close to 1

Precision-Recall: The precision-recall curve shows the tradeoff between precision and recall for different threshold. A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate

library(reshape2)
library(dplyr)
library(ggplot2)
library(recommenderlab)
library(tidyr)


## 2. Define recommender lab helper functions

Helper functions for the RMarkdown notebook are created

• eval – Gives details of RMSE, MSE and MAE of ML algorithm
• evalRecomMethods – Evaluates different recommender methods and plot the ROC and Precision-Recall curves
# This function returns the error for the chosen algorithm and also predicts the estimates
# for the given data
eval <- function(data, train1, k1,given1,goodRating1,recomType1="UBCF"){
set.seed(2022)
e<- evaluationScheme(data,
method = "split",
train = train1,
k = k1,
given = given1,
goodRating = goodRating1)

r1 <- Recommender(getData(e, "train"), recomType1)
print(r1)

p1 <- predict(r1, getData(e, "known"), type="ratings")
print(p1)

error = calcPredictionAccuracy(p1, getData(e, "unknown"))

print(error)
p2 <- predict(r1, data, type="ratingMatrix")
p2
}
# This function will evaluate the different recommender algorithms and plot the AUC and ROC curves
evalRecomMethods <- function(data,k1,given1,goodRating1){
set.seed(2022)
e<- evaluationScheme(data,
method = "cross",
k = k1,
given = given1,
goodRating = goodRating1)

models_to_evaluate <- list(
IBCF Cosinus = list(name = "IBCF",
param = list(method = "cosine")),
IBCF Pearson = list(name = "IBCF",
param = list(method = "pearson")),
UBCF Cosinus = list(name = "UBCF",
param = list(method = "cosine")),
UBCF Pearson = list(name = "UBCF",
param = list(method = "pearson")),
Zufälliger Vorschlag = list(name = "RANDOM", param=NULL)
)

n_recommendations <- c(1, 5, seq(10, 100, 10))
list_results <- evaluate(x = e,
method = models_to_evaluate,
n = n_recommendations)
plot(list_results, annotate=c(1,3), legend="bottomright")
plot(list_results, "prec/rec", annotate=3, legend="topleft")
}


## 3. Batsman performance estimation

The section below regenerates the performance for batsmen based on incomplete data for the different fields in the data frame namely balls faced, fours, sixes, strike rate, times out. The recommender lab allows one to test several different algorithms all at once namely

1. User based – Cosine similarity method, Pearson similarity
2. Item based – Cosine similarity method, Pearson similarity
3. Popular
4. Random
5. SVD and a few others

## 3a. Batting dataframe

head(df)

##   batsman1         bowler1 ballsFaced totalRuns fours sixes  SR timesOut
## 1 A Badoni        A Mishra          0         0     0     0 NaN        0
## 2 A Badoni        A Nortje          0         0     0     0 NaN        0
## 3 A Badoni         A Zampa          0         0     0     0 NaN        0
## 4 A Badoni     Abdul Samad          0         0     0     0 NaN        0
## 5 A Badoni Abhishek Sharma          0         0     0     0 NaN        0
## 6 A Badoni      AD Russell          0         0     0     0 NaN        0


## 3b Data set and data preparation

For this analysis the data from Cricsheet has been processed using my R package yorkr to obtain the following 2 data sets – batsmenVsBowler – This dataset will contain the performance of the batsmen against the bowler and will capture a) ballsFaced b) totalRuns c) Fours d) Sixes e) SR f) timesOut – bowlerVsBatsmen – This data set will contain the performance of the bowler against the difference batsmen and will include a) deliveries b) runsConceded c) EconomyRate d) wicketsTaken

Obviously many rows/columns will be empty

This is a large data set and hence I have filtered for the period > Jan 2020 and < Dec 2022 which gives 2 datasets a) batsmanVsBowler20_22.rdata b) bowlerVsBatsman20_22.rdata

I also have 2 other datasets of all batsmen and bowlers in these 2 dataset in the files c) all-batsmen20_22.rds d) all-bowlers20_22.rds

You can download the data and this RMarkdown notebook from Github at PlayerPerformanceEstimation

Feel free to download and analyze the data and use any recommendation engine you choose

## 3c. Exploratory analysis

Initially an exploratory analysis is done on the data

df3 <- select(df, batsman1,bowler1,timesOut)
df6 <- xtabs(timesOut ~ ., df3)
df7 <- as.data.frame.matrix(df6)
df8 <- data.matrix(df7)
df8[df8 == 0] <- NA
print(df8[1:10,1:10])

##                 A Mishra A Nortje A Zampa Abdul Samad Abhishek Sharma
## A Badoni              NA       NA      NA          NA              NA
## A Manohar             NA       NA      NA          NA              NA
## A Nortje              NA       NA      NA          NA              NA
## AB de Villiers        NA        4       3          NA              NA
## Abdul Samad           NA       NA      NA          NA              NA
## Abhishek Sharma       NA       NA      NA          NA              NA
## AD Russell             1       NA      NA          NA              NA
## AF Milne              NA       NA      NA          NA              NA
## AJ Finch              NA       NA      NA          NA               3
## AJ Tye                NA       NA      NA          NA              NA
##                 AD Russell AF Milne AJ Tye AK Markram Akash Deep
## A Badoni                NA       NA     NA         NA         NA
## A Manohar               NA       NA     NA         NA         NA
## A Nortje                NA       NA     NA         NA         NA
## AB de Villiers           3       NA      3         NA         NA
## Abdul Samad             NA       NA     NA         NA         NA
## Abhishek Sharma         NA       NA     NA         NA         NA
## AD Russell              NA       NA      6         NA         NA
## AF Milne                NA       NA     NA         NA         NA
## AJ Finch                NA       NA     NA         NA         NA
## AJ Tye                  NA       NA     NA         NA         NA


The dots below represent data for which there is no performance data. These cells need to be estimated by the algorithm

set.seed(2022)
r <- as(df8,"realRatingMatrix")
getRatingMatrix(r)[1:15,1:15]

## 15 x 15 sparse Matrix of class "dgCMatrix"

##    [[ suppressing 15 column names 'A Mishra', 'A Nortje', 'A Zampa' ... ]]

##
## A Badoni         . . . . . . . . . . . . . . .
## A Manohar        . . . . . . . . . . . . . . .
## A Nortje         . . . . . . . . . . . . . . .
## AB de Villiers   . 4 3 . . 3 . 3 . . . 4 3 . .
## Abdul Samad      . . . . . . . . . . . . . . .
## Abhishek Sharma  . . . . . . . . . . . 1 . . .
## AD Russell       1 . . . . . . 6 . . . 3 3 3 .
## AF Milne         . . . . . . . . . . . . . . .
## AJ Finch         . . . . 3 . . . . . . 1 . . .
## AJ Tye           . . . . . . . . . . . 1 . . .
## AK Markram       . . . 3 . . . . . . . . . . .
## AM Rahane        9 . . . . 3 . 3 . . . 3 3 . .
## Anmolpreet Singh . . . . . . . . . . . . . . .
## Anuj Rawat       . . . . . . . . . . . . . . .
## AR Patel         . . . . . . . 1 . . . . . . .

r0=r[(rowCounts(r) > 10),]
getRatingMatrix(r0)[1:15,1:15]

## 15 x 15 sparse Matrix of class "dgCMatrix"

##    [[ suppressing 15 column names 'A Mishra', 'A Nortje', 'A Zampa' ... ]]

##
## AB de Villiers  . 4 3 . . 3 . 3 . . . 4 3 . .
## Abdul Samad     . . . . . . . . . . . . . . .
## Abhishek Sharma . . . . . . . . . . . 1 . . .
## AD Russell      1 . . . . . . 6 . . . 3 3 3 .
## AJ Finch        . . . . 3 . . . . . . 1 . . .
## AM Rahane       9 . . . . 3 . 3 . . . 3 3 . .
## AR Patel        . . . . . . . 1 . . . . . . .
## AT Rayudu       2 . . . . . 1 . . . . 3 . . .
## B Kumar         3 . 3 . . . . . . . . . . 3 .
## BA Stokes       . . . . . . 3 4 . . . 3 . . .
## CA Lynn         . . . . . . . 9 . . . 3 . . .
## CH Gayle        . . . . . 6 . 3 . . . 6 . . .
## CH Morris       . 3 . . . . . . . . . 3 . . .
## D Padikkal      . 4 . . . 3 . . . . . . 3 . .
## DA Miller       . . . . . 3 . . . . . 3 . . .

