Man of the match — Is there a quantitative way to measure it?

Source: Skymet Weather

AB de Villiers gave away his man of the match (MoM) award to a fellow teammate KP Appanna in 2012. Then again, in 2015 he gave his MoM title to Mandeep Singh.

Source: Twitter — RCBTweets

Team sports have an age-old tradition of picking the ‘best’ player of the day. MVP or MoM (man of the match), these individuals stand out due to their remarkable performances — sometimes despite their team losing, they get picked.

Dream11 has a system of allocating points to players based on — batting, bowling and fielding. It looks holistic and brings bowlers, batsmen and fielders on the same scale. Dream11 has a way of allocating points based on a player’s performance.

Source: Dream11

At this point am flooded with questions, I want to know if this scoring technique is holistic enough? If there is a connection between the highest-ranked Dream11 player and the man of the match for a given game?

Data Time!

So once again while on a hunt for data, I found three sets that had various parameters from 60 IPL matches in 2020.

  1. DailyMatchData
  2. Fantasy_points_Dream11
  3. IPLMatchResults
Source: Kaggle (2020)

These three datasets have a wide variety of information. It took some time to figure out and after a bunch of ‘vlookups’ to connect multiple datasets, this is what I ended up with —

de-cluttered data

A quick overview of what these columns represent, we have the Match column that represents different games played. The datasheet consists of 60 matches. So, the column MOM; represents the name of the player who has been awarded the man of the match. Score_MOM is the Dream11 points accumulated by the said player. For the same match, the highest-ranked player (according to Dream11 scores) is mentioned in the Highest_Player column. Highest_score_player represents scores of the highest-ranked player by Dream11.

The data is now filtered and consolidated. So let’s try to plot it in R and see if we have an answer to our question —

Is there a connection between the highest-ranked Dream11 player and the man of the match (MOM) for a given match?

% of times when MOM was the same as the Dream 11 highest-ranked player
#Pie chart for % times man of the match has the highest Dream11 score#Create the first column
outcome<-c("yes","no")
#Insert Values into the rows
counts<-c(55,45)
#Create the final table
maintable<-data.frame(outcome,counts)
#Plot pie chart using ggplot2 libraryggplot(maintable, aes(x="",y = counts, fill = outcome)) +
geom_col() +
geom_text(aes(label=counts),position=position_stack(vjust=0.5))+
labs(title = " Percentage of times MOM has the highest Dream11 score",x="",caption = "MK")+coord_polar(theta = "y")

We see that majority of the time (55% to be exact), Dream11’s highest rank player does get the man of the match title. However, we still are not very sure about the association. So, let’s bring some statistics into the picture. Correlation is a great parameter to draw a relationship between two parameters. It is judged on a scale of 0 to 1; 0 indicates no correlation and 1 indicates high correlation. In other words, if the correlation is closer to 1, the two parameters have a significant connection.

The correlation of these two is 0.82

Let’s see what that means, graphically…

Scatter plot: Score of highest ranked Dream11 player vs man of the match
#Take the table ScoreMOMHighest and store it in a tableDream<-ScoreMOMHighest
ggplot(Dream)+geom_point(aes(Score_MOM,Score_Highest),col="maroon")+geom_smooth(aes(Score_MOM,Score_Highest),method="lm",col="dark grey")

Insights

Dream11 scores are part of a fantasy league that is quantified using a set of parameters. If we look at how the weightage is given for Dream11 scores, we see that it attempts to cover all avenues of playing the game. Hence, trying to find a correlation between the highest-ranked player of Dream11 and the man of the match will help us understand the relationship a little better. On calculating the correlation, we see that they have a strong connection. There is a high chance that the man of the match may also have a decent (if not the highest) score in the Dream11 world.

Tracking the outlier(1)

From the scatter plot, we see that there are certain outliers. If you look at the above table, Sanju Samson (despite a significant low Dream11 score) was awarded the man of the match. Mayank Agarwal had a good score of 158. Now the reason why Sanju Samson was given the MOM is an obvious one — his team won! It was a match between Kings XI Punjab and Rajasthan Royals. Mayank Agarwal scored high on the Dream11 board because of his 106 runs worth contribution to the total set by the KXIP which the RR successfully chased down.

Tracking the outlier (2)

Another interesting outlier is in the match between RCB and RR. Here Chris Morris has a Dream11 score of 120 and AB de Villiers has a score of 88. They both belong to the winning team (i.e., RCB). However, the MOM title is awarded to AB de Villiers. To see why that might have been the case, we can re-visit the scorecard to get some more insights.

Source: IPL
Source: IPL

RR set a total of 177. On one hand, Chris Morris bagged 4 wickets with a commendable economy restricting RR to a chasable total. On the other hand, AB de Villiers finished off in style with a brilliant strike rate! Factors that may have worked in favour of de Villiers —

  1. Batsmen are usually preferred over bowlers.
  2. The popularity of players has some influence on the selection of MoM
  3. The chase at the end is usually more memorable. This could impact the decision made by the panel.

Maybe, even this time AB should have given away his title to Chris?

--

--

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store