Indian Predictable League

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Cricket is a funny game, especially its limited overs version, you cannot actually predict what a game of cricket is hiding for you until you watch it full. Indian Premier League is a perfect cocktail for this gentlemen’s game. It has all the masala (x-factor) which you would desire to watch in a cricket match. Individual performances like Chris Gayle’s 66 ball 175 knock in IPL 2013 or Adam Zampa’s 6 wickets for 19 Runs in IPL 2016 makes it even tougher to predict.

There are many factors which can affect an outcome of a cricket match like playing eleven’s current form, pitch conditions, venue, type of opposition, toss, past performances, team composition to name a few.

Having said that, what I am about to say might seem quite the opposite, what I have here is a list of factors which I have used to predict IPL 2016 matches.

 

Objective: Predicting an outcome of a cricket match for all the IPL Matches

Factors that affect an outcome of a cricket match:

  1. The Playing Eleven

First of all, I have allotted a batting score and a bowling score to each and every player. The score assigned is based on the past performance of the players in IPL. For a particular match, total team batting score and total team bowling score is calculated as a summation of player scores. Team Batting Score and Team Bowling Score are the two most important factors for this prediction approach.

playing eleven

  1. Ground Type

Each cricket ground brings up some twists and turns. Based on the past matches in a particular cricket ground, each ground has been assigned a tag of either Batting Friendly or Bowling Friendly. As the name suggests, a Batting Friendly ground gives some extra points to the Team Batting Score whereas a Bowling Friendly ground earns extra points for the Team Bowling Score. Apart from that, Ground dimensions are also taken into account. All the grounds are divided into 3 categories: Small Ground Dimensions, Medium Ground Dimensions and Large Ground Dimensions. For Small and Medium grounds extra points are given to players who fall in Powerhitter category (Strike Rate >130 and >50% Runs in Boundaries). For Large ground dimensions, spinners are given extra points.

Ground Type

  1. Type of Players

It is important to check the team composition to get some meaningful insights. Four categories are taken into account for the analysis: Batsman, Bowler, All Rounder, and Powerhitter (Strike Rate >130 and >50% Runs in Boundaries). Each player can fall into more than one category. Based on the team composition (Number of Batsmen, Bowlers, All Rounders and Powerhitters) extra points are added to Team Batting Score and Team Bowling Score.

Player Types

  1. Home Advantage

Some teams have an added advantage while playing at their home ground. Some teams understand their home conditions better than their opponents. This factor takes into account Number of Wins in the last 5 Home matches. Extra points are given if a team has won more than 2 matches out of the last 5 Home matches. Some points are deducted if a team has won less than 3 matches out of the last 5 Home matches

.Home Advantage

  1. Recent Performance of the team

Current form of the team is one of the most important factors in cricket. If you are continuously winning matches, your confidence will be high and the probability of your winning will also be high. This factor takes into account Number of Wins in the last 5 matches played. Extra points are given if a team has won more than 2 matches out of the last 5. Some points are deducted if a team has won less than 3 matches out of the last 5.

Recent Performance

  1. Decision of Batting First at the Venue

If you are playing a night match in Wankhede Stadium (Mumbai), you have to bat second because of the dew which makes it very difficult to grip the bowl in the second half of the match. Conditions of this type make the decision of Batting First/Second at a given venue a very important factor. Extra points are given to the team taking a fair call of Batting First/Second at a given venue based on the previous match outcomes.

Decision of Batting First

After combining these 6 factors, final Probability of Winning is calculated for both the teams and the team with higher win probability is our predicted winner of the match.

Yes, it is so much easy to predict a cricket match. Now let us see how these factors actually predicted an outcome of an IPL match between Royal Challengers Bangalore (RCB) and Mumbai Indians (MI) played on 11th May, 2016 at M. Chinnaswamy Stadium (Bengaluru).

 

Factor 1: Playing Eleven

RCB  
Player NameBAT_RATBOWL_RAT
 Virat Kohli79.920.00
 AB de Villiers98.450.00
 Shane Watson68.3665.21
 Sachin Baby0.000.00
 Chris Gayle80.690.00
 Lokesh Rahul32.990.00
 Stuart Binny26.6749.37
 Varun Aaron0.0066.01
 Sreenath Aravind0.000.00
 Chris Jordan0.000.00
 Yuzvendra Chahal0.0089.18

 

MI  
Player NameBAT_RATBOWL_RAT
 Rohit Sharma83.700.00
 Parthiv Patel60.580.00
 Jos Buttler0.000.00
 Ambati Rayudu61.030.00
 Kieron Pollard74.293.05
 Harbhajan Singh37.0991.05
 Nitish Rana0.000.00
 Mitchell McClenaghan0.0064.40
 Jasprit Bumrah0.0044.79
 Tim Southee0.0042.01
 Krunal Pandya0.000.00