# Get the summary of the data
summary(getRatings(r0))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##   1.000   3.000   3.000   3.463   4.000  21.000

# Normalize the data
r0_m <- normalize(r0)
getRatingMatrix(r0_m)[1:15,1:15]

## 15 x 15 sparse Matrix of class "dgCMatrix"

##    [[ suppressing 15 column names 'A Mishra', 'A Nortje', 'A Zampa' ... ]]

##
## AB de Villiers   .         -0.7857143 -1.7857143 .  .       -1.7857143
## Abdul Samad      .          .          .         .  .        .
## Abhishek Sharma  .          .          .         .  .        .
## AD Russell      -2.6562500  .          .         .  .        .
## AJ Finch         .          .          .         . -0.03125  .
## AM Rahane        4.6041667  .          .         .  .       -1.3958333
## AR Patel         .          .          .         .  .        .
## AT Rayudu       -2.1363636  .          .         .  .        .
## B Kumar          0.3636364  .          0.3636364 .  .        .
## BA Stokes        .          .          .         .  .        .
## CA Lynn          .          .          .         .  .        .
## CH Gayle         .          .          .         .  .        1.5476190
## CH Morris        .          0.3500000  .         .  .        .
## D Padikkal       .          0.6250000  .         .  .       -0.3750000
## DA Miller        .          .          .         .  .       -0.7037037
##
## AB de Villiers   .         -1.7857143 . . . -0.7857143 -1.785714  .         .
## Abdul Samad      .          .         . . .  .          .         .         .
## Abhishek Sharma  .          .         . . . -1.6000000  .         .         .
## AD Russell       .          2.3437500 . . . -0.6562500 -0.656250 -0.6562500 .
## AJ Finch         .          .         . . . -2.0312500  .         .         .
## AM Rahane        .         -1.3958333 . . . -1.3958333 -1.395833  .         .
## AR Patel         .         -2.3333333 . . .  .          .         .         .
## AT Rayudu       -3.1363636  .         . . . -1.1363636  .         .         .
## B Kumar          .          .         . . .  .          .         0.3636364 .
## BA Stokes       -0.6086957  0.3913043 . . . -0.6086957  .         .         .
## CA Lynn          .          5.3200000 . . . -0.6800000  .         .         .
## CH Gayle         .         -1.4523810 . . .  1.5476190  .         .         .
## CH Morris        .          .         . . .  0.3500000  .         .         .
## D Padikkal       .          .         . . .  .         -0.375000  .         .
## DA Miller        .          .         . . . -0.7037037  .         .         .


## 4. Create a visual representation of the rating data before and after the normalization

The histograms show the bias in the data is removed after normalization

r0=r[(m=rowCounts(r) > 10),]
getRatingMatrix(r0)[1:15,1:10]

## 15 x 10 sparse Matrix of class "dgCMatrix"

##    [[ suppressing 10 column names 'A Mishra', 'A Nortje', 'A Zampa' ... ]]

##
## AB de Villiers  . 4 3 . . 3 . 3 . .
## Abdul Samad     . . . . . . . . . .
## Abhishek Sharma . . . . . . . . . .
## AD Russell      1 . . . . . . 6 . .
## AJ Finch        . . . . 3 . . . . .
## AM Rahane       9 . . . . 3 . 3 . .
## AR Patel        . . . . . . . 1 . .
## AT Rayudu       2 . . . . . 1 . . .
## B Kumar         3 . 3 . . . . . . .
## BA Stokes       . . . . . . 3 4 . .
## CA Lynn         . . . . . . . 9 . .
## CH Gayle        . . . . . 6 . 3 . .
## CH Morris       . 3 . . . . . . . .
## D Padikkal      . 4 . . . 3 . . . .
## DA Miller       . . . . . 3 . . . .

#Plot ratings
image(r0, main = "Raw Ratings")

#Plot normalized ratings
r0_m <- normalize(r0)
getRatingMatrix(r0_m)[1:15,1:15]

## 15 x 15 sparse Matrix of class "dgCMatrix"

##    [[ suppressing 15 column names 'A Mishra', 'A Nortje', 'A Zampa' ... ]]

##
## AB de Villiers   .         -0.7857143 -1.7857143 .  .       -1.7857143
## Abdul Samad      .          .          .         .  .        .
## Abhishek Sharma  .          .          .         .  .        .
## AD Russell      -2.6562500  .          .         .  .        .
## AJ Finch         .          .          .         . -0.03125  .
## AM Rahane        4.6041667  .          .         .  .       -1.3958333
## AR Patel         .          .          .         .  .        .
## AT Rayudu       -2.1363636  .          .         .  .        .
## B Kumar          0.3636364  .          0.3636364 .  .        .
## BA Stokes        .          .          .         .  .        .
## CA Lynn          .          .          .         .  .        .
## CH Gayle         .          .          .         .  .        1.5476190
## CH Morris        .          0.3500000  .         .  .        .
## D Padikkal       .          0.6250000  .         .  .       -0.3750000
## DA Miller        .          .          .         .  .       -0.7037037
##
## AB de Villiers   .         -1.7857143 . . . -0.7857143 -1.785714  .         .
## Abdul Samad      .          .         . . .  .          .         .         .
## Abhishek Sharma  .          .         . . . -1.6000000  .         .         .
## AD Russell       .          2.3437500 . . . -0.6562500 -0.656250 -0.6562500 .
## AJ Finch         .          .         . . . -2.0312500  .         .         .
## AM Rahane        .         -1.3958333 . . . -1.3958333 -1.395833  .         .
## AR Patel         .         -2.3333333 . . .  .          .         .         .
## AT Rayudu       -3.1363636  .         . . . -1.1363636  .         .         .
## B Kumar          .          .         . . .  .          .         0.3636364 .
## BA Stokes       -0.6086957  0.3913043 . . . -0.6086957  .         .         .
## CA Lynn          .          5.3200000 . . . -0.6800000  .         .         .
## CH Gayle         .         -1.4523810 . . .  1.5476190  .         .         .
## CH Morris        .          .         . . .  0.3500000  .         .         .
## D Padikkal       .          .         . . .  .         -0.375000  .         .
## DA Miller        .          .         . . . -0.7037037  .         .         .

image(r0_m, main = "Normalized Ratings")

set.seed(1234)
hist(getRatings(r0), breaks=25)

hist(getRatings(r0_m), breaks=25)


## 4a. Data for analysis

The data frame of the batsman vs bowlers from the period 2020 -2022 is read as a dataframe. To remove rows with very low number of ratings(timesOut, SR, Fours, Sixes etc), the rows are filtered so that there are at least more 10 values in the row. For the player estimation the dataframe is converted into a wide-format as a matrix (m x n) of batsman x bowler with each of the columns of the dataframe i.e. timesOut, SR, fours or sixes. These different matrices can be considered as a rating matrix for estimation.

A similar approach is taken for estimating bowler performance. Here a wide form matrix (m x n) of bowler x batsman is created for each of the columns of deliveries, runsConceded, ER, wicketsTaken

## 5. Batsman’s times Out

The code below estimates the number of times the batsmen would lose his/her wicket to the bowler. As discussed in the algorithm above, the recommendation engine will make an initial estimate features for the bowler and an initial estimate for the parameter vector for the batsmen. Then using gradient descent the recommender engine will determine the feature and parameter values such that the over Mean Squared Error is minimum

From the plot for the different algorithms it can be seen that UBCF performs the best. However the AUC & ROC curves are not optimal and the AUC> 0.5

df3 <- select(df, batsman1,bowler1,timesOut)
df6 <- xtabs(timesOut ~ ., df3)
df7 <- as.data.frame.matrix(df6)
df8 <- data.matrix(df7)
df8[df8 == 0] <- NA
r <- as(df8,"realRatingMatrix")
# Filter only rows where the row count is > 10
r0=r[(rowCounts(r) > 10),]
getRatingMatrix(r0)[1:10,1:10]

## 10 x 10 sparse Matrix of class "dgCMatrix"

##    [[ suppressing 10 column names 'A Mishra', 'A Nortje', 'A Zampa' ... ]]

##
## AB de Villiers  . 4 3 . . 3 . 3 . .
## Abdul Samad     . . . . . . . . . .
## Abhishek Sharma . . . . . . . . . .
## AD Russell      1 . . . . . . 6 . .
## AJ Finch        . . . . 3 . . . . .
## AM Rahane       9 . . . . 3 . 3 . .
## AR Patel        . . . . . . . 1 . .
## AT Rayudu       2 . . . . . 1 . . .
## B Kumar         3 . 3 . . . . . . .
## BA Stokes       . . . . . . 3 4 . .

summary(getRatings(r0))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##   1.000   3.000   3.000   3.463   4.000  21.000

# Evaluate the different plotting methods
evalRecomMethods(r0[1:dim(r0)[1]],k1=5,given=7,goodRating1=median(getRatings(r0)))

#Evaluate the error
a=eval(r0[1:dim(r0)[1]],0.8,k1=5,given1=7,goodRating1=median(getRatings(r0)),"UBCF")

## Recommender of type 'UBCF' for 'realRatingMatrix'
## learned using 70 users.
## 18 x 145 rating matrix of class 'realRatingMatrix' with 1755 ratings.
##     RMSE      MSE      MAE
## 2.069027 4.280872 1.496388

b=round(as(a,"matrix")[1:10,1:10])
c <- as(b,"realRatingMatrix")
m=as(c,"data.frame")
names(m) =c("batsman","bowler","TimesOut")


## 6. Batsman’s Strike rate

This section deals with the Strike rate of batsmen versus bowlers and estimates the values for those where the data is incomplete using UBCF method.

