Ahlad- Data Discovery Stage 2

AIM: What factors influence match outcomes in cricket, and how do toss decisions and venues impact the performance of teams?

The purpose is to investigate the factors influencing cricket match outcomes, with a specific focus on toss decisions and venue performance.

library(ggplot2)
library(dplyr)

data <- read.csv("~/Documents/Rdocs/matches.csv", stringsAsFactors = TRUE)

head(data)

Assumptions:

The dataset accurately represents match outcomes and decisions. It is reasonable as the dataset is sourced from official records. Toss decisions and venue impact are significant predictors of match outcomes. It aligns with cricket strategies commonly observed in practice. Wins are a fair measure of team performance. It is acceptable as match results are universally measured by wins.

Hypothesis testing

Hypothesis : There is a significant difference in the average result margin between teams that chose to bat first and those that chose to field first.

Alternate Hypothesis : Average result margin is independent of toss decision.

data_margin <- data_clean %>% filter(!is.na(result_margin))

# Create two seperate groups based on toss decision 
bat_first <- data_margin %>% filter(toss_decision == 'bat') %>% pull(result_margin)
field_first <- data_margin %>% filter(toss_decision == 'field') %>% pull(result_margin)

t_test_result <- t.test(bat_first, field_first, var.equal = TRUE)

# Print test result
print(t_test_result)

    Two Sample t-test

data:  bat_first and field_first
t = -0.54765, df = 1074, p-value = 0.584
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -3.480792  1.961765
sample estimates:
mean of x mean of y 
 16.77083  17.53035 

We got p value of 0.584 from the t test. we fail to reject the null hypothesis and conclude that there is significant difference in the result margins based on toss decisions. Lets check a visualization to understand well.

From this boxplot we can see there are differences between result margin by toss decision. Teams that chose field first have won by bigger margin than that of teams that chose to bat first. So we can say that they are not independent.

Hypothesis: Do teams winning the toss have a significant advantage in winning the match statistically?

Alternative Hypothesis: The chances of winning a match is higher if a team wins the toss.

Does the teams that field after winning the toss have a higher probability of winning?. Lets perform Chi-square test for independence between toss decision and match outcome.

print(chi_test_result)

    Chi-squared test for given probabilities

data:  toss_match_table
X-squared = 0.29725, df = 1, p-value = 0.5856

We got p value from chi squared test.The p-value from the test is more than the significance level (0.05), so we fail to reject the null hypothesis, indicating that winning the toss does not give a statistically significant advantage in winning the match. Lets check a visualizatio to understand better.

Hypothesis : There is no association between the toss decision, venue and match outcome.

Alternative Hypothesis: There is an association between the two variables.

Does certain venues provide a home-ground advantage?.Lets build a logistic regression to evaluate the probability of home team wins at specific venues. The home team can be team1 in the dataset as it is scheduled as home vs away normally.

data$home_advantage <- ifelse(data$team1 == data$winner | data$team2 == data$winner, 1, 0)
venue_model <- glm(home_advantage ~ venue, data = data, family = binomial)
summary(venue_model)

Based on this test, we can see which teams have more home advantage to others.

chisq.test(toss_outcomes)

    Pearson's Chi-squared test with Yates' continuity correction

data:  toss_outcomes
X-squared = 6.8585, df = 1, p-value = 0.008822

Given the p-value = 0.008822, which suggests to reject the null hypothesis.So we can interpret that there is statistically significant evidence of an association between the toss decision and match outcome.

Key Insights:

Teams that choose to field after winning the toss have a higher win rate.Some venues significantly favor home teams, indicating a potential advantage. Toss decisions and venue analysis can guide pre-match strategies.Venues with positive significant coefficients in LRM indicate that winning toss provides clear advantage. Teams should focus on optimizing toss decisions. similarly when the coefficient is negative or neutral, teams have to rely on players and conditions rather than depending on toss.

Based on these insights, we can recommend the following. For Teams: Prioritize fielding after winning the toss in specific conditions. For Organizers: Ensure balanced scheduling to reduce venue bias. For Analysts: Continuously update models with recent data for real-time strategy recommendations.