{"id":25174,"date":"2026-03-28T21:23:20","date_gmt":"2026-03-28T15:53:20","guid":{"rendered":"http:\/\/www.sachdevajk.in\/?p=25174"},"modified":"2026-03-28T21:23:20","modified_gmt":"2026-03-28T15:53:20","slug":"a-comparative-statistical-analysis-of-ipl-franchises","status":"publish","type":"post","link":"http:\/\/www.sachdevajk.in\/?p=25174","title":{"rendered":"A Comparative Statistical Analysis of IPL Franchises"},"content":{"rendered":"

A Comparative Statistical Analysis of IPL Franchises <\/span><\/b><\/p>\n

Author<\/span><\/b>: Tejas Navnath Nilange 92<\/b><\/span><\/p>\n

\u00a0<\/span><\/p>\n

Introduction:<\/span><\/b><\/p>\n

The Indian Premier League (IPL) is one of the most popular cricket tournaments, where team performance is closely followed by fans and analysts. Evaluating the ratings of IPL teams helps in understanding their consistency, strengths, and competitiveness. Beyond on-field results, a franchise’s success is deeply influenced by structural management, brand value, and operational efficiency. This study examines the perceived performance of major franchises and teams are rated by fans and observers.<\/p>\n

<\/b><\/span><\/p>\n

Objective:<\/span><\/b><\/p>\n

To analyze the ratings of IPL teams using one\u2011way ANOVA in order to determine whether significant differences exist among them.<\/p>\n

<\/span><\/p>\n

Literature review:<\/span><\/b><\/p>\n

Efficiency and Performance Metrics<\/span><\/b><\/p>\n

Singh (2011) the study discusses the operational efficiency and the performance parameters of different franchises in the Indian Premier League. The study highlights that, in spite of the success of the teams in the games, the structural management also has a crucial role to play in the overall rating of the teams. It concludes that different franchises are working at different levels of efficiency, thereby making the rating of a team a function of different parameters.<\/span><\/p>\n

Comparative Methodology and Statistical Modeling<\/span><\/b><\/p>\n

Kumar and Nagorao (2022) the study aims to examine the statistical consistency of IPL team performances. This review aims to discuss how, through more advanced statistical modeling techniques, moving beyond simply calculating a win-loss ratio, a more holistic understanding of a franchise’s stability can be achieved. The study suggests that more established franchises may possess a higher mean rating, whereas newer franchises may possess a higher variance in their data, thus requiring ANOVA techniques.<\/p>\n

<\/span><\/p>\n

Data collection:<\/span><\/b><\/p>\n

The data for this study was collected using primary research methods via a structured survey distributed through Google Forms. A total of 50 responses were recorded. Participants were asked to rate the franchises\u2014Mumbai Indians, Chennai Super Kings, Royal Challengers Bengaluru, and Kolkata Knight Riders\u2014on a scale of 1 to 10 based on their overall performance, brand value, and team consistency,<\/span> and a One-Way ANOVA was calculated on the compiled data.<\/p>\n

<\/span><\/p>\n

Data Analysis:<\/span><\/b><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Anova: Single Factor<\/span><\/b><\/p>\n<\/td>\n

