ODI Cricket Result Predictor
Is it possible to guess the winner of a cricket match even before the match starts? What are the factors which influence the result of one-day cricket matches? These and many similar questions have always boggled my mind while growing up in 1990's and 2000's India, for whom "Cricket was a religion and Tendulkar was God!".
Inspired by the ICC Cricket World Cup (2019) and passion for data science and machine learning, I started working on building a machine learning classification model to try to answer some of these questions, with state of the art precision. Work on a detailed write-up about the project, as the project itself, is in progress.
Here's my predictions for winners of each match of ICC Cricket World Cup 2019. The table is arranged as follows:
- Date and Venue of match, and names of both Teams.
- Prediction of result as calculated by my predictor, in terms of winning probability of each team, before the match begins.
- Winning prediction probability calculated by Google, captured for the sake of validation.
- Actual winner at the end of match
- And last but not the least is Correctness of prediction, represented as: Match yet to be played, Successful prediction, Incorrect prediction, Match abandoned
- Update on 7th July 2019: Prediction by my model calculated on 22nd June for top 4 team-standing and semi-final draws matched with 100% accuracy with the final standing at completion of round-robin format on 6th July! Here's the original post on Twitter.
- Update on 14th July 2019: Not only predicted the result of final accurately, the model also predicted that it's gonna be a very close match!
Date |
Team 1 |
Team 2 |
Venue |
Prediction (Mine) |
Prediction (Google) |
Winner |
|
2019-07-14 |
New Zealand |
England |
London |
NZ: 47 | ENG: 53 |
NZ: 25 | ENG: 75 |
England |
|
2019-07-11 |
England |
Australia |
Birmingham |
ENG: 55 | AUS: 45 |
ENG: 58 | AUS: 42 |
England |
|
2019-07-09 |
India |
New Zealand |
Manchester |
IND: 59 | NZ: 41 |
IND: 72 | NZ: 28 |
New Zealand |
|
2019-07-06 |
Australia |
South Africa |
Manchester |
AUS: 53.13 | SA: 46.87 |
AUS: 69 | SA: 31 |
South Africa |
|
2019-07-06 |
Sri Lanka |
India |
Leeds |
SL: 28.6 | IND: 71.4 |
SL: 14 | IND: 86 |
India |
|
2019-07-05 |
Pakistan |
Bangladesh |
London |
PAK: 70 | BAN: 30 |
PAK: 67 | BAN: 33 |
Pakistan |
|
2019-07-04 |
Afghanistan |
West Indies |
Leeds |
AFG: 48 | WI: 52 |
AFG: 22 | WI: 78 |
West Indies |
|
2019-07-03 |
England |
New Zealand |
Durham |
ENG: 59.2 | NZ: 40.8 |
ENG: 70 | NZ: 30 |
England |
|
2019-07-02 |
Bangladesh |
India |
Birmingham |
BAN: 20.27 | IND: 79.73 |
BAN: 16 | IND: 84 |
India |
|
2019-07-01 |
Sri Lanka |
West Indies |
Durham |
SL: 65.63 | WI: 34.37 |
SL: 36 | WI: 64 |
Sri Lanka |
|
2019-06-30 |
England |
India |
Birmingham |
ENG: 46.22 | IND: 53.78 |
ENG: 48 | IND: 52 |
England |
|
2019-06-29 |
New Zealand |
Australia |
London |
NZ: 47.67 | AUS: 52.33 |
NZ: 35 | AUS: 65 |
Australia |
|
2019-06-29 |
Pakistan |
Afghanistan |
Leeds |
PAK: 82.73 | AFG: 17.27 |
PAK: 82 | AFG: 18 |
Pakistan |
|
2019-06-28 |
Sri Lanka |
South Africa |
Durham |
SL: 32 | SA: 68 |
SL: 34 | SA: 66 |
South Africa |
|
2019-06-27 |
West Indies |
India |
Manchester |
WI: 39.42 | IND: 60.58 |
WI: 27 | IND: 73 |
India |
|
2019-06-26 |
New Zealand |
Pakistan |
Birmingham |
NZ: 57.6 | PAK: 42.4 |
NZ: 57 | PAK: 43 |
Pakistan |
|
2019-06-25 |
England |
Australia |
London |
ENG: 66.28 | AUS: 33.72 |
ENG: 57 | AUS: 43 |
Australia |
|
2019-06-24 |
Bangladesh |
