Sports prediction using machine learning
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I am trying to predict soccer scores using past results. The dataset I have only consists of the home team, the away team, goals scored by the home team and goals scored by the away team in each match. What is the best way to go about this. I need 2 algorithms so that I can be able to compare how they perform against each other
machine-learning predictive-modeling prediction
asked 36 mins ago
CheopsCheops
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I am trying to predict soccer scores using past results. The dataset I have only consists of the home team, the away team, goals scored by the home team and goals scored by the away team in each match. What is the best way to go about this. I need 2 algorithms so that I can be able to compare how they perform against each other
machine-learning predictive-modeling prediction
asked 36 mins ago
CheopsCheops
1
New contributor
Cheops is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I am trying to predict soccer scores using past results. The dataset I have only consists of the home team, the away team, goals scored by the home team and goals scored by the away team in each match. What is the best way to go about this. I need 2 algorithms so that I can be able to compare how they perform against each other
machine-learning predictive-modeling prediction
asked 36 mins ago
CheopsCheops
1
New contributor
Cheops is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am trying to predict soccer scores using past results. The dataset I have only consists of the home team, the away team, goals scored by the home team and goals scored by the away team in each match. What is the best way to go about this. I need 2 algorithms so that I can be able to compare how they perform against each other
machine-learning predictive-modeling prediction
machine-learning predictive-modeling prediction
asked 36 mins ago
CheopsCheops
1
New contributor
Cheops is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 36 mins ago
CheopsCheops
1
New contributor
Cheops is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 36 mins ago
CheopsCheops
1
New contributor
Cheops is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked 36 mins ago
CheopsCheops
1
asked 36 mins ago
CheopsCheops
1
1
New contributor
Cheops is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Cheops is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Cheops is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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You have two categorical features (team names) and two continuous features (goals scored). The continuous features will probably remain bounded at a lower value since soccer isn't typically a high scoring game, so no normalization will be necessary.
You may need dummy variables for the team names, as the text names will likely not play well as a feature for most classification libraries. Research the sklearn Ordinal Encoder for this task.
Look at the sklearn classification algorithms that are available and try implementing a few of those.
Your response variables will be the two predicted scores. One for home and one for away. Or you may decide that the response variable could simply be one 'spread
' feature for the expected spread of the score.
answered 12 mins ago
StevenTheDataGuyStevenTheDataGuy
564
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1 Answer
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1 Answer
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$begingroup$
You have two categorical features (team names) and two continuous features (goals scored). The continuous features will probably remain bounded at a lower value since soccer isn't typically a high scoring game, so no normalization will be necessary.
You may need dummy variables for the team names, as the text names will likely not play well as a feature for most classification libraries. Research the sklearn Ordinal Encoder for this task.
Look at the sklearn classification algorithms that are available and try implementing a few of those.
Your response variables will be the two predicted scores. One for home and one for away. Or you may decide that the response variable could simply be one 'spread
' feature for the expected spread of the score.
answered 12 mins ago
StevenTheDataGuyStevenTheDataGuy
564
$endgroup$
add a comment |
$begingroup$
You have two categorical features (team names) and two continuous features (goals scored). The continuous features will probably remain bounded at a lower value since soccer isn't typically a high scoring game, so no normalization will be necessary.
You may need dummy variables for the team names, as the text names will likely not play well as a feature for most classification libraries. Research the sklearn Ordinal Encoder for this task.
Look at the sklearn classification algorithms that are available and try implementing a few of those.
Your response variables will be the two predicted scores. One for home and one for away. Or you may decide that the response variable could simply be one 'spread
' feature for the expected spread of the score.
answered 12 mins ago
StevenTheDataGuyStevenTheDataGuy
564
$endgroup$
add a comment |
$begingroup$
You have two categorical features (team names) and two continuous features (goals scored). The continuous features will probably remain bounded at a lower value since soccer isn't typically a high scoring game, so no normalization will be necessary.
You may need dummy variables for the team names, as the text names will likely not play well as a feature for most classification libraries. Research the sklearn Ordinal Encoder for this task.
Look at the sklearn classification algorithms that are available and try implementing a few of those.
Your response variables will be the two predicted scores. One for home and one for away. Or you may decide that the response variable could simply be one 'spread
' feature for the expected spread of the score.
answered 12 mins ago
StevenTheDataGuyStevenTheDataGuy
564
$endgroup$
You have two categorical features (team names) and two continuous features (goals scored). The continuous features will probably remain bounded at a lower value since soccer isn't typically a high scoring game, so no normalization will be necessary.
You may need dummy variables for the team names, as the text names will likely not play well as a feature for most classification libraries. Research the sklearn Ordinal Encoder for this task.
Look at the sklearn classification algorithms that are available and try implementing a few of those.
Your response variables will be the two predicted scores. One for home and one for away. Or you may decide that the response variable could simply be one 'spread
' feature for the expected spread of the score.
answered 12 mins ago
StevenTheDataGuyStevenTheDataGuy
564
answered 12 mins ago
StevenTheDataGuyStevenTheDataGuy
564
answered 12 mins ago
StevenTheDataGuyStevenTheDataGuy
564
answered 12 mins ago
StevenTheDataGuyStevenTheDataGuy
564
564
add a comment |
add a comment |
Cheops is a new contributor. Be nice, and check out our Code of Conduct.
Cheops is a new contributor. Be nice, and check out our Code of Conduct.
Cheops is a new contributor. Be nice, and check out our Code of Conduct.
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