Small Data Science Project for Basketball
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£35(approx. $44)
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Wordpress |Web developer|Graphic Designer |Logo Designer | Scrapping |Excel VBA |Data Scientist
Rawalpindi
Virtual Assistant, Web Scraping, Data Mining, Python Bot creation, Data Entry, Photoshop
Salem
PPH TOP Website & App Developer✮LOGO & Graphic Designer✮Content Writer✮Translator
Dubai
Website Developer, Graphic Designer, Transcriber, Content writer, CAD Expert, Python Developer, Photo Editor, Web Scrapper, JAVA developer, Android developer, Wix/Shopify Expert,
Regensburg
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Karachi
WordPress Expert✮Shopify Expert✮Graphic Designer✮AutoCAD 2D & 3D✮CV Writer & Designer✮Fullstack developer
Rawalpindi
Data Scientist | Machine Learning Engineer | MLOps | Natural Language Processing | Python Developer
Setif
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Description
Experience Level: Entry
We have provided game by game player stats data for approx. 5 years of the NBA.
Your goal is to do some exploratory data analysis, and then try to build a model to predict
probabilities for (price) player proposition markets for Basketball.
An example of these markets are:
● {Player A} Total Points
● {Player A} Total 3 Pointers
● {Player A} Total Rebounds
These are traditionally called ‘Over/Under’ markets in sports betting, so depending on your base
level knowledge - some sports betting research may be required.
The aim is to price these markets for a hypothetical match between two teams selected at
random. For example, what would be the player prop market probabilities if GSW play TOR?
We suggest starting with one market, then seeing if that approach or methodology can be
applied to the others.
Some questions that will be useful to investigate:
● What effect does the number of minutes played have on these predictions?
● Who are the most effective players in this area?
● If a new player has joined the league (from the NCAA) - are there any methods we can
apply to price these markets?
The deliverable should be in a Jupyter Notebook and the programming language should be Python.
Dataset:
The dataset can be downloaded from this link: https://drive.google.com/file/d/1A5wb1xFoRPOJjOkD2iNYtnUglW4pCMmI/view?usp=sharing
2013-2018 game by game player stats data for the NBA.
Each row contains basic player data and their URL slug on basketball-reference.com. It also
includes the data of the game they played in, what team they were on, the team they were
facing, and all of their basic stats from the game (Pts, 3P, BLK, TRB, etc).
Data has been scraped from basketball-reference, please find the glossary
here: https://www.basketball-reference.com/about/glossary.html
Your goal is to do some exploratory data analysis, and then try to build a model to predict
probabilities for (price) player proposition markets for Basketball.
An example of these markets are:
● {Player A} Total Points
● {Player A} Total 3 Pointers
● {Player A} Total Rebounds
These are traditionally called ‘Over/Under’ markets in sports betting, so depending on your base
level knowledge - some sports betting research may be required.
The aim is to price these markets for a hypothetical match between two teams selected at
random. For example, what would be the player prop market probabilities if GSW play TOR?
We suggest starting with one market, then seeing if that approach or methodology can be
applied to the others.
Some questions that will be useful to investigate:
● What effect does the number of minutes played have on these predictions?
● Who are the most effective players in this area?
● If a new player has joined the league (from the NCAA) - are there any methods we can
apply to price these markets?
The deliverable should be in a Jupyter Notebook and the programming language should be Python.
Dataset:
The dataset can be downloaded from this link: https://drive.google.com/file/d/1A5wb1xFoRPOJjOkD2iNYtnUglW4pCMmI/view?usp=sharing
2013-2018 game by game player stats data for the NBA.
Each row contains basic player data and their URL slug on basketball-reference.com. It also
includes the data of the game they played in, what team they were on, the team they were
facing, and all of their basic stats from the game (Pts, 3P, BLK, TRB, etc).
Data has been scraped from basketball-reference, please find the glossary
here: https://www.basketball-reference.com/about/glossary.html
Achilles K.
100% (1)Projects Completed
1
Freelancers worked with
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Projects awarded
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Last project
23 May 2021
United Kingdom
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