Even here all the algorithms do not perform too efficiently. I did try out a few variations but could not lower the error (suggestions welcome!!)

df3 <- select(df, batsman1,bowler1,SR)
df6 <- xtabs(SR ~ ., df3)
df7 <- as.data.frame.matrix(df6)
df8 <- data.matrix(df7)
df8[df8 == 0] <- NA
r <- as(df8,"realRatingMatrix")
r0=r[(rowCounts(r) > 10),]
getRatingMatrix(r0)[1:10,1:10]

## 10 x 10 sparse Matrix of class "dgCMatrix"

##    [[ suppressing 10 column names 'A Mishra', 'A Nortje', 'A Zampa' ... ]]

##
## AB de Villiers   96.8254 171.4286  33.33333  . 66.66667 223.07692   .
## Abdul Samad       .      228.0000   .        .  .       100.00000   .
## Abhishek Sharma 150.0000   .        .        .  .        66.66667   .
## AD Russell      111.4286   .        .        .  .         .         .
## AJ Finch        250.0000 116.6667   .        . 50.00000  85.71429 112.5000
## AJ Tye            .        .        .        .  .         .       100.0000
## AK Markram        .        .        .       50  .         .         .
## AM Rahane       121.1111   .        .        .  .       113.82979 117.9487
## AR Patel        183.3333   .      200.00000  .  .       433.33333   .
## AT Rayudu       126.5432 200.0000 122.22222  .  .       105.55556   .
##
## AB de Villiers  109.52381 .   .
## Abdul Samad       .       .   .
## Abhishek Sharma   .       .   .
## AD Russell      195.45455 .   .
## AJ Finch          .       .   .
## AJ Tye            .       .   .
## AK Markram        .       .   .
## AM Rahane        33.33333 . 200
## AR Patel        171.42857 .   .
## AT Rayudu       204.76190 .   .

summary(getRatings(r0))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##   5.882  85.714 116.667 128.529 160.606 600.000

evalRecomMethods(r0[1:dim(r0)[1]],k1=5,given=7,goodRating1=median(getRatings(r0)))

a=eval(r0[1:dim(r0)[1]],0.8, k1=5,given1=7,goodRating1=median(getRatings(r0)),"UBCF")

## Recommender of type 'UBCF' for 'realRatingMatrix'
## learned using 105 users.
## 27 x 145 rating matrix of class 'realRatingMatrix' with 3220 ratings.
##       RMSE        MSE        MAE
##   77.71979 6040.36508   58.58484

b=round(as(a,"matrix")[1:10,1:10])
c <- as(b,"realRatingMatrix")
n=as(c,"data.frame")
names(n) =c("batsman","bowler","SR")


## 7. Batsman’s Sixes

The snippet of code estimes the sixes of the batsman against bowlers. The ROC and AUC curve for UBCF looks a lot better here, as it significantly greater than 0.5

df3 <- select(df, batsman1,bowler1,sixes)
df6 <- xtabs(sixes ~ ., df3)
df7 <- as.data.frame.matrix(df6)
df8 <- data.matrix(df7)
df8[df8 == 0] <- NA
r <- as(df8,"realRatingMatrix")
r0=r[(rowCounts(r) > 10),]
getRatingMatrix(r0)[1:10,1:10]

## 10 x 10 sparse Matrix of class "dgCMatrix"

##    [[ suppressing 10 column names 'A Mishra', 'A Nortje', 'A Zampa' ... ]]

##
## AB de Villiers  3 3 . . . 18 .  3 . .
## AD Russell      3 . . . .  . . 12 . .
## AJ Finch        2 . . . .  . .  . . .
## AM Rahane       7 . . . .  3 1  . . .
## AR Patel        4 . 3 . .  6 .  1 . .
## AT Rayudu       5 2 . . .  . .  1 . .
## BA Stokes       . . . . .  . .  . . .
## CA Lynn         . . . . .  . .  9 . .
## CH Gayle       17 . . . . 17 .  . . .
## CH Morris       . . 3 . .  . .  . . .

summary(getRatings(r0))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##    1.00    3.00    3.00    4.68    6.00   33.00

evalRecomMethods(r0[1:dim(r0)[1]],k1=5,given=7,goodRating1=median(getRatings(r0)))

## Timing stopped at: 0.003 0 0.002

a=eval(r0[1:dim(r0)[1]],0.8, k1=5,given1=7,goodRating1=median(getRatings(r0)),"UBCF")

## Recommender of type 'UBCF' for 'realRatingMatrix'
## learned using 52 users.
## 14 x 145 rating matrix of class 'realRatingMatrix' with 1634 ratings.
##      RMSE       MSE       MAE
##  3.529922 12.460350  2.532122

b=round(as(a,"matrix")[1:10,1:10])
c <- as(b,"realRatingMatrix")
o=as(c,"data.frame")
names(o) =c("batsman","bowler","Sixes")


## 8. Batsman’s Fours

The code below estimates 4s for the batsmen

df3 <- select(df, batsman1,bowler1,fours)
df6 <- xtabs(fours ~ ., df3)
df7 <- as.data.frame.matrix(df6)
df8 <- data.matrix(df7)
df8[df8 == 0] <- NA
r <- as(df8,"realRatingMatrix")
r0=r[(rowCounts(r) > 10),]
getRatingMatrix(r0)[1:10,1:10]

## 10 x 10 sparse Matrix of class "dgCMatrix"

##    [[ suppressing 10 column names 'A Mishra', 'A Nortje', 'A Zampa' ... ]]

##
## AB de Villiers   . 1 . . . 24 . 3 . .
## Abhishek Sharma  . . . . .  . . . . .
## AD Russell       1 . . . .  . . 9 . .
## AJ Finch         . 1 . . .  3 2 . . .
## AK Markram       . . . . .  . . . . .
## AM Rahane       11 . . . .  8 7 . . 3
## AR Patel         . . . . .  . . 3 . .
## AT Rayudu       11 2 3 . .  6 . 6 . .
## BA Stokes        1 . . . .  . . . . .
## CA Lynn          . . . . .  . . 6 . .

summary(getRatings(r0))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##   1.000   3.000   4.000   6.339   9.000  55.000

evalRecomMethods(r0[1:dim(r0)[1]],k1=5,given=7,goodRating1=median(getRatings(r0)))

## Timing stopped at: 0.008 0 0.008

## Warning in .local(x, method, ...):
##   Recommender 'UBCF Pearson' has failed and has been removed from the results!

a=eval(r0[1:dim(r0)[1]],0.8, k1=5,given1=7,goodRating1=median(getRatings(r0)),"UBCF")

## Recommender of type 'UBCF' for 'realRatingMatrix'
## learned using 67 users.
## 17 x 145 rating matrix of class 'realRatingMatrix' with 2083 ratings.
##      RMSE       MSE       MAE
##  5.486661 30.103447  4.060990

b=round(as(a,"matrix")[1:10,1:10])
c <- as(b,"realRatingMatrix")
p=as(c,"data.frame")
names(p) =c("batsman","bowler","Fours")


## 9. Batsman’s Total Runs

The code below estimates the total runs that would have scored by the batsman against different bowlers

df3 <- select(df, batsman1,bowler1,totalRuns)
df6 <- xtabs(totalRuns ~ ., df3)
df7 <- as.data.frame.matrix(df6)
df8 <- data.matrix(df7)
df8[df8 == 0] <- NA
r <- as(df8,"realRatingMatrix")
r0=r[(rowCounts(r) > 10),]
getRatingMatrix(r)[1:10,1:10]