 

Total Batting Score for RCB          = Sum of Individual BAT_RAT      = 387.08

Total Bowling Score for RCB         = Sum of Individual BOWL_RAT = 269.77

Total Batting Score for MI             = Sum of Individual BAT_RAT      = 316.68

Total Bowling Score for MI           = Sum of Individual BOWL_RAT = 245.29

 

Factor 2: Ground Type

Playing Venue: M. Chinnaswamy Stadium (Bengaluru)

Ground Type (Based on Research): Batting Friendly

Ground Dimensions (Based on Research): Small

Factor 3: Player Type

RCB    
Player NameBatSpinFastPower
 Virat Kohli1001
 AB de Villiers1001
 Shane Watson1011
 Sachin Baby1000
 Chris Gayle1001
 Lokesh Rahul1000
 Stuart Binny1010
 Varun Aaron0010
 Sreenath Aravind0010
 Chris Jordan1010
 Yuzvendra Chahal0100

 

MI    
Player NameBatSpinFastPower
 Rohit Sharma1001
 Parthiv Patel1001
 Jos Buttler1000
 Ambati Rayudu1001
 Kieron Pollard1011
 Harbhajan Singh0101
 Nitish Rana1000
 Mitchell McClenaghan0010
 Jasprit Bumrah0010
 Tim Southee0010
 Krunal Pandya1100

 

Number of Batsmen for RCB (Bat_RCB)                = 8

Number of Spin Bowlers for RCB (SBowl_RCB)    = 1

Number of Fast Bowlers for RCB (FBowl_RCB)    = 5

Number of Powerhitters for RCB (PH_RCB)         = 4

Number of Batsmen for MI (Bat_MI)                    = 7

Number of Spin Bowlers for MI (SBowl_MI)        = 2

Number of Fast Bowlers for MI (FBowl_MI)        = 4

Number of Powerhitters for MI (PH_MI)             = 5

Bat_Factor for RCB          = ( Bat_RCB / 10 ) + ( PH_RCB / 10 ) + 1                   = 2.2

Bowl_Factor for RCB       = ( SBowl_RCB / 10 ) + ( FBowl_RCB / 10 ) + 1       = 1.6

Bat_Factor for MI            = ( Bat_MI / 10 ) + ( PH_MI / 10 ) + 1                        = 2.2

Bowl_Factor for MI         = ( SBowl_MI / 10 ) + ( FBowl_MI / 10 ) + 1           = 1.6

 

Factor 4: Home Advantage

Home Team                                                                    = RCB

Wins of RCB in last 5 Home matches (Home_Win)    = 2

Home_Factor                                                               = 0.5 + ( Home_Win / 5 )             = 0.9

Factor 5: Recent Performance of the team

Wins in Last 5 matches for RCB (Recent_RCB)          = 2

Recent5_Factor for RCB                                              = 0.5 + ( Recent_RCB / 5 )             = 0.9

Wins in Last 5 matches for MI (Recent_MI)              = 3

Recent5_Factor for MI                                               = 0.5 + ( Recent_MI / 5 )               = 1.1

Factor 6: Decision of Batting First at the Venue

Batting First Team                                                            = RCB

Wins of Team Batting First at M. Chinnaswamy Stadium in last 5 matches (Bat_First_Win)                                                                                               = 1

Bat_First_Factor                                                               = 0.5 + ( Bat_First_Win / 5 )         = 0.7

Combining the Factors:

Net Batting Score for RCB (Bat_A)             = 965.68

Net Bowling Score for RCB (Bowl_A)         = 152.96

Net Batting Score for MI (Bat_B)               = 1532.73

Net Bowling Score for MI (Bowl_B)          = 269.82

 

Win Probability:

Winning Probability of RCB

= ( ( Bat_A / ( Bat_A + Bat_B ) ) + ( Bowl_A / ( Bowl_A + Bowl_B ) ) ) / 2

= 37%

Winning Probability of MI

= ( ( Bat_B / ( Bat_A + Bat_B ) ) + ( Bowl_B / ( Bowl_A + Bowl_B ) ) ) / 2

= 63%

Where, Bat_A    = Net Batting Score for RCB

                Bat_B    = Net Batting Score for MI

                Bowl_A = Net Bowling Score for RCB

                Bowl_B = Net Bowling Score for MI

Hence, the Predicted Winner comes out to be Mumbai Indians (MI) with Win probability 63% and the Actual Winner of the match was also Mumbai Indians (MI).

Blog Author: 
akshit sharma
Categories: 
Uncategorized

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