\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
\n

SUMMARY<\/span><\/b><\/p>\n<\/td>\n

\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
\n

Groups<\/span><\/i><\/b><\/p>\n<\/td>\n

\n

Count<\/span><\/i><\/b><\/p>\n<\/td>\n

\n

Sum<\/span><\/i><\/b><\/p>\n<\/td>\n

\n

Average<\/span><\/i><\/b><\/p>\n<\/td>\n

\n

Variance<\/span><\/i><\/b><\/p>\n<\/td>\n

\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
\n

Mumbai Indians<\/span><\/b><\/p>\n<\/td>\n

\n

50<\/span><\/p>\n<\/td>\n

\n

454<\/span><\/p>\n<\/td>\n

\n

9.08<\/span><\/p>\n<\/td>\n

\n

3.217959<\/span><\/p>\n<\/td>\n

\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
\n

Chennai Super Kings<\/span><\/b><\/p>\n<\/td>\n

\n

50<\/span><\/p>\n<\/td>\n

\n

347<\/span><\/p>\n<\/td>\n

\n

6.94<\/span><\/p>\n<\/td>\n

\n

6.914694<\/span><\/p>\n<\/td>\n

\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
\n

Royal Challengers Bangalor-<\/span><\/b><\/p>\n<\/td>\n

\n

50<\/span><\/p>\n<\/td>\n

\n

386<\/span><\/p>\n<\/td>\n

\n

7.72<\/span><\/p>\n<\/td>\n

\n

6.409796<\/span><\/p>\n<\/td>\n

\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
\n

Kolkata Knight Riders<\/span><\/b><\/p>\n<\/td>\n

\n

50<\/span><\/p>\n<\/td>\n

\n

294<\/span><\/p>\n<\/td>\n

\n

5.88<\/span><\/p>\n<\/td>\n

\n

5.209796<\/span><\/p>\n<\/td>\n

\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
\n

ANOVA<\/span><\/b><\/p>\n<\/td>\n

\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
\n

Source of Variation<\/span><\/i><\/b><\/p>\n<\/td>\n

\n

SS<\/span><\/i><\/b><\/p>\n<\/td>\n

\n

df<\/span><\/i><\/b><\/p>\n<\/td>\n

\n

MS<\/span><\/i><\/b><\/p>\n<\/td>\n

\n

F<\/span><\/i><\/b><\/p>\n<\/td>\n

\n

P-value<\/span><\/i><\/b><\/p>\n<\/td>\n

\n

F crit<\/span><\/i><\/b><\/p>\n<\/td>\n<\/tr>\n

\n

Between Groups<\/span><\/b><\/p>\n<\/td>\n

\n

272.335<\/span><\/p>\n<\/td>\n

\n

3<\/span><\/p>\n<\/td>\n

\n

90.77833<\/span><\/p>\n<\/td>\n

\n

16.69314<\/span><\/p>\n<\/td>\n

\n

1.07E-09<\/span><\/p>\n<\/td>\n

\n

2.650677<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Within Groups<\/span><\/b><\/p>\n<\/td>\n

\n

1065.86<\/span><\/p>\n<\/td>\n

\n

196<\/span><\/p>\n<\/td>\n

\n

5.438061<\/span><\/p>\n<\/td>\n

\n

\u00a0<\/span><\/p>\n<\/td>\n

\n

\u00a0<\/span><\/p>\n<\/td>\n

\n

\u00a0<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

\u00a0<\/span><\/b><\/p>\n<\/td>\n

\n

\u00a0<\/span><\/p>\n<\/td>\n

\n

\u00a0<\/span><\/p>\n<\/td>\n

\n

\u00a0<\/span><\/p>\n<\/td>\n

\n

\u00a0<\/span><\/p>\n<\/td>\n

\n

\u00a0<\/span><\/p>\n<\/td>\n

\n

\u00a0<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Total<\/span><\/b><\/p>\n<\/td>\n

\n

1338.195<\/span><\/p>\n<\/td>\n

\n

199<\/span><\/p>\n<\/td>\n

\n

\u00a0<\/span><\/p>\n<\/td>\n

\n

\u00a0<\/span><\/p>\n<\/td>\n

\n

\u00a0<\/span><\/p>\n<\/td>\n

\n

\u00a0<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

H0: Mumbai Indians = Chennai Super Kings = Royal Challengers Bengaluru = Kolkata Knight Riders<\/span><\/h3>\n

H1: Any one of them is different.<\/span><\/h3>\n

Conclusion: <\/span><\/b><\/h3>\n

As calculated, F\u00a0 <\/span>(16.69314) is more than F crit\u00a0 <\/span>(2.650677). Accept H1, meaning any one of them is different.<\/p>\n

<\/span><\/p>\n

Reference:<\/span><\/b><\/p>\n