## 10 x 10 sparse Matrix of class "dgCMatrix"

##    [[ suppressing 10 column names 'A Mishra', 'A Nortje', 'A Zampa' ... ]]

##
## A Badoni         .  . . . .   . .   . . .
## A Manohar        .  . . . .   . .   . . .
## A Nortje         .  . . . .   . .   . . .
## AB de Villiers  61 36 3 . 6 261 .  69 . .
## Abdul Samad      . 57 . . .  12 .   . . .
## Abhishek Sharma  3  . . . .   6 .   . . .
## AD Russell      39  . . . .   . . 129 . .
## AF Milne         .  . . . .   . .   . . .
## AJ Finch        15  7 . . 3  18 9   . . .
## AJ Tye           .  . . . .   . 4   . . .

summary(getRatings(r0))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##    1.00    9.00   24.00   41.36   54.00  452.00

evalRecomMethods(r0[1:dim(r0)[1]],k1=5,given1=7,goodRating1=median(getRatings(r0)))

a=eval(r0[1:dim(r0)[1]],0.8, k1=5,given1=7,goodRating1=median(getRatings(r0)),"UBCF")

## Recommender of type 'UBCF' for 'realRatingMatrix'
## learned using 105 users.
## 27 x 145 rating matrix of class 'realRatingMatrix' with 3256 ratings.
##       RMSE        MSE        MAE
##   41.50985 1723.06788   29.52958

b=round(as(a,"matrix")[1:10,1:10])
c <- as(b,"realRatingMatrix")
q=as(c,"data.frame")
names(q) =c("batsman","bowler","TotalRuns")


## 10. Batsman’s Balls Faced

The snippet estimates the balls faced by batsmen versus bowlers

df3 <- select(df, batsman1,bowler1,ballsFaced)
df6 <- xtabs(ballsFaced ~ ., df3)
df7 <- as.data.frame.matrix(df6)
df8 <- data.matrix(df7)
df8[df8 == 0] <- NA
r <- as(df8,"realRatingMatrix")
r0=r[(rowCounts(r) > 10),]
getRatingMatrix(r)[1:10,1:10]

## 10 x 10 sparse Matrix of class "dgCMatrix"

##    [[ suppressing 10 column names 'A Mishra', 'A Nortje', 'A Zampa' ... ]]

##
## A Badoni         .  . . . .   . .  . . .
## A Manohar        .  . . . .   . .  . . .
## A Nortje         .  . . . .   . .  . . .
## AB de Villiers  63 21 9 . 9 117 . 63 . .
## Abdul Samad      . 25 . . .  12 .  . . .
## Abhishek Sharma  2  . . . .   9 .  . . .
## AD Russell      35  . . . .   . . 66 . .
## AF Milne         .  . . . .   . .  . . .
## AJ Finch         6  6 . . 6  21 8  . . .
## AJ Tye           .  . . . .   9 4  . . .

summary(getRatings(r0))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##    1.00    9.00   18.00   30.21   39.00  384.00

evalRecomMethods(r0[1:dim(r0)[1]],k1=5,given=7,goodRating1=median(getRatings(r0)))

a=eval(r0[1:dim(r0)[1]],0.8, k1=5,given1=7,goodRating1=median(getRatings(r0)),"UBCF")

## Recommender of type 'UBCF' for 'realRatingMatrix'
## learned using 112 users.
## 28 x 145 rating matrix of class 'realRatingMatrix' with 3378 ratings.
##       RMSE        MSE        MAE
##   33.91251 1150.05835   23.39439

b=round(as(a,"matrix")[1:10,1:10])
c <- as(b,"realRatingMatrix")
r=as(c,"data.frame")
names(r) =c("batsman","bowler","BallsFaced")


## 11. Generate the Batsmen Performance Estimate

This code generates the estimated dataframe with known and ‘predicted’ values

a1=merge(m,n,by=c("batsman","bowler"))
a2=merge(a1,o,by=c("batsman","bowler"))
a3=merge(a2,p,by=c("batsman","bowler"))
a4=merge(a3,q,by=c("batsman","bowler"))
a5=merge(a4,r,by=c("batsman","bowler"))
a6= select(a5, batsman,bowler,BallsFaced,TotalRuns,Fours, Sixes, SR,TimesOut)

##          batsman          bowler BallsFaced TotalRuns Fours Sixes  SR TimesOut
## 1 AB de Villiers        A Mishra         94       124     7     5 144        5
## 2 AB de Villiers        A Nortje         26        42     4     3 148        3
## 3 AB de Villiers         A Zampa         28        42     5     7 106        4
## 4 AB de Villiers Abhishek Sharma         22        28     0    10 136        5
## 5 AB de Villiers      AD Russell         70       135    14    12 207        4
## 6 AB de Villiers        AF Milne         31        45     6     6 130        3


## 12. Bowler analysis

Just like the batsman performance estimation we can consider the bowler’s performances also for estimation. Consider the following table

As in the batsman analysis, for every batsman a set of features like (“strong backfoot player”, “360 degree player”,“Power hitter”) can be estimated with a set of initial values. Also every bowler will have an associated parameter vector θθ. Different bowlers will have performance data for different set of batsmen. Based on the initial estimate of the features and the parameters, gradient descent can be used to minimize actual values {for e.g. wicketsTaken(ratings)}.

load("recom_data/bowlerVsBatsman20_22.rdata")


## 12a. Bowler dataframe

Inspecting the bowler dataframe

head(df2)

##    bowler1        batsman1 balls runsConceded       ER wicketTaken
## 1 A Mishra        A Badoni     0            0 0.000000           0
## 2 A Mishra       A Manohar     0            0 0.000000           0
## 3 A Mishra        A Nortje     0            0 0.000000           0
## 4 A Mishra  AB de Villiers    63           61 5.809524           0
## 5 A Mishra     Abdul Samad     0            0 0.000000           0
## 6 A Mishra Abhishek Sharma     2            3 9.000000           0

names(df2)

## [1] "bowler1"      "batsman1"     "balls"        "runsConceded" "ER"
## [6] "wicketTaken"


## 13. Balls bowled by bowler

The below section estimates the balls bowled for each bowler. We can see that UBCF Pearson and UBCF Cosine both perform well

df3 <- select(df2, bowler1,batsman1,balls)
df6 <- xtabs(balls ~ ., df3)
df7 <- as.data.frame.matrix(df6)
df8 <- data.matrix(df7)
df8[df8 == 0] <- NA
r <- as(df8,"realRatingMatrix")
r0=r[(rowCounts(r) > 10),]
getRatingMatrix(r0)[1:10,1:10]

## 10 x 10 sparse Matrix of class "dgCMatrix"

##    [[ suppressing 10 column names 'A Badoni', 'A Manohar', 'A Nortje' ... ]]

##
## A Mishra        . . .  63  .  2 35 .  6 .
## A Nortje        . . .  21 25  .  . .  6 .
## A Zampa         . . .   9  .  .  . .  . .
## Abhishek Sharma . . .   9  .  .  . .  6 .
## AD Russell      . . . 117 12  9  . . 21 9
## AF Milne        . . .   .  .  .  . .  8 4
## AJ Tye          . . .  63  .  . 66 .  . .
## Akash Deep      . . .   .  .  .  . .  . .
## AR Patel        . . . 188  5  1 84 . 29 5
## Arshdeep Singh  . . .   6  6 24 18 . 12 .

summary(getRatings(r0))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##    1.00    9.00   18.00   29.61   36.00  384.00

evalRecomMethods(r0[1:dim(r0)[1]],k1=5,given=7,goodRating1=median(getRatings(r0)))

a=eval(r0[1:dim(r0)[1]],0.8,k1=5,given1=7,goodRating1=median(getRatings(r0)),"UBCF")

## Recommender of type 'UBCF' for 'realRatingMatrix'
## learned using 96 users.
## 24 x 195 rating matrix of class 'realRatingMatrix' with 3954 ratings.
##      RMSE       MSE       MAE
##  30.72284 943.89294  19.89204

b=round(as(a,"matrix")[1:10,1:10])
c <- as(b,"realRatingMatrix")
s=as(c,"data.frame")
names(s) =c("bowler","batsman","BallsBowled")


## 14. Runs conceded by bowler

This section estimates the runs conceded by the bowler. The UBCF Cosinus algorithm performs the best with TPR increasing fastewr than FPR

df3 <- select(df2, bowler1,batsman1,runsConceded)
df6 <- xtabs(runsConceded ~ ., df3)
df7 <- as.data.frame.matrix(df6)
df8 <- data.matrix(df7)
df8[df8 == 0] <- NA
r <- as(df8,"realRatingMatrix")
r0=r[(rowCounts(r) > 10),]
getRatingMatrix(r0)[1:10,1:10]