Afghanistan |
Southampton |
BAN: 69.55 | AFG: 30.45 |
BAN: 78 | AFG: 22 |
Bangladesh |
|
2019-06-23 |
Pakistan |
South Africa |
London |
PAK: 35.87 | SA: 64.13 |
PAK: 44 | SA: 56 |
Pakistan |
|
2019-06-22 |
West Indies |
New Zealand |
Manchester |
WI: 36.69 | NZ: 63.31 |
WI: 40 | NZ: 60 |
New Zealand |
|
2019-06-22 |
India |
Afghanistan |
Southampton |
IND: 84.23 | AFG: 15.77 |
IND: 94 | AFG: 6 |
India |
|
2019-06-21 |
England |
Sri Lanka |
Leeds |
ENG: 69.1 | SL: 30.9 |
ENG: 87 | SL: 13 |
Sri Lanka |
|
2019-06-20 |
Australia |
Bangladesh |
Nottingham |
AUS: 82.19 | BAN: 17.81 |
AUS: 84 | BAN: 14 |
Australia |
|
2019-06-19 |
New Zealand |
South Africa |
Birmingham |
NZ: 51.45 | SA: 48.55 |
NZ: 56 | SA: 44 |
New Zealand |
|
2019-06-18 |
England |
Afghanistan |
Manchester |
ENG: 86.44 | AFG: 13.56 |
ENG: 93 | AFG: 7 |
England |
|
2019-06-17 |
West Indies |
Bangladesh |
Taunton |
WI: 46.48 | BAN: 53.52 |
WI: 68 | BAN: 32 |
Bangladesh |
|
2019-06-16 |
India |
Pakistan |
Manchester |
IND: 61.52 | PAK: 38.48 |
IND: 71 | PAK: 29 |
India |
|
2019-06-15 |
South Africa |
Afghanistan |
Cardiff |
SA: 73.07 | AFG: 26.93 |
SA: 86 | AFG: 14 |
South Africa |
|
2019-06-15 |
Sri Lanka |
Australia |
London |
SL: 29.73 | AUS: 70.27 |
SL: 15 | AUS: 85 |
Australia |
|
2019-06-14 |
England |
West Indies |
Southampton |
ENG: 72.97 | WI: 27.03 |
ENG: 76 | WI: 24 |
England |
|
2019-06-13 |
India |
New Zealand |
Taunton |
AUS: 75.14 | PAK: 24.86 |
AUS: 73 | PAK: 27 |
Rain |
|
2019-06-12 |
Australia |
Pakistan |
Taunton |
AUS: 75.14 | PAK: 24.86 |
AUS: 73 | PAK: 27 |
Australia |
|
2019-06-11 |
Bangladesh |
Sri Lanka |
Bristol |
BAN: 60.8 | SL: 39.2 |
BAN: 55 | SL: 45 |
Rain |
|
2019-06-10 |
South Africa |
West Indies |
Southampton |
SA: 71.38 | WI: 28.62 |
SA: 50 | WI: 50 |
Rain |
|
2019-06-09 |
India |
Australia |
London |
IND: 45.43 | AUS: 54.57 |
IND: 54 | AUS: 46 |
India |
|
2019-06-08 |
Afghanistan |
New Zealand |
Taunton |
AFG: 18.83 | NZ: 81.17 |
AFG: 15 | NZ: 85 |
New Zealand |
|
2019-06-08 |
England |
Bangladesh |
Cardiff |
ENG: 73.41 | BAN: 26.59 |
ENG: 85 | BAN: 15 |
England |
|
2019-06-07 |
Pakistan |
Sri Lanka |
Bristol |
PAK: 65.66 | SL: 34.34 |
PAK: 70 | SL: 30 |
Rain |
|
2019-06-06 |
Australia |
West Indies |
Nottingham |
AUS: 74.3 | WI: 25.7 |
AUS: 63 | WI: 37 |
Australia |
|
2019-06-05 |
Bangladesh |
New Zealand |
London |
BAN: 37.58 | NZ: 62.42 |
BAN: 28 | NZ: 72 |
New Zealand |
|
2019-06-05 |
South Africa |
India |
Southampton |
SA: 44.01 | IND: 55.99 |
SA:35 | IND: 65 |
India |
|
2019-06-04 |
Afghanistan |
Sri Lanka |
Cardiff |
AFG: 49 | SL: 51 |
- |
Sri Lanka |
|
2019-06-03 |
England |
Pakistan |
Nottingham |
ENG: 65.73 | PAK: 34.27 |
- |
Pakistan |
|
2019-06-02 |
South Africa |
Bangladesh |
London |
SA: 78.84 | BAN: 21.16 |
- |
Bangladesh |
|
2019-06-01 |
Afghanistan |
Australia |
Bristol |
AFG: 15.9 | AUS: 84.1 |
- |
Australia |
|
2019-06-01 |
New Zealand |
Sri Lanka |
Cardiff |
NZ: 72.15 | SL: 27.85 |
- |
New Zealand |
|
2019-05-31 |
Pakistan |
West Indies |
Nottingham |
PAK: 58.37 | WI: 41.63 |
- |
West Indies |
|
2019-05-30 |
England |
South Africa |
London |
ENG: 45.06 | SA: 54.94 |
- |
England |
|
This predictor is made with 💖 and improving every day. Thanks for visiting 🌼
Predictions are posted daily on my handle @supratim_h, please follow me for regular updates.
For any feedback, comment or suggestion, or just to say Hi, please write me an anytime to supratimh@gmail.com.
For other similar projects I'm working on, please visit my home page