Thursday, 4 April 2019

IPL 2019 Prediction Model : Data Points





Cricket: The game of uncertainties and the force which binds the country as big as India. Cricket has a new definition these days, it’s a more entertaining game than in past, because of the induction of new format called T-20 cricket.

All over the world the business folks are trying to cash up this new popularity and Indian Premier League is one of the diamonds of these new business models.

IPL is more than cricket, Iits money, its flashy, its surprise cricket, its entertainment and more than that its business. There are people earning more than players, franchise and sponsors and there has been a lot of debates about this already.

I have been following IPL for so many years and its as unpredictable as one game can get, its just as good as a Soccer Match , but can mathematics achieve the possibility of finding who will win the match , is there a pattern IPL is following and who will win today ?
I have been collecting data points and based on the historic result trying to predict the winner and scores and let’s see how close it can get.

The following criteria are a few in thousands to predict if the team is going to win the match or not.
The model is in development phase and data collection has been a challenge, hoping to add more attributes going forward and train my Predictor.

Attributes 
Description
Team 1
Name of the Team whose victory is to be predicted
No of players changed
The count of players changed from previous Game
Recent Form
50% Last 5 match ,20% close match , 20% last cup , 10% controversies
No of bowlers in top 5 list
Players count in top 5 of tournament
No of batters  in top 5 list
Players count in top 5 of tournament
AUS/NZ Players
Players from NZ Australia Region
SA Players
Players from SA  Region
ASIA Players
Players from ASIA
UK Players
Players from UK
Spinners
Spinners
Fast Bowlers
Pacers /Mediam pacers
Wicket Rating
slow , fast , flat , bouncy
Avg Score
Average score in pitch
Avg Wickets Taken
Out of 20 how many wickets fall
Captain Form
30% team win , 40% own form ,20% last cups , 10% controversies/pressure 
Key players
superstar /single handed match winning players
close matches win
last 3 balls win(40%) + 180+ chases win (30%) + 150- defends (30%)
Regular player
No of Players playing 80% times not playing or injured
Team 2- Opposition

No of players changed
The count of players changed from previous Game
Recent Form
50% Last 5 match ,20% close match , 20% last cup , 10% controversies
No of bowlers in top 5 list
Players count in top 5 of tournament
No of batters  in top 5 list
Players count in top 5 of tournament
AUS/NZ Players
Players from NZ Australia Region
SA Players
Players from SA  Region
ASIA Players
Players from ASIA
UK Players
Players from UK
Spinners
Spinners
Fast Bowlers
Pacers /Mediam pacers
Wicket Rating
slow , fast , flat , bouncy
Avg Score
Average score in pitch
Avg Wickets Taken
Out of 20 how many wickets fall
Captain Form
30% team win , 40% own form ,20% last cups , 10% controversies/pressure 
Key players
superstar /single handed match winning players
close matches win
last 3 balls win(40%) + 180+ chases win (30%) + 150- defends (30%)
Regular player
No of Players playing 80% times not playing or injured
Team 1 is Winner
Y/N

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