## 10 x 10 sparse Matrix of class "dgCMatrix"

##    [[ suppressing 10 column names 'A Badoni', 'A Manohar', 'A Nortje' ... ]]

##
## A Mishra        . . .  61  .  3  41 . 15  .
## A Nortje        . . .  36 57  .   . .  8  .
## A Zampa         . . .   3  .  .   . .  .  .
## Abhishek Sharma . . .   6  .  .   . .  3  .
## AD Russell      . . . 276 12  6   . . 21  .
## AF Milne        . . .   .  .  .   . . 10  4
## AJ Tye          . . .  69  .  . 138 .  .  .
## Akash Deep      . . .   .  .  .   . .  .  .
## AR Patel        . . . 205  5  . 165 . 33 13
## Arshdeep Singh  . . .  18  3 51  51 .  6  .

summary(getRatings(r0))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##    1.00    9.00   24.00   41.34   54.00  458.00

evalRecomMethods(r0[1:dim(r0)[1]],k1=5,given=7,goodRating1=median(getRatings(r0)))

## Timing stopped at: 0.004 0 0.004

## Warning in .local(x, method, ...):
##   Recommender 'UBCF Pearson' has failed and has been removed from the results!

a=eval(r0[1:dim(r0)[1]],0.8,k1=5,given1=7,goodRating1=median(getRatings(r0)),"UBCF")

## Recommender of type 'UBCF' for 'realRatingMatrix'
## learned using 95 users.
## 24 x 195 rating matrix of class 'realRatingMatrix' with 3820 ratings.
##       RMSE        MSE        MAE
##   43.16674 1863.36749   30.32709

b=round(as(a,"matrix")[1:10,1:10])
c <- as(b,"realRatingMatrix")
t=as(c,"data.frame")
names(t) =c("bowler","batsman","RunsConceded")


## 15. Economy Rate of the bowler

This section computes the economy rate of the bowler. The performance is not all that good

df3 <- select(df2, bowler1,batsman1,ER)
df6 <- xtabs(ER ~ ., df3)
df7 <- as.data.frame.matrix(df6)
df8 <- data.matrix(df7)
df8[df8 == 0] <- NA
r <- as(df8,"realRatingMatrix")
r0=r[(rowCounts(r) > 10),]
getRatingMatrix(r0)[1:10,1:10]

## 10 x 10 sparse Matrix of class "dgCMatrix"

##    [[ suppressing 10 column names 'A Badoni', 'A Manohar', 'A Nortje' ... ]]

##
## A Mishra        . . .  5.809524  .     9.00  7.028571 . 15.000000  .
## A Nortje        . . . 10.285714 13.68  .     .        .  8.000000  .
## A Zampa         . . .  2.000000  .     .     .        .  .         .
## Abhishek Sharma . . .  4.000000  .     .     .        .  3.000000  .
## AD Russell      . . . 14.153846  6.00  4.00  .        .  6.000000  .
## AF Milne        . . .  .         .     .     .        .  7.500000  6.0
## AJ Tye          . . .  6.571429  .     .    12.545455 .  .         .
## Akash Deep      . . .  .         .     .     .        .  .         .
## AR Patel        . . .  6.542553  6.00  .    11.785714 .  6.827586 15.6
## Arshdeep Singh  . . . 18.000000  3.00 12.75 17.000000 .  3.000000  .

summary(getRatings(r0))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##  0.3529  5.2500  7.1126  7.8139  9.8000 36.0000

evalRecomMethods(r0[1:dim(r0)[1]],k1=5,given=7,goodRating1=median(getRatings(r0)))

## Timing stopped at: 0.003 0 0.004

## Warning in .local(x, method, ...):
##   Recommender 'UBCF Pearson' has failed and has been removed from the results!

a=eval(r0[1:dim(r0)[1]],0.8,k1=5,given1=7,goodRating1=median(getRatings(r0)),"UBCF")

## Recommender of type 'UBCF' for 'realRatingMatrix'
## learned using 95 users.
## 24 x 195 rating matrix of class 'realRatingMatrix' with 3839 ratings.
##      RMSE       MSE       MAE
##  4.380680 19.190356  3.316556

b=round(as(a,"matrix")[1:10,1:10])
c <- as(b,"realRatingMatrix")
u=as(c,"data.frame")
names(u) =c("bowler","batsman","EconomyRate")


## 16. Wickets Taken by bowler

The code below computes the wickets taken by the bowler versus different batsmen

df3 <- select(df2, bowler1,batsman1,wicketTaken)
df6 <- xtabs(wicketTaken ~ ., df3)
df7 <- as.data.frame.matrix(df6)
df8 <- data.matrix(df7)
df8[df8 == 0] <- NA
r <- as(df8,"realRatingMatrix")
r0=r[(rowCounts(r) > 10),]
getRatingMatrix(r0)[1:10,1:10]

## 10 x 10 sparse Matrix of class "dgCMatrix"

##    [[ suppressing 10 column names 'A Badoni', 'A Manohar', 'A Nortje' ... ]]

##
## A Mishra       . . . . . . 1 . . .
## A Nortje       . . . 4 . . . . . .
## A Zampa        . . . 3 . . . . . .
## AD Russell     . . . 3 . . . . . .
## AJ Tye         . . . 3 . . 6 . . .
## AR Patel       . . . 4 . 1 3 . 1 1
## Arshdeep Singh . . . 3 . . 3 . . .
## AS Rajpoot     . . . . . . 3 . . .
## Avesh Khan     . . . . . . 1 . 3 .
## B Kumar        . . . 9 . . 3 . 1 .

summary(getRatings(r0))

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##   1.000   3.000   3.000   3.423   3.000  21.000

evalRecomMethods(r0[1:dim(r0)[1]],k1=5,given=7,goodRating1=median(getRatings(r0)))

## Timing stopped at: 0.003 0 0.003

## Warning in .local(x, method, ...):
##   Recommender 'UBCF Pearson' has failed and has been removed from the results!

a=eval(r0[1:dim(r0)[1]],0.8,k1=5,given1=7,goodRating1=median(getRatings(r0)),"UBCF")

## Recommender of type 'UBCF' for 'realRatingMatrix'
## learned using 64 users.
## 16 x 195 rating matrix of class 'realRatingMatrix' with 1908 ratings.
##     RMSE      MSE      MAE
## 2.672677 7.143203 1.956934

b=round(as(a,"matrix")[1:10,1:10])
c <- as(b,"realRatingMatrix")
v=as(c,"data.frame")
names(v) =c("bowler","batsman","WicketTaken")


## 17. Generate the Bowler Performance estmiate

The entire dataframe is regenerated with known and ‘predicted’ values

r1=merge(s,t,by=c("bowler","batsman"))
r2=merge(r1,u,by=c("bowler","batsman"))
r3=merge(r2,v,by=c("bowler","batsman"))
r4= select(r3,bowler, batsman, BallsBowled,RunsConceded,EconomyRate, WicketTaken)

##     bowler         batsman BallsBowled RunsConceded EconomyRate WicketTaken
## 1 A Mishra  AB de Villiers         102          144           8           4
## 2 A Mishra     Abdul Samad          13           20           7           4
## 3 A Mishra Abhishek Sharma          14           26           8           2
## 4 A Mishra      AD Russell          47           85           9           3
## 5 A Mishra        AJ Finch          45           61          11           4
## 6 A Mishra          AJ Tye          14           20           5           4


## 18. Conclusion

This post showed an approach for performing the Batsmen Performance Estimate & Bowler Performance Estimate. The performance of the recommender engine could have been better. In any case, I think this approach will work for player estimation provided the recommender algorithm is able to achieve a high degree of accuracy. This will be a good way to estimate as the algorithm will be able to determine features and nuances of batsmen and bowlers which cannot be captured by data.

## Also see

To see all posts click Index of posts

# IPL 2022: Near real-time analytics with GooglyPlusPlus!!!

It is that time of the year when there is “a song in the air, the lark’s on the wing, and the snail’s on the the thorn“. Yes, it is the that time of year when the grand gala event of IPL 2022 is underway. So, I managed to wake myself from my Covid-induced slumber, worked up my ‘creaking bones‘ and cranked up the GooglyPlusPlus machinery.

So now, every morning, a scheduled CRON tab entry will automatically download the previous night’s match data from Cricsheet, unzip, process and transform it into the necessary format required by my R package yorkr, and make it available to my Shiny app GooglyPlusPlus. Hence the data is current and you have access to ‘analytics-in-the-now’!.

As you know in 2021, I added a lot of new features to GooglyPlusPlus, new tabs to do even more. analytics – or in other words there is “more GooglyPlusPlus per click!!”. So now, you have the following

• Batsman tab: For detailed analysis of batsmen
• Bowler tab: For detailed analysis of bowlers
• Match tab: Analysis of individual matches, plot of Runs vs SR, Wickets vs ER in power play, middle and death overs
• Head-to-head tab: Detailed analysis of team-vs-team batting/bowling scorecard, batting, bowling performances, performances in power play, middle and death overs
• Team performance tab: Analysis of team-vs-all other teams with batting /bowling scorecard, batting, bowling performances, performances in power play, middle and death overs
• Optimisation tab: Allows one to pit batsmen vs bowlers and vice-versa. This tab also uses integer programming to optimise batting and bowling lineup
• Batting analysis tab: Ranks batsmen using Runs or SR. Also plots performances of batsmen in power play, middle and death overs and plots them in a 4×4 grid
• Bowling analysis tab: Ranks bowlers based on Wickets or ER. Also plots performances of bowlers in power play, middle and death overs and plots them in a 4×4 grid

Also note all these tabs and features are available for all T20 formats namely IPL, Intl. T20 (men, women), BBL, NTB, PSL, CPL, SSM.

Note: All charts are interactive, which means that you can hover, zoom-in, zoom-out, pan etc on the charts

The latest avatar of GooglyPlusPlus2022 is based on my R package yorkr with data from Cricsheet.

Go ahead, give GooglyPlusPlus a try!!!

To know all the new features and how to use them, check out these posts

1. Ranking of batsmen, bowlers – GooglyPlusPlus2021 interactively ranks T20 batsmen and bowlers!!!
2. Interactive charts – GooglyPlusPlus2021 is now fully interactive!!!
3. Detailed batsmen/bowler analytics – GooglyPlusPlus2021 enhanced with drill-down batsman, bowler analytics
4. Addition of Date Range picker to charts – GooglyPlusPlus2021 adds new bells and whistles!!
5. Analysis of power play, middle and death overs across players, teams – GooglyPlusPlus2021 now with power play, middle and death over analysis
6. Analysis based on 4 x 4 grid of players – GooglyPlusPlus2021: Towards more picturesque analytics!
7. Optimisation of batsmen/bowlers – GooglyPlusPlus2022 optimizes batting/bowling lineup

Here are some random analysis that can be done by GooglyPlusPlus across the tabs. Note the app will be updated daily and the analytics will be current throughout the season of IPL 2022

A) Match tab

a) GT vs DC – 2 Apr 2022

Runs vs SR – Gujarat Titans

b) CSK vs LSG – 31 Mar 2022

Runs across 20 overs

c) KKR vs PBKS -Match wicket worm chart – 1 Apr 2022

B) Batsmen tab

a) Faf Du Plessis – Runs vs Deliveries

b) Sanju Samson – Runs against opposition

C) Bowler’s tab

a) D J Bravo – No of deliveries to wicket

b) Trent Boult – Wickets at Venues

a) DC vs MI – Mar -2019 till date : Batting scorecard

b) CSK vs KKR – Jan 2019 till date : Runs vs SR

E) Team vs All Teams tab

a) Punjab Kings vs all Teams – Wickets vs ER in Power play

b) Rajasthan Royals vs all Teams : Jan 2019 till date : Runs vs SR in Power play

F) Optimisation tab

a) Batsmen vs Bowlers

b) Bowlers vs batsmen

G) Batting analysis

This tab is for ranking batsmen

a) Batsmen rank from 2019 till date (Runs over SR)

b) Overall Runs vs SR (Jan 2020 till date)

Best batsmen in top right quadrant

zooming in on the above (right-most)

H) Bowling analysis tab

a) Best middle over bowlers in IPL (2019 onwards)

The bottom right quadrant are the best bowlers

b) Best bowlers in death overs (bottom-right)

Check out GooglyPlusPlus!!!

Also see

To see all posts click Index of posts

# Analyzing player performance with animated charts!

Analytics is by definition, the science (& art) of identifying, discovering and interpreting patterns in data. There are different ways of capturing these patterns through charts (bar, pie, cumulative data, moving average etc.). One such way is the motion or animated chart which captures the changes in data across different time periods. This was made famous by Hans Rosling in his Gapminder charts.

In this post, I use animated charts, based on gganimate(), to display the rise and fall of batsmen and bowlers in IPL and Intl. T20 (men). I only did this for these 2 formats as they have sufficient data over at least 10+ years.

To construct these animated charts, I use a ‘sliding window’ of 3 years, so that we get a clearer view of batsman and bowler consistency. The animated charts show the performance of players for this moving window for e.g. Jan 2008- Dec 2010, Jan 2009-Dec 2011, Jan 2010- Dec 2012 and so on till Jan 2019- Dec 2021. This is done for both batting( total runs) and bowling (total wickets). If you would like to analyse the performance of particular batsmen, bowler during specific periods or for a team vs another team or in the overall T20 format, check out my post GooglyPlusPlus2021: Towards more picturesque analytics!

You clone/fork the code from Github here animation.

Note: This code is based on a snippet from this blog How to create animations in R with gganimate by Ander Fernandez Jauregui

Included below are the animated charts.

Important note: The year which is displayed on the side actually represents the last 3 years, for e.g. 2015 (2013, 2014, 2015) or 2019 (2017, 2018, 2019)

1. IPL Batting performance

We can see that Kohli stays in the top 3 from 2015-2019

2. IPL Bowling performance

Malinga ruled from 2010- 2015. Bumrah is in top 3 from 2019-2021

3. IPL Batting in Power play

Adam Gilchrist, Tendulkar, Warner, KL Rahul, Shikhar Dhawan have a stay at the top

4. IPL Batting in Middle overs

Rohit Sharma, Kohli, Pant have their stay at the top

5. IPL Batting Death overs

MS Dhoni is lord and master of the death overs in IPL for a rolling period of 10 years from 2011-2020. No wonder, he is the best finisher of T20 cricket

6. IPL Bowling Power Play

Bhuvanesh Kumar is in top 3 from 2014-2018 and then Deepak Chahar

7. IPL Bowling Middle overs

Toppers Harbhajan Singh, YS Chahal, Rashid Khan

8. IPL Bowling Death overs

SL Malinga, B. Kumar, JJ Bumrah and Rabada top the list across the years

9. T20 (men) Batting performance

Kohli, Babar Azam, P R Stirling are best performers

10. T20 (men) bowling performance

Saaed Ajmal tops from 2010-2014 and Rashid Khan 2018-2020

11. T20 (men) batting Power play

Shahzad, D Warner, Rohit Sharma, PR Stirling best performers

12. T20 (men) batting middle overs

Babar Azam is the best middle overs player from 2018-2021

13. T20(men) batting death overs

MS Dhoni, Shoaib Malik, V Kohli, David Miller are the best death over players

14. T20 (men) bowling Power play

Mohammad Nabi, Mujeeb ur Rahman, TG Southee are the best bowlers in power play

15. T20 (men) bowling middle overs

Imran Tahir from 2015-2017, Shadab Khan from 2018-2020, T Shamsi in 2021 top the tables

16. T20 (men) bowling death overs

Saaed Ajmal, A J Tye, Bumrah, Haris Rauf occupy the top slot in different periods

Also see

To see all posts click Index of posts

# GooglyPlusPlus2021: Towards more picturesque analytics!

Analytics for e.g. sports analytics, business analytics or analytics in e-commerce or in other domain has 2 main requirements namely a) What kind of analytics (set of parameters,function) will squeeze out the most intelligence from the data b) How to represent the analytics so that an expert can garner maximum insight?

While it may appear that the former is more important, the latter is also equally, if not, more vital to the problem. Indeed, a picture is worth a thousand words, and often times is more insightful than a large table of numbers. However, in the case of sports analytics, for e.g. in cricket a batting or bowling scorecard captures more information and can never be represented in chart.

So, my Shiny app GooglyPlusPlus includes both charts and tables for different aspects of the analysis. In this post, a newer type of chart, popular among senior management experts, namely the 4 quadrant graph is introduced, which helps in categorising batsmen and bowlers into 4 categories as shown below

a) Batting Performances – Top right quadrant (High runs, High Strike rate)

b) Bowling Performances – Bottom right quadrant( High wickets, Low Economy Rate)

A. Match Tab

All the functions below are at match level

1. Team Runs vs SR Plot
2. Team Wickets vs ER Plot
3. Team Runs vs SR Power play plot
4. Team Runs vs SR Middle overs plot
5. Team Runs vs SR Death overs plot
6. Team Wickets vs ER Power Play
7. Team Wickets vs ER Middle overs
8. Team Wickets vs ER Death overs

The below functions are based on all matches between 2 teams’

1. Team Runs vs SR Plot all Matches
2. Team Wickets vs ER Plot all Matches
3. Team Runs vs SR Power play plot all Matches
4. Team Runs vs SR Middle overs plot all Matches
5. Team Runs vs SR Death overs plot all Matches
6. Team Wickets vs ER Power Play plot all Matches
7. Team Wickets vs ER Middle overs plot all Matches
8. Team Wickets vs ER Death overs plot all Matches

C. Team Performance tab

The below functions are based on a team’s performance against all other teams

1. Team Runs vs SR Plot overall
2. Team Wickets vs ER Plot overall
3. Team Runs vs SR Power play plot overall
4. Team Runs vs SR Middle overs plot overall
5. Team Runs vs SR Death overs plot overall
6. Team Wickets vs ER Power Play overall
7. Team Wickets vs ER Middle overs overall
8. Team Wickets vs ER Death overs overall

D. T20 format Batting Analysis

This analysis is at T20 format level (IPL, Intl. T20(men), Intl. T20 (women), PSL, CPL etc.)

1. Overall Runs vs SR plot
2. Overall Runs vs SR Power play plot
3. Overall Runs vs SR Middle overs plot
4. Overall Runs vs SR Death overs plot

E. T20 Bowling Analysis

This analysis is at T20 format level (IPL, Intl. T20(men), Intl. T20 (women), PSL, CPL etc.)

1. Overall Wickets vs ER plot
2. Team Wickets vs ER Power Play
3. Team Wickets vs ER Middle overs
4. Team Wickets vs ER Death overs

These 32 functions have been added to my yorkr package and so all these functions become plug-n-play in my Shiny app GooglyPlusPlus2021 which means that the 32 functions apply across all the nine T20 formats that the app supports i.e. IPL, Intl. T20 (men), Intl. T20 (women), BBL, NTB, PSL, CPL, SSM, WBB.

Hence the multiplicative factor of the new addition is 32 x 9 = 288 additional ways of exploring match, team and player data

The data for GooglyPlusPlus is taken from Cricsheet. My shiny app GooglyPlusPlus2021 is based on my R package yorkr.

You can clone/fork GooglyPlusPlus from Github at gpp2021-10

Check out my app GooglyPlusPlus2021 and analyze batsmen, bowlers, teams, overall performance. The data for all the nine T20 formats have been updated to include the latest data.

Hence, the app is just in time for the IPL mega auction. You should be able to analyse players in IPL, Intl. T20 or in any of the other formats from where they could be drawn and check out their relative standings

I am including some random plots to demonstrate the newly minted functions

Note 1: All plots are interactive. The controls are on the top right. You can hover over data, zoom-in, zoom-out, compare data etc by choosing the appropriate control. To know more about how to use the interactive charts see GooglyPlusPlus2021 is now fully interactive!!!

You can also check my short video on how to navigate interactive charts

Note 2: To know about Powerplay, Middle overs and Death over analysis see my post GooglyPlusPlus2021 now with power play, middle and death over analysis

Note 3: All tabs(except Match tab) now include Date range pickers to focus on the period of interest. See my post GooglyPlusPlus2021 enhanced with drill-down batsman, bowler analytics

I) Match tab

New Zealand vs Australia (2021-11-14)

New Zealand batting, except K Williamson, the rest did not fire as much

For Australia, Warner, Maxwell and Marsh played good knocks to wrest control

a) Wickets vs ER during Power play of Mumbai Indians in all matches against Chennai Super Kings (IPL)

b) Karachi Kings Runs vs SR during middle overs against Multan Sultans (PSL)

c) Wickets vs ER during death overs of Barbados Tridents in all matches against Jamaica Tallawahs (CPL)

III) Teams overall batting performance

India’s best T20 performers in Power play since 2018 (Intl. T20)

e) Australia’s best performers in Death overs since Mar 2017 (Intl. T20)

f) India’s Intl. T20 (women) best Runs vs SR since 2018

g) England’s Intl. T20 (women) best bowlers in Death overs

IV) Overall Batting Performance across T20

This tab gives the batsmen’s rank and overall batting performance across the T20 format.

a) Why was Hardik Pandya chosen, and why this was in error?

Of course, it provides an insight into why Hardik Pandya was chosen in India’s World cup team despite poor performances recently. Here are the best Intl. T20 death over batsmen

Of course, we can zoom in to get a better look

This is further substantiated when we performances in IPL

However, if you move the needle forward a year at a time, you see Hardik Pandya’s performance drops significantly

and further down

Rather, Dinesh Karthik, Sanju Samson or Ruturaj Gaikwad would have been better options

b) Best batsmen Intl. T20 (women) in Power play since 2018

V) Overall bowling performance

This tab gives the bowler’s rank and overall bowling performance in Power play, middle and death overs across all T20 formats

a) Intl. T20 (men) best bowlers in Power Play from 2019 (zoomed in)

b) Intl. T20(men) best bowlers in Death overs since 2019

c) Was B. Kumar a good choice for India team in World cup?

Bhuvi was one of India’s best bowler in Power play only if we go back to the beginning of time

i) From 2008

But if we move forward to 2020 onwards we see Arshdeep Singh or D Chahar would have been a better choice

ii) From 2020 onwards

iii) 2021 onwards

Hence D Chahar & Arshdeep Singh are the natural choice moving forwards for India

iv) T20 Best batsman

If we look at Intl. T20 performances since 2017, Babar Azam leads the pack, however his Strike rate needs to move up.

v) T20 best bowlers

As mentioned above go ahead and give GooglyPlusPlus2021 a spin!!!

You can download/fork the code for the Shiny app from Github at gpp2021-10

Also see

To see all posts click Index of posts

# GooglyPlusPlus2021 now with power play, middle and death over analysis

This latest edition of GooglyPlusPlus2021 now includes detailed analysis of teams, batsmen and bowlers in power play, middle and death overs. The T20 format is based on 3 phases as each side faces 20 overs.

Power play: Overs: 0 – 6 – No more than 2 players can be outside the 30 yard circle

Middle overs: Overs: 7- 16 – During these overs the batting side tries to consolidate their innings

Death overs: Overs: 16 -20 – During these 5 overs the batting side tries to accelerate the scoring rate, while the bowling side will try to restrict the batsmen against going for big hits

This is shown below

This latest update of GooglyPlusPlus2021 includes the following functions

a) Match tab

1. teamRunsAcrossOvers
2. teamSRAcrossOvers
3. teamWicketsAcrossOvers
4. teamERAcrossOvers
5. matchWormWickets

1. teamRunsAcrossOversOppnAllMatches
2. teamSRAcrossOversOppnAllMatches
3. teamWicketsAcrossOversOppnAllMatches
4. teamERAcrossOversOppnAllMatches
5. topRunsBatsmenAcrossOversOppnAllMatches
6. topSRBatsmenAcrossOversOppnAllMatches
7. topWicketsBowlersAcrossOversOppnAllMatches
8. topERBowlerAcrossOverOppnAllMatches

c) Overall performance tab

1. teamRunsAcrossOversAllOppnAllMatches
2. teamSRAcrossOversAllOppnAllMatches
3. teamWicketsAcrossOversAllOppnAllMatches
4. teamERAcrossOversAllOppnAllMatches
5. topRunsBatsmenAcrossOversAllOppnAllMatches
6. topSRBatsmenAcrossOversAllOppnAllMatches
7. topWicketsBowlersAcrossOversAllOppnAllMatches
8. topERBowlerAcrossOverAllOppnAllMatches

Hence a total of 8 + 8 + 5 = 21 functions have been added. These functions can be utilized across all the 9 T20 formats that are supported in GooglyPlusPlus2021 namely

i) IPL ii) Intl. T20 (men) iii) Intl. T20 (women) iv) BBL v) NTB vi) PSL vii) CPL viii) SSM ix) WBB

Hence there are a total of 21 x 9 = 189 new possibilities to explore in GooglyPlusPlus2021

GooglyPlusPlus2021 is based on my R package yorkr and is based on data from Cricsheet. To know how to use GooglyPlusPlus see any of earlier posts GooglyPlusPlus2021 is now fully interactive!!!, GooglyPlusPlus2021 adds new bells and whistles!!, GooglyPlusPlus2021 enhanced with drill-down batsman, bowler analytics

Take GooglyPlusPlus for a spin here GooglyPlusPlus2021-9

You can clone/fork the code for the Shiny app from Github – gpp2021-9

Included below is a random selection of options from the 189 possibilities mentioned above. Feel free to try out for yourself

A) IPL – CSK vs KKR 2018-04-10

a) Team Runs in power play, middle and death overs

b) Team Strike rate in power play, middle and death overs

B) Intl. T20 (men) – India vs Afghanistan (2021-11-03)

a) Team wickets in power play, middle and death overs

b) Team Economy rate in power play, middle and death overs

a) Team Runs in all matches in power play, middle and death overs

a) Team vs team Strike rate : Karachi Kings vs Lahore Qalanders since 2019 in power play, middle and death overs

E) Team overall performance in all matches against all opposition

a) BBL : Brisbane Heats : Team Wickets between 2015 – 2018 in power play, middle and death overs

F) Top Runs and Strike rate Batsman of Mumbai Indians vs Royal Challengers Bangalore since 2018

a) Top runs scorers for Mumbai Indians (MI) in power play, middle and death overs

b) Top strike rate for RCB in power play, middle and death overs

F) Intl. T20 (women) India vs England since 2018

a) Top wicket takers for England in power play, middle and death overs since 2018

b) Top wicket takers for India in power play, middle and death overs since 2018

G) Intl. T20 (men) All time best batsmen and bowlers for India

a) Most runs in power play, middle and death overs

b) Highest strike rate in power play, middle and death overs

H) Match worm wicket chart

Note: You can zoom to the area where you would like to focus more

The option of looking at the Match worm chart (without wickets) also exists.

Go ahead take GooglyPlusPlus2021 for a test drive and check out how your favourite players perform in power play, middle and death overs. Click GooglyPlusPlus2021-9

Hope you have fun with GooglyPlusPlus

You may also like

To see all posts click Index of posts

# GooglyPlusPlus2021:ICC WC T20:Pavilion-view analytics as-it-happens!

This year 2021, we are witnessing a rare spectacle in the cricketing universe, where IPL playoffs are immediately followed by ICC World Cup T20. Cricket pundits have claimed such a phenomenon occurs once in 127 years! Jokes apart, the World cup T20 is underway and as usual GooglyPlusPlus is ready for the action.

GooglyPlusPlus will provide near-real time analytics, by automatically downloading the latest match data daily, processing and organising the match data into appropriate folders so that my R package yorkr can slice and dice the data to provide the pavilion-view analytics.

The charts capture all the breathless, heart-pounding, and nail-biting action in great details in the many tables and plots. Every table and chart tell a story. You just have to ‘read between the lines!’

GooglyPlusPlus2021 will update itself automatically every day, so the data will be current and you can analyse all matches upto the previous day, along with the historical performances of the teams. So make sure you check it everyday.

Note:

1. All charts are interactive. To know how to use the interactive charts see my post GooglyPlusPlus2021 is now fully interactive!!!
2. The are 5 tabs for each of the formats supported by GooglyPlusPlus2021 which now supports IPL, Intl. T20(men), Intl. T20(women), BBL, NTB, PSL, CPL, SSM, WBB. Besides, it also supports ODI (men) and ODI (women)
3. Each of the formats have 5 tabs – Batsman, Bowler, Match, Head-to-head and Overall Performace.
4. All T20 formats also include a ranking functionality for the batsmen and bowlers
5. You can now perform drill-down analytics for batsmen, bowlers, head-to-head and overall performance based on date-range selector functionality. The ranking tabs also include date range selector granular analysis. For more details see GooglyPlusPlus2021 enhanced with drill-down batsman, bowler analytics

Try out GooglyPlusPlus2021 here gpp2021-8!!

You can clone fork the code from Github gpp2021-8

I am including some random screenshots of things that can be done with GooglyPlusPlus2021

A. Papua New Guinea vs Oman (2021-10-17)

a. Batting partnership

B. Match worm chart (New Papua Guinea v Oman)

This was a no contest as Oman cruised to victory

b. March worm chart (Scotland vs Bangladesh)

Fortunes see-sawed one way, then another, as can be seen in the match worm chart

C. Netherlands vs Ireland (2021-10-18)

a. Batman vs Bowler

a. Sri Lanka vs West Indies (2019-2021) – Batting partnerships

b. India vs England (2018 – 2021) – Bowling scorecard

c) Australia vs South Africa – Team wicket opposition

E) Overall performance

a. Pakistan batting scorecard since 2019

a. Win loss of Australia since 2019

F) Batsman Performance

a. PR Stirling’s runs against opposition since 2019

b. KJ Brien’s cumulative average runs since 2019

G. Bowler performance

a. PWH De Silva’s wicket prediction since 2019

b. T Shamsi’s cumulative average wickets since 2019

H. Ranking Intl. T20 batsman since 2019

a. Runs over Strike rate

b. Strike rate over runs

I. Ranking bowlers since 2019

a. Wickets over Economy rate

b. Economy rate over wickets

As mentioned above GooglyPlusPlus2021 will be updated daily automatically, so you won’t miss any analytic action.

Do give GooglyPlusPlus2021 a spin!

Clone/fork the code for the Shiny app from Github gpp2021-8

You may also like

To see all post click Index of posts

# GooglyPlusPlus2021 enhanced with drill-down batsman, bowler analytics

This latest update to GooglyPlusPlus2021 includes the following changes

a) All the functions in the ‘Batsman’ and ‘Bowler ‘tabs now include a date range, which allows you specify a period of interest.

b) The ‘Rank Batsman’ and ‘Rank Bowler’ tabs also include a date range selector, against the earlier version which had a ‘Since year’ slider see GooglyPlusPlus2021 bubbles up top T20 players in all formats!. The earlier ‘Since year’ slider option could only rank for the latest year or for all years up to the current year. Now with the new ‘date range’ picker we can check the batsman and bowler ranks in any IPL season or (any T20 format) or for a range of years.

There are 10 batsman functions and 9 bowler function that have changed for the following T20 and ODI formats and Rank batsman and bowler includes the ‘date range’ and has changed for all T20 formats.

GooglyPlusPlus2021 supports all the following T20 formats

i) IPL ii) Intl T20(men) iii) Intl T20(women) iv) BBL v) NTB vi) PSL vii) WBB viii) CPL ix) SSM T20 formats – ( 9 T20 panels)

i) ODI (men) ii) ODI (women) – 2 ODI panels

i.e. the changes impact (10 + 9) x 11 + (1 + 1 ) x 9 = 227 tabs which have been changed

The addition of date range enables a fine-grained analysis of players as the players progress through the years.

Note: All charts are interactive. To see how to use interactive charts of GooglyPlusPlus2021 see

GooglyPlusPlus2021 is now fully interactive!!!

GooglyPlusPlus2021 is based on my R package yorkr. The data is take from Cricsheet

You can clone/fork this latest version of GooglyPlusPlus2021 from Github at gpp2021-7

Check out the Shiny app here GooglyPlusPlus2021!!!

I have included some random screen shots of some of using these tabs and options in GooglyPlusPlus2021.

A) KL Rahul’s Cumulative average in IPL 2021 vs IPL 2020

a) KL Rahul in IPL 2021

b) KL Rahul in IPL 2020

B) Performance of Babar Azam in Intl. T20 (men)

a) Babar Azam’s cumulative average from 2019

b) Babar Azam’s Runs against opposition since 2019

Note: Intl. T20 (women) data available upto Mar 2020 from Cricsheet

a) A J Healy performance between 2010 – 2015

b) A J Healy performance between 2015 – 2020

D) M S Dhoni’s performance with the bat pre-2020 and post 2020

There has been a significant decline in Dhoni’s performance in the last couple of years

I) Dhoni’s performance from Jan 2010 to Dec 2019

a) Moving average at 25+ (Dhoni before)

The moving average actually moves up…

b) Cumulative average at 25+ (Dhoni before)

c) Cumulative Strike rate 140+ (Dhoni before)

d) Dhoni’s moving average is ~10-12 (post 2020)

e) Dhoni’s cumulative average (post 2020)

f) Dhoni’s strike rate ~80 (post 2020)

E) Bumrah’s performance in IPL

a) Bumrah’s performance in IPL 2020

b) Bumrah’s performance in IPL 2021

F) Moving average wickets for A. Shrubsole in ODI (women)

G) Chris Jordan’s cumulative economy rate

We can see that Jordan has become more expensive over the years

G) Ranking players

In this latest version the ‘Since year slider’ has been replaced with a Date Range selector. With this we can identify the player ranks in any IPL, CPL, PSL or BBL season. We can also check the performance over the last couple of years. Note: The matches played and Runs over Strike rate or Strike rate over runs can be computed. Similarly for bowlers we have Wickets over Economy rate and Economy rate over wickets options.

a) Ranking IPL batsman in IPL season 2020

b) Ranking Intl. T20 (batsmen) from Jan 2019 to Jul 2021

c) Ranking Intl. T20 bowlers (women) from Jan 2019 – Jul 2021

d) Best IPL bowlers over the last 3 seasons (Wickets over Economy rate)

e) Best IPL bowlers over the last 3 seasons (Economy rate over wickets)